Lots of refactoring and cleanup.

* Move 'test time pool' to Module that can be used by any model, remove from DPN
* Remove ResNext model file and combine with ResNet
* Remove fbresnet200 as it was an old conversion and pretrained performance not worth param count
* Cleanup adaptive avgmax pooling and add back conctat variant
* Factor out checkpoint load fn

data/utils.py

@@ -39,8 +39,7 @@ class PrefetchLoader:
             with torch.cuda.stream(stream):
                 next_input = next_input.cuda(non_blocking=True)
                 next_target = next_target.cuda(non_blocking=True)
-                next_input = next_input.float()
+                next_input = next_input.float().sub_(self.mean).div_(self.std)
-                next_input = next_input.sub_(self.mean).div_(self.std)
                 if self.random_erasing is not None:
                     next_input = self.random_erasing(next_input)
 
@@ -74,6 +73,7 @@ def create_loader(
         std=IMAGENET_DEFAULT_STD,
         num_workers=1,
         distributed=False,
+        crop_pct=None,
 ):
 
     if is_training:
@@ -87,7 +87,8 @@ def create_loader(
             img_size,
             use_prefetcher=use_prefetcher,
             mean=mean,
-            std=std)
+            std=std,
+            crop_pct=crop_pct)
 
     dataset.transform = transform
 

inference.py

@@ -11,7 +11,7 @@ import argparse
 import numpy as np
 import torch
 
-from models import create_model
+from models import create_model, load_checkpoint, TestTimePoolHead
 from data import Dataset, create_loader, get_model_meanstd
 from utils import AverageMeter
 
@@ -49,21 +49,15 @@ def main():
     model = create_model(
         args.model,
         num_classes=num_classes,
-        pretrained=args.pretrained,
+        pretrained=args.pretrained)
-        test_time_pool=args.test_time_pool)
+
-
+    print('Model %s created, param count: %d' %
-    # resume from a checkpoint
+          (args.model, sum([m.numel() for m in model.parameters()])))
-    if args.checkpoint and os.path.isfile(args.checkpoint):
+
-        print("=> loading checkpoint '{}'".format(args.checkpoint))
+    # load a checkpoint
-        checkpoint = torch.load(args.checkpoint)
+    if not args.pretrained:
-        if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
+        if not load_checkpoint(model, args.checkpoint):
-            model.load_state_dict(checkpoint['state_dict'])
+            exit(1)
-        else:
-            model.load_state_dict(checkpoint)
-        print("=> loaded checkpoint '{}'".format(args.checkpoint))
-    elif not args.pretrained:
-        print("=> no checkpoint found at '{}'".format(args.checkpoint))
-        exit(1)
 
     if args.num_gpu > 1:
         model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()

models/__init__.py

@@ -1,2 +1,3 @@
-from .model_factory import create_model
+from .model_factory import create_model, load_checkpoint
+from .test_time_pool import TestTimePoolHead
 

models/adaptive_avgmax_pool.py

@@ -14,29 +14,70 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 
-def adaptive_avgmax_pool2d(x, pool_type='avg', output_size=1):
+def adaptive_pool_feat_mult(pool_type='avg'):
+    if pool_type == 'catavgmax':
+        return 2
+    else:
+        return 1
+
+
+def adaptive_avgmax_pool2d(x, output_size=1):
+    x_avg = F.adaptive_avg_pool2d(x, output_size)
+    x_max = F.adaptive_max_pool2d(x, output_size)
+    return 0.5 * (x_avg + x_max)
+
+
+def adaptive_catavgmax_pool2d(x, output_size=1):
+    x_avg = F.adaptive_avg_pool2d(x, output_size)
+    x_max = F.adaptive_max_pool2d(x, output_size)
+    return torch.cat((x_avg, x_max), 1)
+
+
+def select_adaptive_pool2d(x, pool_type='avg', output_size=1):
     """Selectable global pooling function with dynamic input kernel size
     """
-    if pool_type == 'avgmax':
+    if pool_type == 'avg':
-        x_avg = F.adaptive_avg_pool2d(x, output_size)
+        x = F.adaptive_avg_pool2d(x, output_size)
-        x_max = F.adaptive_max_pool2d(x, output_size)
+    elif pool_type == 'avgmax':
-        x = 0.5 * (x_avg + x_max)
+        x = adaptive_avgmax_pool2d(x, output_size)
+    elif pool_type == 'catavgmax':
+        x = adaptive_catavgmax_pool2d(x, output_size)
     elif pool_type == 'max':
         x = F.adaptive_max_pool2d(x, output_size)
     else:
-        x = F.adaptive_avg_pool2d(x, output_size)
+        assert False, 'Invalid pool type: %s' % pool_type
     return x
 
 
 class AdaptiveAvgMaxPool2d(torch.nn.Module):
+    def __init__(self, output_size=1):
+        super(AdaptiveAvgMaxPool2d, self).__init__()
+        self.output_size = output_size
+
+    def forward(self, x):
+        return adaptive_avgmax_pool2d(x, self.output_size)
+
+
+class AdaptiveCatAvgMaxPool2d(torch.nn.Module):
+    def __init__(self, output_size=1):
+        super(AdaptiveCatAvgMaxPool2d, self).__init__()
+        self.output_size = output_size
+
+    def forward(self, x):
+        return adaptive_catavgmax_pool2d(x, self.output_size)
+
+
+class SelectAdaptivePool2d(torch.nn.Module):
     """Selectable global pooling layer with dynamic input kernel size
     """
     def __init__(self, output_size=1, pool_type='avg'):
-        super(AdaptiveAvgMaxPool2d, self).__init__()
+        super(SelectAdaptivePool2d, self).__init__()
         self.output_size = output_size
         self.pool_type = pool_type
         if pool_type == 'avgmax':
-            self.pool = nn.ModuleList([nn.AdaptiveAvgPool2d(output_size), nn.AdaptiveMaxPool2d(output_size)])
+            self.pool = AdaptiveAvgMaxPool2d(output_size)
+        elif pool_type == 'catavgmax':
+            self.pool = AdaptiveCatAvgMaxPool2d(output_size)
         elif pool_type == 'max':
             self.pool = nn.AdaptiveMaxPool2d(output_size)
         else:
@@ -45,11 +86,10 @@ class AdaptiveAvgMaxPool2d(torch.nn.Module):
             self.pool = nn.AdaptiveAvgPool2d(output_size)
 
     def forward(self, x):
-        if self.pool_type == 'avgmax':
+        return self.pool(x)
-            x = 0.5 * torch.sum(torch.stack([p(x) for p in self.pool]), 0).squeeze(dim=0)
+
-        else:
+    def feat_mult(self):
-            x = self.pool(x)
+        return adaptive_pool_feat_mult(self.pool_type)
-        return x
 
     def __repr__(self):
         return self.__class__.__name__ + ' (' \

models/densenet.py

@@ -83,7 +83,6 @@ def densenet161(pretrained=False, **kwargs):
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
     """
-    print(kwargs)
     model = DenseNet(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24), **kwargs)
     if pretrained:
         state_dict = model_zoo.load_url(model_urls['densenet161'])
@@ -193,7 +192,7 @@ class DenseNet(nn.Module):
         x = self.features(x)
         x = F.relu(x, inplace=True)
         if pool:
-            x = adaptive_avgmax_pool2d(x, self.global_pool)
+            x = select_adaptive_pool2d(x, self.global_pool)
             x = x.view(x.size(0), -1)
         return x
 

models/dpn.py

@@ -16,7 +16,7 @@ import torch.nn.functional as F
 import torch.utils.model_zoo as model_zoo
 from collections import OrderedDict
 
-from .adaptive_avgmax_pool import adaptive_avgmax_pool2d
+from .adaptive_avgmax_pool import select_adaptive_pool2d
 
 __all__ = ['DPN', 'dpn68', 'dpn92', 'dpn98', 'dpn131', 'dpn107']
 
@@ -41,31 +41,31 @@ model_urls = {
 }
 
 
-def dpn68(num_classes=1000, pretrained=False, test_time_pool=7):
+def dpn68(num_classes=1000, pretrained=False):
     model = DPN(
         small=True, num_init_features=10, k_r=128, groups=32,
         k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64),
-        num_classes=num_classes, test_time_pool=test_time_pool)
+        num_classes=num_classes)
     if pretrained:
         model.load_state_dict(model_zoo.load_url(model_urls['dpn68']))
     return model
 
 
-def dpn68b(num_classes=1000, pretrained=False, test_time_pool=7):
+def dpn68b(num_classes=1000, pretrained=False):
     model = DPN(
         small=True, num_init_features=10, k_r=128, groups=32,
         b=True, k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64),
-        num_classes=num_classes, test_time_pool=test_time_pool)
+        num_classes=num_classes)
     if pretrained:
         model.load_state_dict(model_zoo.load_url(model_urls['dpn68b_extra']))
     return model
 
 
-def dpn92(num_classes=1000, pretrained=False, test_time_pool=7, extra=True):
+def dpn92(num_classes=1000, pretrained=False, extra=True):
     model = DPN(
         num_init_features=64, k_r=96, groups=32,
         k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128),
-        num_classes=num_classes, test_time_pool=test_time_pool)
+        num_classes=num_classes)
     if pretrained:
         if extra:
             model.load_state_dict(model_zoo.load_url(model_urls['dpn92_extra']))
@@ -74,31 +74,31 @@ def dpn92(num_classes=1000, pretrained=False, test_time_pool=7, extra=True):
     return model
 
 
-def dpn98(num_classes=1000, pretrained=False, test_time_pool=7):
+def dpn98(num_classes=1000, pretrained=False):
     model = DPN(
         num_init_features=96, k_r=160, groups=40,
         k_sec=(3, 6, 20, 3), inc_sec=(16, 32, 32, 128),
-        num_classes=num_classes, test_time_pool=test_time_pool)
+        num_classes=num_classes)
     if pretrained:
         model.load_state_dict(model_zoo.load_url(model_urls['dpn98']))
     return model
 
 
-def dpn131(num_classes=1000, pretrained=False, test_time_pool=7):
+def dpn131(num_classes=1000, pretrained=False):
     model = DPN(
         num_init_features=128, k_r=160, groups=40,
         k_sec=(4, 8, 28, 3), inc_sec=(16, 32, 32, 128),
-        num_classes=num_classes, test_time_pool=test_time_pool)
+        num_classes=num_classes)
     if pretrained:
         model.load_state_dict(model_zoo.load_url(model_urls['dpn131']))
     return model
 
 
-def dpn107(num_classes=1000, pretrained=False, test_time_pool=7):
+def dpn107(num_classes=1000, pretrained=False):
     model = DPN(
         num_init_features=128, k_r=200, groups=50,
         k_sec=(4, 8, 20, 3), inc_sec=(20, 64, 64, 128),
-        num_classes=num_classes, test_time_pool=test_time_pool)
+        num_classes=num_classes)
     if pretrained:
         model.load_state_dict(model_zoo.load_url(model_urls['dpn107_extra']))
     return model
@@ -212,10 +212,9 @@ class DualPathBlock(nn.Module):
 class DPN(nn.Module):
     def __init__(self, small=False, num_init_features=64, k_r=96, groups=32,
                  b=False, k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128),
-                 num_classes=1000, test_time_pool=0, fc_act=nn.ELU(inplace=True)):
+                 num_classes=1000, fc_act=nn.ELU(inplace=True)):
         super(DPN, self).__init__()
         self.num_classes = num_classes
-        self.test_time_pool = test_time_pool
         self.b = b
         bw_factor = 1 if small else 4
 
@@ -287,20 +286,12 @@ class DPN(nn.Module):
     def forward_features(self, x, pool=True):
         x = self.features(x)
         if pool:
-            x = adaptive_avgmax_pool2d(x, pool_type='avg')
+            x = select_adaptive_pool2d(x, pool_type='avg')
-            x = x.view(x.size(0), -1)
         return x
 
     def forward(self, x):
-        x = self.features(x)
+        x = self.forward_features(x)
-        if not self.training and self.test_time_pool:
+        out = self.classifier(x)
-            x = F.avg_pool2d(x, kernel_size=self.test_time_pool, stride=1)
-            out = self.classifier(x)
-            # The extra test time pool should be pooling an img_size//32 - 6 size patch
-            out = adaptive_avgmax_pool2d(out, pool_type='avgmax')
-        else:
-            x = adaptive_avgmax_pool2d(x, pool_type='avg')
-            out = self.classifier(x)
         return out.view(out.size(0), -1)
 
 

models/inception_resnet_v2.py

@@ -305,7 +305,7 @@ class InceptionResnetV2(nn.Module):
         x = self.block8(x)
         x = self.conv2d_7b(x)
         if pool:
-            x = adaptive_avgmax_pool2d(x, self.global_pool)
+            x = select_adaptive_pool2d(x, self.global_pool)
             #x = F.avg_pool2d(x, 8, count_include_pad=False)
             x = x.view(x.size(0), -1)
         return x

models/inception_v4.py

@@ -272,7 +272,7 @@ class InceptionV4(nn.Module):
     def forward_features(self, x, pool=True):
         x = self.features(x)
         if pool:
-            x = adaptive_avgmax_pool2d(x, self.global_pool, count_include_pad=False)
+            x = select_adaptive_pool2d(x, self.global_pool, count_include_pad=False)
             x = x.view(x.size(0), -1)
         return x
 

models/model_factory.py

@@ -1,15 +1,16 @@
 import torch
 import os
+from collections import OrderedDict
 
 from .inception_v4 import inception_v4
 from .inception_resnet_v2 import inception_resnet_v2
 from .densenet import densenet161, densenet121, densenet169, densenet201
-from .resnet import resnet18, resnet34, resnet50, resnet101, resnet152
+from .resnet import resnet18, resnet34, resnet50, resnet101, resnet152, \
-from .fbresnet200 import fbresnet200
+    resnext50_32x4d, resnext101_32x4d, resnext101_64x4d, resnext152_32x4d
 from .dpn import dpn68, dpn68b, dpn92, dpn98, dpn131, dpn107
 from .senet import seresnet18, seresnet34, seresnet50, seresnet101, seresnet152, \
     seresnext26_32x4d, seresnext50_32x4d, seresnext101_32x4d
-from .resnext import resnext50, resnext101, resnext152
+#from .resnext import resnext50, resnext101, resnext152
 from .xception import xception
 
 model_config_dict = {
@@ -57,26 +58,18 @@ def create_model(
         checkpoint_path='',
         **kwargs):
 
-    test_time_pool = kwargs.pop('test_time_pool') if 'test_time_pool' in kwargs else 0
-
     if model_name == 'dpn68':
-        model = dpn68(
+        model = dpn68(num_classes=num_classes, pretrained=pretrained)
-            num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
     elif model_name == 'dpn68b':
-        model = dpn68b(
+        model = dpn68b(num_classes=num_classes, pretrained=pretrained)
-            num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
     elif model_name == 'dpn92':
-        model = dpn92(
+        model = dpn92(num_classes=num_classes, pretrained=pretrained)
-            num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
     elif model_name == 'dpn98':
-        model = dpn98(
+        model = dpn98(num_classes=num_classes, pretrained=pretrained)
-            num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
     elif model_name == 'dpn131':
-        model = dpn131(
+        model = dpn131(num_classes=num_classes, pretrained=pretrained)
-            num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
     elif model_name == 'dpn107':
-        model = dpn107(
+        model = dpn107(num_classes=num_classes, pretrained=pretrained)
-            num_classes=num_classes, pretrained=pretrained, test_time_pool=test_time_pool)
     elif model_name == 'resnet18':
         model = resnet18(num_classes=num_classes, pretrained=pretrained, **kwargs)
     elif model_name == 'resnet34':
@@ -99,8 +92,6 @@ def create_model(
         model = inception_resnet_v2(num_classes=num_classes, pretrained=pretrained, **kwargs)
     elif model_name == 'inception_v4':
         model = inception_v4(num_classes=num_classes, pretrained=pretrained, **kwargs)
-    elif model_name == 'fbresnet200':
-        model = fbresnet200(num_classes=num_classes, pretrained=pretrained, **kwargs)
     elif model_name == 'seresnet18':
         model = seresnet18(num_classes=num_classes, pretrained=pretrained, **kwargs)
     elif model_name == 'seresnet34':
@@ -117,12 +108,14 @@ def create_model(
         model = seresnext50_32x4d(num_classes=num_classes, pretrained=pretrained, **kwargs)
     elif model_name == 'seresnext101_32x4d':
         model = seresnext101_32x4d(num_classes=num_classes, pretrained=pretrained, **kwargs)
-    elif model_name == 'resnext50':
+    elif model_name == 'resnext50_32x4d':
-        model = resnext50(num_classes=num_classes, pretrained=pretrained, **kwargs)
+        model = resnext50_32x4d(num_classes=num_classes, pretrained=pretrained, **kwargs)
-    elif model_name == 'resnext101':
+    elif model_name == 'resnext101_32x4d':
-        model = resnext101(num_classes=num_classes, pretrained=pretrained, **kwargs)
+        model = resnext101_32x4d(num_classes=num_classes, pretrained=pretrained, **kwargs)
-    elif model_name == 'resnext152':
+    elif model_name == 'resnext101_64x4d':
-        model = resnext152(num_classes=num_classes, pretrained=pretrained, **kwargs)
+        model = resnext101_32x4d(num_classes=num_classes, pretrained=pretrained, **kwargs)
+    elif model_name == 'resnext152_32x4d':
+        model = resnext152_32x4d(num_classes=num_classes, pretrained=pretrained, **kwargs)
     elif model_name == 'xception':
         model = xception(num_classes=num_classes, pretrained=pretrained)
     else:
@@ -136,13 +129,22 @@ def create_model(
 
 
 def load_checkpoint(model, checkpoint_path):
-    if checkpoint_path is not None and os.path.isfile(checkpoint_path):
+    if checkpoint_path and os.path.isfile(checkpoint_path):
-        print('Loading checkpoint', checkpoint_path)
+        print("=> Loading checkpoint '{}'".format(checkpoint_path))
         checkpoint = torch.load(checkpoint_path)
         if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
-            model.load_state_dict(checkpoint['state_dict'])
+            new_state_dict = OrderedDict()
+            for k, v in checkpoint['state_dict'].items():
+                if k.startswith('module'):
+                    name = k[7:]  # remove `module.`
+                else:
+                    name = k
+                new_state_dict[name] = v
+            model.load_state_dict(new_state_dict)
         else:
             model.load_state_dict(checkpoint)
+        print("=> Loaded checkpoint '{}'".format(checkpoint_path))
+        return True
     else:
-        print("Error: No checkpoint found at %s." % checkpoint_path)
+        print("=> Error: No checkpoint found at '{}'".format(checkpoint_path))
-
+        return False

models/resnet.py

@@ -7,7 +7,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 import math
 import torch.utils.model_zoo as model_zoo
-from .adaptive_avgmax_pool import AdaptiveAvgMaxPool2d
+from .adaptive_avgmax_pool import SelectAdaptivePool2d
 
 __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
 
@@ -29,8 +29,13 @@ def conv3x3(in_planes, out_planes, stride=1):
 class BasicBlock(nn.Module):
     expansion = 1
 
-    def __init__(self, inplanes, planes, stride=1, downsample=None, drop_rate=0.0):
+    def __init__(self, inplanes, planes, stride=1, downsample=None,
+                 cardinality=1, base_width=64, drop_rate=0.0):
         super(BasicBlock, self).__init__()
+
+        assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
+        assert base_width == 64, 'BasicBlock doest not support changing base width'
+
         self.conv1 = conv3x3(inplanes, planes, stride)
         self.bn1 = nn.BatchNorm2d(planes)
         self.relu = nn.ReLU(inplace=True)
@@ -65,14 +70,18 @@ class BasicBlock(nn.Module):
 class Bottleneck(nn.Module):
     expansion = 4
 
-    def __init__(self, inplanes, planes, stride=1, downsample=None, drop_rate=0.0):
+    def __init__(self, inplanes, planes, stride=1, downsample=None,
+                 cardinality=1, base_width=64, drop_rate=0.0):
         super(Bottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+
-        self.bn1 = nn.BatchNorm2d(planes)
+        width = int(math.floor(planes * (base_width / 64)) * cardinality)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+
-                               padding=1, bias=False)
+        self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
+        self.bn1 = nn.BatchNorm2d(width)
-        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride,
+                               padding=1, groups=cardinality, bias=False)
+        self.bn2 = nn.BatchNorm2d(width)
+        self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
         self.bn3 = nn.BatchNorm2d(planes * 4)
         self.relu = nn.ReLU(inplace=True)
         self.downsample = downsample
@@ -108,10 +117,13 @@ class Bottleneck(nn.Module):
 class ResNet(nn.Module):
 
     def __init__(self, block, layers, num_classes=1000,
+                 cardinality=1, base_width=64,
                  drop_rate=0.0, block_drop_rate=0.0,
                  global_pool='avg'):
         self.num_classes = num_classes
         self.inplanes = 64
+        self.cardinality = cardinality
+        self.base_width = base_width
         self.drop_rate = drop_rate
         self.expansion = block.expansion
         super(ResNet, self).__init__()
@@ -123,31 +135,29 @@ class ResNet(nn.Module):
         self.layer2 = self._make_layer(block, 128, layers[1], stride=2, drop_rate=block_drop_rate)
         self.layer3 = self._make_layer(block, 256, layers[2], stride=2, drop_rate=block_drop_rate)
         self.layer4 = self._make_layer(block, 512, layers[3], stride=2, drop_rate=block_drop_rate)
-        self.global_pool = AdaptiveAvgMaxPool2d(pool_type=global_pool)
+        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
         self.num_features = 512 * block.expansion
-        self.fc = nn.Linear(self.num_features, num_classes)
+        self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
 
         for m in self.modules():
             if isinstance(m, nn.Conv2d):
-                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-                m.weight.data.normal_(0, math.sqrt(2. / n))
             elif isinstance(m, nn.BatchNorm2d):
-                m.weight.data.fill_(1)
+                nn.init.constant_(m.weight, 1.)
-                m.bias.data.zero_()
+                nn.init.constant_(m.bias, 0.)
 
     def _make_layer(self, block, planes, blocks, stride=1, drop_rate=0.):
         downsample = None
         if stride != 1 or self.inplanes != planes * block.expansion:
             downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion,
+                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
-                          kernel_size=1, stride=stride, bias=False),
                 nn.BatchNorm2d(planes * block.expansion),
             )
 
-        layers = [block(self.inplanes, planes, stride, downsample, drop_rate)]
+        layers = [block(self.inplanes, planes, stride, downsample, self.cardinality, self.base_width, drop_rate)]
         self.inplanes = planes * block.expansion
         for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes))
+            layers.append(block(self.inplanes, planes, cardinality=self.cardinality, base_width=self.base_width))
 
         return nn.Sequential(*layers)
 
@@ -155,11 +165,11 @@ class ResNet(nn.Module):
         return self.fc
 
     def reset_classifier(self, num_classes, global_pool='avg'):
-        self.global_pool = AdaptiveAvgMaxPool2d(pool_type=global_pool)
+        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
         self.num_classes = num_classes
         del self.fc
         if num_classes:
-            self.fc = nn.Linear(512 * self.expansion, num_classes)
+            self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
         else:
             self.fc = None
 
@@ -245,3 +255,51 @@ def resnet152(pretrained=False, **kwargs):
     if pretrained:
         model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
     return model
+
+
+def resnext50_32x4d(cardinality=32, base_width=4, pretrained=False, **kwargs):
+    """Constructs a ResNeXt50-32x4d model.
+
+    Args:
+        cardinality (int): Cardinality of the aggregated transform
+        base_width (int): Base width of the grouped convolution
+    """
+    model = ResNet(
+        Bottleneck, [3, 4, 6, 3], cardinality=cardinality, base_width=base_width, **kwargs)
+    return model
+
+
+def resnext101_32x4d(cardinality=32, base_width=4, pretrained=False, **kwargs):
+    """Constructs a ResNeXt-101 model.
+
+    Args:
+        cardinality (int): Cardinality of the aggregated transform
+        base_width (int): Base width of the grouped convolution
+    """
+    model = ResNet(
+        Bottleneck, [3, 4, 23, 3], cardinality=cardinality, base_width=base_width, **kwargs)
+    return model
+
+
+def resnext101_64x4d(cardinality=64, base_width=4, pretrained=False, **kwargs):
+    """Constructs a ResNeXt101-64x4d model.
+
+    Args:
+        cardinality (int): Cardinality of the aggregated transform
+        base_width (int): Base width of the grouped convolution
+    """
+    model = ResNet(
+        Bottleneck, [3, 4, 23, 3], cardinality=cardinality, base_width=base_width, **kwargs)
+    return model
+
+
+def resnext152_32x4d(cardinality=32, base_width=4, pretrained=False, **kwargs):
+    """Constructs a ResNeXt152-32x4d model.
+
+    Args:
+        cardinality (int): Cardinality of the aggregated transform
+        base_width (int): Base width of the grouped convolution
+    """
+    model = ResNet(
+        Bottleneck, [3, 8, 36, 3], cardinality=cardinality, base_width=base_width, **kwargs)
+    return model

models/resnext.py

@@ -1,175 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-import math
-import torch.utils.model_zoo as model_zoo
-from models.adaptive_avgmax_pool import AdaptiveAvgMaxPool2d
-
-__all__ = ['ResNeXt', 'resnext50', 'resnext101', 'resnext152']
-
-
-def conv3x3(in_planes, out_planes, stride=1):
-    "3x3 convolution with padding"
-    return nn.Conv2d(
-        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
-
-
-class ResNeXtBottleneckC(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=32, base_width=4):
-        super(ResNeXtBottleneckC, self).__init__()
-
-        width = math.floor(planes * (base_width / 64)) * cardinality
-
-        self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(width)
-        self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride,
-                               padding=1, bias=False, groups=cardinality)
-        self.bn2 = nn.BatchNorm2d(width)
-        self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * 4)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class ResNeXt(nn.Module):
-
-    def __init__(self, block, layers, num_classes=1000, cardinality=32, base_width=4,
-                 drop_rate=0., global_pool='avg'):
-        self.num_classes = num_classes
-        self.inplanes = 64
-        self.cardinality = cardinality
-        self.base_width = base_width
-        self.drop_rate = drop_rate
-        super(ResNeXt, self).__init__()
-        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
-        self.avgpool = AdaptiveAvgMaxPool2d(pool_type=global_pool)
-        self.num_features = 512 * block.expansion
-        self.fc = nn.Linear(self.num_features, num_classes)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.constant_(m.weight, 1.)
-                nn.init.constant_(m.bias, 0.)
-
-    def _make_layer(self, block, planes, blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(
-                    self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(planes * block.expansion),
-            )
-
-        layers = [block(self.inplanes, planes, stride, downsample, self.cardinality, self.base_width)]
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes, 1, None, self.cardinality, self.base_width))
-
-        return nn.Sequential(*layers)
-
-    def get_classifier(self):
-        return self.fc
-
-    def reset_classifier(self, num_classes, global_pool='avg'):
-        self.avgpool = AdaptiveAvgMaxPool2d(pool_type=global_pool)
-        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, x, pool=True):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        if pool:
-            x = self.avgpool(x)
-            x = x.view(x.size(0), -1)
-        return x
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        if self.drop_rate > 0.:
-            x = F.dropout(x, p=self.drop_rate, training=self.training)
-        x = self.fc(x)
-
-        return x
-
-
-def resnext50(cardinality=32, base_width=4, pretrained=False, **kwargs):
-    """Constructs a ResNeXt-50 model.
-
-    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)
-    return model
-
-
-def resnext101(cardinality=32, base_width=4, pretrained=False, **kwargs):
-    """Constructs a ResNeXt-101 model.
-
-    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)
-    return model
-
-
-def resnext152(cardinality=32, base_width=4, pretrained=False, **kwargs):
-    """Constructs a ResNeXt-152 model.
-
-    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)
-    return model

models/senet.py

@@ -9,7 +9,7 @@ import math
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.utils import model_zoo
-from models.adaptive_avgmax_pool import AdaptiveAvgMaxPool2d
+from models.adaptive_avgmax_pool import SelectAdaptivePool2d
 
 __all__ = ['SENet', 'senet154', 'seresnet50', 'seresnet101', 'seresnet152',
            'seresnext50_32x4d', 'seresnext101_32x4d']
@@ -307,7 +307,7 @@ class SENet(nn.Module):
             downsample_kernel_size=downsample_kernel_size,
             downsample_padding=downsample_padding
         )
-        self.avg_pool = AdaptiveAvgMaxPool2d(pool_type=global_pool)
+        self.avg_pool = SelectAdaptivePool2d(pool_type=global_pool)
         self.drop_rate = drop_rate
         self.num_features = 512 * block.expansion
         self.last_linear = nn.Linear(self.num_features, num_classes)

models/test_time_pool.py

@@ -0,0 +1,27 @@
+from torch import nn
+import torch.nn.functional as F
+from models.adaptive_avgmax_pool import adaptive_avgmax_pool2d
+
+
+class TestTimePoolHead(nn.Module):
+    def __init__(self, base, original_pool=7):
+        super(TestTimePoolHead, self).__init__()
+        self.base = base
+        self.original_pool = original_pool
+        base_fc = self.base.get_classifier()
+        if isinstance(base_fc, nn.Conv2d):
+            self.fc = base_fc
+        else:
+            self.fc = nn.Conv2d(
+                self.base.num_features, self.base.num_classes, kernel_size=1, bias=True)
+            self.fc.weight.data.copy_(base_fc.weight.data.view(self.fc.weight.size()))
+            self.fc.bias.data.copy_(base_fc.bias.data.view(self.fc.bias.size()))
+        self.base.reset_classifier(0)  # delete original fc layer
+
+    def forward(self, x):
+        x = self.base.forward_features(x, pool=False)
+        x = F.avg_pool2d(x, kernel_size=self.original_pool, stride=1)
+        x = self.fc(x)
+        x = adaptive_avgmax_pool2d(x, 1)
+        return x.view(x.size(0), -1)
+

validate.py

@@ -9,9 +9,8 @@ import torch
 import torch.backends.cudnn as cudnn
 import torch.nn as nn
 import torch.nn.parallel
-from collections import OrderedDict
 
-from models import create_model
+from models import create_model, load_checkpoint, TestTimePoolHead
 from data import Dataset, create_loader, get_model_meanstd
 
 
@@ -41,40 +40,26 @@ parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true',
 def main():
     args = parser.parse_args()
 
-    test_time_pool = False
-    if 'dpn' in args.model and args.img_size > 224 and not args.no_test_pool:
-        test_time_pool = True
-
     # create model
     num_classes = 1000
     model = create_model(
         args.model,
         num_classes=num_classes,
-        pretrained=args.pretrained,
+        pretrained=args.pretrained)
-        test_time_pool=test_time_pool)
 
     print('Model %s created, param count: %d' %
           (args.model, sum([m.numel() for m in model.parameters()])))
 
-    # optionally resume from a checkpoint
+    # load a checkpoint
-    if args.checkpoint and os.path.isfile(args.checkpoint):
+    if not args.pretrained:
-        print("=> loading checkpoint '{}'".format(args.checkpoint))
+        if not load_checkpoint(model, args.checkpoint):
-        checkpoint = torch.load(args.checkpoint)
+            exit(1)
-        if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
+
-            new_state_dict = OrderedDict()
+    test_time_pool = False
-            for k, v in checkpoint['state_dict'].items():
+    # FIXME make this work for networks with default img size != 224 and default pool k != 7
-                if k.startswith('module'):
+    if args.img_size > 224 and not args.no_test_pool:
-                    name = k[7:]  # remove `module.`
+        model = TestTimePoolHead(model)
-                else:
+        test_time_pool = True
-                    name = k
-                new_state_dict[name] = v
-            model.load_state_dict(new_state_dict)
-        else:
-            model.load_state_dict(checkpoint)
-        print("=> loaded checkpoint '{}'".format(args.checkpoint))
-    elif not args.pretrained:
-        print("=> no checkpoint found at '{}'".format(args.checkpoint))
-        exit(1)
 
     if args.num_gpu > 1:
         model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
@@ -94,7 +79,8 @@ def main():
         use_prefetcher=True,
         mean=data_mean,
         std=data_std,
-        num_workers=args.workers)
+        num_workers=args.workers,
+        crop_pct=1.0 if test_time_pool else None)
 
     batch_time = AverageMeter()
     losses = AverageMeter()