Add wide resnet101/50

python/paddle/tests/test_vision_models.py

@@ -71,6 +71,12 @@ def test_resnet101(self):
     def test_resnet152(self):
         self.models_infer('resnet152')
 
+    def test_wide_resnet50(self):
+        self.models_infer('wide_resnet50_2')
+
+    def test_wide_resnet101(self):
+        self.models_infer('wide_resnet101_2')
+
     def test_vgg16_num_classes(self):
         vgg16 = models.__dict__['vgg16'](pretrained=False, num_classes=10)
 

python/paddle/vision/models/__init__.py

@@ -18,6 +18,8 @@
 from .resnet import resnet50  # noqa: F401
 from .resnet import resnet101  # noqa: F401
 from .resnet import resnet152  # noqa: F401
+from .resnet import wide_resnet50_2  # noqa: F401
+from .resnet import wide_resnet101_2  # noqa: F401
 from .mobilenetv1 import MobileNetV1  # noqa: F401
 from .mobilenetv1 import mobilenet_v1  # noqa: F401
 from .mobilenetv2 import MobileNetV2  # noqa: F401
@@ -36,6 +38,8 @@
     'resnet50',
     'resnet101',
     'resnet152',
+    'wide_resnet50_2',
+    'wide_resnet101_2',
     'VGG',
     'vgg11',
     'vgg13',

python/paddle/vision/models/resnet.py

@@ -176,7 +176,9 @@ def __init__(self, block, depth, num_classes=1000, with_pool=True):
             34: [3, 4, 6, 3],
             50: [3, 4, 6, 3],
             101: [3, 4, 23, 3],
-            152: [3, 8, 36, 3]
+            152: [3, 8, 36, 3],
+            "50_2": [3, 4, 6, 3],
+            "101_2": [3, 4, 23, 3]
         }
         layers = layer_cfg[depth]
         self.num_classes = num_classes
@@ -365,3 +367,34 @@ def resnet152(pretrained=False, **kwargs):
             # model = resnet152(pretrained=True)
     """
     return _resnet('resnet152', BottleneckBlock, 152, pretrained, **kwargs)
+
+def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
+    r"""Wide ResNet-50-2 model from
+    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
+    The model is the same as ResNet except for the bottleneck number of channels
+    which is twice larger in every block. The number of channels in outer 1x1
+    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
+    channels, and in Wide ResNet-50-2 has 2048-1024-2048.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    kwargs['width_per_group'] = 64 * 2
+    return _resnet('wide_resnet50_2', BottleneckBlock, "50_2",
+                   pretrained, progress, **kwargs)
+
+
+def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
+    r"""Wide ResNet-101-2 model from
+    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
+    The model is the same as ResNet except for the bottleneck number of channels
+    which is twice larger in every block. The number of channels in outer 1x1
+    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
+    channels, and in Wide ResNet-50-2 has 2048-1024-2048.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    kwargs['width_per_group'] = 64 * 2
+    return _resnet('wide_resnet101_2', BottleneckBlock, "101_2",
+                   pretrained, progress, **kwargs)