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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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@@ -11,176 +11,8 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.model_zoo as model_zoo
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from collections import OrderedDict
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__all__ = ["DPN", "dpn68", "dpn68b", "dpn92", "dpn98", "dpn131", "dpn107"]
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def dpn68(num_classes=1000, pretrained="imagenet"):
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model = DPN(
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small=True,
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num_init_features=10,
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k_r=128,
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groups=32,
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k_sec=(3, 4, 12, 3),
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inc_sec=(16, 32, 32, 64),
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num_classes=num_classes,
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test_time_pool=True,
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)
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if pretrained:
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settings = pretrained_settings["dpn68"][pretrained]
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assert num_classes == settings["num_classes"], (
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"num_classes should be {}, but is {}".format(
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settings["num_classes"], num_classes
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)
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)
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model.load_state_dict(model_zoo.load_url(settings["url"]))
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model.input_space = settings["input_space"]
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model.input_size = settings["input_size"]
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model.input_range = settings["input_range"]
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model.mean = settings["mean"]
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model.std = settings["std"]
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return model
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def dpn68b(num_classes=1000, pretrained="imagenet+5k"):
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model = DPN(
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small=True,
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num_init_features=10,
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k_r=128,
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groups=32,
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b=True,
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k_sec=(3, 4, 12, 3),
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inc_sec=(16, 32, 32, 64),
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num_classes=num_classes,
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test_time_pool=True,
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)
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if pretrained:
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settings = pretrained_settings["dpn68b"][pretrained]
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assert num_classes == settings["num_classes"], (
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"num_classes should be {}, but is {}".format(
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settings["num_classes"], num_classes
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)
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)
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model.load_state_dict(model_zoo.load_url(settings["url"]))
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model.input_space = settings["input_space"]
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model.input_size = settings["input_size"]
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model.input_range = settings["input_range"]
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model.mean = settings["mean"]
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model.std = settings["std"]
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return model
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def dpn92(num_classes=1000, pretrained="imagenet+5k"):
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model = DPN(
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num_init_features=64,
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k_r=96,
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groups=32,
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k_sec=(3, 4, 20, 3),
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inc_sec=(16, 32, 24, 128),
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num_classes=num_classes,
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test_time_pool=True,
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)
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if pretrained:
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settings = pretrained_settings["dpn92"][pretrained]
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assert num_classes == settings["num_classes"], (
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"num_classes should be {}, but is {}".format(
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settings["num_classes"], num_classes
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)
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)
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model.load_state_dict(model_zoo.load_url(settings["url"]))
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model.input_space = settings["input_space"]
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model.input_size = settings["input_size"]
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model.input_range = settings["input_range"]
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model.mean = settings["mean"]
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model.std = settings["std"]
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return model
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def dpn98(num_classes=1000, pretrained="imagenet"):
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model = DPN(
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num_init_features=96,
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k_r=160,
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groups=40,
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k_sec=(3, 6, 20, 3),
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inc_sec=(16, 32, 32, 128),
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num_classes=num_classes,
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test_time_pool=True,
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)
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if pretrained:
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settings = pretrained_settings["dpn98"][pretrained]
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assert num_classes == settings["num_classes"], (
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"num_classes should be {}, but is {}".format(
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settings["num_classes"], num_classes
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)
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)
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model.load_state_dict(model_zoo.load_url(settings["url"]))
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model.input_space = settings["input_space"]
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model.input_size = settings["input_size"]
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model.input_range = settings["input_range"]
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model.mean = settings["mean"]
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model.std = settings["std"]
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return model
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def dpn131(num_classes=1000, pretrained="imagenet"):
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model = DPN(
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num_init_features=128,
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k_r=160,
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groups=40,
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k_sec=(4, 8, 28, 3),
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inc_sec=(16, 32, 32, 128),
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num_classes=num_classes,
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test_time_pool=True,
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)
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if pretrained:
142-
settings = pretrained_settings["dpn131"][pretrained]
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assert num_classes == settings["num_classes"], (
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"num_classes should be {}, but is {}".format(
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settings["num_classes"], num_classes
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)
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)
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model.load_state_dict(model_zoo.load_url(settings["url"]))
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model.input_space = settings["input_space"]
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model.input_size = settings["input_size"]
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model.input_range = settings["input_range"]
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model.mean = settings["mean"]
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model.std = settings["std"]
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return model
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def dpn107(num_classes=1000, pretrained="imagenet+5k"):
159-
model = DPN(
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num_init_features=128,
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k_r=200,
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groups=50,
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k_sec=(4, 8, 20, 3),
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inc_sec=(20, 64, 64, 128),
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num_classes=num_classes,
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test_time_pool=True,
167-
)
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if pretrained:
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settings = pretrained_settings["dpn107"][pretrained]
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assert num_classes == settings["num_classes"], (
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"num_classes should be {}, but is {}".format(
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settings["num_classes"], num_classes
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)
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)
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model.load_state_dict(model_zoo.load_url(settings["url"]))
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model.input_space = settings["input_space"]
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model.input_size = settings["input_size"]
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model.input_range = settings["input_range"]
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model.mean = settings["mean"]
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model.std = settings["std"]
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return model
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class CatBnAct(nn.Module):
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def __init__(self, in_chs, activation_fn=nn.ReLU(inplace=True)):

‎segmentation_models_pytorch/encoders/_efficientnet.py

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Original file line numberDiff line numberDiff line change
@@ -716,7 +716,6 @@ def forward(self, x):
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# efficientnet_params: A function to query compound coefficient
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# get_model_params and efficientnet:
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# Functions to get BlockArgs and GlobalParams for efficientnet
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# url_map and url_map_advprop: Dicts of url_map for pretrained weights
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class BlockDecoder(object):
@@ -882,31 +881,3 @@ def get_model_params(model_name, override_params):
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# ValueError will be raised here if override_params has fields not included in global_params.
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global_params = global_params._replace(**override_params)
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return blocks_args, global_params
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# train with Standard methods
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# check more details in paper(EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks)
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url_map = {
890-
"efficientnet-b0": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth",
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"efficientnet-b1": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b1-f1951068.pth",
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"efficientnet-b2": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth",
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"efficientnet-b3": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b3-5fb5a3c3.pth",
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"efficientnet-b4": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth",
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"efficientnet-b5": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth",
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"efficientnet-b6": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b6-c76e70fd.pth",
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"efficientnet-b7": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth",
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}
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# train with Adversarial Examples(AdvProp)
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# check more details in paper(Adversarial Examples Improve Image Recognition)
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url_map_advprop = {
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"efficientnet-b0": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b0-b64d5a18.pth",
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"efficientnet-b1": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b1-0f3ce85a.pth",
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"efficientnet-b2": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b2-6e9d97e5.pth",
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"efficientnet-b3": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b3-cdd7c0f4.pth",
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"efficientnet-b4": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b4-44fb3a87.pth",
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"efficientnet-b5": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b5-86493f6b.pth",
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"efficientnet-b6": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b6-ac80338e.pth",
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"efficientnet-b7": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b7-4652b6dd.pth",
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"efficientnet-b8": "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b8-22a8fe65.pth",
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}

‎segmentation_models_pytorch/encoders/_inceptionresnetv2.py

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@@ -1,9 +1,5 @@
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from __future__ import print_function, division, absolute_import
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import torch
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import torch.nn as nn
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import torch.utils.model_zoo as model_zoo
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__all__ = ["InceptionResNetV2", "inceptionresnetv2"]
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84

95
class BasicConv2d(nn.Module):
@@ -303,36 +299,3 @@ def forward(self, input):
303299
x = self.features(input)
304300
x = self.logits(x)
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return x
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def inceptionresnetv2(num_classes=1000, pretrained="imagenet"):
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r"""InceptionResNetV2 model architecture from the
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`"InceptionV4, Inception-ResNet..." <https://arxiv.org/abs/1602.07261>`_ paper.
311-
"""
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if pretrained:
313-
settings = pretrained_settings["inceptionresnetv2"][pretrained]
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assert num_classes == settings["num_classes"], (
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"num_classes should be {}, but is {}".format(
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settings["num_classes"], num_classes
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)
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)
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# both 'imagenet'&'imagenet+background' are loaded from same parameters
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model = InceptionResNetV2(num_classes=1001)
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model.load_state_dict(model_zoo.load_url(settings["url"]))
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324-
if pretrained == "imagenet":
325-
new_last_linear = nn.Linear(1536, 1000)
326-
new_last_linear.weight.data = model.last_linear.weight.data[1:]
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new_last_linear.bias.data = model.last_linear.bias.data[1:]
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model.last_linear = new_last_linear
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model.input_space = settings["input_space"]
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model.input_size = settings["input_size"]
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model.input_range = settings["input_range"]
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model.mean = settings["mean"]
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model.std = settings["std"]
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else:
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model = InceptionResNetV2(num_classes=num_classes)
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return model

‎segmentation_models_pytorch/encoders/_inceptionv4.py

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@@ -1,10 +1,6 @@
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from __future__ import print_function, division, absolute_import
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.model_zoo as model_zoo
6-
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__all__ = ["InceptionV4", "inceptionv4"]
84

95

106
class BasicConv2d(nn.Module):
@@ -293,32 +289,3 @@ def forward(self, input):
293289
x = self.features(input)
294290
x = self.logits(x)
295291
return x
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298-
def inceptionv4(num_classes=1000, pretrained="imagenet"):
299-
if pretrained:
300-
settings = pretrained_settings["inceptionv4"][pretrained]
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assert num_classes == settings["num_classes"], (
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"num_classes should be {}, but is {}".format(
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settings["num_classes"], num_classes
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)
305-
)
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307-
# both 'imagenet'&'imagenet+background' are loaded from same parameters
308-
model = InceptionV4(num_classes=1001)
309-
model.load_state_dict(model_zoo.load_url(settings["url"]))
310-
311-
if pretrained == "imagenet":
312-
new_last_linear = nn.Linear(1536, 1000)
313-
new_last_linear.weight.data = model.last_linear.weight.data[1:]
314-
new_last_linear.bias.data = model.last_linear.bias.data[1:]
315-
model.last_linear = new_last_linear
316-
317-
model.input_space = settings["input_space"]
318-
model.input_size = settings["input_size"]
319-
model.input_range = settings["input_range"]
320-
model.mean = settings["mean"]
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model.std = settings["std"]
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else:
323-
model = InceptionV4(num_classes=num_classes)
324-
return model

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