Refactor modules (ultralytics#7823)

Author: glenn-jocher

models/experimental.py

@@ -78,9 +78,7 @@ def __init__(self):
         super().__init__()
 
     def forward(self, x, augment=False, profile=False, visualize=False):
-        y = []
-        for module in self:
-            y.append(module(x, augment, profile, visualize)[0])
+        y = [module(x, augment, profile, visualize)[0] for module in self]
         # y = torch.stack(y).max(0)[0]  # max ensemble
         # y = torch.stack(y).mean(0)  # mean ensemble
         y = torch.cat(y, 1)  # nms ensemble
@@ -102,21 +100,19 @@ def attempt_load(weights, map_location=None, inplace=True, fuse=True):
         t = type(m)
         if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
             m.inplace = inplace  # torch 1.7.0 compatibility
-            if t 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)
+            if t is Detect and not isinstance(m.anchor_grid, list):
+                delattr(m, 'anchor_grid')
+                setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
         elif t is Conv:
             m._non_persistent_buffers_set = set()  # torch 1.6.0 compatibility
         elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'):
             m.recompute_scale_factor = None  # torch 1.11.0 compatibility
 
     if len(model) == 1:
         return model[-1]  # return model
-    else:
-        print(f'Ensemble created with {weights}\n')
-        for k in 'names', 'nc', 'yaml':
-            setattr(model, k, getattr(model[0], k))
-        model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride  # max stride
-        assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}'
-        return model  # return ensemble
+    print(f'Ensemble created with {weights}\n')
+    for k in 'names', 'nc', 'yaml':
+        setattr(model, k, getattr(model[0], k))
+    model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride  # max stride
+    assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}'
+    return model  # return ensemble

models/tf.py

@@ -362,7 +362,7 @@ def predict(self,
                 conf_thres=0.25):
         y = []  # outputs
         x = inputs
-        for i, m in enumerate(self.model.layers):
+        for m in 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
 
@@ -377,7 +377,6 @@ def predict(self,
             scores = probs * classes
             if agnostic_nms:
                 nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres)
-                return nms, x[1]
             else:
                 boxes = tf.expand_dims(boxes, 2)
                 nms = tf.image.combined_non_max_suppression(boxes,
@@ -387,8 +386,7 @@ def predict(self,
                                                             iou_thres,
                                                             conf_thres,
                                                             clip_boxes=False)
-                return nms, x[1]
-
+            return nms, x[1]
         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
@@ -444,10 +442,10 @@ def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25):  # agnostic NMS
 def representative_dataset_gen(dataset, ncalib=100):
     # Representative dataset generator for use with converter.representative_dataset, returns a generator of np arrays
     for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):
-        input = np.transpose(img, [1, 2, 0])
-        input = np.expand_dims(input, axis=0).astype(np.float32)
-        input /= 255
-        yield [input]
+        im = np.transpose(img, [1, 2, 0])
+        im = np.expand_dims(im, axis=0).astype(np.float32)
+        im /= 255
+        yield [im]
         if n >= ncalib:
             break
 

models/yolo.py

@@ -197,7 +197,7 @@ def _profile_one_layer(self, m, x, dt):
             m(x.copy() if c else x)
         dt.append((time_sync() - t) * 100)
         if m == self.model[0]:
-            LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  {'module'}")
+            LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  module")
         LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')
         if c:
             LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")