From 5b6528ca432268bbc3963290fe058dc9da9bb124 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Fri, 9 Oct 2020 16:03:46 +0800 Subject: [PATCH 01/92] Add models/tf.py for TensorFlow and TFLite export --- models/tf.py | 486 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 486 insertions(+) create mode 100644 models/tf.py diff --git a/models/tf.py b/models/tf.py new file mode 100644 index 000000000000..13cb3b276cda --- /dev/null +++ b/models/tf.py @@ -0,0 +1,486 @@ +import argparse +from copy import deepcopy +from pathlib import Path +import sys, traceback +import os +import logging + +import torch +import torch.nn as nn +import tensorflow as tf +if tf.__version__.startswith('1'): + tf.enable_eager_execution() +from tensorflow import keras +from tensorflow.keras import layers +from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 +import numpy as np + +from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad +from models.experimental import MixConv2d, CrossConv, C3 +from utils.general import check_anchor_order, make_divisible, check_file, set_logging +from utils.torch_utils import ( + time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, select_device) +from models.yolo import Detect +from utils.datasets import LoadImages + + +logger = logging.getLogger(__name__) + + +class tf_BN(keras.layers.Layer): + # TensorFlow BatchNormalization wrapper + def __init__(self, w=None): + super(tf_BN, self).__init__() + self.bn = keras.layers.BatchNormalization( + beta_initializer=keras.initializers.Constant(w.bias.numpy()), + gamma_initializer=keras.initializers.Constant(w.weight.numpy()), + moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()), + moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy())) + + def call(self, inputs): + return self.bn(inputs) + + +class tf_Pad(keras.layers.Layer): + def __init__(self, pad): + super(tf_Pad, self).__init__() + self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]]) + + def call(self, inputs): + return tf.pad(inputs, self.pad, mode='constant', constant_values=0) + + +class tf_Conv(keras.layers.Layer): + # Standard convolution + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): # ch_in, ch_out, weights, kernel, stride, padding, groups + super(tf_Conv, self).__init__() + assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" + assert isinstance(k, int), "Convolution with multiple kernels are not allowed." + + # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding) + # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch + if s == 1: + self.conv = keras.layers.Conv2D( + c2, k, s, 'SAME', use_bias=False, + kernel_initializer= + keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) + else: + self.pad = tf_Pad(autopad(k, p)) + self.conv = keras.Sequential([ + self.pad, + keras.layers.Conv2D( + c2, k, s, 'VALID', use_bias=False, + kernel_initializer= + keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) + ]) + + self.bn = tf_BN(w.bn) + + # YOLOv5 v3 uses Hardswish for activations + if isinstance(w.act, nn.LeakyReLU): + self.act = (lambda x: keras.activations.relu(x, alpha=0.1)) if act else tf.identity + elif isinstance(w.act, nn.Hardswish): + self.act = (lambda x: x * tf.nn.relu6(x+3) * 0.166666667) if act else tf.identity + + def call(self, inputs): + return self.act(self.bn(self.conv(inputs))) + + +class tf_Focus(keras.layers.Layer): + # Focus wh information into c-space + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): # ch_in, ch_out, kernel, stride, padding, groups + super(tf_Focus, self).__init__() + self.conv = tf_Conv(c1 * 4, c2, k, s, p, g, act, w.conv) + + def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c) + return self.conv(tf.concat([inputs[:, ::2, ::2, :], + inputs[:, 1::2, ::2, :], + inputs[:, ::2, 1::2, :], + inputs[:, 1::2, 1::2, :]], 3)) + + +class tf_Bottleneck(keras.layers.Layer): + # Standard bottleneck + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, shortcut, groups, expansion + super(tf_Bottleneck, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = tf_Conv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = tf_Conv(c_, c2, 3, 1, g=g, w=w.cv2) + self.add = shortcut and c1 == c2 + + def call(self, inputs): + return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs)) + + +class tf_Conv2d(keras.layers.Layer): + # Substitution for PyTorch nn.Conv2D + def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None): + super(tf_Conv2d, self).__init__() + assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" + self.conv = keras.layers.Conv2D( + c2, k, s, 'VALID', use_bias=bias, + kernel_initializer= + keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer= + keras.initializers.Constant(w.bias.numpy()) if bias else None, + ) + + def call(self, inputs): + return self.conv(inputs) + + +class tf_BottleneckCSP(keras.layers.Layer): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, number, shortcut, groups, expansion + super(tf_BottleneckCSP, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = tf_Conv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = tf_Conv2d(c1, c_, 1, 1, bias=False, w=w.cv2) + self.cv3 = tf_Conv2d(c_, c_, 1, 1, bias=False, w=w.cv3) + self.cv4 = tf_Conv(2 * c_, c2, 1, 1, w=w.cv4) + self.bn = tf_BN(w.bn) + self.act = lambda x: keras.activations.relu(x, alpha=0.1) + self.m = keras.Sequential([tf_Bottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) + + def call(self, inputs): + y1 = self.cv3(self.m(self.cv1(inputs))) + y2 = self.cv2(inputs) + return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3)))) + + +class tf_SPP(keras.layers.Layer): + # Spatial pyramid pooling layer used in YOLOv3-SPP + def __init__(self, c1, c2, k=(5, 9, 13), w=None): + super(tf_SPP, self).__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = tf_Conv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = tf_Conv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2) + self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding='SAME') for x in k] + + def call(self, inputs): + x = self.cv1(inputs) + return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3)) + + +class tf_Detect(keras.layers.Layer): + def __init__(self, nc=80, anchors=(), ch=(), w=None): # detection layer + super(tf_Detect, self).__init__() + # self.stride = None # strides computed during build + self.stride = tf.Variable([8, 16, 32], dtype=tf.float32) + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [tf.zeros(1)] * self.nl # init grid + a = tf.reshape(tf.convert_to_tensor(anchors, dtype=tf.float32), [self.nl, -1 ,2]) + self.anchors = tf.Variable(a, trainable=False) + self.anchor_grid = tf.reshape(tf.Variable(a, trainable=False), [self.nl, 1, -1, 1, 2]) + self.m = [tf_Conv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] + self.export = False # onnx export + self.training = True # set to False after building model + for i in range(self.nl): + ny, nx = opt.img_size[0] // self.stride[i], opt.img_size[1] // self.stride[i] + self.grid[i] = self._make_grid(nx, ny) + + def call(self, inputs): + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + x = [] + for i in range(self.nl): + x.append(self.m[i](inputs[i])) + # x(bs,20,20,255) to x(bs,3,20,20,85) + ny, nx = opt.img_size[0] // self.stride[i], opt.img_size[1] // self.stride[i] + x[i] = tf.transpose(tf.reshape(x[i], [-1, ny * nx, self.na, self.no]), [0, 2, 1, 3]) + + if not self.training: # inference + y = tf.sigmoid(x[i]) + xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy + wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] + y = tf.concat([xy, wh, y[..., 4:]], -1) + z.append(tf.reshape(y, [opt.batch_size, 3 * ny * nx, self.no])) + + return x if self.training else (tf.concat(z, 1), x) + + @staticmethod + def _make_grid(nx=20, ny=20): + # yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + # return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny)) + return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32) + + +class tf_Upsample(keras.layers.Layer): + def __init__(self, size, scale_factor, mode, w=None): + super(tf_Upsample, self).__init__() + assert scale_factor == 2, "scale_factor must be 2" + # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode) + self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * 2, x.shape[2] * 2), method=mode) + + def call(self, inputs): + return self.upsample(inputs) + + +class tf_Concat(keras.layers.Layer): + def __init__(self, dimension=1, w=None): + super(tf_Concat, self).__init__() + assert dimension == 1, "convert only NCHW to NHWC concat" + self.d = 3 + + def call(self, inputs): + return tf.concat(inputs, self.d) + + +def parse_model(d, ch, model): # model_dict, input_channels(3) + logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) + anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] + na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors + no = na * (nc + 5) # number of outputs = anchors * (classes + 5) + + layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out + for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args + m_str = m + m = eval(m) if isinstance(m, str) else m # eval strings + for j, a in enumerate(args): + try: + args[j] = eval(a) if isinstance(a, str) else a # eval strings + except: + pass + + n = max(round(n * gd), 1) if n > 1 else n # depth gain + if m in [nn.Conv2d, Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]: + c1, c2 = ch[f], args[0] + c2 = make_divisible(c2 * gw, 8) if c2 != no else c2 + + args = [c1, c2, *args[1:]] + if m in [BottleneckCSP, C3]: + args.insert(2, n) + n = 1 + elif m is nn.BatchNorm2d: + args = [ch[f]] + elif m is Concat: + c2 = sum([ch[-1 if x == -1 else x + 1] for x in f]) + elif m is Detect: + args.append([ch[x + 1] for x in f]) + if isinstance(args[1], int): # number of anchors + args[1] = [list(range(args[1] * 2))] * len(f) + else: + c2 = ch[f] + + tf_m = eval('tf_' + m_str.replace('nn.', '')) + m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \ + else tf_m(*args, w=model.model[i]) # module + + torch_m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module + t = str(m)[8:-2].replace('__main__.', '') # module type + np = sum([x.numel() for x in torch_m_.parameters()]) # number params + m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params + logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print + save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist + layers.append(m_) + ch.append(c2) + return keras.Sequential(layers), sorted(save) + + +def tf_check_anchor_order(m): + # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary + a = tf.reshape(tf.math.reduce_prod(m.anchor_grid, -1), [-1]) + da = a[-1] - a[0] # delta a + ds = m.stride[-1] - m.stride[0] # delta s + # Add numpy() in comparison for TensorFlow v1.15 + if tf.math.sign(da).numpy() != tf.math.sign(ds).numpy(): # same order + print('Reversing anchor order') + m.anchors[:] = tf.reverse(m.anchors, 0) + m.anchor_grid[:] = tf.reverse(m.anchor_grid, [0]) + + +class tf_Model(): + def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None): # model, input channels, number of classes + super(tf_Model, self).__init__() + if isinstance(cfg, dict): + self.yaml = cfg # model dict + else: # is *.yaml + import yaml # for torch hub + self.yaml_file = Path(cfg).name + with open(cfg) as f: + self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict + + # Define model + if nc and nc != self.yaml['nc']: + print('Overriding %s nc=%g with nc=%g' % (cfg, self.yaml['nc'], nc)) + self.yaml['nc'] = nc # override yaml value + self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model) # model, savelist, ch_out + # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) + + # Build strides, anchors + m = self.model.layers[-1] # Detect() + if isinstance(m, tf_Detect): + # s = 128 # 2x min stride + # m.stride = tf.convert_to_tensor([s / x.shape[-2] for x in self.predict(tf.zeros([1, s, s, ch]))]) # forward + m.anchors = tf.cast(m.anchors, dtype=tf.float32) / tf.reshape(m.stride, [-1, 1, 1]) + tf_check_anchor_order(m) + # self.stride = m.stride + # self._initialize_biases() # only run once + # print('Strides: %s' % m.stride.tolist()) + + # Init weights, biases + # torch_utils.initialize_weights(self) + + # keras.layers.Layer has no summary/info method + # self.info() + + def predict(self, inputs, profile=False): + y, dt = [], [] # outputs + x = inputs + for i, m in enumerate(self.model.layers): + if m.f != -1: # if not from previous layer + x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers + + if profile: + try: + import thop + o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # FLOPS + except: + o = 0 + t = torch_utils.time_synchronized() + for _ in range(10): + _ = m(x) + dt.append((torch_utils.time_synchronized() - t) * 100) + print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type)) + + x = m(x) # run + y.append(x if m.i in self.savelist else None) # save output + + if profile: + print('%.1fms total' % sum(dt)) + return x + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--cfg', type=str, default='./models/yolov5s.yaml', help='cfg path') + parser.add_argument('--weights', type=str, default='./weights/yolov5s.pt', help='weights path') + parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') + parser.add_argument('--batch-size', type=int, default=1, help='batch size') + parser.add_argument('--no-tfl-detect', action='store_true', dest='no_tfl_detect', help='remove Detect module in TFLite model') + parser.add_argument('--source', type=str, default='/dataset/coco/coco2017/train2017', help='source') # file/folder, 0 for webcam + parser.add_argument('--ncalib', type=int, default=100, help='number of calibration images') # file/folder, 0 for webcam + parser.add_argument('--tfl-int8', action='store_true', dest='tfl_int8', help='export TFLite int8 model') + opt = parser.parse_args() + opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand + print(opt) + + # Input + img = torch.zeros((opt.batch_size, 3, *opt.img_size)) # image size(1,3,320,192) iDetection + + # Load PyTorch model + model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float() + model.eval() + model.model[-1].export = False # set Detect() layer export=True + y = model(img) # dry run + nc = y[0].shape[-1] - 5 + + # TensorFlow saved_model export + try: + print('\nStarting TensorFlow saved_model export with TensorFlow %s...' % tf.__version__) + tf_model = tf_Model(opt.cfg, model=model, nc=nc) + img = tf.zeros((opt.batch_size, *opt.img_size, 3)) # NHWC Input for TensorFlow + + m = tf_model.model.layers[-1] + assert isinstance(m, tf_Detect), "the last layer must be Detect" + m.training = False + y = tf_model.predict(img) + + inputs = keras.Input(shape=(*opt.img_size, 3)) + keras_model = keras.Model(inputs=inputs, outputs=tf_model.predict(inputs)) + keras_model.summary() + path = opt.weights.replace('.pt', '_saved_model') # filename + keras_model.save(path, save_format='tf') + print('TensorFlow saved_model export success, saved as %s' % path) + except Exception as e: + print('TensorFlow saved_model export failure: %s' % e) + traceback.print_exc(file=sys.stdout) + + # TensorFlow GraphDef export + try: + print('\nStarting TensorFlow GraphDef export with TensorFlow %s...' % tf.__version__) + + # https://github.com/leimao/Frozen_Graph_TensorFlow + full_model = tf.function(lambda x: keras_model(x)) + full_model = full_model.get_concrete_function( + tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)) + + frozen_func = convert_variables_to_constants_v2(full_model) + frozen_func.graph.as_graph_def() + f = opt.weights.replace('.pt', '.pb') # filename + tf.io.write_graph(graph_or_graph_def=frozen_func.graph, + logdir=os.path.dirname(f), + name=os.path.basename(f), + as_text=False) + + print('TensorFlow GraphDef export success, saved as %s' % f) + except Exception as e: + print('TensorFlow GraphDef export failure: %s' % e) + traceback.print_exc(file=sys.stdout) + + # TFLite model export + try: + print('\nStarting TFLite export with TensorFlow %s...' % tf.__version__) + if opt.no_tfl_detect: + print("Don't export Detect module") + m.training = True + keras_model = keras.Model(inputs=inputs, outputs=tf_model.predict(inputs)) + # fp32 TFLite model export + # converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) + # converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] + # converter.allow_custom_ops = False + # converter.experimental_new_converter = True + # tflite_model = converter.convert() + # f = opt.weights.replace('.pt', '.tflite') # filename + # open(f, "wb").write(tflite_model) + + # fp16 TFLite model export + converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) + converter.optimizations = [tf.lite.Optimize.DEFAULT] + converter.target_spec.supported_types = [tf.float16] + converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] + converter.allow_custom_ops = False + converter.experimental_new_converter = True + tflite_model = converter.convert() + f = opt.weights.replace('.pt', '-fp16.tflite') # filename + open(f, "wb").write(tflite_model) + print('\nTFLite export success, saved as %s' % f) + + # int8 TFLite model export + if opt.tfl_int8: + converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) + converter.optimizations = [tf.lite.Optimize.DEFAULT] + dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) + + def representative_dataset_gen(): + n = 0 + for path, img, im0s, vid_cap in dataset: + # Get sample input data as a numpy array in a method of your choosing. + n += 1 + input = np.transpose(img, [1, 2, 0]) + input = np.expand_dims(input, axis=0).astype(np.float32) + input /= 255.0 + yield [input] + if n >= opt.ncalib: + break + + converter.representative_dataset = representative_dataset_gen + converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] + converter.inference_input_type = tf.uint8 # or tf.int8 + converter.inference_output_type = tf.uint8 # or tf.int8 + converter.allow_custom_ops = False + converter.experimental_new_converter = True + tflite_model = converter.convert() + f = opt.weights.replace('.pt', '-int8.tflite') # filename + open(f, "wb").write(tflite_model) + print('\nTFLite (int8) export success, saved as %s' % f) + + except Exception as e: + print('\nTFLite export failure: %s' % e) + traceback.print_exc(file=sys.stdout) + From a30dad4ec48ad528d3136b1e31c77324458393a6 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Fri, 9 Oct 2020 17:32:15 +0800 Subject: [PATCH 02/92] Set auto=False for int8 calibration --- utils/datasets.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/utils/datasets.py b/utils/datasets.py index cd6151e364ba..8d6b45a32848 100755 --- a/utils/datasets.py +++ b/utils/datasets.py @@ -118,7 +118,7 @@ def __iter__(self): class LoadImages: # for inference - def __init__(self, path, img_size=640): + def __init__(self, path, img_size=640, auto=True): p = str(Path(path)) # os-agnostic p = os.path.abspath(p) # absolute path if '*' in p: @@ -139,6 +139,7 @@ def __init__(self, path, img_size=640): self.nf = ni + nv # number of files self.video_flag = [False] * ni + [True] * nv self.mode = 'images' + self.auto = auto if any(videos): self.new_video(videos[0]) # new video else: @@ -180,7 +181,7 @@ def __next__(self): print('image %g/%g %s: ' % (self.count, self.nf, path), end='') # Padded resize - img = letterbox(img0, new_shape=self.img_size)[0] + img = letterbox(img0, new_shape=self.img_size, auto=self.auto)[0] # Convert img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 @@ -255,7 +256,7 @@ def __len__(self): class LoadStreams: # multiple IP or RTSP cameras - def __init__(self, sources='streams.txt', img_size=640): + def __init__(self, sources='streams.txt', img_size=640, auto=True): self.mode = 'images' self.img_size = img_size @@ -268,6 +269,7 @@ def __init__(self, sources='streams.txt', img_size=640): n = len(sources) self.imgs = [None] * n self.sources = sources + self.auto = auto for i, s in enumerate(sources): # Start the thread to read frames from the video stream print('%g/%g: %s... ' % (i + 1, n, s), end='') @@ -283,7 +285,7 @@ def __init__(self, sources='streams.txt', img_size=640): print('') # newline # check for common shapes - s = np.stack([letterbox(x, new_shape=self.img_size)[0].shape for x in self.imgs], 0) # inference shapes + s = np.stack([letterbox(x, new_shape=self.img_size, auto=self.auto)[0].shape for x in self.imgs], 0) # inference shapes self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal if not self.rect: print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.') @@ -312,7 +314,7 @@ def __next__(self): raise StopIteration # Letterbox - img = [letterbox(x, new_shape=self.img_size, auto=self.rect)[0] for x in img0] + img = [letterbox(x, new_shape=self.img_size, auto=self.rect and self.auto)[0] for x in img0] # Stack img = np.stack(img, 0) From f0cb6e280c0d021c714e38ca8ed60abdea46044f Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Tue, 20 Oct 2020 19:41:50 +0800 Subject: [PATCH 03/92] Update requirements.txt for TensorFlow and TFLite export --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 5e813fb0420c..543874b789fa 100755 --- a/requirements.txt +++ b/requirements.txt @@ -24,6 +24,7 @@ pandas # coremltools==4.0 # onnx>=1.8.0 # scikit-learn==0.19.2 # for coreml quantization +# tensorflow==2.3.1 # extras -------------------------------------- # thop # FLOPS computation From ce73d3d7290d4320fb0bcf51377092ff0e602555 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Fri, 23 Oct 2020 17:51:01 +0800 Subject: [PATCH 04/92] Read anchors directly from PyTorch weights --- models/tf.py | 23 +++++------------------ 1 file changed, 5 insertions(+), 18 deletions(-) diff --git a/models/tf.py b/models/tf.py index 13cb3b276cda..6a3b8feff45b 100644 --- a/models/tf.py +++ b/models/tf.py @@ -165,16 +165,16 @@ def call(self, inputs): class tf_Detect(keras.layers.Layer): def __init__(self, nc=80, anchors=(), ch=(), w=None): # detection layer super(tf_Detect, self).__init__() - # self.stride = None # strides computed during build - self.stride = tf.Variable([8, 16, 32], dtype=tf.float32) + self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32) self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors self.grid = [tf.zeros(1)] * self.nl # init grid - a = tf.reshape(tf.convert_to_tensor(anchors, dtype=tf.float32), [self.nl, -1 ,2]) - self.anchors = tf.Variable(a, trainable=False) - self.anchor_grid = tf.reshape(tf.Variable(a, trainable=False), [self.nl, 1, -1, 1, 2]) + self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32) + self.anchor_grid = tf.reshape( + tf.convert_to_tensor(w.anchor_grid.numpy(), dtype=tf.float32), + [self.nl, 1, -1, 1, 2]) self.m = [tf_Conv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] self.export = False # onnx export self.training = True # set to False after building model @@ -310,24 +310,11 @@ def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None): # model, inp print('Overriding %s nc=%g with nc=%g' % (cfg, self.yaml['nc'], nc)) self.yaml['nc'] = nc # override yaml value self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model) # model, savelist, ch_out - # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) # Build strides, anchors m = self.model.layers[-1] # Detect() if isinstance(m, tf_Detect): - # s = 128 # 2x min stride - # m.stride = tf.convert_to_tensor([s / x.shape[-2] for x in self.predict(tf.zeros([1, s, s, ch]))]) # forward - m.anchors = tf.cast(m.anchors, dtype=tf.float32) / tf.reshape(m.stride, [-1, 1, 1]) tf_check_anchor_order(m) - # self.stride = m.stride - # self._initialize_biases() # only run once - # print('Strides: %s' % m.stride.tolist()) - - # Init weights, biases - # torch_utils.initialize_weights(self) - - # keras.layers.Layer has no summary/info method - # self.info() def predict(self, inputs, profile=False): y, dt = [], [] # outputs From d101f7e4329d671fbde62afeb3a9692470b28c11 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Mon, 2 Nov 2020 18:34:19 +0800 Subject: [PATCH 05/92] Add --tf-nms to append NMS in TensorFlow SavedModel and GraphDef export --- models/tf.py | 150 +++++++++++++++++++++++++++------------------------ 1 file changed, 81 insertions(+), 69 deletions(-) diff --git a/models/tf.py b/models/tf.py index 6a3b8feff45b..50c1066c7287 100644 --- a/models/tf.py +++ b/models/tf.py @@ -317,29 +317,33 @@ def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None): # model, inp tf_check_anchor_order(m) def predict(self, inputs, profile=False): - y, dt = [], [] # outputs + y = [] # outputs x = inputs for i, m in enumerate(self.model.layers): if m.f != -1: # if not from previous layer x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers - if profile: - try: - import thop - o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # FLOPS - except: - o = 0 - t = torch_utils.time_synchronized() - for _ in range(10): - _ = m(x) - dt.append((torch_utils.time_synchronized() - t) * 100) - print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type)) - x = m(x) # run y.append(x if m.i in self.savelist else None) # save output - if profile: - print('%.1fms total' % sum(dt)) + # Add TensorFlow NMS + if opt.tf_nms: + xy = x[0][:, :, 0:2] + wh = x[0][:, :, 2:4] + x1 = xy[:, :, 0:1] - wh[:, :, 0:1] / 2 + x2 = xy[:, :, 0:1] + wh[:, :, 0:1] / 2 + y1 = xy[:, :, 1:2] - wh[:, :, 1:2] / 2 + y2 = xy[:, :, 1:2] + wh[:, :, 1:2] / 2 + xyxy = tf.concat([x1, y1, x2, y2], 2) + boxes = tf.expand_dims(xyxy, 2) + probs = x[0][:, :, 4:5] + classes = x[0][:, :, 5:] + scores = probs * classes + nms = tf.image.combined_non_max_suppression( + boxes, scores, opt.topk_per_class, opt.topk_all, opt.iou_thres, opt.score_thres, + clip_boxes=False) + return nms, x[1] + return x @@ -353,6 +357,11 @@ def predict(self, inputs, profile=False): parser.add_argument('--source', type=str, default='/dataset/coco/coco2017/train2017', help='source') # file/folder, 0 for webcam parser.add_argument('--ncalib', type=int, default=100, help='number of calibration images') # file/folder, 0 for webcam parser.add_argument('--tfl-int8', action='store_true', dest='tfl_int8', help='export TFLite int8 model') + parser.add_argument('--tf-nms', action='store_true', dest='tf_nms', help='add TensorFlow NMS (without TFLite export)') + parser.add_argument('--topk-per-class', type=int, default=100, help='topk per class to keep in NMS') + parser.add_argument('--topk-all', type=int, default=100, help='topk for all classes to keep in NMS') + parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS') + parser.add_argument('--score-thres', type=float, default=0.4, help='score threshold for NMS') opt = parser.parse_args() opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand print(opt) @@ -410,64 +419,67 @@ def predict(self, inputs, profile=False): print('TensorFlow GraphDef export failure: %s' % e) traceback.print_exc(file=sys.stdout) - # TFLite model export - try: - print('\nStarting TFLite export with TensorFlow %s...' % tf.__version__) - if opt.no_tfl_detect: - print("Don't export Detect module") - m.training = True - keras_model = keras.Model(inputs=inputs, outputs=tf_model.predict(inputs)) - # fp32 TFLite model export - # converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) - # converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] - # converter.allow_custom_ops = False - # converter.experimental_new_converter = True - # tflite_model = converter.convert() - # f = opt.weights.replace('.pt', '.tflite') # filename - # open(f, "wb").write(tflite_model) - - # fp16 TFLite model export - converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) - converter.optimizations = [tf.lite.Optimize.DEFAULT] - converter.target_spec.supported_types = [tf.float16] - converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] - converter.allow_custom_ops = False - converter.experimental_new_converter = True - tflite_model = converter.convert() - f = opt.weights.replace('.pt', '-fp16.tflite') # filename - open(f, "wb").write(tflite_model) - print('\nTFLite export success, saved as %s' % f) - - # int8 TFLite model export - if opt.tfl_int8: + # NMS doesn't support TFLite export + + if not opt.tf_nms: + # TFLite model export + try: + print('\nStarting TFLite export with TensorFlow %s...' % tf.__version__) + if opt.no_tfl_detect: + print("Don't export Detect module") + m.training = True + keras_model = keras.Model(inputs=inputs, outputs=tf_model.predict(inputs)) + # fp32 TFLite model export + # converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) + # converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] + # converter.allow_custom_ops = False + # converter.experimental_new_converter = True + # tflite_model = converter.convert() + # f = opt.weights.replace('.pt', '.tflite') # filename + # open(f, "wb").write(tflite_model) + + # fp16 TFLite model export converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] - dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) - - def representative_dataset_gen(): - n = 0 - for path, img, im0s, vid_cap in dataset: - # Get sample input data as a numpy array in a method of your choosing. - n += 1 - input = np.transpose(img, [1, 2, 0]) - input = np.expand_dims(input, axis=0).astype(np.float32) - input /= 255.0 - yield [input] - if n >= opt.ncalib: - break - - converter.representative_dataset = representative_dataset_gen - converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] - converter.inference_input_type = tf.uint8 # or tf.int8 - converter.inference_output_type = tf.uint8 # or tf.int8 + converter.target_spec.supported_types = [tf.float16] + converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] converter.allow_custom_ops = False converter.experimental_new_converter = True tflite_model = converter.convert() - f = opt.weights.replace('.pt', '-int8.tflite') # filename + f = opt.weights.replace('.pt', '-fp16.tflite') # filename open(f, "wb").write(tflite_model) - print('\nTFLite (int8) export success, saved as %s' % f) - - except Exception as e: - print('\nTFLite export failure: %s' % e) - traceback.print_exc(file=sys.stdout) + print('\nTFLite export success, saved as %s' % f) + + # int8 TFLite model export + if opt.tfl_int8: + converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) + converter.optimizations = [tf.lite.Optimize.DEFAULT] + dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) + + def representative_dataset_gen(): + n = 0 + for path, img, im0s, vid_cap in dataset: + # Get sample input data as a numpy array in a method of your choosing. + n += 1 + input = np.transpose(img, [1, 2, 0]) + input = np.expand_dims(input, axis=0).astype(np.float32) + input /= 255.0 + yield [input] + if n >= opt.ncalib: + break + + converter.representative_dataset = representative_dataset_gen + converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] + converter.inference_input_type = tf.uint8 # or tf.int8 + converter.inference_output_type = tf.uint8 # or tf.int8 + converter.allow_custom_ops = False + converter.experimental_new_converter = True + tflite_model = converter.convert() + f = opt.weights.replace('.pt', '-int8.tflite') # filename + open(f, "wb").write(tflite_model) + print('\nTFLite (int8) export success, saved as %s' % f) + + except Exception as e: + print('\nTFLite export failure: %s' % e) + traceback.print_exc(file=sys.stdout) From 55fb2eb2063629d4065433970074a16b89f7e031 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Mon, 16 Nov 2020 11:11:00 +0800 Subject: [PATCH 06/92] Remove check_anchor_order, check_file, set_logging from import --- models/tf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/models/tf.py b/models/tf.py index 50c1066c7287..7d16e6ca22f7 100644 --- a/models/tf.py +++ b/models/tf.py @@ -17,7 +17,7 @@ from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad from models.experimental import MixConv2d, CrossConv, C3 -from utils.general import check_anchor_order, make_divisible, check_file, set_logging +from utils.general import make_divisible from utils.torch_utils import ( time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, select_device) from models.yolo import Detect From 3dd69d9dcf8560d33a0d387615809a6e47953205 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 28 Nov 2020 14:10:48 +0100 Subject: [PATCH 07/92] Reformat code and optimize imports --- models/tf.py | 124 ++++++++++++++++++++++----------------------------- 1 file changed, 54 insertions(+), 70 deletions(-) diff --git a/models/tf.py b/models/tf.py index 7d16e6ca22f7..9e1a01549f02 100644 --- a/models/tf.py +++ b/models/tf.py @@ -1,29 +1,27 @@ import argparse +import logging +import os +import sys +import traceback from copy import deepcopy from pathlib import Path -import sys, traceback -import os -import logging +import tensorflow as tf import torch import torch.nn as nn -import tensorflow as tf + if tf.__version__.startswith('1'): tf.enable_eager_execution() from tensorflow import keras -from tensorflow.keras import layers from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 import numpy as np from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad from models.experimental import MixConv2d, CrossConv, C3 from utils.general import make_divisible -from utils.torch_utils import ( - time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, select_device) from models.yolo import Detect from utils.datasets import LoadImages - logger = logging.getLogger(__name__) @@ -32,10 +30,10 @@ class tf_BN(keras.layers.Layer): def __init__(self, w=None): super(tf_BN, self).__init__() self.bn = keras.layers.BatchNormalization( - beta_initializer=keras.initializers.Constant(w.bias.numpy()), - gamma_initializer=keras.initializers.Constant(w.weight.numpy()), - moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()), - moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy())) + beta_initializer=keras.initializers.Constant(w.bias.numpy()), + gamma_initializer=keras.initializers.Constant(w.weight.numpy()), + moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()), + moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy())) def call(self, inputs): return self.bn(inputs) @@ -52,7 +50,8 @@ def call(self, inputs): class tf_Conv(keras.layers.Layer): # Standard convolution - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): # ch_in, ch_out, weights, kernel, stride, padding, groups + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): + # ch_in, ch_out, weights, kernel, stride, padding, groups super(tf_Conv, self).__init__() assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" assert isinstance(k, int), "Convolution with multiple kernels are not allowed." @@ -61,38 +60,38 @@ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): # ch_in, c # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch if s == 1: self.conv = keras.layers.Conv2D( - c2, k, s, 'SAME', use_bias=False, - kernel_initializer= - keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) + c2, k, s, 'SAME', use_bias=False, + kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) else: self.pad = tf_Pad(autopad(k, p)) self.conv = keras.Sequential([ - self.pad, + self.pad, keras.layers.Conv2D( c2, k, s, 'VALID', use_bias=False, - kernel_initializer= - keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) + kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) ]) - self.bn = tf_BN(w.bn) + self.bn = tf_BN(w.bn) if hasattr(w, 'bn') else tf.identity # YOLOv5 v3 uses Hardswish for activations if isinstance(w.act, nn.LeakyReLU): self.act = (lambda x: keras.activations.relu(x, alpha=0.1)) if act else tf.identity - elif isinstance(w.act, nn.Hardswish): - self.act = (lambda x: x * tf.nn.relu6(x+3) * 0.166666667) if act else tf.identity + elif isinstance(w.act, nn.Hardswish): + self.act = (lambda x: x * tf.nn.relu6(x + 3) * 0.166666667) if act else tf.identity def call(self, inputs): - return self.act(self.bn(self.conv(inputs))) + return self.act(self.bn(self.conv(inputs))) if hasattr(self, 'bn') else self.act(self.conv(inputs)) class tf_Focus(keras.layers.Layer): # Focus wh information into c-space - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): # ch_in, ch_out, kernel, stride, padding, groups + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): + # ch_in, ch_out, kernel, stride, padding, groups super(tf_Focus, self).__init__() self.conv = tf_Conv(c1 * 4, c2, k, s, p, g, act, w.conv) def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c) + # inputs = inputs / 255. # normalize 0-255 to 0-1 return self.conv(tf.concat([inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], @@ -118,12 +117,9 @@ def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None): super(tf_Conv2d, self).__init__() assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" self.conv = keras.layers.Conv2D( - c2, k, s, 'VALID', use_bias=bias, - kernel_initializer= - keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()), - bias_initializer= - keras.initializers.Constant(w.bias.numpy()) if bias else None, - ) + c2, k, s, 'VALID', use_bias=bias, + kernel_initializer=keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None, ) def call(self, inputs): return self.conv(inputs) @@ -131,7 +127,8 @@ def call(self, inputs): class tf_BottleneckCSP(keras.layers.Layer): # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, number, shortcut, groups, expansion + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): + # ch_in, ch_out, number, shortcut, groups, expansion super(tf_BottleneckCSP, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = tf_Conv(c1, c_, 1, 1, w=w.cv1) @@ -172,12 +169,11 @@ def __init__(self, nc=80, anchors=(), ch=(), w=None): # detection layer self.na = len(anchors[0]) // 2 # number of anchors self.grid = [tf.zeros(1)] * self.nl # init grid self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32) - self.anchor_grid = tf.reshape( - tf.convert_to_tensor(w.anchor_grid.numpy(), dtype=tf.float32), - [self.nl, 1, -1, 1, 2]) + self.anchor_grid = tf.reshape(tf.convert_to_tensor(w.anchor_grid.numpy(), dtype=tf.float32), + [self.nl, 1, -1, 1, 2]) self.m = [tf_Conv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] self.export = False # onnx export - self.training = True # set to False after building model + self.training = True # set to False after building model for i in range(self.nl): ny, nx = opt.img_size[0] // self.stride[i], opt.img_size[1] // self.stride[i] self.grid[i] = self._make_grid(nx, ny) @@ -269,7 +265,7 @@ def parse_model(d, ch, model): # model_dict, input_channels(3) tf_m = eval('tf_' + m_str.replace('nn.', '')) m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \ - else tf_m(*args, w=model.model[i]) # module + else tf_m(*args, w=model.model[i]) # module torch_m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module t = str(m)[8:-2].replace('__main__.', '') # module type @@ -282,18 +278,6 @@ def parse_model(d, ch, model): # model_dict, input_channels(3) return keras.Sequential(layers), sorted(save) -def tf_check_anchor_order(m): - # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary - a = tf.reshape(tf.math.reduce_prod(m.anchor_grid, -1), [-1]) - da = a[-1] - a[0] # delta a - ds = m.stride[-1] - m.stride[0] # delta s - # Add numpy() in comparison for TensorFlow v1.15 - if tf.math.sign(da).numpy() != tf.math.sign(ds).numpy(): # same order - print('Reversing anchor order') - m.anchors[:] = tf.reverse(m.anchors, 0) - m.anchor_grid[:] = tf.reverse(m.anchor_grid, [0]) - - class tf_Model(): def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None): # model, input channels, number of classes super(tf_Model, self).__init__() @@ -311,11 +295,6 @@ def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None): # model, inp self.yaml['nc'] = nc # override yaml value self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model) # model, savelist, ch_out - # Build strides, anchors - m = self.model.layers[-1] # Detect() - if isinstance(m, tf_Detect): - tf_check_anchor_order(m) - def predict(self, inputs, profile=False): y = [] # outputs x = inputs @@ -326,24 +305,30 @@ def predict(self, inputs, profile=False): x = m(x) # run y.append(x if m.i in self.savelist else None) # save output + # Boxes + xy = x[0][:, :, 0:2] + wh = x[0][:, :, 2:4] + x1 = xy[:, :, 0:1] - wh[:, :, 0:1] / 2 + x2 = xy[:, :, 0:1] + wh[:, :, 0:1] / 2 + y1 = xy[:, :, 1:2] - wh[:, :, 1:2] / 2 + y2 = xy[:, :, 1:2] + wh[:, :, 1:2] / 2 + xyxy = tf.concat([x1, y1, x2, y2], 2) + # Add TensorFlow NMS if opt.tf_nms: - xy = x[0][:, :, 0:2] - wh = x[0][:, :, 2:4] - x1 = xy[:, :, 0:1] - wh[:, :, 0:1] / 2 - x2 = xy[:, :, 0:1] + wh[:, :, 0:1] / 2 - y1 = xy[:, :, 1:2] - wh[:, :, 1:2] / 2 - y2 = xy[:, :, 1:2] + wh[:, :, 1:2] / 2 - xyxy = tf.concat([x1, y1, x2, y2], 2) boxes = tf.expand_dims(xyxy, 2) probs = x[0][:, :, 4:5] classes = x[0][:, :, 5:] scores = probs * classes nms = tf.image.combined_non_max_suppression( - boxes, scores, opt.topk_per_class, opt.topk_all, opt.iou_thres, opt.score_thres, - clip_boxes=False) + boxes, scores, opt.topk_per_class, opt.topk_all, opt.iou_thres, opt.score_thres, clip_boxes=False) return nms, x[1] + # x = x[0] + # conf = x[..., 4:5] + # cls = x[..., 5:] + # return [conf * cls, xyxy] # output only first tensor, [1,6300,80], [1,6300,4] + # return x[0] # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...] return x @@ -351,13 +336,13 @@ def predict(self, inputs, profile=False): parser = argparse.ArgumentParser() parser.add_argument('--cfg', type=str, default='./models/yolov5s.yaml', help='cfg path') parser.add_argument('--weights', type=str, default='./weights/yolov5s.pt', help='weights path') - parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') + parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width parser.add_argument('--batch-size', type=int, default=1, help='batch size') - parser.add_argument('--no-tfl-detect', action='store_true', dest='no_tfl_detect', help='remove Detect module in TFLite model') - parser.add_argument('--source', type=str, default='/dataset/coco/coco2017/train2017', help='source') # file/folder, 0 for webcam - parser.add_argument('--ncalib', type=int, default=100, help='number of calibration images') # file/folder, 0 for webcam + parser.add_argument('--no-tfl-detect', action='store_true', help='remove Detect() from TFLite model') + parser.add_argument('--source', type=str, default='data/images', help='source') + parser.add_argument('--ncalib', type=int, default=100, help='number of calibration images') parser.add_argument('--tfl-int8', action='store_true', dest='tfl_int8', help='export TFLite int8 model') - parser.add_argument('--tf-nms', action='store_true', dest='tf_nms', help='add TensorFlow NMS (without TFLite export)') + parser.add_argument('--tf-nms', action='store_true', dest='tf_nms', help='TF NMS (without TFLite export)') parser.add_argument('--topk-per-class', type=int, default=100, help='topk per class to keep in NMS') parser.add_argument('--topk-all', type=int, default=100, help='topk for all classes to keep in NMS') parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS') @@ -370,7 +355,7 @@ def predict(self, inputs, profile=False): img = torch.zeros((opt.batch_size, 3, *opt.img_size)) # image size(1,3,320,192) iDetection # Load PyTorch model - model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float() + model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float() # .fuse() model.eval() model.model[-1].export = False # set Detect() layer export=True y = model(img) # dry run @@ -404,7 +389,7 @@ def predict(self, inputs, profile=False): # https://github.com/leimao/Frozen_Graph_TensorFlow full_model = tf.function(lambda x: keras_model(x)) full_model = full_model.get_concrete_function( - tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)) + tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)) frozen_func = convert_variables_to_constants_v2(full_model) frozen_func.graph.as_graph_def() @@ -482,4 +467,3 @@ def representative_dataset_gen(): except Exception as e: print('\nTFLite export failure: %s' % e) traceback.print_exc(file=sys.stdout) - From e8a9ad2f1ae63025e1788b0e415d72e61e03e5a2 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 28 Nov 2020 14:29:55 +0100 Subject: [PATCH 08/92] Autodownload model and check cfg --- models/tf.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/models/tf.py b/models/tf.py index 9e1a01549f02..b433442840cb 100644 --- a/models/tf.py +++ b/models/tf.py @@ -18,9 +18,10 @@ from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad from models.experimental import MixConv2d, CrossConv, C3 -from utils.general import make_divisible +from utils.general import make_divisible, check_file from models.yolo import Detect from utils.datasets import LoadImages +from utils.google_utils import attempt_download logger = logging.getLogger(__name__) @@ -334,8 +335,8 @@ def predict(self, inputs, profile=False): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--cfg', type=str, default='./models/yolov5s.yaml', help='cfg path') - parser.add_argument('--weights', type=str, default='./weights/yolov5s.pt', help='weights path') + parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='cfg path') + parser.add_argument('--weights', type=str, default='yolov5s.pt', help='weights path') parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width parser.add_argument('--batch-size', type=int, default=1, help='batch size') parser.add_argument('--no-tfl-detect', action='store_true', help='remove Detect() from TFLite model') @@ -348,6 +349,7 @@ def predict(self, inputs, profile=False): parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS') parser.add_argument('--score-thres', type=float, default=0.4, help='score threshold for NMS') opt = parser.parse_args() + opt.cfg = check_file(opt.cfg) # check file opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand print(opt) @@ -355,6 +357,7 @@ def predict(self, inputs, profile=False): img = torch.zeros((opt.batch_size, 3, *opt.img_size)) # image size(1,3,320,192) iDetection # Load PyTorch model + attempt_download(opt.weights) model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float() # .fuse() model.eval() model.model[-1].export = False # set Detect() layer export=True @@ -441,6 +444,7 @@ def predict(self, inputs, profile=False): converter.optimizations = [tf.lite.Optimize.DEFAULT] dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) + def representative_dataset_gen(): n = 0 for path, img, im0s, vid_cap in dataset: @@ -453,6 +457,7 @@ def representative_dataset_gen(): if n >= opt.ncalib: break + converter.representative_dataset = representative_dataset_gen converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.inference_input_type = tf.uint8 # or tf.int8 From efbb853efcbf9893f33de4d71c7c07477d04aa7e Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 28 Nov 2020 16:24:21 +0100 Subject: [PATCH 09/92] update --source path, img-size to 320, single output --- models/tf.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/models/tf.py b/models/tf.py index b433442840cb..4f915ed8ae93 100644 --- a/models/tf.py +++ b/models/tf.py @@ -329,18 +329,17 @@ def predict(self, inputs, profile=False): # conf = x[..., 4:5] # cls = x[..., 5:] # return [conf * cls, xyxy] # output only first tensor, [1,6300,80], [1,6300,4] - # return x[0] # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...] - return x + return x[0] # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...] if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='cfg path') parser.add_argument('--weights', type=str, default='yolov5s.pt', help='weights path') - parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width + parser.add_argument('--img-size', nargs='+', type=int, default=[320, 320], help='image size') # height, width parser.add_argument('--batch-size', type=int, default=1, help='batch size') parser.add_argument('--no-tfl-detect', action='store_true', help='remove Detect() from TFLite model') - parser.add_argument('--source', type=str, default='data/images', help='source') + parser.add_argument('--source', type=str, default='../data/images', help='source') parser.add_argument('--ncalib', type=int, default=100, help='number of calibration images') parser.add_argument('--tfl-int8', action='store_true', dest='tfl_int8', help='export TFLite int8 model') parser.add_argument('--tf-nms', action='store_true', dest='tf_nms', help='TF NMS (without TFLite export)') @@ -442,6 +441,12 @@ def predict(self, inputs, profile=False): if opt.tfl_int8: converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] + + # # Download COCO128 + # with open(check_file('coco128.yaml')) as f: + # data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict + # check_dataset(data_dict) # check + dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) From aed53cee1bd242366af5c53c304aafcd8adaceb8 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 28 Nov 2020 16:58:19 +0100 Subject: [PATCH 10/92] Adjust representative_dataset --- models/tf.py | 59 +++++++++++++++++++++++++++------------------------- 1 file changed, 31 insertions(+), 28 deletions(-) diff --git a/models/tf.py b/models/tf.py index 4f915ed8ae93..d0bf2920d2fe 100644 --- a/models/tf.py +++ b/models/tf.py @@ -9,6 +9,7 @@ import tensorflow as tf import torch import torch.nn as nn +import yaml if tf.__version__.startswith('1'): tf.enable_eager_execution() @@ -18,7 +19,7 @@ from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad from models.experimental import MixConv2d, CrossConv, C3 -from utils.general import make_divisible, check_file +from utils.general import make_divisible, check_file, check_dataset from models.yolo import Detect from utils.datasets import LoadImages from utils.google_utils import attempt_download @@ -332,6 +333,20 @@ def predict(self, inputs, profile=False): return x[0] # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...] +def representative_dataset_gen(): + # Representative dataset for use with converter.representative_dataset + n = 0 + for path, img, im0s, vid_cap in dataset: + # Get sample input data as a numpy array in a method of your choosing. + n += 1 + input = np.transpose(img, [1, 2, 0]) + input = np.expand_dims(input, axis=0).astype(np.float32) + input /= 255.0 + yield [input] + if n >= opt.ncalib: + break + + if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='cfg path') @@ -339,7 +354,7 @@ def predict(self, inputs, profile=False): parser.add_argument('--img-size', nargs='+', type=int, default=[320, 320], help='image size') # height, width parser.add_argument('--batch-size', type=int, default=1, help='batch size') parser.add_argument('--no-tfl-detect', action='store_true', help='remove Detect() from TFLite model') - parser.add_argument('--source', type=str, default='../data/images', help='source') + parser.add_argument('--source', type=str, default='../data/coco128.yaml', help='dir of images or data.yaml file') parser.add_argument('--ncalib', type=int, default=100, help='number of calibration images') parser.add_argument('--tfl-int8', action='store_true', dest='tfl_int8', help='export TFLite int8 model') parser.add_argument('--tf-nms', action='store_true', dest='tf_nms', help='TF NMS (without TFLite export)') @@ -416,7 +431,16 @@ def predict(self, inputs, profile=False): print("Don't export Detect module") m.training = True keras_model = keras.Model(inputs=inputs, outputs=tf_model.predict(inputs)) - # fp32 TFLite model export + + # Representative Dataset + if opt.source.endswith('.yaml'): + with open(check_file(opt.source)) as f: + data = yaml.load(f, Loader=yaml.FullLoader) # data dict + check_dataset(data) # check + opt.source = data['train'] + dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) + + # fp32 TFLite model export --------------------------------------------------------------------------------- # converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) # converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] # converter.allow_custom_ops = False @@ -425,10 +449,11 @@ def predict(self, inputs, profile=False): # f = opt.weights.replace('.pt', '.tflite') # filename # open(f, "wb").write(tflite_model) - # fp16 TFLite model export + # fp16 TFLite model export --------------------------------------------------------------------------------- converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] - converter.target_spec.supported_types = [tf.float16] + # converter.representative_dataset = representative_dataset_gen + # converter.target_spec.supported_types = [tf.float16] converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] converter.allow_custom_ops = False converter.experimental_new_converter = True @@ -437,32 +462,10 @@ def predict(self, inputs, profile=False): open(f, "wb").write(tflite_model) print('\nTFLite export success, saved as %s' % f) - # int8 TFLite model export + # int8 TFLite model export --------------------------------------------------------------------------------- if opt.tfl_int8: converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] - - # # Download COCO128 - # with open(check_file('coco128.yaml')) as f: - # data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict - # check_dataset(data_dict) # check - - dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) - - - def representative_dataset_gen(): - n = 0 - for path, img, im0s, vid_cap in dataset: - # Get sample input data as a numpy array in a method of your choosing. - n += 1 - input = np.transpose(img, [1, 2, 0]) - input = np.expand_dims(input, axis=0).astype(np.float32) - input /= 255.0 - yield [input] - if n >= opt.ncalib: - break - - converter.representative_dataset = representative_dataset_gen converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.inference_input_type = tf.uint8 # or tf.int8 From ccb233634464b044c6c23e6a24e2204245990553 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Mon, 30 Nov 2020 18:17:56 +0800 Subject: [PATCH 11/92] Put representative dataset in tfl_int8 block --- models/tf.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/models/tf.py b/models/tf.py index d0bf2920d2fe..ace450434a6d 100644 --- a/models/tf.py +++ b/models/tf.py @@ -432,14 +432,6 @@ def representative_dataset_gen(): m.training = True keras_model = keras.Model(inputs=inputs, outputs=tf_model.predict(inputs)) - # Representative Dataset - if opt.source.endswith('.yaml'): - with open(check_file(opt.source)) as f: - data = yaml.load(f, Loader=yaml.FullLoader) # data dict - check_dataset(data) # check - opt.source = data['train'] - dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) - # fp32 TFLite model export --------------------------------------------------------------------------------- # converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) # converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] @@ -464,6 +456,14 @@ def representative_dataset_gen(): # int8 TFLite model export --------------------------------------------------------------------------------- if opt.tfl_int8: + # Representative Dataset + if opt.source.endswith('.yaml'): + with open(check_file(opt.source)) as f: + data = yaml.load(f, Loader=yaml.FullLoader) # data dict + check_dataset(data) # check + opt.source = data['train'] + dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) + converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.representative_dataset = representative_dataset_gen From 9f893c8a838d6b50809ff9a4e1b7a86fc766623f Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 2 Dec 2020 12:25:07 +0100 Subject: [PATCH 12/92] detect.py TF inference --- detect.py | 76 +++++++++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 66 insertions(+), 10 deletions(-) diff --git a/detect.py b/detect.py index 63feaa6adef0..dc38bbe540fb 100644 --- a/detect.py +++ b/detect.py @@ -5,6 +5,7 @@ import cv2 import torch import torch.backends.cudnn as cudnn +import yaml from numpy import random from models.experimental import attempt_load @@ -30,10 +31,54 @@ def detect(save_img=False): half = device.type != 'cpu' # half precision only supported on CUDA # Load model - model = attempt_load(weights, map_location=device) # load FP32 model - imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size - if half: - model.half() # to FP16 + suffix = Path(weights[0]).suffix + if suffix == '.pt': + backend = 'pytorch' + model = attempt_load(weights, map_location=device) # load FP32 model + imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size + names = model.module.names if hasattr(model, 'module') else model.names # class names + if half: + model.half() # to FP16 + else: + import tensorflow as tf + from tensorflow import keras + + with open('data/coco.yaml') as f: + names = yaml.load(f, Loader=yaml.FullLoader)['names'] # class names (assume COCO) + + if suffix == '.pb': + backend = 'graph_def' + + # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt + # https://github.com/leimao/Frozen_Graph_TensorFlow + def wrap_frozen_graph(graph_def, inputs, outputs): + def _imports_graph_def(): + tf.compat.v1.import_graph_def(graph_def, name="") + + wrapped_import = tf.compat.v1.wrap_function(_imports_graph_def, []) + import_graph = wrapped_import.graph + return wrapped_import.prune( + tf.nest.map_structure(import_graph.as_graph_element, inputs), + tf.nest.map_structure(import_graph.as_graph_element, outputs)) + + graph = tf.Graph() + graph_def = graph.as_graph_def() + graph_def.ParseFromString(open(weights[0], 'rb').read()) + frozen_func = wrap_frozen_graph(graph_def=graph_def, inputs="x:0", outputs="Identity:0") + + elif suffix == '.tflite': + backend = 'tflite' + # Load TFLite model and allocate tensors + interpreter = tf.lite.Interpreter(model_path=opt.weights[0]) + interpreter.allocate_tensors() + + # Get input and output tensors + input_details = interpreter.get_input_details() + output_details = interpreter.get_output_details() + + else: + backend = 'saved_model' + model = keras.models.load_model(weights[0]) # Second-stage classifier classify = False @@ -46,29 +91,40 @@ def detect(save_img=False): if webcam: view_img = True cudnn.benchmark = True # set True to speed up constant image size inference - dataset = LoadStreams(source, img_size=imgsz) + dataset = LoadStreams(source, img_size=imgsz, auto=backend == 'pytorch') else: save_img = True - dataset = LoadImages(source, img_size=imgsz) + dataset = LoadImages(source, img_size=imgsz, auto=backend == 'pytorch') # Get names and colors - names = model.module.names if hasattr(model, 'module') else model.names colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] # Run inference t0 = time.time() img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img - _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once + _ = model(img.half() if half else img) if (device.type != 'cpu' and backend == 'pytorch') else None # run once for path, img, im0s, vid_cap in dataset: img = torch.from_numpy(img).to(device) - img = img.half() if half else img.float() # uint8 to fp16/32 + img = img.half() if half and backend == 'pytorch' else img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 if img.ndimension() == 3: img = img.unsqueeze(0) # Inference t1 = time_synchronized() - pred = model(img, augment=opt.augment)[0] + if backend == 'pytorch': + pred = model(img, augment=opt.augment)[0] + else: + if backend == 'saved_model': + pred = model(img.permute(0, 2, 3, 1).cpu().numpy(), training=False)[0].numpy() + elif backend == 'graph_def': + pred = frozen_func(x=tf.constant(img.permute(0, 2, 3, 1).cpu().numpy())).numpy() + elif backend == 'tflite': + input_data = img.permute(0, 2, 3, 1).cpu().numpy() + interpreter.set_tensor(input_details[0]['index'], input_data) + interpreter.invoke() + pred = interpreter.get_tensor(output_details[0]['index']) + pred = torch.tensor(pred) # Apply NMS pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) From 49a9e053f0ad22dbddf21f205608a5d29742ca90 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 2 Dec 2020 12:45:34 +0100 Subject: [PATCH 13/92] weights to string --- detect.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/detect.py b/detect.py index dc38bbe540fb..6d89c356951a 100644 --- a/detect.py +++ b/detect.py @@ -31,7 +31,8 @@ def detect(save_img=False): half = device.type != 'cpu' # half precision only supported on CUDA # Load model - suffix = Path(weights[0]).suffix + weights = weights[0] if isinstance(weights, list) else weights + suffix = Path(weights).suffix if suffix == '.pt': backend = 'pytorch' model = attempt_load(weights, map_location=device) # load FP32 model @@ -63,7 +64,7 @@ def _imports_graph_def(): graph = tf.Graph() graph_def = graph.as_graph_def() - graph_def.ParseFromString(open(weights[0], 'rb').read()) + graph_def.ParseFromString(open(weights, 'rb').read()) frozen_func = wrap_frozen_graph(graph_def=graph_def, inputs="x:0", outputs="Identity:0") elif suffix == '.tflite': @@ -78,7 +79,7 @@ def _imports_graph_def(): else: backend = 'saved_model' - model = keras.models.load_model(weights[0]) + model = keras.models.load_model(weights) # Second-stage classifier classify = False From 1867bb404ca7bbede5a4545dc43d5a11465396e9 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 2 Dec 2020 12:48:04 +0100 Subject: [PATCH 14/92] weights to string --- detect.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/detect.py b/detect.py index 6d89c356951a..d54d2c97e4d6 100644 --- a/detect.py +++ b/detect.py @@ -70,7 +70,7 @@ def _imports_graph_def(): elif suffix == '.tflite': backend = 'tflite' # Load TFLite model and allocate tensors - interpreter = tf.lite.Interpreter(model_path=opt.weights[0]) + interpreter = tf.lite.Interpreter(model_path=weights) interpreter.allocate_tensors() # Get input and output tensors From 4eed60863e4b01024e85939bbf77d15425e24fed Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Fri, 4 Dec 2020 12:07:01 +0100 Subject: [PATCH 15/92] cleanup tf.py --- models/tf.py | 56 +++++++++++++++++++--------------------------------- 1 file changed, 20 insertions(+), 36 deletions(-) diff --git a/models/tf.py b/models/tf.py index ace450434a6d..41fe44150a76 100644 --- a/models/tf.py +++ b/models/tf.py @@ -6,22 +6,19 @@ from copy import deepcopy from pathlib import Path +import numpy as np import tensorflow as tf import torch import torch.nn as nn import yaml - -if tf.__version__.startswith('1'): - tf.enable_eager_execution() from tensorflow import keras from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 -import numpy as np from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad from models.experimental import MixConv2d, CrossConv, C3 -from utils.general import make_divisible, check_file, check_dataset from models.yolo import Detect from utils.datasets import LoadImages +from utils.general import make_divisible, check_file, check_dataset from utils.google_utils import attempt_download logger = logging.getLogger(__name__) @@ -57,22 +54,13 @@ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): super(tf_Conv, self).__init__() assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" assert isinstance(k, int), "Convolution with multiple kernels are not allowed." - # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding) # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch - if s == 1: - self.conv = keras.layers.Conv2D( - c2, k, s, 'SAME', use_bias=False, - kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) - else: - self.pad = tf_Pad(autopad(k, p)) - self.conv = keras.Sequential([ - self.pad, - keras.layers.Conv2D( - c2, k, s, 'VALID', use_bias=False, - kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) - ]) + conv = keras.layers.Conv2D( + c2, k, s, 'SAME' if s == 1 else 'VALID', use_bias=False, + kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) + self.conv = conv if s == 1 else keras.Sequential([tf_Pad(autopad(k, p)), conv]) self.bn = tf_BN(w.bn) if hasattr(w, 'bn') else tf.identity # YOLOv5 v3 uses Hardswish for activations @@ -82,7 +70,7 @@ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): self.act = (lambda x: x * tf.nn.relu6(x + 3) * 0.166666667) if act else tf.identity def call(self, inputs): - return self.act(self.bn(self.conv(inputs))) if hasattr(self, 'bn') else self.act(self.conv(inputs)) + return self.act(self.bn(self.conv(inputs))) class tf_Focus(keras.layers.Layer): @@ -307,18 +295,9 @@ def predict(self, inputs, profile=False): x = m(x) # run y.append(x if m.i in self.savelist else None) # save output - # Boxes - xy = x[0][:, :, 0:2] - wh = x[0][:, :, 2:4] - x1 = xy[:, :, 0:1] - wh[:, :, 0:1] / 2 - x2 = xy[:, :, 0:1] + wh[:, :, 0:1] / 2 - y1 = xy[:, :, 1:2] - wh[:, :, 1:2] / 2 - y2 = xy[:, :, 1:2] + wh[:, :, 1:2] / 2 - xyxy = tf.concat([x1, y1, x2, y2], 2) - # Add TensorFlow NMS if opt.tf_nms: - boxes = tf.expand_dims(xyxy, 2) + boxes = tf.expand_dims(xywh2xyxy(x[0][..., :4]), 2) probs = x[0][:, :, 4:5] classes = x[0][:, :, 5:] scores = probs * classes @@ -326,11 +305,18 @@ def predict(self, inputs, profile=False): boxes, scores, opt.topk_per_class, opt.topk_all, opt.iou_thres, opt.score_thres, clip_boxes=False) return nms, x[1] - # x = x[0] - # conf = x[..., 4:5] - # cls = x[..., 5:] - # return [conf * cls, xyxy] # output only first tensor, [1,6300,80], [1,6300,4] return x[0] # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...] + # x = x[0][0] # [x(1,6300,85), ...] to x(6300,85) + # xywh = x[..., :4] # x(6300,4) boxes + # conf = x[..., 4:5] # x(6300,1) confidences + # cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1)) # x(6300,1) classes + # return tf.concat([conf, cls, xywh], 1) + + +def xywh2xyxy(xywh): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1) + return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1) def representative_dataset_gen(): @@ -421,10 +407,8 @@ def representative_dataset_gen(): print('TensorFlow GraphDef export failure: %s' % e) traceback.print_exc(file=sys.stdout) - # NMS doesn't support TFLite export - + # TFLite model export if not opt.tf_nms: - # TFLite model export try: print('\nStarting TFLite export with TensorFlow %s...' % tf.__version__) if opt.no_tfl_detect: From 8ba2ca9640212e2d09c50c4466c71ae98074f7ba Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Tue, 22 Dec 2020 20:46:25 +0800 Subject: [PATCH 16/92] Add --dynamic-batch-size --- models/tf.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/models/tf.py b/models/tf.py index 41fe44150a76..a5cc8888da51 100644 --- a/models/tf.py +++ b/models/tf.py @@ -339,6 +339,7 @@ def representative_dataset_gen(): parser.add_argument('--weights', type=str, default='yolov5s.pt', help='weights path') parser.add_argument('--img-size', nargs='+', type=int, default=[320, 320], help='image size') # height, width parser.add_argument('--batch-size', type=int, default=1, help='batch size') + parser.add_argument('--dynamic-batch-size', action='store_true', help='dynamic batch size') parser.add_argument('--no-tfl-detect', action='store_true', help='remove Detect() from TFLite model') parser.add_argument('--source', type=str, default='../data/coco128.yaml', help='dir of images or data.yaml file') parser.add_argument('--ncalib', type=int, default=100, help='number of calibration images') @@ -375,7 +376,7 @@ def representative_dataset_gen(): m.training = False y = tf_model.predict(img) - inputs = keras.Input(shape=(*opt.img_size, 3)) + inputs = keras.Input(shape=(*opt.img_size, 3), batch_size=None if opt.dynamic_batch_size else opt.batch_size) keras_model = keras.Model(inputs=inputs, outputs=tf_model.predict(inputs)) keras_model.summary() path = opt.weights.replace('.pt', '_saved_model') # filename From 4d9104ae4af0bf69b54e64f1403f4c5cd9cc28c0 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Tue, 22 Dec 2020 19:28:48 +0800 Subject: [PATCH 17/92] Add xywh normalization to reduce calibration error --- detect.py | 14 +++++++++++++- models/tf.py | 4 +++- 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/detect.py b/detect.py index d54d2c97e4d6..dc4756336d2b 100644 --- a/detect.py +++ b/detect.py @@ -7,6 +7,7 @@ import torch.backends.cudnn as cudnn import yaml from numpy import random +import numpy as np from models.experimental import attempt_load from utils.datasets import LoadStreams, LoadImages @@ -117,14 +118,24 @@ def _imports_graph_def(): pred = model(img, augment=opt.augment)[0] else: if backend == 'saved_model': - pred = model(img.permute(0, 2, 3, 1).cpu().numpy(), training=False)[0].numpy() + pred = model(img.permute(0, 2, 3, 1).cpu().numpy(), training=False).numpy() elif backend == 'graph_def': pred = frozen_func(x=tf.constant(img.permute(0, 2, 3, 1).cpu().numpy())).numpy() elif backend == 'tflite': input_data = img.permute(0, 2, 3, 1).cpu().numpy() + if opt.tfl_int8: + scale, zero_point = input_details[0]['quantization'] + input_data = input_data / scale + zero_point + input_data = input_data.astype(np.uint8) interpreter.set_tensor(input_details[0]['index'], input_data) interpreter.invoke() pred = interpreter.get_tensor(output_details[0]['index']) + if opt.tfl_int8: + scale, zero_point = output_details[0]['quantization'] + pred = pred.astype(np.float32) + pred = (pred - zero_point) * scale + # Denormalize xywh + pred[..., :4] *= opt.img_size pred = torch.tensor(pred) # Apply NMS @@ -218,6 +229,7 @@ def _imports_graph_def(): parser.add_argument('--project', default='runs/detect', help='save results to project/name') parser.add_argument('--name', default='exp', help='save results to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--tfl-int8', action='store_true', help='use int8 quantized TFLite model') opt = parser.parse_args() print(opt) diff --git a/models/tf.py b/models/tf.py index a5cc8888da51..e2b9eccd8dba 100644 --- a/models/tf.py +++ b/models/tf.py @@ -183,6 +183,9 @@ def call(self, inputs): y = tf.sigmoid(x[i]) xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] + # Normalize xywh to 0-1 to reduce calibration error + xy /= tf.constant([[opt.img_size[1], opt.img_size[0]]], dtype=tf.float32) + wh /= tf.constant([[opt.img_size[1], opt.img_size[0]]], dtype=tf.float32) y = tf.concat([xy, wh, y[..., 4:]], -1) z.append(tf.reshape(y, [opt.batch_size, 3 * ny * nx, self.no])) @@ -448,7 +451,6 @@ def representative_dataset_gen(): check_dataset(data) # check opt.source = data['train'] dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) - converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.representative_dataset = representative_dataset_gen From cabb802a27b3b8626b016b7cd4c6c4e67ffb8f6d Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Wed, 23 Dec 2020 17:40:35 +0800 Subject: [PATCH 18/92] Update requirements.txt TensorFlow 2.3.1 -> 2.4.0 to avoid int8 quantization error --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 11fec991d7ef..812d5442bb60 100755 --- a/requirements.txt +++ b/requirements.txt @@ -24,7 +24,7 @@ pandas # coremltools==4.0 # onnx>=1.8.0 # scikit-learn==0.19.2 # for coreml quantization -# tensorflow==2.3.1 +# tensorflow==2.4.0 # extras -------------------------------------- thop # FLOPS computation From a5967f82f7e522c77d6740e90607617e41514080 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Thu, 24 Dec 2020 10:44:49 +0800 Subject: [PATCH 19/92] Fix imports Move C3 from models.experimental to models.common --- models/tf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/models/tf.py b/models/tf.py index e2b9eccd8dba..8a7af6d4384b 100644 --- a/models/tf.py +++ b/models/tf.py @@ -14,8 +14,8 @@ from tensorflow import keras from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 -from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad -from models.experimental import MixConv2d, CrossConv, C3 +from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad, C3 +from models.experimental import MixConv2d, CrossConv from models.yolo import Detect from utils.datasets import LoadImages from utils.general import make_divisible, check_file, check_dataset From dbc7f7106adaee6c41de2e2b15efdfebc2a7ad3a Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Fri, 9 Oct 2020 16:03:46 +0800 Subject: [PATCH 20/92] Add models/tf.py for TensorFlow and TFLite export --- models/tf.py | 486 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 486 insertions(+) create mode 100644 models/tf.py diff --git a/models/tf.py b/models/tf.py new file mode 100644 index 000000000000..13cb3b276cda --- /dev/null +++ b/models/tf.py @@ -0,0 +1,486 @@ +import argparse +from copy import deepcopy +from pathlib import Path +import sys, traceback +import os +import logging + +import torch +import torch.nn as nn +import tensorflow as tf +if tf.__version__.startswith('1'): + tf.enable_eager_execution() +from tensorflow import keras +from tensorflow.keras import layers +from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 +import numpy as np + +from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad +from models.experimental import MixConv2d, CrossConv, C3 +from utils.general import check_anchor_order, make_divisible, check_file, set_logging +from utils.torch_utils import ( + time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, select_device) +from models.yolo import Detect +from utils.datasets import LoadImages + + +logger = logging.getLogger(__name__) + + +class tf_BN(keras.layers.Layer): + # TensorFlow BatchNormalization wrapper + def __init__(self, w=None): + super(tf_BN, self).__init__() + self.bn = keras.layers.BatchNormalization( + beta_initializer=keras.initializers.Constant(w.bias.numpy()), + gamma_initializer=keras.initializers.Constant(w.weight.numpy()), + moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()), + moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy())) + + def call(self, inputs): + return self.bn(inputs) + + +class tf_Pad(keras.layers.Layer): + def __init__(self, pad): + super(tf_Pad, self).__init__() + self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]]) + + def call(self, inputs): + return tf.pad(inputs, self.pad, mode='constant', constant_values=0) + + +class tf_Conv(keras.layers.Layer): + # Standard convolution + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): # ch_in, ch_out, weights, kernel, stride, padding, groups + super(tf_Conv, self).__init__() + assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" + assert isinstance(k, int), "Convolution with multiple kernels are not allowed." + + # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding) + # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch + if s == 1: + self.conv = keras.layers.Conv2D( + c2, k, s, 'SAME', use_bias=False, + kernel_initializer= + keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) + else: + self.pad = tf_Pad(autopad(k, p)) + self.conv = keras.Sequential([ + self.pad, + keras.layers.Conv2D( + c2, k, s, 'VALID', use_bias=False, + kernel_initializer= + keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) + ]) + + self.bn = tf_BN(w.bn) + + # YOLOv5 v3 uses Hardswish for activations + if isinstance(w.act, nn.LeakyReLU): + self.act = (lambda x: keras.activations.relu(x, alpha=0.1)) if act else tf.identity + elif isinstance(w.act, nn.Hardswish): + self.act = (lambda x: x * tf.nn.relu6(x+3) * 0.166666667) if act else tf.identity + + def call(self, inputs): + return self.act(self.bn(self.conv(inputs))) + + +class tf_Focus(keras.layers.Layer): + # Focus wh information into c-space + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): # ch_in, ch_out, kernel, stride, padding, groups + super(tf_Focus, self).__init__() + self.conv = tf_Conv(c1 * 4, c2, k, s, p, g, act, w.conv) + + def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c) + return self.conv(tf.concat([inputs[:, ::2, ::2, :], + inputs[:, 1::2, ::2, :], + inputs[:, ::2, 1::2, :], + inputs[:, 1::2, 1::2, :]], 3)) + + +class tf_Bottleneck(keras.layers.Layer): + # Standard bottleneck + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, shortcut, groups, expansion + super(tf_Bottleneck, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = tf_Conv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = tf_Conv(c_, c2, 3, 1, g=g, w=w.cv2) + self.add = shortcut and c1 == c2 + + def call(self, inputs): + return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs)) + + +class tf_Conv2d(keras.layers.Layer): + # Substitution for PyTorch nn.Conv2D + def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None): + super(tf_Conv2d, self).__init__() + assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" + self.conv = keras.layers.Conv2D( + c2, k, s, 'VALID', use_bias=bias, + kernel_initializer= + keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer= + keras.initializers.Constant(w.bias.numpy()) if bias else None, + ) + + def call(self, inputs): + return self.conv(inputs) + + +class tf_BottleneckCSP(keras.layers.Layer): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, number, shortcut, groups, expansion + super(tf_BottleneckCSP, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = tf_Conv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = tf_Conv2d(c1, c_, 1, 1, bias=False, w=w.cv2) + self.cv3 = tf_Conv2d(c_, c_, 1, 1, bias=False, w=w.cv3) + self.cv4 = tf_Conv(2 * c_, c2, 1, 1, w=w.cv4) + self.bn = tf_BN(w.bn) + self.act = lambda x: keras.activations.relu(x, alpha=0.1) + self.m = keras.Sequential([tf_Bottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) + + def call(self, inputs): + y1 = self.cv3(self.m(self.cv1(inputs))) + y2 = self.cv2(inputs) + return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3)))) + + +class tf_SPP(keras.layers.Layer): + # Spatial pyramid pooling layer used in YOLOv3-SPP + def __init__(self, c1, c2, k=(5, 9, 13), w=None): + super(tf_SPP, self).__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = tf_Conv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = tf_Conv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2) + self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding='SAME') for x in k] + + def call(self, inputs): + x = self.cv1(inputs) + return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3)) + + +class tf_Detect(keras.layers.Layer): + def __init__(self, nc=80, anchors=(), ch=(), w=None): # detection layer + super(tf_Detect, self).__init__() + # self.stride = None # strides computed during build + self.stride = tf.Variable([8, 16, 32], dtype=tf.float32) + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [tf.zeros(1)] * self.nl # init grid + a = tf.reshape(tf.convert_to_tensor(anchors, dtype=tf.float32), [self.nl, -1 ,2]) + self.anchors = tf.Variable(a, trainable=False) + self.anchor_grid = tf.reshape(tf.Variable(a, trainable=False), [self.nl, 1, -1, 1, 2]) + self.m = [tf_Conv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] + self.export = False # onnx export + self.training = True # set to False after building model + for i in range(self.nl): + ny, nx = opt.img_size[0] // self.stride[i], opt.img_size[1] // self.stride[i] + self.grid[i] = self._make_grid(nx, ny) + + def call(self, inputs): + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + x = [] + for i in range(self.nl): + x.append(self.m[i](inputs[i])) + # x(bs,20,20,255) to x(bs,3,20,20,85) + ny, nx = opt.img_size[0] // self.stride[i], opt.img_size[1] // self.stride[i] + x[i] = tf.transpose(tf.reshape(x[i], [-1, ny * nx, self.na, self.no]), [0, 2, 1, 3]) + + if not self.training: # inference + y = tf.sigmoid(x[i]) + xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy + wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] + y = tf.concat([xy, wh, y[..., 4:]], -1) + z.append(tf.reshape(y, [opt.batch_size, 3 * ny * nx, self.no])) + + return x if self.training else (tf.concat(z, 1), x) + + @staticmethod + def _make_grid(nx=20, ny=20): + # yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + # return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny)) + return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32) + + +class tf_Upsample(keras.layers.Layer): + def __init__(self, size, scale_factor, mode, w=None): + super(tf_Upsample, self).__init__() + assert scale_factor == 2, "scale_factor must be 2" + # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode) + self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * 2, x.shape[2] * 2), method=mode) + + def call(self, inputs): + return self.upsample(inputs) + + +class tf_Concat(keras.layers.Layer): + def __init__(self, dimension=1, w=None): + super(tf_Concat, self).__init__() + assert dimension == 1, "convert only NCHW to NHWC concat" + self.d = 3 + + def call(self, inputs): + return tf.concat(inputs, self.d) + + +def parse_model(d, ch, model): # model_dict, input_channels(3) + logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) + anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] + na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors + no = na * (nc + 5) # number of outputs = anchors * (classes + 5) + + layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out + for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args + m_str = m + m = eval(m) if isinstance(m, str) else m # eval strings + for j, a in enumerate(args): + try: + args[j] = eval(a) if isinstance(a, str) else a # eval strings + except: + pass + + n = max(round(n * gd), 1) if n > 1 else n # depth gain + if m in [nn.Conv2d, Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]: + c1, c2 = ch[f], args[0] + c2 = make_divisible(c2 * gw, 8) if c2 != no else c2 + + args = [c1, c2, *args[1:]] + if m in [BottleneckCSP, C3]: + args.insert(2, n) + n = 1 + elif m is nn.BatchNorm2d: + args = [ch[f]] + elif m is Concat: + c2 = sum([ch[-1 if x == -1 else x + 1] for x in f]) + elif m is Detect: + args.append([ch[x + 1] for x in f]) + if isinstance(args[1], int): # number of anchors + args[1] = [list(range(args[1] * 2))] * len(f) + else: + c2 = ch[f] + + tf_m = eval('tf_' + m_str.replace('nn.', '')) + m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \ + else tf_m(*args, w=model.model[i]) # module + + torch_m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module + t = str(m)[8:-2].replace('__main__.', '') # module type + np = sum([x.numel() for x in torch_m_.parameters()]) # number params + m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params + logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print + save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist + layers.append(m_) + ch.append(c2) + return keras.Sequential(layers), sorted(save) + + +def tf_check_anchor_order(m): + # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary + a = tf.reshape(tf.math.reduce_prod(m.anchor_grid, -1), [-1]) + da = a[-1] - a[0] # delta a + ds = m.stride[-1] - m.stride[0] # delta s + # Add numpy() in comparison for TensorFlow v1.15 + if tf.math.sign(da).numpy() != tf.math.sign(ds).numpy(): # same order + print('Reversing anchor order') + m.anchors[:] = tf.reverse(m.anchors, 0) + m.anchor_grid[:] = tf.reverse(m.anchor_grid, [0]) + + +class tf_Model(): + def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None): # model, input channels, number of classes + super(tf_Model, self).__init__() + if isinstance(cfg, dict): + self.yaml = cfg # model dict + else: # is *.yaml + import yaml # for torch hub + self.yaml_file = Path(cfg).name + with open(cfg) as f: + self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict + + # Define model + if nc and nc != self.yaml['nc']: + print('Overriding %s nc=%g with nc=%g' % (cfg, self.yaml['nc'], nc)) + self.yaml['nc'] = nc # override yaml value + self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model) # model, savelist, ch_out + # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) + + # Build strides, anchors + m = self.model.layers[-1] # Detect() + if isinstance(m, tf_Detect): + # s = 128 # 2x min stride + # m.stride = tf.convert_to_tensor([s / x.shape[-2] for x in self.predict(tf.zeros([1, s, s, ch]))]) # forward + m.anchors = tf.cast(m.anchors, dtype=tf.float32) / tf.reshape(m.stride, [-1, 1, 1]) + tf_check_anchor_order(m) + # self.stride = m.stride + # self._initialize_biases() # only run once + # print('Strides: %s' % m.stride.tolist()) + + # Init weights, biases + # torch_utils.initialize_weights(self) + + # keras.layers.Layer has no summary/info method + # self.info() + + def predict(self, inputs, profile=False): + y, dt = [], [] # outputs + x = inputs + for i, m in enumerate(self.model.layers): + if m.f != -1: # if not from previous layer + x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers + + if profile: + try: + import thop + o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # FLOPS + except: + o = 0 + t = torch_utils.time_synchronized() + for _ in range(10): + _ = m(x) + dt.append((torch_utils.time_synchronized() - t) * 100) + print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type)) + + x = m(x) # run + y.append(x if m.i in self.savelist else None) # save output + + if profile: + print('%.1fms total' % sum(dt)) + return x + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--cfg', type=str, default='./models/yolov5s.yaml', help='cfg path') + parser.add_argument('--weights', type=str, default='./weights/yolov5s.pt', help='weights path') + parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') + parser.add_argument('--batch-size', type=int, default=1, help='batch size') + parser.add_argument('--no-tfl-detect', action='store_true', dest='no_tfl_detect', help='remove Detect module in TFLite model') + parser.add_argument('--source', type=str, default='/dataset/coco/coco2017/train2017', help='source') # file/folder, 0 for webcam + parser.add_argument('--ncalib', type=int, default=100, help='number of calibration images') # file/folder, 0 for webcam + parser.add_argument('--tfl-int8', action='store_true', dest='tfl_int8', help='export TFLite int8 model') + opt = parser.parse_args() + opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand + print(opt) + + # Input + img = torch.zeros((opt.batch_size, 3, *opt.img_size)) # image size(1,3,320,192) iDetection + + # Load PyTorch model + model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float() + model.eval() + model.model[-1].export = False # set Detect() layer export=True + y = model(img) # dry run + nc = y[0].shape[-1] - 5 + + # TensorFlow saved_model export + try: + print('\nStarting TensorFlow saved_model export with TensorFlow %s...' % tf.__version__) + tf_model = tf_Model(opt.cfg, model=model, nc=nc) + img = tf.zeros((opt.batch_size, *opt.img_size, 3)) # NHWC Input for TensorFlow + + m = tf_model.model.layers[-1] + assert isinstance(m, tf_Detect), "the last layer must be Detect" + m.training = False + y = tf_model.predict(img) + + inputs = keras.Input(shape=(*opt.img_size, 3)) + keras_model = keras.Model(inputs=inputs, outputs=tf_model.predict(inputs)) + keras_model.summary() + path = opt.weights.replace('.pt', '_saved_model') # filename + keras_model.save(path, save_format='tf') + print('TensorFlow saved_model export success, saved as %s' % path) + except Exception as e: + print('TensorFlow saved_model export failure: %s' % e) + traceback.print_exc(file=sys.stdout) + + # TensorFlow GraphDef export + try: + print('\nStarting TensorFlow GraphDef export with TensorFlow %s...' % tf.__version__) + + # https://github.com/leimao/Frozen_Graph_TensorFlow + full_model = tf.function(lambda x: keras_model(x)) + full_model = full_model.get_concrete_function( + tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)) + + frozen_func = convert_variables_to_constants_v2(full_model) + frozen_func.graph.as_graph_def() + f = opt.weights.replace('.pt', '.pb') # filename + tf.io.write_graph(graph_or_graph_def=frozen_func.graph, + logdir=os.path.dirname(f), + name=os.path.basename(f), + as_text=False) + + print('TensorFlow GraphDef export success, saved as %s' % f) + except Exception as e: + print('TensorFlow GraphDef export failure: %s' % e) + traceback.print_exc(file=sys.stdout) + + # TFLite model export + try: + print('\nStarting TFLite export with TensorFlow %s...' % tf.__version__) + if opt.no_tfl_detect: + print("Don't export Detect module") + m.training = True + keras_model = keras.Model(inputs=inputs, outputs=tf_model.predict(inputs)) + # fp32 TFLite model export + # converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) + # converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] + # converter.allow_custom_ops = False + # converter.experimental_new_converter = True + # tflite_model = converter.convert() + # f = opt.weights.replace('.pt', '.tflite') # filename + # open(f, "wb").write(tflite_model) + + # fp16 TFLite model export + converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) + converter.optimizations = [tf.lite.Optimize.DEFAULT] + converter.target_spec.supported_types = [tf.float16] + converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] + converter.allow_custom_ops = False + converter.experimental_new_converter = True + tflite_model = converter.convert() + f = opt.weights.replace('.pt', '-fp16.tflite') # filename + open(f, "wb").write(tflite_model) + print('\nTFLite export success, saved as %s' % f) + + # int8 TFLite model export + if opt.tfl_int8: + converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) + converter.optimizations = [tf.lite.Optimize.DEFAULT] + dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) + + def representative_dataset_gen(): + n = 0 + for path, img, im0s, vid_cap in dataset: + # Get sample input data as a numpy array in a method of your choosing. + n += 1 + input = np.transpose(img, [1, 2, 0]) + input = np.expand_dims(input, axis=0).astype(np.float32) + input /= 255.0 + yield [input] + if n >= opt.ncalib: + break + + converter.representative_dataset = representative_dataset_gen + converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] + converter.inference_input_type = tf.uint8 # or tf.int8 + converter.inference_output_type = tf.uint8 # or tf.int8 + converter.allow_custom_ops = False + converter.experimental_new_converter = True + tflite_model = converter.convert() + f = opt.weights.replace('.pt', '-int8.tflite') # filename + open(f, "wb").write(tflite_model) + print('\nTFLite (int8) export success, saved as %s' % f) + + except Exception as e: + print('\nTFLite export failure: %s' % e) + traceback.print_exc(file=sys.stdout) + From 565e6206b9113d8341b40602786d238c39bf6138 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Fri, 9 Oct 2020 17:32:15 +0800 Subject: [PATCH 21/92] Set auto=False for int8 calibration --- utils/datasets.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/utils/datasets.py b/utils/datasets.py index 9001832aadec..15d71933b77a 100755 --- a/utils/datasets.py +++ b/utils/datasets.py @@ -119,7 +119,7 @@ def __iter__(self): class LoadImages: # for inference - def __init__(self, path, img_size=640): + def __init__(self, path, img_size=640, auto=True): p = str(Path(path)) # os-agnostic p = os.path.abspath(p) # absolute path if '*' in p: @@ -140,6 +140,7 @@ def __init__(self, path, img_size=640): self.nf = ni + nv # number of files self.video_flag = [False] * ni + [True] * nv self.mode = 'image' + self.auto = auto if any(videos): self.new_video(videos[0]) # new video else: @@ -181,7 +182,7 @@ def __next__(self): print('image %g/%g %s: ' % (self.count, self.nf, path), end='') # Padded resize - img = letterbox(img0, new_shape=self.img_size)[0] + img = letterbox(img0, new_shape=self.img_size, auto=self.auto)[0] # Convert img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 @@ -256,7 +257,7 @@ def __len__(self): class LoadStreams: # multiple IP or RTSP cameras - def __init__(self, sources='streams.txt', img_size=640): + def __init__(self, sources='streams.txt', img_size=640, auto=True): self.mode = 'stream' self.img_size = img_size @@ -269,6 +270,7 @@ def __init__(self, sources='streams.txt', img_size=640): n = len(sources) self.imgs = [None] * n self.sources = [clean_str(x) for x in sources] # clean source names for later + self.auto = auto for i, s in enumerate(sources): # Start the thread to read frames from the video stream print('%g/%g: %s... ' % (i + 1, n, s), end='') @@ -284,7 +286,7 @@ def __init__(self, sources='streams.txt', img_size=640): print('') # newline # check for common shapes - s = np.stack([letterbox(x, new_shape=self.img_size)[0].shape for x in self.imgs], 0) # inference shapes + s = np.stack([letterbox(x, new_shape=self.img_size, auto=self.auto)[0].shape for x in self.imgs], 0) # inference shapes self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal if not self.rect: print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.') @@ -313,7 +315,7 @@ def __next__(self): raise StopIteration # Letterbox - img = [letterbox(x, new_shape=self.img_size, auto=self.rect)[0] for x in img0] + img = [letterbox(x, new_shape=self.img_size, auto=self.rect and self.auto)[0] for x in img0] # Stack img = np.stack(img, 0) From fc265614e7e92da6bccb4108ffdb0da70f421564 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Tue, 20 Oct 2020 19:41:50 +0800 Subject: [PATCH 22/92] Update requirements.txt for TensorFlow and TFLite export --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 3c23f2b750a2..11fec991d7ef 100755 --- a/requirements.txt +++ b/requirements.txt @@ -24,6 +24,7 @@ pandas # coremltools==4.0 # onnx>=1.8.0 # scikit-learn==0.19.2 # for coreml quantization +# tensorflow==2.3.1 # extras -------------------------------------- thop # FLOPS computation From 05cc3892b481fd094e7f4e3845fa4830a7c0e4de Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Fri, 23 Oct 2020 17:51:01 +0800 Subject: [PATCH 23/92] Read anchors directly from PyTorch weights --- models/tf.py | 23 +++++------------------ 1 file changed, 5 insertions(+), 18 deletions(-) diff --git a/models/tf.py b/models/tf.py index 13cb3b276cda..6a3b8feff45b 100644 --- a/models/tf.py +++ b/models/tf.py @@ -165,16 +165,16 @@ def call(self, inputs): class tf_Detect(keras.layers.Layer): def __init__(self, nc=80, anchors=(), ch=(), w=None): # detection layer super(tf_Detect, self).__init__() - # self.stride = None # strides computed during build - self.stride = tf.Variable([8, 16, 32], dtype=tf.float32) + self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32) self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors self.grid = [tf.zeros(1)] * self.nl # init grid - a = tf.reshape(tf.convert_to_tensor(anchors, dtype=tf.float32), [self.nl, -1 ,2]) - self.anchors = tf.Variable(a, trainable=False) - self.anchor_grid = tf.reshape(tf.Variable(a, trainable=False), [self.nl, 1, -1, 1, 2]) + self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32) + self.anchor_grid = tf.reshape( + tf.convert_to_tensor(w.anchor_grid.numpy(), dtype=tf.float32), + [self.nl, 1, -1, 1, 2]) self.m = [tf_Conv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] self.export = False # onnx export self.training = True # set to False after building model @@ -310,24 +310,11 @@ def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None): # model, inp print('Overriding %s nc=%g with nc=%g' % (cfg, self.yaml['nc'], nc)) self.yaml['nc'] = nc # override yaml value self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model) # model, savelist, ch_out - # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) # Build strides, anchors m = self.model.layers[-1] # Detect() if isinstance(m, tf_Detect): - # s = 128 # 2x min stride - # m.stride = tf.convert_to_tensor([s / x.shape[-2] for x in self.predict(tf.zeros([1, s, s, ch]))]) # forward - m.anchors = tf.cast(m.anchors, dtype=tf.float32) / tf.reshape(m.stride, [-1, 1, 1]) tf_check_anchor_order(m) - # self.stride = m.stride - # self._initialize_biases() # only run once - # print('Strides: %s' % m.stride.tolist()) - - # Init weights, biases - # torch_utils.initialize_weights(self) - - # keras.layers.Layer has no summary/info method - # self.info() def predict(self, inputs, profile=False): y, dt = [], [] # outputs From 817fcf8db1ad4d4597973aef794a0233175fb134 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Mon, 2 Nov 2020 18:34:19 +0800 Subject: [PATCH 24/92] Add --tf-nms to append NMS in TensorFlow SavedModel and GraphDef export --- models/tf.py | 150 +++++++++++++++++++++++++++------------------------ 1 file changed, 81 insertions(+), 69 deletions(-) diff --git a/models/tf.py b/models/tf.py index 6a3b8feff45b..50c1066c7287 100644 --- a/models/tf.py +++ b/models/tf.py @@ -317,29 +317,33 @@ def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None): # model, inp tf_check_anchor_order(m) def predict(self, inputs, profile=False): - y, dt = [], [] # outputs + y = [] # outputs x = inputs for i, m in enumerate(self.model.layers): if m.f != -1: # if not from previous layer x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers - if profile: - try: - import thop - o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # FLOPS - except: - o = 0 - t = torch_utils.time_synchronized() - for _ in range(10): - _ = m(x) - dt.append((torch_utils.time_synchronized() - t) * 100) - print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type)) - x = m(x) # run y.append(x if m.i in self.savelist else None) # save output - if profile: - print('%.1fms total' % sum(dt)) + # Add TensorFlow NMS + if opt.tf_nms: + xy = x[0][:, :, 0:2] + wh = x[0][:, :, 2:4] + x1 = xy[:, :, 0:1] - wh[:, :, 0:1] / 2 + x2 = xy[:, :, 0:1] + wh[:, :, 0:1] / 2 + y1 = xy[:, :, 1:2] - wh[:, :, 1:2] / 2 + y2 = xy[:, :, 1:2] + wh[:, :, 1:2] / 2 + xyxy = tf.concat([x1, y1, x2, y2], 2) + boxes = tf.expand_dims(xyxy, 2) + probs = x[0][:, :, 4:5] + classes = x[0][:, :, 5:] + scores = probs * classes + nms = tf.image.combined_non_max_suppression( + boxes, scores, opt.topk_per_class, opt.topk_all, opt.iou_thres, opt.score_thres, + clip_boxes=False) + return nms, x[1] + return x @@ -353,6 +357,11 @@ def predict(self, inputs, profile=False): parser.add_argument('--source', type=str, default='/dataset/coco/coco2017/train2017', help='source') # file/folder, 0 for webcam parser.add_argument('--ncalib', type=int, default=100, help='number of calibration images') # file/folder, 0 for webcam parser.add_argument('--tfl-int8', action='store_true', dest='tfl_int8', help='export TFLite int8 model') + parser.add_argument('--tf-nms', action='store_true', dest='tf_nms', help='add TensorFlow NMS (without TFLite export)') + parser.add_argument('--topk-per-class', type=int, default=100, help='topk per class to keep in NMS') + parser.add_argument('--topk-all', type=int, default=100, help='topk for all classes to keep in NMS') + parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS') + parser.add_argument('--score-thres', type=float, default=0.4, help='score threshold for NMS') opt = parser.parse_args() opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand print(opt) @@ -410,64 +419,67 @@ def predict(self, inputs, profile=False): print('TensorFlow GraphDef export failure: %s' % e) traceback.print_exc(file=sys.stdout) - # TFLite model export - try: - print('\nStarting TFLite export with TensorFlow %s...' % tf.__version__) - if opt.no_tfl_detect: - print("Don't export Detect module") - m.training = True - keras_model = keras.Model(inputs=inputs, outputs=tf_model.predict(inputs)) - # fp32 TFLite model export - # converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) - # converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] - # converter.allow_custom_ops = False - # converter.experimental_new_converter = True - # tflite_model = converter.convert() - # f = opt.weights.replace('.pt', '.tflite') # filename - # open(f, "wb").write(tflite_model) - - # fp16 TFLite model export - converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) - converter.optimizations = [tf.lite.Optimize.DEFAULT] - converter.target_spec.supported_types = [tf.float16] - converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] - converter.allow_custom_ops = False - converter.experimental_new_converter = True - tflite_model = converter.convert() - f = opt.weights.replace('.pt', '-fp16.tflite') # filename - open(f, "wb").write(tflite_model) - print('\nTFLite export success, saved as %s' % f) - - # int8 TFLite model export - if opt.tfl_int8: + # NMS doesn't support TFLite export + + if not opt.tf_nms: + # TFLite model export + try: + print('\nStarting TFLite export with TensorFlow %s...' % tf.__version__) + if opt.no_tfl_detect: + print("Don't export Detect module") + m.training = True + keras_model = keras.Model(inputs=inputs, outputs=tf_model.predict(inputs)) + # fp32 TFLite model export + # converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) + # converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] + # converter.allow_custom_ops = False + # converter.experimental_new_converter = True + # tflite_model = converter.convert() + # f = opt.weights.replace('.pt', '.tflite') # filename + # open(f, "wb").write(tflite_model) + + # fp16 TFLite model export converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] - dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) - - def representative_dataset_gen(): - n = 0 - for path, img, im0s, vid_cap in dataset: - # Get sample input data as a numpy array in a method of your choosing. - n += 1 - input = np.transpose(img, [1, 2, 0]) - input = np.expand_dims(input, axis=0).astype(np.float32) - input /= 255.0 - yield [input] - if n >= opt.ncalib: - break - - converter.representative_dataset = representative_dataset_gen - converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] - converter.inference_input_type = tf.uint8 # or tf.int8 - converter.inference_output_type = tf.uint8 # or tf.int8 + converter.target_spec.supported_types = [tf.float16] + converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] converter.allow_custom_ops = False converter.experimental_new_converter = True tflite_model = converter.convert() - f = opt.weights.replace('.pt', '-int8.tflite') # filename + f = opt.weights.replace('.pt', '-fp16.tflite') # filename open(f, "wb").write(tflite_model) - print('\nTFLite (int8) export success, saved as %s' % f) - - except Exception as e: - print('\nTFLite export failure: %s' % e) - traceback.print_exc(file=sys.stdout) + print('\nTFLite export success, saved as %s' % f) + + # int8 TFLite model export + if opt.tfl_int8: + converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) + converter.optimizations = [tf.lite.Optimize.DEFAULT] + dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) + + def representative_dataset_gen(): + n = 0 + for path, img, im0s, vid_cap in dataset: + # Get sample input data as a numpy array in a method of your choosing. + n += 1 + input = np.transpose(img, [1, 2, 0]) + input = np.expand_dims(input, axis=0).astype(np.float32) + input /= 255.0 + yield [input] + if n >= opt.ncalib: + break + + converter.representative_dataset = representative_dataset_gen + converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] + converter.inference_input_type = tf.uint8 # or tf.int8 + converter.inference_output_type = tf.uint8 # or tf.int8 + converter.allow_custom_ops = False + converter.experimental_new_converter = True + tflite_model = converter.convert() + f = opt.weights.replace('.pt', '-int8.tflite') # filename + open(f, "wb").write(tflite_model) + print('\nTFLite (int8) export success, saved as %s' % f) + + except Exception as e: + print('\nTFLite export failure: %s' % e) + traceback.print_exc(file=sys.stdout) From 9a4ce5e8c5a796de303729010295eecfceccb607 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Mon, 16 Nov 2020 11:11:00 +0800 Subject: [PATCH 25/92] Remove check_anchor_order, check_file, set_logging from import --- models/tf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/models/tf.py b/models/tf.py index 50c1066c7287..7d16e6ca22f7 100644 --- a/models/tf.py +++ b/models/tf.py @@ -17,7 +17,7 @@ from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad from models.experimental import MixConv2d, CrossConv, C3 -from utils.general import check_anchor_order, make_divisible, check_file, set_logging +from utils.general import make_divisible from utils.torch_utils import ( time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, select_device) from models.yolo import Detect From 719479ef223eece64d08a78cd31eda458e9aad26 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 28 Nov 2020 14:10:48 +0100 Subject: [PATCH 26/92] Reformat code and optimize imports --- models/tf.py | 124 ++++++++++++++++++++++----------------------------- 1 file changed, 54 insertions(+), 70 deletions(-) diff --git a/models/tf.py b/models/tf.py index 7d16e6ca22f7..9e1a01549f02 100644 --- a/models/tf.py +++ b/models/tf.py @@ -1,29 +1,27 @@ import argparse +import logging +import os +import sys +import traceback from copy import deepcopy from pathlib import Path -import sys, traceback -import os -import logging +import tensorflow as tf import torch import torch.nn as nn -import tensorflow as tf + if tf.__version__.startswith('1'): tf.enable_eager_execution() from tensorflow import keras -from tensorflow.keras import layers from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 import numpy as np from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad from models.experimental import MixConv2d, CrossConv, C3 from utils.general import make_divisible -from utils.torch_utils import ( - time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, select_device) from models.yolo import Detect from utils.datasets import LoadImages - logger = logging.getLogger(__name__) @@ -32,10 +30,10 @@ class tf_BN(keras.layers.Layer): def __init__(self, w=None): super(tf_BN, self).__init__() self.bn = keras.layers.BatchNormalization( - beta_initializer=keras.initializers.Constant(w.bias.numpy()), - gamma_initializer=keras.initializers.Constant(w.weight.numpy()), - moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()), - moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy())) + beta_initializer=keras.initializers.Constant(w.bias.numpy()), + gamma_initializer=keras.initializers.Constant(w.weight.numpy()), + moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()), + moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy())) def call(self, inputs): return self.bn(inputs) @@ -52,7 +50,8 @@ def call(self, inputs): class tf_Conv(keras.layers.Layer): # Standard convolution - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): # ch_in, ch_out, weights, kernel, stride, padding, groups + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): + # ch_in, ch_out, weights, kernel, stride, padding, groups super(tf_Conv, self).__init__() assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" assert isinstance(k, int), "Convolution with multiple kernels are not allowed." @@ -61,38 +60,38 @@ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): # ch_in, c # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch if s == 1: self.conv = keras.layers.Conv2D( - c2, k, s, 'SAME', use_bias=False, - kernel_initializer= - keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) + c2, k, s, 'SAME', use_bias=False, + kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) else: self.pad = tf_Pad(autopad(k, p)) self.conv = keras.Sequential([ - self.pad, + self.pad, keras.layers.Conv2D( c2, k, s, 'VALID', use_bias=False, - kernel_initializer= - keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) + kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) ]) - self.bn = tf_BN(w.bn) + self.bn = tf_BN(w.bn) if hasattr(w, 'bn') else tf.identity # YOLOv5 v3 uses Hardswish for activations if isinstance(w.act, nn.LeakyReLU): self.act = (lambda x: keras.activations.relu(x, alpha=0.1)) if act else tf.identity - elif isinstance(w.act, nn.Hardswish): - self.act = (lambda x: x * tf.nn.relu6(x+3) * 0.166666667) if act else tf.identity + elif isinstance(w.act, nn.Hardswish): + self.act = (lambda x: x * tf.nn.relu6(x + 3) * 0.166666667) if act else tf.identity def call(self, inputs): - return self.act(self.bn(self.conv(inputs))) + return self.act(self.bn(self.conv(inputs))) if hasattr(self, 'bn') else self.act(self.conv(inputs)) class tf_Focus(keras.layers.Layer): # Focus wh information into c-space - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): # ch_in, ch_out, kernel, stride, padding, groups + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): + # ch_in, ch_out, kernel, stride, padding, groups super(tf_Focus, self).__init__() self.conv = tf_Conv(c1 * 4, c2, k, s, p, g, act, w.conv) def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c) + # inputs = inputs / 255. # normalize 0-255 to 0-1 return self.conv(tf.concat([inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], @@ -118,12 +117,9 @@ def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None): super(tf_Conv2d, self).__init__() assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" self.conv = keras.layers.Conv2D( - c2, k, s, 'VALID', use_bias=bias, - kernel_initializer= - keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()), - bias_initializer= - keras.initializers.Constant(w.bias.numpy()) if bias else None, - ) + c2, k, s, 'VALID', use_bias=bias, + kernel_initializer=keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None, ) def call(self, inputs): return self.conv(inputs) @@ -131,7 +127,8 @@ def call(self, inputs): class tf_BottleneckCSP(keras.layers.Layer): # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, number, shortcut, groups, expansion + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): + # ch_in, ch_out, number, shortcut, groups, expansion super(tf_BottleneckCSP, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = tf_Conv(c1, c_, 1, 1, w=w.cv1) @@ -172,12 +169,11 @@ def __init__(self, nc=80, anchors=(), ch=(), w=None): # detection layer self.na = len(anchors[0]) // 2 # number of anchors self.grid = [tf.zeros(1)] * self.nl # init grid self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32) - self.anchor_grid = tf.reshape( - tf.convert_to_tensor(w.anchor_grid.numpy(), dtype=tf.float32), - [self.nl, 1, -1, 1, 2]) + self.anchor_grid = tf.reshape(tf.convert_to_tensor(w.anchor_grid.numpy(), dtype=tf.float32), + [self.nl, 1, -1, 1, 2]) self.m = [tf_Conv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] self.export = False # onnx export - self.training = True # set to False after building model + self.training = True # set to False after building model for i in range(self.nl): ny, nx = opt.img_size[0] // self.stride[i], opt.img_size[1] // self.stride[i] self.grid[i] = self._make_grid(nx, ny) @@ -269,7 +265,7 @@ def parse_model(d, ch, model): # model_dict, input_channels(3) tf_m = eval('tf_' + m_str.replace('nn.', '')) m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \ - else tf_m(*args, w=model.model[i]) # module + else tf_m(*args, w=model.model[i]) # module torch_m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module t = str(m)[8:-2].replace('__main__.', '') # module type @@ -282,18 +278,6 @@ def parse_model(d, ch, model): # model_dict, input_channels(3) return keras.Sequential(layers), sorted(save) -def tf_check_anchor_order(m): - # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary - a = tf.reshape(tf.math.reduce_prod(m.anchor_grid, -1), [-1]) - da = a[-1] - a[0] # delta a - ds = m.stride[-1] - m.stride[0] # delta s - # Add numpy() in comparison for TensorFlow v1.15 - if tf.math.sign(da).numpy() != tf.math.sign(ds).numpy(): # same order - print('Reversing anchor order') - m.anchors[:] = tf.reverse(m.anchors, 0) - m.anchor_grid[:] = tf.reverse(m.anchor_grid, [0]) - - class tf_Model(): def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None): # model, input channels, number of classes super(tf_Model, self).__init__() @@ -311,11 +295,6 @@ def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None): # model, inp self.yaml['nc'] = nc # override yaml value self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model) # model, savelist, ch_out - # Build strides, anchors - m = self.model.layers[-1] # Detect() - if isinstance(m, tf_Detect): - tf_check_anchor_order(m) - def predict(self, inputs, profile=False): y = [] # outputs x = inputs @@ -326,24 +305,30 @@ def predict(self, inputs, profile=False): x = m(x) # run y.append(x if m.i in self.savelist else None) # save output + # Boxes + xy = x[0][:, :, 0:2] + wh = x[0][:, :, 2:4] + x1 = xy[:, :, 0:1] - wh[:, :, 0:1] / 2 + x2 = xy[:, :, 0:1] + wh[:, :, 0:1] / 2 + y1 = xy[:, :, 1:2] - wh[:, :, 1:2] / 2 + y2 = xy[:, :, 1:2] + wh[:, :, 1:2] / 2 + xyxy = tf.concat([x1, y1, x2, y2], 2) + # Add TensorFlow NMS if opt.tf_nms: - xy = x[0][:, :, 0:2] - wh = x[0][:, :, 2:4] - x1 = xy[:, :, 0:1] - wh[:, :, 0:1] / 2 - x2 = xy[:, :, 0:1] + wh[:, :, 0:1] / 2 - y1 = xy[:, :, 1:2] - wh[:, :, 1:2] / 2 - y2 = xy[:, :, 1:2] + wh[:, :, 1:2] / 2 - xyxy = tf.concat([x1, y1, x2, y2], 2) boxes = tf.expand_dims(xyxy, 2) probs = x[0][:, :, 4:5] classes = x[0][:, :, 5:] scores = probs * classes nms = tf.image.combined_non_max_suppression( - boxes, scores, opt.topk_per_class, opt.topk_all, opt.iou_thres, opt.score_thres, - clip_boxes=False) + boxes, scores, opt.topk_per_class, opt.topk_all, opt.iou_thres, opt.score_thres, clip_boxes=False) return nms, x[1] + # x = x[0] + # conf = x[..., 4:5] + # cls = x[..., 5:] + # return [conf * cls, xyxy] # output only first tensor, [1,6300,80], [1,6300,4] + # return x[0] # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...] return x @@ -351,13 +336,13 @@ def predict(self, inputs, profile=False): parser = argparse.ArgumentParser() parser.add_argument('--cfg', type=str, default='./models/yolov5s.yaml', help='cfg path') parser.add_argument('--weights', type=str, default='./weights/yolov5s.pt', help='weights path') - parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') + parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width parser.add_argument('--batch-size', type=int, default=1, help='batch size') - parser.add_argument('--no-tfl-detect', action='store_true', dest='no_tfl_detect', help='remove Detect module in TFLite model') - parser.add_argument('--source', type=str, default='/dataset/coco/coco2017/train2017', help='source') # file/folder, 0 for webcam - parser.add_argument('--ncalib', type=int, default=100, help='number of calibration images') # file/folder, 0 for webcam + parser.add_argument('--no-tfl-detect', action='store_true', help='remove Detect() from TFLite model') + parser.add_argument('--source', type=str, default='data/images', help='source') + parser.add_argument('--ncalib', type=int, default=100, help='number of calibration images') parser.add_argument('--tfl-int8', action='store_true', dest='tfl_int8', help='export TFLite int8 model') - parser.add_argument('--tf-nms', action='store_true', dest='tf_nms', help='add TensorFlow NMS (without TFLite export)') + parser.add_argument('--tf-nms', action='store_true', dest='tf_nms', help='TF NMS (without TFLite export)') parser.add_argument('--topk-per-class', type=int, default=100, help='topk per class to keep in NMS') parser.add_argument('--topk-all', type=int, default=100, help='topk for all classes to keep in NMS') parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS') @@ -370,7 +355,7 @@ def predict(self, inputs, profile=False): img = torch.zeros((opt.batch_size, 3, *opt.img_size)) # image size(1,3,320,192) iDetection # Load PyTorch model - model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float() + model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float() # .fuse() model.eval() model.model[-1].export = False # set Detect() layer export=True y = model(img) # dry run @@ -404,7 +389,7 @@ def predict(self, inputs, profile=False): # https://github.com/leimao/Frozen_Graph_TensorFlow full_model = tf.function(lambda x: keras_model(x)) full_model = full_model.get_concrete_function( - tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)) + tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)) frozen_func = convert_variables_to_constants_v2(full_model) frozen_func.graph.as_graph_def() @@ -482,4 +467,3 @@ def representative_dataset_gen(): except Exception as e: print('\nTFLite export failure: %s' % e) traceback.print_exc(file=sys.stdout) - From 243fa7fa62b4158313ce3326752c9625862c7526 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 28 Nov 2020 14:29:55 +0100 Subject: [PATCH 27/92] Autodownload model and check cfg --- models/tf.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/models/tf.py b/models/tf.py index 9e1a01549f02..b433442840cb 100644 --- a/models/tf.py +++ b/models/tf.py @@ -18,9 +18,10 @@ from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad from models.experimental import MixConv2d, CrossConv, C3 -from utils.general import make_divisible +from utils.general import make_divisible, check_file from models.yolo import Detect from utils.datasets import LoadImages +from utils.google_utils import attempt_download logger = logging.getLogger(__name__) @@ -334,8 +335,8 @@ def predict(self, inputs, profile=False): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--cfg', type=str, default='./models/yolov5s.yaml', help='cfg path') - parser.add_argument('--weights', type=str, default='./weights/yolov5s.pt', help='weights path') + parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='cfg path') + parser.add_argument('--weights', type=str, default='yolov5s.pt', help='weights path') parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width parser.add_argument('--batch-size', type=int, default=1, help='batch size') parser.add_argument('--no-tfl-detect', action='store_true', help='remove Detect() from TFLite model') @@ -348,6 +349,7 @@ def predict(self, inputs, profile=False): parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS') parser.add_argument('--score-thres', type=float, default=0.4, help='score threshold for NMS') opt = parser.parse_args() + opt.cfg = check_file(opt.cfg) # check file opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand print(opt) @@ -355,6 +357,7 @@ def predict(self, inputs, profile=False): img = torch.zeros((opt.batch_size, 3, *opt.img_size)) # image size(1,3,320,192) iDetection # Load PyTorch model + attempt_download(opt.weights) model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float() # .fuse() model.eval() model.model[-1].export = False # set Detect() layer export=True @@ -441,6 +444,7 @@ def predict(self, inputs, profile=False): converter.optimizations = [tf.lite.Optimize.DEFAULT] dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) + def representative_dataset_gen(): n = 0 for path, img, im0s, vid_cap in dataset: @@ -453,6 +457,7 @@ def representative_dataset_gen(): if n >= opt.ncalib: break + converter.representative_dataset = representative_dataset_gen converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.inference_input_type = tf.uint8 # or tf.int8 From 3b6cf12da445a03335f2dba50ac9f3936dae786a Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 28 Nov 2020 16:24:21 +0100 Subject: [PATCH 28/92] update --source path, img-size to 320, single output --- models/tf.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/models/tf.py b/models/tf.py index b433442840cb..4f915ed8ae93 100644 --- a/models/tf.py +++ b/models/tf.py @@ -329,18 +329,17 @@ def predict(self, inputs, profile=False): # conf = x[..., 4:5] # cls = x[..., 5:] # return [conf * cls, xyxy] # output only first tensor, [1,6300,80], [1,6300,4] - # return x[0] # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...] - return x + return x[0] # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...] if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='cfg path') parser.add_argument('--weights', type=str, default='yolov5s.pt', help='weights path') - parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width + parser.add_argument('--img-size', nargs='+', type=int, default=[320, 320], help='image size') # height, width parser.add_argument('--batch-size', type=int, default=1, help='batch size') parser.add_argument('--no-tfl-detect', action='store_true', help='remove Detect() from TFLite model') - parser.add_argument('--source', type=str, default='data/images', help='source') + parser.add_argument('--source', type=str, default='../data/images', help='source') parser.add_argument('--ncalib', type=int, default=100, help='number of calibration images') parser.add_argument('--tfl-int8', action='store_true', dest='tfl_int8', help='export TFLite int8 model') parser.add_argument('--tf-nms', action='store_true', dest='tf_nms', help='TF NMS (without TFLite export)') @@ -442,6 +441,12 @@ def predict(self, inputs, profile=False): if opt.tfl_int8: converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] + + # # Download COCO128 + # with open(check_file('coco128.yaml')) as f: + # data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict + # check_dataset(data_dict) # check + dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) From 5a5f949d6e7c8e846460a9de7894b99087fce462 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 28 Nov 2020 16:58:19 +0100 Subject: [PATCH 29/92] Adjust representative_dataset --- models/tf.py | 59 +++++++++++++++++++++++++++------------------------- 1 file changed, 31 insertions(+), 28 deletions(-) diff --git a/models/tf.py b/models/tf.py index 4f915ed8ae93..d0bf2920d2fe 100644 --- a/models/tf.py +++ b/models/tf.py @@ -9,6 +9,7 @@ import tensorflow as tf import torch import torch.nn as nn +import yaml if tf.__version__.startswith('1'): tf.enable_eager_execution() @@ -18,7 +19,7 @@ from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad from models.experimental import MixConv2d, CrossConv, C3 -from utils.general import make_divisible, check_file +from utils.general import make_divisible, check_file, check_dataset from models.yolo import Detect from utils.datasets import LoadImages from utils.google_utils import attempt_download @@ -332,6 +333,20 @@ def predict(self, inputs, profile=False): return x[0] # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...] +def representative_dataset_gen(): + # Representative dataset for use with converter.representative_dataset + n = 0 + for path, img, im0s, vid_cap in dataset: + # Get sample input data as a numpy array in a method of your choosing. + n += 1 + input = np.transpose(img, [1, 2, 0]) + input = np.expand_dims(input, axis=0).astype(np.float32) + input /= 255.0 + yield [input] + if n >= opt.ncalib: + break + + if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='cfg path') @@ -339,7 +354,7 @@ def predict(self, inputs, profile=False): parser.add_argument('--img-size', nargs='+', type=int, default=[320, 320], help='image size') # height, width parser.add_argument('--batch-size', type=int, default=1, help='batch size') parser.add_argument('--no-tfl-detect', action='store_true', help='remove Detect() from TFLite model') - parser.add_argument('--source', type=str, default='../data/images', help='source') + parser.add_argument('--source', type=str, default='../data/coco128.yaml', help='dir of images or data.yaml file') parser.add_argument('--ncalib', type=int, default=100, help='number of calibration images') parser.add_argument('--tfl-int8', action='store_true', dest='tfl_int8', help='export TFLite int8 model') parser.add_argument('--tf-nms', action='store_true', dest='tf_nms', help='TF NMS (without TFLite export)') @@ -416,7 +431,16 @@ def predict(self, inputs, profile=False): print("Don't export Detect module") m.training = True keras_model = keras.Model(inputs=inputs, outputs=tf_model.predict(inputs)) - # fp32 TFLite model export + + # Representative Dataset + if opt.source.endswith('.yaml'): + with open(check_file(opt.source)) as f: + data = yaml.load(f, Loader=yaml.FullLoader) # data dict + check_dataset(data) # check + opt.source = data['train'] + dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) + + # fp32 TFLite model export --------------------------------------------------------------------------------- # converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) # converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] # converter.allow_custom_ops = False @@ -425,10 +449,11 @@ def predict(self, inputs, profile=False): # f = opt.weights.replace('.pt', '.tflite') # filename # open(f, "wb").write(tflite_model) - # fp16 TFLite model export + # fp16 TFLite model export --------------------------------------------------------------------------------- converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] - converter.target_spec.supported_types = [tf.float16] + # converter.representative_dataset = representative_dataset_gen + # converter.target_spec.supported_types = [tf.float16] converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] converter.allow_custom_ops = False converter.experimental_new_converter = True @@ -437,32 +462,10 @@ def predict(self, inputs, profile=False): open(f, "wb").write(tflite_model) print('\nTFLite export success, saved as %s' % f) - # int8 TFLite model export + # int8 TFLite model export --------------------------------------------------------------------------------- if opt.tfl_int8: converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] - - # # Download COCO128 - # with open(check_file('coco128.yaml')) as f: - # data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict - # check_dataset(data_dict) # check - - dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) - - - def representative_dataset_gen(): - n = 0 - for path, img, im0s, vid_cap in dataset: - # Get sample input data as a numpy array in a method of your choosing. - n += 1 - input = np.transpose(img, [1, 2, 0]) - input = np.expand_dims(input, axis=0).astype(np.float32) - input /= 255.0 - yield [input] - if n >= opt.ncalib: - break - - converter.representative_dataset = representative_dataset_gen converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.inference_input_type = tf.uint8 # or tf.int8 From 00d50fd4775e97d3b704ba09caa78132f4c96ae1 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 2 Dec 2020 12:25:07 +0100 Subject: [PATCH 30/92] detect.py TF inference --- detect.py | 76 +++++++++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 66 insertions(+), 10 deletions(-) diff --git a/detect.py b/detect.py index 80e006251b69..d5e6dbaffeef 100644 --- a/detect.py +++ b/detect.py @@ -5,6 +5,7 @@ import cv2 import torch import torch.backends.cudnn as cudnn +import yaml from numpy import random from models.experimental import attempt_load @@ -30,10 +31,54 @@ def detect(save_img=False): half = device.type != 'cpu' # half precision only supported on CUDA # Load model - model = attempt_load(weights, map_location=device) # load FP32 model - imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size - if half: - model.half() # to FP16 + suffix = Path(weights[0]).suffix + if suffix == '.pt': + backend = 'pytorch' + model = attempt_load(weights, map_location=device) # load FP32 model + imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size + names = model.module.names if hasattr(model, 'module') else model.names # class names + if half: + model.half() # to FP16 + else: + import tensorflow as tf + from tensorflow import keras + + with open('data/coco.yaml') as f: + names = yaml.load(f, Loader=yaml.FullLoader)['names'] # class names (assume COCO) + + if suffix == '.pb': + backend = 'graph_def' + + # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt + # https://github.com/leimao/Frozen_Graph_TensorFlow + def wrap_frozen_graph(graph_def, inputs, outputs): + def _imports_graph_def(): + tf.compat.v1.import_graph_def(graph_def, name="") + + wrapped_import = tf.compat.v1.wrap_function(_imports_graph_def, []) + import_graph = wrapped_import.graph + return wrapped_import.prune( + tf.nest.map_structure(import_graph.as_graph_element, inputs), + tf.nest.map_structure(import_graph.as_graph_element, outputs)) + + graph = tf.Graph() + graph_def = graph.as_graph_def() + graph_def.ParseFromString(open(weights[0], 'rb').read()) + frozen_func = wrap_frozen_graph(graph_def=graph_def, inputs="x:0", outputs="Identity:0") + + elif suffix == '.tflite': + backend = 'tflite' + # Load TFLite model and allocate tensors + interpreter = tf.lite.Interpreter(model_path=opt.weights[0]) + interpreter.allocate_tensors() + + # Get input and output tensors + input_details = interpreter.get_input_details() + output_details = interpreter.get_output_details() + + else: + backend = 'saved_model' + model = keras.models.load_model(weights[0]) # Second-stage classifier classify = False @@ -46,29 +91,40 @@ def detect(save_img=False): if webcam: view_img = True cudnn.benchmark = True # set True to speed up constant image size inference - dataset = LoadStreams(source, img_size=imgsz) + dataset = LoadStreams(source, img_size=imgsz, auto=backend == 'pytorch') else: save_img = True - dataset = LoadImages(source, img_size=imgsz) + dataset = LoadImages(source, img_size=imgsz, auto=backend == 'pytorch') # Get names and colors - names = model.module.names if hasattr(model, 'module') else model.names colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] # Run inference t0 = time.time() img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img - _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once + _ = model(img.half() if half else img) if (device.type != 'cpu' and backend == 'pytorch') else None # run once for path, img, im0s, vid_cap in dataset: img = torch.from_numpy(img).to(device) - img = img.half() if half else img.float() # uint8 to fp16/32 + img = img.half() if half and backend == 'pytorch' else img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 if img.ndimension() == 3: img = img.unsqueeze(0) # Inference t1 = time_synchronized() - pred = model(img, augment=opt.augment)[0] + if backend == 'pytorch': + pred = model(img, augment=opt.augment)[0] + else: + if backend == 'saved_model': + pred = model(img.permute(0, 2, 3, 1).cpu().numpy(), training=False)[0].numpy() + elif backend == 'graph_def': + pred = frozen_func(x=tf.constant(img.permute(0, 2, 3, 1).cpu().numpy())).numpy() + elif backend == 'tflite': + input_data = img.permute(0, 2, 3, 1).cpu().numpy() + interpreter.set_tensor(input_details[0]['index'], input_data) + interpreter.invoke() + pred = interpreter.get_tensor(output_details[0]['index']) + pred = torch.tensor(pred) # Apply NMS pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) From 061907b48eb76026ac349ee39975fa8e0d043a49 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Mon, 30 Nov 2020 18:17:56 +0800 Subject: [PATCH 31/92] Put representative dataset in tfl_int8 block --- models/tf.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/models/tf.py b/models/tf.py index d0bf2920d2fe..ace450434a6d 100644 --- a/models/tf.py +++ b/models/tf.py @@ -432,14 +432,6 @@ def representative_dataset_gen(): m.training = True keras_model = keras.Model(inputs=inputs, outputs=tf_model.predict(inputs)) - # Representative Dataset - if opt.source.endswith('.yaml'): - with open(check_file(opt.source)) as f: - data = yaml.load(f, Loader=yaml.FullLoader) # data dict - check_dataset(data) # check - opt.source = data['train'] - dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) - # fp32 TFLite model export --------------------------------------------------------------------------------- # converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) # converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] @@ -464,6 +456,14 @@ def representative_dataset_gen(): # int8 TFLite model export --------------------------------------------------------------------------------- if opt.tfl_int8: + # Representative Dataset + if opt.source.endswith('.yaml'): + with open(check_file(opt.source)) as f: + data = yaml.load(f, Loader=yaml.FullLoader) # data dict + check_dataset(data) # check + opt.source = data['train'] + dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) + converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.representative_dataset = representative_dataset_gen From ca4550ba800211612a6aafda7a390775a431b6f0 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 2 Dec 2020 12:45:34 +0100 Subject: [PATCH 32/92] weights to string --- detect.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/detect.py b/detect.py index d5e6dbaffeef..1de512d6d60b 100644 --- a/detect.py +++ b/detect.py @@ -31,7 +31,8 @@ def detect(save_img=False): half = device.type != 'cpu' # half precision only supported on CUDA # Load model - suffix = Path(weights[0]).suffix + weights = weights[0] if isinstance(weights, list) else weights + suffix = Path(weights).suffix if suffix == '.pt': backend = 'pytorch' model = attempt_load(weights, map_location=device) # load FP32 model @@ -63,7 +64,7 @@ def _imports_graph_def(): graph = tf.Graph() graph_def = graph.as_graph_def() - graph_def.ParseFromString(open(weights[0], 'rb').read()) + graph_def.ParseFromString(open(weights, 'rb').read()) frozen_func = wrap_frozen_graph(graph_def=graph_def, inputs="x:0", outputs="Identity:0") elif suffix == '.tflite': @@ -78,7 +79,7 @@ def _imports_graph_def(): else: backend = 'saved_model' - model = keras.models.load_model(weights[0]) + model = keras.models.load_model(weights) # Second-stage classifier classify = False From 5e04c5cb4291297588ad86a1840f04035fee6a68 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 2 Dec 2020 12:48:04 +0100 Subject: [PATCH 33/92] weights to string --- detect.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/detect.py b/detect.py index 1de512d6d60b..253be4b552df 100644 --- a/detect.py +++ b/detect.py @@ -70,7 +70,7 @@ def _imports_graph_def(): elif suffix == '.tflite': backend = 'tflite' # Load TFLite model and allocate tensors - interpreter = tf.lite.Interpreter(model_path=opt.weights[0]) + interpreter = tf.lite.Interpreter(model_path=weights) interpreter.allocate_tensors() # Get input and output tensors From 9121a87363a709518e048bb005967412d2b0f06a Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Fri, 4 Dec 2020 12:07:01 +0100 Subject: [PATCH 34/92] cleanup tf.py --- models/tf.py | 56 +++++++++++++++++++--------------------------------- 1 file changed, 20 insertions(+), 36 deletions(-) diff --git a/models/tf.py b/models/tf.py index ace450434a6d..41fe44150a76 100644 --- a/models/tf.py +++ b/models/tf.py @@ -6,22 +6,19 @@ from copy import deepcopy from pathlib import Path +import numpy as np import tensorflow as tf import torch import torch.nn as nn import yaml - -if tf.__version__.startswith('1'): - tf.enable_eager_execution() from tensorflow import keras from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 -import numpy as np from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad from models.experimental import MixConv2d, CrossConv, C3 -from utils.general import make_divisible, check_file, check_dataset from models.yolo import Detect from utils.datasets import LoadImages +from utils.general import make_divisible, check_file, check_dataset from utils.google_utils import attempt_download logger = logging.getLogger(__name__) @@ -57,22 +54,13 @@ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): super(tf_Conv, self).__init__() assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" assert isinstance(k, int), "Convolution with multiple kernels are not allowed." - # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding) # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch - if s == 1: - self.conv = keras.layers.Conv2D( - c2, k, s, 'SAME', use_bias=False, - kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) - else: - self.pad = tf_Pad(autopad(k, p)) - self.conv = keras.Sequential([ - self.pad, - keras.layers.Conv2D( - c2, k, s, 'VALID', use_bias=False, - kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) - ]) + conv = keras.layers.Conv2D( + c2, k, s, 'SAME' if s == 1 else 'VALID', use_bias=False, + kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) + self.conv = conv if s == 1 else keras.Sequential([tf_Pad(autopad(k, p)), conv]) self.bn = tf_BN(w.bn) if hasattr(w, 'bn') else tf.identity # YOLOv5 v3 uses Hardswish for activations @@ -82,7 +70,7 @@ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): self.act = (lambda x: x * tf.nn.relu6(x + 3) * 0.166666667) if act else tf.identity def call(self, inputs): - return self.act(self.bn(self.conv(inputs))) if hasattr(self, 'bn') else self.act(self.conv(inputs)) + return self.act(self.bn(self.conv(inputs))) class tf_Focus(keras.layers.Layer): @@ -307,18 +295,9 @@ def predict(self, inputs, profile=False): x = m(x) # run y.append(x if m.i in self.savelist else None) # save output - # Boxes - xy = x[0][:, :, 0:2] - wh = x[0][:, :, 2:4] - x1 = xy[:, :, 0:1] - wh[:, :, 0:1] / 2 - x2 = xy[:, :, 0:1] + wh[:, :, 0:1] / 2 - y1 = xy[:, :, 1:2] - wh[:, :, 1:2] / 2 - y2 = xy[:, :, 1:2] + wh[:, :, 1:2] / 2 - xyxy = tf.concat([x1, y1, x2, y2], 2) - # Add TensorFlow NMS if opt.tf_nms: - boxes = tf.expand_dims(xyxy, 2) + boxes = tf.expand_dims(xywh2xyxy(x[0][..., :4]), 2) probs = x[0][:, :, 4:5] classes = x[0][:, :, 5:] scores = probs * classes @@ -326,11 +305,18 @@ def predict(self, inputs, profile=False): boxes, scores, opt.topk_per_class, opt.topk_all, opt.iou_thres, opt.score_thres, clip_boxes=False) return nms, x[1] - # x = x[0] - # conf = x[..., 4:5] - # cls = x[..., 5:] - # return [conf * cls, xyxy] # output only first tensor, [1,6300,80], [1,6300,4] return x[0] # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...] + # x = x[0][0] # [x(1,6300,85), ...] to x(6300,85) + # xywh = x[..., :4] # x(6300,4) boxes + # conf = x[..., 4:5] # x(6300,1) confidences + # cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1)) # x(6300,1) classes + # return tf.concat([conf, cls, xywh], 1) + + +def xywh2xyxy(xywh): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1) + return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1) def representative_dataset_gen(): @@ -421,10 +407,8 @@ def representative_dataset_gen(): print('TensorFlow GraphDef export failure: %s' % e) traceback.print_exc(file=sys.stdout) - # NMS doesn't support TFLite export - + # TFLite model export if not opt.tf_nms: - # TFLite model export try: print('\nStarting TFLite export with TensorFlow %s...' % tf.__version__) if opt.no_tfl_detect: From e9bc606b50e35e63633952784176993ce332f7f2 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Tue, 22 Dec 2020 20:46:25 +0800 Subject: [PATCH 35/92] Add --dynamic-batch-size --- models/tf.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/models/tf.py b/models/tf.py index 41fe44150a76..a5cc8888da51 100644 --- a/models/tf.py +++ b/models/tf.py @@ -339,6 +339,7 @@ def representative_dataset_gen(): parser.add_argument('--weights', type=str, default='yolov5s.pt', help='weights path') parser.add_argument('--img-size', nargs='+', type=int, default=[320, 320], help='image size') # height, width parser.add_argument('--batch-size', type=int, default=1, help='batch size') + parser.add_argument('--dynamic-batch-size', action='store_true', help='dynamic batch size') parser.add_argument('--no-tfl-detect', action='store_true', help='remove Detect() from TFLite model') parser.add_argument('--source', type=str, default='../data/coco128.yaml', help='dir of images or data.yaml file') parser.add_argument('--ncalib', type=int, default=100, help='number of calibration images') @@ -375,7 +376,7 @@ def representative_dataset_gen(): m.training = False y = tf_model.predict(img) - inputs = keras.Input(shape=(*opt.img_size, 3)) + inputs = keras.Input(shape=(*opt.img_size, 3), batch_size=None if opt.dynamic_batch_size else opt.batch_size) keras_model = keras.Model(inputs=inputs, outputs=tf_model.predict(inputs)) keras_model.summary() path = opt.weights.replace('.pt', '_saved_model') # filename From dacd8afb43817f6836854c795ff0991e01779655 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Tue, 22 Dec 2020 19:28:48 +0800 Subject: [PATCH 36/92] Add xywh normalization to reduce calibration error --- detect.py | 14 +++++++++++++- models/tf.py | 4 +++- 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/detect.py b/detect.py index 253be4b552df..77c4cd4f5273 100644 --- a/detect.py +++ b/detect.py @@ -7,6 +7,7 @@ import torch.backends.cudnn as cudnn import yaml from numpy import random +import numpy as np from models.experimental import attempt_load from utils.datasets import LoadStreams, LoadImages @@ -117,14 +118,24 @@ def _imports_graph_def(): pred = model(img, augment=opt.augment)[0] else: if backend == 'saved_model': - pred = model(img.permute(0, 2, 3, 1).cpu().numpy(), training=False)[0].numpy() + pred = model(img.permute(0, 2, 3, 1).cpu().numpy(), training=False).numpy() elif backend == 'graph_def': pred = frozen_func(x=tf.constant(img.permute(0, 2, 3, 1).cpu().numpy())).numpy() elif backend == 'tflite': input_data = img.permute(0, 2, 3, 1).cpu().numpy() + if opt.tfl_int8: + scale, zero_point = input_details[0]['quantization'] + input_data = input_data / scale + zero_point + input_data = input_data.astype(np.uint8) interpreter.set_tensor(input_details[0]['index'], input_data) interpreter.invoke() pred = interpreter.get_tensor(output_details[0]['index']) + if opt.tfl_int8: + scale, zero_point = output_details[0]['quantization'] + pred = pred.astype(np.float32) + pred = (pred - zero_point) * scale + # Denormalize xywh + pred[..., :4] *= opt.img_size pred = torch.tensor(pred) # Apply NMS @@ -217,6 +228,7 @@ def _imports_graph_def(): parser.add_argument('--project', default='runs/detect', help='save results to project/name') parser.add_argument('--name', default='exp', help='save results to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--tfl-int8', action='store_true', help='use int8 quantized TFLite model') opt = parser.parse_args() print(opt) check_requirements() diff --git a/models/tf.py b/models/tf.py index a5cc8888da51..e2b9eccd8dba 100644 --- a/models/tf.py +++ b/models/tf.py @@ -183,6 +183,9 @@ def call(self, inputs): y = tf.sigmoid(x[i]) xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] + # Normalize xywh to 0-1 to reduce calibration error + xy /= tf.constant([[opt.img_size[1], opt.img_size[0]]], dtype=tf.float32) + wh /= tf.constant([[opt.img_size[1], opt.img_size[0]]], dtype=tf.float32) y = tf.concat([xy, wh, y[..., 4:]], -1) z.append(tf.reshape(y, [opt.batch_size, 3 * ny * nx, self.no])) @@ -448,7 +451,6 @@ def representative_dataset_gen(): check_dataset(data) # check opt.source = data['train'] dataset = LoadImages(opt.source, img_size=opt.img_size, auto=False) - converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.representative_dataset = representative_dataset_gen From b492af9ab2492e3a887a9fa0275dd31ed8236a68 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Wed, 23 Dec 2020 17:40:35 +0800 Subject: [PATCH 37/92] Update requirements.txt TensorFlow 2.3.1 -> 2.4.0 to avoid int8 quantization error --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 11fec991d7ef..812d5442bb60 100755 --- a/requirements.txt +++ b/requirements.txt @@ -24,7 +24,7 @@ pandas # coremltools==4.0 # onnx>=1.8.0 # scikit-learn==0.19.2 # for coreml quantization -# tensorflow==2.3.1 +# tensorflow==2.4.0 # extras -------------------------------------- thop # FLOPS computation From fbf5a458cd0f70f30d6639d1f86985228c9722fb Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Thu, 24 Dec 2020 10:44:49 +0800 Subject: [PATCH 38/92] Fix imports Move C3 from models.experimental to models.common --- models/tf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/models/tf.py b/models/tf.py index e2b9eccd8dba..8a7af6d4384b 100644 --- a/models/tf.py +++ b/models/tf.py @@ -14,8 +14,8 @@ from tensorflow import keras from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 -from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad -from models.experimental import MixConv2d, CrossConv, C3 +from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad, C3 +from models.experimental import MixConv2d, CrossConv from models.yolo import Detect from utils.datasets import LoadImages from utils.general import make_divisible, check_file, check_dataset From c761637b51701565a9efbd585a05f093f8aa5f41 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Thu, 7 Jan 2021 16:52:02 -0800 Subject: [PATCH 39/92] implement C3() and SiLU() --- models/tf.py | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/models/tf.py b/models/tf.py index 8a7af6d4384b..8c9d89430e34 100644 --- a/models/tf.py +++ b/models/tf.py @@ -63,11 +63,13 @@ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): self.conv = conv if s == 1 else keras.Sequential([tf_Pad(autopad(k, p)), conv]) self.bn = tf_BN(w.bn) if hasattr(w, 'bn') else tf.identity - # YOLOv5 v3 uses Hardswish for activations + # YOLOv5 activations if isinstance(w.act, nn.LeakyReLU): self.act = (lambda x: keras.activations.relu(x, alpha=0.1)) if act else tf.identity elif isinstance(w.act, nn.Hardswish): self.act = (lambda x: x * tf.nn.relu6(x + 3) * 0.166666667) if act else tf.identity + elif isinstance(w.act, nn.SiLU): + self.act = (lambda x: keras.activations.swish(x)) if act else tf.identity def call(self, inputs): return self.act(self.bn(self.conv(inputs))) @@ -135,6 +137,21 @@ def call(self, inputs): return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3)))) +class tf_C3(keras.layers.Layer): + # CSP Bottleneck with 3 convolutions + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): + # ch_in, ch_out, number, shortcut, groups, expansion + super(tf_C3, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = tf_Conv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = tf_Conv(c1, c_, 1, 1, w=w.cv2) + self.cv3 = tf_Conv(2 * c_, c2, 1, 1, w=w.cv3) + self.m = keras.Sequential([tf_Bottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) + + def call(self, inputs): + return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3)) + + class tf_SPP(keras.layers.Layer): # Spatial pyramid pooling layer used in YOLOv3-SPP def __init__(self, c1, c2, k=(5, 9, 13), w=None): From e610134b72e8e505ef885bce86cd1c9fad0d8b57 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Sat, 10 Oct 2020 09:52:05 +0800 Subject: [PATCH 40/92] Add TensorFlow and TFLite Detection --- detect.py | 159 ++++++++++++++++++++++++++++++++++++------------------ 1 file changed, 106 insertions(+), 53 deletions(-) diff --git a/detect.py b/detect.py index 77c4cd4f5273..281830c84489 100644 --- a/detect.py +++ b/detect.py @@ -32,55 +32,87 @@ def detect(save_img=False): half = device.type != 'cpu' # half precision only supported on CUDA # Load model - weights = weights[0] if isinstance(weights, list) else weights - suffix = Path(weights).suffix - if suffix == '.pt': + if weights[0].split('.')[-1] == 'pt': backend = 'pytorch' - model = attempt_load(weights, map_location=device) # load FP32 model - imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size - names = model.module.names if hasattr(model, 'module') else model.names # class names - if half: - model.half() # to FP16 + elif weights[0].split('.')[-1] == 'pb': + backend = 'graph_def' + elif weights[0].split('.')[-1] == 'tflite': + backend = 'tflite' else: - import tensorflow as tf - from tensorflow import keras - - with open('data/coco.yaml') as f: - names = yaml.load(f, Loader=yaml.FullLoader)['names'] # class names (assume COCO) + backend = 'saved_model' - if suffix == '.pb': - backend = 'graph_def' + if backend == 'saved_model' or backend =='graph_def' or backend=='tflite': + import tensorflow as tf + from tensorflow import keras + if backend == 'pytorch': + model = attempt_load(weights, map_location=device) # load FP32 model + elif backend == 'saved_model': + if tf.__version__.startswith('1'): + config = tf.ConfigProto() + config.gpu_options.allow_growth=True + sess = tf.Session(config=config) + loaded = tf.saved_model.load(sess, [tf.saved_model.tag_constants.SERVING], weights[0]) + tf_input = loaded.signature_def['serving_default'].inputs['input_1'] + tf_output = loaded.signature_def['serving_default'].outputs['tf__detect'] + else: + model = keras.models.load_model(weights[0]) + elif backend == 'graph_def': + if tf.__version__.startswith('1'): + config = tf.ConfigProto() + config.gpu_options.allow_growth=True + sess = tf.Session(config=config) + graph = tf.Graph() + graph_def = graph.as_graph_def() + graph_def.ParseFromString(open(weights[0], 'rb').read()) + tf.import_graph_def(graph_def, name='') + default_graph = tf.get_default_graph() + tf_input = default_graph.get_tensor_by_name('x:0') + tf_output = default_graph.get_tensor_by_name('Identity:0') + else: # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt # https://github.com/leimao/Frozen_Graph_TensorFlow - def wrap_frozen_graph(graph_def, inputs, outputs): + def wrap_frozen_graph(graph_def, inputs, outputs, print_graph=False): def _imports_graph_def(): tf.compat.v1.import_graph_def(graph_def, name="") wrapped_import = tf.compat.v1.wrap_function(_imports_graph_def, []) import_graph = wrapped_import.graph + + if print_graph == True: + print("-" * 50) + print("Frozen model layers: ") + layers = [op.name for op in import_graph.get_operations()] + for layer in layers: + print(layer) + print("-" * 50) + return wrapped_import.prune( tf.nest.map_structure(import_graph.as_graph_element, inputs), tf.nest.map_structure(import_graph.as_graph_element, outputs)) graph = tf.Graph() graph_def = graph.as_graph_def() - graph_def.ParseFromString(open(weights, 'rb').read()) - frozen_func = wrap_frozen_graph(graph_def=graph_def, inputs="x:0", outputs="Identity:0") - - elif suffix == '.tflite': - backend = 'tflite' - # Load TFLite model and allocate tensors - interpreter = tf.lite.Interpreter(model_path=weights) - interpreter.allocate_tensors() - - # Get input and output tensors - input_details = interpreter.get_input_details() - output_details = interpreter.get_output_details() + graph_def.ParseFromString(open(weights[0], 'rb').read()) + frozen_func = wrap_frozen_graph(graph_def=graph_def, + inputs="x:0", + outputs="Identity:0", + print_graph=False) + + elif backend == 'tflite': + # Load TFLite model and allocate tensors. + interpreter = tf.lite.Interpreter(model_path=opt.weights[0]) + interpreter.allocate_tensors() + + # Get input and output tensors. + input_details = interpreter.get_input_details() + output_details = interpreter.get_output_details() + + if backend == 'pytorch': + imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size - else: - backend = 'saved_model' - model = keras.models.load_model(weights) + if half and backend == 'pytorch': + model.half() # to FP16 # Second-stage classifier classify = False @@ -104,7 +136,24 @@ def _imports_graph_def(): # Run inference t0 = time.time() img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img - _ = model(img.half() if half else img) if (device.type != 'cpu' and backend == 'pytorch') else None # run once + if backend == 'pytorch': + _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once + elif backend == 'saved_model': + if tf.__version__.startswith('1'): + _ = sess.run(tf_output.name, feed_dict={tf_input.name: img.permute(0, 2, 3, 1).cpu().numpy()}) + else: + _ = model(img.permute(0, 2, 3, 1).cpu().numpy(), training=False) + elif backend == 'graph_def': + if tf.__version__.startswith('1'): + _ = sess.run(tf_output.name, feed_dict={tf_input.name: img.permute(0, 2, 3, 1).cpu().numpy()}) + else: + _ = frozen_func(x=tf.constant(img.permute(0, 2, 3, 1).cpu().numpy())) + elif backend == 'tflite': + input_data = img.permute(0, 2, 3, 1).cpu().numpy() + interpreter.set_tensor(input_details[0]['index'], input_data) + interpreter.invoke() + output_data = interpreter.get_tensor(output_details[0]['index']) + for path, img, im0s, vid_cap in dataset: img = torch.from_numpy(img).to(device) img = img.half() if half and backend == 'pytorch' else img.float() # uint8 to fp16/32 @@ -116,27 +165,31 @@ def _imports_graph_def(): t1 = time_synchronized() if backend == 'pytorch': pred = model(img, augment=opt.augment)[0] - else: - if backend == 'saved_model': - pred = model(img.permute(0, 2, 3, 1).cpu().numpy(), training=False).numpy() - elif backend == 'graph_def': - pred = frozen_func(x=tf.constant(img.permute(0, 2, 3, 1).cpu().numpy())).numpy() - elif backend == 'tflite': - input_data = img.permute(0, 2, 3, 1).cpu().numpy() - if opt.tfl_int8: - scale, zero_point = input_details[0]['quantization'] - input_data = input_data / scale + zero_point - input_data = input_data.astype(np.uint8) - interpreter.set_tensor(input_details[0]['index'], input_data) - interpreter.invoke() - pred = interpreter.get_tensor(output_details[0]['index']) - if opt.tfl_int8: - scale, zero_point = output_details[0]['quantization'] - pred = pred.astype(np.float32) - pred = (pred - zero_point) * scale - # Denormalize xywh - pred[..., :4] *= opt.img_size - pred = torch.tensor(pred) + + elif backend == 'saved_model': + if tf.__version__.startswith('1'): + pred = sess.run(tf_output.name, feed_dict={tf_input.name: img.permute(0, 2, 3, 1).cpu().numpy()}) + pred = torch.tensor(pred) + else: + res = model(img.permute(0, 2, 3, 1).cpu().numpy(), training=False) + pred = res[0].numpy() + pred = torch.tensor(pred) + + elif backend == 'graph_def': + if tf.__version__.startswith('1'): + pred = sess.run(tf_output.name, feed_dict={tf_input.name: img.permute(0, 2, 3, 1).cpu().numpy()}) + pred = torch.tensor(pred) + else: + pred = frozen_func(x=tf.constant(img.permute(0, 2, 3, 1).cpu().numpy())) + pred = torch.tensor(pred.numpy()) + + elif backend == 'tflite': + input_data = img.permute(0, 2, 3, 1).cpu().numpy() + interpreter.set_tensor(input_details[0]['index'], input_data) + interpreter.invoke() + output_data = interpreter.get_tensor(output_details[0]['index']) + pred = torch.tensor(output_data) + # Apply NMS pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) From 98bd2499e61990d75d78889766131786dd785a7b Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Sat, 10 Oct 2020 14:00:42 +0800 Subject: [PATCH 41/92] Add --tfl-detect for TFLite Detection --- detect.py | 40 ++++++++++++++++++++++++++++++++++++---- 1 file changed, 36 insertions(+), 4 deletions(-) diff --git a/detect.py b/detect.py index 281830c84489..a33f2b230f55 100644 --- a/detect.py +++ b/detect.py @@ -10,6 +10,7 @@ import numpy as np from models.experimental import attempt_load +from models.yolo import Detect from utils.datasets import LoadStreams, LoadImages from utils.general import check_img_size, check_requirements, non_max_suppression, apply_classifier, scale_coords, \ xyxy2xywh, strip_optimizer, set_logging, increment_path @@ -187,9 +188,39 @@ def _imports_graph_def(): input_data = img.permute(0, 2, 3, 1).cpu().numpy() interpreter.set_tensor(input_details[0]['index'], input_data) interpreter.invoke() - output_data = interpreter.get_tensor(output_details[0]['index']) - pred = torch.tensor(output_data) - + if not opt.tfl_detect: + output_data = interpreter.get_tensor(output_details[0]['index']) + pred = torch.tensor(output_data) + else: + import yaml + yaml_file = Path(opt.cfg).name + with open(opt.cfg) as f: + yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict + + anchors = yaml['anchors'] + nc = yaml['nc'] + nl = len(anchors) + x = [torch.tensor(interpreter.get_tensor(output_details[i]['index']), device=device) for i in range(nl)] + def _make_grid(nx=20, ny=20): + yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + return torch.stack((xv, yv), 2).view((1, 1, ny * nx, 2)).float() + + no = nc + 5 + grid = [torch.zeros(1)] * nl # init grid + a = torch.tensor(anchors).float().view(nl, -1, 2).to(device) + anchor_grid = a.clone().view(nl, 1, -1, 1, 2) # shape(nl,1,na,1,2) + z = [] # inference output + for i in range(nl): + _, _, ny_nx, _ = x[i].shape + nx = ny = int(np.sqrt(ny_nx)) + grid[i] = _make_grid(nx, ny).to(x[i].device) + stride = imgsz // ny + y = x[i].sigmoid() + y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + grid[i].to(x[i].device)) * stride # xy + y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * anchor_grid[i] # wh + z.append(y.view(-1, no)) + + pred = torch.unsqueeze(torch.cat(z, 0), 0) # Apply NMS pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) @@ -281,7 +312,8 @@ def _imports_graph_def(): parser.add_argument('--project', default='runs/detect', help='save results to project/name') parser.add_argument('--name', default='exp', help='save results to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--tfl-int8', action='store_true', help='use int8 quantized TFLite model') + parser.add_argument('--tfl-detect', action='store_true', help='add Detect module in TFLite') + parser.add_argument('--cfg', type=str, default='./models/yolov5s.yaml', help='cfg path') opt = parser.parse_args() print(opt) check_requirements() From aeffcb08c879b920e56d3a35485d9c6eb1194642 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Mon, 12 Oct 2020 11:58:42 +0800 Subject: [PATCH 42/92] Add int8 quantized TFLite inference in detect.py --- detect.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/detect.py b/detect.py index a33f2b230f55..6cc7ad16d664 100644 --- a/detect.py +++ b/detect.py @@ -151,6 +151,8 @@ def _imports_graph_def(): _ = frozen_func(x=tf.constant(img.permute(0, 2, 3, 1).cpu().numpy())) elif backend == 'tflite': input_data = img.permute(0, 2, 3, 1).cpu().numpy() + if opt.tfl_int8: + input_data = input_data.astype(np.uint8) interpreter.set_tensor(input_details[0]['index'], input_data) interpreter.invoke() output_data = interpreter.get_tensor(output_details[0]['index']) @@ -186,6 +188,10 @@ def _imports_graph_def(): elif backend == 'tflite': input_data = img.permute(0, 2, 3, 1).cpu().numpy() + if opt.tfl_int8: + scale, zero_point = input_details[0]['quantization'] + input_data = input_data / scale + zero_point + input_data = input_data.astype(np.uint8) interpreter.set_tensor(input_details[0]['index'], input_data) interpreter.invoke() if not opt.tfl_detect: @@ -195,12 +201,18 @@ def _imports_graph_def(): import yaml yaml_file = Path(opt.cfg).name with open(opt.cfg) as f: - yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict + yaml = yaml.load(f, Loader=yaml.FullLoader) anchors = yaml['anchors'] nc = yaml['nc'] nl = len(anchors) x = [torch.tensor(interpreter.get_tensor(output_details[i]['index']), device=device) for i in range(nl)] + if opt.tfl_int8: + for i in range(nl): + scale, zero_point = output_details[i]['quantization'] + x[i] = x[i].float() + x[i] = (x[i] - zero_point) * scale + def _make_grid(nx=20, ny=20): yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) return torch.stack((xv, yv), 2).view((1, 1, ny * nx, 2)).float() @@ -314,6 +326,7 @@ def _make_grid(nx=20, ny=20): parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--tfl-detect', action='store_true', help='add Detect module in TFLite') parser.add_argument('--cfg', type=str, default='./models/yolov5s.yaml', help='cfg path') + parser.add_argument('--tfl-int8', action='store_true', help='use int8 quantized TFLite model') opt = parser.parse_args() print(opt) check_requirements() From c10bd951ec3e1b895fbf28046092886e215d909f Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Wed, 30 Dec 2020 15:59:10 +0800 Subject: [PATCH 43/92] Add --edgetpu for Edge TPU detection --- detect.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/detect.py b/detect.py index 6cc7ad16d664..e1bc85fd2aa7 100644 --- a/detect.py +++ b/detect.py @@ -102,7 +102,10 @@ def _imports_graph_def(): elif backend == 'tflite': # Load TFLite model and allocate tensors. - interpreter = tf.lite.Interpreter(model_path=opt.weights[0]) + interpreter = tf.lite.Interpreter( + model_path=opt.weights[0], + experimental_delegates= + [tf.lite.experimental.load_delegate('libedgetpu.so.1')] if opt.edgetpu else None) interpreter.allocate_tensors() # Get input and output tensors. @@ -327,6 +330,8 @@ def _make_grid(nx=20, ny=20): parser.add_argument('--tfl-detect', action='store_true', help='add Detect module in TFLite') parser.add_argument('--cfg', type=str, default='./models/yolov5s.yaml', help='cfg path') parser.add_argument('--tfl-int8', action='store_true', help='use int8 quantized TFLite model') + parser.add_argument('--no-tf-nms', action='store_true', help='dont proceed NMS due to model w/ TensorFlow NMS') + parser.add_argument('--edgetpu', action='store_true', help='inference with Edge TPU') opt = parser.parse_args() print(opt) check_requirements() From 0b943fe1a2bf061b220b70092d1ff5bd326e9b1e Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Tue, 20 Oct 2020 19:36:27 +0800 Subject: [PATCH 44/92] Fix --img-size to add rectangle TensorFlow and TFLite input --- detect.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/detect.py b/detect.py index e1bc85fd2aa7..f4b0183d98b5 100644 --- a/detect.py +++ b/detect.py @@ -139,7 +139,10 @@ def _imports_graph_def(): # Run inference t0 = time.time() - img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img + if isinstance(imgsz, int): + imgsz = (imgsz, imgsz) + img = torch.zeros((1, 3, *imgsz), device=device) # init img + if backend == 'pytorch': _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once elif backend == 'saved_model': @@ -227,9 +230,11 @@ def _make_grid(nx=20, ny=20): z = [] # inference output for i in range(nl): _, _, ny_nx, _ = x[i].shape - nx = ny = int(np.sqrt(ny_nx)) + r = imgsz[0] / imgsz[1] + nx = int(np.sqrt(ny_nx / r)) + ny = int(r * nx) grid[i] = _make_grid(nx, ny).to(x[i].device) - stride = imgsz // ny + stride = imgsz[0] // ny y = x[i].sigmoid() y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + grid[i].to(x[i].device)) * stride # xy y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * anchor_grid[i] # wh @@ -313,7 +318,7 @@ def _make_grid(nx=20, ny=20): parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)') parser.add_argument('--source', type=str, default='data/images', help='source') # file/folder, 0 for webcam - parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--img-size', nargs='+', type=int, default=640, help='inference size (pixels)') parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') From 15e7be4049650fd63bbb8aaecc0bb18a6f81e9b7 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Mon, 2 Nov 2020 18:36:56 +0800 Subject: [PATCH 45/92] Add --no-tf-nms to detect objects using models combined with TensorFlow NMS --- detect.py | 70 ++++++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 57 insertions(+), 13 deletions(-) diff --git a/detect.py b/detect.py index f4b0183d98b5..40b70948947b 100644 --- a/detect.py +++ b/detect.py @@ -55,7 +55,14 @@ def detect(save_img=False): sess = tf.Session(config=config) loaded = tf.saved_model.load(sess, [tf.saved_model.tag_constants.SERVING], weights[0]) tf_input = loaded.signature_def['serving_default'].inputs['input_1'] - tf_output = loaded.signature_def['serving_default'].outputs['tf__detect'] + if not opt.no_tf_nms: + tf_output = loaded.signature_def['serving_default'].outputs['tf__detect'] + else: + tf_outputs = [loaded.signature_def['serving_default'].outputs['tf_op_layer_CombinedNonMaxSuppression'], + loaded.signature_def['serving_default'].outputs['tf_op_layer_CombinedNonMaxSuppression_1'], + loaded.signature_def['serving_default'].outputs['tf_op_layer_CombinedNonMaxSuppression_2'], + loaded.signature_def['serving_default'].outputs['tf_op_layer_CombinedNonMaxSuppression_3'] + ] else: model = keras.models.load_model(weights[0]) elif backend == 'graph_def': @@ -69,7 +76,15 @@ def detect(save_img=False): tf.import_graph_def(graph_def, name='') default_graph = tf.get_default_graph() tf_input = default_graph.get_tensor_by_name('x:0') - tf_output = default_graph.get_tensor_by_name('Identity:0') + if not opt.no_tf_nms: + tf_output = default_graph.get_tensor_by_name('Identity:0') + else: + tf_outputs = [default_graph.get_tensor_by_name('Identity:0'), + default_graph.get_tensor_by_name('Identity_1:0'), + default_graph.get_tensor_by_name('Identity_2:0'), + default_graph.get_tensor_by_name('Identity_3:0') + ] + else: # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt # https://github.com/leimao/Frozen_Graph_TensorFlow @@ -97,7 +112,8 @@ def _imports_graph_def(): graph_def.ParseFromString(open(weights[0], 'rb').read()) frozen_func = wrap_frozen_graph(graph_def=graph_def, inputs="x:0", - outputs="Identity:0", + outputs="Identity:0" if not opt.no_tf_nms else + ["Identity:0", "Identity_1:0", "Identity_2:0", "Identity_3:0"], print_graph=False) elif backend == 'tflite': @@ -142,17 +158,19 @@ def _imports_graph_def(): if isinstance(imgsz, int): imgsz = (imgsz, imgsz) img = torch.zeros((1, 3, *imgsz), device=device) # init img + if (backend == 'saved_model' or backend == 'graph_def') and tf.__version__.startswith('1'): + fetches = tf_output.name if not opt.no_tf_nms else [o.name for o in tf_outputs] if backend == 'pytorch': _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once elif backend == 'saved_model': if tf.__version__.startswith('1'): - _ = sess.run(tf_output.name, feed_dict={tf_input.name: img.permute(0, 2, 3, 1).cpu().numpy()}) + _ = sess.run(fetches, feed_dict={tf_input.name: img.permute(0, 2, 3, 1).cpu().numpy()}) else: _ = model(img.permute(0, 2, 3, 1).cpu().numpy(), training=False) elif backend == 'graph_def': if tf.__version__.startswith('1'): - _ = sess.run(tf_output.name, feed_dict={tf_input.name: img.permute(0, 2, 3, 1).cpu().numpy()}) + _ = sess.run(fetches, feed_dict={tf_input.name: img.permute(0, 2, 3, 1).cpu().numpy()}) else: _ = frozen_func(x=tf.constant(img.permute(0, 2, 3, 1).cpu().numpy())) elif backend == 'tflite': @@ -177,20 +195,26 @@ def _imports_graph_def(): elif backend == 'saved_model': if tf.__version__.startswith('1'): - pred = sess.run(tf_output.name, feed_dict={tf_input.name: img.permute(0, 2, 3, 1).cpu().numpy()}) - pred = torch.tensor(pred) + pred = sess.run(fetches, feed_dict={tf_input.name: img.permute(0, 2, 3, 1).cpu().numpy()}) + if not opt.no_tf_nms: + pred = torch.tensor(pred) else: res = model(img.permute(0, 2, 3, 1).cpu().numpy(), training=False) - pred = res[0].numpy() - pred = torch.tensor(pred) + if not opt.no_tf_nms: + pred = res[0].numpy() + pred = torch.tensor(pred) + else: + pred = res[0] elif backend == 'graph_def': if tf.__version__.startswith('1'): - pred = sess.run(tf_output.name, feed_dict={tf_input.name: img.permute(0, 2, 3, 1).cpu().numpy()}) - pred = torch.tensor(pred) + pred = sess.run(fetches, feed_dict={tf_input.name: img.permute(0, 2, 3, 1).cpu().numpy()}) + if not opt.no_tf_nms: + pred = torch.tensor(pred) else: pred = frozen_func(x=tf.constant(img.permute(0, 2, 3, 1).cpu().numpy())) - pred = torch.tensor(pred.numpy()) + if not opt.no_tf_nms: + pred = torch.tensor(pred.numpy()) elif backend == 'tflite': input_data = img.permute(0, 2, 3, 1).cpu().numpy() @@ -243,7 +267,27 @@ def _make_grid(nx=20, ny=20): pred = torch.unsqueeze(torch.cat(z, 0), 0) # Apply NMS - pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) + if not opt.no_tf_nms: + pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) + else: + nmsed_boxes, nmsed_scores, nmsed_classes, valid_detections = pred + if not tf.__version__.startswith('1'): + nmsed_boxes = torch.tensor(nmsed_boxes.numpy()) + nmsed_scores = torch.tensor(nmsed_scores.numpy()) + nmsed_classes = torch.tensor(nmsed_classes.numpy()) + valid_detections = torch.tensor(valid_detections.numpy()) + else: + nmsed_boxes = torch.tensor(nmsed_boxes) + nmsed_scores = torch.tensor(nmsed_scores) + nmsed_classes = torch.tensor(nmsed_classes) + valid_detections = torch.tensor(valid_detections) + bs = nmsed_boxes.shape[0] + pred = [None] * bs + for i in range(bs): + pred[i] = torch.cat([nmsed_boxes[i, :valid_detections[i], :], + torch.unsqueeze(nmsed_scores[i, :valid_detections[i]], -1), + torch.unsqueeze(nmsed_classes[i, :valid_detections[i]], -1)], -1) + t2 = time_synchronized() # Apply Classifier From 83852c2229ee1ec59f5d6f6f35f9d8d468d26d0f Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Mon, 2 Nov 2020 20:30:31 +0800 Subject: [PATCH 46/92] Fix --img-size list type input --- detect.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/detect.py b/detect.py index 40b70948947b..48308fb96af3 100644 --- a/detect.py +++ b/detect.py @@ -27,6 +27,9 @@ def detect(save_img=False): save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + if len(imgsz) == 1: + imgsz = imgsz[0] + # Initialize set_logging() device = select_device(opt.device) @@ -362,7 +365,7 @@ def _make_grid(nx=20, ny=20): parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)') parser.add_argument('--source', type=str, default='data/images', help='source') # file/folder, 0 for webcam - parser.add_argument('--img-size', nargs='+', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--img-size', nargs='+', type=int, default=[640], help='inference size (pixels)') parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') From 992ec81ed5acaf471d6ebb32d2a49de9a794dc38 Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Mon, 9 Nov 2020 16:36:17 +0800 Subject: [PATCH 47/92] Update README.md --- README.md | 188 +++++++++++++----------------------------------------- 1 file changed, 45 insertions(+), 143 deletions(-) diff --git a/README.md b/README.md index a3b5b00f4d7e..2dcab5cab472 100755 --- a/README.md +++ b/README.md @@ -1,155 +1,57 @@ - - -  +## Info +This branch provides detection and Android code complement to branch `tf-only-export`. +`models/tf.py` uses TF2 API to construct a tf.Keras model according to `*.yaml` config files and reads weights from `*.pt`, without using ONNX. -![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg) +**Because this branch persistently rebases to master branch of ultralytics/yolov5, use `git pull --rebase` instead of `git pull`.** -This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk. - -** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8. - -- **January 5, 2021**: [v4.0 release](https://github.com/ultralytics/yolov5/releases/tag/v4.0): nn.SiLU() activations, [Weights & Biases](https://wandb.ai/) logging, [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/) integration. -- **August 13, 2020**: [v3.0 release](https://github.com/ultralytics/yolov5/releases/tag/v3.0): nn.Hardswish() activations, data autodownload, native AMP. -- **July 23, 2020**: [v2.0 release](https://github.com/ultralytics/yolov5/releases/tag/v2.0): improved model definition, training and mAP. -- **June 22, 2020**: [PANet](https://arxiv.org/abs/1803.01534) updates: new heads, reduced parameters, improved speed and mAP [364fcfd](https://github.com/ultralytics/yolov5/commit/364fcfd7dba53f46edd4f04c037a039c0a287972). -- **June 19, 2020**: [FP16](https://pytorch.org/docs/stable/nn.html#torch.nn.Module.half) as new default for smaller checkpoints and faster inference [d4c6674](https://github.com/ultralytics/yolov5/commit/d4c6674c98e19df4c40e33a777610a18d1961145). - - -## Pretrained Checkpoints - -| Model | size | APval | APtest | AP50 | SpeedV100 | FPSV100 || params | GFLOPS | -|---------- |------ |------ |------ |------ | -------- | ------| ------ |------ | :------: | -| [YOLOv5s](https://github.com/ultralytics/yolov5/releases) |640 |36.8 |36.8 |55.6 |**2.2ms** |**455** ||7.3M |17.0 -| [YOLOv5m](https://github.com/ultralytics/yolov5/releases) |640 |44.5 |44.5 |63.1 |2.9ms |345 ||21.4M |51.3 -| [YOLOv5l](https://github.com/ultralytics/yolov5/releases) |640 |48.1 |48.1 |66.4 |3.8ms |264 ||47.0M |115.4 -| [YOLOv5x](https://github.com/ultralytics/yolov5/releases) |640 |**50.1** |**50.1** |**68.7** |6.0ms |167 ||87.7M |218.8 -| | | | | | | || | -| [YOLOv5x](https://github.com/ultralytics/yolov5/releases) + TTA |832 |**51.9** |**51.9** |**69.6** |24.9ms |40 ||87.7M |1005.3 - - - -** APtest denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy. -** All AP numbers are for single-model single-scale without ensemble or TTA. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` -** SpeedGPU averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes image preprocessing, FP16 inference, postprocessing and NMS. NMS is 1-2ms/img. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45` -** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). -** Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) runs at 3 image sizes. **Reproduce TTA** by `python test.py --data coco.yaml --img 832 --iou 0.65 --augment` - - -## Requirements - -Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run: -```bash -$ pip install -r requirements.txt +## Usage +### 1. Git clone `yolov5` and checkout `tf-android` ``` - - -## Tutorials - -* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)  🚀 RECOMMENDED -* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)  🌟 NEW -* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475) -* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)  ⭐ NEW -* [ONNX and TorchScript Export](https://github.com/ultralytics/yolov5/issues/251) -* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303) -* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318) -* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304) -* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607) -* [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)  ⭐ NEW -* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx) - - -## Environments - -YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): - -- **Google Colab Notebook** with free GPU: Open In Colab -- **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov5](https://www.kaggle.com/ultralytics/yolov5) -- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) -- **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker) - - -## Inference - -detect.py runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. -```bash -$ python detect.py --source 0 # webcam - file.jpg # image - file.mp4 # video - path/ # directory - path/*.jpg # glob - rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream - rtmp://192.168.1.105/live/test # rtmp stream - http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream +git clone https://github.com/zldrobit/yolov5.git +cd yolov5 +git checkout tf-android ``` - -To run inference on example images in `data/images`: -```bash -$ python detect.py --source data/images --weights yolov5s.pt --conf 0.25 - -Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', img_size=640, iou_thres=0.45, save_conf=False, save_dir='runs/detect', save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov5s.pt']) -Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB) - -Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5s.pt to yolov5s.pt... 100%|██████████████| 14.5M/14.5M [00:00<00:00, 21.3MB/s] - -Fusing layers... -Model Summary: 232 layers, 7459581 parameters, 0 gradients -image 1/2 data/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.012s) -image 2/2 data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.012s) -Results saved to runs/detect/exp -Done. (0.113s) +and download pretrained weights from ``` - - -### PyTorch Hub - -To run **batched inference** with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36): -```python -import torch -from PIL import Image - -# Model -model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True) - -# Images -img1 = Image.open('zidane.jpg') -img2 = Image.open('bus.jpg') -imgs = [img1, img2] # batched list of images - -# Inference -result = model(imgs) +https://github.com/ultralytics/yolov5.git ``` - -## Training - -Run commands below to reproduce results on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices). -```bash -$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64 - yolov5m 40 - yolov5l 24 - yolov5x 16 +### 2. Install requirements +``` +pip install -r requirements.txt +pip install tensorflow==2.3.1 ``` - - - -## Citation - -[![DOI](https://zenodo.org/badge/264818686.svg)](https://zenodo.org/badge/latestdoi/264818686) - - -## About Us - -Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including: -- **Cloud-based AI** systems operating on **hundreds of HD video streams in realtime.** -- **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.** -- **Custom data training**, hyperparameter evolution, and model exportation to any destination. -For business inquiries and professional support requests please visit us at https://www.ultralytics.com. +### 3. Convert and verify +- Convert weights to TensorFlow SavedModel and GraphDef, and verify them with +``` +PYTHONPATH=. python models/tf.py --weights weights/yolov5s.pt --cfg models/yolov5s.yaml --img 640 +python3 detect.py --weight weights/yolov5s.pb --img 640 +python3 detect.py --weight weights/yolov5s_saved_model/ --img 640 +``` +- Convert weights to TensorFlow SavedModel and GraphDef **integrated with NMS**, and verify them with +``` +PYTHONPATH=. python3 models/tf.py --img 640 --weight weights/yolov5s.pt --cfg models/yolov5s.yaml --tf-nms +python3 detect.py --img 640 --weight weights/yolov5s.pb --no-tf-nms +python3 detect.py --img 640 --weight weights/yolov5s_saved_model --no-tf-nms +``` +- Convert weights to fp16 TFLite model, and verify it with +``` +PYTHONPATH=. python3 models/tf.py --weight weights/yolov5s.pt --cfg models/yolov5s.yaml --img 640 --no-tfl-detect +python3 detect.py --weight weights/yolov5s-fp16.tflite --img 640 --tfl-detect +``` +- Convert weights to int8 TFLite model, and verify it with (Post-Training Quantization needs train or val images from [COCO 2017 dataset](https://cocodataset.org/#download)) +``` +PYTHONPATH=. python3 models/tf.py --weight weights/yolov5s.pt --cfg models/yolov5s.yaml --img 640 --no-tfl-detect --tfl-int8 --source /data/dataset/coco/coco2017/train2017 --ncalib 100 +python3 detect.py --weight weights/yolov5s-int8.tflite --img 640 --tfl-int8 --tfl-detect +``` +### 4. Put TFLite models in `assets` folder of Android project, and change +- `inputSize` to `--img` +- `output_width` according to new/old `inputSize` ratio +- `anchors` to `m.anchor_grid` as https://github.com/ultralytics/yolov5/pull/1127#issuecomment-714651073 +in android/app/src/main/java/org/tensorflow/lite/examples/detection/tflite/DetectorFactory.java -## Contact +Then run the program in Android Studio. -**Issues should be raised directly in the repository.** For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com. +If you have further question, plz ask in https://github.com/ultralytics/yolov5/pull/1127 From 058afbf3fd0b72fc27d279324fe495684792389a Mon Sep 17 00:00:00 2001 From: Jiacong Fang Date: Wed, 14 Oct 2020 09:50:17 +0800 Subject: [PATCH 48/92] Add Android project for TFLite inference --- android/.gitignore | 13 + android/app/.gitignore | 2 + android/app/build.gradle | 68 ++ android/app/download_model.gradle | 26 + android/app/proguard-rules.pro | 21 + android/app/src/androidTest/assets/table.jpg | Bin 0 -> 25291 bytes .../src/androidTest/assets/table_results.txt | 4 + .../src/androidTest/java/AndroidManifest.xml | 5 + .../lite/examples/detection/DetectorTest.java | 165 +++++ android/app/src/main/AndroidManifest.xml | 38 ++ android/app/src/main/assets/coco.txt | 80 +++ android/app/src/main/assets/kite.jpg | Bin 0 -> 1415684 bytes android/app/src/main/assets/labelmap.txt.bak | 91 +++ .../examples/detection/CameraActivity.java | 624 ++++++++++++++++++ .../detection/CameraConnectionFragment.java | 569 ++++++++++++++++ .../examples/detection/DetectorActivity.java | 323 +++++++++ .../LegacyCameraConnectionFragment.java | 199 ++++++ .../lite/examples/detection/MainActivity.java | 169 +++++ .../customview/AutoFitTextureView.java | 72 ++ .../detection/customview/OverlayView.java | 48 ++ .../customview/RecognitionScoreView.java | 67 ++ 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android/app/src/main/res/drawable/rectangle.xml create mode 100644 android/app/src/main/res/layout/activity_main.xml create mode 100644 android/app/src/main/res/layout/deviceview_row.xml create mode 100644 android/app/src/main/res/layout/listview_row.xml create mode 100644 android/app/src/main/res/layout/tfe_od_activity_camera.xml create mode 100644 android/app/src/main/res/layout/tfe_od_camera_connection_fragment_tracking.xml create mode 100644 android/app/src/main/res/layout/tfe_od_layout_bottom_sheet.xml create mode 100644 android/app/src/main/res/mipmap-anydpi-v26/ic_launcher.xml create mode 100644 android/app/src/main/res/mipmap-anydpi-v26/ic_launcher_round.xml create mode 100644 android/app/src/main/res/mipmap-hdpi/ic_launcher.png create mode 100644 android/app/src/main/res/mipmap-hdpi/ic_launcher_foreground.png create mode 100644 android/app/src/main/res/mipmap-hdpi/ic_launcher_round.png create mode 100644 android/app/src/main/res/mipmap-mdpi/ic_launcher.png create mode 100644 android/app/src/main/res/mipmap-mdpi/ic_launcher_foreground.png create mode 100644 android/app/src/main/res/mipmap-mdpi/ic_launcher_round.png create mode 100644 android/app/src/main/res/mipmap-xhdpi/ic_launcher.png create mode 100644 android/app/src/main/res/mipmap-xhdpi/ic_launcher_foreground.png create mode 100644 android/app/src/main/res/mipmap-xhdpi/ic_launcher_round.png create mode 100644 android/app/src/main/res/mipmap-xxhdpi/ic_launcher.png create mode 100644 android/app/src/main/res/mipmap-xxhdpi/ic_launcher_foreground.png create mode 100644 android/app/src/main/res/mipmap-xxhdpi/ic_launcher_round.png create mode 100644 android/app/src/main/res/mipmap-xxxhdpi/ic_launcher.png create mode 100644 android/app/src/main/res/mipmap-xxxhdpi/ic_launcher_foreground.png create mode 100644 android/app/src/main/res/mipmap-xxxhdpi/ic_launcher_round.png create mode 100644 android/app/src/main/res/values/colors.xml create mode 100644 android/app/src/main/res/values/dimens.xml create mode 100644 android/app/src/main/res/values/strings.xml create mode 100644 android/app/src/main/res/values/styles.xml create mode 100644 android/build.gradle create mode 100644 android/gradle.properties create mode 100644 android/gradle/wrapper/gradle-wrapper.jar create mode 100644 android/gradle/wrapper/gradle-wrapper.properties create mode 100644 android/gradlew create mode 100644 android/gradlew.bat create mode 100644 android/settings.gradle diff --git a/android/.gitignore b/android/.gitignore new file mode 100644 index 000000000000..4e94553f8871 --- /dev/null +++ b/android/.gitignore @@ -0,0 +1,13 @@ +*.iml +.gradle +/local.properties +/.idea/libraries +/.idea/modules.xml +/.idea/workspace.xml +.DS_Store +/build +/captures +.externalNativeBuild + +/.gradle/ +/.idea/ diff --git a/android/app/.gitignore b/android/app/.gitignore new file mode 100644 index 000000000000..4ccdcdfc6cc3 --- /dev/null +++ b/android/app/.gitignore @@ -0,0 +1,2 @@ +/build +/build/ \ No newline at end of file diff --git a/android/app/build.gradle b/android/app/build.gradle new file mode 100644 index 000000000000..8733a8451958 --- /dev/null +++ b/android/app/build.gradle @@ -0,0 +1,68 @@ +apply plugin: 'com.android.application' +apply plugin: 'de.undercouch.download' + +android { + compileSdkVersion 28 + buildToolsVersion '28.0.3' + defaultConfig { + applicationId "org.tensorflow.lite.examples.detection" + minSdkVersion 21 + targetSdkVersion 28 + versionCode 1 + versionName "1.0" + + ndk { + abiFilters 'armeabi-v7a', 'arm64-v8a', 'x86' + } + } + buildTypes { + release { + minifyEnabled false + proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro' + } + } + aaptOptions { + noCompress "tflite" + } + compileOptions { + sourceCompatibility = '1.8' + targetCompatibility = '1.8' + } + lintOptions { + abortOnError false + } +} + +// import DownloadModels task +project.ext.ASSET_DIR = projectDir.toString() + '/src/main/assets' +project.ext.TMP_DIR = project.buildDir.toString() + '/downloads' + +// Download default models; if you wish to use your own models then +// place them in the "assets" directory and comment out this line. +//apply from: "download_model.gradle" + +//apply from: 'download_model.gradle' + +dependencies { +// implementation fileTree(dir: 'libs', include: ['*.jar', '*.aar']) +// implementation files('bin/gdbserver') + implementation 'androidx.appcompat:appcompat:1.1.0' + implementation 'androidx.coordinatorlayout:coordinatorlayout:1.1.0' + implementation 'com.google.android.material:material:1.1.0' + implementation 'org.tensorflow:tensorflow-lite:2.3.0' + implementation 'org.tensorflow:tensorflow-lite-gpu:2.3.0' +// implementation 'org.tensorflow:tensorflow-lite-select-tf-ops:2.3.0' +// implementation 'org.tensorflow:tensorflow-lite:2.2.0' +// implementation 'org.tensorflow:tensorflow-lite-gpu:2.2.0' +// implementation 'org.tensorflow:tensorflow-lite:1.14.0' +// implementation(name:'tensorflow-lite', ext:'aar') +// implementation(name:'tensorflow-lite-gpu', ext:'aar') + +// implementation 'org.tensorflow:tensorflow-lite-gpu:1.14.0' +// implementation 'org.tensorflow:tensorflow-lite:0.0.0-gpu-experimental' + implementation 'androidx.constraintlayout:constraintlayout:1.1.3' + implementation 'com.google.code.gson:gson:2.8.6' + androidTestImplementation 'androidx.test.ext:junit:1.1.1' + androidTestImplementation 'com.android.support.test:rules:1.0.2' + androidTestImplementation 'com.google.truth:truth:1.0.1' +} diff --git a/android/app/download_model.gradle b/android/app/download_model.gradle new file mode 100644 index 000000000000..08b86fdc3c6c --- /dev/null +++ b/android/app/download_model.gradle @@ -0,0 +1,26 @@ + +task downloadZipFile(type: Download) { + src 'http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip' + dest new File(buildDir, 'zips/') + overwrite false +} + + +task downloadAndUnzipFile(dependsOn: downloadZipFile, type: Copy) { + from zipTree(downloadZipFile.dest) + into project.ext.ASSET_DIR +} + + +task extractModels(type: Copy) { + dependsOn downloadAndUnzipFile +} + +tasks.whenTaskAdded { task -> + if (task.name == 'assembleDebug') { + task.dependsOn 'extractModels' + } + if (task.name == 'assembleRelease') { + task.dependsOn 'extractModels' + } +} \ No newline at end of file diff --git a/android/app/proguard-rules.pro b/android/app/proguard-rules.pro new file mode 100644 index 000000000000..f1b424510da5 --- /dev/null +++ b/android/app/proguard-rules.pro @@ -0,0 +1,21 @@ +# Add project specific ProGuard rules here. +# You can control the set of applied configuration files using the +# proguardFiles setting in build.gradle. +# +# For more details, see +# http://developer.android.com/guide/developing/tools/proguard.html + +# If your project uses WebView with JS, uncomment the following +# and specify the fully qualified class name to the JavaScript interface +# class: +#-keepclassmembers class 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444.8627 0.48828125 +knife 342.53433 243.71082 583.89185 416.34595 0.4765625 +cup 68.025925 197.5857 202.02031 374.2206 0.4375 +book 185.43098 139.64153 244.51149 203.37737 0.3125 diff --git a/android/app/src/androidTest/java/AndroidManifest.xml b/android/app/src/androidTest/java/AndroidManifest.xml new file mode 100644 index 000000000000..484ab4769b22 --- /dev/null +++ b/android/app/src/androidTest/java/AndroidManifest.xml @@ -0,0 +1,5 @@ + + + + \ No newline at end of file diff --git a/android/app/src/androidTest/java/org/tensorflow/lite/examples/detection/DetectorTest.java b/android/app/src/androidTest/java/org/tensorflow/lite/examples/detection/DetectorTest.java new file mode 100644 index 000000000000..14b9e678070c --- /dev/null +++ b/android/app/src/androidTest/java/org/tensorflow/lite/examples/detection/DetectorTest.java @@ -0,0 +1,165 @@ +/* + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.tensorflow.lite.examples.detection; + +import static com.google.common.truth.Truth.assertThat; +import static java.lang.Math.abs; +import static java.lang.Math.max; +import static java.lang.Math.min; + +import android.content.res.AssetManager; +import android.graphics.Bitmap; +import android.graphics.Bitmap.Config; +import android.graphics.BitmapFactory; +import android.graphics.Canvas; +import android.graphics.Matrix; +import android.graphics.RectF; +import android.util.Size; +import androidx.test.ext.junit.runners.AndroidJUnit4; +import androidx.test.platform.app.InstrumentationRegistry; +import java.io.IOException; +import java.io.InputStream; +import java.util.ArrayList; +import java.util.List; +import java.util.Scanner; +import org.junit.Before; +import org.junit.Test; +import org.junit.runner.RunWith; +import org.tensorflow.lite.examples.detection.env.ImageUtils; +import org.tensorflow.lite.examples.detection.tflite.Classifier; +import org.tensorflow.lite.examples.detection.tflite.Classifier.Recognition; +import org.tensorflow.lite.examples.detection.tflite.TFLiteObjectDetectionAPIModel; + +/** Golden test for Object Detection Reference app. */ +@RunWith(AndroidJUnit4.class) +public class DetectorTest { + + private static final int MODEL_INPUT_SIZE = 300; + private static final boolean IS_MODEL_QUANTIZED = true; + private static final String MODEL_FILE = "detect.tflite"; + private static final String LABELS_FILE = "file:///android_asset/coco.txt"; + private static final Size IMAGE_SIZE = new Size(640, 480); + + private Classifier detector; + private Bitmap croppedBitmap; + private Matrix frameToCropTransform; + private Matrix cropToFrameTransform; + + @Before + public void setUp() throws IOException { + AssetManager assetManager = + InstrumentationRegistry.getInstrumentation().getContext().getAssets(); + detector = + TFLiteObjectDetectionAPIModel.create( + assetManager, + MODEL_FILE, + LABELS_FILE, + MODEL_INPUT_SIZE, + IS_MODEL_QUANTIZED); + int cropSize = MODEL_INPUT_SIZE; + int previewWidth = IMAGE_SIZE.getWidth(); + int previewHeight = IMAGE_SIZE.getHeight(); + int sensorOrientation = 0; + croppedBitmap = Bitmap.createBitmap(cropSize, cropSize, Config.ARGB_8888); + + frameToCropTransform = + ImageUtils.getTransformationMatrix( + previewWidth, previewHeight, + cropSize, cropSize, + sensorOrientation, false); + cropToFrameTransform = new Matrix(); + frameToCropTransform.invert(cropToFrameTransform); + } + + @Test + public void detectionResultsShouldNotChange() throws Exception { + Canvas canvas = new Canvas(croppedBitmap); + canvas.drawBitmap(loadImage("table.jpg"), frameToCropTransform, null); + final List results = detector.recognizeImage(croppedBitmap); + final List expected = loadRecognitions("table_results.txt"); + + for (Recognition target : expected) { + // Find a matching result in results + boolean matched = false; + for (Recognition item : results) { + RectF bbox = new RectF(); + cropToFrameTransform.mapRect(bbox, item.getLocation()); + if (item.getTitle().equals(target.getTitle()) + && matchBoundingBoxes(bbox, target.getLocation()) + && matchConfidence(item.getConfidence(), target.getConfidence())) { + matched = true; + break; + } + } + assertThat(matched).isTrue(); + } + } + + // Confidence tolerance: absolute 1% + private static boolean matchConfidence(float a, float b) { + return abs(a - b) < 0.01; + } + + // Bounding Box tolerance: overlapped area > 95% of each one + private static boolean matchBoundingBoxes(RectF a, RectF b) { + float areaA = a.width() * a.height(); + float areaB = b.width() * b.height(); + RectF overlapped = + new RectF( + max(a.left, b.left), max(a.top, b.top), min(a.right, b.right), min(a.bottom, b.bottom)); + float overlappedArea = overlapped.width() * overlapped.height(); + return overlappedArea > 0.95 * areaA && overlappedArea > 0.95 * areaB; + } + + private static Bitmap loadImage(String fileName) throws Exception { + AssetManager assetManager = + InstrumentationRegistry.getInstrumentation().getContext().getAssets(); + InputStream inputStream = assetManager.open(fileName); + return BitmapFactory.decodeStream(inputStream); + } + + // The format of result: + // category bbox.left bbox.top bbox.right bbox.bottom confidence + // ... + // Example: + // Apple 99 25 30 75 80 0.99 + // Banana 25 90 75 200 0.98 + // ... + private static List loadRecognitions(String fileName) throws Exception { + AssetManager assetManager = + InstrumentationRegistry.getInstrumentation().getContext().getAssets(); + InputStream inputStream = assetManager.open(fileName); + Scanner scanner = new Scanner(inputStream); + List result = new ArrayList<>(); + while (scanner.hasNext()) { + String category = scanner.next(); + category = category.replace('_', ' '); + if (!scanner.hasNextFloat()) { + break; + } + float left = scanner.nextFloat(); + float top = scanner.nextFloat(); + float right = scanner.nextFloat(); + float bottom = scanner.nextFloat(); + RectF boundingBox = new RectF(left, top, right, bottom); + float confidence = scanner.nextFloat(); + Recognition recognition = new Recognition(null, category, confidence, boundingBox); + result.add(recognition); + } + return result; + } +} diff --git a/android/app/src/main/AndroidManifest.xml b/android/app/src/main/AndroidManifest.xml new file mode 100644 index 000000000000..66c4000ffeef --- /dev/null +++ b/android/app/src/main/AndroidManifest.xml @@ -0,0 +1,38 @@ + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/android/app/src/main/assets/coco.txt b/android/app/src/main/assets/coco.txt new file mode 100644 index 000000000000..ec82f0ffde81 --- /dev/null +++ b/android/app/src/main/assets/coco.txt @@ -0,0 +1,80 @@ +person +bicycle +car +motorbike +aeroplane +bus +train +truck +boat +traffic light +fire hydrant +stop sign +parking meter +bench +bird +cat +dog +horse +sheep +cow +elephant +bear +zebra +giraffe +backpack +umbrella +handbag +tie +suitcase +frisbee +skis +snowboard +sports ball +kite +baseball bat +baseball glove +skateboard +surfboard +tennis racket +bottle +wine glass +cup +fork +knife +spoon +bowl +banana +apple +sandwich +orange +broccoli +carrot +hot dog +pizza +donut +cake +chair +sofa +potted plant +bed +dining table +toilet +tvmonitor +laptop +mouse +remote +keyboard +cell phone +microwave +oven +toaster +sink +refrigerator +book +clock +vase +scissors 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cropValueTextView, inferenceTimeTextView; + protected ImageView bottomSheetArrowImageView; + private ImageView plusImageView, minusImageView; + protected ListView deviceView; + protected TextView threadsTextView; + protected ListView modelView; + /** Current indices of device and model. */ + int currentDevice = -1; + int currentModel = -1; + int currentNumThreads = -1; + + ArrayList deviceStrings = new ArrayList(); + + @Override + protected void onCreate(final Bundle savedInstanceState) { + LOGGER.d("onCreate " + this); + super.onCreate(null); + getWindow().addFlags(WindowManager.LayoutParams.FLAG_KEEP_SCREEN_ON); + + setContentView(R.layout.tfe_od_activity_camera); + Toolbar toolbar = findViewById(R.id.toolbar); + setSupportActionBar(toolbar); + getSupportActionBar().setDisplayShowTitleEnabled(false); + + if (hasPermission()) { + setFragment(); + } else { + requestPermission(); + } + + threadsTextView = findViewById(R.id.threads); + currentNumThreads = Integer.parseInt(threadsTextView.getText().toString().trim()); + plusImageView = findViewById(R.id.plus); + minusImageView = findViewById(R.id.minus); + deviceView = findViewById(R.id.device_list); + deviceStrings.add("CPU"); + deviceStrings.add("GPU"); + deviceStrings.add("NNAPI"); + deviceView.setChoiceMode(ListView.CHOICE_MODE_SINGLE); + ArrayAdapter deviceAdapter = + new ArrayAdapter<>( + CameraActivity.this , R.layout.deviceview_row, R.id.deviceview_row_text, deviceStrings); + deviceView.setAdapter(deviceAdapter); + deviceView.setItemChecked(defaultDeviceIndex, true); + currentDevice = defaultDeviceIndex; + deviceView.setOnItemClickListener( + new AdapterView.OnItemClickListener() { + @Override + public void onItemClick(AdapterView parent, View view, int position, long id) { + updateActiveModel(); + } + }); + + bottomSheetLayout = findViewById(R.id.bottom_sheet_layout); + gestureLayout = findViewById(R.id.gesture_layout); + sheetBehavior = BottomSheetBehavior.from(bottomSheetLayout); + bottomSheetArrowImageView = findViewById(R.id.bottom_sheet_arrow); + modelView = findViewById((R.id.model_list)); + + modelStrings = getModelStrings(getAssets(), ASSET_PATH); + modelView.setChoiceMode(ListView.CHOICE_MODE_SINGLE); + ArrayAdapter modelAdapter = + new ArrayAdapter<>( + CameraActivity.this , R.layout.listview_row, R.id.listview_row_text, modelStrings); + modelView.setAdapter(modelAdapter); + modelView.setItemChecked(defaultModelIndex, true); + currentModel = defaultModelIndex; + modelView.setOnItemClickListener( + new AdapterView.OnItemClickListener() { + @Override + public void onItemClick(AdapterView parent, View view, int position, long id) { + updateActiveModel(); + } + }); + + ViewTreeObserver vto = gestureLayout.getViewTreeObserver(); + vto.addOnGlobalLayoutListener( + new ViewTreeObserver.OnGlobalLayoutListener() { + @Override + public void onGlobalLayout() { + if (Build.VERSION.SDK_INT < Build.VERSION_CODES.JELLY_BEAN) { + gestureLayout.getViewTreeObserver().removeGlobalOnLayoutListener(this); + } else { + gestureLayout.getViewTreeObserver().removeOnGlobalLayoutListener(this); + } + // int width = bottomSheetLayout.getMeasuredWidth(); + int height = gestureLayout.getMeasuredHeight(); + + sheetBehavior.setPeekHeight(height); + } + }); + sheetBehavior.setHideable(false); + + sheetBehavior.setBottomSheetCallback( + new BottomSheetBehavior.BottomSheetCallback() { + @Override + public void onStateChanged(@NonNull View bottomSheet, int newState) { + switch (newState) { + case BottomSheetBehavior.STATE_HIDDEN: + break; + case BottomSheetBehavior.STATE_EXPANDED: + { + bottomSheetArrowImageView.setImageResource(R.drawable.icn_chevron_down); + } + break; + case BottomSheetBehavior.STATE_COLLAPSED: + { + bottomSheetArrowImageView.setImageResource(R.drawable.icn_chevron_up); + } + break; + case BottomSheetBehavior.STATE_DRAGGING: + break; + case BottomSheetBehavior.STATE_SETTLING: + bottomSheetArrowImageView.setImageResource(R.drawable.icn_chevron_up); + break; + } + } + + @Override + public void onSlide(@NonNull View bottomSheet, float slideOffset) {} + }); + + frameValueTextView = findViewById(R.id.frame_info); + cropValueTextView = findViewById(R.id.crop_info); + inferenceTimeTextView = findViewById(R.id.inference_info); + + plusImageView.setOnClickListener(this); + minusImageView.setOnClickListener(this); + } + + + + protected ArrayList getModelStrings(AssetManager mgr, String path){ + ArrayList res = new ArrayList(); + try { + String[] files = mgr.list(path); + for (String file : files) { + String[] splits = file.split("\\."); + if (splits[splits.length - 1].equals("tflite")) { + res.add(file); + } + } + + } + catch (IOException e){ + System.err.println("getModelStrings: " + e.getMessage()); + } + return res; + } + + protected int[] getRgbBytes() { + imageConverter.run(); + return rgbBytes; + } + + protected int getLuminanceStride() { + return yRowStride; + } + + protected byte[] getLuminance() { + return yuvBytes[0]; + } + + /** Callback for android.hardware.Camera API */ + @Override + public void onPreviewFrame(final byte[] bytes, final Camera camera) { + if (isProcessingFrame) { + LOGGER.w("Dropping frame!"); + return; + } + + try { + // Initialize the storage bitmaps once when the resolution is known. + if (rgbBytes == null) { + Camera.Size previewSize = camera.getParameters().getPreviewSize(); + previewHeight = previewSize.height; + previewWidth = previewSize.width; + rgbBytes = new int[previewWidth * previewHeight]; + onPreviewSizeChosen(new Size(previewSize.width, previewSize.height), 90); + } + } catch (final Exception e) { + LOGGER.e(e, "Exception!"); + return; + } + + isProcessingFrame = true; + yuvBytes[0] = bytes; + yRowStride = previewWidth; + + imageConverter = + new Runnable() { + @Override + public void run() { + ImageUtils.convertYUV420SPToARGB8888(bytes, previewWidth, previewHeight, rgbBytes); + } + }; + + postInferenceCallback = + new Runnable() { + @Override + public void run() { + camera.addCallbackBuffer(bytes); + isProcessingFrame = false; + } + }; + processImage(); + } + + /** Callback for Camera2 API */ + @Override + public void onImageAvailable(final ImageReader reader) { + // We need wait until we have some size from onPreviewSizeChosen + if (previewWidth == 0 || previewHeight == 0) { + return; + } + if (rgbBytes == null) { + rgbBytes = new int[previewWidth * previewHeight]; + } + try { + final Image image = reader.acquireLatestImage(); + + if (image == null) { + return; + } + + if (isProcessingFrame) { + image.close(); + return; + } + isProcessingFrame = true; + Trace.beginSection("imageAvailable"); + final Plane[] planes = image.getPlanes(); + fillBytes(planes, yuvBytes); + yRowStride = planes[0].getRowStride(); + final int uvRowStride = planes[1].getRowStride(); + final int uvPixelStride = planes[1].getPixelStride(); + + imageConverter = + new Runnable() { + @Override + public void run() { + ImageUtils.convertYUV420ToARGB8888( + yuvBytes[0], + yuvBytes[1], + yuvBytes[2], + previewWidth, + previewHeight, + yRowStride, + uvRowStride, + uvPixelStride, + rgbBytes); + } + }; + + postInferenceCallback = + new Runnable() { + @Override + public void run() { + image.close(); + isProcessingFrame = false; + } + }; + + processImage(); + } catch (final Exception e) { + LOGGER.e(e, "Exception!"); + Trace.endSection(); + return; + } + Trace.endSection(); + } + + @Override + public synchronized void onStart() { + LOGGER.d("onStart " + this); + super.onStart(); + } + + @Override + public synchronized void onResume() { + LOGGER.d("onResume " + this); + super.onResume(); + + handlerThread = new HandlerThread("inference"); + handlerThread.start(); + handler = new Handler(handlerThread.getLooper()); + } + + @Override + public synchronized void onPause() { + LOGGER.d("onPause " + this); + + handlerThread.quitSafely(); + try { + handlerThread.join(); + handlerThread = null; + handler = null; + } catch (final InterruptedException e) { + LOGGER.e(e, "Exception!"); + } + + super.onPause(); + } + + @Override + public synchronized void onStop() { + LOGGER.d("onStop " + this); + super.onStop(); + } + + @Override + public synchronized void onDestroy() { + LOGGER.d("onDestroy " + this); + super.onDestroy(); + } + + protected synchronized void runInBackground(final Runnable r) { + if (handler != null) { + handler.post(r); + } + } + + @Override + public void onRequestPermissionsResult( + final int requestCode, final String[] permissions, final int[] grantResults) { + super.onRequestPermissionsResult(requestCode, permissions, grantResults); + if (requestCode == PERMISSIONS_REQUEST) { + if (allPermissionsGranted(grantResults)) { + setFragment(); + } else { + requestPermission(); + } + } + } + + private static boolean allPermissionsGranted(final int[] grantResults) { + for (int result : grantResults) { + if (result != PackageManager.PERMISSION_GRANTED) { + return false; + } + } + return true; + } + + private boolean hasPermission() { + if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.M) { + return checkSelfPermission(PERMISSION_CAMERA) == PackageManager.PERMISSION_GRANTED; + } else { + return true; + } + } + + private void requestPermission() { + if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.M) { + if (shouldShowRequestPermissionRationale(PERMISSION_CAMERA)) { + Toast.makeText( + CameraActivity.this, + "Camera permission is required for this demo", + Toast.LENGTH_LONG) + .show(); + } + requestPermissions(new String[] {PERMISSION_CAMERA}, PERMISSIONS_REQUEST); + } + } + + // Returns true if the device supports the required hardware level, or better. + private boolean isHardwareLevelSupported( + CameraCharacteristics characteristics, int requiredLevel) { + int deviceLevel = characteristics.get(CameraCharacteristics.INFO_SUPPORTED_HARDWARE_LEVEL); + if (deviceLevel == CameraCharacteristics.INFO_SUPPORTED_HARDWARE_LEVEL_LEGACY) { + return requiredLevel == deviceLevel; + } + // deviceLevel is not LEGACY, can use numerical sort + return requiredLevel <= deviceLevel; + } + + private String chooseCamera() { + final CameraManager manager = (CameraManager) getSystemService(Context.CAMERA_SERVICE); + try { + for (final String cameraId : manager.getCameraIdList()) { + final CameraCharacteristics characteristics = manager.getCameraCharacteristics(cameraId); + + // We don't use a front facing camera in this sample. + final Integer facing = characteristics.get(CameraCharacteristics.LENS_FACING); + if (facing != null && facing == CameraCharacteristics.LENS_FACING_FRONT) { + continue; + } + + final StreamConfigurationMap map = + characteristics.get(CameraCharacteristics.SCALER_STREAM_CONFIGURATION_MAP); + + if (map == null) { + continue; + } + + // Fallback to camera1 API for internal cameras that don't have full support. + // This should help with legacy situations where using the camera2 API causes + // distorted or otherwise broken previews. + useCamera2API = + (facing == CameraCharacteristics.LENS_FACING_EXTERNAL) + || isHardwareLevelSupported( + characteristics, CameraCharacteristics.INFO_SUPPORTED_HARDWARE_LEVEL_FULL); + LOGGER.i("Camera API lv2?: %s", useCamera2API); + return cameraId; + } + } catch (CameraAccessException e) { + LOGGER.e(e, "Not allowed to access camera"); + } + + return null; + } + + protected void setFragment() { + String cameraId = chooseCamera(); + + Fragment fragment; + if (useCamera2API) { + CameraConnectionFragment camera2Fragment = + CameraConnectionFragment.newInstance( + new CameraConnectionFragment.ConnectionCallback() { + @Override + public void onPreviewSizeChosen(final Size size, final int rotation) { + previewHeight = size.getHeight(); + previewWidth = size.getWidth(); + CameraActivity.this.onPreviewSizeChosen(size, rotation); + } + }, + this, + getLayoutId(), + getDesiredPreviewFrameSize()); + + camera2Fragment.setCamera(cameraId); + fragment = camera2Fragment; + } else { + fragment = + new LegacyCameraConnectionFragment(this, getLayoutId(), getDesiredPreviewFrameSize()); + } + + getFragmentManager().beginTransaction().replace(R.id.container, fragment).commit(); + } + + protected void fillBytes(final Plane[] planes, final byte[][] yuvBytes) { + // Because of the variable row stride it's not possible to know in + // advance the actual necessary dimensions of the yuv planes. + for (int i = 0; i < planes.length; ++i) { + final ByteBuffer buffer = planes[i].getBuffer(); + if (yuvBytes[i] == null) { + LOGGER.d("Initializing buffer %d at size %d", i, buffer.capacity()); + yuvBytes[i] = new byte[buffer.capacity()]; + } + buffer.get(yuvBytes[i]); + } + } + + public boolean isDebug() { + return debug; + } + + protected void readyForNextImage() { + if (postInferenceCallback != null) { + postInferenceCallback.run(); + } + } + + protected int getScreenOrientation() { + switch (getWindowManager().getDefaultDisplay().getRotation()) { + case Surface.ROTATION_270: + return 270; + case Surface.ROTATION_180: + return 180; + case Surface.ROTATION_90: + return 90; + default: + return 0; + } + } + +// @Override +// public void onCheckedChanged(CompoundButton buttonView, boolean isChecked) { +// setUseNNAPI(isChecked); +// if (isChecked) apiSwitchCompat.setText("NNAPI"); +// else apiSwitchCompat.setText("TFLITE"); +// } + + @Override + public void onClick(View v) { + if (v.getId() == R.id.plus) { + String threads = threadsTextView.getText().toString().trim(); + int numThreads = Integer.parseInt(threads); + if (numThreads >= 9) return; + numThreads++; + threadsTextView.setText(String.valueOf(numThreads)); + setNumThreads(numThreads); + } else if (v.getId() == R.id.minus) { + String threads = threadsTextView.getText().toString().trim(); + int numThreads = Integer.parseInt(threads); + if (numThreads == 1) { + return; + } + numThreads--; + threadsTextView.setText(String.valueOf(numThreads)); + setNumThreads(numThreads); + } + } + + protected void showFrameInfo(String frameInfo) { + frameValueTextView.setText(frameInfo); + } + + protected void showCropInfo(String cropInfo) { + cropValueTextView.setText(cropInfo); + } + + protected void showInference(String inferenceTime) { + inferenceTimeTextView.setText(inferenceTime); + } + + protected abstract void updateActiveModel(); + protected abstract void processImage(); + + protected abstract void onPreviewSizeChosen(final Size size, final int rotation); + + protected abstract int getLayoutId(); + + protected abstract Size getDesiredPreviewFrameSize(); + + protected abstract void setNumThreads(int numThreads); + + protected abstract void setUseNNAPI(boolean isChecked); +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/CameraConnectionFragment.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/CameraConnectionFragment.java new file mode 100644 index 000000000000..641f3084bdaa --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/CameraConnectionFragment.java @@ -0,0 +1,569 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.tensorflow.lite.examples.detection; + +import android.annotation.SuppressLint; +import android.app.Activity; +import android.app.AlertDialog; +import android.app.Dialog; +import android.app.DialogFragment; +import android.app.Fragment; +import android.content.Context; +import android.content.DialogInterface; +import android.content.res.Configuration; +import android.graphics.ImageFormat; +import android.graphics.Matrix; +import android.graphics.RectF; +import android.graphics.SurfaceTexture; +import android.hardware.camera2.CameraAccessException; +import android.hardware.camera2.CameraCaptureSession; +import android.hardware.camera2.CameraCharacteristics; +import android.hardware.camera2.CameraDevice; +import android.hardware.camera2.CameraManager; +import android.hardware.camera2.CaptureRequest; +import android.hardware.camera2.CaptureResult; +import android.hardware.camera2.TotalCaptureResult; +import android.hardware.camera2.params.StreamConfigurationMap; +import android.media.ImageReader; +import android.media.ImageReader.OnImageAvailableListener; +import android.os.Bundle; +import android.os.Handler; +import android.os.HandlerThread; +import android.text.TextUtils; +import android.util.Size; +import android.util.SparseIntArray; +import android.view.LayoutInflater; +import android.view.Surface; +import android.view.TextureView; +import android.view.View; +import android.view.ViewGroup; +import android.widget.Toast; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Collections; +import java.util.Comparator; +import java.util.List; +import java.util.concurrent.Semaphore; +import java.util.concurrent.TimeUnit; +import org.tensorflow.lite.examples.detection.customview.AutoFitTextureView; +import org.tensorflow.lite.examples.detection.env.Logger; + +@SuppressLint("ValidFragment") +public class CameraConnectionFragment extends Fragment { + private static final Logger LOGGER = new Logger(); + + /** + * The camera preview size will be chosen to be the smallest frame by pixel size capable of + * containing a DESIRED_SIZE x DESIRED_SIZE square. + */ + private static final int MINIMUM_PREVIEW_SIZE = 320; + + /** Conversion from screen rotation to JPEG orientation. */ + private static final SparseIntArray ORIENTATIONS = new SparseIntArray(); + + private static final String FRAGMENT_DIALOG = "dialog"; + + static { + ORIENTATIONS.append(Surface.ROTATION_0, 90); + ORIENTATIONS.append(Surface.ROTATION_90, 0); + ORIENTATIONS.append(Surface.ROTATION_180, 270); + ORIENTATIONS.append(Surface.ROTATION_270, 180); + } + + /** A {@link Semaphore} to prevent the app from exiting before closing the camera. */ + private final Semaphore cameraOpenCloseLock = new Semaphore(1); + /** A {@link OnImageAvailableListener} to receive frames as they are available. */ + private final OnImageAvailableListener imageListener; + /** The input size in pixels desired by TensorFlow (width and height of a square bitmap). */ + private final Size inputSize; + /** The layout identifier to inflate for this Fragment. */ + private final int layout; + + private final ConnectionCallback cameraConnectionCallback; + private final CameraCaptureSession.CaptureCallback captureCallback = + new CameraCaptureSession.CaptureCallback() { + @Override + public void onCaptureProgressed( + final CameraCaptureSession session, + final CaptureRequest request, + final CaptureResult partialResult) {} + + @Override + public void onCaptureCompleted( + final CameraCaptureSession session, + final CaptureRequest request, + final TotalCaptureResult result) {} + }; + /** ID of the current {@link CameraDevice}. */ + private String cameraId; + /** An {@link AutoFitTextureView} for camera preview. */ + private AutoFitTextureView textureView; + /** A {@link CameraCaptureSession } for camera preview. */ + private CameraCaptureSession captureSession; + /** A reference to the opened {@link CameraDevice}. */ + private CameraDevice cameraDevice; + /** The rotation in degrees of the camera sensor from the display. */ + private Integer sensorOrientation; + /** The {@link Size} of camera preview. */ + private Size previewSize; + /** An additional thread for running tasks that shouldn't block the UI. */ + private HandlerThread backgroundThread; + /** A {@link Handler} for running tasks in the background. */ + private Handler backgroundHandler; + /** An {@link ImageReader} that handles preview frame capture. */ + private ImageReader previewReader; + /** {@link CaptureRequest.Builder} for the camera preview */ + private CaptureRequest.Builder previewRequestBuilder; + /** {@link CaptureRequest} generated by {@link #previewRequestBuilder} */ + private CaptureRequest previewRequest; + /** {@link CameraDevice.StateCallback} is called when {@link CameraDevice} changes its state. */ + private final CameraDevice.StateCallback stateCallback = + new CameraDevice.StateCallback() { + @Override + public void onOpened(final CameraDevice cd) { + // This method is called when the camera is opened. We start camera preview here. + cameraOpenCloseLock.release(); + cameraDevice = cd; + createCameraPreviewSession(); + } + + @Override + public void onDisconnected(final CameraDevice cd) { + cameraOpenCloseLock.release(); + cd.close(); + cameraDevice = null; + } + + @Override + public void onError(final CameraDevice cd, final int error) { + cameraOpenCloseLock.release(); + cd.close(); + cameraDevice = null; + final Activity activity = getActivity(); + if (null != activity) { + activity.finish(); + } + } + }; + /** + * {@link TextureView.SurfaceTextureListener} handles several lifecycle events on a {@link + * TextureView}. + */ + private final TextureView.SurfaceTextureListener surfaceTextureListener = + new TextureView.SurfaceTextureListener() { + @Override + public void onSurfaceTextureAvailable( + final SurfaceTexture texture, final int width, final int height) { + openCamera(width, height); + } + + @Override + public void onSurfaceTextureSizeChanged( + final SurfaceTexture texture, final int width, final int height) { + configureTransform(width, height); + } + + @Override + public boolean onSurfaceTextureDestroyed(final SurfaceTexture texture) { + return true; + } + + @Override + public void onSurfaceTextureUpdated(final SurfaceTexture texture) {} + }; + + private CameraConnectionFragment( + final ConnectionCallback connectionCallback, + final OnImageAvailableListener imageListener, + final int layout, + final Size inputSize) { + this.cameraConnectionCallback = connectionCallback; + this.imageListener = imageListener; + this.layout = layout; + this.inputSize = inputSize; + } + + /** + * Given {@code choices} of {@code Size}s supported by a camera, chooses the smallest one whose + * width and height are at least as large as the minimum of both, or an exact match if possible. + * + * @param choices The list of sizes that the camera supports for the intended output class + * @param width The minimum desired width + * @param height The minimum desired height + * @return The optimal {@code Size}, or an arbitrary one if none were big enough + */ + protected static Size chooseOptimalSize(final Size[] choices, final int width, final int height) { + final int minSize = Math.max(Math.min(width, height), MINIMUM_PREVIEW_SIZE); + final Size desiredSize = new Size(width, height); + + // Collect the supported resolutions that are at least as big as the preview Surface + boolean exactSizeFound = false; + final List bigEnough = new ArrayList(); + final List tooSmall = new ArrayList(); + for (final Size option : choices) { + if (option.equals(desiredSize)) { + // Set the size but don't return yet so that remaining sizes will still be logged. + exactSizeFound = true; + } + + if (option.getHeight() >= minSize && option.getWidth() >= minSize) { + bigEnough.add(option); + } else { + tooSmall.add(option); + } + } + + LOGGER.i("Desired size: " + desiredSize + ", min size: " + minSize + "x" + minSize); + LOGGER.i("Valid preview sizes: [" + TextUtils.join(", ", bigEnough) + "]"); + LOGGER.i("Rejected preview sizes: [" + TextUtils.join(", ", tooSmall) + "]"); + + if (exactSizeFound) { + LOGGER.i("Exact size match found."); + return desiredSize; + } + + // Pick the smallest of those, assuming we found any + if (bigEnough.size() > 0) { + final Size chosenSize = Collections.min(bigEnough, new CompareSizesByArea()); + LOGGER.i("Chosen size: " + chosenSize.getWidth() + "x" + chosenSize.getHeight()); + return chosenSize; + } else { + LOGGER.e("Couldn't find any suitable preview size"); + return choices[0]; + } + } + + public static CameraConnectionFragment newInstance( + final ConnectionCallback callback, + final OnImageAvailableListener imageListener, + final int layout, + final Size inputSize) { + return new CameraConnectionFragment(callback, imageListener, layout, inputSize); + } + + /** + * Shows a {@link Toast} on the UI thread. + * + * @param text The message to show + */ + private void showToast(final String text) { + final Activity activity = getActivity(); + if (activity != null) { + activity.runOnUiThread( + new Runnable() { + @Override + public void run() { + Toast.makeText(activity, text, Toast.LENGTH_SHORT).show(); + } + }); + } + } + + @Override + public View onCreateView( + final LayoutInflater inflater, final ViewGroup container, final Bundle savedInstanceState) { + return inflater.inflate(layout, container, false); + } + + @Override + public void onViewCreated(final View view, final Bundle savedInstanceState) { + textureView = (AutoFitTextureView) view.findViewById(R.id.texture); + } + + @Override + public void onActivityCreated(final Bundle savedInstanceState) { + super.onActivityCreated(savedInstanceState); + } + + @Override + public void onResume() { + super.onResume(); + startBackgroundThread(); + + // When the screen is turned off and turned back on, the SurfaceTexture is already + // available, and "onSurfaceTextureAvailable" will not be called. In that case, we can open + // a camera and start preview from here (otherwise, we wait until the surface is ready in + // the SurfaceTextureListener). + if (textureView.isAvailable()) { + openCamera(textureView.getWidth(), textureView.getHeight()); + } else { + textureView.setSurfaceTextureListener(surfaceTextureListener); + } + } + + @Override + public void onPause() { + closeCamera(); + stopBackgroundThread(); + super.onPause(); + } + + public void setCamera(String cameraId) { + this.cameraId = cameraId; + } + + /** Sets up member variables related to camera. */ + private void setUpCameraOutputs() { + final Activity activity = getActivity(); + final CameraManager manager = (CameraManager) activity.getSystemService(Context.CAMERA_SERVICE); + try { + final CameraCharacteristics characteristics = manager.getCameraCharacteristics(cameraId); + + final StreamConfigurationMap map = + characteristics.get(CameraCharacteristics.SCALER_STREAM_CONFIGURATION_MAP); + + sensorOrientation = characteristics.get(CameraCharacteristics.SENSOR_ORIENTATION); + + // Danger, W.R.! Attempting to use too large a preview size could exceed the camera + // bus' bandwidth limitation, resulting in gorgeous previews but the storage of + // garbage capture data. + previewSize = + chooseOptimalSize( + map.getOutputSizes(SurfaceTexture.class), + inputSize.getWidth(), + inputSize.getHeight()); + + // We fit the aspect ratio of TextureView to the size of preview we picked. + final int orientation = getResources().getConfiguration().orientation; + if (orientation == Configuration.ORIENTATION_LANDSCAPE) { + textureView.setAspectRatio(previewSize.getWidth(), previewSize.getHeight()); + } else { + textureView.setAspectRatio(previewSize.getHeight(), previewSize.getWidth()); + } + } catch (final CameraAccessException e) { + LOGGER.e(e, "Exception!"); + } catch (final NullPointerException e) { + // Currently an NPE is thrown when the Camera2API is used but not supported on the + // device this code runs. + ErrorDialog.newInstance(getString(R.string.tfe_od_camera_error)) + .show(getChildFragmentManager(), FRAGMENT_DIALOG); + throw new IllegalStateException(getString(R.string.tfe_od_camera_error)); + } + + cameraConnectionCallback.onPreviewSizeChosen(previewSize, sensorOrientation); + } + + /** Opens the camera specified by {@link CameraConnectionFragment#cameraId}. */ + private void openCamera(final int width, final int height) { + setUpCameraOutputs(); + configureTransform(width, height); + final Activity activity = getActivity(); + final CameraManager manager = (CameraManager) activity.getSystemService(Context.CAMERA_SERVICE); + try { + if (!cameraOpenCloseLock.tryAcquire(2500, TimeUnit.MILLISECONDS)) { + throw new RuntimeException("Time out waiting to lock camera opening."); + } + manager.openCamera(cameraId, stateCallback, backgroundHandler); + } catch (final CameraAccessException e) { + LOGGER.e(e, "Exception!"); + } catch (final InterruptedException e) { + throw new RuntimeException("Interrupted while trying to lock camera opening.", e); + } + } + + /** Closes the current {@link CameraDevice}. */ + private void closeCamera() { + try { + cameraOpenCloseLock.acquire(); + if (null != captureSession) { + captureSession.close(); + captureSession = null; + } + if (null != cameraDevice) { + cameraDevice.close(); + cameraDevice = null; + } + if (null != previewReader) { + previewReader.close(); + previewReader = null; + } + } catch (final InterruptedException e) { + throw new RuntimeException("Interrupted while trying to lock camera closing.", e); + } finally { + cameraOpenCloseLock.release(); + } + } + + /** Starts a background thread and its {@link Handler}. */ + private void startBackgroundThread() { + backgroundThread = new HandlerThread("ImageListener"); + backgroundThread.start(); + backgroundHandler = new Handler(backgroundThread.getLooper()); + } + + /** Stops the background thread and its {@link Handler}. */ + private void stopBackgroundThread() { + backgroundThread.quitSafely(); + try { + backgroundThread.join(); + backgroundThread = null; + backgroundHandler = null; + } catch (final InterruptedException e) { + LOGGER.e(e, "Exception!"); + } + } + + /** Creates a new {@link CameraCaptureSession} for camera preview. */ + private void createCameraPreviewSession() { + try { + final SurfaceTexture texture = textureView.getSurfaceTexture(); + assert texture != null; + + // We configure the size of default buffer to be the size of camera preview we want. + texture.setDefaultBufferSize(previewSize.getWidth(), previewSize.getHeight()); + + // This is the output Surface we need to start preview. + final Surface surface = new Surface(texture); + + // We set up a CaptureRequest.Builder with the output Surface. + previewRequestBuilder = cameraDevice.createCaptureRequest(CameraDevice.TEMPLATE_PREVIEW); + previewRequestBuilder.addTarget(surface); + + LOGGER.i("Opening camera preview: " + previewSize.getWidth() + "x" + previewSize.getHeight()); + + // Create the reader for the preview frames. + previewReader = + ImageReader.newInstance( + previewSize.getWidth(), previewSize.getHeight(), ImageFormat.YUV_420_888, 2); + + previewReader.setOnImageAvailableListener(imageListener, backgroundHandler); + previewRequestBuilder.addTarget(previewReader.getSurface()); + + // Here, we create a CameraCaptureSession for camera preview. + cameraDevice.createCaptureSession( + Arrays.asList(surface, previewReader.getSurface()), + new CameraCaptureSession.StateCallback() { + + @Override + public void onConfigured(final CameraCaptureSession cameraCaptureSession) { + // The camera is already closed + if (null == cameraDevice) { + return; + } + + // When the session is ready, we start displaying the preview. + captureSession = cameraCaptureSession; + try { + // Auto focus should be continuous for camera preview. + previewRequestBuilder.set( + CaptureRequest.CONTROL_AF_MODE, + CaptureRequest.CONTROL_AF_MODE_CONTINUOUS_PICTURE); + // Flash is automatically enabled when necessary. + previewRequestBuilder.set( + CaptureRequest.CONTROL_AE_MODE, CaptureRequest.CONTROL_AE_MODE_ON_AUTO_FLASH); + + // Finally, we start displaying the camera preview. + previewRequest = previewRequestBuilder.build(); + captureSession.setRepeatingRequest( + previewRequest, captureCallback, backgroundHandler); + } catch (final CameraAccessException e) { + LOGGER.e(e, "Exception!"); + } + } + + @Override + public void onConfigureFailed(final CameraCaptureSession cameraCaptureSession) { + showToast("Failed"); + } + }, + null); + } catch (final CameraAccessException e) { + LOGGER.e(e, "Exception!"); + } + } + + /** + * Configures the necessary {@link Matrix} transformation to `mTextureView`. This method should be + * called after the camera preview size is determined in setUpCameraOutputs and also the size of + * `mTextureView` is fixed. + * + * @param viewWidth The width of `mTextureView` + * @param viewHeight The height of `mTextureView` + */ + private void configureTransform(final int viewWidth, final int viewHeight) { + final Activity activity = getActivity(); + if (null == textureView || null == previewSize || null == activity) { + return; + } + final int rotation = activity.getWindowManager().getDefaultDisplay().getRotation(); + final Matrix matrix = new Matrix(); + final RectF viewRect = new RectF(0, 0, viewWidth, viewHeight); + final RectF bufferRect = new RectF(0, 0, previewSize.getHeight(), previewSize.getWidth()); + final float centerX = viewRect.centerX(); + final float centerY = viewRect.centerY(); + if (Surface.ROTATION_90 == rotation || Surface.ROTATION_270 == rotation) { + bufferRect.offset(centerX - bufferRect.centerX(), centerY - bufferRect.centerY()); + matrix.setRectToRect(viewRect, bufferRect, Matrix.ScaleToFit.FILL); + final float scale = + Math.max( + (float) viewHeight / previewSize.getHeight(), + (float) viewWidth / previewSize.getWidth()); + matrix.postScale(scale, scale, centerX, centerY); + matrix.postRotate(90 * (rotation - 2), centerX, centerY); + } else if (Surface.ROTATION_180 == rotation) { + matrix.postRotate(180, centerX, centerY); + } + textureView.setTransform(matrix); + } + + /** + * Callback for Activities to use to initialize their data once the selected preview size is + * known. + */ + public interface ConnectionCallback { + void onPreviewSizeChosen(Size size, int cameraRotation); + } + + /** Compares two {@code Size}s based on their areas. */ + static class CompareSizesByArea implements Comparator { + @Override + public int compare(final Size lhs, final Size rhs) { + // We cast here to ensure the multiplications won't overflow + return Long.signum( + (long) lhs.getWidth() * lhs.getHeight() - (long) rhs.getWidth() * rhs.getHeight()); + } + } + + /** Shows an error message dialog. */ + public static class ErrorDialog extends DialogFragment { + private static final String ARG_MESSAGE = "message"; + + public static ErrorDialog newInstance(final String message) { + final ErrorDialog dialog = new ErrorDialog(); + final Bundle args = new Bundle(); + args.putString(ARG_MESSAGE, message); + dialog.setArguments(args); + return dialog; + } + + @Override + public Dialog onCreateDialog(final Bundle savedInstanceState) { + final Activity activity = getActivity(); + return new AlertDialog.Builder(activity) + .setMessage(getArguments().getString(ARG_MESSAGE)) + .setPositiveButton( + android.R.string.ok, + new DialogInterface.OnClickListener() { + @Override + public void onClick(final DialogInterface dialogInterface, final int i) { + activity.finish(); + } + }) + .create(); + } + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/DetectorActivity.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/DetectorActivity.java new file mode 100644 index 000000000000..3458571ac4b6 --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/DetectorActivity.java @@ -0,0 +1,323 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.tensorflow.lite.examples.detection; + +import android.graphics.Bitmap; +import android.graphics.Bitmap.Config; +import android.graphics.Canvas; +import android.graphics.Color; +import android.graphics.Matrix; +import android.graphics.Paint; +import android.graphics.Paint.Style; +import android.graphics.RectF; +import android.graphics.Typeface; +import android.media.ImageReader.OnImageAvailableListener; +import android.os.SystemClock; +import android.util.Log; +import android.util.Size; +import android.util.TypedValue; +import android.widget.Toast; + +import java.io.IOException; +import java.util.LinkedList; +import java.util.List; + +import org.tensorflow.lite.examples.detection.customview.OverlayView; +import org.tensorflow.lite.examples.detection.customview.OverlayView.DrawCallback; +import org.tensorflow.lite.examples.detection.env.BorderedText; +import org.tensorflow.lite.examples.detection.env.ImageUtils; +import org.tensorflow.lite.examples.detection.env.Logger; +import org.tensorflow.lite.examples.detection.tflite.Classifier; +import org.tensorflow.lite.examples.detection.tflite.DetectorFactory; +import org.tensorflow.lite.examples.detection.tflite.YoloV5ClassifierDetect; +import org.tensorflow.lite.examples.detection.tracking.MultiBoxTracker; + +/** + * An activity that uses a TensorFlowMultiBoxDetector and ObjectTracker to detect and then track + * objects. + */ +public class DetectorActivity extends CameraActivity implements OnImageAvailableListener { + private static final Logger LOGGER = new Logger(); + + private static final DetectorMode MODE = DetectorMode.TF_OD_API; + private static final float MINIMUM_CONFIDENCE_TF_OD_API = 0.3f; + private static final boolean MAINTAIN_ASPECT = true; + private static final Size DESIRED_PREVIEW_SIZE = new Size(640, 640); + private static final boolean SAVE_PREVIEW_BITMAP = false; + private static final float TEXT_SIZE_DIP = 10; + OverlayView trackingOverlay; + private Integer sensorOrientation; + + private YoloV5ClassifierDetect detector; + + private long lastProcessingTimeMs; + private Bitmap rgbFrameBitmap = null; + private Bitmap croppedBitmap = null; + private Bitmap cropCopyBitmap = null; + + private boolean computingDetection = false; + + private long timestamp = 0; + + private Matrix frameToCropTransform; + private Matrix cropToFrameTransform; + + private MultiBoxTracker tracker; + + private BorderedText borderedText; + + @Override + public void onPreviewSizeChosen(final Size size, final int rotation) { + final float textSizePx = + TypedValue.applyDimension( + TypedValue.COMPLEX_UNIT_DIP, TEXT_SIZE_DIP, getResources().getDisplayMetrics()); + borderedText = new BorderedText(textSizePx); + borderedText.setTypeface(Typeface.MONOSPACE); + + tracker = new MultiBoxTracker(this); + + final int modelIndex = modelView.getCheckedItemPosition(); + final String modelString = modelStrings.get(modelIndex); + + try { + detector = DetectorFactory.getDetector(getAssets(), modelString); + } catch (final IOException e) { + e.printStackTrace(); + LOGGER.e(e, "Exception initializing classifier!"); + Toast toast = + Toast.makeText( + getApplicationContext(), "Classifier could not be initialized", Toast.LENGTH_SHORT); + toast.show(); + finish(); + } + + int cropSize = detector.getInputSize(); + + previewWidth = size.getWidth(); + previewHeight = size.getHeight(); + + sensorOrientation = rotation - getScreenOrientation(); + LOGGER.i("Camera orientation relative to screen canvas: %d", sensorOrientation); + + LOGGER.i("Initializing at size %dx%d", previewWidth, previewHeight); + rgbFrameBitmap = Bitmap.createBitmap(previewWidth, previewHeight, Config.ARGB_8888); + croppedBitmap = Bitmap.createBitmap(cropSize, cropSize, Config.ARGB_8888); + + frameToCropTransform = + ImageUtils.getTransformationMatrix( + previewWidth, previewHeight, + cropSize, cropSize, + sensorOrientation, MAINTAIN_ASPECT); + + cropToFrameTransform = new Matrix(); + frameToCropTransform.invert(cropToFrameTransform); + + trackingOverlay = (OverlayView) findViewById(R.id.tracking_overlay); + trackingOverlay.addCallback( + new DrawCallback() { + @Override + public void drawCallback(final Canvas canvas) { + tracker.draw(canvas); + if (isDebug()) { + tracker.drawDebug(canvas); + } + } + }); + + tracker.setFrameConfiguration(previewWidth, previewHeight, sensorOrientation); + } + + protected void updateActiveModel() { + // Get UI information before delegating to background + final int modelIndex = modelView.getCheckedItemPosition(); + final int deviceIndex = deviceView.getCheckedItemPosition(); + String threads = threadsTextView.getText().toString().trim(); + final int numThreads = Integer.parseInt(threads); + + handler.post(() -> { + if (modelIndex == currentModel && deviceIndex == currentDevice + && numThreads == currentNumThreads) { + return; + } + currentModel = modelIndex; + currentDevice = deviceIndex; + currentNumThreads = numThreads; + + // Disable classifier while updating + if (detector != null) { + detector.close(); + detector = null; + } + + // Lookup names of parameters. + String modelString = modelStrings.get(modelIndex); + String device = deviceStrings.get(deviceIndex); + + LOGGER.i("Changing model to " + modelString + " device " + device); + + // Try to load model. + + try { + detector = DetectorFactory.getDetector(getAssets(), modelString); + // Customize the interpreter to the type of device we want to use. + if (detector == null) { + return; + } + } + catch(IOException e) { + e.printStackTrace(); + LOGGER.e(e, "Exception in updateActiveModel()"); + Toast toast = + Toast.makeText( + getApplicationContext(), "Classifier could not be initialized", Toast.LENGTH_SHORT); + toast.show(); + finish(); + } + + + if (device.equals("CPU")) { + detector.useCPU(); + } else if (device.equals("GPU")) { + detector.useGpu(); + } else if (device.equals("NNAPI")) { + detector.useNNAPI(); + } + detector.setNumThreads(numThreads); + + int cropSize = detector.getInputSize(); + croppedBitmap = Bitmap.createBitmap(cropSize, cropSize, Config.ARGB_8888); + + frameToCropTransform = + ImageUtils.getTransformationMatrix( + previewWidth, previewHeight, + cropSize, cropSize, + sensorOrientation, MAINTAIN_ASPECT); + + cropToFrameTransform = new Matrix(); + frameToCropTransform.invert(cropToFrameTransform); + }); + } + + @Override + protected void processImage() { + ++timestamp; + final long currTimestamp = timestamp; + trackingOverlay.postInvalidate(); + + // No mutex needed as this method is not reentrant. + if (computingDetection) { + readyForNextImage(); + return; + } + computingDetection = true; + LOGGER.i("Preparing image " + currTimestamp + " for detection in bg thread."); + + rgbFrameBitmap.setPixels(getRgbBytes(), 0, previewWidth, 0, 0, previewWidth, previewHeight); + + readyForNextImage(); + + final Canvas canvas = new Canvas(croppedBitmap); + canvas.drawBitmap(rgbFrameBitmap, frameToCropTransform, null); + // For examining the actual TF input. + if (SAVE_PREVIEW_BITMAP) { + ImageUtils.saveBitmap(croppedBitmap); + } + + runInBackground( + new Runnable() { + @Override + public void run() { + LOGGER.i("Running detection on image " + currTimestamp); + final long startTime = SystemClock.uptimeMillis(); + final List results = detector.recognizeImage(croppedBitmap); + lastProcessingTimeMs = SystemClock.uptimeMillis() - startTime; + + Log.e("CHECK", "run: " + results.size()); + + cropCopyBitmap = Bitmap.createBitmap(croppedBitmap); + final Canvas canvas = new Canvas(cropCopyBitmap); + final Paint paint = new Paint(); + paint.setColor(Color.RED); + paint.setStyle(Style.STROKE); + paint.setStrokeWidth(2.0f); + + float minimumConfidence = MINIMUM_CONFIDENCE_TF_OD_API; + switch (MODE) { + case TF_OD_API: + minimumConfidence = MINIMUM_CONFIDENCE_TF_OD_API; + break; + } + + final List mappedRecognitions = + new LinkedList(); + + for (final Classifier.Recognition result : results) { + final RectF location = result.getLocation(); + if (location != null && result.getConfidence() >= minimumConfidence) { + canvas.drawRect(location, paint); + + cropToFrameTransform.mapRect(location); + + result.setLocation(location); + mappedRecognitions.add(result); + } + } + + tracker.trackResults(mappedRecognitions, currTimestamp); + trackingOverlay.postInvalidate(); + + computingDetection = false; + + runOnUiThread( + new Runnable() { + @Override + public void run() { + showFrameInfo(previewWidth + "x" + previewHeight); + showCropInfo(cropCopyBitmap.getWidth() + "x" + cropCopyBitmap.getHeight()); + showInference(lastProcessingTimeMs + "ms"); + } + }); + } + }); + } + + @Override + protected int getLayoutId() { + return R.layout.tfe_od_camera_connection_fragment_tracking; + } + + @Override + protected Size getDesiredPreviewFrameSize() { + return DESIRED_PREVIEW_SIZE; + } + + // Which detection model to use: by default uses Tensorflow Object Detection API frozen + // checkpoints. + private enum DetectorMode { + TF_OD_API; + } + + @Override + protected void setUseNNAPI(final boolean isChecked) { + runInBackground(() -> detector.setUseNNAPI(isChecked)); + } + + @Override + protected void setNumThreads(final int numThreads) { + runInBackground(() -> detector.setNumThreads(numThreads)); + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/LegacyCameraConnectionFragment.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/LegacyCameraConnectionFragment.java new file mode 100644 index 000000000000..afb9d7599950 --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/LegacyCameraConnectionFragment.java @@ -0,0 +1,199 @@ +package org.tensorflow.lite.examples.detection; + +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +import android.app.Fragment; +import android.graphics.SurfaceTexture; +import android.hardware.Camera; +import android.hardware.Camera.CameraInfo; +import android.os.Bundle; +import android.os.Handler; +import android.os.HandlerThread; +import android.util.Size; +import android.util.SparseIntArray; +import android.view.LayoutInflater; +import android.view.Surface; +import android.view.TextureView; +import android.view.View; +import android.view.ViewGroup; +import java.io.IOException; +import java.util.List; +import org.tensorflow.lite.examples.detection.customview.AutoFitTextureView; +import org.tensorflow.lite.examples.detection.env.ImageUtils; +import org.tensorflow.lite.examples.detection.env.Logger; + +public class LegacyCameraConnectionFragment extends Fragment { + private static final Logger LOGGER = new Logger(); + /** Conversion from screen rotation to JPEG orientation. */ + private static final SparseIntArray ORIENTATIONS = new SparseIntArray(); + + static { + ORIENTATIONS.append(Surface.ROTATION_0, 90); + ORIENTATIONS.append(Surface.ROTATION_90, 0); + ORIENTATIONS.append(Surface.ROTATION_180, 270); + ORIENTATIONS.append(Surface.ROTATION_270, 180); + } + + private Camera camera; + private Camera.PreviewCallback imageListener; + private Size desiredSize; + /** The layout identifier to inflate for this Fragment. */ + private int layout; + /** An {@link AutoFitTextureView} for camera preview. */ + private AutoFitTextureView textureView; + /** + * {@link TextureView.SurfaceTextureListener} handles several lifecycle events on a {@link + * TextureView}. + */ + private final TextureView.SurfaceTextureListener surfaceTextureListener = + new TextureView.SurfaceTextureListener() { + @Override + public void onSurfaceTextureAvailable( + final SurfaceTexture texture, final int width, final int height) { + + int index = getCameraId(); + camera = Camera.open(index); + + try { + Camera.Parameters parameters = camera.getParameters(); + List focusModes = parameters.getSupportedFocusModes(); + if (focusModes != null + && focusModes.contains(Camera.Parameters.FOCUS_MODE_CONTINUOUS_PICTURE)) { + parameters.setFocusMode(Camera.Parameters.FOCUS_MODE_CONTINUOUS_PICTURE); + } + List cameraSizes = parameters.getSupportedPreviewSizes(); + Size[] sizes = new Size[cameraSizes.size()]; + int i = 0; + for (Camera.Size size : cameraSizes) { + sizes[i++] = new Size(size.width, size.height); + } + Size previewSize = + CameraConnectionFragment.chooseOptimalSize( + sizes, desiredSize.getWidth(), desiredSize.getHeight()); + parameters.setPreviewSize(previewSize.getWidth(), previewSize.getHeight()); + camera.setDisplayOrientation(90); + camera.setParameters(parameters); + camera.setPreviewTexture(texture); + } catch (IOException exception) { + camera.release(); + } + + camera.setPreviewCallbackWithBuffer(imageListener); + Camera.Size s = camera.getParameters().getPreviewSize(); + camera.addCallbackBuffer(new byte[ImageUtils.getYUVByteSize(s.height, s.width)]); + + textureView.setAspectRatio(s.height, s.width); + + camera.startPreview(); + } + + @Override + public void onSurfaceTextureSizeChanged( + final SurfaceTexture texture, final int width, final int height) {} + + @Override + public boolean onSurfaceTextureDestroyed(final SurfaceTexture texture) { + return true; + } + + @Override + public void onSurfaceTextureUpdated(final SurfaceTexture texture) {} + }; + /** An additional thread for running tasks that shouldn't block the UI. */ + private HandlerThread backgroundThread; + + public LegacyCameraConnectionFragment( + final Camera.PreviewCallback imageListener, final int layout, final Size desiredSize) { + this.imageListener = imageListener; + this.layout = layout; + this.desiredSize = desiredSize; + } + + @Override + public View onCreateView( + final LayoutInflater inflater, final ViewGroup container, final Bundle savedInstanceState) { + return inflater.inflate(layout, container, false); + } + + @Override + public void onViewCreated(final View view, final Bundle savedInstanceState) { + textureView = (AutoFitTextureView) view.findViewById(R.id.texture); + } + + @Override + public void onActivityCreated(final Bundle savedInstanceState) { + super.onActivityCreated(savedInstanceState); + } + + @Override + public void onResume() { + super.onResume(); + startBackgroundThread(); + // When the screen is turned off and turned back on, the SurfaceTexture is already + // available, and "onSurfaceTextureAvailable" will not be called. In that case, we can open + // a camera and start preview from here (otherwise, we wait until the surface is ready in + // the SurfaceTextureListener). + + if (textureView.isAvailable()) { + camera.startPreview(); + } else { + textureView.setSurfaceTextureListener(surfaceTextureListener); + } + } + + @Override + public void onPause() { + stopCamera(); + stopBackgroundThread(); + super.onPause(); + } + + /** Starts a background thread and its {@link Handler}. */ + private void startBackgroundThread() { + backgroundThread = new HandlerThread("CameraBackground"); + backgroundThread.start(); + } + + /** Stops the background thread and its {@link Handler}. */ + private void stopBackgroundThread() { + backgroundThread.quitSafely(); + try { + backgroundThread.join(); + backgroundThread = null; + } catch (final InterruptedException e) { + LOGGER.e(e, "Exception!"); + } + } + + protected void stopCamera() { + if (camera != null) { + camera.stopPreview(); + camera.setPreviewCallback(null); + camera.release(); + camera = null; + } + } + + private int getCameraId() { + CameraInfo ci = new CameraInfo(); + for (int i = 0; i < Camera.getNumberOfCameras(); i++) { + Camera.getCameraInfo(i, ci); + if (ci.facing == CameraInfo.CAMERA_FACING_BACK) return i; + } + return -1; // No camera found + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/MainActivity.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/MainActivity.java new file mode 100644 index 000000000000..21d24dbe2222 --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/MainActivity.java @@ -0,0 +1,169 @@ +package org.tensorflow.lite.examples.detection; + +import androidx.appcompat.app.AppCompatActivity; + +import android.app.ActivityManager; +import android.content.Context; +import android.content.Intent; +import android.content.pm.ConfigurationInfo; +import android.graphics.Bitmap; +import android.graphics.Canvas; +import android.graphics.Color; +import android.graphics.Matrix; +import android.graphics.Paint; +import android.graphics.RectF; +import android.os.Bundle; +import android.os.Handler; +import android.widget.Button; +import android.widget.ImageView; +import android.widget.Toast; + +import org.tensorflow.lite.examples.detection.customview.OverlayView; +import org.tensorflow.lite.examples.detection.env.ImageUtils; +import org.tensorflow.lite.examples.detection.env.Logger; +import org.tensorflow.lite.examples.detection.env.Utils; +import org.tensorflow.lite.examples.detection.tflite.Classifier; +import org.tensorflow.lite.examples.detection.tflite.YoloV5Classifier; +import org.tensorflow.lite.examples.detection.tracking.MultiBoxTracker; + +import java.io.IOException; +import java.util.LinkedList; +import java.util.List; + +public class MainActivity extends AppCompatActivity { + + public static final float MINIMUM_CONFIDENCE_TF_OD_API = 0.3f; + + @Override + protected void onCreate(Bundle savedInstanceState) { + super.onCreate(savedInstanceState); + setContentView(R.layout.activity_main); + + cameraButton = findViewById(R.id.cameraButton); + detectButton = findViewById(R.id.detectButton); + imageView = findViewById(R.id.imageView); + + cameraButton.setOnClickListener(v -> startActivity(new Intent(MainActivity.this, DetectorActivity.class))); + + detectButton.setOnClickListener(v -> { + Handler handler = new Handler(); + + new Thread(() -> { + final List results = detector.recognizeImage(cropBitmap); + handler.post(new Runnable() { + @Override + public void run() { + handleResult(cropBitmap, results); + } + }); + }).start(); + + }); + this.sourceBitmap = Utils.getBitmapFromAsset(MainActivity.this, "kite.jpg"); + + this.cropBitmap = Utils.processBitmap(sourceBitmap, TF_OD_API_INPUT_SIZE); + + this.imageView.setImageBitmap(cropBitmap); + + initBox(); + ActivityManager activityManager = (ActivityManager) getSystemService(Context.ACTIVITY_SERVICE); + ConfigurationInfo configurationInfo = activityManager.getDeviceConfigurationInfo(); + + System.err.println(Double.parseDouble(configurationInfo.getGlEsVersion())); + System.err.println(configurationInfo.reqGlEsVersion >= 0x30000); + System.err.println(String.format("%X", configurationInfo.reqGlEsVersion)); + } + + private static final Logger LOGGER = new Logger(); + + public static final int TF_OD_API_INPUT_SIZE = 640; + + private static final boolean TF_OD_API_IS_QUANTIZED = false; + + private static final String TF_OD_API_MODEL_FILE = "yolov5s.tflite"; + + private static final String TF_OD_API_LABELS_FILE = "file:///android_asset/coco.txt"; + + // Minimum detection confidence to track a detection. + private static final boolean MAINTAIN_ASPECT = true; + private Integer sensorOrientation = 90; + + private Classifier detector; + + private Matrix frameToCropTransform; + private Matrix cropToFrameTransform; + private MultiBoxTracker tracker; + private OverlayView trackingOverlay; + + protected int previewWidth = 0; + protected int previewHeight = 0; + + private Bitmap sourceBitmap; + private Bitmap cropBitmap; + + private Button cameraButton, detectButton; + private ImageView imageView; + + private void initBox() { + previewHeight = TF_OD_API_INPUT_SIZE; + previewWidth = TF_OD_API_INPUT_SIZE; + frameToCropTransform = + ImageUtils.getTransformationMatrix( + previewWidth, previewHeight, + TF_OD_API_INPUT_SIZE, TF_OD_API_INPUT_SIZE, + sensorOrientation, MAINTAIN_ASPECT); + + cropToFrameTransform = new Matrix(); + frameToCropTransform.invert(cropToFrameTransform); + + tracker = new MultiBoxTracker(this); + trackingOverlay = findViewById(R.id.tracking_overlay); + trackingOverlay.addCallback( + canvas -> tracker.draw(canvas)); + + tracker.setFrameConfiguration(TF_OD_API_INPUT_SIZE, TF_OD_API_INPUT_SIZE, sensorOrientation); + + try { + detector = + YoloV5Classifier.create( + getAssets(), + TF_OD_API_MODEL_FILE, + TF_OD_API_LABELS_FILE, + TF_OD_API_IS_QUANTIZED, + TF_OD_API_INPUT_SIZE); + } catch (final IOException e) { + e.printStackTrace(); + LOGGER.e(e, "Exception initializing classifier!"); + Toast toast = + Toast.makeText( + getApplicationContext(), "Classifier could not be initialized", Toast.LENGTH_SHORT); + toast.show(); + finish(); + } + } + + private void handleResult(Bitmap bitmap, List results) { + final Canvas canvas = new Canvas(bitmap); + final Paint paint = new Paint(); + paint.setColor(Color.RED); + paint.setStyle(Paint.Style.STROKE); + paint.setStrokeWidth(2.0f); + + final List mappedRecognitions = + new LinkedList(); + + for (final Classifier.Recognition result : results) { + final RectF location = result.getLocation(); + if (location != null && result.getConfidence() >= MINIMUM_CONFIDENCE_TF_OD_API) { + canvas.drawRect(location, paint); +// cropToFrameTransform.mapRect(location); +// +// result.setLocation(location); +// mappedRecognitions.add(result); + } + } +// tracker.trackResults(mappedRecognitions, new Random().nextInt()); +// trackingOverlay.postInvalidate(); + imageView.setImageBitmap(bitmap); + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/customview/AutoFitTextureView.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/customview/AutoFitTextureView.java new file mode 100644 index 000000000000..8f41eb71336b --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/customview/AutoFitTextureView.java @@ -0,0 +1,72 @@ +/* + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.tensorflow.lite.examples.detection.customview; + +import android.content.Context; +import android.util.AttributeSet; +import android.view.TextureView; + +/** A {@link TextureView} that can be adjusted to a specified aspect ratio. */ +public class AutoFitTextureView extends TextureView { + private int ratioWidth = 0; + private int ratioHeight = 0; + + public AutoFitTextureView(final Context context) { + this(context, null); + } + + public AutoFitTextureView(final Context context, final AttributeSet attrs) { + this(context, attrs, 0); + } + + public AutoFitTextureView(final Context context, final AttributeSet attrs, final int defStyle) { + super(context, attrs, defStyle); + } + + /** + * Sets the aspect ratio for this view. The size of the view will be measured based on the ratio + * calculated from the parameters. Note that the actual sizes of parameters don't matter, that is, + * calling setAspectRatio(2, 3) and setAspectRatio(4, 6) make the same result. + * + * @param width Relative horizontal size + * @param height Relative vertical size + */ + public void setAspectRatio(final int width, final int height) { + if (width < 0 || height < 0) { + throw new IllegalArgumentException("Size cannot be negative."); + } + ratioWidth = width; + ratioHeight = height; + requestLayout(); + } + + @Override + protected void onMeasure(final int widthMeasureSpec, final int heightMeasureSpec) { + super.onMeasure(widthMeasureSpec, heightMeasureSpec); + final int width = MeasureSpec.getSize(widthMeasureSpec); + final int height = MeasureSpec.getSize(heightMeasureSpec); + if (0 == ratioWidth || 0 == ratioHeight) { + setMeasuredDimension(width, height); + } else { + if (width < height * ratioWidth / ratioHeight) { + setMeasuredDimension(width, width * ratioHeight / ratioWidth); + } else { + setMeasuredDimension(height * ratioWidth / ratioHeight, height); + } + } + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/customview/OverlayView.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/customview/OverlayView.java new file mode 100644 index 000000000000..8f7e66102a7d --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/customview/OverlayView.java @@ -0,0 +1,48 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package org.tensorflow.lite.examples.detection.customview; + +import android.content.Context; +import android.graphics.Canvas; +import android.util.AttributeSet; +import android.view.View; +import java.util.LinkedList; +import java.util.List; + +/** A simple View providing a render callback to other classes. */ +public class OverlayView extends View { + private final List callbacks = new LinkedList(); + + public OverlayView(final Context context, final AttributeSet attrs) { + super(context, attrs); + } + + public void addCallback(final DrawCallback callback) { + callbacks.add(callback); + } + + @Override + public synchronized void draw(final Canvas canvas) { + for (final DrawCallback callback : callbacks) { + callback.drawCallback(canvas); + } + } + + /** Interface defining the callback for client classes. */ + public interface DrawCallback { + public void drawCallback(final Canvas canvas); + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/customview/RecognitionScoreView.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/customview/RecognitionScoreView.java new file mode 100644 index 000000000000..daf862d30e5c --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/customview/RecognitionScoreView.java @@ -0,0 +1,67 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package org.tensorflow.lite.examples.detection.customview; + +import android.content.Context; +import android.graphics.Canvas; +import android.graphics.Paint; +import android.util.AttributeSet; +import android.util.TypedValue; +import android.view.View; +import java.util.List; +import org.tensorflow.lite.examples.detection.tflite.Classifier.Recognition; + +public class RecognitionScoreView extends View implements ResultsView { + private static final float TEXT_SIZE_DIP = 14; + private final float textSizePx; + private final Paint fgPaint; + private final Paint bgPaint; + private List results; + + public RecognitionScoreView(final Context context, final AttributeSet set) { + super(context, set); + + textSizePx = + TypedValue.applyDimension( + TypedValue.COMPLEX_UNIT_DIP, TEXT_SIZE_DIP, getResources().getDisplayMetrics()); + fgPaint = new Paint(); + fgPaint.setTextSize(textSizePx); + + bgPaint = new Paint(); + bgPaint.setColor(0xcc4285f4); + } + + @Override + public void setResults(final List results) { + this.results = results; + postInvalidate(); + } + + @Override + public void onDraw(final Canvas canvas) { + final int x = 10; + int y = (int) (fgPaint.getTextSize() * 1.5f); + + canvas.drawPaint(bgPaint); + + if (results != null) { + for (final Recognition recog : results) { + canvas.drawText(recog.getTitle() + ": " + recog.getConfidence(), x, y, fgPaint); + y += (int) (fgPaint.getTextSize() * 1.5f); + } + } + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/customview/ResultsView.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/customview/ResultsView.java new file mode 100644 index 000000000000..6d54020d7bc0 --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/customview/ResultsView.java @@ -0,0 +1,23 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package org.tensorflow.lite.examples.detection.customview; + +import java.util.List; +import org.tensorflow.lite.examples.detection.tflite.Classifier.Recognition; + +public interface ResultsView { + public void setResults(final List results); +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/env/BorderedText.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/env/BorderedText.java new file mode 100644 index 000000000000..006c4c7847ee --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/env/BorderedText.java @@ -0,0 +1,128 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package org.tensorflow.lite.examples.detection.env; + +import android.graphics.Canvas; +import android.graphics.Color; +import android.graphics.Paint; +import android.graphics.Paint.Align; +import android.graphics.Paint.Style; +import android.graphics.Rect; +import android.graphics.Typeface; +import java.util.Vector; + +/** A class that encapsulates the tedious bits of rendering legible, bordered text onto a canvas. */ +public class BorderedText { + private final Paint interiorPaint; + private final Paint exteriorPaint; + + private final float textSize; + + /** + * Creates a left-aligned bordered text object with a white interior, and a black exterior with + * the specified text size. + * + * @param textSize text size in pixels + */ + public BorderedText(final float textSize) { + this(Color.WHITE, Color.BLACK, textSize); + } + + /** + * Create a bordered text object with the specified interior and exterior colors, text size and + * alignment. + * + * @param interiorColor the interior text color + * @param exteriorColor the exterior text color + * @param textSize text size in pixels + */ + public BorderedText(final int interiorColor, final int exteriorColor, final float textSize) { + interiorPaint = new Paint(); + interiorPaint.setTextSize(textSize); + interiorPaint.setColor(interiorColor); + interiorPaint.setStyle(Style.FILL); + interiorPaint.setAntiAlias(false); + interiorPaint.setAlpha(255); + + exteriorPaint = new Paint(); + exteriorPaint.setTextSize(textSize); + exteriorPaint.setColor(exteriorColor); + exteriorPaint.setStyle(Style.FILL_AND_STROKE); + exteriorPaint.setStrokeWidth(textSize / 8); + exteriorPaint.setAntiAlias(false); + exteriorPaint.setAlpha(255); + + this.textSize = textSize; + } + + public void setTypeface(Typeface typeface) { + interiorPaint.setTypeface(typeface); + exteriorPaint.setTypeface(typeface); + } + + public void drawText(final Canvas canvas, final float posX, final float posY, final String text) { + canvas.drawText(text, posX, posY, exteriorPaint); + canvas.drawText(text, posX, posY, interiorPaint); + } + + public void drawText( + final Canvas canvas, final float posX, final float posY, final String text, Paint bgPaint) { + + float width = exteriorPaint.measureText(text); + float textSize = exteriorPaint.getTextSize(); + Paint paint = new Paint(bgPaint); + paint.setStyle(Paint.Style.FILL); + paint.setAlpha(160); + canvas.drawRect(posX, (posY + (int) (textSize)), (posX + (int) (width)), posY, paint); + + canvas.drawText(text, posX, (posY + textSize), interiorPaint); + } + + public void drawLines(Canvas canvas, final float posX, final float posY, Vector lines) { + int lineNum = 0; + for (final String line : lines) { + drawText(canvas, posX, posY - getTextSize() * (lines.size() - lineNum - 1), line); + ++lineNum; + } + } + + public void setInteriorColor(final int color) { + interiorPaint.setColor(color); + } + + public void setExteriorColor(final int color) { + exteriorPaint.setColor(color); + } + + public float getTextSize() { + return textSize; + } + + public void setAlpha(final int alpha) { + interiorPaint.setAlpha(alpha); + exteriorPaint.setAlpha(alpha); + } + + public void getTextBounds( + final String line, final int index, final int count, final Rect lineBounds) { + interiorPaint.getTextBounds(line, index, count, lineBounds); + } + + public void setTextAlign(final Align align) { + interiorPaint.setTextAlign(align); + exteriorPaint.setTextAlign(align); + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/env/ImageUtils.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/env/ImageUtils.java new file mode 100644 index 000000000000..df7b0999a80b --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/env/ImageUtils.java @@ -0,0 +1,219 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package org.tensorflow.lite.examples.detection.env; + +import android.graphics.Bitmap; +import android.graphics.Matrix; +import android.os.Environment; +import java.io.File; +import java.io.FileOutputStream; + +/** Utility class for manipulating images. */ +public class ImageUtils { + // This value is 2 ^ 18 - 1, and is used to clamp the RGB values before their ranges + // are normalized to eight bits. + static final int kMaxChannelValue = 262143; + + @SuppressWarnings("unused") + private static final Logger LOGGER = new Logger(); + + /** + * Utility method to compute the allocated size in bytes of a YUV420SP image of the given + * dimensions. + */ + public static int getYUVByteSize(final int width, final int height) { + // The luminance plane requires 1 byte per pixel. + final int ySize = width * height; + + // The UV plane works on 2x2 blocks, so dimensions with odd size must be rounded up. + // Each 2x2 block takes 2 bytes to encode, one each for U and V. + final int uvSize = ((width + 1) / 2) * ((height + 1) / 2) * 2; + + return ySize + uvSize; + } + + /** + * Saves a Bitmap object to disk for analysis. + * + * @param bitmap The bitmap to save. + */ + public static void saveBitmap(final Bitmap bitmap) { + saveBitmap(bitmap, "preview.png"); + } + + /** + * Saves a Bitmap object to disk for analysis. + * + * @param bitmap The bitmap to save. + * @param filename The location to save the bitmap to. + */ + public static void saveBitmap(final Bitmap bitmap, final String filename) { + final String root = + Environment.getExternalStorageDirectory().getAbsolutePath() + File.separator + "tensorflow"; + LOGGER.i("Saving %dx%d bitmap to %s.", bitmap.getWidth(), bitmap.getHeight(), root); + final File myDir = new File(root); + + if (!myDir.mkdirs()) { + LOGGER.i("Make dir failed"); + } + + final String fname = filename; + final File file = new File(myDir, fname); + if (file.exists()) { + file.delete(); + } + try { + final FileOutputStream out = new FileOutputStream(file); + bitmap.compress(Bitmap.CompressFormat.PNG, 99, out); + out.flush(); + out.close(); + } catch (final Exception e) { + LOGGER.e(e, "Exception!"); + } + } + + public static void convertYUV420SPToARGB8888(byte[] input, int width, int height, int[] output) { + final int frameSize = width * height; + for (int j = 0, yp = 0; j < height; j++) { + int uvp = frameSize + (j >> 1) * width; + int u = 0; + int v = 0; + + for (int i = 0; i < width; i++, yp++) { + int y = 0xff & input[yp]; + if ((i & 1) == 0) { + v = 0xff & input[uvp++]; + u = 0xff & input[uvp++]; + } + + output[yp] = YUV2RGB(y, u, v); + } + } + } + + private static int YUV2RGB(int y, int u, int v) { + // Adjust and check YUV values + y = (y - 16) < 0 ? 0 : (y - 16); + u -= 128; + v -= 128; + + // This is the floating point equivalent. We do the conversion in integer + // because some Android devices do not have floating point in hardware. + // nR = (int)(1.164 * nY + 2.018 * nU); + // nG = (int)(1.164 * nY - 0.813 * nV - 0.391 * nU); + // nB = (int)(1.164 * nY + 1.596 * nV); + int y1192 = 1192 * y; + int r = (y1192 + 1634 * v); + int g = (y1192 - 833 * v - 400 * u); + int b = (y1192 + 2066 * u); + + // Clipping RGB values to be inside boundaries [ 0 , kMaxChannelValue ] + r = r > kMaxChannelValue ? kMaxChannelValue : (r < 0 ? 0 : r); + g = g > kMaxChannelValue ? kMaxChannelValue : (g < 0 ? 0 : g); + b = b > kMaxChannelValue ? kMaxChannelValue : (b < 0 ? 0 : b); + + return 0xff000000 | ((r << 6) & 0xff0000) | ((g >> 2) & 0xff00) | ((b >> 10) & 0xff); + } + + public static void convertYUV420ToARGB8888( + byte[] yData, + byte[] uData, + byte[] vData, + int width, + int height, + int yRowStride, + int uvRowStride, + int uvPixelStride, + int[] out) { + int yp = 0; + for (int j = 0; j < height; j++) { + int pY = yRowStride * j; + int pUV = uvRowStride * (j >> 1); + + for (int i = 0; i < width; i++) { + int uv_offset = pUV + (i >> 1) * uvPixelStride; + + out[yp++] = YUV2RGB(0xff & yData[pY + i], 0xff & uData[uv_offset], 0xff & vData[uv_offset]); + } + } + } + + /** + * Returns a transformation matrix from one reference frame into another. Handles cropping (if + * maintaining aspect ratio is desired) and rotation. + * + * @param srcWidth Width of source frame. + * @param srcHeight Height of source frame. + * @param dstWidth Width of destination frame. + * @param dstHeight Height of destination frame. + * @param applyRotation Amount of rotation to apply from one frame to another. Must be a multiple + * of 90. + * @param maintainAspectRatio If true, will ensure that scaling in x and y remains constant, + * cropping the image if necessary. + * @return The transformation fulfilling the desired requirements. + */ + public static Matrix getTransformationMatrix( + final int srcWidth, + final int srcHeight, + final int dstWidth, + final int dstHeight, + final int applyRotation, + final boolean maintainAspectRatio) { + final Matrix matrix = new Matrix(); + + if (applyRotation != 0) { + if (applyRotation % 90 != 0) { + LOGGER.w("Rotation of %d % 90 != 0", applyRotation); + } + + // Translate so center of image is at origin. + matrix.postTranslate(-srcWidth / 2.0f, -srcHeight / 2.0f); + + // Rotate around origin. + matrix.postRotate(applyRotation); + } + + // Account for the already applied rotation, if any, and then determine how + // much scaling is needed for each axis. + final boolean transpose = (Math.abs(applyRotation) + 90) % 180 == 0; + + final int inWidth = transpose ? srcHeight : srcWidth; + final int inHeight = transpose ? srcWidth : srcHeight; + + // Apply scaling if necessary. + if (inWidth != dstWidth || inHeight != dstHeight) { + final float scaleFactorX = dstWidth / (float) inWidth; + final float scaleFactorY = dstHeight / (float) inHeight; + + if (maintainAspectRatio) { + // Scale by minimum factor so that dst is filled completely while + // maintaining the aspect ratio. Some image may fall off the edge. + final float scaleFactor = Math.max(scaleFactorX, scaleFactorY); + matrix.postScale(scaleFactor, scaleFactor); + } else { + // Scale exactly to fill dst from src. + matrix.postScale(scaleFactorX, scaleFactorY); + } + } + + if (applyRotation != 0) { + // Translate back from origin centered reference to destination frame. + matrix.postTranslate(dstWidth / 2.0f, dstHeight / 2.0f); + } + + return matrix; + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/env/Logger.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/env/Logger.java new file mode 100644 index 000000000000..9dc05f4d1bd9 --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/env/Logger.java @@ -0,0 +1,186 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package org.tensorflow.lite.examples.detection.env; + +import android.util.Log; +import java.util.HashSet; +import java.util.Set; + +/** Wrapper for the platform log function, allows convenient message prefixing and log disabling. */ +public final class Logger { + private static final String DEFAULT_TAG = "tensorflow"; + private static final int DEFAULT_MIN_LOG_LEVEL = Log.DEBUG; + + // Classes to be ignored when examining the stack trace + private static final Set IGNORED_CLASS_NAMES; + + static { + IGNORED_CLASS_NAMES = new HashSet(3); + IGNORED_CLASS_NAMES.add("dalvik.system.VMStack"); + IGNORED_CLASS_NAMES.add("java.lang.Thread"); + IGNORED_CLASS_NAMES.add(Logger.class.getCanonicalName()); + } + + private final String tag; + private final String messagePrefix; + private int minLogLevel = DEFAULT_MIN_LOG_LEVEL; + + /** + * Creates a Logger using the class name as the message prefix. + * + * @param clazz the simple name of this class is used as the message prefix. + */ + public Logger(final Class clazz) { + this(clazz.getSimpleName()); + } + + /** + * Creates a Logger using the specified message prefix. + * + * @param messagePrefix is prepended to the text of every message. + */ + public Logger(final String messagePrefix) { + this(DEFAULT_TAG, messagePrefix); + } + + /** + * Creates a Logger with a custom tag and a custom message prefix. If the message prefix is set to + * + * 

null
+ * + * , the caller's class name is used as the prefix. + * + * @param tag identifies the source of a log message. + * @param messagePrefix prepended to every message if non-null. If null, the name of the caller is + * being used + */ + public Logger(final String tag, final String messagePrefix) { + this.tag = tag; + final String prefix = messagePrefix == null ? getCallerSimpleName() : messagePrefix; + this.messagePrefix = (prefix.length() > 0) ? prefix + ": " : prefix; + } + + /** Creates a Logger using the caller's class name as the message prefix. */ + public Logger() { + this(DEFAULT_TAG, null); + } + + /** Creates a Logger using the caller's class name as the message prefix. */ + public Logger(final int minLogLevel) { + this(DEFAULT_TAG, null); + this.minLogLevel = minLogLevel; + } + + /** + * Return caller's simple name. + * + *

Android getStackTrace() returns an array that looks like this: stackTrace[0]: + * dalvik.system.VMStack stackTrace[1]: java.lang.Thread stackTrace[2]: + * com.google.android.apps.unveil.env.UnveilLogger stackTrace[3]: + * com.google.android.apps.unveil.BaseApplication + * + *

This function returns the simple version of the first non-filtered name. + * + * @return caller's simple name + */ + private static String getCallerSimpleName() { + // Get the current callstack so we can pull the class of the caller off of it. + final StackTraceElement[] stackTrace = Thread.currentThread().getStackTrace(); + + for (final StackTraceElement elem : stackTrace) { + final String className = elem.getClassName(); + if (!IGNORED_CLASS_NAMES.contains(className)) { + // We're only interested in the simple name of the class, not the complete package. + final String[] classParts = className.split("\\."); + return classParts[classParts.length - 1]; + } + } + + return Logger.class.getSimpleName(); + } + + public void setMinLogLevel(final int minLogLevel) { + this.minLogLevel = minLogLevel; + } + + public boolean isLoggable(final int logLevel) { + return logLevel >= minLogLevel || Log.isLoggable(tag, logLevel); + } + + private String toMessage(final String format, final Object... args) { + return messagePrefix + (args.length > 0 ? String.format(format, args) : format); + } + + public void v(final String format, final Object... args) { + if (isLoggable(Log.VERBOSE)) { + Log.v(tag, toMessage(format, args)); + } + } + + public void v(final Throwable t, final String format, final Object... args) { + if (isLoggable(Log.VERBOSE)) { + Log.v(tag, toMessage(format, args), t); + } + } + + public void d(final String format, final Object... args) { + if (isLoggable(Log.DEBUG)) { + Log.d(tag, toMessage(format, args)); + } + } + + public void d(final Throwable t, final String format, final Object... args) { + if (isLoggable(Log.DEBUG)) { + Log.d(tag, toMessage(format, args), t); + } + } + + public void i(final String format, final Object... args) { + if (isLoggable(Log.INFO)) { + Log.i(tag, toMessage(format, args)); + } + } + + public void i(final Throwable t, final String format, final Object... args) { + if (isLoggable(Log.INFO)) { + Log.i(tag, toMessage(format, args), t); + } + } + + public void w(final String format, final Object... args) { + if (isLoggable(Log.WARN)) { + Log.w(tag, toMessage(format, args)); + } + } + + public void w(final Throwable t, final String format, final Object... args) { + if (isLoggable(Log.WARN)) { + Log.w(tag, toMessage(format, args), t); + } + } + + public void e(final String format, final Object... args) { + if (isLoggable(Log.ERROR)) { + Log.e(tag, toMessage(format, args)); + } + } + + public void e(final Throwable t, final String format, final Object... args) { + if (isLoggable(Log.ERROR)) { + Log.e(tag, toMessage(format, args), t); + } + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/env/Size.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/env/Size.java new file mode 100644 index 000000000000..e3f71e0e03ce --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/env/Size.java @@ -0,0 +1,142 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package org.tensorflow.lite.examples.detection.env; + +import android.graphics.Bitmap; +import android.text.TextUtils; +import java.io.Serializable; +import java.util.ArrayList; +import java.util.List; + +/** Size class independent of a Camera object. */ +public class Size implements Comparable, Serializable { + + // 1.4 went out with this UID so we'll need to maintain it to preserve pending queries when + // upgrading. + public static final long serialVersionUID = 7689808733290872361L; + + public final int width; + public final int height; + + public Size(final int width, final int height) { + this.width = width; + this.height = height; + } + + public Size(final Bitmap bmp) { + this.width = bmp.getWidth(); + this.height = bmp.getHeight(); + } + + /** + * Rotate a size by the given number of degrees. + * + * @param size Size to rotate. + * @param rotation Degrees {0, 90, 180, 270} to rotate the size. + * @return Rotated size. + */ + public static Size getRotatedSize(final Size size, final int rotation) { + if (rotation % 180 != 0) { + // The phone is portrait, therefore the camera is sideways and frame should be rotated. + return new Size(size.height, size.width); + } + return size; + } + + public static Size parseFromString(String sizeString) { + if (TextUtils.isEmpty(sizeString)) { + return null; + } + + sizeString = sizeString.trim(); + + // The expected format is "x". + final String[] components = sizeString.split("x"); + if (components.length == 2) { + try { + final int width = Integer.parseInt(components[0]); + final int height = Integer.parseInt(components[1]); + return new Size(width, height); + } catch (final NumberFormatException e) { + return null; + } + } else { + return null; + } + } + + public static List sizeStringToList(final String sizes) { + final List sizeList = new ArrayList(); + if (sizes != null) { + final String[] pairs = sizes.split(","); + for (final String pair : pairs) { + final Size size = Size.parseFromString(pair); + if (size != null) { + sizeList.add(size); + } + } + } + return sizeList; + } + + public static String sizeListToString(final List sizes) { + String sizesString = ""; + if (sizes != null && sizes.size() > 0) { + sizesString = sizes.get(0).toString(); + for (int i = 1; i < sizes.size(); i++) { + sizesString += "," + sizes.get(i).toString(); + } + } + return sizesString; + } + + public static final String dimensionsAsString(final int width, final int height) { + return width + "x" + height; + } + + public final float aspectRatio() { + return (float) width / (float) height; + } + + @Override + public int compareTo(final Size other) { + return width * height - other.width * other.height; + } + + @Override + public boolean equals(final Object other) { + if (other == null) { + return false; + } + + if (!(other instanceof Size)) { + return false; + } + + final Size otherSize = (Size) other; + return (width == otherSize.width && height == otherSize.height); + } + + @Override + public int hashCode() { + return width * 32713 + height; + } + + @Override + public String toString() { + return dimensionsAsString(width, height); + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/env/Utils.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/env/Utils.java new file mode 100644 index 000000000000..47fd3e50dfc9 --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/env/Utils.java @@ -0,0 +1,188 @@ +package org.tensorflow.lite.examples.detection.env; + +import android.content.Context; +import android.content.res.AssetFileDescriptor; +import android.content.res.AssetManager; +import android.graphics.Bitmap; +import android.graphics.BitmapFactory; +import android.graphics.Canvas; +import android.graphics.Matrix; +import android.os.Environment; +import android.util.Log; + +import org.tensorflow.lite.examples.detection.MainActivity; + +import java.io.File; +import java.io.FileInputStream; +import java.io.FileOutputStream; +import java.io.IOException; +import java.io.InputStream; +import java.io.OutputStreamWriter; +import java.nio.MappedByteBuffer; +import java.nio.channels.FileChannel; + +public class Utils { + + /** + * Memory-map the model file in Assets. + */ + public static MappedByteBuffer loadModelFile(AssetManager assets, String modelFilename) + throws IOException { + AssetFileDescriptor fileDescriptor = assets.openFd(modelFilename); + FileInputStream inputStream = new FileInputStream(fileDescriptor.getFileDescriptor()); + FileChannel fileChannel = inputStream.getChannel(); + long startOffset = fileDescriptor.getStartOffset(); + long declaredLength = fileDescriptor.getDeclaredLength(); + return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength); + } + + public static void softmax(final float[] vals) { + float max = Float.NEGATIVE_INFINITY; + for (final float val : vals) { + max = Math.max(max, val); + } + float sum = 0.0f; + for (int i = 0; i < vals.length; ++i) { + vals[i] = (float) Math.exp(vals[i] - max); + sum += vals[i]; + } + for (int i = 0; i < vals.length; ++i) { + vals[i] = vals[i] / sum; + } + } + + public static float expit(final float x) { + return (float) (1. / (1. + Math.exp(-x))); + } + +// public static Bitmap scale(Context context, String filePath) { +// AssetManager assetManager = context.getAssets(); +// +// InputStream istr; +// Bitmap bitmap = null; +// try { +// istr = assetManager.open(filePath); +// bitmap = BitmapFactory.decodeStream(istr); +// bitmap = Bitmap.createScaledBitmap(bitmap, MainActivity.TF_OD_API_INPUT_SIZE, MainActivity.TF_OD_API_INPUT_SIZE, false); +// } catch (IOException e) { +// // handle exception +// Log.e("getBitmapFromAsset", "getBitmapFromAsset: " + e.getMessage()); +// } +// +// return bitmap; +// } + + public static Bitmap getBitmapFromAsset(Context context, String filePath) { + AssetManager assetManager = context.getAssets(); + + InputStream istr; + Bitmap bitmap = null; + try { + istr = assetManager.open(filePath); + bitmap = BitmapFactory.decodeStream(istr); +// return bitmap.copy(Bitmap.Config.ARGB_8888,true); + } catch (IOException e) { + // handle exception + Log.e("getBitmapFromAsset", "getBitmapFromAsset: " + e.getMessage()); + } + + return bitmap; + } + + /** + * Returns a transformation matrix from one reference frame into another. + * Handles cropping (if maintaining aspect ratio is desired) and rotation. + * + * @param srcWidth Width of source frame. + * @param srcHeight Height of source frame. + * @param dstWidth Width of destination frame. + * @param dstHeight Height of destination frame. + * @param applyRotation Amount of rotation to apply from one frame to another. + * Must be a multiple of 90. + * @param maintainAspectRatio If true, will ensure that scaling in x and y remains constant, + * cropping the image if necessary. + * @return The transformation fulfilling the desired requirements. + */ + public static Matrix getTransformationMatrix( + final int srcWidth, + final int srcHeight, + final int dstWidth, + final int dstHeight, + final int applyRotation, + final boolean maintainAspectRatio) { + final Matrix matrix = new Matrix(); + + if (applyRotation != 0) { + // Translate so center of image is at origin. + matrix.postTranslate(-srcWidth / 2.0f, -srcHeight / 2.0f); + + // Rotate around origin. + matrix.postRotate(applyRotation); + } + + // Account for the already applied rotation, if any, and then determine how + // much scaling is needed for each axis. + final boolean transpose = (Math.abs(applyRotation) + 90) % 180 == 0; + + final int inWidth = transpose ? srcHeight : srcWidth; + final int inHeight = transpose ? srcWidth : srcHeight; + + // Apply scaling if necessary. + if (inWidth != dstWidth || inHeight != dstHeight) { + final float scaleFactorX = dstWidth / (float) inWidth; + final float scaleFactorY = dstHeight / (float) inHeight; + + if (maintainAspectRatio) { + // Scale by minimum factor so that dst is filled completely while + // maintaining the aspect ratio. Some image may fall off the edge. + final float scaleFactor = Math.max(scaleFactorX, scaleFactorY); + matrix.postScale(scaleFactor, scaleFactor); + } else { + // Scale exactly to fill dst from src. + matrix.postScale(scaleFactorX, scaleFactorY); + } + } + + if (applyRotation != 0) { + // Translate back from origin centered reference to destination frame. + matrix.postTranslate(dstWidth / 2.0f, dstHeight / 2.0f); + } + + return matrix; + } + + public static Bitmap processBitmap(Bitmap source, int size){ + + int image_height = source.getHeight(); + int image_width = source.getWidth(); + + Bitmap croppedBitmap = Bitmap.createBitmap(size, size, Bitmap.Config.ARGB_8888); + + Matrix frameToCropTransformations = getTransformationMatrix(image_width,image_height,size,size,0,false); + Matrix cropToFrameTransformations = new Matrix(); + frameToCropTransformations.invert(cropToFrameTransformations); + + final Canvas canvas = new Canvas(croppedBitmap); + canvas.drawBitmap(source, frameToCropTransformations, null); + + return croppedBitmap; + } + + public static void writeToFile(String data, Context context) { + try { + String baseDir = Environment.getExternalStorageDirectory().getAbsolutePath(); + String fileName = "myFile.txt"; + + File file = new File(baseDir + File.separator + fileName); + + FileOutputStream stream = new FileOutputStream(file); + try { + stream.write(data.getBytes()); + } finally { + stream.close(); + } + } catch (IOException e) { + Log.e("Exception", "File write failed: " + e.toString()); + } + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/tflite/Classifier.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/tflite/Classifier.java new file mode 100644 index 000000000000..cffb0afaf64b --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/tflite/Classifier.java @@ -0,0 +1,134 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package org.tensorflow.lite.examples.detection.tflite; + +import android.graphics.Bitmap; +import android.graphics.RectF; + +import java.util.List; + +/** + * Generic interface for interacting with different recognition engines. + */ +public interface Classifier { + List recognizeImage(Bitmap bitmap); + + void enableStatLogging(final boolean debug); + + String getStatString(); + + void close(); + + void setNumThreads(int num_threads); + + void setUseNNAPI(boolean isChecked); + + abstract float getObjThresh(); + + /** + * An immutable result returned by a Classifier describing what was recognized. + */ + public class Recognition { + /** + * A unique identifier for what has been recognized. Specific to the class, not the instance of + * the object. + */ + private final String id; + + /** + * Display name for the recognition. + */ + private final String title; + + /** + * A sortable score for how good the recognition is relative to others. Higher should be better. + */ + private final Float confidence; + + /** + * Optional location within the source image for the location of the recognized object. + */ + private RectF location; + + private int detectedClass; + + public Recognition( + final String id, final String title, final Float confidence, final RectF location) { + this.id = id; + this.title = title; + this.confidence = confidence; + this.location = location; + } + + public Recognition(final String id, final String title, final Float confidence, final RectF location, int detectedClass) { + this.id = id; + this.title = title; + this.confidence = confidence; + this.location = location; + this.detectedClass = detectedClass; + } + + public String getId() { + return id; + } + + public String getTitle() { + return title; + } + + public Float getConfidence() { + return confidence; + } + + public RectF getLocation() { + return new RectF(location); + } + + public void setLocation(RectF location) { + this.location = location; + } + + public int getDetectedClass() { + return detectedClass; + } + + public void setDetectedClass(int detectedClass) { + this.detectedClass = detectedClass; + } + + @Override + public String toString() { + String resultString = ""; + if (id != null) { + resultString += "[" + id + "] "; + } + + if (title != null) { + resultString += title + " "; + } + + if (confidence != null) { + resultString += String.format("(%.1f%%) ", confidence * 100.0f); + } + + if (location != null) { + resultString += location + " "; + } + + return resultString.trim(); + } + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/tflite/DetectorFactory.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/tflite/DetectorFactory.java new file mode 100644 index 000000000000..f9cb14a8260a --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/tflite/DetectorFactory.java @@ -0,0 +1,53 @@ +package org.tensorflow.lite.examples.detection.tflite; + +import android.content.res.AssetManager; + +import java.io.IOException; + +public class DetectorFactory { + public static YoloV5ClassifierDetect getDetector( + final AssetManager assetManager, + final String modelFilename) + throws IOException { + String labelFilename = null; + boolean isQuantized = false; + int inputSize = 0; + int[] output_width = new int[]{0}; + int[][] masks = new int[][]{{0}}; + int[] anchors = new int[]{0}; + + if (modelFilename.equals("yolov5s.tflite")) { + labelFilename = "file:///android_asset/coco.txt"; + isQuantized = false; + inputSize = 640; + output_width = new int[]{80, 40, 20}; + masks = new int[][]{{0, 1, 2}, {3, 4, 5}, {6, 7, 8}}; + anchors = new int[]{ + 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 + }; + } + else if (modelFilename.equals("yolov5s-fp16.tflite")) { + labelFilename = "file:///android_asset/coco.txt"; + isQuantized = false; + inputSize = 640; + output_width = new int[]{80, 40, 20}; + masks = new int[][]{{0, 1, 2}, {3, 4, 5}, {6, 7, 8}}; + anchors = new int[]{ + 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 + }; + } + else if (modelFilename.equals("yolov5s-int8.tflite")) { + labelFilename = "file:///android_asset/coco.txt"; + isQuantized = true; + inputSize = 640; + output_width = new int[]{80, 40, 20}; + masks = new int[][]{{0, 1, 2}, {3, 4, 5}, {6, 7, 8}}; + anchors = new int[]{ + 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 + }; + } + return YoloV5ClassifierDetect.create(assetManager, modelFilename, labelFilename, isQuantized, + inputSize, output_width, masks, anchors); + } + +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/tflite/YoloV5Classifier.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/tflite/YoloV5Classifier.java new file mode 100644 index 000000000000..4a90befbba1f --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/tflite/YoloV5Classifier.java @@ -0,0 +1,463 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package org.tensorflow.lite.examples.detection.tflite; + +import android.content.res.AssetManager; +import android.graphics.Bitmap; +import android.graphics.RectF; +import android.os.Build; +import android.util.Log; + +import org.tensorflow.lite.Interpreter; +import org.tensorflow.lite.examples.detection.MainActivity; +import org.tensorflow.lite.examples.detection.env.Logger; +import org.tensorflow.lite.examples.detection.env.Utils; +import org.tensorflow.lite.gpu.GpuDelegate; +import org.tensorflow.lite.nnapi.NnApiDelegate; + +import java.io.BufferedReader; +import java.io.IOException; +import java.io.InputStream; +import java.io.InputStreamReader; +import java.nio.ByteBuffer; +import java.nio.ByteOrder; +import java.nio.MappedByteBuffer; +import java.util.ArrayList; +import java.util.Comparator; +import java.util.HashMap; +import java.util.Map; +import java.util.PriorityQueue; +import java.util.Vector; + + +/** + * Wrapper for frozen detection models trained using the Tensorflow Object Detection API: + * - https://github.com/tensorflow/models/tree/master/research/object_detection + * where you can find the training code. + *

+ * To use pretrained models in the API or convert to TF Lite models, please see docs for details: + * - https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md + * - https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_mobile_tensorflowlite.md#running-our-model-on-android + */ +public class YoloV5Classifier implements Classifier { + + /** + * Initializes a native TensorFlow session for classifying images. + * + * @param assetManager The asset manager to be used to load assets. + * @param modelFilename The filepath of the model GraphDef protocol buffer. + * @param labelFilename The filepath of label file for classes. + * @param isQuantized Boolean representing model is quantized or not + */ + public static YoloV5Classifier create( + final AssetManager assetManager, + final String modelFilename, + final String labelFilename, + final boolean isQuantized, + final int inputSize + /*final int[] output_width, + final int[][] masks, + final int[] anchors*/) + throws IOException { + final YoloV5Classifier d = new YoloV5Classifier(); + + String actualFilename = labelFilename.split("file:///android_asset/")[1]; + InputStream labelsInput = assetManager.open(actualFilename); + BufferedReader br = new BufferedReader(new InputStreamReader(labelsInput)); + String line; + while ((line = br.readLine()) != null) { + LOGGER.w(line); + d.labels.add(line); + } + br.close(); + + try { + Interpreter.Options options = (new Interpreter.Options()); + options.setNumThreads(NUM_THREADS); + if (isNNAPI) { + d.nnapiDelegate = null; + // Initialize interpreter with NNAPI delegate for Android Pie or above + if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.P) { + d.nnapiDelegate = new NnApiDelegate(); + options.addDelegate(d.nnapiDelegate); + options.setNumThreads(NUM_THREADS); +// options.setUseNNAPI(false); +// options.setAllowFp16PrecisionForFp32(true); +// options.setAllowBufferHandleOutput(true); + options.setUseNNAPI(true); + } + } + if (isGPU) { + GpuDelegate.Options gpu_options = new GpuDelegate.Options(); + gpu_options.setPrecisionLossAllowed(true); // It seems that the default is true + gpu_options.setInferencePreference(GpuDelegate.Options.INFERENCE_PREFERENCE_SUSTAINED_SPEED); + d.gpuDelegate = new GpuDelegate(gpu_options); + options.addDelegate(d.gpuDelegate); + } + d.tfliteModel = Utils.loadModelFile(assetManager, modelFilename); + d.tfLite = new Interpreter(d.tfliteModel, options); + } catch (Exception e) { + throw new RuntimeException(e); + } + + d.isModelQuantized = isQuantized; + // Pre-allocate buffers. + int numBytesPerChannel; + if (isQuantized) { + numBytesPerChannel = 1; // Quantized + } else { + numBytesPerChannel = 4; // Floating point + } + d.imgData = ByteBuffer.allocateDirect(1 * d.INPUT_SIZE * d.INPUT_SIZE * 3 * numBytesPerChannel); + d.imgData.order(ByteOrder.nativeOrder()); + d.intValues = new int[d.INPUT_SIZE * d.INPUT_SIZE]; + d.INPUT_SIZE = inputSize; + d.output_box = (int) ((Math.pow((inputSize / 32), 2) + Math.pow((inputSize / 16), 2) + Math.pow((inputSize / 8), 2)) * 3); +// d.OUTPUT_WIDTH = output_width; +// d.MASKS = masks; +// d.ANCHORS = anchors; + return d; + } + + public int getInputSize() { + return INPUT_SIZE; + } + @Override + public void enableStatLogging(final boolean logStats) { + } + + @Override + public String getStatString() { + return ""; + } + + @Override + public void close() { + tfLite.close(); + tfLite = null; + if (gpuDelegate != null) { + gpuDelegate.close(); + gpuDelegate = null; + } + if (nnapiDelegate != null) { + nnapiDelegate.close(); + nnapiDelegate = null; + } + tfliteModel = null; + } + + public void setNumThreads(int num_threads) { + if (tfLite != null) tfLite.setNumThreads(num_threads); + } + + @Override + public void setUseNNAPI(boolean isChecked) { + if (tfLite != null) tfLite.setUseNNAPI(isChecked); + } + + private void recreateInterpreter() { + if (tfLite != null) { + tfLite.close(); + tfLite = new Interpreter(tfliteModel, tfliteOptions); + } + } + + public void useGpu() { + if (gpuDelegate == null) { + gpuDelegate = new GpuDelegate(); + tfliteOptions.addDelegate(gpuDelegate); + recreateInterpreter(); + } + } + + public void useCPU() { + recreateInterpreter(); + } + + public void useNNAPI() { + nnapiDelegate = new NnApiDelegate(); + tfliteOptions.addDelegate(nnapiDelegate); + recreateInterpreter(); + } + + @Override + public float getObjThresh() { + return MainActivity.MINIMUM_CONFIDENCE_TF_OD_API; + } + + private static final Logger LOGGER = new Logger(); + + // Float model + private final float IMAGE_MEAN = 0; + + private final float IMAGE_STD = 255.0f; + + //config yolo + private int INPUT_SIZE = -1; + +// private int[] OUTPUT_WIDTH; +// private int[][] MASKS; +// private int[] ANCHORS; + private int output_box; + + private static final float[] XYSCALE = new float[]{1.2f, 1.1f, 1.05f}; + + private static final int NUM_BOXES_PER_BLOCK = 3; + + // Number of threads in the java app + private static final int NUM_THREADS = 1; + private static boolean isNNAPI = false; + private static boolean isGPU = false; + + private boolean isModelQuantized; + + /** holds a gpu delegate */ + GpuDelegate gpuDelegate = null; + /** holds an nnapi delegate */ + NnApiDelegate nnapiDelegate = null; + + /** The loaded TensorFlow Lite model. */ + private MappedByteBuffer tfliteModel; + + /** Options for configuring the Interpreter. */ + private final Interpreter.Options tfliteOptions = new Interpreter.Options(); + + // Config values. + + // Pre-allocated buffers. + private Vector labels = new Vector(); + private int[] intValues; + + private ByteBuffer imgData; + + private Interpreter tfLite; + + private YoloV5Classifier() { + } + + //non maximum suppression + protected ArrayList nms(ArrayList list) { + ArrayList nmsList = new ArrayList(); + + for (int k = 0; k < labels.size(); k++) { + //1.find max confidence per class + PriorityQueue pq = + new PriorityQueue( + 50, + new Comparator() { + @Override + public int compare(final Recognition lhs, final Recognition rhs) { + // Intentionally reversed to put high confidence at the head of the queue. + return Float.compare(rhs.getConfidence(), lhs.getConfidence()); + } + }); + + for (int i = 0; i < list.size(); ++i) { + if (list.get(i).getDetectedClass() == k) { + pq.add(list.get(i)); + } + } + + //2.do non maximum suppression + while (pq.size() > 0) { + //insert detection with max confidence + Recognition[] a = new Recognition[pq.size()]; + Recognition[] detections = pq.toArray(a); + Recognition max = detections[0]; + nmsList.add(max); + pq.clear(); + + for (int j = 1; j < detections.length; j++) { + Recognition detection = detections[j]; + RectF b = detection.getLocation(); + if (box_iou(max.getLocation(), b) < mNmsThresh) { + pq.add(detection); + } + } + } + } + return nmsList; + } + + protected float mNmsThresh = 0.6f; + + protected float box_iou(RectF a, RectF b) { + return box_intersection(a, b) / box_union(a, b); + } + + protected float box_intersection(RectF a, RectF b) { + float w = overlap((a.left + a.right) / 2, a.right - a.left, + (b.left + b.right) / 2, b.right - b.left); + float h = overlap((a.top + a.bottom) / 2, a.bottom - a.top, + (b.top + b.bottom) / 2, b.bottom - b.top); + if (w < 0 || h < 0) return 0; + float area = w * h; + return area; + } + + protected float box_union(RectF a, RectF b) { + float i = box_intersection(a, b); + float u = (a.right - a.left) * (a.bottom - a.top) + (b.right - b.left) * (b.bottom - b.top) - i; + return u; + } + + protected float overlap(float x1, float w1, float x2, float w2) { + float l1 = x1 - w1 / 2; + float l2 = x2 - w2 / 2; + float left = l1 > l2 ? l1 : l2; + float r1 = x1 + w1 / 2; + float r2 = x2 + w2 / 2; + float right = r1 < r2 ? r1 : r2; + return right - left; + } + + protected static final int BATCH_SIZE = 1; + protected static final int PIXEL_SIZE = 3; + + /** + * Writes Image data into a {@code ByteBuffer}. + */ + protected ByteBuffer convertBitmapToByteBuffer(Bitmap bitmap) { + ByteBuffer byteBuffer = ByteBuffer.allocateDirect(4 * BATCH_SIZE * INPUT_SIZE * INPUT_SIZE * PIXEL_SIZE); + byteBuffer.order(ByteOrder.nativeOrder()); + int[] intValues = new int[INPUT_SIZE * INPUT_SIZE]; + bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight()); + int pixel = 0; + for (int i = 0; i < INPUT_SIZE; ++i) { + for (int j = 0; j < INPUT_SIZE; ++j) { + final int val = intValues[pixel++]; + byteBuffer.putFloat(((val >> 16) & 0xFF) / 255.0f); + byteBuffer.putFloat(((val >> 8) & 0xFF) / 255.0f); + byteBuffer.putFloat((val & 0xFF) / 255.0f); + } + } + return byteBuffer; + } + + public ArrayList recognizeImage(Bitmap bitmap) { + ByteBuffer byteBuffer = convertBitmapToByteBuffer(bitmap); + + Map outputMap = new HashMap<>(); + + float[][][] outbuf = new float[1][output_box][labels.size() + 5]; + outputMap.put(0, outbuf); + Log.d("YoloV5Classifier", "mObjThresh: " + getObjThresh()); + + Object[] inputArray = {byteBuffer}; + tfLite.runForMultipleInputsOutputs(inputArray, outputMap); + + ArrayList detections = new ArrayList(); + + float[][][] out = (float[][][]) outputMap.get(0); + + Log.d("YoloV5Classifier", "out[0] detect start"); + for (int i = 0; i < output_box; ++i){ + final int offset = 0; + final float confidence = out[0][i][4]; + int detectedClass = -1; + float maxClass = 0; + + final float[] classes = new float[labels.size()]; + for (int c = 0; c < labels.size(); ++c) { + classes[c] = out[0][i][5 + c]; + } + + for (int c = 0; c < labels.size(); ++c) { + if (classes[c] > maxClass) { + detectedClass = c; + maxClass = classes[c]; + } + } + + final float confidenceInClass = maxClass * confidence; + if (confidenceInClass > getObjThresh()) { + final float xPos = out[0][i][0]; + final float yPos = out[0][i][1]; + + final float w = out[0][i][2]; + final float h = out[0][i][3]; + Log.d("YoloV5Classifier", + Float.toString(xPos) + ',' + yPos + ',' + w + ',' + h); + + final RectF rect = + new RectF( + Math.max(0, xPos - w / 2), + Math.max(0, yPos - h / 2), + Math.min(bitmap.getWidth() - 1, xPos + w / 2), + Math.min(bitmap.getHeight() - 1, yPos + h / 2)); + detections.add(new Recognition("" + offset, labels.get(detectedClass), + confidenceInClass, rect, detectedClass)); + } + } + + Log.d("YoloV5Classifier", "detect end"); + final ArrayList recognitions = nms(detections); +// final ArrayList recognitions = detections; + return recognitions; + } + + public boolean checkInvalidateBox(float x, float y, float width, float height, float oriW, float oriH, int intputSize) { + // (1) (x, y, w, h) --> (xmin, ymin, xmax, ymax) + float halfHeight = height / 2.0f; + float halfWidth = width / 2.0f; + + float[] pred_coor = new float[]{x - halfWidth, y - halfHeight, x + halfWidth, y + halfHeight}; + + // (2) (xmin, ymin, xmax, ymax) -> (xmin_org, ymin_org, xmax_org, ymax_org) + float resize_ratioW = 1.0f * intputSize / oriW; + float resize_ratioH = 1.0f * intputSize / oriH; + + float resize_ratio = resize_ratioW > resize_ratioH ? resize_ratioH : resize_ratioW; //min + + float dw = (intputSize - resize_ratio * oriW) / 2; + float dh = (intputSize - resize_ratio * oriH) / 2; + + pred_coor[0] = 1.0f * (pred_coor[0] - dw) / resize_ratio; + pred_coor[2] = 1.0f * (pred_coor[2] - dw) / resize_ratio; + + pred_coor[1] = 1.0f * (pred_coor[1] - dh) / resize_ratio; + pred_coor[3] = 1.0f * (pred_coor[3] - dh) / resize_ratio; + + // (3) clip some boxes those are out of range + pred_coor[0] = pred_coor[0] > 0 ? pred_coor[0] : 0; + pred_coor[1] = pred_coor[1] > 0 ? pred_coor[1] : 0; + + pred_coor[2] = pred_coor[2] < (oriW - 1) ? pred_coor[2] : (oriW - 1); + pred_coor[3] = pred_coor[3] < (oriH - 1) ? pred_coor[3] : (oriH - 1); + + if ((pred_coor[0] > pred_coor[2]) || (pred_coor[1] > pred_coor[3])) { + pred_coor[0] = 0; + pred_coor[1] = 0; + pred_coor[2] = 0; + pred_coor[3] = 0; + } + + // (4) discard some invalid boxes + float temp1 = pred_coor[2] - pred_coor[0]; + float temp2 = pred_coor[3] - pred_coor[1]; + float temp = temp1 * temp2; + if (temp < 0) { + Log.e("checkInvalidateBox", "temp < 0"); + return false; + } + if (Math.sqrt(temp) > Float.MAX_VALUE) { + Log.e("checkInvalidateBox", "temp max"); + return false; + } + + return true; + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/tflite/YoloV5ClassifierDetect.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/tflite/YoloV5ClassifierDetect.java new file mode 100644 index 000000000000..8140780d1d27 --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/tflite/YoloV5ClassifierDetect.java @@ -0,0 +1,524 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package org.tensorflow.lite.examples.detection.tflite; + +import android.content.res.AssetManager; +import android.graphics.Bitmap; +import android.graphics.RectF; +import android.os.Build; +import android.util.Log; + +import org.tensorflow.lite.Interpreter; +import org.tensorflow.lite.Tensor; +import org.tensorflow.lite.examples.detection.MainActivity; +import org.tensorflow.lite.examples.detection.env.Logger; +import org.tensorflow.lite.examples.detection.env.Utils; +import org.tensorflow.lite.gpu.GpuDelegate; +import org.tensorflow.lite.nnapi.NnApiDelegate; + +import java.io.BufferedReader; +import java.io.IOException; +import java.io.InputStream; +import java.io.InputStreamReader; +import java.nio.ByteBuffer; +import java.nio.ByteOrder; +import java.nio.MappedByteBuffer; +import java.util.ArrayList; +import java.util.Comparator; +import java.util.HashMap; +import java.util.Map; +import java.util.PriorityQueue; +import java.util.Vector; + +import static org.tensorflow.lite.examples.detection.env.Utils.expit; + + +/** + * Wrapper for frozen detection models trained using the Tensorflow Object Detection API: + * - https://github.com/tensorflow/models/tree/master/research/object_detection + * where you can find the training code. + *

+ * To use pretrained models in the API or convert to TF Lite models, please see docs for details: + * - https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md + * - https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_mobile_tensorflowlite.md#running-our-model-on-android + */ +public class YoloV5ClassifierDetect implements Classifier { + + /** + * Initializes a native TensorFlow session for classifying images. + * + * @param assetManager The asset manager to be used to load assets. + * @param modelFilename The filepath of the model GraphDef protocol buffer. + * @param labelFilename The filepath of label file for classes. + * @param isQuantized Boolean representing model is quantized or not + */ + public static YoloV5ClassifierDetect create( + final AssetManager assetManager, + final String modelFilename, + final String labelFilename, + final boolean isQuantized, + final int inputSize, + final int[] output_width, + final int[][] masks, + final int[] anchors) + throws IOException { + final YoloV5ClassifierDetect d = new YoloV5ClassifierDetect(); + + String actualFilename = labelFilename.split("file:///android_asset/")[1]; + InputStream labelsInput = assetManager.open(actualFilename); + BufferedReader br = new BufferedReader(new InputStreamReader(labelsInput)); + String line; + while ((line = br.readLine()) != null) { + LOGGER.w(line); + d.labels.add(line); + } + br.close(); + + try { + Interpreter.Options options = (new Interpreter.Options()); + options.setNumThreads(NUM_THREADS); + if (isNNAPI) { + d.nnapiDelegate = null; + // Initialize interpreter with NNAPI delegate for Android Pie or above + if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.P) { + d.nnapiDelegate = new NnApiDelegate(); + options.addDelegate(d.nnapiDelegate); + options.setNumThreads(NUM_THREADS); +// options.setUseNNAPI(false); +// options.setAllowFp16PrecisionForFp32(true); +// options.setAllowBufferHandleOutput(true); + options.setUseNNAPI(true); + } + } + if (isGPU) { + GpuDelegate.Options gpu_options = new GpuDelegate.Options(); + gpu_options.setPrecisionLossAllowed(true); // It seems that the default is true + gpu_options.setInferencePreference(GpuDelegate.Options.INFERENCE_PREFERENCE_SUSTAINED_SPEED); + d.gpuDelegate = new GpuDelegate(gpu_options); + options.addDelegate(d.gpuDelegate); + } + d.tfliteModel = Utils.loadModelFile(assetManager, modelFilename); + d.tfLite = new Interpreter(d.tfliteModel, options); + } catch (Exception e) { + throw new RuntimeException(e); + } + + d.isModelQuantized = isQuantized; + // Pre-allocate buffers. + int numBytesPerChannel; + if (isQuantized) { + numBytesPerChannel = 1; // Quantized + } else { + numBytesPerChannel = 4; // Floating point + } + d.INPUT_SIZE = inputSize; + d.OUTPUT_WIDTH = output_width; + d.imgData = ByteBuffer.allocateDirect(1 * d.INPUT_SIZE * d.INPUT_SIZE * 3 * numBytesPerChannel); + d.imgData.order(ByteOrder.nativeOrder()); + d.outData = new ByteBuffer[masks.length]; + + int[] shape = d.tfLite.getOutputTensor(0).shape(); + int numClass = shape[shape.length - 1]; + for (int i = 0; i < masks.length; ++i){ + d.outData[i] = ByteBuffer.allocateDirect(1 * d.OUTPUT_WIDTH[i] * d.OUTPUT_WIDTH[i] * + masks[i].length * (5 + numClass) * numBytesPerChannel); + d.outData[i].order(ByteOrder.nativeOrder()); + } + + d.intValues = new int[d.INPUT_SIZE * d.INPUT_SIZE]; + if (d.isModelQuantized){ + Tensor inpten = d.tfLite.getInputTensor(0); + d.inp_scale = inpten.quantizationParams().getScale(); + d.inp_zero_point = inpten.quantizationParams().getZeroPoint(); + + d.oup_scales = new float[masks.length]; + d.oup_zero_points = new int[masks.length]; + + for (int i = 0; i < masks.length; ++i) { + Tensor oupten = d.tfLite.getOutputTensor(i); + d.oup_scales[i] = oupten.quantizationParams().getScale(); + d.oup_zero_points[i] = oupten.quantizationParams().getZeroPoint(); + } + } + d.MASKS = masks; + d.ANCHORS = anchors; + return d; + } + + public int getInputSize() { + return INPUT_SIZE; + } + @Override + public void enableStatLogging(final boolean logStats) { + } + + @Override + public String getStatString() { + return ""; + } + + @Override + public void close() { + tfLite.close(); + tfLite = null; + if (gpuDelegate != null) { + gpuDelegate.close(); + gpuDelegate = null; + } + if (nnapiDelegate != null) { + nnapiDelegate.close(); + nnapiDelegate = null; + } + tfliteModel = null; + } + + public void setNumThreads(int num_threads) { + if (tfLite != null) tfLite.setNumThreads(num_threads); + } + + @Override + public void setUseNNAPI(boolean isChecked) { + if (tfLite != null) tfLite.setUseNNAPI(isChecked); + } + + private void recreateInterpreter() { + if (tfLite != null) { + tfLite.close(); + tfLite = new Interpreter(tfliteModel, tfliteOptions); + } + } + + public void useGpu() { + if (gpuDelegate == null) { + gpuDelegate = new GpuDelegate(); + tfliteOptions.addDelegate(gpuDelegate); + recreateInterpreter(); + } + } + + public void useCPU() { + recreateInterpreter(); + } + + public void useNNAPI() { + nnapiDelegate = new NnApiDelegate(); + tfliteOptions.addDelegate(nnapiDelegate); + recreateInterpreter(); + } + + @Override + public float getObjThresh() { + return MainActivity.MINIMUM_CONFIDENCE_TF_OD_API; + } + + private static final Logger LOGGER = new Logger(); + + // Float model + private final float IMAGE_MEAN = 0; + + private final float IMAGE_STD = 255.0f; + + //config yolo + private int INPUT_SIZE = -1; + + private int[] OUTPUT_WIDTH; + private int[][] MASKS; + private int[] ANCHORS; + + private static final float[] XYSCALE = new float[]{1.2f, 1.1f, 1.05f}; + + private static final int NUM_BOXES_PER_BLOCK = 3; + + // Number of threads in the java app + private static final int NUM_THREADS = 1; + private static boolean isNNAPI = false; + private static boolean isGPU = false; + + private boolean isModelQuantized; + + /** holds a gpu delegate */ + GpuDelegate gpuDelegate = null; + /** holds an nnapi delegate */ + NnApiDelegate nnapiDelegate = null; + + /** The loaded TensorFlow Lite model. */ + private MappedByteBuffer tfliteModel; + + /** Options for configuring the Interpreter. */ + private final Interpreter.Options tfliteOptions = new Interpreter.Options(); + + // Config values. + + // Pre-allocated buffers. + private Vector labels = new Vector(); + private int[] intValues; + + private ByteBuffer imgData; + private ByteBuffer[] outData; + private Interpreter tfLite; + + private float inp_scale; + private int inp_zero_point; + private float[] oup_scales; + private int[] oup_zero_points; + private YoloV5ClassifierDetect() { + } + + //non maximum suppression + protected ArrayList nms(ArrayList list) { + ArrayList nmsList = new ArrayList(); + + for (int k = 0; k < labels.size(); k++) { + //1.find max confidence per class + PriorityQueue pq = + new PriorityQueue( + 50, + new Comparator() { + @Override + public int compare(final Recognition lhs, final Recognition rhs) { + // Intentionally reversed to put high confidence at the head of the queue. + return Float.compare(rhs.getConfidence(), lhs.getConfidence()); + } + }); + + for (int i = 0; i < list.size(); ++i) { + if (list.get(i).getDetectedClass() == k) { + pq.add(list.get(i)); + } + } + + //2.do non maximum suppression + while (pq.size() > 0) { + //insert detection with max confidence + Recognition[] a = new Recognition[pq.size()]; + Recognition[] detections = pq.toArray(a); + Recognition max = detections[0]; + nmsList.add(max); + pq.clear(); + + for (int j = 1; j < detections.length; j++) { + Recognition detection = detections[j]; + RectF b = detection.getLocation(); + if (box_iou(max.getLocation(), b) < mNmsThresh) { + pq.add(detection); + } + } + } + } + return nmsList; + } + + protected float mNmsThresh = 0.6f; + + protected float box_iou(RectF a, RectF b) { + return box_intersection(a, b) / box_union(a, b); + } + + protected float box_intersection(RectF a, RectF b) { + float w = overlap((a.left + a.right) / 2, a.right - a.left, + (b.left + b.right) / 2, b.right - b.left); + float h = overlap((a.top + a.bottom) / 2, a.bottom - a.top, + (b.top + b.bottom) / 2, b.bottom - b.top); + if (w < 0 || h < 0) return 0; + float area = w * h; + return area; + } + + protected float box_union(RectF a, RectF b) { + float i = box_intersection(a, b); + float u = (a.right - a.left) * (a.bottom - a.top) + (b.right - b.left) * (b.bottom - b.top) - i; + return u; + } + + protected float overlap(float x1, float w1, float x2, float w2) { + float l1 = x1 - w1 / 2; + float l2 = x2 - w2 / 2; + float left = l1 > l2 ? l1 : l2; + float r1 = x1 + w1 / 2; + float r2 = x2 + w2 / 2; + float right = r1 < r2 ? r1 : r2; + return right - left; + } + + protected static final int BATCH_SIZE = 1; + protected static final int PIXEL_SIZE = 3; + + public ArrayList recognizeImage(Bitmap bitmap) { + Map outputMap = new HashMap<>(); + + bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight()); + imgData.rewind(); + for (int i = 0; i < INPUT_SIZE; ++i) { + for (int j = 0; j < INPUT_SIZE; ++j) { + int pixelValue = intValues[i * INPUT_SIZE + j]; + if (isModelQuantized) { + // Quantized model + imgData.put((byte) ((((pixelValue >> 16) & 0xFF) - IMAGE_MEAN) / IMAGE_STD / inp_scale + inp_zero_point)); + imgData.put((byte) ((((pixelValue >> 8) & 0xFF) - IMAGE_MEAN) / IMAGE_STD / inp_scale + inp_zero_point)); + imgData.put((byte) (((pixelValue & 0xFF) - IMAGE_MEAN) / IMAGE_STD / inp_scale + inp_zero_point)); + } else { // Float model + imgData.putFloat((((pixelValue >> 16) & 0xFF) - IMAGE_MEAN) / IMAGE_STD); + imgData.putFloat((((pixelValue >> 8) & 0xFF) - IMAGE_MEAN) / IMAGE_STD); + imgData.putFloat(((pixelValue & 0xFF) - IMAGE_MEAN) / IMAGE_STD); + } + } + } + + for (int i = 0; i < OUTPUT_WIDTH.length; i++) { + outData[i].rewind(); + outputMap.put(i, outData[i]); + } + + Log.d("YoloV5Classifier", "mObjThresh: " + getObjThresh()); + + Object[] inputArray = {imgData}; + tfLite.runForMultipleInputsOutputs(inputArray, outputMap); + + ArrayList detections = new ArrayList(); + + for (int i = 0; i < OUTPUT_WIDTH.length; i++) { + int gridWidth = OUTPUT_WIDTH[i]; + ByteBuffer byteBuffer = (ByteBuffer) outputMap.get(i); + byteBuffer.rewind(); + float[][][][] out = new float[1][NUM_BOXES_PER_BLOCK][gridWidth * gridWidth][5 + labels.size()]; + if (isModelQuantized) { + for (int b = 0; b < NUM_BOXES_PER_BLOCK; ++b) { + for (int y = 0; y < gridWidth; ++y) { + for (int x = 0; x < gridWidth; ++x) { + for (int c = 0; c < 5 + labels.size(); ++c) { + out[0][b][y * gridWidth + x][c] = + oup_scales[i] * (((int) byteBuffer.get() & 0xFF) - oup_zero_points[i]); + } + } + } + } + } + else { + for (int b = 0; b < NUM_BOXES_PER_BLOCK; ++b) { + for (int y = 0; y < gridWidth; ++y) { + for (int x = 0; x < gridWidth; ++x) { + for (int c = 0; c < 5 + labels.size(); ++c) { + out[0][b][y * gridWidth + x][c] = byteBuffer.getFloat(); + } + } + } + } + } + Log.d("YoloV5Classifier", "out[" + i + "] detect start"); + for (int y = 0; y < gridWidth; ++y) { + for (int x = 0; x < gridWidth; ++x) { + for (int b = 0; b < NUM_BOXES_PER_BLOCK; ++b) { + final int offset = + (gridWidth * (NUM_BOXES_PER_BLOCK * (labels.size() + 5))) * y + + (NUM_BOXES_PER_BLOCK * (labels.size() + 5)) * x + + (labels.size() + 5) * b; + + final float confidence = expit(out[0][b][y * gridWidth + x][4]); + int detectedClass = -1; + float maxClass = 0; + + final float[] classes = new float[labels.size()]; + for (int c = 0; c < labels.size(); ++c) { + classes[c] = expit(out[0][b][y * gridWidth + x][5 + c]); + } + + for (int c = 0; c < labels.size(); ++c) { + if (classes[c] > maxClass) { + detectedClass = c; + maxClass = classes[c]; + } + } + + final float confidenceInClass = maxClass * confidence; + if (confidenceInClass > getObjThresh()) { + final float xPos = (x + expit(out[0][b][y * gridWidth + x][0]) * 2.f - 0.5f) * (1.0f * INPUT_SIZE / gridWidth); + final float yPos = (y + expit(out[0][b][y * gridWidth + x][1]) * 2.f - 0.5f) * (1.0f * INPUT_SIZE / gridWidth); + + final float w = (float) (Math.pow(expit(out[0][b][y * gridWidth + x][2]) * 2, 2) * ANCHORS[2 * MASKS[i][b]]); + final float h = (float) (Math.pow(expit(out[0][b][y * gridWidth + x][3]) * 2, 2) * ANCHORS[2 * MASKS[i][b] + 1]); + + final RectF rect = + new RectF( + Math.max(0, xPos - w / 2), + Math.max(0, yPos - h / 2), + Math.min(bitmap.getWidth() - 1, xPos + w / 2), + Math.min(bitmap.getHeight() - 1, yPos + h / 2)); + detections.add(new Recognition("" + offset, labels.get(detectedClass), + confidenceInClass, rect, detectedClass)); + } + } + } + } + Log.d("YoloV5Classifier", "out[" + i + "] detect end"); + } + + final ArrayList recognitions = nms(detections); + + return recognitions; + } + + public boolean checkInvalidateBox(float x, float y, float width, float height, float oriW, float oriH, int intputSize) { + // (1) (x, y, w, h) --> (xmin, ymin, xmax, ymax) + float halfHeight = height / 2.0f; + float halfWidth = width / 2.0f; + + float[] pred_coor = new float[]{x - halfWidth, y - halfHeight, x + halfWidth, y + halfHeight}; + + // (2) (xmin, ymin, xmax, ymax) -> (xmin_org, ymin_org, xmax_org, ymax_org) + float resize_ratioW = 1.0f * intputSize / oriW; + float resize_ratioH = 1.0f * intputSize / oriH; + + float resize_ratio = resize_ratioW > resize_ratioH ? resize_ratioH : resize_ratioW; //min + + float dw = (intputSize - resize_ratio * oriW) / 2; + float dh = (intputSize - resize_ratio * oriH) / 2; + + pred_coor[0] = 1.0f * (pred_coor[0] - dw) / resize_ratio; + pred_coor[2] = 1.0f * (pred_coor[2] - dw) / resize_ratio; + + pred_coor[1] = 1.0f * (pred_coor[1] - dh) / resize_ratio; + pred_coor[3] = 1.0f * (pred_coor[3] - dh) / resize_ratio; + + // (3) clip some boxes those are out of range + pred_coor[0] = pred_coor[0] > 0 ? pred_coor[0] : 0; + pred_coor[1] = pred_coor[1] > 0 ? pred_coor[1] : 0; + + pred_coor[2] = pred_coor[2] < (oriW - 1) ? pred_coor[2] : (oriW - 1); + pred_coor[3] = pred_coor[3] < (oriH - 1) ? pred_coor[3] : (oriH - 1); + + if ((pred_coor[0] > pred_coor[2]) || (pred_coor[1] > pred_coor[3])) { + pred_coor[0] = 0; + pred_coor[1] = 0; + pred_coor[2] = 0; + pred_coor[3] = 0; + } + + // (4) discard some invalid boxes + float temp1 = pred_coor[2] - pred_coor[0]; + float temp2 = pred_coor[3] - pred_coor[1]; + float temp = temp1 * temp2; + if (temp < 0) { + Log.e("checkInvalidateBox", "temp < 0"); + return false; + } + if (Math.sqrt(temp) > Float.MAX_VALUE) { + Log.e("checkInvalidateBox", "temp max"); + return false; + } + + return true; + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/detection/tracking/MultiBoxTracker.java b/android/app/src/main/java/org/tensorflow/lite/examples/detection/tracking/MultiBoxTracker.java new file mode 100644 index 000000000000..8f58cb5b4827 --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/detection/tracking/MultiBoxTracker.java @@ -0,0 +1,212 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +package org.tensorflow.lite.examples.detection.tracking; + +import android.content.Context; +import android.graphics.Canvas; +import android.graphics.Color; +import android.graphics.Matrix; +import android.graphics.Paint; +import android.graphics.Paint.Cap; +import android.graphics.Paint.Join; +import android.graphics.Paint.Style; +import android.graphics.RectF; +import android.text.TextUtils; +import android.util.Pair; +import android.util.TypedValue; +import java.util.LinkedList; +import java.util.List; +import java.util.Queue; +import org.tensorflow.lite.examples.detection.env.BorderedText; +import org.tensorflow.lite.examples.detection.env.ImageUtils; +import org.tensorflow.lite.examples.detection.env.Logger; +import org.tensorflow.lite.examples.detection.tflite.Classifier.Recognition; + +/** A tracker that handles non-max suppression and matches existing objects to new detections. */ +public class MultiBoxTracker { + private static final float TEXT_SIZE_DIP = 18; + private static final float MIN_SIZE = 16.0f; + private static final int[] COLORS = { + Color.BLUE, + Color.RED, + Color.GREEN, + Color.YELLOW, + Color.CYAN, + Color.MAGENTA, + Color.WHITE, + Color.parseColor("#55FF55"), + Color.parseColor("#FFA500"), + Color.parseColor("#FF8888"), + Color.parseColor("#AAAAFF"), + Color.parseColor("#FFFFAA"), + Color.parseColor("#55AAAA"), + Color.parseColor("#AA33AA"), + Color.parseColor("#0D0068") + }; + final List> screenRects = new LinkedList>(); + private final Logger logger = new Logger(); + private final Queue availableColors = new LinkedList(); + private final List trackedObjects = new LinkedList(); + private final Paint boxPaint = new Paint(); + private final float textSizePx; + private final BorderedText borderedText; + private Matrix frameToCanvasMatrix; + private int frameWidth; + private int frameHeight; + private int sensorOrientation; + + public MultiBoxTracker(final Context context) { + for (final int color : COLORS) { + availableColors.add(color); + } + + boxPaint.setColor(Color.RED); + boxPaint.setStyle(Style.STROKE); + boxPaint.setStrokeWidth(10.0f); + boxPaint.setStrokeCap(Cap.ROUND); + boxPaint.setStrokeJoin(Join.ROUND); + boxPaint.setStrokeMiter(100); + + textSizePx = + TypedValue.applyDimension( + TypedValue.COMPLEX_UNIT_DIP, TEXT_SIZE_DIP, context.getResources().getDisplayMetrics()); + borderedText = new BorderedText(textSizePx); + } + + public synchronized void setFrameConfiguration( + final int width, final int height, final int sensorOrientation) { + frameWidth = width; + frameHeight = height; + this.sensorOrientation = sensorOrientation; + } + + public synchronized void drawDebug(final Canvas canvas) { + final Paint textPaint = new Paint(); + textPaint.setColor(Color.WHITE); + textPaint.setTextSize(60.0f); + + final Paint boxPaint = new Paint(); + boxPaint.setColor(Color.RED); + boxPaint.setAlpha(200); + boxPaint.setStyle(Style.STROKE); + + for (final Pair detection : screenRects) { + final RectF rect = detection.second; + canvas.drawRect(rect, boxPaint); + canvas.drawText("" + detection.first, rect.left, rect.top, textPaint); + borderedText.drawText(canvas, rect.centerX(), rect.centerY(), "" + detection.first); + } + } + + public synchronized void trackResults(final List results, final long timestamp) { + logger.i("Processing %d results from %d", results.size(), timestamp); + processResults(results); + } + + private Matrix getFrameToCanvasMatrix() { + return frameToCanvasMatrix; + } + + public synchronized void draw(final Canvas canvas) { + final boolean rotated = sensorOrientation % 180 == 90; + final float multiplier = + Math.min( + canvas.getHeight() / (float) (rotated ? frameWidth : frameHeight), + canvas.getWidth() / (float) (rotated ? frameHeight : frameWidth)); + frameToCanvasMatrix = + ImageUtils.getTransformationMatrix( + frameWidth, + frameHeight, + (int) (multiplier * (rotated ? frameHeight : frameWidth)), + (int) (multiplier * (rotated ? frameWidth : frameHeight)), + sensorOrientation, + false); + for (final TrackedRecognition recognition : trackedObjects) { + final RectF trackedPos = new RectF(recognition.location); + + getFrameToCanvasMatrix().mapRect(trackedPos); + boxPaint.setColor(recognition.color); + + float cornerSize = Math.min(trackedPos.width(), trackedPos.height()) / 8.0f; + canvas.drawRoundRect(trackedPos, cornerSize, cornerSize, boxPaint); + + final String labelString = + !TextUtils.isEmpty(recognition.title) + ? String.format("%s %.2f", recognition.title, (100 * recognition.detectionConfidence)) + : String.format("%.2f", (100 * recognition.detectionConfidence)); + // borderedText.drawText(canvas, trackedPos.left + cornerSize, trackedPos.top, + // labelString); + borderedText.drawText( + canvas, trackedPos.left + cornerSize, trackedPos.top, labelString + "%", boxPaint); + } + } + + private void processResults(final List results) { + final List> rectsToTrack = new LinkedList>(); + + screenRects.clear(); + final Matrix rgbFrameToScreen = new Matrix(getFrameToCanvasMatrix()); + + for (final Recognition result : results) { + if (result.getLocation() == null) { + continue; + } + final RectF detectionFrameRect = new RectF(result.getLocation()); + + final RectF detectionScreenRect = new RectF(); + rgbFrameToScreen.mapRect(detectionScreenRect, detectionFrameRect); + + logger.v( + "Result! Frame: " + result.getLocation() + " mapped to screen:" + detectionScreenRect); + + screenRects.add(new Pair(result.getConfidence(), detectionScreenRect)); + + if (detectionFrameRect.width() < MIN_SIZE || detectionFrameRect.height() < MIN_SIZE) { + logger.w("Degenerate rectangle! " + detectionFrameRect); + continue; + } + + rectsToTrack.add(new Pair(result.getConfidence(), result)); + } + + trackedObjects.clear(); + if (rectsToTrack.isEmpty()) { + logger.v("Nothing to track, aborting."); + return; + } + + for (final Pair potential : rectsToTrack) { + final TrackedRecognition trackedRecognition = new TrackedRecognition(); + trackedRecognition.detectionConfidence = potential.first; + trackedRecognition.location = new RectF(potential.second.getLocation()); + trackedRecognition.title = potential.second.getTitle(); +// trackedRecognition.color = COLORS[trackedObjects.size() % COLORS.length]; + trackedRecognition.color = COLORS[potential.second.getDetectedClass() % COLORS.length]; + trackedObjects.add(trackedRecognition); + +// if (trackedObjects.size() >= COLORS.length) { +// break; +// } + } + } + + private static class TrackedRecognition { + RectF location; + float detectionConfidence; + int color; + String title; + } +} diff --git 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