Add verbose option to pytorch hub models (#2926)

* Add verbose and update print to logging

* Fix positonal param

* Revert auto formatting changes

* Update hubconf.py

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

models/yolo.py

@@ -84,7 +84,7 @@ class Model(nn.Module):
             self.yaml['anchors'] = round(anchors)  # override yaml value
         self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
         self.names = [str(i) for i in range(self.yaml['nc'])]  # default names
-        # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
+        # logger.info([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
 
         # Build strides, anchors
         m = self.model[-1]  # Detect()
@@ -95,7 +95,7 @@ class Model(nn.Module):
             check_anchor_order(m)
             self.stride = m.stride
             self._initialize_biases()  # only run once
-            # print('Strides: %s' % m.stride.tolist())
+            # logger.info('Strides: %s' % m.stride.tolist())
 
         # Init weights, biases
         initialize_weights(self)
@@ -134,13 +134,13 @@ class Model(nn.Module):
                 for _ in range(10):
                     _ = m(x)
                 dt.append((time_synchronized() - t) * 100)
-                print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
+                logger.info('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
 
             x = m(x)  # run
             y.append(x if m.i in self.save else None)  # save output
 
         if profile:
-            print('%.1fms total' % sum(dt))
+            logger.info('%.1fms total' % sum(dt))
         return x
 
     def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
@@ -157,15 +157,15 @@ class Model(nn.Module):
         m = self.model[-1]  # Detect() module
         for mi in m.m:  # from
             b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
-            print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
+            logger.info(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
 
     # def _print_weights(self):
     #     for m in self.model.modules():
     #         if type(m) is Bottleneck:
-    #             print('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights
+    #             logger.info('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights
 
     def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
-        print('Fusing layers... ')
+        logger.info('Fusing layers... ')
         for m in self.model.modules():
             if type(m) is Conv and hasattr(m, 'bn'):
                 m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
@@ -177,19 +177,19 @@ class Model(nn.Module):
     def nms(self, mode=True):  # add or remove NMS module
         present = type(self.model[-1]) is NMS  # last layer is NMS
         if mode and not present:
-            print('Adding NMS... ')
+            logger.info('Adding NMS... ')
             m = NMS()  # module
             m.f = -1  # from
             m.i = self.model[-1].i + 1  # index
             self.model.add_module(name='%s' % m.i, module=m)  # add
             self.eval()
         elif not mode and present:
-            print('Removing NMS... ')
+            logger.info('Removing NMS... ')
             self.model = self.model[:-1]  # remove
         return self
 
     def autoshape(self):  # add autoShape module
-        print('Adding autoShape... ')
+        logger.info('Adding autoShape... ')
         m = autoShape(self)  # wrap model
         copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=())  # copy attributes
         return m
@@ -272,6 +272,6 @@ if __name__ == '__main__':
     # Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898)
     # from torch.utils.tensorboard import SummaryWriter
     # tb_writer = SummaryWriter('.')
-    # print("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/")
+    # logger.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/")
     # tb_writer.add_graph(torch.jit.trace(model, img, strict=False), [])  # add model graph
     # tb_writer.add_image('test', img[0], dataformats='CWH')  # add model to tensorboard