Merge branch 'yoniaflalo-adding_ECA_resnet'

rwightman · rwightman · commit 7a9942a75ef2 · 2020-05-01T15:10:23.000-07:00
diff --git a/timm/models/helpers.py b/timm/models/helpers.py
@@ -1,8 +1,11 @@
 import torch
+import torch.nn as nn
+from copy import deepcopy
 import torch.utils.model_zoo as model_zoo
 import os
 import logging
 from collections import OrderedDict
+from timm.models.layers.conv2d_same import Conv2dSame
 
 
 def load_state_dict(checkpoint_path, use_ema=False):
@@ -98,7 +101,96 @@ def load_pretrained(model, cfg=None, num_classes=1000, in_chans=3, filter_fn=Non
     model.load_state_dict(state_dict, strict=strict)
 
 
-
-
-
-
+def extract_layer(model, layer):
+    layer = layer.split('.')
+    module = model
+    if hasattr(model, 'module') and layer[0] != 'module':
+        module = model.module
+    if not hasattr(model, 'module') and layer[0] == 'module':
+        layer = layer[1:]
+    for l in layer:
+        if hasattr(module, l):
+            if not l.isdigit():
+                module = getattr(module, l)
+            else:
+                module = module[int(l)]
+        else:
+            return module
+    return module
+
+
+def set_layer(model, layer, val):
+    layer = layer.split('.')
+    module = model
+    if hasattr(model, 'module') and layer[0] != 'module':
+        module = model.module
+    lst_index = 0
+    module2 = module
+    for l in layer:
+        if hasattr(module2, l):
+            if not l.isdigit():
+                module2 = getattr(module2, l)
+            else:
+                module2 = module2[int(l)]
+            lst_index += 1
+    lst_index -= 1
+    for l in layer[:lst_index]:
+        if not l.isdigit():
+            module = getattr(module, l)
+        else:
+            module = module[int(l)]
+    l = layer[lst_index]
+    setattr(module, l, val)
+
+
+def adapt_model_from_string(parent_module, model_string):
+    separator = '***'
+    state_dict = {}
+    lst_shape = model_string.split(separator)
+    for k in lst_shape:
+        k = k.split(':')
+        key = k[0]
+        shape = k[1][1:-1].split(',')
+        if shape[0] != '':
+            state_dict[key] = [int(i) for i in shape]
+
+    new_module = deepcopy(parent_module)
+    for n, m in parent_module.named_modules():
+        old_module = extract_layer(parent_module, n)
+        if isinstance(old_module, nn.Conv2d) or isinstance(old_module, Conv2dSame):
+            if isinstance(old_module, Conv2dSame):
+                conv = Conv2dSame
+            else:
+                conv = nn.Conv2d
+            s = state_dict[n + '.weight']
+            in_channels = s[1]
+            out_channels = s[0]
+            g = 1
+            if old_module.groups > 1:
+                in_channels = out_channels
+                g = in_channels
+            new_conv = conv(
+                in_channels=in_channels, out_channels=out_channels, kernel_size=old_module.kernel_size,
+                bias=old_module.bias is not None, padding=old_module.padding, dilation=old_module.dilation,
+                groups=g, stride=old_module.stride)
+            set_layer(new_module, n, new_conv)
+        if isinstance(old_module, nn.BatchNorm2d):
+            new_bn = nn.BatchNorm2d(
+                num_features=state_dict[n + '.weight'][0], eps=old_module.eps, momentum=old_module.momentum,
+                affine=old_module.affine, track_running_stats=True)
+            set_layer(new_module, n, new_bn)
+        if isinstance(old_module, nn.Linear):
+            new_fc = nn.Linear(
+                in_features=state_dict[n + '.weight'][1], out_features=old_module.out_features,
+                bias=old_module.bias is not None)
+            set_layer(new_module, n, new_fc)
+    new_module.eval()
+    parent_module.eval()
+
+    return new_module
+
+
+def adapt_model_from_file(parent_module, model_variant):
+    adapt_file = os.path.join(os.path.dirname(__file__), 'pruned', model_variant + '.txt')
+    with open(adapt_file, 'r') as f:
+        return adapt_model_from_string(parent_module, f.read().strip())
diff --git a/timm/models/pruned/ecaresnet101d_pruned.txt b/timm/models/pruned/ecaresnet101d_pruned.txt
@@ -0,0 +1 @@
+conv1.0.weight:[32, 3, 3, 3]***conv1.1.weight:[32]***conv1.3.weight:[32, 32, 3, 3]***conv1.4.weight:[32]***conv1.6.weight:[64, 32, 3, 3]***bn1.weight:[64]***layer1.0.conv1.weight:[45, 64, 1, 1]***layer1.0.bn1.weight:[45]***layer1.0.conv2.weight:[25, 45, 3, 3]***layer1.0.bn2.weight:[25]***layer1.0.conv3.weight:[26, 25, 1, 1]***layer1.0.bn3.weight:[26]***layer1.0.se.conv.weight:[1, 1, 5]***layer1.0.downsample.1.weight:[26, 64, 1, 1]***layer1.0.downsample.2.weight:[26]***layer1.1.conv1.weight:[53, 26, 1, 1]***layer1.1.bn1.weight:[53]***layer1.1.conv2.weight:[20, 53, 3, 3]***layer1.1.bn2.weight:[20]***layer1.1.conv3.weight:[26, 20, 1, 1]***layer1.1.bn3.weight:[26]***layer1.1.se.conv.weight:[1, 1, 5]***layer1.2.conv1.weight:[60, 26, 1, 1]***layer1.2.bn1.weight:[60]***layer1.2.conv2.weight:[27, 60, 3, 3]***layer1.2.bn2.weight:[27]***layer1.2.conv3.weight:[26, 27, 1, 1]***layer1.2.bn3.weight:[26]***layer1.2.se.conv.weight:[1, 1, 5]***layer2.0.conv1.weight:[81, 26, 1, 1]***layer2.0.bn1.weight:[81]***layer2.0.conv2.weight:[24, 81, 3, 3]***layer2.0.bn2.weight:[24]***layer2.0.conv3.weight:[142, 24, 1, 1]***layer2.0.bn3.weight:[142]***layer2.0.se.conv.weight:[1, 1, 5]***layer2.0.downsample.1.weight:[142, 26, 1, 1]***layer2.0.downsample.2.weight:[142]***layer2.1.conv1.weight:[93, 142, 1, 1]***layer2.1.bn1.weight:[93]***layer2.1.conv2.weight:[49, 93, 3, 3]***layer2.1.bn2.weight:[49]***layer2.1.conv3.weight:[142, 49, 1, 1]***layer2.1.bn3.weight:[142]***layer2.1.se.conv.weight:[1, 1, 5]***layer2.2.conv1.weight:[102, 142, 1, 1]***layer2.2.bn1.weight:[102]***layer2.2.conv2.weight:[54, 102, 3, 3]***layer2.2.bn2.weight:[54]***layer2.2.conv3.weight:[142, 54, 1, 1]***layer2.2.bn3.weight:[142]***layer2.2.se.conv.weight:[1, 1, 5]***layer2.3.conv1.weight:[122, 142, 1, 1]***layer2.3.bn1.weight:[122]***layer2.3.conv2.weight:[78, 122, 3, 3]***layer2.3.bn2.weight:[78]***layer2.3.conv3.weight:[142, 78, 1, 1]***layer2.3.bn3.weight:[142]***layer2.3.se.conv.weight:[1, 1, 5]***layer3.0.conv1.weight:[101, 142, 1, 1]***layer3.0.bn1.weight:[101]***layer3.0.conv2.weight:[25, 101, 3, 3]***layer3.0.bn2.weight:[25]***layer3.0.conv3.weight:[278, 25, 1, 1]***layer3.0.bn3.weight:[278]***layer3.0.se.conv.weight:[1, 1, 5]***layer3.0.downsample.1.weight:[278, 142, 1, 1]***layer3.0.downsample.2.weight:[278]***layer3.1.conv1.weight:[239, 278, 1, 1]***layer3.1.bn1.weight:[239]***layer3.1.conv2.weight:[160, 239, 3, 3]***layer3.1.bn2.weight:[160]***layer3.1.conv3.weight:[278, 160, 1, 1]***layer3.1.bn3.weight:[278]***layer3.1.se.conv.weight:[1, 1, 5]***layer3.2.conv1.weight:[234, 278, 1, 1]***layer3.2.bn1.weight:[234]***layer3.2.conv2.weight:[156, 234, 3, 3]***layer3.2.bn2.weight:[156]***layer3.2.conv3.weight:[278, 156, 1, 1]***layer3.2.bn3.weight:[278]***layer3.2.se.conv.weight:[1, 1, 5]***layer3.3.conv1.weight:[250, 278, 1, 1]***layer3.3.bn1.weight:[250]***layer3.3.conv2.weight:[176, 250, 3, 3]***layer3.3.bn2.weight:[176]***layer3.3.conv3.weight:[278, 176, 1, 1]***layer3.3.bn3.weight:[278]***layer3.3.se.conv.weight:[1, 1, 5]***layer3.4.conv1.weight:[253, 278, 1, 1]***layer3.4.bn1.weight:[253]***layer3.4.conv2.weight:[191, 253, 3, 3]***layer3.4.bn2.weight:[191]***layer3.4.conv3.weight:[278, 191, 1, 1]***layer3.4.bn3.weight:[278]***layer3.4.se.conv.weight:[1, 1, 5]***layer3.5.conv1.weight:[251, 278, 1, 1]***layer3.5.bn1.weight:[251]***layer3.5.conv2.weight:[175, 251, 3, 3]***layer3.5.bn2.weight:[175]***layer3.5.conv3.weight:[278, 175, 1, 1]***layer3.5.bn3.weight:[278]***layer3.5.se.conv.weight:[1, 1, 5]***layer3.6.conv1.weight:[230, 278, 1, 1]***layer3.6.bn1.weight:[230]***layer3.6.conv2.weight:[128, 230, 3, 3]***layer3.6.bn2.weight:[128]***layer3.6.conv3.weight:[278, 128, 1, 1]***layer3.6.bn3.weight:[278]***layer3.6.se.conv.weight:[1, 1, 5]***layer3.7.conv1.weight:[244, 278, 1, 1]***layer3.7.bn1.weight:[244]***layer3.7.conv2.weight:[154, 244, 3, 3]***layer3.7.bn2.weight:[154]***layer3.7.conv3.weight:[278, 154, 1, 1]***layer3.7.bn3.weight:[278]***layer3.7.se.conv.weight:[1, 1, 5]***layer3.8.conv1.weight:[244, 278, 1, 1]***layer3.8.bn1.weight:[244]***layer3.8.conv2.weight:[159, 244, 3, 3]***layer3.8.bn2.weight:[159]***layer3.8.conv3.weight:[278, 159, 1, 1]***layer3.8.bn3.weight:[278]***layer3.8.se.conv.weight:[1, 1, 5]***layer3.9.conv1.weight:[238, 278, 1, 1]***layer3.9.bn1.weight:[238]***layer3.9.conv2.weight:[97, 238, 3, 3]***layer3.9.bn2.weight:[97]***layer3.9.conv3.weight:[278, 97, 1, 1]***layer3.9.bn3.weight:[278]***layer3.9.se.conv.weight:[1, 1, 5]***layer3.10.conv1.weight:[244, 278, 1, 1]***layer3.10.bn1.weight:[244]***layer3.10.conv2.weight:[149, 244, 3, 3]***layer3.10.bn2.weight:[149]***layer3.10.conv3.weight:[278, 149, 1, 1]***layer3.10.bn3.weight:[278]***layer3.10.se.conv.weight:[1, 1, 5]***layer3.11.conv1.weight:[253, 278, 1, 1]***layer3.11.bn1.weight:[253]***layer3.11.conv2.weight:[181, 253, 3, 3]***layer3.11.bn2.weight:[181]***layer3.11.conv3.weight:[278, 181, 1, 1]***layer3.11.bn3.weight:[278]***layer3.11.se.conv.weight:[1, 1, 5]***layer3.12.conv1.weight:[245, 278, 1, 1]***layer3.12.bn1.weight:[245]***layer3.12.conv2.weight:[119, 245, 3, 3]***layer3.12.bn2.weight:[119]***layer3.12.conv3.weight:[278, 119, 1, 1]***layer3.12.bn3.weight:[278]***layer3.12.se.conv.weight:[1, 1, 5]***layer3.13.conv1.weight:[255, 278, 1, 1]***layer3.13.bn1.weight:[255]***layer3.13.conv2.weight:[216, 255, 3, 3]***layer3.13.bn2.weight:[216]***layer3.13.conv3.weight:[278, 216, 1, 1]***layer3.13.bn3.weight:[278]***layer3.13.se.conv.weight:[1, 1, 5]***layer3.14.conv1.weight:[256, 278, 1, 1]***layer3.14.bn1.weight:[256]***layer3.14.conv2.weight:[201, 256, 3, 3]***layer3.14.bn2.weight:[201]***layer3.14.conv3.weight:[278, 201, 1, 1]***layer3.14.bn3.weight:[278]***layer3.14.se.conv.weight:[1, 1, 5]***layer3.15.conv1.weight:[253, 278, 1, 1]***layer3.15.bn1.weight:[253]***layer3.15.conv2.weight:[149, 253, 3, 3]***layer3.15.bn2.weight:[149]***layer3.15.conv3.weight:[278, 149, 1, 1]***layer3.15.bn3.weight:[278]***layer3.15.se.conv.weight:[1, 1, 5]***layer3.16.conv1.weight:[254, 278, 1, 1]***layer3.16.bn1.weight:[254]***layer3.16.conv2.weight:[141, 254, 3, 3]***layer3.16.bn2.weight:[141]***layer3.16.conv3.weight:[278, 141, 1, 1]***layer3.16.bn3.weight:[278]***layer3.16.se.conv.weight:[1, 1, 5]***layer3.17.conv1.weight:[256, 278, 1, 1]***layer3.17.bn1.weight:[256]***layer3.17.conv2.weight:[190, 256, 3, 3]***layer3.17.bn2.weight:[190]***layer3.17.conv3.weight:[278, 190, 1, 1]***layer3.17.bn3.weight:[278]***layer3.17.se.conv.weight:[1, 1, 5]***layer3.18.conv1.weight:[256, 278, 1, 1]***layer3.18.bn1.weight:[256]***layer3.18.conv2.weight:[217, 256, 3, 3]***layer3.18.bn2.weight:[217]***layer3.18.conv3.weight:[278, 217, 1, 1]***layer3.18.bn3.weight:[278]***layer3.18.se.conv.weight:[1, 1, 5]***layer3.19.conv1.weight:[255, 278, 1, 1]***layer3.19.bn1.weight:[255]***layer3.19.conv2.weight:[156, 255, 3, 3]***layer3.19.bn2.weight:[156]***layer3.19.conv3.weight:[278, 156, 1, 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diff --git a/timm/models/pruned/ecaresnet50d_pruned.txt b/timm/models/pruned/ecaresnet50d_pruned.txt
@@ -0,0 +1 @@
+conv1.0.weight:[32, 3, 3, 3]***conv1.1.weight:[32]***conv1.3.weight:[32, 32, 3, 3]***conv1.4.weight:[32]***conv1.6.weight:[64, 32, 3, 3]***bn1.weight:[64]***layer1.0.conv1.weight:[47, 64, 1, 1]***layer1.0.bn1.weight:[47]***layer1.0.conv2.weight:[18, 47, 3, 3]***layer1.0.bn2.weight:[18]***layer1.0.conv3.weight:[19, 18, 1, 1]***layer1.0.bn3.weight:[19]***layer1.0.se.conv.weight:[1, 1, 5]***layer1.0.downsample.1.weight:[19, 64, 1, 1]***layer1.0.downsample.2.weight:[19]***layer1.1.conv1.weight:[52, 19, 1, 1]***layer1.1.bn1.weight:[52]***layer1.1.conv2.weight:[22, 52, 3, 3]***layer1.1.bn2.weight:[22]***layer1.1.conv3.weight:[19, 22, 1, 1]***layer1.1.bn3.weight:[19]***layer1.1.se.conv.weight:[1, 1, 5]***layer1.2.conv1.weight:[64, 19, 1, 1]***layer1.2.bn1.weight:[64]***layer1.2.conv2.weight:[35, 64, 3, 3]***layer1.2.bn2.weight:[35]***layer1.2.conv3.weight:[19, 35, 1, 1]***layer1.2.bn3.weight:[19]***layer1.2.se.conv.weight:[1, 1, 5]***layer2.0.conv1.weight:[85, 19, 1, 1]***layer2.0.bn1.weight:[85]***layer2.0.conv2.weight:[37, 85, 3, 3]***layer2.0.bn2.weight:[37]***layer2.0.conv3.weight:[171, 37, 1, 1]***layer2.0.bn3.weight:[171]***layer2.0.se.conv.weight:[1, 1, 5]***layer2.0.downsample.1.weight:[171, 19, 1, 1]***layer2.0.downsample.2.weight:[171]***layer2.1.conv1.weight:[107, 171, 1, 1]***layer2.1.bn1.weight:[107]***layer2.1.conv2.weight:[80, 107, 3, 3]***layer2.1.bn2.weight:[80]***layer2.1.conv3.weight:[171, 80, 1, 1]***layer2.1.bn3.weight:[171]***layer2.1.se.conv.weight:[1, 1, 5]***layer2.2.conv1.weight:[120, 171, 1, 1]***layer2.2.bn1.weight:[120]***layer2.2.conv2.weight:[85, 120, 3, 3]***layer2.2.bn2.weight:[85]***layer2.2.conv3.weight:[171, 85, 1, 1]***layer2.2.bn3.weight:[171]***layer2.2.se.conv.weight:[1, 1, 5]***layer2.3.conv1.weight:[125, 171, 1, 1]***layer2.3.bn1.weight:[125]***layer2.3.conv2.weight:[87, 125, 3, 3]***layer2.3.bn2.weight:[87]***layer2.3.conv3.weight:[171, 87, 1, 1]***layer2.3.bn3.weight:[171]***layer2.3.se.conv.weight:[1, 1, 5]***layer3.0.conv1.weight:[198, 171, 1, 1]***layer3.0.bn1.weight:[198]***layer3.0.conv2.weight:[126, 198, 3, 3]***layer3.0.bn2.weight:[126]***layer3.0.conv3.weight:[818, 126, 1, 1]***layer3.0.bn3.weight:[818]***layer3.0.se.conv.weight:[1, 1, 5]***layer3.0.downsample.1.weight:[818, 171, 1, 1]***layer3.0.downsample.2.weight:[818]***layer3.1.conv1.weight:[255, 818, 1, 1]***layer3.1.bn1.weight:[255]***layer3.1.conv2.weight:[232, 255, 3, 3]***layer3.1.bn2.weight:[232]***layer3.1.conv3.weight:[818, 232, 1, 1]***layer3.1.bn3.weight:[818]***layer3.1.se.conv.weight:[1, 1, 5]***layer3.2.conv1.weight:[256, 818, 1, 1]***layer3.2.bn1.weight:[256]***layer3.2.conv2.weight:[233, 256, 3, 3]***layer3.2.bn2.weight:[233]***layer3.2.conv3.weight:[818, 233, 1, 1]***layer3.2.bn3.weight:[818]***layer3.2.se.conv.weight:[1, 1, 5]***layer3.3.conv1.weight:[253, 818, 1, 1]***layer3.3.bn1.weight:[253]***layer3.3.conv2.weight:[235, 253, 3, 3]***layer3.3.bn2.weight:[235]***layer3.3.conv3.weight:[818, 235, 1, 1]***layer3.3.bn3.weight:[818]***layer3.3.se.conv.weight:[1, 1, 5]***layer3.4.conv1.weight:[256, 818, 1, 1]***layer3.4.bn1.weight:[256]***layer3.4.conv2.weight:[225, 256, 3, 3]***layer3.4.bn2.weight:[225]***layer3.4.conv3.weight:[818, 225, 1, 1]***layer3.4.bn3.weight:[818]***layer3.4.se.conv.weight:[1, 1, 5]***layer3.5.conv1.weight:[256, 818, 1, 1]***layer3.5.bn1.weight:[256]***layer3.5.conv2.weight:[239, 256, 3, 3]***layer3.5.bn2.weight:[239]***layer3.5.conv3.weight:[818, 239, 1, 1]***layer3.5.bn3.weight:[818]***layer3.5.se.conv.weight:[1, 1, 5]***layer4.0.conv1.weight:[492, 818, 1, 1]***layer4.0.bn1.weight:[492]***layer4.0.conv2.weight:[237, 492, 3, 3]***layer4.0.bn2.weight:[237]***layer4.0.conv3.weight:[2022, 237, 1, 1]***layer4.0.bn3.weight:[2022]***layer4.0.se.conv.weight:[1, 1, 7]***layer4.0.downsample.1.weight:[2022, 818, 1, 1]***layer4.0.downsample.2.weight:[2022]***layer4.1.conv1.weight:[512, 2022, 1, 1]***layer4.1.bn1.weight:[512]***layer4.1.conv2.weight:[500, 512, 3, 3]***layer4.1.bn2.weight:[500]***layer4.1.conv3.weight:[2022, 500, 1, 1]***layer4.1.bn3.weight:[2022]***layer4.1.se.conv.weight:[1, 1, 7]***layer4.2.conv1.weight:[512, 2022, 1, 1]***layer4.2.bn1.weight:[512]***layer4.2.conv2.weight:[490, 512, 3, 3]***layer4.2.bn2.weight:[490]***layer4.2.conv3.weight:[2022, 490, 1, 1]***layer4.2.bn3.weight:[2022]***layer4.2.se.conv.weight:[1, 1, 7]***fc.weight:[1000, 2022]***layer1_2_conv3_M.weight:[256, 19]***layer2_3_conv3_M.weight:[512, 171]***layer3_5_conv3_M.weight:[1024, 818]***layer4_2_conv3_M.weight:[2048, 2022]
diff --git a/timm/models/resnet.py b/timm/models/resnet.py

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