Replace all None by nn.Identity() in HRNet modules

Author: Animatory

timm/models/hrnet.py

@@ -13,20 +13,16 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
 import logging
-import functools
 
-import torch
 import torch.nn as nn
-import torch._utils
 import torch.nn.functional as F
 
-from .resnet import BasicBlock, Bottleneck  # leveraging ResNet blocks w/ additional features like SE
-from .registry import register_model
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .helpers import load_pretrained
 from .layers import SelectAdaptivePool2d
-from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from .registry import register_model
+from .resnet import BasicBlock, Bottleneck  # leveraging ResNet blocks w/ additional features like SE
 
 _BN_MOMENTUM = 0.1
 logger = logging.getLogger(__name__)
@@ -101,7 +97,7 @@ def _cfg(url='', **kwargs):
         ),
     ),
 
-    hrnet_w18_small_v2 = dict(
+    hrnet_w18_small_v2=dict(
         STEM_WIDTH=64,
         STAGE1=dict(
             NUM_MODULES=1,
@@ -137,7 +133,7 @@ def _cfg(url='', **kwargs):
         ),
     ),
 
-    hrnet_w18 = dict(
+    hrnet_w18=dict(
         STEM_WIDTH=64,
         STAGE1=dict(
             NUM_MODULES=1,
@@ -173,7 +169,7 @@ def _cfg(url='', **kwargs):
         ),
     ),
 
-    hrnet_w30 = dict(
+    hrnet_w30=dict(
         STEM_WIDTH=64,
         STAGE1=dict(
             NUM_MODULES=1,
@@ -209,7 +205,7 @@ def _cfg(url='', **kwargs):
         ),
     ),
 
-    hrnet_w32 = dict(
+    hrnet_w32=dict(
         STEM_WIDTH=64,
         STAGE1=dict(
             NUM_MODULES=1,
@@ -245,7 +241,7 @@ def _cfg(url='', **kwargs):
         ),
     ),
 
-    hrnet_w40 = dict(
+    hrnet_w40=dict(
         STEM_WIDTH=64,
         STAGE1=dict(
             NUM_MODULES=1,
@@ -281,7 +277,7 @@ def _cfg(url='', **kwargs):
         ),
     ),
 
-    hrnet_w44 = dict(
+    hrnet_w44=dict(
         STEM_WIDTH=64,
         STAGE1=dict(
             NUM_MODULES=1,
@@ -317,7 +313,7 @@ def _cfg(url='', **kwargs):
         ),
     ),
 
-    hrnet_w48 = dict(
+    hrnet_w48=dict(
         STEM_WIDTH=64,
         STAGE1=dict(
             NUM_MODULES=1,
@@ -353,7 +349,7 @@ def _cfg(url='', **kwargs):
         ),
     ),
 
-    hrnet_w64 = dict(
+    hrnet_w64=dict(
         STEM_WIDTH=64,
         STAGE1=dict(
             NUM_MODULES=1,
@@ -456,7 +452,7 @@ def _make_branches(self, num_branches, block, num_blocks, num_channels):
 
     def _make_fuse_layers(self):
         if self.num_branches == 1:
-            return None
+            return nn.Identity()
 
         num_branches = self.num_branches
         num_inchannels = self.num_inchannels
@@ -470,7 +466,7 @@ def _make_fuse_layers(self):
                         nn.BatchNorm2d(num_inchannels[i], momentum=_BN_MOMENTUM),
                         nn.Upsample(scale_factor=2 ** (j - i), mode='nearest')))
                 elif j == i:
-                    fuse_layer.append(None)
+                    fuse_layer.append(nn.Identity())
                 else:
                     conv3x3s = []
                     for k in range(i - j):
@@ -619,7 +615,7 @@ def _make_transition_layer(self, num_channels_pre_layer, num_channels_cur_layer)
                         nn.BatchNorm2d(num_channels_cur_layer[i], momentum=_BN_MOMENTUM),
                         nn.ReLU(inplace=True)))
                 else:
-                    transition_layers.append(None)
+                    transition_layers.append(nn.Identity())
             else:
                 conv3x3s = []
                 for j in range(i + 1 - num_branches_pre):
@@ -686,8 +682,11 @@ def get_classifier(self):
     def reset_classifier(self, num_classes, global_pool='avg'):
         self.num_classes = num_classes
         self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
-        self.classifier = nn.Linear(
-            self.num_features * self.global_pool.feat_mult(), num_classes) if num_classes else None
+        num_features = self.num_features * self.global_pool.feat_mult()
+        if num_classes:
+            self.classifier = nn.Linear(num_features, num_classes)
+        else:
+            self.classifier = nn.Identity()
 
     def forward_features(self, x):
         x = self.conv1(x)
@@ -699,24 +698,21 @@ def forward_features(self, x):
         x = self.layer1(x)
 
         x_list = []
-        for i in range(self.stage2_cfg['NUM_BRANCHES']):
-            if self.transition1[i] is not None:
-                x_list.append(self.transition1[i](x))
-            else:
-                x_list.append(x)
+        for i in range(len(self.transition1)):
+            x_list.append(self.transition1[i](x))
         y_list = self.stage2(x_list)
 
         x_list = []
-        for i in range(self.stage3_cfg['NUM_BRANCHES']):
-            if self.transition2[i] is not None:
+        for i in range(len(self.transition2)):
+            if not isinstance(self.transition2[i], nn.Identity):
                 x_list.append(self.transition2[i](y_list[-1]))
             else:
                 x_list.append(y_list[i])
         y_list = self.stage3(x_list)
 
         x_list = []
-        for i in range(self.stage4_cfg['NUM_BRANCHES']):
-            if self.transition3[i] is not None:
+        for i in range(len(self.transition3)):
+            if not isinstance(self.transition3[i], nn.Identity):
                 x_list.append(self.transition3[i](y_list[-1]))
             else:
                 x_list.append(y_list[i])