precommit: yapf (#5494)

* precommit: yapf * align isort * fix # Conflicts: # utils/plots.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update setup.cfg * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update setup.cfg * Update setup.cfg * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update wandb_utils.py * Update augmentations.py * Update setup.cfg * Update yolo.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update val.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * simplify colorstr * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * val run fix * export.py last comma * Update export.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update hubconf.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * PyTorch Hub tuple fix * PyTorch Hub tuple fix2 * PyTorch Hub tuple fix3 * Update setup Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2022-03-31 16:52:34 +02:00
1 parent df9008ee69
commit c3d5ac151e
@@ -227,11 +227,12 @@ class GhostBottleneck(nn.Module):
    def __init__(self, c1, c2, k=3, s=1):  # ch_in, ch_out, kernel, stride
        super().__init__()
        c_ = c2 // 2
-        self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1),  # pw
-                                  DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
-                                  GhostConv(c_, c2, 1, 1, act=False))  # pw-linear
-        self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
-                                      Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
+        self.conv = nn.Sequential(
+            GhostConv(c1, c_, 1, 1),  # pw
+            DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
+            GhostConv(c_, c2, 1, 1, act=False))  # pw-linear
+        self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1,
+                                                                            act=False)) if s == 2 else nn.Identity()

    def forward(self, x):
        return self.conv(x) + self.shortcut(x)
@@ -387,9 +388,10 @@ class DetectMultiBackend(nn.Module):
                    Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,
                if edgetpu:  # Edge TPU https://coral.ai/software/#edgetpu-runtime
                    LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
-                    delegate = {'Linux': 'libedgetpu.so.1',
-                                'Darwin': 'libedgetpu.1.dylib',
-                                'Windows': 'edgetpu.dll'}[platform.system()]
+                    delegate = {
+                        'Linux': 'libedgetpu.so.1',
+                        'Darwin': 'libedgetpu.1.dylib',
+                        'Windows': 'edgetpu.dll'}[platform.system()]
                    interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
                else:  # Lite
                    LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
@@ -531,7 +533,7 @@ class AutoShape(nn.Module):
                return self.model(imgs.to(p.device).type_as(p), augment, profile)  # inference

        # Pre-process
-        n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs])  # number of images, list of images
+        n, imgs = (len(imgs), list(imgs)) if isinstance(imgs, (list, tuple)) else (1, [imgs])  # number, list of images
        shape0, shape1, files = [], [], []  # image and inference shapes, filenames
        for i, im in enumerate(imgs):
            f = f'image{i}'  # filename
@@ -561,8 +563,13 @@ class AutoShape(nn.Module):
            t.append(time_sync())

            # Post-process
-            y = non_max_suppression(y if self.dmb else y[0], self.conf, self.iou, self.classes, self.agnostic,
-                                    self.multi_label, max_det=self.max_det)  # NMS
+            y = non_max_suppression(y if self.dmb else y[0],
+                                    self.conf,
+                                    self.iou,
+                                    self.classes,
+                                    self.agnostic,
+                                    self.multi_label,
+                                    max_det=self.max_det)  # NMS
            for i in range(n):
                scale_coords(shape1, y[i][:, :4], shape0[i])

@@ -603,8 +610,12 @@ class Detections:
                        label = f'{self.names[int(cls)]} {conf:.2f}'
                        if crop:
                            file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
-                            crops.append({'box': box, 'conf': conf, 'cls': cls, 'label': label,
-                                          'im': save_one_box(box, im, file=file, save=save)})
+                            crops.append({
+                                'box': box,
+                                'conf': conf,
+                                'cls': cls,
+                                'label': label,
+                                'im': save_one_box(box, im, file=file, save=save)})
                        else:  # all others
                            annotator.box_label(box, label if labels else '', color=colors(cls))
                    im = annotator.im
@@ -63,8 +63,8 @@ class MixConv2d(nn.Module):
            a[0] = 1
            c_ = np.linalg.lstsq(a, b, rcond=None)[0].round()  # solve for equal weight indices, ax = b

-        self.m = nn.ModuleList(
-            [nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
+        self.m = nn.ModuleList([
+            nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
        self.bn = nn.BatchNorm2d(c2)
        self.act = nn.SiLU()

@@ -69,7 +69,11 @@ class TFConv(keras.layers.Layer):
        # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch

        conv = keras.layers.Conv2D(
-            c2, k, s, 'SAME' if s == 1 else 'VALID', use_bias=False if hasattr(w, 'bn') else True,
+            c2,
+            k,
+            s,
+            'SAME' if s == 1 else 'VALID',
+            use_bias=False if hasattr(w, 'bn') else True,
            kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
            bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy()))
        self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
@@ -98,10 +102,10 @@ class TFFocus(keras.layers.Layer):

    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, :],
-                                    inputs[:, 1::2, 1::2, :]], 3))
+        return self.conv(
+            tf.concat(
+                [inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], inputs[:, 1::2, 1::2, :]],
+                3))


 class TFBottleneck(keras.layers.Layer):
@@ -123,9 +127,14 @@ class TFConv2d(keras.layers.Layer):
        super().__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,
+            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, )
+            bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None,
+        )

    def call(self, inputs):
        return self.conv(inputs)
@@ -206,8 +215,7 @@ class TFDetect(keras.layers.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(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]),
-                                      [self.nl, 1, -1, 1, 2])
+        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
        self.imgsz = imgsz
@@ -339,7 +347,13 @@ class TFModel:
            self.yaml['nc'] = nc  # override yaml value
        self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz)

-    def predict(self, inputs, tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
+    def predict(self,
+                inputs,
+                tf_nms=False,
+                agnostic_nms=False,
+                topk_per_class=100,
+                topk_all=100,
+                iou_thres=0.45,
                conf_thres=0.25):
        y = []  # outputs
        x = inputs
@@ -361,8 +375,13 @@ class TFModel:
                return nms, x[1]
            else:
                boxes = tf.expand_dims(boxes, 2)
-                nms = tf.image.combined_non_max_suppression(
-                    boxes, scores, topk_per_class, topk_all, iou_thres, conf_thres, clip_boxes=False)
+                nms = tf.image.combined_non_max_suppression(boxes,
+                                                            scores,
+                                                            topk_per_class,
+                                                            topk_all,
+                                                            iou_thres,
+                                                            conf_thres,
+                                                            clip_boxes=False)
                return nms, x[1]

        return x[0]  # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...]
@@ -383,7 +402,8 @@ class AgnosticNMS(keras.layers.Layer):
    # TF Agnostic NMS
    def call(self, input, topk_all, iou_thres, conf_thres):
        # wrap map_fn to avoid TypeSpec related error https://stackoverflow.com/a/65809989/3036450
-        return tf.map_fn(lambda x: self._nms(x, topk_all, iou_thres, conf_thres), input,
+        return tf.map_fn(lambda x: self._nms(x, topk_all, iou_thres, conf_thres),
+                         input,
                         fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32),
                         name='agnostic_nms')

@@ -392,20 +412,26 @@ class AgnosticNMS(keras.layers.Layer):
        boxes, classes, scores = x
        class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32)
        scores_inp = tf.reduce_max(scores, -1)
-        selected_inds = tf.image.non_max_suppression(
-            boxes, scores_inp, max_output_size=topk_all, iou_threshold=iou_thres, score_threshold=conf_thres)
+        selected_inds = tf.image.non_max_suppression(boxes,
+                                                     scores_inp,
+                                                     max_output_size=topk_all,
+                                                     iou_threshold=iou_thres,
+                                                     score_threshold=conf_thres)
        selected_boxes = tf.gather(boxes, selected_inds)
        padded_boxes = tf.pad(selected_boxes,
                              paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]],
-                              mode="CONSTANT", constant_values=0.0)
+                              mode="CONSTANT",
+                              constant_values=0.0)
        selected_scores = tf.gather(scores_inp, selected_inds)
        padded_scores = tf.pad(selected_scores,
                               paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
-                               mode="CONSTANT", constant_values=-1.0)
+                               mode="CONSTANT",
+                               constant_values=-1.0)
        selected_classes = tf.gather(class_inds, selected_inds)
        padded_classes = tf.pad(selected_classes,
                                paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
-                                mode="CONSTANT", constant_values=-1.0)
+                                mode="CONSTANT",
+                                constant_values=-1.0)
        valid_detections = tf.shape(selected_inds)[0]
        return padded_boxes, padded_scores, padded_classes, valid_detections

@@ -421,11 +447,12 @@ def representative_dataset_gen(dataset, ncalib=100):
            break


-def run(weights=ROOT / 'yolov5s.pt',  # weights path
+def run(
+        weights=ROOT / 'yolov5s.pt',  # weights path
        imgsz=(640, 640),  # inference size h,w
        batch_size=1,  # batch size
        dynamic=False,  # dynamic batch size
-        ):
+):
    # PyTorch model
    im = torch.zeros((batch_size, 3, *imgsz))  # BCHW image
    model = attempt_load(weights, map_location=torch.device('cpu'), inplace=True, fuse=False)
@@ -260,8 +260,8 @@ def parse_model(d, ch):  # model_dict, input_channels(3)
                pass

        n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
-        if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
-                 BottleneckCSP, C3, C3TR, C3SPP, C3Ghost]:
+        if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
+                 BottleneckCSP, C3, C3TR, C3SPP, C3Ghost):
            c1, c2 = ch[f], args[0]
            if c2 != no:  # if not output
                c2 = make_divisible(c2 * gw, 8)