Add resnext50_32x4d

Author: barrh

pretrainedmodels/__init__.py

@@ -11,6 +11,7 @@
 from .models.fbresnet import fbresnet152
 from .models.cafferesnet import cafferesnet101
 from .models.bninception import bninception
+from .models.resnext import resnext50_32x4d
 from .models.resnext import resnext101_32x4d
 from .models.resnext import resnext101_64x4d
 from .models.inceptionv4 import inceptionv4

pretrainedmodels/models/__init__.py

@@ -5,6 +5,7 @@
 
 from .bninception import bninception
 
+from .resnext import resnext50_32x4d
 from .resnext import resnext101_32x4d
 from .resnext import resnext101_64x4d
 

pretrainedmodels/models/resnext.py

@@ -3,13 +3,26 @@
 import torch
 import torch.nn as nn
 import torch.utils.model_zoo as model_zoo
+from .resnext_features import resnext50_32x4d_features
 from .resnext_features import resnext101_32x4d_features
 from .resnext_features import resnext101_64x4d_features
 
-__all__ = ['ResNeXt101_32x4d', 'resnext101_32x4d',
+__all__ = ['ResNeXt50_32x4d', 'resnext50_32x4d',
+           'ResNeXt101_32x4d', 'resnext101_32x4d',
            'ResNeXt101_64x4d', 'resnext101_64x4d']
 
 pretrained_settings = {
+    'resnext50_32x4d': {
+        'imagenet': {
+            'url': 'file:/data/resnext50_32x4d-dc76b0bd094076dae.pth', # TODO (barrh): upload model,
+            'input_space': 'RGB',
+            'input_size': [3, 224, 224],
+            'input_range': [0, 1],
+            'mean': [0.485, 0.456, 0.406],
+            'std': [0.229, 0.224, 0.225],
+            'num_classes': 1000
+        }
+    },
     'resnext101_32x4d': {
         'imagenet': {
             'url': 'http://data.lip6.fr/cadene/pretrainedmodels/resnext101_32x4d-29e315fa.pth',
@@ -34,6 +47,27 @@
     }
 }
 
+class ResNeXt50_32x4d(nn.Module):
+
+    def __init__(self, num_classes=1000):
+        super(ResNeXt50_32x4d, self).__init__()
+        self.num_classes = num_classes
+        self.features = resnext50_32x4d_features
+        self.avg_pool = nn.AvgPool2d((7, 7), (1, 1))
+        self.last_linear = nn.Linear(2048, num_classes)
+
+    def logits(self, input):
+        x = self.avg_pool(input)
+        x = x.view(x.size(0), -1)
+        x = self.last_linear(x)
+        return x
+
+    def forward(self, input):
+        x = self.features(input)
+        x = self.logits(x)
+        return x
+
+
 class ResNeXt101_32x4d(nn.Module):
 
     def __init__(self, num_classes=1000):
@@ -76,6 +110,20 @@ def forward(self, input):
         return x
 
 
+def resnext50_32x4d(num_classes=1000, pretrained='imagenet'):
+    model = ResNeXt50_32x4d(num_classes=num_classes)
+    if pretrained is not None:
+        settings = pretrained_settings['resnext50_32x4d'][pretrained]
+        assert num_classes == settings['num_classes'], \
+            "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)
+        model.load_state_dict(model_zoo.load_url(settings['url']))
+        model.input_space = settings['input_space']
+        model.input_size = settings['input_size']
+        model.input_range = settings['input_range']
+        model.mean = settings['mean']
+        model.std = settings['std']
+    return model
+
 def resnext101_32x4d(num_classes=1000, pretrained='imagenet'):
     model = ResNeXt101_32x4d(num_classes=num_classes)
     if pretrained is not None:

pretrainedmodels/models/resnext_features/__init__.py

@@ -1,3 +1,4 @@
 from __future__ import print_function, division, absolute_import
+from .resnext50_32x4d_features import resnext50_32x4d_features
 from .resnext101_32x4d_features import resnext101_32x4d_features
 from .resnext101_64x4d_features import resnext101_64x4d_features