Enhance ShufflenetV2 (#892)

* Enhance ShufflenetV2

Class shufflenetv2 receives `stages_repeats` and `stages_out_channels` arguments.

* remove explicit num_classes argument from utility functions

torchvision/models/shufflenetv2.py

@@ -84,13 +84,17 @@ class InvertedResidual(nn.Module):
 
 
 class ShuffleNetV2(nn.Module):
-    def __init__(self, stage_out_channels, num_classes=1000):
+    def __init__(self, stages_repeats, stages_out_channels, num_classes=1000):
         super(ShuffleNetV2, self).__init__()
 
-        self.stage_out_channels = stage_out_channels
-        input_channels = 3
-        output_channels = self.stage_out_channels[0]
+        if len(stages_repeats) != 3:
+            raise ValueError('expected stages_repeats as list of 3 positive ints')
+        if len(stages_out_channels) != 5:
+            raise ValueError('expected stages_out_channels as list of 5 positive ints')
+        self._stage_out_channels = stages_out_channels
 
+        input_channels = 3
+        output_channels = self._stage_out_channels[0]
         self.conv1 = nn.Sequential(
             nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
             nn.BatchNorm2d(output_channels),
@@ -101,16 +105,15 @@ class ShuffleNetV2(nn.Module):
         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
 
         stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
-        stage_repeats = [4, 8, 4]
         for name, repeats, output_channels in zip(
-                stage_names, stage_repeats, self.stage_out_channels[1:]):
+                stage_names, stages_repeats, self._stage_out_channels[1:]):
             seq = [InvertedResidual(input_channels, output_channels, 2)]
             for i in range(repeats - 1):
                 seq.append(InvertedResidual(output_channels, output_channels, 1))
             setattr(self, name, nn.Sequential(*seq))
             input_channels = output_channels
 
-        output_channels = self.stage_out_channels[-1]
+        output_channels = self._stage_out_channels[-1]
         self.conv5 = nn.Sequential(
             nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),
             nn.BatchNorm2d(output_channels),
@@ -131,8 +134,8 @@ class ShuffleNetV2(nn.Module):
         return x
 
 
-def _shufflenetv2(arch, pretrained, progress, stage_out_channels, **kwargs):
-    model = ShuffleNetV2(stage_out_channels=stage_out_channels, **kwargs)
+def _shufflenetv2(arch, pretrained, progress, *args, **kwargs):
+    model = ShuffleNetV2(*args, **kwargs)
 
     if pretrained:
         model_url = model_urls[arch]
@@ -146,16 +149,20 @@ def _shufflenetv2(arch, pretrained, progress, stage_out_channels, **kwargs):
 
 
 def shufflenetv2_x0_5(pretrained=False, progress=True, **kwargs):
-    return _shufflenetv2('shufflenetv2_x0.5', pretrained, progress, [24, 48, 96, 192, 1024], **kwargs)
+    return _shufflenetv2('shufflenetv2_x0.5', pretrained, progress,
+                         [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs)
 
 
 def shufflenetv2_x1_0(pretrained=False, progress=True, **kwargs):
-    return _shufflenetv2('shufflenetv2_x1.0', pretrained, progress, [24, 116, 232, 464, 1024], **kwargs)
+    return _shufflenetv2('shufflenetv2_x1.0', pretrained, progress,
+                         [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs)
 
 
 def shufflenetv2_x1_5(pretrained=False, progress=True, **kwargs):
-    return _shufflenetv2('shufflenetv2_x1.5', pretrained, progress, [24, 176, 352, 704, 1024], **kwargs)
+    return _shufflenetv2('shufflenetv2_x1.5', pretrained, progress,
+                         [4, 8, 4], [24, 176, 352, 704, 1024], **kwargs)
 
 
 def shufflenetv2_x2_0(pretrained=False, progress=True, **kwargs):
-    return _shufflenetv2('shufflenetv2_x2.0', pretrained, progress, [24, 244, 488, 976, 2048], **kwargs)
+    return _shufflenetv2('shufflenetv2_x2.0', pretrained, progress,
+                         [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs)