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adamjstewartadamjstewart
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Remove unnecessary code
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‎segmentation_models_pytorch/encoders/_dpn.py

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__all__ = ["DPN", "dpn68", "dpn68b", "dpn92", "dpn98", "dpn131", "dpn107"]
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pretrained_settings = {
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"dpn68": {
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"imagenet": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/dpn68-4af7d88d2.pth",
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"input_space": "RGB",
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"input_size": [3, 224, 224],
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"input_range": [0, 1],
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"mean": [124 / 255, 117 / 255, 104 / 255],
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"std": [1 / (0.0167 * 255)] * 3,
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"num_classes": 1000,
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}
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},
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"dpn68b": {
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"imagenet+5k": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/dpn68b_extra-363ab9c19.pth",
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"input_space": "RGB",
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"input_size": [3, 224, 224],
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"input_range": [0, 1],
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"mean": [124 / 255, 117 / 255, 104 / 255],
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"std": [1 / (0.0167 * 255)] * 3,
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"num_classes": 1000,
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}
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},
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"dpn92": {
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# 'imagenet': {
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# 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/dpn68-66bebafa7.pth',
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# 'input_space': 'RGB',
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# 'input_size': [3, 224, 224],
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# 'input_range': [0, 1],
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# 'mean': [124 / 255, 117 / 255, 104 / 255],
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# 'std': [1 / (.0167 * 255)] * 3,
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# 'num_classes': 1000
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# },
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"imagenet+5k": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/dpn92_extra-fda993c95.pth",
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"input_space": "RGB",
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"input_size": [3, 224, 224],
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"input_range": [0, 1],
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"mean": [124 / 255, 117 / 255, 104 / 255],
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"std": [1 / (0.0167 * 255)] * 3,
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"num_classes": 1000,
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}
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},
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"dpn98": {
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"imagenet": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/dpn98-722954780.pth",
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"input_space": "RGB",
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"input_size": [3, 224, 224],
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"input_range": [0, 1],
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"mean": [124 / 255, 117 / 255, 104 / 255],
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"std": [1 / (0.0167 * 255)] * 3,
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"num_classes": 1000,
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}
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},
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"dpn131": {
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"imagenet": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/dpn131-7af84be88.pth",
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"input_space": "RGB",
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"input_size": [3, 224, 224],
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"input_range": [0, 1],
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"mean": [124 / 255, 117 / 255, 104 / 255],
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"std": [1 / (0.0167 * 255)] * 3,
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"num_classes": 1000,
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}
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},
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"dpn107": {
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"imagenet+5k": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/dpn107_extra-b7f9f4cc9.pth",
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"input_space": "RGB",
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"input_size": [3, 224, 224],
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"input_range": [0, 1],
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"mean": [124 / 255, 117 / 255, 104 / 255],
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"std": [1 / (0.0167 * 255)] * 3,
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"num_classes": 1000,
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}
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},
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}
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def dpn68(num_classes=1000, pretrained="imagenet"):
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model = DPN(

‎segmentation_models_pytorch/encoders/_inceptionresnetv2.py

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__all__ = ["InceptionResNetV2", "inceptionresnetv2"]
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pretrained_settings = {
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"inceptionresnetv2": {
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"imagenet": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.pth",
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"input_space": "RGB",
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"input_size": [3, 299, 299],
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"input_range": [0, 1],
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"mean": [0.5, 0.5, 0.5],
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"std": [0.5, 0.5, 0.5],
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"num_classes": 1000,
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},
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"imagenet+background": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.pth",
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"input_space": "RGB",
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"input_size": [3, 299, 299],
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"input_range": [0, 1],
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"mean": [0.5, 0.5, 0.5],
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"std": [0.5, 0.5, 0.5],
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"num_classes": 1001,
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},
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}
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}
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class BasicConv2d(nn.Module):
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def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
@@ -359,23 +336,3 @@ def inceptionresnetv2(num_classes=1000, pretrained="imagenet"):
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else:
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model = InceptionResNetV2(num_classes=num_classes)
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return model
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"""
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TEST
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Run this code with:
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```
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cd $HOME/pretrained-models.pytorch
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python -m pretrainedmodels.inceptionresnetv2
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```
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"""
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if __name__ == "__main__":
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assert inceptionresnetv2(num_classes=10, pretrained=None)
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print("success")
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assert inceptionresnetv2(num_classes=1000, pretrained="imagenet")
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print("success")
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assert inceptionresnetv2(num_classes=1001, pretrained="imagenet+background")
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print("success")
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# fail
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assert inceptionresnetv2(num_classes=1001, pretrained="imagenet")

‎segmentation_models_pytorch/encoders/_inceptionv4.py

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__all__ = ["InceptionV4", "inceptionv4"]
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pretrained_settings = {
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"inceptionv4": {
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"imagenet": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth",
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"input_space": "RGB",
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"input_size": [3, 299, 299],
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"input_range": [0, 1],
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"mean": [0.5, 0.5, 0.5],
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"std": [0.5, 0.5, 0.5],
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"num_classes": 1000,
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},
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"imagenet+background": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth",
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"input_space": "RGB",
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"input_size": [3, 299, 299],
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"input_range": [0, 1],
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"mean": [0.5, 0.5, 0.5],
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"std": [0.5, 0.5, 0.5],
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"num_classes": 1001,
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},
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}
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}
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class BasicConv2d(nn.Module):
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def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
@@ -345,23 +322,3 @@ def inceptionv4(num_classes=1000, pretrained="imagenet"):
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else:
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model = InceptionV4(num_classes=num_classes)
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return model
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"""
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TEST
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Run this code with:
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```
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cd $HOME/pretrained-models.pytorch
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python -m pretrainedmodels.inceptionv4
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```
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"""
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if __name__ == "__main__":
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assert inceptionv4(num_classes=10, pretrained=None)
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print("success")
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assert inceptionv4(num_classes=1000, pretrained="imagenet")
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print("success")
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assert inceptionv4(num_classes=1001, pretrained="imagenet+background")
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print("success")
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# fail
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assert inceptionv4(num_classes=1001, pretrained="imagenet")

‎segmentation_models_pytorch/encoders/_senet.py

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"se_resnext101_32x4d",
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]
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pretrained_settings = {
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"senet154": {
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"imagenet": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth",
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"input_space": "RGB",
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"input_size": [3, 224, 224],
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"input_range": [0, 1],
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"mean": [0.485, 0.456, 0.406],
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"std": [0.229, 0.224, 0.225],
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"num_classes": 1000,
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}
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},
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"se_resnet50": {
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"imagenet": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/se_resnet50-ce0d4300.pth",
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"input_space": "RGB",
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"input_size": [3, 224, 224],
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"input_range": [0, 1],
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"mean": [0.485, 0.456, 0.406],
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"std": [0.229, 0.224, 0.225],
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"num_classes": 1000,
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}
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},
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"se_resnet101": {
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"imagenet": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/se_resnet101-7e38fcc6.pth",
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"input_space": "RGB",
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"input_size": [3, 224, 224],
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"input_range": [0, 1],
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"mean": [0.485, 0.456, 0.406],
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"std": [0.229, 0.224, 0.225],
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"num_classes": 1000,
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}
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},
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"se_resnet152": {
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"imagenet": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/se_resnet152-d17c99b7.pth",
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"input_space": "RGB",
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"input_size": [3, 224, 224],
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"input_range": [0, 1],
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"mean": [0.485, 0.456, 0.406],
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"std": [0.229, 0.224, 0.225],
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"num_classes": 1000,
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}
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},
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"se_resnext50_32x4d": {
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"imagenet": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth",
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"input_space": "RGB",
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"input_size": [3, 224, 224],
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"input_range": [0, 1],
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"mean": [0.485, 0.456, 0.406],
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"std": [0.229, 0.224, 0.225],
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"num_classes": 1000,
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}
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},
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"se_resnext101_32x4d": {
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"imagenet": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth",
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"input_space": "RGB",
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"input_size": [3, 224, 224],
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"input_range": [0, 1],
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"mean": [0.485, 0.456, 0.406],
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"std": [0.229, 0.224, 0.225],
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"num_classes": 1000,
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}
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},
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}
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class SEModule(nn.Module):
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def __init__(self, channels, reduction):

‎segmentation_models_pytorch/encoders/_xception.py

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__all__ = ["xception"]
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pretrained_settings = {
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"xception": {
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"imagenet": {
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"url": "http://data.lip6.fr/cadene/pretrainedmodels/xception-43020ad28.pth",
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"input_space": "RGB",
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"input_size": [3, 299, 299],
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"input_range": [0, 1],
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"mean": [0.5, 0.5, 0.5],
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"std": [0.5, 0.5, 0.5],
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"num_classes": 1000,
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"scale": 0.8975, # The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
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}
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}
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}
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class SeparableConv2d(nn.Module):
4934
def __init__(

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