More models supporting feature extraction, xception, gluon_xception, inception_v3, inception_v4, pnasnet, nasnet, dla. Fix DLA unused projection params.

tests/test_models.py

@@ -37,8 +37,7 @@ def test_model_forward(model_name, batch_size):
 
 
 @pytest.mark.timeout(120)
-# DLA models have an issue TBD, add them to exclusions
-@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + ['dla*']))
+@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
 @pytest.mark.parametrize('batch_size', [2])
 def test_model_backward(model_name, batch_size):
     """Run a single forward pass with each model"""

timm/models/dla.py

@@ -12,7 +12,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import load_pretrained
+from .helpers import build_model_with_cfg
 from .layers import SelectAdaptivePool2d
 from .registry import register_model
 
@@ -212,10 +212,19 @@ class DlaTree(nn.Module):
             root_dim = 2 * out_channels
         if level_root:
             root_dim += in_channels
+        self.downsample = nn.MaxPool2d(stride, stride=stride) if stride > 1 else nn.Identity()
+        self.project = nn.Identity()
         cargs = dict(dilation=dilation, cardinality=cardinality, base_width=base_width)
         if levels == 1:
             self.tree1 = block(in_channels, out_channels, stride, **cargs)
             self.tree2 = block(out_channels, out_channels, 1, **cargs)
+            if in_channels != out_channels:
+                # NOTE the official impl/weights have  project layers in levels > 1 case that are never
+                # used, I've moved the project layer here to avoid wasted params but old checkpoints will
+                # need strict=False while loading.
+                self.project = nn.Sequential(
+                    nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False),
+                    nn.BatchNorm2d(out_channels))
         else:
             cargs.update(dict(root_kernel_size=root_kernel_size, root_residual=root_residual))
             self.tree1 = DlaTree(
@@ -226,22 +235,12 @@ class DlaTree(nn.Module):
             self.root = DlaRoot(root_dim, out_channels, root_kernel_size, root_residual)
         self.level_root = level_root
         self.root_dim = root_dim
-        self.downsample = nn.MaxPool2d(stride, stride=stride) if stride > 1 else None
-        self.project = None
-        if in_channels != out_channels:
-            self.project = nn.Sequential(
-                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False),
-                nn.BatchNorm2d(out_channels)
-            )
         self.levels = levels
 
     def forward(self, x, residual=None, children=None):
         children = [] if children is None else children
-        # FIXME the way downsample / project are used here and residual is passed to next level up
-        # the tree, the residual is overridden and some project weights are thus never used and
-        # have no gradients. This appears to be an issue with the original model / weights.
-        bottom = self.downsample(x) if self.downsample is not None else x
-        residual = self.project(bottom) if self.project is not None else bottom
+        bottom = self.downsample(x)
+        residual = self.project(bottom)
         if self.level_root:
             children.append(bottom)
         x1 = self.tree1(x, residual)
@@ -255,8 +254,8 @@ class DlaTree(nn.Module):
 
 
 class DLA(nn.Module):
-    def __init__(self, levels, channels, num_classes=1000, in_chans=3, cardinality=1, base_width=64,
-                 block=DlaBottle2neck, residual_root=False, linear_root=False,
+    def __init__(self, levels, channels, output_stride=32, num_classes=1000, in_chans=3,
+                 cardinality=1, base_width=64, block=DlaBottle2neck, residual_root=False,
                  drop_rate=0.0, global_pool='avg'):
         super(DLA, self).__init__()
         self.channels = channels
@@ -264,6 +263,7 @@ class DLA(nn.Module):
         self.cardinality = cardinality
         self.base_width = base_width
         self.drop_rate = drop_rate
+        assert output_stride == 32  # FIXME support dilation
 
         self.base_layer = nn.Sequential(
             nn.Conv2d(in_chans, channels[0], kernel_size=7, stride=1, padding=3, bias=False),
@@ -276,6 +276,14 @@ class DLA(nn.Module):
         self.level3 = DlaTree(levels[3], block, channels[2], channels[3], 2, level_root=True, **cargs)
         self.level4 = DlaTree(levels[4], block, channels[3], channels[4], 2, level_root=True, **cargs)
         self.level5 = DlaTree(levels[5], block, channels[4], channels[5], 2, level_root=True, **cargs)
+        self.feature_info = [
+            dict(num_chs=channels[0], reduction=1, module='level0'),  # rare to have a meaningful stride 1 level
+            dict(num_chs=channels[1], reduction=2, module='level1'),
+            dict(num_chs=channels[2], reduction=4, module='level2'),
+            dict(num_chs=channels[3], reduction=8, module='level3'),
+            dict(num_chs=channels[4], reduction=16, module='level4'),
+            dict(num_chs=channels[5], reduction=32, module='level5'),
+        ]
 
         self.num_features = channels[-1]
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
@@ -331,142 +339,103 @@ class DLA(nn.Module):
         return x.flatten(1)
 
 
+def _create_dla(variant, pretrained=False, **kwargs):
+    return build_model_with_cfg(
+        DLA, variant, pretrained, default_cfg=default_cfgs[variant],
+        pretrained_strict=False, feature_cfg=dict(out_indices=(1, 2, 3, 4, 5)), **kwargs)
+
+
 @register_model
-def dla60_res2net(pretrained=None, num_classes=1000, in_chans=3, **kwargs):
-    default_cfg = default_cfgs['dla60_res2net']
-    model = DLA(levels=(1, 1, 1, 2, 3, 1), channels=(16, 32, 128, 256, 512, 1024),
-                block=DlaBottle2neck, cardinality=1, base_width=28,
-                num_classes=num_classes, in_chans=in_chans, **kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-    return model
+def dla60_res2net(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        levels=(1, 1, 1, 2, 3, 1), channels=(16, 32, 128, 256, 512, 1024),
+        block=DlaBottle2neck, cardinality=1, base_width=28, **kwargs)
+    return _create_dla('dla60_res2net', pretrained, **model_kwargs)
 
 
 @register_model
-def dla60_res2next(pretrained=None, num_classes=1000, in_chans=3, **kwargs):
-    default_cfg = default_cfgs['dla60_res2next']
-    model = DLA(levels=(1, 1, 1, 2, 3, 1), channels=(16, 32, 128, 256, 512, 1024),
-                block=DlaBottle2neck, cardinality=8, base_width=4,
-                num_classes=num_classes, in_chans=in_chans, **kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-    return model
+def dla60_res2next(pretrained=False,**kwargs):
+    model_kwargs = dict(
+        levels=(1, 1, 1, 2, 3, 1), channels=(16, 32, 128, 256, 512, 1024),
+        block=DlaBottle2neck, cardinality=8, base_width=4, **kwargs)
+    return _create_dla('dla60_res2next', pretrained, **model_kwargs)
 
 
 @register_model
-def dla34(pretrained=None, num_classes=1000, in_chans=3, **kwargs):  # DLA-34
-    default_cfg = default_cfgs['dla34']
-    model = DLA([1, 1, 1, 2, 2, 1], [16, 32, 64, 128, 256, 512], block=DlaBasic,
-                num_classes=num_classes, in_chans=in_chans, **kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-    return model
+def dla34(pretrained=False, **kwargs):  # DLA-34
+    model_kwargs = dict(
+        levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 128, 256, 512],
+        block=DlaBasic, **kwargs)
+    return _create_dla('dla34', pretrained, **model_kwargs)
 
 
 @register_model
-def dla46_c(pretrained=None, num_classes=1000, in_chans=3, **kwargs):  # DLA-46-C
-    default_cfg = default_cfgs['dla46_c']
-    model = DLA(levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 64, 128, 256],
-                block=DlaBottleneck, num_classes=num_classes, in_chans=in_chans, **kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-    return model
+def dla46_c(pretrained=False, **kwargs):  # DLA-46-C
+    model_kwargs = dict(
+        levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 64, 128, 256],
+        block=DlaBottleneck, **kwargs)
+    return _create_dla('dla46_c', pretrained, **model_kwargs)
 
 
 @register_model
-def dla46x_c(pretrained=None, num_classes=1000, in_chans=3, **kwargs):  # DLA-X-46-C
-    default_cfg = default_cfgs['dla46x_c']
-    model = DLA(levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 64, 128, 256],
-                block=DlaBottleneck, cardinality=32, base_width=4,
-                num_classes=num_classes, in_chans=in_chans, **kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-    return model
+def dla46x_c(pretrained=False, **kwargs):  # DLA-X-46-C
+    model_kwargs = dict(
+        levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 64, 128, 256],
+        block=DlaBottleneck, cardinality=32, base_width=4, **kwargs)
+    return _create_dla('dla46x_c', pretrained, **model_kwargs)
 
 
 @register_model
-def dla60x_c(pretrained=None, num_classes=1000, in_chans=3, **kwargs):  # DLA-X-60-C
-    default_cfg = default_cfgs['dla60x_c']
-    model = DLA([1, 1, 1, 2, 3, 1], [16, 32, 64, 64, 128, 256],
-                block=DlaBottleneck, cardinality=32, base_width=4,
-                num_classes=num_classes, in_chans=in_chans, **kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-    return model
+def dla60x_c(pretrained=False, **kwargs):  # DLA-X-60-C
+    model_kwargs = dict(
+        levels=[1, 1, 1, 2, 3, 1], channels=[16, 32, 64, 64, 128, 256],
+        block=DlaBottleneck, cardinality=32, base_width=4, **kwargs)
+    return _create_dla('dla60x_c', pretrained, **model_kwargs)
 
 
 @register_model
-def dla60(pretrained=None, num_classes=1000, in_chans=3, **kwargs):  # DLA-60
-    default_cfg = default_cfgs['dla60']
-    model = DLA([1, 1, 1, 2, 3, 1], [16, 32, 128, 256, 512, 1024],
-                block=DlaBottleneck, num_classes=num_classes, in_chans=in_chans, **kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-    return model
+def dla60(pretrained=False, **kwargs):  # DLA-60
+    model_kwargs = dict(
+        levels=[1, 1, 1, 2, 3, 1], channels=[16, 32, 128, 256, 512, 1024],
+        block=DlaBottleneck, **kwargs)
+    return _create_dla('dla60', pretrained, **model_kwargs)
 
 
 @register_model
-def dla60x(pretrained=None, num_classes=1000, in_chans=3, **kwargs):  # DLA-X-60
-    default_cfg = default_cfgs['dla60x']
-    model = DLA([1, 1, 1, 2, 3, 1], [16, 32, 128, 256, 512, 1024],
-                block=DlaBottleneck, cardinality=32, base_width=4,
-                num_classes=num_classes, in_chans=in_chans, **kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-    return model
+def dla60x(pretrained=False, **kwargs):  # DLA-X-60
+    model_kwargs = dict(
+        levels=[1, 1, 1, 2, 3, 1], channels=[16, 32, 128, 256, 512, 1024],
+        block=DlaBottleneck, cardinality=32, base_width=4, **kwargs)
+    return _create_dla('dla60x', pretrained, **model_kwargs)
 
 
 @register_model
-def dla102(pretrained=None, num_classes=1000, in_chans=3, **kwargs):  # DLA-102
-    default_cfg = default_cfgs['dla102']
-    model = DLA([1, 1, 1, 3, 4, 1], [16, 32, 128, 256, 512, 1024],
-                block=DlaBottleneck, residual_root=True,
-                num_classes=num_classes, in_chans=in_chans, **kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-    return model
+def dla102(pretrained=False, **kwargs):  # DLA-102
+    model_kwargs = dict(
+        levels=[1, 1, 1, 3, 4, 1], channels=[16, 32, 128, 256, 512, 1024],
+        block=DlaBottleneck, residual_root=True, **kwargs)
+    return _create_dla('dla102', pretrained, **model_kwargs)
 
 
 @register_model
-def dla102x(pretrained=None, num_classes=1000, in_chans=3, **kwargs):  # DLA-X-102
-    default_cfg = default_cfgs['dla102x']
-    model = DLA([1, 1, 1, 3, 4, 1], [16, 32, 128, 256, 512, 1024],
-                block=DlaBottleneck, cardinality=32, base_width=4, residual_root=True,
-                num_classes=num_classes, in_chans=in_chans, **kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-    return model
+def dla102x(pretrained=False, **kwargs):  # DLA-X-102
+    model_kwargs = dict(
+        levels=[1, 1, 1, 3, 4, 1], channels=[16, 32, 128, 256, 512, 1024],
+        block=DlaBottleneck, cardinality=32, base_width=4, residual_root=True, **kwargs)
+    return _create_dla('dla102x', pretrained, **model_kwargs)
 
 
 @register_model
-def dla102x2(pretrained=None, num_classes=1000, in_chans=3, **kwargs):  # DLA-X-102 64
-    default_cfg = default_cfgs['dla102x2']
-    model = DLA([1, 1, 1, 3, 4, 1], [16, 32, 128, 256, 512, 1024],
-                block=DlaBottleneck, cardinality=64, base_width=4, residual_root=True,
-                num_classes=num_classes, in_chans=in_chans, **kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-    return model
+def dla102x2(pretrained=False, **kwargs):  # DLA-X-102 64
+    model_kwargs = dict(
+        levels=[1, 1, 1, 3, 4, 1], channels=[16, 32, 128, 256, 512, 1024],
+        block=DlaBottleneck, cardinality=64, base_width=4, residual_root=True, **kwargs)
+    return _create_dla('dla102x2', pretrained, **model_kwargs)
 
 
 @register_model
-def dla169(pretrained=None, num_classes=1000, in_chans=3, **kwargs):  # DLA-169
-    default_cfg = default_cfgs['dla169']
-    model = DLA([1, 1, 2, 3, 5, 1], [16, 32, 128, 256, 512, 1024],
-                block=DlaBottleneck, residual_root=True,
-                num_classes=num_classes, in_chans=in_chans, **kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-    return model
+def dla169(pretrained=False, **kwargs):  # DLA-169
+    model_kwargs = dict(
+        levels=[1, 1, 2, 3, 5, 1], channels=[16, 32, 128, 256, 512, 1024],
+        block=DlaBottleneck, residual_root=True, **kwargs)
+    return _create_dla('dla169', pretrained, **model_kwargs)

timm/models/gluon_xception.py

@@ -12,7 +12,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from .helpers import load_pretrained
+from .helpers import build_model_with_cfg
 from .layers import SelectAdaptivePool2d, get_padding
 from .registry import register_model
 
@@ -141,13 +141,15 @@ class Xception65(nn.Module):
         # Entry flow
         self.conv1 = nn.Conv2d(in_chans, 32, kernel_size=3, stride=2, padding=1, bias=False)
         self.bn1 = norm_layer(num_features=32, **norm_kwargs)
-        self.relu = nn.ReLU(inplace=True)
+        self.act1 = nn.ReLU(inplace=True)
 
         self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False)
         self.bn2 = norm_layer(num_features=64)
+        self.act2 = nn.ReLU(inplace=True)
 
         self.block1 = Block(
             64, 128, stride=2, start_with_relu=False, norm_layer=norm_layer, norm_kwargs=norm_kwargs)
+        self.block1_act = nn.ReLU(inplace=True)
         self.block2 = Block(
             128, 256, stride=2, start_with_relu=False, norm_layer=norm_layer, norm_kwargs=norm_kwargs)
         self.block3 = Block(
@@ -162,22 +164,34 @@ class Xception65(nn.Module):
         self.block20 = Block(
             728, (728, 1024, 1024), stride=exit_block20_stride, dilation=exit_block_dilations[0],
             norm_layer=norm_layer, norm_kwargs=norm_kwargs)
+        self.block20_act = nn.ReLU(inplace=True)
 
         self.conv3 = SeparableConv2d(
             1024, 1536, 3, stride=1, dilation=exit_block_dilations[1],
             norm_layer=norm_layer, norm_kwargs=norm_kwargs)
         self.bn3 = norm_layer(num_features=1536, **norm_kwargs)
+        self.act3 = nn.ReLU(inplace=True)
 
         self.conv4 = SeparableConv2d(
             1536, 1536, 3, stride=1, dilation=exit_block_dilations[1],
             norm_layer=norm_layer, norm_kwargs=norm_kwargs)
         self.bn4 = norm_layer(num_features=1536, **norm_kwargs)
+        self.act4 = nn.ReLU(inplace=True)
 
         self.num_features = 2048
         self.conv5 = SeparableConv2d(
             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=self.num_features, **norm_kwargs)
+        self.act5 = nn.ReLU(inplace=True)
+        self.feature_info = [
+            dict(num_chs=64, reduction=2, module='act2'),
+            dict(num_chs=128, reduction=4, module='block1_act'),
+            dict(num_chs=256, reduction=8, module='block3.rep.act1'),
+            dict(num_chs=728, reduction=16, module='block20.rep.act1'),
+            dict(num_chs=2048, reduction=32, module='act5'),
+        ]
+
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
         self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
 
@@ -193,15 +207,14 @@ class Xception65(nn.Module):
         # Entry flow
         x = self.conv1(x)
         x = self.bn1(x)
-        x = self.relu(x)
+        x = self.act1(x)
 
         x = self.conv2(x)
         x = self.bn2(x)
-        x = self.relu(x)
+        x = self.act2(x)
 
         x = self.block1(x)
-        # add relu here
-        x = self.relu(x)
+        x = self.block1_act(x)
         # c1 = x
         x = self.block2(x)
         # c2 = x
@@ -213,18 +226,18 @@ class Xception65(nn.Module):
 
         # Exit flow
         x = self.block20(x)
-        x = self.relu(x)
+        x = self.block20_act(x)
         x = self.conv3(x)
         x = self.bn3(x)
-        x = self.relu(x)
+        x = self.act3(x)
 
         x = self.conv4(x)
         x = self.bn4(x)
-        x = self.relu(x)
+        x = self.act4(x)
 
         x = self.conv5(x)
         x = self.bn5(x)
-        x = self.relu(x)
+        x = self.act5(x)
         return x
 
     def forward(self, x):
@@ -236,13 +249,14 @@ class Xception65(nn.Module):
         return x
 
 
+def _create_gluon_xception(variant, pretrained=False, **kwargs):
+    return build_model_with_cfg(
+        Xception65, variant, pretrained, default_cfg=default_cfgs[variant],
+        feature_cfg=dict(use_hooks=True), **kwargs)
+
+
 @register_model
-def gluon_xception65(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+def gluon_xception65(pretrained=False, **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
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-    return model
+    return _create_gluon_xception('gluon_xception65', pretrained, **kwargs)

timm/models/helpers.py

@@ -8,7 +8,7 @@ import torch
 import torch.nn as nn
 import torch.utils.model_zoo as model_zoo
 
-from .features import FeatureNet
+from .features import FeatureNet, FeatureHookNet
 from .layers import Conv2dSame
 
 
@@ -207,6 +207,7 @@ def build_model_with_cfg(
         default_cfg: dict,
         model_cfg: dict = None,
         feature_cfg: dict = None,
+        pretrained_strict: bool = True,
         pretrained_filter_fn: Callable = None,
         **kwargs):
     pruned = kwargs.pop('pruned', False)
@@ -230,10 +231,18 @@ def build_model_with_cfg(
             model,
             num_classes=kwargs.get('num_classes', 0),
             in_chans=kwargs.get('in_chans', 3),
-            filter_fn=pretrained_filter_fn)
+            filter_fn=pretrained_filter_fn, strict=pretrained_strict)
     
     if features:
         feature_cls = feature_cfg.pop('feature_cls', FeatureNet)
+        if isinstance(feature_cls, str):
+            feature_cls = feature_cls.lower()
+            if feature_cls == 'hook' or feature_cls == 'featurehooknet':
+                feature_cls = FeatureHookNet
+            else:
+                assert False, f'Unknown feature class {feature_cls}'
+        if feature_cls == FeatureHookNet and hasattr(model, 'reset_classifier'):
+            model.reset_classifier(0)
         model = feature_cls(model, **feature_cfg)
     
     return model

timm/models/hrnet.py

@@ -735,6 +735,7 @@ class HighResolutionNet(nn.Module):
 
 
 def _create_hrnet(variant, pretrained, **model_kwargs):
+    assert not model_kwargs.pop('features_only', False)  # feature extraction not figured out yet
     return build_model_with_cfg(
         HighResolutionNet, variant, pretrained, default_cfg=default_cfgs[variant],
         model_cfg=cfg_cls[variant], **model_kwargs)

timm/models/inception_v3.py

@@ -3,7 +3,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
-from .helpers import load_pretrained
+from .helpers import build_model_with_cfg
 from .registry import register_model
 from .layers import trunc_normal_, SelectAdaptivePool2d
 
@@ -44,231 +44,6 @@ default_cfgs = {
 }
 
 
-class InceptionV3Aux(nn.Module):
-    """InceptionV3 with AuxLogits
-    """
-
-    def __init__(self, inception_blocks=None, num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg'):
-        super(InceptionV3Aux, self).__init__()
-        self.num_classes = num_classes
-        self.drop_rate = drop_rate
-
-        if inception_blocks is None:
-            inception_blocks = [
-                BasicConv2d, InceptionA, InceptionB, InceptionC,
-                InceptionD, InceptionE, InceptionAux
-            ]
-        assert len(inception_blocks) == 7
-        conv_block = inception_blocks[0]
-        inception_a = inception_blocks[1]
-        inception_b = inception_blocks[2]
-        inception_c = inception_blocks[3]
-        inception_d = inception_blocks[4]
-        inception_e = inception_blocks[5]
-        inception_aux = inception_blocks[6]
-
-        self.Conv2d_1a_3x3 = conv_block(in_chans, 32, kernel_size=3, stride=2)
-        self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3)
-        self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1)
-        self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1)
-        self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3)
-        self.Mixed_5b = inception_a(192, pool_features=32)
-        self.Mixed_5c = inception_a(256, pool_features=64)
-        self.Mixed_5d = inception_a(288, pool_features=64)
-        self.Mixed_6a = inception_b(288)
-        self.Mixed_6b = inception_c(768, channels_7x7=128)
-        self.Mixed_6c = inception_c(768, channels_7x7=160)
-        self.Mixed_6d = inception_c(768, channels_7x7=160)
-        self.Mixed_6e = inception_c(768, channels_7x7=192)
-        self.AuxLogits = inception_aux(768, num_classes)
-        self.Mixed_7a = inception_d(768)
-        self.Mixed_7b = inception_e(1280)
-        self.Mixed_7c = inception_e(2048)
-
-        self.num_features = 2048
-        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
-        self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
-                stddev = m.stddev if hasattr(m, 'stddev') else 0.1
-                trunc_normal_(m.weight, std=stddev)
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-    def forward_features(self, x):
-        # N x 3 x 299 x 299
-        x = self.Conv2d_1a_3x3(x)
-        # N x 32 x 149 x 149
-        x = self.Conv2d_2a_3x3(x)
-        # N x 32 x 147 x 147
-        x = self.Conv2d_2b_3x3(x)
-        # N x 64 x 147 x 147
-        x = F.max_pool2d(x, kernel_size=3, stride=2)
-        # N x 64 x 73 x 73
-        x = self.Conv2d_3b_1x1(x)
-        # N x 80 x 73 x 73
-        x = self.Conv2d_4a_3x3(x)
-        # N x 192 x 71 x 71
-        x = F.max_pool2d(x, kernel_size=3, stride=2)
-        # N x 192 x 35 x 35
-        x = self.Mixed_5b(x)
-        # N x 256 x 35 x 35
-        x = self.Mixed_5c(x)
-        # N x 288 x 35 x 35
-        x = self.Mixed_5d(x)
-        # N x 288 x 35 x 35
-        x = self.Mixed_6a(x)
-        # N x 768 x 17 x 17
-        x = self.Mixed_6b(x)
-        # N x 768 x 17 x 17
-        x = self.Mixed_6c(x)
-        # N x 768 x 17 x 17
-        x = self.Mixed_6d(x)
-        # N x 768 x 17 x 17
-        x = self.Mixed_6e(x)
-        # N x 768 x 17 x 17
-        aux = self.AuxLogits(x) if self.training else None
-        # N x 768 x 17 x 17
-        x = self.Mixed_7a(x)
-        # N x 1280 x 8 x 8
-        x = self.Mixed_7b(x)
-        # N x 2048 x 8 x 8
-        x = self.Mixed_7c(x)
-        # N x 2048 x 8 x 8
-        return x, aux
-
-    def get_classifier(self):
-        return self.fc
-
-    def reset_classifier(self, num_classes, global_pool='avg'):
-        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
-        self.num_classes = num_classes
-        if self.num_classes > 0:
-            self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
-        else:
-            self.fc = nn.Identity()
-
-    def forward(self, x):
-        x, aux = self.forward_features(x)
-        x = self.global_pool(x).flatten(1)
-        if self.drop_rate > 0:
-            x = F.dropout(x, p=self.drop_rate, training=self.training)
-        x = self.fc(x)
-        return x, aux
-
-
-class InceptionV3(nn.Module):
-    """Inception-V3 with no AuxLogits
-    FIXME two class defs are redundant, but less screwing around with torchsript fussyness and inconsistent returns
-    """
-
-    def __init__(self, inception_blocks=None, num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg'):
-        super(InceptionV3, self).__init__()
-        self.num_classes = num_classes
-        self.drop_rate = drop_rate
-
-        if inception_blocks is None:
-            inception_blocks = [
-                BasicConv2d, InceptionA, InceptionB, InceptionC, InceptionD, InceptionE]
-        assert len(inception_blocks) >= 6
-        conv_block = inception_blocks[0]
-        inception_a = inception_blocks[1]
-        inception_b = inception_blocks[2]
-        inception_c = inception_blocks[3]
-        inception_d = inception_blocks[4]
-        inception_e = inception_blocks[5]
-
-        self.Conv2d_1a_3x3 = conv_block(in_chans, 32, kernel_size=3, stride=2)
-        self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3)
-        self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1)
-        self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1)
-        self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3)
-        self.Mixed_5b = inception_a(192, pool_features=32)
-        self.Mixed_5c = inception_a(256, pool_features=64)
-        self.Mixed_5d = inception_a(288, pool_features=64)
-        self.Mixed_6a = inception_b(288)
-        self.Mixed_6b = inception_c(768, channels_7x7=128)
-        self.Mixed_6c = inception_c(768, channels_7x7=160)
-        self.Mixed_6d = inception_c(768, channels_7x7=160)
-        self.Mixed_6e = inception_c(768, channels_7x7=192)
-        self.Mixed_7a = inception_d(768)
-        self.Mixed_7b = inception_e(1280)
-        self.Mixed_7c = inception_e(2048)
-
-        self.num_features = 2048
-        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
-        self.fc = nn.Linear(2048, num_classes)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
-                stddev = m.stddev if hasattr(m, 'stddev') else 0.1
-                trunc_normal_(m.weight, std=stddev)
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-    def forward_features(self, x):
-        # N x 3 x 299 x 299
-        x = self.Conv2d_1a_3x3(x)
-        # N x 32 x 149 x 149
-        x = self.Conv2d_2a_3x3(x)
-        # N x 32 x 147 x 147
-        x = self.Conv2d_2b_3x3(x)
-        # N x 64 x 147 x 147
-        x = F.max_pool2d(x, kernel_size=3, stride=2)
-        # N x 64 x 73 x 73
-        x = self.Conv2d_3b_1x1(x)
-        # N x 80 x 73 x 73
-        x = self.Conv2d_4a_3x3(x)
-        # N x 192 x 71 x 71
-        x = F.max_pool2d(x, kernel_size=3, stride=2)
-        # N x 192 x 35 x 35
-        x = self.Mixed_5b(x)
-        # N x 256 x 35 x 35
-        x = self.Mixed_5c(x)
-        # N x 288 x 35 x 35
-        x = self.Mixed_5d(x)
-        # N x 288 x 35 x 35
-        x = self.Mixed_6a(x)
-        # N x 768 x 17 x 17
-        x = self.Mixed_6b(x)
-        # N x 768 x 17 x 17
-        x = self.Mixed_6c(x)
-        # N x 768 x 17 x 17
-        x = self.Mixed_6d(x)
-        # N x 768 x 17 x 17
-        x = self.Mixed_6e(x)
-        # N x 768 x 17 x 17
-        x = self.Mixed_7a(x)
-        # N x 1280 x 8 x 8
-        x = self.Mixed_7b(x)
-        # N x 2048 x 8 x 8
-        x = self.Mixed_7c(x)
-        # N x 2048 x 8 x 8
-        return  x
-
-    def get_classifier(self):
-        return self.fc
-
-    def reset_classifier(self, num_classes, global_pool='avg'):
-        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
-        self.num_classes = num_classes
-        if self.num_classes > 0:
-            self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
-        else:
-            self.fc = nn.Identity()
-
-    def forward(self, x):
-        x = self.forward_features(x)
-        x = self.global_pool(x).flatten(1)
-        if self.drop_rate > 0:
-            x = F.dropout(x, p=self.drop_rate, training=self.training)
-        x = self.fc(x)
-        return x
-
-
 class InceptionA(nn.Module):
 
     def __init__(self, in_channels, pool_features, conv_block=None):
@@ -504,26 +279,163 @@ class BasicConv2d(nn.Module):
         return F.relu(x, inplace=True)
 
 
+class InceptionV3(nn.Module):
+    """Inception-V3 with no AuxLogits
+    FIXME two class defs are redundant, but less screwing around with torchsript fussyness and inconsistent returns
+    """
+
+    def __init__(self, num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg', aux_logits=False):
+        super(InceptionV3, self).__init__()
+        self.num_classes = num_classes
+        self.drop_rate = drop_rate
+        self.aux_logits = aux_logits
+
+        self.Conv2d_1a_3x3 = BasicConv2d(in_chans, 32, kernel_size=3, stride=2)
+        self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3)
+        self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1)
+        self.Pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
+        self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1)
+        self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3)
+        self.Pool2 = nn.MaxPool2d(kernel_size=3, stride=2)
+        self.Mixed_5b = InceptionA(192, pool_features=32)
+        self.Mixed_5c = InceptionA(256, pool_features=64)
+        self.Mixed_5d = InceptionA(288, pool_features=64)
+        self.Mixed_6a = InceptionB(288)
+        self.Mixed_6b = InceptionC(768, channels_7x7=128)
+        self.Mixed_6c = InceptionC(768, channels_7x7=160)
+        self.Mixed_6d = InceptionC(768, channels_7x7=160)
+        self.Mixed_6e = InceptionC(768, channels_7x7=192)
+        if aux_logits:
+            self.AuxLogits = InceptionAux(768, num_classes)
+        else:
+            self.AuxLogits = None
+        self.Mixed_7a = InceptionD(768)
+        self.Mixed_7b = InceptionE(1280)
+        self.Mixed_7c = InceptionE(2048)
+        self.feature_info = [
+            dict(num_chs=64, reduction=2, module='Conv2d_2b_3x3'),
+            dict(num_chs=192, reduction=4, module='Conv2d_4a_3x3'),
+            dict(num_chs=288, reduction=8, module='Mixed_5d'),
+            dict(num_chs=768, reduction=16, module='Mixed_6e'),
+            dict(num_chs=2048, reduction=32, module='Mixed_7c'),
+        ]
+
+        self.num_features = 2048
+        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
+        self.fc = nn.Linear(2048, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
+                stddev = m.stddev if hasattr(m, 'stddev') else 0.1
+                trunc_normal_(m.weight, std=stddev)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+    def forward_preaux(self, x):
+        # N x 3 x 299 x 299
+        x = self.Conv2d_1a_3x3(x)
+        # N x 32 x 149 x 149
+        x = self.Conv2d_2a_3x3(x)
+        # N x 32 x 147 x 147
+        x = self.Conv2d_2b_3x3(x)
+        # N x 64 x 147 x 147
+        x = self.Pool1(x)
+        # N x 64 x 73 x 73
+        x = self.Conv2d_3b_1x1(x)
+        # N x 80 x 73 x 73
+        x = self.Conv2d_4a_3x3(x)
+        # N x 192 x 71 x 71
+        x = self.Pool2(x)
+        # N x 192 x 35 x 35
+        x = self.Mixed_5b(x)
+        # N x 256 x 35 x 35
+        x = self.Mixed_5c(x)
+        # N x 288 x 35 x 35
+        x = self.Mixed_5d(x)
+        # N x 288 x 35 x 35
+        x = self.Mixed_6a(x)
+        # N x 768 x 17 x 17
+        x = self.Mixed_6b(x)
+        # N x 768 x 17 x 17
+        x = self.Mixed_6c(x)
+        # N x 768 x 17 x 17
+        x = self.Mixed_6d(x)
+        # N x 768 x 17 x 17
+        x = self.Mixed_6e(x)
+        # N x 768 x 17 x 17
+        return x
+
+    def forward_postaux(self, x):
+        x = self.Mixed_7a(x)
+        # N x 1280 x 8 x 8
+        x = self.Mixed_7b(x)
+        # N x 2048 x 8 x 8
+        x = self.Mixed_7c(x)
+        # N x 2048 x 8 x 8
+        return x
+
+    def forward_features(self, x):
+        x = self.forward_preaux(x)
+        x = self.forward_postaux(x)
+        return x
+
+    def get_classifier(self):
+        return self.fc
+
+    def reset_classifier(self, num_classes, global_pool='avg'):
+        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
+        self.num_classes = num_classes
+        if self.num_classes > 0:
+            self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
+        else:
+            self.fc = nn.Identity()
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.global_pool(x).flatten(1)
+        if self.drop_rate > 0:
+            x = F.dropout(x, p=self.drop_rate, training=self.training)
+        x = self.fc(x)
+        return x
+
+
+class InceptionV3Aux(InceptionV3):
+    """InceptionV3 with AuxLogits
+    """
+
+    def __init__(self, num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg', aux_logits=True):
+        super(InceptionV3Aux, self).__init__(
+            num_classes, in_chans, drop_rate, global_pool, aux_logits)
+
+    def forward_features(self, x):
+        x = self.forward_preaux(x)
+        aux = self.AuxLogits(x) if self.training else None
+        x = self.forward_postaux(x)
+        return x, aux
+
+    def forward(self, x):
+        x, aux = self.forward_features(x)
+        x = self.global_pool(x).flatten(1)
+        if self.drop_rate > 0:
+            x = F.dropout(x, p=self.drop_rate, training=self.training)
+        x = self.fc(x)
+        return x, aux
+
+
 def _create_inception_v3(variant, pretrained=False, **kwargs):
-    assert not kwargs.pop('features_only', False)
     default_cfg = default_cfgs[variant]
     aux_logits = kwargs.pop('aux_logits', False)
     if aux_logits:
-        model_class = InceptionV3Aux
+        assert not kwargs.pop('features_only', False)
+        model_cls = InceptionV3Aux
         load_strict = default_cfg['has_aux']
     else:
-        model_class = InceptionV3
+        model_cls = InceptionV3
         load_strict = not default_cfg['has_aux']
-
-    model = model_class(**kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(
-            model,
-            num_classes=kwargs.get('num_classes', 0),
-            in_chans=kwargs.get('in_chans', 3),
-            strict=load_strict)
-    return model
+    return build_model_with_cfg(
+        model_cls, variant, pretrained, default_cfg=default_cfgs[variant],
+        pretrained_strict=load_strict, **kwargs)
 
 
 @register_model

timm/models/inception_v4.py

@@ -7,7 +7,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
-from .helpers import load_pretrained
+from .helpers import build_model_with_cfg
 from .layers import SelectAdaptivePool2d
 from .registry import register_model
 
@@ -39,9 +39,9 @@ class BasicConv2d(nn.Module):
         return x
 
 
-class Mixed_3a(nn.Module):
+class Mixed3a(nn.Module):
     def __init__(self):
-        super(Mixed_3a, self).__init__()
+        super(Mixed3a, self).__init__()
         self.maxpool = nn.MaxPool2d(3, stride=2)
         self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2)
 
@@ -52,9 +52,9 @@ class Mixed_3a(nn.Module):
         return out
 
 
-class Mixed_4a(nn.Module):
+class Mixed4a(nn.Module):
     def __init__(self):
-        super(Mixed_4a, self).__init__()
+        super(Mixed4a, self).__init__()
 
         self.branch0 = nn.Sequential(
             BasicConv2d(160, 64, kernel_size=1, stride=1),
@@ -75,9 +75,9 @@ class Mixed_4a(nn.Module):
         return out
 
 
-class Mixed_5a(nn.Module):
+class Mixed5a(nn.Module):
     def __init__(self):
-        super(Mixed_5a, self).__init__()
+        super(Mixed5a, self).__init__()
         self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2)
         self.maxpool = nn.MaxPool2d(3, stride=2)
 
@@ -88,9 +88,9 @@ class Mixed_5a(nn.Module):
         return out
 
 
-class Inception_A(nn.Module):
+class InceptionA(nn.Module):
     def __init__(self):
-        super(Inception_A, self).__init__()
+        super(InceptionA, self).__init__()
         self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1)
 
         self.branch1 = nn.Sequential(
@@ -118,9 +118,9 @@ class Inception_A(nn.Module):
         return out
 
 
-class Reduction_A(nn.Module):
+class ReductionA(nn.Module):
     def __init__(self):
-        super(Reduction_A, self).__init__()
+        super(ReductionA, self).__init__()
         self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2)
 
         self.branch1 = nn.Sequential(
@@ -139,9 +139,9 @@ class Reduction_A(nn.Module):
         return out
 
 
-class Inception_B(nn.Module):
+class InceptionB(nn.Module):
     def __init__(self):
-        super(Inception_B, self).__init__()
+        super(InceptionB, self).__init__()
         self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1)
 
         self.branch1 = nn.Sequential(
@@ -172,9 +172,9 @@ class Inception_B(nn.Module):
         return out
 
 
-class Reduction_B(nn.Module):
+class ReductionB(nn.Module):
     def __init__(self):
-        super(Reduction_B, self).__init__()
+        super(ReductionB, self).__init__()
 
         self.branch0 = nn.Sequential(
             BasicConv2d(1024, 192, kernel_size=1, stride=1),
@@ -198,9 +198,9 @@ class Reduction_B(nn.Module):
         return out
 
 
-class Inception_C(nn.Module):
+class InceptionC(nn.Module):
     def __init__(self):
-        super(Inception_C, self).__init__()
+        super(InceptionC, self).__init__()
 
         self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1)
 
@@ -241,8 +241,9 @@ class Inception_C(nn.Module):
 
 
 class InceptionV4(nn.Module):
-    def __init__(self, num_classes=1001, in_chans=3, drop_rate=0., global_pool='avg'):
+    def __init__(self, num_classes=1001, in_chans=3, output_stride=32, drop_rate=0., global_pool='avg'):
         super(InceptionV4, self).__init__()
+        assert output_stride == 32
         self.drop_rate = drop_rate
         self.num_classes = num_classes
         self.num_features = 1536
@@ -251,26 +252,33 @@ class InceptionV4(nn.Module):
             BasicConv2d(in_chans, 32, kernel_size=3, stride=2),
             BasicConv2d(32, 32, kernel_size=3, stride=1),
             BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1),
-            Mixed_3a(),
-            Mixed_4a(),
-            Mixed_5a(),
-            Inception_A(),
-            Inception_A(),
-            Inception_A(),
-            Inception_A(),
-            Reduction_A(),  # Mixed_6a
-            Inception_B(),
-            Inception_B(),
-            Inception_B(),
-            Inception_B(),
-            Inception_B(),
-            Inception_B(),
-            Inception_B(),
-            Reduction_B(),  # Mixed_7a
-            Inception_C(),
-            Inception_C(),
-            Inception_C(),
+            Mixed3a(),
+            Mixed4a(),
+            Mixed5a(),
+            InceptionA(),
+            InceptionA(),
+            InceptionA(),
+            InceptionA(),
+            ReductionA(),  # Mixed6a
+            InceptionB(),
+            InceptionB(),
+            InceptionB(),
+            InceptionB(),
+            InceptionB(),
+            InceptionB(),
+            InceptionB(),
+            ReductionB(),  # Mixed7a
+            InceptionC(),
+            InceptionC(),
+            InceptionC(),
         )
+        self.feature_info = [
+            dict(num_chs=64, reduction=2, module='features.2'),
+            dict(num_chs=160, reduction=4, module='features.3'),
+            dict(num_chs=384, reduction=8, module='features.9'),
+            dict(num_chs=1024, reduction=16, module='features.17'),
+            dict(num_chs=1536, reduction=32, module='features.21'),
+        ]
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
         self.last_linear = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
 
@@ -298,11 +306,12 @@ class InceptionV4(nn.Module):
         return x
 
 
+def _create_inception_v4(variant, pretrained=False, **kwargs):
+    return build_model_with_cfg(
+        InceptionV4, variant, pretrained, default_cfg=default_cfgs[variant],
+        feature_cfg=dict(flatten_sequential=True), **kwargs)
+
+
 @register_model
-def inception_v4(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
-    default_cfg = default_cfgs['inception_v4']
-    model = InceptionV4(num_classes=num_classes, in_chans=in_chans, **kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-    return model
+def inception_v4(pretrained=False, **kwargs):
+    return _create_inception_v4('inception_v4', pretrained, **kwargs)

timm/models/nasnet.py

@@ -1,8 +1,11 @@
+"""
+
+"""
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from .helpers import load_pretrained
+from .helpers import build_model_with_cfg
 from .layers import SelectAdaptivePool2d, ConvBnAct, create_conv2d, create_pool2d
 from .registry import register_model
 
@@ -484,8 +487,15 @@ class NASNetALarge(nn.Module):
         self.cell_17 = NormalCell(
             in_chs_left=24 * channels, out_chs_left=4 * channels,
             in_chs_right=24 * channels, out_chs_right=4 * channels, pad_type=pad_type)
-
         self.act = nn.ReLU(inplace=True)
+        self.feature_info = [
+            dict(num_chs=96, reduction=2, module='conv0'),
+            dict(num_chs=168, reduction=4, module='cell_stem_1.conv_1x1.act'),
+            dict(num_chs=1008, reduction=8, module='reduction_cell_0.conv_1x1.act'),
+            dict(num_chs=2016, reduction=16, module='reduction_cell_1.conv_1x1.act'),
+            dict(num_chs=4032, reduction=32, module='act'),
+        ]
+
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
         self.last_linear = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
 
@@ -503,11 +513,9 @@ class NASNetALarge(nn.Module):
 
     def forward_features(self, x):
         x_conv0 = self.conv0(x)
-        #0
 
         x_stem_0 = self.cell_stem_0(x_conv0)
         x_stem_1 = self.cell_stem_1(x_conv0, x_stem_0)
-        #1
 
         x_cell_0 = self.cell_0(x_stem_1, x_stem_0)
         x_cell_1 = self.cell_1(x_cell_0, x_stem_1)
@@ -515,7 +523,6 @@ class NASNetALarge(nn.Module):
         x_cell_3 = self.cell_3(x_cell_2, x_cell_1)
         x_cell_4 = self.cell_4(x_cell_3, x_cell_2)
         x_cell_5 = self.cell_5(x_cell_4, x_cell_3)
-        #2
 
         x_reduction_cell_0 = self.reduction_cell_0(x_cell_5, x_cell_4)
         x_cell_6 = self.cell_6(x_reduction_cell_0, x_cell_4)
@@ -524,7 +531,6 @@ class NASNetALarge(nn.Module):
         x_cell_9 = self.cell_9(x_cell_8, x_cell_7)
         x_cell_10 = self.cell_10(x_cell_9, x_cell_8)
         x_cell_11 = self.cell_11(x_cell_10, x_cell_9)
-        #3
 
         x_reduction_cell_1 = self.reduction_cell_1(x_cell_11, x_cell_10)
         x_cell_12 = self.cell_12(x_reduction_cell_1, x_cell_10)
@@ -534,8 +540,6 @@ class NASNetALarge(nn.Module):
         x_cell_16 = self.cell_16(x_cell_15, x_cell_14)
         x_cell_17 = self.cell_17(x_cell_16, x_cell_15)
         x = self.act(x_cell_17)
-        #4
-
         return x
 
     def forward(self, x):
@@ -547,14 +551,16 @@ class NASNetALarge(nn.Module):
         return x
 
 
+def _create_nasnet(variant, pretrained=False, **kwargs):
+    return build_model_with_cfg(
+        NASNetALarge, variant, pretrained, default_cfg=default_cfgs[variant],
+        feature_cfg=dict(feature_cls='hook'),  # not possible to re-write this model, must use FeatureHookNet
+        **kwargs)
+
+
 @register_model
-def nasnetalarge(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+def nasnetalarge(pretrained=False, **kwargs):
     """NASNet-A large model architecture.
     """
-    default_cfg = default_cfgs['nasnetalarge']
-    model = NASNetALarge(num_classes=num_classes, in_chans=in_chans, **kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-
-    return model
+    model_kwargs = dict(pad_type='same', **kwargs)
+    return _create_nasnet('nasnetalarge', pretrained, **model_kwargs)

timm/models/pnasnet.py

@@ -5,15 +5,13 @@
  https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
 
 """
-from __future__ import print_function, division, absolute_import
-
 from collections import OrderedDict
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from .helpers import load_pretrained
+from .helpers import build_model_with_cfg
 from .layers import SelectAdaptivePool2d, ConvBnAct, create_conv2d, create_pool2d
 from .registry import register_model
 
@@ -147,35 +145,35 @@ class CellBase(nn.Module):
 
 class CellStem0(CellBase):
 
-    def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, padding=''):
+    def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
         super(CellStem0, self).__init__()
-        self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, kernel_size=1, padding=padding)
+        self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, kernel_size=1, padding=pad_type)
 
         self.comb_iter_0_left = BranchSeparables(
-            in_chs_left, out_chs_left, kernel_size=5, stride=2, stem_cell=True, padding=padding)
+            in_chs_left, out_chs_left, kernel_size=5, stride=2, stem_cell=True, padding=pad_type)
         self.comb_iter_0_right = nn.Sequential(OrderedDict([
-            ('max_pool', create_pool2d('max', 3, stride=2, padding=padding)),
-            ('conv', create_conv2d(in_chs_left, out_chs_left, kernel_size=1, padding=padding)),
+            ('max_pool', create_pool2d('max', 3, stride=2, padding=pad_type)),
+            ('conv', create_conv2d(in_chs_left, out_chs_left, kernel_size=1, padding=pad_type)),
             ('bn', nn.BatchNorm2d(out_chs_left, eps=0.001)),
         ]))
 
         self.comb_iter_1_left = BranchSeparables(
-            out_chs_right, out_chs_right, kernel_size=7, stride=2, padding=padding)
-        self.comb_iter_1_right = create_pool2d('max', 3, stride=2, padding=padding)
+            out_chs_right, out_chs_right, kernel_size=7, stride=2, padding=pad_type)
+        self.comb_iter_1_right = create_pool2d('max', 3, stride=2, padding=pad_type)
 
         self.comb_iter_2_left = BranchSeparables(
-            out_chs_right, out_chs_right, kernel_size=5, stride=2, padding=padding)
+            out_chs_right, out_chs_right, kernel_size=5, stride=2, padding=pad_type)
         self.comb_iter_2_right = BranchSeparables(
-            out_chs_right, out_chs_right, kernel_size=3, stride=2, padding=padding)
+            out_chs_right, out_chs_right, kernel_size=3, stride=2, padding=pad_type)
 
         self.comb_iter_3_left = BranchSeparables(
-            out_chs_right, out_chs_right, kernel_size=3, padding=padding)
-        self.comb_iter_3_right = create_pool2d('max', 3, stride=2, padding=padding)
+            out_chs_right, out_chs_right, kernel_size=3, padding=pad_type)
+        self.comb_iter_3_right = create_pool2d('max', 3, stride=2, padding=pad_type)
 
         self.comb_iter_4_left = BranchSeparables(
-            in_chs_right, out_chs_right, kernel_size=3, stride=2, stem_cell=True, padding=padding)
+            in_chs_right, out_chs_right, kernel_size=3, stride=2, stem_cell=True, padding=pad_type)
         self.comb_iter_4_right = ActConvBn(
-            out_chs_right, out_chs_right, kernel_size=1, stride=2, padding=padding)
+            out_chs_right, out_chs_right, kernel_size=1, stride=2, padding=pad_type)
 
     def forward(self, x_left):
         x_right = self.conv_1x1(x_left)
@@ -185,12 +183,12 @@ class CellStem0(CellBase):
 
 class Cell(CellBase):
 
-    def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, padding='',
+    def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type='',
                  is_reduction=False, match_prev_layer_dims=False):
         super(Cell, self).__init__()
 
         # If `is_reduction` is set to `True` stride 2 is used for
-        # convolutional and pooling layers to reduce the spatial size of
+        # convolution and pooling layers to reduce the spatial size of
         # the output of a cell approximately by a factor of 2.
         stride = 2 if is_reduction else 1
 
@@ -199,32 +197,32 @@ class Cell(CellBase):
         # of the left input of a cell approximately by a factor of 2.
         self.match_prev_layer_dimensions = match_prev_layer_dims
         if match_prev_layer_dims:
-            self.conv_prev_1x1 = FactorizedReduction(in_chs_left, out_chs_left, padding=padding)
+            self.conv_prev_1x1 = FactorizedReduction(in_chs_left, out_chs_left, padding=pad_type)
         else:
-            self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, kernel_size=1, padding=padding)
-        self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, kernel_size=1, padding=padding)
+            self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, kernel_size=1, padding=pad_type)
+        self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, kernel_size=1, padding=pad_type)
 
         self.comb_iter_0_left = BranchSeparables(
-            out_chs_left, out_chs_left, kernel_size=5, stride=stride, padding=padding)
-        self.comb_iter_0_right = create_pool2d('max', 3, stride=stride, padding=padding)
+            out_chs_left, out_chs_left, kernel_size=5, stride=stride, padding=pad_type)
+        self.comb_iter_0_right = create_pool2d('max', 3, stride=stride, padding=pad_type)
 
         self.comb_iter_1_left = BranchSeparables(
-            out_chs_right, out_chs_right, kernel_size=7, stride=stride, padding=padding)
-        self.comb_iter_1_right = create_pool2d('max', 3, stride=stride, padding=padding)
+            out_chs_right, out_chs_right, kernel_size=7, stride=stride, padding=pad_type)
+        self.comb_iter_1_right = create_pool2d('max', 3, stride=stride, padding=pad_type)
 
         self.comb_iter_2_left = BranchSeparables(
-            out_chs_right, out_chs_right, kernel_size=5, stride=stride, padding=padding)
+            out_chs_right, out_chs_right, kernel_size=5, stride=stride, padding=pad_type)
         self.comb_iter_2_right = BranchSeparables(
-            out_chs_right, out_chs_right, kernel_size=3, stride=stride, padding=padding)
+            out_chs_right, out_chs_right, kernel_size=3, stride=stride, padding=pad_type)
 
         self.comb_iter_3_left = BranchSeparables(out_chs_right, out_chs_right, kernel_size=3)
-        self.comb_iter_3_right = create_pool2d('max', 3, stride=stride, padding=padding)
+        self.comb_iter_3_right = create_pool2d('max', 3, stride=stride, padding=pad_type)
 
         self.comb_iter_4_left = BranchSeparables(
-            out_chs_left, out_chs_left, kernel_size=3, stride=stride, padding=padding)
+            out_chs_left, out_chs_left, kernel_size=3, stride=stride, padding=pad_type)
         if is_reduction:
             self.comb_iter_4_right = ActConvBn(
-                out_chs_right, out_chs_right, kernel_size=1, stride=stride, padding=padding)
+                out_chs_right, out_chs_right, kernel_size=1, stride=stride, padding=pad_type)
         else:
             self.comb_iter_4_right = None
 
@@ -236,7 +234,7 @@ class Cell(CellBase):
 
 
 class PNASNet5Large(nn.Module):
-    def __init__(self, num_classes=1001, in_chans=3, output_stride=32, drop_rate=0.5, global_pool='avg', padding=''):
+    def __init__(self, num_classes=1001, in_chans=3, output_stride=32, drop_rate=0., global_pool='avg', pad_type=''):
         super(PNASNet5Large, self).__init__()
         self.num_classes = num_classes
         self.drop_rate = drop_rate
@@ -248,43 +246,51 @@ class PNASNet5Large(nn.Module):
             norm_kwargs=dict(eps=0.001, momentum=0.1), act_layer=None)
 
         self.cell_stem_0 = CellStem0(
-            in_chs_left=96, out_chs_left=54, in_chs_right=96, out_chs_right=54, padding=padding)
+            in_chs_left=96, out_chs_left=54, in_chs_right=96, out_chs_right=54, pad_type=pad_type)
 
         self.cell_stem_1 = Cell(
-            in_chs_left=96, out_chs_left=108, in_chs_right=270, out_chs_right=108, padding=padding,
+            in_chs_left=96, out_chs_left=108, in_chs_right=270, out_chs_right=108, pad_type=pad_type,
             match_prev_layer_dims=True, is_reduction=True)
         self.cell_0 = Cell(
-            in_chs_left=270, out_chs_left=216, in_chs_right=540, out_chs_right=216, padding=padding,
+            in_chs_left=270, out_chs_left=216, in_chs_right=540, out_chs_right=216, pad_type=pad_type,
             match_prev_layer_dims=True)
         self.cell_1 = Cell(
-            in_chs_left=540, out_chs_left=216, in_chs_right=1080, out_chs_right=216, padding=padding)
+            in_chs_left=540, out_chs_left=216, in_chs_right=1080, out_chs_right=216, pad_type=pad_type)
         self.cell_2 = Cell(
-            in_chs_left=1080, out_chs_left=216, in_chs_right=1080, out_chs_right=216, padding=padding)
+            in_chs_left=1080, out_chs_left=216, in_chs_right=1080, out_chs_right=216, pad_type=pad_type)
         self.cell_3 = Cell(
-            in_chs_left=1080, out_chs_left=216, in_chs_right=1080, out_chs_right=216, padding=padding)
+            in_chs_left=1080, out_chs_left=216, in_chs_right=1080, out_chs_right=216, pad_type=pad_type)
 
         self.cell_4 = Cell(
-            in_chs_left=1080, out_chs_left=432, in_chs_right=1080, out_chs_right=432, padding=padding,
+            in_chs_left=1080, out_chs_left=432, in_chs_right=1080, out_chs_right=432, pad_type=pad_type,
             is_reduction=True)
         self.cell_5 = Cell(
-            in_chs_left=1080, out_chs_left=432, in_chs_right=2160, out_chs_right=432, padding=padding,
+            in_chs_left=1080, out_chs_left=432, in_chs_right=2160, out_chs_right=432, pad_type=pad_type,
             match_prev_layer_dims=True)
         self.cell_6 = Cell(
-            in_chs_left=2160, out_chs_left=432, in_chs_right=2160, out_chs_right=432, padding=padding)
+            in_chs_left=2160, out_chs_left=432, in_chs_right=2160, out_chs_right=432, pad_type=pad_type)
         self.cell_7 = Cell(
-            in_chs_left=2160, out_chs_left=432, in_chs_right=2160, out_chs_right=432, padding=padding)
+            in_chs_left=2160, out_chs_left=432, in_chs_right=2160, out_chs_right=432, pad_type=pad_type)
 
         self.cell_8 = Cell(
-            in_chs_left=2160, out_chs_left=864, in_chs_right=2160, out_chs_right=864, padding=padding,
+            in_chs_left=2160, out_chs_left=864, in_chs_right=2160, out_chs_right=864, pad_type=pad_type,
             is_reduction=True)
         self.cell_9 = Cell(
-            in_chs_left=2160, out_chs_left=864, in_chs_right=4320, out_chs_right=864, padding=padding,
+            in_chs_left=2160, out_chs_left=864, in_chs_right=4320, out_chs_right=864, pad_type=pad_type,
             match_prev_layer_dims=True)
         self.cell_10 = Cell(
-            in_chs_left=4320, out_chs_left=864, in_chs_right=4320, out_chs_right=864, padding=padding)
+            in_chs_left=4320, out_chs_left=864, in_chs_right=4320, out_chs_right=864, pad_type=pad_type)
         self.cell_11 = Cell(
-            in_chs_left=4320, out_chs_left=864, in_chs_right=4320, out_chs_right=864, padding=padding)
-        self.relu = nn.ReLU()
+            in_chs_left=4320, out_chs_left=864, in_chs_right=4320, out_chs_right=864, pad_type=pad_type)
+        self.act = nn.ReLU()
+        self.feature_info = [
+            dict(num_chs=96, reduction=2, module='conv_0'),
+            dict(num_chs=270, reduction=4, module='cell_stem_1.conv_1x1.act'),
+            dict(num_chs=1080, reduction=8, module='cell_4.conv_1x1.act'),
+            dict(num_chs=2160, reduction=16, module='cell_8.conv_1x1.act'),
+            dict(num_chs=4320, reduction=32, module='act'),
+        ]
+
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
         self.last_linear = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
 
@@ -316,7 +322,7 @@ 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)
-        x = self.relu(x_cell_11)
+        x = self.act(x_cell_11)
         return x
 
     def forward(self, x):
@@ -328,16 +334,18 @@ class PNASNet5Large(nn.Module):
         return x
 
 
+def _create_pnasnet(variant, pretrained=False, **kwargs):
+    return build_model_with_cfg(
+        PNASNet5Large, variant, pretrained, default_cfg=default_cfgs[variant],
+        feature_cfg=dict(feature_cls='hook'),  # not possible to re-write this model, must use FeatureHookNet
+        **kwargs)
+
+
 @register_model
-def pnasnet5large(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+def pnasnet5large(pretrained=False, **kwargs):
     r"""PNASNet-5 model architecture from the
     `"Progressive Neural Architecture Search"
     <https://arxiv.org/abs/1712.00559>`_ paper.
     """
-    default_cfg = default_cfgs['pnasnet5large']
-    model = PNASNet5Large(num_classes=num_classes, in_chans=in_chans, padding='same', **kwargs)
-    model.default_cfg = default_cfg
-    if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
-
-    return model
+    model_kwargs = dict(pad_type='same', **kwargs)
+    return _create_pnasnet('pnasnet5large', pretrained, **model_kwargs)

timm/models/xception.py

@@ -154,6 +154,13 @@ class Xception(nn.Module):
         self.conv4 = SeparableConv2d(1536, self.num_features, 3, 1, 1)
         self.bn4 = nn.BatchNorm2d(self.num_features)
         self.act4 = nn.ReLU(inplace=True)
+        self.feature_info = [
+            dict(num_chs=64, reduction=2, module='act2'),
+            dict(num_chs=128, reduction=4, module='block2.rep.0'),
+            dict(num_chs=256, reduction=8, module='block3.rep.0'),
+            dict(num_chs=728, reduction=16, module='block12.rep.0'),
+            dict(num_chs=2048, reduction=32, module='act4'),
+        ]
 
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
         self.fc = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
@@ -221,7 +228,7 @@ class Xception(nn.Module):
 def _xception(variant, pretrained=False, **kwargs):
     return build_model_with_cfg(
         Xception, variant, pretrained, default_cfg=default_cfgs[variant],
-        feature_cfg=dict(), **kwargs)
+        feature_cfg=dict(use_hooks=True), **kwargs)
 
 
 @register_model