Merge pull request #1 from ultralytics/master

Updata

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,7 +1,9 @@
+<!--
 Thank you for submitting a YOLOv5 🚀 Pull Request! We want to make contributing to YOLOv5 as easy and transparent as possible. A few tips to get you started:
 
 - Search existing YOLOv5 [PRs](https://github.com/ultralytics/yolov5/pull) to see if a similar PR already exists.
 - Link this PR to a YOLOv5 [issue](https://github.com/ultralytics/yolov5/issues) to help us understand what bug fix or feature is being implemented.
 - Provide before and after profiling/inference/training results to help us quantify the improvement your PR provides (if applicable).
 
 Please see our ✅ [Contributing Guide](https://github.com/ultralytics/yolov5/blob/master/CONTRIBUTING.md) for more details.
+-->

.github/SECURITY.md

@@ -0,0 +1,7 @@
+# Security Policy
+
+We aim to make YOLOv5 🚀 as secure as possible! If you find potential vulnerabilities or have any concerns please let us know so we can investigate and take corrective action if needed.
+
+### Reporting a Vulnerability
+
+To report vulnerabilities please email us at [email protected] or visit https://ultralytics.com/contact. Thank you!

.github/workflows/ci-testing.yml

@@ -25,9 +25,9 @@ jobs:
     # Timeout: https://stackoverflow.com/a/59076067/4521646
     timeout-minutes: 60
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v3
         with:
           python-version: ${{ matrix.python-version }}
 

.github/workflows/codeql-analysis.yml

@@ -22,7 +22,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
 
       # Initializes the CodeQL tools for scanning.
       - name: Initialize CodeQL

.github/workflows/rebase.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout the latest code
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
         with:
           token: ${{ secrets.ACTIONS_TOKEN }}
           fetch-depth: 0 # otherwise, you will fail to push refs to dest repo

Dockerfile

@@ -11,15 +11,16 @@ COPY requirements.txt .
 RUN python -m pip install --upgrade pip
 RUN pip uninstall -y torch torchvision torchtext
 RUN pip install --no-cache -r requirements.txt albumentations wandb gsutil notebook \
-    torch==1.10.2+cu113 torchvision==0.11.3+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
+    torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
 # RUN pip install --no-cache -U torch torchvision
 
 # Create working directory
 RUN mkdir -p /usr/src/app
 WORKDIR /usr/src/app
 
 # Copy contents
-COPY . /usr/src/app
+RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
+# COPY . /usr/src/app
 
 # Downloads to user config dir
 ADD https://ultralytics.com/assets/Arial.ttf /root/.config/Ultralytics/

README.md

@@ -245,7 +245,7 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
 |[YOLOv5x][assets]      |640  |50.7   |68.9   |766    |12.1   |4.8    |86.7   |205.7
 |                       |     |       |       |       |       |       |       |
 |[YOLOv5n6][assets]     |1280 |36.0   |54.4   |153    |8.1    |2.1    |3.2    |4.6
-|[YOLOv5s6][assets]     |1280 |44.8   |63.7   |385    |8.2    |3.6    |16.8   |12.6
+|[YOLOv5s6][assets]     |1280 |44.8   |63.7   |385    |8.2    |3.6    |12.6   |16.8
 |[YOLOv5m6][assets]     |1280 |51.3   |69.3   |887    |11.1   |6.8    |35.7   |50.0
 |[YOLOv5l6][assets]     |1280 |53.7   |71.3   |1784   |15.8   |10.5   |76.8   |111.4
 |[YOLOv5x6][assets]<br>+ [TTA][TTA]|1280<br>1536 |55.0<br>**55.8** |72.7<br>**72.7** |3136<br>- |26.2<br>- |19.4<br>- |140.7<br>- |209.8<br>-

data/hyps/hyp.VOC.yaml

@@ -4,37 +4,37 @@
 # See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
 
 # YOLOv5 Hyperparameter Evolution Results
-# Best generation: 319
-# Last generation: 434
+# Best generation: 467
+# Last generation: 996
 #    metrics/precision,       metrics/recall,      metrics/mAP_0.5, metrics/mAP_0.5:0.95,         val/box_loss,         val/obj_loss,         val/cls_loss
-#              0.86236,              0.86184,              0.91274,              0.72647,            0.0077056,            0.0042449,            0.0013846
+#              0.87729,              0.85125,              0.91286,              0.72664,            0.0076739,            0.0042529,            0.0013865
 
-lr0: 0.0033
-lrf: 0.15184
-momentum: 0.74747
+lr0: 0.00334
+lrf: 0.15135
+momentum: 0.74832
 weight_decay: 0.00025
-warmup_epochs: 3.4278
-warmup_momentum: 0.59032
-warmup_bias_lr: 0.18742
+warmup_epochs: 3.3835
+warmup_momentum: 0.59462
+warmup_bias_lr: 0.18657
 box: 0.02
-cls: 0.21563
+cls: 0.21638
 cls_pw: 0.5
-obj: 0.50843
-obj_pw: 0.6729
+obj: 0.51728
+obj_pw: 0.67198
 iou_t: 0.2
-anchor_t: 3.4172
+anchor_t: 3.3744
 fl_gamma: 0.0
-hsv_h: 0.01032
-hsv_s: 0.5562
-hsv_v: 0.28255
+hsv_h: 0.01041
+hsv_s: 0.54703
+hsv_v: 0.27739
 degrees: 0.0
-translate: 0.04575
-scale: 0.73711
+translate: 0.04591
+scale: 0.75544
 shear: 0.0
 perspective: 0.0
 flipud: 0.0
 fliplr: 0.5
-mosaic: 0.87158
-mixup: 0.04294
+mosaic: 0.85834
+mixup: 0.04266
 copy_paste: 0.0
-anchors: 3.3556
+anchors: 3.412

detect.py

@@ -89,15 +89,10 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
 
     # Load model
     device = select_device(device)
-    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data)
-    stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine
+    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
+    stride, names, pt = model.stride, model.names, model.pt
     imgsz = check_img_size(imgsz, s=stride)  # check image size
 
-    # Half
-    half &= (pt or jit or onnx or engine) and device.type != 'cpu'  # FP16 supported on limited backends with CUDA
-    if pt or jit:
-        model.model.half() if half else model.model.float()
-
     # Dataloader
     if webcam:
         view_img = check_imshow()
@@ -110,12 +105,12 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
     vid_path, vid_writer = [None] * bs, [None] * bs
 
     # Run inference
-    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz), half=half)  # warmup
+    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
     dt, seen = [0.0, 0.0, 0.0], 0
     for path, im, im0s, vid_cap, s in dataset:
         t1 = time_sync()
         im = torch.from_numpy(im).to(device)
-        im = im.half() if half else im.float()  # uint8 to fp16/32
+        im = im.half() if model.fp16 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

export.py

@@ -75,18 +75,18 @@
 
 def export_formats():
     # YOLOv5 export formats
-    x = [['PyTorch', '-', '.pt'],
-         ['TorchScript', 'torchscript', '.torchscript'],
-         ['ONNX', 'onnx', '.onnx'],
-         ['OpenVINO', 'openvino', '_openvino_model'],
-         ['TensorRT', 'engine', '.engine'],
-         ['CoreML', 'coreml', '.mlmodel'],
-         ['TensorFlow SavedModel', 'saved_model', '_saved_model'],
-         ['TensorFlow GraphDef', 'pb', '.pb'],
-         ['TensorFlow Lite', 'tflite', '.tflite'],
-         ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite'],
-         ['TensorFlow.js', 'tfjs', '_web_model']]
-    return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix'])
+    x = [['PyTorch', '-', '.pt', True],
+         ['TorchScript', 'torchscript', '.torchscript', True],
+         ['ONNX', 'onnx', '.onnx', True],
+         ['OpenVINO', 'openvino', '_openvino_model', False],
+         ['TensorRT', 'engine', '.engine', True],
+         ['CoreML', 'coreml', '.mlmodel', False],
+         ['TensorFlow SavedModel', 'saved_model', '_saved_model', True],
+         ['TensorFlow GraphDef', 'pb', '.pb', True],
+         ['TensorFlow Lite', 'tflite', '.tflite', False],
+         ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False],
+         ['TensorFlow.js', 'tfjs', '_web_model', False]]
+    return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'GPU'])
 
 
 def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
@@ -218,6 +218,7 @@ def export_engine(model, im, file, train, half, simplify, workspace=4, verbose=F
         builder = trt.Builder(logger)
         config = builder.create_builder_config()
         config.max_workspace_size = workspace * 1 << 30
+        # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30)  # fix TRT 8.4 deprecation notice
 
         flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
         network = builder.create_network(flag)
@@ -233,9 +234,8 @@ def export_engine(model, im, file, train, half, simplify, workspace=4, verbose=F
         for out in outputs:
             LOGGER.info(f'{prefix}\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}')
 
-        half &= builder.platform_has_fast_fp16
-        LOGGER.info(f'{prefix} building FP{16 if half else 32} engine in {f}')
-        if half:
+        LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 else 32} engine in {f}')
+        if builder.platform_has_fast_fp16:
             config.set_flag(trt.BuilderFlag.FP16)
         with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
             t.write(engine.serialize())
@@ -260,9 +260,9 @@ def export_saved_model(model, im, file, dynamic,
         batch_size, ch, *imgsz = list(im.shape)  # BCHW
 
         tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
-        im = tf.zeros((batch_size, *imgsz, 3))  # BHWC order for TensorFlow
+        im = tf.zeros((batch_size, *imgsz, ch))  # BHWC order for TensorFlow
         _ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
-        inputs = tf.keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
+        inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size)
         outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
         keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
         keras_model.trainable = False
@@ -275,7 +275,7 @@ def export_saved_model(model, im, file, dynamic,
             m = m.get_concrete_function(spec)
             frozen_func = convert_variables_to_constants_v2(m)
             tfm = tf.Module()
-            tfm.__call__ = tf.function(lambda x: frozen_func(x), [spec])
+            tfm.__call__ = tf.function(lambda x: frozen_func(x)[0], [spec])
             tfm.__call__(im)
             tf.saved_model.save(
                 tfm,
@@ -331,7 +331,7 @@ def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('Te
             converter.target_spec.supported_types = []
             converter.inference_input_type = tf.uint8  # or tf.int8
             converter.inference_output_type = tf.uint8  # or tf.int8
-            converter.experimental_new_quantizer = False
+            converter.experimental_new_quantizer = True
             f = str(file).replace('.pt', '-int8.tflite')
 
         tflite_model = converter.convert()
@@ -494,7 +494,7 @@ def run(data=ROOT / 'data/coco128.yaml',  # 'dataset.yaml path'
         if int8 or edgetpu:  # TFLite --int8 bug https://github.com/ultralytics/yolov5/issues/5707
             check_requirements(('flatbuffers==1.12',))  # required before `import tensorflow`
         assert not (tflite and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.'
-        model, f[5] = export_saved_model(model, im, file, dynamic, tf_nms=nms or agnostic_nms or tfjs,
+        model, f[5] = export_saved_model(model.cpu(), im, file, dynamic, tf_nms=nms or agnostic_nms or tfjs,
                                          agnostic_nms=agnostic_nms or tfjs, topk_per_class=topk_per_class,
                                          topk_all=topk_all, conf_thres=conf_thres, iou_thres=iou_thres)  # keras model
         if pb or tfjs:  # pb prerequisite to tfjs

models/common.py

@@ -31,7 +31,7 @@
 def autopad(k, p=None):  # kernel, padding
     # Pad to 'same'
     if p is None:
-        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
+        p = k // 2 if isinstance(k, int) else (x // 2 for x in k)  # auto-pad
     return p
 
 
@@ -133,7 +133,7 @@ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, nu
         self.cv2 = Conv(c1, c_, 1, 1)
         self.cv3 = Conv(2 * c_, c2, 1)  # act=FReLU(c2)
         self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
-        # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
+        # self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)))
 
     def forward(self, x):
         return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
@@ -194,7 +194,7 @@ def forward(self, x):
             warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
             y1 = self.m(x)
             y2 = self.m(y1)
-            return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
+            return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
 
 
 class Focus(nn.Module):
@@ -205,7 +205,7 @@ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, k
         # self.contract = Contract(gain=2)
 
     def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
-        return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
+        return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1))
         # return self.conv(self.contract(x))
 
 
@@ -219,7 +219,7 @@ def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out, kernel, s
 
     def forward(self, x):
         y = self.cv1(x)
-        return torch.cat([y, self.cv2(y)], 1)
+        return torch.cat((y, self.cv2(y)), 1)
 
 
 class GhostBottleneck(nn.Module):
@@ -277,7 +277,7 @@ def forward(self, x):
 
 class DetectMultiBackend(nn.Module):
     # YOLOv5 MultiBackend class for python inference on various backends
-    def __init__(self, weights='yolov5s.pt', device=None, dnn=False, data=None):
+    def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False):
         # Usage:
         #   PyTorch:              weights = *.pt
         #   TorchScript:                    *.torchscript
@@ -297,6 +297,7 @@ def __init__(self, weights='yolov5s.pt', device=None, dnn=False, data=None):
         pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = self.model_type(w)  # get backend
         stride, names = 64, [f'class{i}' for i in range(1000)]  # assign defaults
         w = attempt_download(w)  # download if not local
+        fp16 &= (pt or jit or onnx or engine) and device.type != 'cpu'  # FP16
         if data:  # data.yaml path (optional)
             with open(data, errors='ignore') as f:
                 names = yaml.safe_load(f)['names']  # class names
@@ -305,11 +306,13 @@ def __init__(self, weights='yolov5s.pt', device=None, dnn=False, data=None):
             model = attempt_load(weights if isinstance(weights, list) else w, map_location=device)
             stride = max(int(model.stride.max()), 32)  # model stride
             names = model.module.names if hasattr(model, 'module') else model.names  # get class names
+            model.half() if fp16 else model.float()
             self.model = model  # explicitly assign for to(), cpu(), cuda(), half()
         elif 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)
+            model.half() if fp16 else model.float()
             if extra_files['config.txt']:
                 d = json.loads(extra_files['config.txt'])  # extra_files dict
                 stride, names = int(d['stride']), d['names']
@@ -342,12 +345,15 @@ def __init__(self, weights='yolov5s.pt', device=None, dnn=False, data=None):
             with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
                 model = runtime.deserialize_cuda_engine(f.read())
             bindings = OrderedDict()
+            fp16 = False  # default updated below
             for index in range(model.num_bindings):
                 name = model.get_binding_name(index)
                 dtype = trt.nptype(model.get_binding_dtype(index))
                 shape = tuple(model.get_binding_shape(index))
                 data = torch.from_numpy(np.empty(shape, dtype=np.dtype(dtype))).to(device)
                 bindings[name] = Binding(name, dtype, shape, data, int(data.data_ptr()))
+                if model.binding_is_input(index) and dtype == np.float16:
+                    fp16 = True
             binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
             context = model.create_execution_context()
             batch_size = bindings['images'].shape[0]
@@ -435,7 +441,7 @@ def forward(self, im, augment=False, visualize=False, val=False):
         else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
             im = im.permute(0, 2, 3, 1).cpu().numpy()  # torch BCHW to numpy BHWC shape(1,320,192,3)
             if self.saved_model:  # SavedModel
-                y = (self.model(im, training=False) if self.keras else self.model(im)[0]).numpy()
+                y = (self.model(im, training=False) if self.keras else self.model(im)).numpy()
             elif self.pb:  # GraphDef
                 y = self.frozen_func(x=self.tf.constant(im)).numpy()
             else:  # Lite or Edge TPU
@@ -452,15 +458,17 @@ def forward(self, im, augment=False, visualize=False, val=False):
                     y = (y.astype(np.float32) - zero_point) * scale  # re-scale
             y[..., :4] *= [w, h, w, h]  # xywh normalized to pixels
 
-        y = torch.tensor(y) if isinstance(y, np.ndarray) else y
+        if isinstance(y, np.ndarray):
+            y = torch.tensor(y, device=self.device)
         return (y, []) if val else y
 
-    def warmup(self, imgsz=(1, 3, 640, 640), half=False):
+    def warmup(self, imgsz=(1, 3, 640, 640)):
         # Warmup model by running inference once
-        if self.pt or self.jit or self.onnx or self.engine:  # warmup types
-            if isinstance(self.device, torch.device) and self.device.type != 'cpu':  # only warmup GPU models
-                im = torch.zeros(*imgsz).to(self.device).type(torch.half if half else torch.float)  # input image
-                self.forward(im)  # warmup
+        if any((self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb)):  # warmup types
+            if self.device.type != 'cpu':  # only warmup GPU models
+                im = torch.zeros(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device)  # input
+                for _ in range(2 if self.jit else 1):  #
+                    self.forward(im)  # warmup
 
     @staticmethod
     def model_type(p='path/to/model.pt'):
@@ -541,10 +549,9 @@ def forward(self, imgs, size=640, augment=False, profile=False):
             g = (size / max(s))  # gain
             shape1.append([y * g for y in s])
             imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
-        shape1 = [make_divisible(x, self.stride) for x in np.stack(shape1, 0).max(0)]  # inference shape
-        x = [letterbox(im, new_shape=shape1 if self.pt else size, auto=False)[0] for im in imgs]  # pad
-        x = np.stack(x, 0) if n > 1 else x[0][None]  # stack
-        x = np.ascontiguousarray(x.transpose((0, 3, 1, 2)))  # BHWC to BCHW
+        shape1 = [make_divisible(x, self.stride) if self.pt else size for x in np.array(shape1).max(0)]  # inf shape
+        x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs]  # pad
+        x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2)))  # stack and BHWC to BCHW
         x = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32
         t.append(time_sync())