diff --git a/data/coco128.yaml b/data/coco128.yaml index b1dfb004afa1..84a91b18359d 100644 --- a/data/coco128.yaml +++ b/data/coco128.yaml @@ -27,4 +27,4 @@ names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 't # Download script/URL (optional) -download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip +download: https://ultralytics.com/assets/coco128.zip diff --git a/detect.py b/detect.py index 661a0b86bc99..108f8f138052 100644 --- a/detect.py +++ b/detect.py @@ -14,12 +14,10 @@ import argparse import os -import platform import sys from pathlib import Path import cv2 -import numpy as np import torch import torch.backends.cudnn as cudnn @@ -29,13 +27,12 @@ sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative -from models.experimental import attempt_load +from models.common import DetectMultiBackend from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams -from utils.general import (LOGGER, apply_classifier, check_file, check_img_size, check_imshow, check_requirements, - check_suffix, colorstr, increment_path, non_max_suppression, print_args, scale_coords, - strip_optimizer, xyxy2xywh) +from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr, + increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh) from utils.plots import Annotator, colors, save_one_box -from utils.torch_utils import load_classifier, select_device, time_sync +from utils.torch_utils import select_device, time_sync @torch.no_grad() @@ -77,120 +74,45 @@ def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s) save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir - # Initialize + # Load model device = select_device(device) - half &= device.type != 'cpu' # half precision only supported on CUDA + model = DetectMultiBackend(weights, device=device, dnn=dnn) + stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx + imgsz = check_img_size(imgsz, s=stride) # check image size - # Load model - w = str(weights[0] if isinstance(weights, list) else weights) - classify, suffix, suffixes = False, Path(w).suffix.lower(), ['.pt', '.onnx', '.tflite', '.pb', ''] - check_suffix(w, suffixes) # check weights have acceptable suffix - pt, onnx, tflite, pb, saved_model = (suffix == x for x in suffixes) # backend booleans - stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults + # Half + half &= pt and device.type != 'cpu' # half precision only supported by PyTorch on CUDA if pt: - model = torch.jit.load(w) if 'torchscript' in w else attempt_load(weights, map_location=device) - stride = int(model.stride.max()) # model stride - names = model.module.names if hasattr(model, 'module') else model.names # get class names - if half: - model.half() # to FP16 - if classify: # second-stage classifier - modelc = load_classifier(name='resnet50', n=2) # initialize - modelc.load_state_dict(torch.load('resnet50.pt', map_location=device)['model']).to(device).eval() - elif onnx: - if dnn: - check_requirements(('opencv-python>=4.5.4',)) - net = cv2.dnn.readNetFromONNX(w) - else: - check_requirements(('onnx', 'onnxruntime-gpu' if torch.has_cuda else 'onnxruntime')) - import onnxruntime - session = onnxruntime.InferenceSession(w, None) - else: # TensorFlow models - import tensorflow as tf - if pb: # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt - def wrap_frozen_graph(gd, inputs, outputs): - x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped import - return x.prune(tf.nest.map_structure(x.graph.as_graph_element, inputs), - tf.nest.map_structure(x.graph.as_graph_element, outputs)) - - graph_def = tf.Graph().as_graph_def() - graph_def.ParseFromString(open(w, 'rb').read()) - frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0") - elif saved_model: - model = tf.keras.models.load_model(w) - elif tflite: - if "edgetpu" in w: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python - import tflite_runtime.interpreter as tflri - delegate = {'Linux': 'libedgetpu.so.1', # install libedgetpu https://coral.ai/software/#edgetpu-runtime - 'Darwin': 'libedgetpu.1.dylib', - 'Windows': 'edgetpu.dll'}[platform.system()] - interpreter = tflri.Interpreter(model_path=w, experimental_delegates=[tflri.load_delegate(delegate)]) - else: - interpreter = tf.lite.Interpreter(model_path=w) # load TFLite model - interpreter.allocate_tensors() # allocate - input_details = interpreter.get_input_details() # inputs - output_details = interpreter.get_output_details() # outputs - int8 = input_details[0]['dtype'] == np.uint8 # is TFLite quantized uint8 model - imgsz = check_img_size(imgsz, s=stride) # check image size + model.model.half() if half else model.model.float() # Dataloader if webcam: view_img = check_imshow() cudnn.benchmark = True # set True to speed up constant image size inference - dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt) + dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit) bs = len(dataset) # batch_size else: - dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt) + dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit) bs = 1 # batch_size vid_path, vid_writer = [None] * bs, [None] * bs # Run inference if pt and device.type != 'cpu': - model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.parameters()))) # run once + model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters()))) # warmup dt, seen = [0.0, 0.0, 0.0], 0 - for path, img, im0s, vid_cap, s in dataset: + for path, im, im0s, vid_cap, s in dataset: t1 = time_sync() - if onnx: - img = img.astype('float32') - else: - img = torch.from_numpy(img).to(device) - img = img.half() if half else img.float() # uint8 to fp16/32 - img /= 255 # 0 - 255 to 0.0 - 1.0 - if len(img.shape) == 3: - img = img[None] # expand for batch dim + im = torch.from_numpy(im).to(device) + im = im.half() if half else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + if len(im.shape) == 3: + im = im[None] # expand for batch dim t2 = time_sync() dt[0] += t2 - t1 # Inference - if pt: - visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False - pred = model(img, augment=augment, visualize=visualize)[0] - elif onnx: - if dnn: - net.setInput(img) - pred = torch.tensor(net.forward()) - else: - pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img})) - else: # tensorflow model (tflite, pb, saved_model) - imn = img.permute(0, 2, 3, 1).cpu().numpy() # image in numpy - if pb: - pred = frozen_func(x=tf.constant(imn)).numpy() - elif saved_model: - pred = model(imn, training=False).numpy() - elif tflite: - if int8: - scale, zero_point = input_details[0]['quantization'] - imn = (imn / scale + zero_point).astype(np.uint8) # de-scale - interpreter.set_tensor(input_details[0]['index'], imn) - interpreter.invoke() - pred = interpreter.get_tensor(output_details[0]['index']) - if int8: - scale, zero_point = output_details[0]['quantization'] - pred = (pred.astype(np.float32) - zero_point) * scale # re-scale - pred[..., 0] *= imgsz[1] # x - pred[..., 1] *= imgsz[0] # y - pred[..., 2] *= imgsz[1] # w - pred[..., 3] *= imgsz[0] # h - pred = torch.tensor(pred) + visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False + pred = model(im, augment=augment, visualize=visualize) t3 = time_sync() dt[1] += t3 - t2 @@ -199,8 +121,7 @@ def wrap_frozen_graph(gd, inputs, outputs): dt[2] += time_sync() - t3 # Second-stage classifier (optional) - if classify: - pred = apply_classifier(pred, modelc, img, im0s) + # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) # Process predictions for i, det in enumerate(pred): # per image @@ -212,15 +133,15 @@ def wrap_frozen_graph(gd, inputs, outputs): p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) p = Path(p) # to Path - save_path = str(save_dir / p.name) # img.jpg - txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt - s += '%gx%g ' % img.shape[2:] # print string + save_path = str(save_dir / p.name) # im.jpg + txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt + s += '%gx%g ' % im.shape[2:] # print string gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh imc = im0.copy() if save_crop else im0 # for save_crop annotator = Annotator(im0, line_width=line_thickness, example=str(names)) if len(det): # Rescale boxes from img_size to im0 size - det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() + det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round() # Print results for c in det[:, -1].unique(): diff --git a/export.py b/export.py index f5eb487045b0..4cf30e34fc7b 100644 --- a/export.py +++ b/export.py @@ -21,6 +21,7 @@ """ import argparse +import json import os import subprocess import sys @@ -54,7 +55,9 @@ def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:' f = file.with_suffix('.torchscript.pt') ts = torch.jit.trace(model, im, strict=False) - (optimize_for_mobile(ts) if optimize else ts).save(f) + d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names} + extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap() + (optimize_for_mobile(ts) if optimize else ts).save(f, _extra_files=extra_files) LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') except Exception as e: diff --git a/models/common.py b/models/common.py index f9e4fc69f006..3ea7ba5477a6 100644 --- a/models/common.py +++ b/models/common.py @@ -3,11 +3,14 @@ Common modules """ +import json import math +import platform import warnings from copy import copy from pathlib import Path +import cv2 import numpy as np import pandas as pd import requests @@ -17,7 +20,8 @@ from torch.cuda import amp from utils.datasets import exif_transpose, letterbox -from utils.general import LOGGER, colorstr, increment_path, make_divisible, non_max_suppression, scale_coords, xyxy2xywh +from utils.general import (LOGGER, check_requirements, check_suffix, colorstr, increment_path, make_divisible, + non_max_suppression, scale_coords, xywh2xyxy, xyxy2xywh) from utils.plots import Annotator, colors, save_one_box from utils.torch_utils import time_sync @@ -269,6 +273,128 @@ def forward(self, x): return torch.cat(x, self.d) +class DetectMultiBackend(nn.Module): + # YOLOv5 MultiBackend class for python inference on various backends + def __init__(self, weights='yolov5s.pt', device=None, dnn=True): + # Usage: + # PyTorch: weights = *.pt + # TorchScript: *.torchscript.pt + # CoreML: *.mlmodel + # TensorFlow: *_saved_model + # TensorFlow: *.pb + # TensorFlow Lite: *.tflite + # ONNX Runtime: *.onnx + # OpenCV DNN: *.onnx with dnn=True + super().__init__() + w = str(weights[0] if isinstance(weights, list) else weights) + suffix, suffixes = Path(w).suffix.lower(), ['.pt', '.onnx', '.tflite', '.pb', '', '.mlmodel'] + check_suffix(w, suffixes) # check weights have acceptable suffix + pt, onnx, tflite, pb, saved_model, coreml = (suffix == x for x in suffixes) # backend booleans + jit = pt and 'torchscript' in w.lower() + stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults + + if jit: # TorchScript + LOGGER.info(f'Loading {w} for TorchScript inference...') + extra_files = {'config.txt': ''} # model metadata + model = torch.jit.load(w, _extra_files=extra_files) + if extra_files['config.txt']: + d = json.loads(extra_files['config.txt']) # extra_files dict + stride, names = int(d['stride']), d['names'] + elif pt: # PyTorch + from models.experimental import attempt_load # scoped to avoid circular import + model = torch.jit.load(w) if 'torchscript' in w else attempt_load(weights, map_location=device) + stride = int(model.stride.max()) # model stride + names = model.module.names if hasattr(model, 'module') else model.names # get class names + elif coreml: # CoreML *.mlmodel + import coremltools as ct + model = ct.models.MLModel(w) + elif dnn: # ONNX OpenCV DNN + LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...') + check_requirements(('opencv-python>=4.5.4',)) + net = cv2.dnn.readNetFromONNX(w) + elif onnx: # ONNX Runtime + LOGGER.info(f'Loading {w} for ONNX Runtime inference...') + check_requirements(('onnx', 'onnxruntime-gpu' if torch.has_cuda else 'onnxruntime')) + import onnxruntime + session = onnxruntime.InferenceSession(w, None) + else: # TensorFlow model (TFLite, pb, saved_model) + import tensorflow as tf + if pb: # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt + def wrap_frozen_graph(gd, inputs, outputs): + x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped + return x.prune(tf.nest.map_structure(x.graph.as_graph_element, inputs), + tf.nest.map_structure(x.graph.as_graph_element, outputs)) + + LOGGER.info(f'Loading {w} for TensorFlow *.pb inference...') + graph_def = tf.Graph().as_graph_def() + graph_def.ParseFromString(open(w, 'rb').read()) + frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0") + elif saved_model: + LOGGER.info(f'Loading {w} for TensorFlow saved_model inference...') + model = tf.keras.models.load_model(w) + elif tflite: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python + if 'edgetpu' in w.lower(): + LOGGER.info(f'Loading {w} for TensorFlow Edge TPU inference...') + import tflite_runtime.interpreter as tfli + delegate = {'Linux': 'libedgetpu.so.1', # install https://coral.ai/software/#edgetpu-runtime + 'Darwin': 'libedgetpu.1.dylib', + 'Windows': 'edgetpu.dll'}[platform.system()] + interpreter = tfli.Interpreter(model_path=w, experimental_delegates=[tfli.load_delegate(delegate)]) + else: + LOGGER.info(f'Loading {w} for TensorFlow Lite inference...') + interpreter = tf.lite.Interpreter(model_path=w) # load TFLite model + interpreter.allocate_tensors() # allocate + input_details = interpreter.get_input_details() # inputs + output_details = interpreter.get_output_details() # outputs + self.__dict__.update(locals()) # assign all variables to self + + def forward(self, im, augment=False, visualize=False, val=False): + # YOLOv5 MultiBackend inference + b, ch, h, w = im.shape # batch, channel, height, width + if self.pt: # PyTorch + y = self.model(im) if self.jit else self.model(im, augment=augment, visualize=visualize) + return y if val else y[0] + elif self.coreml: # CoreML *.mlmodel + im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3) + im = Image.fromarray((im[0] * 255).astype('uint8')) + # im = im.resize((192, 320), Image.ANTIALIAS) + y = self.model.predict({'image': im}) # coordinates are xywh normalized + box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels + conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float) + y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1) + elif self.onnx: # ONNX + im = im.cpu().numpy() # torch to numpy + if self.dnn: # ONNX OpenCV DNN + self.net.setInput(im) + y = self.net.forward() + else: # ONNX Runtime + y = self.session.run([self.session.get_outputs()[0].name], {self.session.get_inputs()[0].name: im})[0] + else: # TensorFlow model (TFLite, pb, saved_model) + im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3) + if self.pb: + y = self.frozen_func(x=self.tf.constant(im)).numpy() + elif self.saved_model: + y = self.model(im, training=False).numpy() + elif self.tflite: + input, output = self.input_details[0], self.output_details[0] + int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model + if int8: + scale, zero_point = input['quantization'] + im = (im / scale + zero_point).astype(np.uint8) # de-scale + self.interpreter.set_tensor(input['index'], im) + self.interpreter.invoke() + y = self.interpreter.get_tensor(output['index']) + if int8: + scale, zero_point = output['quantization'] + y = (y.astype(np.float32) - zero_point) * scale # re-scale + y[..., 0] *= w # x + y[..., 1] *= h # y + y[..., 2] *= w # w + y[..., 3] *= h # h + y = torch.tensor(y) + return (y, []) if val else y + + class AutoShape(nn.Module): # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS conf = 0.25 # NMS confidence threshold diff --git a/utils/general.py b/utils/general.py index b0ea1527129a..a6fe603850c8 100755 --- a/utils/general.py +++ b/utils/general.py @@ -785,7 +785,8 @@ def print_mutation(results, hyp, save_dir, bucket): def apply_classifier(x, model, img, im0): - # Apply a second stage classifier to yolo outputs + # Apply a second stage classifier to YOLO outputs + # Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval() im0 = [im0] if isinstance(im0, np.ndarray) else im0 for i, d in enumerate(x): # per image if d is not None and len(d): diff --git a/utils/torch_utils.py b/utils/torch_utils.py index 73acec8e819c..b65b69fe1559 100644 --- a/utils/torch_utils.py +++ b/utils/torch_utils.py @@ -17,7 +17,6 @@ import torch.distributed as dist import torch.nn as nn import torch.nn.functional as F -import torchvision from utils.general import LOGGER @@ -235,25 +234,6 @@ def model_info(model, verbose=False, img_size=640): LOGGER.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}") -def load_classifier(name='resnet101', n=2): - # Loads a pretrained model reshaped to n-class output - model = torchvision.models.__dict__[name](pretrained=True) - - # ResNet model properties - # input_size = [3, 224, 224] - # input_space = 'RGB' - # input_range = [0, 1] - # mean = [0.485, 0.456, 0.406] - # std = [0.229, 0.224, 0.225] - - # Reshape output to n classes - filters = model.fc.weight.shape[1] - model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True) - model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True) - model.fc.out_features = n - return model - - def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) # scales img(bs,3,y,x) by ratio constrained to gs-multiple if ratio == 1.0: diff --git a/val.py b/val.py index d2797f1189ec..2bcbc582a500 100644 --- a/val.py +++ b/val.py @@ -23,10 +23,10 @@ sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative -from models.experimental import attempt_load +from models.common import DetectMultiBackend from utils.callbacks import Callbacks from utils.datasets import create_dataloader -from utils.general import (LOGGER, box_iou, check_dataset, check_img_size, check_requirements, check_suffix, check_yaml, +from utils.general import (LOGGER, box_iou, check_dataset, check_img_size, check_requirements, check_yaml, coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args, scale_coords, xywh2xyxy, xyxy2xywh) from utils.metrics import ConfusionMatrix, ap_per_class @@ -100,6 +100,7 @@ def run(data, name='exp', # save to project/name exist_ok=False, # existing project/name ok, do not increment half=True, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference model=None, dataloader=None, save_dir=Path(''), @@ -110,8 +111,10 @@ def run(data, # Initialize/load model and set device training = model is not None if training: # called by train.py - device = next(model.parameters()).device # get model device + device, pt = next(model.parameters()).device, True # get model device, PyTorch model + half &= device.type != 'cpu' # half precision only supported on CUDA + model.half() if half else model.float() else: # called directly device = select_device(device, batch_size=batch_size) @@ -120,22 +123,21 @@ def run(data, (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model - check_suffix(weights, '.pt') - model = attempt_load(weights, map_location=device) # load FP32 model - gs = max(int(model.stride.max()), 32) # grid size (max stride) - imgsz = check_img_size(imgsz, s=gs) # check image size - - # Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99 - # if device.type != 'cpu' and torch.cuda.device_count() > 1: - # model = nn.DataParallel(model) + model = DetectMultiBackend(weights, device=device, dnn=dnn) + stride, pt = model.stride, model.pt + imgsz = check_img_size(imgsz, s=stride) # check image size + half &= pt and device.type != 'cpu' # half precision only supported by PyTorch on CUDA + if pt: + model.model.half() if half else model.model.float() + else: + half = False + batch_size = 1 # export.py models default to batch-size 1 + device = torch.device('cpu') + LOGGER.info(f'Forcing --batch-size 1 square inference shape(1,3,{imgsz},{imgsz}) for non-PyTorch backends') # Data data = check_dataset(data) # check - # Half - half &= device.type != 'cpu' # half precision only supported on CUDA - model.half() if half else model.float() - # Configure model.eval() is_coco = isinstance(data.get('val'), str) and data['val'].endswith('coco/val2017.txt') # COCO dataset @@ -145,11 +147,11 @@ def run(data, # Dataloader if not training: - if device.type != 'cpu': - model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once + if pt and device.type != 'cpu': + model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.model.parameters()))) # warmup pad = 0.0 if task == 'speed' else 0.5 task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images - dataloader = create_dataloader(data[task], imgsz, batch_size, gs, single_cls, pad=pad, rect=True, + dataloader = create_dataloader(data[task], imgsz, batch_size, stride, single_cls, pad=pad, rect=pt, prefix=colorstr(f'{task}: '))[0] seen = 0 @@ -160,32 +162,33 @@ def run(data, dt, p, r, f1, mp, mr, map50, map = [0.0, 0.0, 0.0], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 loss = torch.zeros(3, device=device) jdict, stats, ap, ap_class = [], [], [], [] - for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)): + for batch_i, (im, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)): t1 = time_sync() - img = img.to(device, non_blocking=True) - img = img.half() if half else img.float() # uint8 to fp16/32 - img /= 255 # 0 - 255 to 0.0 - 1.0 - targets = targets.to(device) - nb, _, height, width = img.shape # batch size, channels, height, width + if pt: + im = im.to(device, non_blocking=True) + targets = targets.to(device) + im = im.half() if half else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + nb, _, height, width = im.shape # batch size, channels, height, width t2 = time_sync() dt[0] += t2 - t1 - # Run model - out, train_out = model(img, augment=augment) # inference and training outputs + # Inference + out, train_out = model(im) if training else model(im, augment=augment, val=True) # inference, loss outputs dt[1] += time_sync() - t2 - # Compute loss + # Loss if compute_loss: loss += compute_loss([x.float() for x in train_out], targets)[1] # box, obj, cls - # Run NMS + # NMS targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling t3 = time_sync() out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls) dt[2] += time_sync() - t3 - # Statistics per image + # Metrics for si, pred in enumerate(out): labels = targets[targets[:, 0] == si, 1:] nl = len(labels) @@ -202,12 +205,12 @@ def run(data, if single_cls: pred[:, 5] = 0 predn = pred.clone() - scale_coords(img[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred + scale_coords(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred # Evaluate if nl: tbox = xywh2xyxy(labels[:, 1:5]) # target boxes - scale_coords(img[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels + scale_coords(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels correct = process_batch(predn, labelsn, iouv) if plots: @@ -221,16 +224,16 @@ def run(data, save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / (path.stem + '.txt')) if save_json: save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary - callbacks.run('on_val_image_end', pred, predn, path, names, img[si]) + callbacks.run('on_val_image_end', pred, predn, path, names, im[si]) # Plot images if plots and batch_i < 3: f = save_dir / f'val_batch{batch_i}_labels.jpg' # labels - Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start() + Thread(target=plot_images, args=(im, targets, paths, f, names), daemon=True).start() f = save_dir / f'val_batch{batch_i}_pred.jpg' # predictions - Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start() + Thread(target=plot_images, args=(im, output_to_target(out), paths, f, names), daemon=True).start() - # Compute statistics + # Compute metrics stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy if len(stats) and stats[0].any(): p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names) @@ -318,6 +321,7 @@ def parse_opt(): parser.add_argument('--name', default='exp', help='save to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') opt = parser.parse_args() opt.data = check_yaml(opt.data) # check YAML opt.save_json |= opt.data.endswith('coco.yaml')