update detect.py for torchscript support

detect.py

@@ -7,6 +7,7 @@
 """
 
 import argparse
+import os
 import sys
 from pathlib import Path
 
@@ -19,7 +20,7 @@
 ROOT = FILE.parents[0]  # YOLOv5 root directory
 if str(ROOT) not in sys.path:
     sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = ROOT.relative_to(Path.cwd())  # relative
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
 from models.experimental import attempt_load
 from utils.datasets import LoadImages, LoadStreams
@@ -55,6 +56,7 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
         hide_labels=False,  # hide labels
         hide_conf=False,  # hide confidences
         half=False,  # use FP16 half-precision inference
+        dnn=False,  # use OpenCV DNN for ONNX inference
         ):
     source = str(source)
     save_img = not nosave and not source.endswith('.txt')  # save inference images
@@ -71,7 +73,7 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
     half &= device.type != 'cpu'  # half precision only supported on CUDA
 
     # Load model
-    w = weights[0] if isinstance(weights, list) else weights
+    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
@@ -80,10 +82,7 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
         if 'torchscript' in w:
             # this is torchscript saved not a pickle that can be loaded with th.load
             # we assume the file exist in the target folder
-            if Path(w).is_file():
-                model = torch.jit.load(w)
-            else:
-                raise ValueError('Cannot find torchscript file {}'.format(Path(w))
+            model = torch.jit.load(w)
         else:
             model = attempt_load(weights, map_location=device)  # load FP32 model
         stride = int(model.stride.max())  # model stride
@@ -94,9 +93,13 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
             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:
-        check_requirements(('onnx', 'onnxruntime'))
-        import onnxruntime
-        session = onnxruntime.InferenceSession(w, None)
+        if dnn:
+            # check_requirements(('opencv-python>=4.5.4',))
+            net = cv2.dnn.readNetFromONNX(w)
+        else:
+            check_requirements(('onnx', 'onnxruntime'))
+            import onnxruntime
+            session = onnxruntime.InferenceSession(w, None)
     else:  # TensorFlow models
         check_requirements(('tensorflow>=2.4.1',))
         import tensorflow as tf
@@ -152,7 +155,11 @@ def wrap_frozen_graph(gd, inputs, outputs):
             visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
             pred = model(img, augment=augment, visualize=visualize)[0]
         elif onnx:
-            pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img}))
+            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:
@@ -288,6 +295,7 @@ def parse_opt():
     parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
     parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
     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.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
     print_args(FILE.stem, opt)

export.py

@@ -21,6 +21,7 @@
 """
 
 import argparse
+import os
 import subprocess
 import sys
 import time
@@ -34,7 +35,7 @@
 ROOT = FILE.parents[0]  # YOLOv5 root directory
 if str(ROOT) not in sys.path:
     sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = ROOT.relative_to(Path.cwd())  # relative
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
 from models.common import Conv
 from models.experimental import attempt_load

models/common.py

@@ -289,6 +289,16 @@ def autoshape(self):
         LOGGER.info('AutoShape already enabled, skipping... ')  # model already converted to model.autoshape()
         return self
 
+    def _apply(self, fn):
+        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
+        self = super()._apply(fn)
+        m = self.model.model[-1]  # Detect()
+        m.stride = fn(m.stride)
+        m.grid = list(map(fn, m.grid))
+        if isinstance(m.anchor_grid, list):
+            m.anchor_grid = list(map(fn, m.anchor_grid))
+        return self
+
     @torch.no_grad()
     def forward(self, imgs, size=640, augment=False, profile=False):
         # Inference from various sources. For height=640, width=1280, RGB images example inputs are:

models/experimental.py

@@ -102,6 +102,10 @@ def attempt_load(weights, map_location=None, inplace=True, fuse=True):
     for m in model.modules():
         if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]:
             m.inplace = inplace  # pytorch 1.7.0 compatibility
+            if type(m) is Detect:
+                if not isinstance(m.anchor_grid, list):  # new Detect Layer compatibility
+                    delattr(m, 'anchor_grid')
+                    setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
         elif type(m) is Conv:
             m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
 

models/tf.py

@@ -193,7 +193,7 @@ def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None):  # detec
         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.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]),
                                       [self.nl, 1, -1, 1, 2])
         self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)]
         self.training = False  # set to False after building model

models/yolo.py

@@ -44,9 +44,8 @@ def __init__(self, nc=80, anchors=(), ch=(), inplace=True):  # detection layer
         self.nl = len(anchors)  # number of detection layers
         self.na = len(anchors[0]) // 2  # number of anchors
         self.grid = [torch.zeros(1)] * self.nl  # init grid
-        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
-        self.register_buffer('anchors', a)  # shape(nl,na,2)
-        self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
+        self.anchor_grid = [torch.zeros(1)] * self.nl  # init anchor grid
+        self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2))  # shape(nl,na,2)
         self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
         self.inplace = inplace  # use in-place ops (e.g. slice assignment)
 
@@ -59,24 +58,27 @@ def forward(self, x):
 
             if not self.training:  # inference
                 if self.grid[i].shape[2:4] != x[i].shape[2:4] or self.onnx_dynamic:
-                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
+                    self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
 
                 y = x[i].sigmoid()
                 if self.inplace:
                     y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
                     y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                 else:  # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
                     xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
-                    wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i].view(1, self.na, 1, 1, 2)  # wh
+                    wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                     y = torch.cat((xy, wh, y[..., 4:]), -1)
                 z.append(y.view(bs, -1, self.no))
 
         return x if self.training else (torch.cat(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()
+    def _make_grid(self, nx=20, ny=20, i=0):
+        d = self.anchors[i].device
+        yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)])
+        grid = torch.stack((xv, yv), 2).expand((1, self.na, ny, nx, 2)).float()
+        anchor_grid = (self.anchors[i].clone() * self.stride[i]) \
+            .view((1, self.na, 1, 1, 2)).expand((1, self.na, ny, nx, 2)).float()
+        return grid, anchor_grid
 
 
 class Model(nn.Module):
@@ -232,6 +234,17 @@ def autoshape(self):  # add AutoShape module
     def info(self, verbose=False, img_size=640):  # print model information
         model_info(self, verbose, img_size)
 
+    def _apply(self, fn):
+        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
+        self = super()._apply(fn)
+        m = self.model[-1]  # Detect()
+        if isinstance(m, Detect):
+            m.stride = fn(m.stride)
+            m.grid = list(map(fn, m.grid))
+            if isinstance(m.anchor_grid, list):
+                m.anchor_grid = list(map(fn, m.anchor_grid))
+        return self
+
 
 def parse_model(d, ch):  # model_dict, input_channels(3)
     LOGGER.info('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))

requirements.txt

@@ -6,6 +6,7 @@ numpy>=1.18.5
 opencv-python>=4.1.2
 Pillow>=7.1.2
 PyYAML>=5.3.1
+requests>=2.23.0
 scipy>=1.4.1
 torch>=1.7.0
 torchvision>=0.8.1
@@ -16,7 +17,7 @@ tensorboard>=2.4.1
 # wandb
 
 # Plotting ------------------------------------
-pandas
+pandas>=1.1.4
 seaborn>=0.11.0
 
 # Export --------------------------------------

train.py

@@ -30,7 +30,7 @@
 ROOT = FILE.parents[0]  # YOLOv5 root directory
 if str(ROOT) not in sys.path:
     sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = ROOT.relative_to(Path.cwd())  # relative
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
 import val  # for end-of-epoch mAP
 from models.experimental import attempt_load
@@ -99,7 +99,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
     plots = not evolve  # create plots
     cuda = device.type != 'cpu'
     init_seeds(1 + RANK)
-    with torch_distributed_zero_first(RANK):
+    with torch_distributed_zero_first(LOCAL_RANK):
         data_dict = data_dict or check_dataset(data)  # check if None
     train_path, val_path = data_dict['train'], data_dict['val']
     nc = 1 if single_cls else int(data_dict['nc'])  # number of classes
@@ -111,7 +111,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
     check_suffix(weights, '.pt')  # check weights
     pretrained = weights.endswith('.pt')
     if pretrained:
-        with torch_distributed_zero_first(RANK):
+        with torch_distributed_zero_first(LOCAL_RANK):
             weights = attempt_download(weights)  # download if not found locally
         ckpt = torch.load(weights, map_location=device)  # load checkpoint
         model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
@@ -208,7 +208,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
 
     # Trainloader
     train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls,
-                                              hyp=hyp, augment=True, cache=opt.cache, rect=opt.rect, rank=RANK,
+                                              hyp=hyp, augment=True, cache=opt.cache, rect=opt.rect, rank=LOCAL_RANK,
                                               workers=workers, image_weights=opt.image_weights, quad=opt.quad,
                                               prefix=colorstr('train: '))
     mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max())  # max label class

utils/autoanchor.py

@@ -15,13 +15,12 @@
 
 def check_anchor_order(m):
     # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
-    a = m.anchor_grid.prod(-1).view(-1)  # anchor area
+    a = m.anchors.prod(-1).view(-1)  # anchor area
     da = a[-1] - a[0]  # delta a
     ds = m.stride[-1] - m.stride[0]  # delta s
     if da.sign() != ds.sign():  # same order
         print('Reversing anchor order')
         m.anchors[:] = m.anchors.flip(0)
-        m.anchor_grid[:] = m.anchor_grid.flip(0)
 
 
 def check_anchors(dataset, model, thr=4.0, imgsz=640):
@@ -41,20 +40,19 @@ def metric(k):  # compute metric
         bpr = (best > 1. / thr).float().mean()  # best possible recall
         return bpr, aat
 
-    anchors = m.anchor_grid.clone().cpu().view(-1, 2)  # current anchors
-    bpr, aat = metric(anchors)
+    anchors = m.anchors.clone() * m.stride.to(m.anchors.device).view(-1, 1, 1)  # current anchors
+    bpr, aat = metric(anchors.cpu().view(-1, 2))
     print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='')
     if bpr < 0.98:  # threshold to recompute
         print('. Attempting to improve anchors, please wait...')
-        na = m.anchor_grid.numel() // 2  # number of anchors
+        na = m.anchors.numel() // 2  # number of anchors
         try:
             anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
         except Exception as e:
             print(f'{prefix}ERROR: {e}')
         new_bpr = metric(anchors)[0]
         if new_bpr > bpr:  # replace anchors
             anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
-            m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid)  # for inference
             m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
             check_anchor_order(m)
             print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.')

utils/plots.py

@@ -264,7 +264,7 @@ def plot_val_study(file='', dir='', x=None):  # from utils.plots import *; plot_
         ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()
 
     fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
-    # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:
+    # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:
     for f in sorted(save_dir.glob('study*.txt')):
         y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
         x = np.arange(y.shape[1]) if x is None else np.array(x)
@@ -284,7 +284,7 @@ def plot_val_study(file='', dir='', x=None):  # from utils.plots import *; plot_
     ax2.grid(alpha=0.2)
     ax2.set_yticks(np.arange(20, 60, 5))
     ax2.set_xlim(0, 57)
-    ax2.set_ylim(30, 55)
+    ax2.set_ylim(25, 55)
     ax2.set_xlabel('GPU Speed (ms/img)')
     ax2.set_ylabel('COCO AP val')
     ax2.legend(loc='lower right')

val.py

@@ -21,7 +21,7 @@
 ROOT = FILE.parents[0]  # YOLOv5 root directory
 if str(ROOT) not in sys.path:
     sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = ROOT.relative_to(Path.cwd())  # relative
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
 from models.experimental import attempt_load
 from utils.datasets import create_dataloader
@@ -147,8 +147,9 @@ def run(data,
     if not training:
         if device.type != 'cpu':
             model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
+        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=0.5, rect=True,
+        dataloader = create_dataloader(data[task], imgsz, batch_size, gs, single_cls, pad=pad, rect=True,
                                        prefix=colorstr(f'{task}: '))[0]
 
     seen = 0
@@ -333,20 +334,21 @@ def main(opt):
         run(**vars(opt))
 
     elif opt.task == 'speed':  # speed benchmarks
+        # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...
         for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
             run(opt.data, weights=w, batch_size=opt.batch_size, imgsz=opt.imgsz, conf_thres=.25, iou_thres=.45,
                 device=opt.device, save_json=False, plots=False)
 
     elif opt.task == 'study':  # run over a range of settings and save/plot
-        # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt
+        # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...
         x = list(range(256, 1536 + 128, 128))  # x axis (image sizes)
         for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
             f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt'  # filename to save to
             y = []  # y axis
             for i in x:  # img-size
                 print(f'\nRunning {f} point {i}...')
                 r, _, t = run(opt.data, weights=w, batch_size=opt.batch_size, imgsz=i, conf_thres=opt.conf_thres,
-                              iou_thres=opt.iou_thres, save_json=opt.save_json, plots=False)
+                              iou_thres=opt.iou_thres, device=opt.device, save_json=opt.save_json, plots=False)
                 y.append(r + t)  # results and times
             np.savetxt(f, y, fmt='%10.4g')  # save
         os.system('zip -r study.zip study_*.txt')