A few more additions to Gluon Xception models to match interface of others.

Author: rwightman

README.md

@@ -120,6 +120,7 @@ I've leveraged the training scripts in this repository to train a few of the mod
 | gluon_resnet152_v1c      | 79.916 (20.084) | 94.842 (5.158) | 60.21  | bicubic | 224 | |
 | gluon_seresnext50_32x4d  | 79.912 (20.088) | 94.818 (5.182) | 27.56  | bicubic | 224 | |
 | gluon_resnet152_v1b      | 79.692 (20.308) | 94.738 (5.262) | 60.19  | bicubic | 224 | |
+| gluon_xception65         | 79.604 (20.396) | 94.748 (5.252) | 39.92  | bicubic | 299 | |
 | gluon_resnet101_v1c      | 79.544 (20.456) | 94.586 (5.414) | 44.57  | bicubic | 224 | |
 | gluon_resnext50_32x4d    | 79.356 (20.644) | 94.424 (5.576) | 25.03  | bicubic | 224 | |
 | gluon_resnet101_v1b      | 79.304 (20.696) | 94.524 (5.476) | 44.55  | bicubic | 224 | |

timm/models/gluon_xception.py

@@ -23,6 +23,7 @@
         'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_xception-7015a15c.pth',
         'input_size': (3, 299, 299),
         'crop_pct': 0.875,
+        'pool_size': (10, 10),
         'interpolation': 'bicubic',
         'mean': IMAGENET_DEFAULT_MEAN,
         'std': IMAGENET_DEFAULT_STD,
@@ -35,6 +36,7 @@
         'url': '',
         'input_size': (3, 299, 299),
         'crop_pct': 0.875,
+        'pool_size': (10, 10),
         'interpolation': 'bicubic',
         'mean': IMAGENET_DEFAULT_MEAN,
         'std': IMAGENET_DEFAULT_STD,
@@ -181,7 +183,9 @@ class Xception65(nn.Module):
     def __init__(self, num_classes=1000, in_chans=3, output_stride=32, norm_layer=nn.BatchNorm2d,
                  norm_kwargs=None, drop_rate=0., global_pool='avg'):
         super(Xception65, self).__init__()
+        self.num_classes = num_classes
         self.drop_rate = drop_rate
+        self.global_pool = global_pool
         norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
         if output_stride == 32:
             entry_block3_stride = 2
@@ -240,14 +244,26 @@ def __init__(self, num_classes=1000, in_chans=3, output_stride=32, norm_layer=nn
             norm_layer=norm_layer, norm_kwargs=norm_kwargs)
         self.bn4 = norm_layer(num_features=1536, **norm_kwargs)
 
+        self.num_features = 2048
         self.conv5 = SeparableConv2d(
-            1536, 2048, 3, stride=1, dilation=exit_block_dilations[1],
+            1536, self.num_features, 3, stride=1, dilation=exit_block_dilations[1],
             norm_layer=norm_layer, norm_kwargs=norm_kwargs)
-        self.bn5 = norm_layer(num_features=2048, **norm_kwargs)
-        self.avgpool = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Linear(in_features=2048, out_features=num_classes)
+        self.bn5 = norm_layer(num_features=self.num_features, **norm_kwargs)
+        self.fc = nn.Linear(in_features=self.num_features, out_features=num_classes)
+
+    def get_classifier(self):
+        return self.fc
+
+    def reset_classifier(self, num_classes, global_pool='avg'):
+        self.num_classes = num_classes
+        self.global_pool = global_pool
+        del self.fc
+        if num_classes:
+            self.fc = nn.Linear(self.num_features, num_classes)
+        else:
+            self.fc = None
 
-    def forward(self, x):
+    def forward_features(self, x, pool=True):
         # Entry flow
         x = self.conv1(x)
         x = self.bn1(x)
@@ -284,10 +300,15 @@ def forward(self, x):
         x = self.bn5(x)
         x = self.relu(x)
 
-        x = self.avgpool(x)
-        x = x.view(x.size(0), -1)
-        if self.drop_rate > 0.:
-            x = F.dropout(x, p=self.drop_rate, training=self.training)
+        if pool:
+            x = select_adaptive_pool2d(x, pool_type=self.global_pool)
+            x = x.view(x.size(0), -1)
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        if self.drop_rate:
+            F.dropout(x, self.drop_rate, training=self.training)
         x = self.fc(x)
         return x
 
@@ -299,7 +320,9 @@ class Xception71(nn.Module):
     def __init__(self, num_classes=1000, in_chans=3, output_stride=32, norm_layer=nn.BatchNorm2d,
                  norm_kwargs=None, drop_rate=0., global_pool='avg'):
         super(Xception71, self).__init__()
+        self.num_classes = num_classes
         self.drop_rate = drop_rate
+        self.global_pool = global_pool
         norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
         if output_stride == 32:
             entry_block3_stride = 2
@@ -365,14 +388,26 @@ def __init__(self, num_classes=1000, in_chans=3, output_stride=32, norm_layer=nn
             norm_layer=norm_layer, norm_kwargs=norm_kwargs)
         self.bn4 = norm_layer(num_features=1536, **norm_kwargs)
 
+        self.num_features = 2048
         self.conv5 = SeparableConv2d(
-            1536, 2048, 3, stride=1, dilation=exit_block_dilations[1],
+            1536, self.num_features, 3, stride=1, dilation=exit_block_dilations[1],
             norm_layer=norm_layer, norm_kwargs=norm_kwargs)
-        self.bn5 = norm_layer(num_features=2048, **norm_kwargs)
-        self.avgpool = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Linear(in_features=2048, out_features=num_classes)
+        self.bn5 = norm_layer(num_features=self.num_features, **norm_kwargs)
+        self.fc = nn.Linear(in_features=self.num_features, out_features=num_classes)
+
+    def get_classifier(self):
+        return self.fc
+
+    def reset_classifier(self, num_classes, global_pool='avg'):
+        self.num_classes = num_classes
+        self.global_pool = global_pool
+        del self.fc
+        if num_classes:
+            self.fc = nn.Linear(self.num_features, num_classes)
+        else:
+            self.fc = None
 
-    def forward(self, x):
+    def forward_features(self, x, pool=True):
         # Entry flow
         x = self.conv1(x)
         x = self.bn1(x)
@@ -409,16 +444,23 @@ def forward(self, x):
         x = self.bn5(x)
         x = self.relu(x)
 
-        x = self.avgpool(x)
-        x = x.view(x.size(0), -1)
-        if self.drop_rate > 0.:
-            x = F.dropout(x, p=self.drop_rate, training=self.training)
+        if pool:
+            x = select_adaptive_pool2d(x, pool_type=self.global_pool)
+            x = x.view(x.size(0), -1)
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        if self.drop_rate:
+            F.dropout(x, self.drop_rate, training=self.training)
         x = self.fc(x)
         return x
 
 
 @register_model
 def gluon_xception65(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """ Modified Aligned Xception-65
+    """
     default_cfg = default_cfgs['gluon_xception65']
     model = Xception65(num_classes=num_classes, in_chans=in_chans, **kwargs)
     model.default_cfg = default_cfg
@@ -429,6 +471,8 @@ def gluon_xception65(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
 
 @register_model
 def gluon_xception71(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    """ Modified Aligned Xception-71
+    """
     default_cfg = default_cfgs['gluon_xception71']
     model = Xception71(num_classes=num_classes, in_chans=in_chans, **kwargs)
     model.default_cfg = default_cfg