Add common model interface to pnasnet and xception, update factory

data/transforms.py

@@ -20,7 +20,7 @@ def get_model_meanstd(model_name):
     model_name = model_name.lower()
     if 'dpn' in model_name:
         return IMAGENET_DPN_MEAN, IMAGENET_DPN_STD
-    elif 'ception' in model_name:
+    elif 'ception' in model_name or 'nasnet' in model_name:
         return IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
     else:
         return IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
@@ -30,7 +30,7 @@ def get_model_mean(model_name):
     model_name = model_name.lower()
     if 'dpn' in model_name:
         return IMAGENET_DPN_STD
-    elif 'ception' in model_name:
+    elif 'ception' in model_name or 'nasnet' in model_name:
         return IMAGENET_INCEPTION_MEAN
     else:
         return IMAGENET_DEFAULT_MEAN
@@ -40,7 +40,7 @@ def get_model_std(model_name):
     model_name = model_name.lower()
     if 'dpn' in model_name:
         return IMAGENET_DEFAULT_STD
-    elif 'ception' in model_name:
+    elif 'ception' in model_name or 'nasnet' in model_name:
         return IMAGENET_INCEPTION_STD
     else:
         return IMAGENET_DEFAULT_STD

models/model_factory.py

@@ -11,6 +11,7 @@ from .senet import seresnet18, seresnet34, seresnet50, seresnet101, seresnet152,
     seresnext26_32x4d, seresnext50_32x4d, seresnext101_32x4d
 from .resnext import resnext50, resnext101, resnext152
 from .xception import xception
+from .pnasnet import pnasnet5large
 
 model_config_dict = {
     'resnet18': {
@@ -47,6 +48,8 @@ model_config_dict = {
         'model_name': 'inception_resnet_v2', 'num_classes': 1000, 'input_size': 299, 'normalizer': 'le'},
     'xception': {
         'model_name': 'xception', 'num_classes': 1000, 'input_size': 299, 'normalizer': 'le'},
+    'pnasnet5large': {
+        'model_name': 'pnasnet5large', 'num_classes': 1000, 'input_size': 331, 'normalizer': 'le'}
 }
 
 
@@ -125,6 +128,8 @@ def create_model(
         model = resnext152(num_classes=num_classes, pretrained=pretrained, **kwargs)
     elif model_name == 'xception':
         model = xception(num_classes=num_classes, pretrained=pretrained)
+    elif model_name == 'pnasnet5large':
+        model = pnasnet5large(num_classes=num_classes, pretrained=pretrained)
     else:
         assert False and "Invalid model"
 

models/pnasnet.py

@@ -5,7 +5,6 @@ import torch
 import torch.nn as nn
 import torch.utils.model_zoo as model_zoo
 
-
 pretrained_settings = {
     'pnasnet5large': {
         'imagenet': {
@@ -292,6 +291,8 @@ class PNASNet5Large(nn.Module):
     def __init__(self, num_classes=1001):
         super(PNASNet5Large, self).__init__()
         self.num_classes = num_classes
+        self.num_features = 4320
+
         self.conv_0 = nn.Sequential(OrderedDict([
             ('conv', nn.Conv2d(3, 96, kernel_size=3, stride=2, bias=False)),
             ('bn', nn.BatchNorm2d(96, eps=0.001))
@@ -335,9 +336,20 @@ class PNASNet5Large(nn.Module):
         self.relu = nn.ReLU()
         self.avg_pool = nn.AvgPool2d(11, stride=1, padding=0)
         self.dropout = nn.Dropout(0.5)
-        self.last_linear = nn.Linear(4320, num_classes)
+        self.last_linear = nn.Linear(self.num_features, num_classes)
 
-    def features(self, x):
+    def get_classifier(self):
+        return self.last_linear
+
+    def reset_classifier(self, num_classes):
+        self.num_classes = num_classes
+        del self.last_linear
+        if num_classes:
+            self.last_linear = nn.Linear(self.num_features, num_classes)
+        else:
+            self.last_linear = None
+
+    def forward_features(self, x, pool=True):
         x_conv_0 = self.conv_0(x)
         x_stem_0 = self.cell_stem_0(x_conv_0)
         x_stem_1 = self.cell_stem_1(x_conv_0, x_stem_0)
@@ -353,19 +365,16 @@ class PNASNet5Large(nn.Module):
         x_cell_9 = self.cell_9(x_cell_7, x_cell_8)
         x_cell_10 = self.cell_10(x_cell_8, x_cell_9)
         x_cell_11 = self.cell_11(x_cell_9, x_cell_10)
-        return x_cell_11
+        x = self.relu(x_cell_11)
-
+        if pool:
-    def logits(self, features):
-        x = self.relu(features)
             x = self.avg_pool(x)
             x = x.view(x.size(0), -1)
-        x = self.dropout(x)
-        x = self.last_linear(x)
         return x
 
     def forward(self, input):
-        x = self.features(input)
+        x = self.forward_features(input)
-        x = self.logits(x)
+        x = self.dropout(x)
+        x = self.last_linear(x)
         return x
 
 
@@ -375,7 +384,7 @@ def pnasnet5large(num_classes=1001, pretrained='imagenet'):
     <https://arxiv.org/abs/1712.00559>`_ paper.
     """
     if pretrained:
-        settings = pretrained_settings['pnasnet5large'][pretrained]
+        settings = pretrained_settings['pnasnet5large']['imagenet']
         assert num_classes == settings[
             'num_classes'], 'num_classes should be {}, but is {}'.format(
             settings['num_classes'], num_classes)
@@ -384,18 +393,12 @@ def pnasnet5large(num_classes=1001, pretrained='imagenet'):
         model = PNASNet5Large(num_classes=1001)
         model.load_state_dict(model_zoo.load_url(settings['url']))
 
-        if pretrained == 'imagenet':
+        #if pretrained == 'imagenet':
         new_last_linear = nn.Linear(model.last_linear.in_features, 1000)
         new_last_linear.weight.data = model.last_linear.weight.data[1:]
         new_last_linear.bias.data = model.last_linear.bias.data[1:]
         model.last_linear = new_last_linear
 
-        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']
     else:
         model = PNASNet5Large(num_classes=num_classes)
     return model

models/resnext.py

@@ -142,7 +142,6 @@ def resnext50(cardinality=32, base_width=4, pretrained=False, **kwargs):
     Args:
         cardinality (int): Cardinality of the aggregated transform
         base_width (int): Base width of the grouped convolution
-        shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
     """
     model = ResNeXt(
         ResNeXtBottleneckC, [3, 4, 6, 3], cardinality=cardinality, base_width=base_width, **kwargs)
@@ -155,7 +154,6 @@ def resnext101(cardinality=32, base_width=4, pretrained=False, **kwargs):
     Args:
         cardinality (int): Cardinality of the aggregated transform
         base_width (int): Base width of the grouped convolution
-        shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
     """
     model = ResNeXt(
         ResNeXtBottleneckC, [3, 4, 23, 3], cardinality=cardinality, base_width=base_width, **kwargs)
@@ -168,7 +166,6 @@ def resnext152(cardinality=32, base_width=4, pretrained=False, **kwargs):
     Args:
         cardinality (int): Cardinality of the aggregated transform
         base_width (int): Base width of the grouped convolution
-        shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
     """
     model = ResNeXt(
         ResNeXtBottleneckC, [3, 8, 36, 3], cardinality=cardinality, base_width=base_width, **kwargs)

models/xception.py

@@ -127,6 +127,7 @@ class Xception(nn.Module):
         """
         super(Xception, self).__init__()
         self.num_classes = num_classes
+        self.num_features = 2048
 
         self.conv1 = nn.Conv2d(3, 32, 3, 2, 0, bias=False)
         self.bn1 = nn.BatchNorm2d(32)
@@ -156,10 +157,10 @@ class Xception(nn.Module):
         self.bn3 = nn.BatchNorm2d(1536)
 
         # do relu here
-        self.conv4 = SeparableConv2d(1536, 2048, 3, 1, 1)
+        self.conv4 = SeparableConv2d(1536, self.num_features, 3, 1, 1)
-        self.bn4 = nn.BatchNorm2d(2048)
+        self.bn4 = nn.BatchNorm2d(self.num_features)
 
-        self.fc = nn.Linear(2048, num_classes)
+        self.fc = nn.Linear(self.num_features, num_classes)
 
         # #------- init weights --------
         for m in self.modules():
@@ -169,7 +170,18 @@ class Xception(nn.Module):
                 m.weight.data.fill_(1)
                 m.bias.data.zero_()
 
-    def forward_features(self, input):
+    def get_classifier(self):
+        return self.fc
+
+    def reset_classifier(self, num_classes):
+        self.num_classes = num_classes
+        del self.fc
+        if num_classes:
+            self.fc = nn.Linear(self.num_features, num_classes)
+        else:
+            self.fc = None
+
+    def forward_features(self, input, pool=True):
         x = self.conv1(input)
         x = self.bn1(x)
         x = self.relu(x)
@@ -197,19 +209,16 @@ class Xception(nn.Module):
 
         x = self.conv4(x)
         x = self.bn4(x)
-        return x
+        x = self.relu(x)
-
-    def logits(self, features):
-        x = self.relu(features)
 
+        if pool:
             x = F.adaptive_avg_pool2d(x, (1, 1))
             x = x.view(x.size(0), -1)
-        x = self.last_linear(x)
         return x
 
     def forward(self, input):
         x = self.forward_features(input)
-        x = self.logits(x)
+        x = self.fc(x)
         return x
 
 
@@ -223,13 +232,4 @@ def xception(num_classes=1000, pretrained=False):
         model = Xception(num_classes=num_classes)
         model.load_state_dict(model_zoo.load_url(config['url']))
 
-        model.input_space = config['input_space']
-        model.input_size = config['input_size']
-        model.input_range = config['input_range']
-        model.mean = config['mean']
-        model.std = config['std']
-
-    # TODO: ugly
-    model.last_linear = model.fc
-    del model.fc
     return model