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""" PyTorch implementation of DualPathNetworks
Ported to PyTorch by [Ross Wightman](https://github.com/rwightman/pytorch-dpn-pretrained)
Based on original MXNet implementation https://github.com/cypw/DPNs with
many ideas from another PyTorch implementation https://github.com/oyam/pytorch-DPNs.
This implementation is compatible with the pretrained weights
from cypw's MXNet implementation.
"""
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from collections import OrderedDict
__all__ = ['DPN', 'dpn68', 'dpn68b', 'dpn92', 'dpn98', 'dpn131', 'dpn107']
pretrained_settings = {
'dpn68': {
'imagenet': {
'url': 'http://data.lip6.fr/cadene/pretrainedmodels/dpn68-66bebafa7.pth',
'input_space': 'RGB',
'input_size': [3, 224, 224],
'input_range': [0, 1],
'mean': [124 / 255, 117 / 255, 104 / 255],
'std': [1 / (.0167 * 255)] * 3,
'num_classes': 1000
}
},
'dpn68b': {
'imagenet+5k': {
'url': 'http://data.lip6.fr/cadene/pretrainedmodels/dpn68b_extra-84854c156.pth',
'input_space': 'RGB',
'input_size': [3, 224, 224],
'input_range': [0, 1],
'mean': [124 / 255, 117 / 255, 104 / 255],
'std': [1 / (.0167 * 255)] * 3,
'num_classes': 1000
}
},
'dpn92': {
# 'imagenet': {
# 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/dpn68-66bebafa7.pth',
# 'input_space': 'RGB',
# 'input_size': [3, 224, 224],
# 'input_range': [0, 1],
# 'mean': [124 / 255, 117 / 255, 104 / 255],
# 'std': [1 / (.0167 * 255)] * 3,
# 'num_classes': 1000
# },
'imagenet+5k': {
'url': 'http://data.lip6.fr/cadene/pretrainedmodels/dpn92_extra-b040e4a9b.pth',
'input_space': 'RGB',
'input_size': [3, 224, 224],
'input_range': [0, 1],
'mean': [124 / 255, 117 / 255, 104 / 255],
'std': [1 / (.0167 * 255)] * 3,
'num_classes': 1000
}
},
'dpn98': {
'imagenet': {
'url': 'http://data.lip6.fr/cadene/pretrainedmodels/dpn98-5b90dec4d.pth',
'input_space': 'RGB',
'input_size': [3, 224, 224],
'input_range': [0, 1],
'mean': [124 / 255, 117 / 255, 104 / 255],
'std': [1 / (.0167 * 255)] * 3,
'num_classes': 1000
}
},
'dpn131': {
'imagenet': {
'url': 'http://data.lip6.fr/cadene/pretrainedmodels/dpn131-71dfe43e0.pth',
'input_space': 'RGB',
'input_size': [3, 224, 224],
'input_range': [0, 1],
'mean': [124 / 255, 117 / 255, 104 / 255],
'std': [1 / (.0167 * 255)] * 3,
'num_classes': 1000
}
},
'dpn107': {
'imagenet+5k': {
'url': 'http://data.lip6.fr/cadene/pretrainedmodels/dpn107_extra-1ac7121e2.pth',
'input_space': 'RGB',
'input_size': [3, 224, 224],
'input_range': [0, 1],
'mean': [124 / 255, 117 / 255, 104 / 255],
'std': [1 / (.0167 * 255)] * 3,
'num_classes': 1000
}
}
}
def dpn68(num_classes=1000, pretrained='imagenet'):
model = DPN(
small=True, num_init_features=10, k_r=128, groups=32,
k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64),
num_classes=num_classes, test_time_pool=True)
if pretrained:
settings = pretrained_settings['dpn68'][pretrained]
assert num_classes == settings['num_classes'], \
"num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)
model.load_state_dict(model_zoo.load_url(settings['url']))
model.input_space = settings['input_space']
model.input_size = settings['input_size']
model.input_range = settings['input_range']
model.mean = settings['mean']
model.std = settings['std']
return model
def dpn68b(num_classes=1000, pretrained='imagenet+5k'):
model = DPN(
small=True, num_init_features=10, k_r=128, groups=32,
b=True, k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64),
num_classes=num_classes, test_time_pool=True)
if pretrained:
settings = pretrained_settings['dpn68b'][pretrained]
assert num_classes == settings['num_classes'], \
"num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)
model.load_state_dict(model_zoo.load_url(settings['url']))
model.input_space = settings['input_space']
model.input_size = settings['input_size']
model.input_range = settings['input_range']
model.mean = settings['mean']
model.std = settings['std']
return model
def dpn92(num_classes=1000, pretrained='imagenet+5k'):
model = DPN(
num_init_features=64, k_r=96, groups=32,
k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128),
num_classes=num_classes, test_time_pool=True)
if pretrained:
settings = pretrained_settings['dpn92'][pretrained]
assert num_classes == settings['num_classes'], \
"num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)
model.load_state_dict(model_zoo.load_url(settings['url']))
model.input_space = settings['input_space']
model.input_size = settings['input_size']
model.input_range = settings['input_range']
model.mean = settings['mean']
model.std = settings['std']
return model
def dpn98(num_classes=1000, pretrained='imagenet'):
model = DPN(
num_init_features=96, k_r=160, groups=40,
k_sec=(3, 6, 20, 3), inc_sec=(16, 32, 32, 128),
num_classes=num_classes, test_time_pool=True)
if pretrained:
settings = pretrained_settings['dpn98'][pretrained]
assert num_classes == settings['num_classes'], \
"num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)
model.load_state_dict(model_zoo.load_url(settings['url']))
model.input_space = settings['input_space']
model.input_size = settings['input_size']
model.input_range = settings['input_range']
model.mean = settings['mean']
model.std = settings['std']
return model
def dpn131(num_classes=1000, pretrained='imagenet'):
model = DPN(
num_init_features=128, k_r=160, groups=40,
k_sec=(4, 8, 28, 3), inc_sec=(16, 32, 32, 128),
num_classes=num_classes, test_time_pool=True)
if pretrained:
settings = pretrained_settings['dpn131'][pretrained]
assert num_classes == settings['num_classes'], \
"num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)
model.load_state_dict(model_zoo.load_url(settings['url']))
model.input_space = settings['input_space']
model.input_size = settings['input_size']
model.input_range = settings['input_range']
model.mean = settings['mean']
model.std = settings['std']
return model
def dpn107(num_classes=1000, pretrained='imagenet+5k'):
model = DPN(
num_init_features=128, k_r=200, groups=50,
k_sec=(4, 8, 20, 3), inc_sec=(20, 64, 64, 128),
num_classes=num_classes, test_time_pool=True)
if pretrained:
settings = pretrained_settings['dpn107'][pretrained]
assert num_classes == settings['num_classes'], \
"num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)
model.load_state_dict(model_zoo.load_url(settings['url']))
model.input_space = settings['input_space']
model.input_size = settings['input_size']
model.input_range = settings['input_range']
model.mean = settings['mean']
model.std = settings['std']
return model
class CatBnAct(nn.Module):
def __init__(self, in_chs, activation_fn=nn.ReLU(inplace=True)):
super(CatBnAct, self).__init__()
self.bn = nn.BatchNorm2d(in_chs, eps=0.001)
self.act = activation_fn
def forward(self, x):
x = torch.cat(x, dim=1) if isinstance(x, tuple) else x
return self.act(self.bn(x))
class BnActConv2d(nn.Module):
def __init__(self, in_chs, out_chs, kernel_size, stride,
padding=0, groups=1, activation_fn=nn.ReLU(inplace=True)):
super(BnActConv2d, self).__init__()
self.bn = nn.BatchNorm2d(in_chs, eps=0.001)
self.act = activation_fn
self.conv = nn.Conv2d(in_chs, out_chs, kernel_size, stride, padding, groups=groups, bias=False)
def forward(self, x):
return self.conv(self.act(self.bn(x)))
class InputBlock(nn.Module):
def __init__(self, num_init_features, kernel_size=7,
padding=3, activation_fn=nn.ReLU(inplace=True)):
super(InputBlock, self).__init__()
self.conv = nn.Conv2d(
3, num_init_features, kernel_size=kernel_size, stride=2, padding=padding, bias=False)
self.bn = nn.BatchNorm2d(num_init_features, eps=0.001)
self.act = activation_fn
self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
x = self.pool(x)
return x
class DualPathBlock(nn.Module):
def __init__(
self, in_chs, num_1x1_a, num_3x3_b, num_1x1_c, inc, groups, block_type='normal', b=False):
super(DualPathBlock, self).__init__()
self.num_1x1_c = num_1x1_c
self.inc = inc
self.b = b
if block_type is 'proj':
self.key_stride = 1
self.has_proj = True
elif block_type is 'down':
self.key_stride = 2
self.has_proj = True
else:
assert block_type is 'normal'
self.key_stride = 1
self.has_proj = False
if self.has_proj:
# Using different member names here to allow easier parameter key matching for conversion
if self.key_stride == 2:
self.c1x1_w_s2 = BnActConv2d(
in_chs=in_chs, out_chs=num_1x1_c + 2 * inc, kernel_size=1, stride=2)
else:
self.c1x1_w_s1 = BnActConv2d(
in_chs=in_chs, out_chs=num_1x1_c + 2 * inc, kernel_size=1, stride=1)
self.c1x1_a = BnActConv2d(in_chs=in_chs, out_chs=num_1x1_a, kernel_size=1, stride=1)
self.c3x3_b = BnActConv2d(
in_chs=num_1x1_a, out_chs=num_3x3_b, kernel_size=3,
stride=self.key_stride, padding=1, groups=groups)
if b:
self.c1x1_c = CatBnAct(in_chs=num_3x3_b)
self.c1x1_c1 = nn.Conv2d(num_3x3_b, num_1x1_c, kernel_size=1, bias=False)
self.c1x1_c2 = nn.Conv2d(num_3x3_b, inc, kernel_size=1, bias=False)
else:
self.c1x1_c = BnActConv2d(in_chs=num_3x3_b, out_chs=num_1x1_c + inc, kernel_size=1, stride=1)
def forward(self, x):
x_in = torch.cat(x, dim=1) if isinstance(x, tuple) else x
if self.has_proj:
if self.key_stride == 2:
x_s = self.c1x1_w_s2(x_in)
else:
x_s = self.c1x1_w_s1(x_in)
x_s1 = x_s[:, :self.num_1x1_c, :, :]
x_s2 = x_s[:, self.num_1x1_c:, :, :]
else:
x_s1 = x[0]
x_s2 = x[1]
x_in = self.c1x1_a(x_in)
x_in = self.c3x3_b(x_in)
if self.b:
x_in = self.c1x1_c(x_in)
out1 = self.c1x1_c1(x_in)
out2 = self.c1x1_c2(x_in)
else:
x_in = self.c1x1_c(x_in)
out1 = x_in[:, :self.num_1x1_c, :, :]
out2 = x_in[:, self.num_1x1_c:, :, :]
resid = x_s1 + out1
dense = torch.cat([x_s2, out2], dim=1)
return resid, dense
class DPN(nn.Module):
def __init__(self, small=False, num_init_features=64, k_r=96, groups=32,
b=False, k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128),
num_classes=1000, test_time_pool=False):
super(DPN, self).__init__()
self.test_time_pool = test_time_pool
self.b = b
bw_factor = 1 if small else 4
self.k_sec = k_sec
self.out_channels = []
self.blocks = OrderedDict()
# conv1
if small:
self.blocks['conv1_1'] = InputBlock(num_init_features, kernel_size=3, padding=1)
else:
self.blocks['conv1_1'] = InputBlock(num_init_features, kernel_size=7, padding=3)
self.out_channels.append(num_init_features)
# conv2
bw = 64 * bw_factor
inc = inc_sec[0]
r = (k_r * bw) // (64 * bw_factor)
self.blocks['conv2_1'] = DualPathBlock(num_init_features, r, r, bw, inc, groups, 'proj', b)
in_chs = bw + 3 * inc
for i in range(2, k_sec[0] + 1):
self.blocks['conv2_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
in_chs += inc
self.out_channels.append(in_chs)
# conv3
bw = 128 * bw_factor
inc = inc_sec[1]
r = (k_r * bw) // (64 * bw_factor)
self.blocks['conv3_1'] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'down', b)
in_chs = bw + 3 * inc
for i in range(2, k_sec[1] + 1):
self.blocks['conv3_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
in_chs += inc
self.out_channels.append(in_chs)
# conv4
bw = 256 * bw_factor
inc = inc_sec[2]
r = (k_r * bw) // (64 * bw_factor)
self.blocks['conv4_1'] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'down', b)
in_chs = bw + 3 * inc
for i in range(2, k_sec[2] + 1):
self.blocks['conv4_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
in_chs += inc
self.out_channels.append(in_chs)
# conv5
bw = 512 * bw_factor
inc = inc_sec[3]
r = (k_r * bw) // (64 * bw_factor)
self.blocks['conv5_1'] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'down', b)
in_chs = bw + 3 * inc
for i in range(2, k_sec[3] + 1):
self.blocks['conv5_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
in_chs += inc
self.blocks['conv5_bn_ac'] = CatBnAct(in_chs)
self.out_channels.append(in_chs)
self.features = nn.Sequential(self.blocks)
# Using 1x1 conv for the FC layer to allow the extra pooling scheme
self.classifier = nn.Conv2d(in_chs, num_classes, kernel_size=1, bias=True)
def logits(self, features):
if not self.training and self.test_time_pool:
x = F.avg_pool2d(features, kernel_size=7, stride=1)
out = self.classifier(x)
# The extra test time pool should be pooling an img_size//32 - 6 size patch
out = adaptive_avgmax_pool2d(out, pool_type='avgmax')
else:
x = adaptive_avgmax_pool2d(features, pool_type='avg')
out = self.classifier(x)
return out.view(out.size(0), -1)
def forward(self, input):
x = self.features(input)
x = self.logits(x)
return x
""" PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles feature dim
Both a functional and a nn.Module version of the pooling is provided.
Author: Ross Wightman (rwightman)
"""
def pooling_factor(pool_type='avg'):
return 2 if pool_type == 'avgmaxc' else 1
def adaptive_avgmax_pool2d(x, pool_type='avg', padding=0, count_include_pad=False):
"""Selectable global pooling function with dynamic input kernel size
"""
if pool_type == 'avgmaxc':
x = torch.cat([
F.avg_pool2d(
x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad),
F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding)
], dim=1)
elif pool_type == 'avgmax':
x_avg = F.avg_pool2d(
x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad)
x_max = F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding)
x = 0.5 * (x_avg + x_max)
elif pool_type == 'max':
x = F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding)
else:
if pool_type != 'avg':
print('Invalid pool type %s specified. Defaulting to average pooling.' % pool_type)
x = F.avg_pool2d(
x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad)
return x
class AdaptiveAvgMaxPool2d(torch.nn.Module):
"""Selectable global pooling layer with dynamic input kernel size
"""
def __init__(self, output_size=1, pool_type='avg'):
super(AdaptiveAvgMaxPool2d, self).__init__()
self.output_size = output_size
self.pool_type = pool_type
if pool_type == 'avgmaxc' or pool_type == 'avgmax':
self.pool = nn.ModuleList([nn.AdaptiveAvgPool2d(output_size), nn.AdaptiveMaxPool2d(output_size)])
elif pool_type == 'max':
self.pool = nn.AdaptiveMaxPool2d(output_size)
else:
if pool_type != 'avg':
print('Invalid pool type %s specified. Defaulting to average pooling.' % pool_type)
self.pool = nn.AdaptiveAvgPool2d(output_size)
def forward(self, x):
if self.pool_type == 'avgmaxc':
x = torch.cat([p(x) for p in self.pool], dim=1)
elif self.pool_type == 'avgmax':
x = 0.5 * torch.sum(torch.stack([p(x) for p in self.pool]), 0).squeeze(dim=0)
else:
x = self.pool(x)
return x
def factor(self):
return pooling_factor(self.pool_type)
def __repr__(self):
return self.__class__.__name__ + ' (' \
+ 'output_size=' + str(self.output_size) \
+ ', pool_type=' + self.pool_type + ')'
if __name__ == "__main__":
model = dpn131()
print(model.features, len(model.features))
print(model.features[2]) |