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@ -16,20 +16,21 @@ 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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from timm.data import IMAGENET_DPN_MEAN, IMAGENET_DPN_STD
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from timm.data import IMAGENET_DPN_MEAN, IMAGENET_DPN_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from .helpers import load_pretrained
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from .layers import SelectAdaptivePool2d
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from .layers import SelectAdaptivePool2d, BatchNormAct2d, create_norm_act, create_conv2d
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from .registry import register_model
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__all__ = ['DPN']
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def _cfg(url=''):
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def _cfg(url='', **kwargs):
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return {
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'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
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'crop_pct': 0.875, 'interpolation': 'bicubic',
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'mean': IMAGENET_DPN_MEAN, 'std': IMAGENET_DPN_STD,
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'first_conv': 'features.conv1_1.conv', 'classifier': 'classifier',
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**kwargs
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}
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@ -37,7 +38,8 @@ default_cfgs = {
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'dpn68': _cfg(
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url='https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn68-66bebafa7.pth'),
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'dpn68b': _cfg(
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url='https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn68b_extra-84854c156.pth'),
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/dpn68b_ra-a31ca160.pth',
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mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
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'dpn92': _cfg(
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url='https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn92_extra-b040e4a9b.pth'),
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'dpn98': _cfg(
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@ -50,10 +52,9 @@ default_cfgs = {
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class CatBnAct(nn.Module):
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def __init__(self, in_chs, activation_fn=nn.ReLU(inplace=True)):
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def __init__(self, in_chs, norm_layer=BatchNormAct2d):
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super(CatBnAct, self).__init__()
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self.bn = nn.BatchNorm2d(in_chs, eps=0.001)
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self.act = activation_fn
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self.bn = norm_layer(in_chs, eps=0.001)
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@torch.jit._overload_method # noqa: F811
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def forward(self, x):
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@ -68,35 +69,29 @@ class CatBnAct(nn.Module):
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def forward(self, x):
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if isinstance(x, tuple):
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x = torch.cat(x, dim=1)
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return self.act(self.bn(x))
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return self.bn(x)
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class BnActConv2d(nn.Module):
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def __init__(self, in_chs, out_chs, kernel_size, stride,
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padding=0, groups=1, activation_fn=nn.ReLU(inplace=True)):
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def __init__(self, in_chs, out_chs, kernel_size, stride, groups=1, norm_layer=BatchNormAct2d):
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super(BnActConv2d, self).__init__()
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self.bn = nn.BatchNorm2d(in_chs, eps=0.001)
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self.act = activation_fn
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self.conv = nn.Conv2d(in_chs, out_chs, kernel_size, stride, padding, groups=groups, bias=False)
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self.bn = norm_layer(in_chs, eps=0.001)
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self.conv = create_conv2d(in_chs, out_chs, kernel_size, stride=stride, groups=groups)
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def forward(self, x):
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return self.conv(self.act(self.bn(x)))
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return self.conv(self.bn(x))
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class InputBlock(nn.Module):
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def __init__(self, num_init_features, kernel_size=7, in_chans=3,
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padding=3, activation_fn=nn.ReLU(inplace=True)):
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def __init__(self, num_init_features, kernel_size=7, in_chans=3, norm_layer=BatchNormAct2d):
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super(InputBlock, self).__init__()
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self.conv = nn.Conv2d(
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in_chans, num_init_features, kernel_size=kernel_size, stride=2, padding=padding, bias=False)
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self.bn = nn.BatchNorm2d(num_init_features, eps=0.001)
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self.act = activation_fn
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self.conv = create_conv2d(in_chans, num_init_features, kernel_size=kernel_size, stride=2)
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self.bn = norm_layer(num_init_features, eps=0.001)
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self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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x = self.act(x)
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x = self.pool(x)
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return x
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@ -132,12 +127,11 @@ class DualPathBlock(nn.Module):
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self.c1x1_a = BnActConv2d(in_chs=in_chs, out_chs=num_1x1_a, kernel_size=1, stride=1)
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self.c3x3_b = BnActConv2d(
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in_chs=num_1x1_a, out_chs=num_3x3_b, kernel_size=3,
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stride=self.key_stride, padding=1, groups=groups)
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in_chs=num_1x1_a, out_chs=num_3x3_b, kernel_size=3, stride=self.key_stride, groups=groups)
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if b:
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self.c1x1_c = CatBnAct(in_chs=num_3x3_b)
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self.c1x1_c1 = nn.Conv2d(num_3x3_b, num_1x1_c, kernel_size=1, bias=False)
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self.c1x1_c2 = nn.Conv2d(num_3x3_b, inc, kernel_size=1, bias=False)
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self.c1x1_c1 = create_conv2d(num_3x3_b, num_1x1_c, kernel_size=1)
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self.c1x1_c2 = create_conv2d(num_3x3_b, inc, kernel_size=1)
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else:
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self.c1x1_c = BnActConv2d(in_chs=num_3x3_b, out_chs=num_1x1_c + inc, kernel_size=1, stride=1)
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self.c1x1_c1 = None
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@ -190,7 +184,7 @@ class DualPathBlock(nn.Module):
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class DPN(nn.Module):
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def __init__(self, small=False, num_init_features=64, k_r=96, groups=32,
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b=False, k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128),
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num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg', fc_act=nn.ELU()):
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num_classes=1000, in_chans=3, drop_rate=0., global_pool='avg', fc_act=nn.ELU):
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super(DPN, self).__init__()
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self.num_classes = num_classes
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self.drop_rate = drop_rate
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@ -201,9 +195,9 @@ class DPN(nn.Module):
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# conv1
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if small:
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blocks['conv1_1'] = InputBlock(num_init_features, in_chans=in_chans, kernel_size=3, padding=1)
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blocks['conv1_1'] = InputBlock(num_init_features, in_chans=in_chans, kernel_size=3)
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else:
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blocks['conv1_1'] = InputBlock(num_init_features, in_chans=in_chans, kernel_size=7, padding=3)
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blocks['conv1_1'] = InputBlock(num_init_features, in_chans=in_chans, kernel_size=7)
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# conv2
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bw = 64 * bw_factor
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@ -244,7 +238,10 @@ class DPN(nn.Module):
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for i in range(2, k_sec[3] + 1):
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blocks['conv5_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
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in_chs += inc
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blocks['conv5_bn_ac'] = CatBnAct(in_chs, activation_fn=fc_act)
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def _fc_norm(f, eps): return BatchNormAct2d(f, eps=eps, act_layer=fc_act, inplace=False)
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blocks['conv5_bn_ac'] = CatBnAct(in_chs, norm_layer=_fc_norm)
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self.num_features = in_chs
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self.features = nn.Sequential(blocks)
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