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dpn.py
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dpn.py
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""" PyTorch implementation of DualPathNetworks
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.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
from collections import OrderedDict
from adaptive_avgmax_pool import adaptive_avgmax_pool2d
__all__ = ['DPN', 'dpn68', 'dpn68b', 'dpn92', 'dpn98', 'dpn131', 'dpn107']
model_urls = {
'dpn68':
'https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn68-66bebafa7.pth',
'dpn68b-extra':
'https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn68b_extra-84854c156.pth',
'dpn92-extra':
'https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn92_extra-b040e4a9b.pth',
'dpn98':
'https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn98-5b90dec4d.pth',
'dpn131':
'https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn131-71dfe43e0.pth',
'dpn107-extra':
'https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn107_extra-1ac7121e2.pth'
}
def dpn68(pretrained=False, test_time_pool=False, **kwargs):
"""Constructs a DPN-68 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet-1K
test_time_pool (bool): If True, pools features for input resolution beyond
standard 224x224 input with avg+max at inference/validation time
**kwargs : Keyword args passed to model __init__
num_classes (int): Number of classes for classifier linear layer, default=1000
"""
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),
test_time_pool=test_time_pool, **kwargs)
if pretrained:
model.load_state_dict(load_state_dict_from_url(model_urls['dpn68']))
return model
def dpn68b(pretrained=False, test_time_pool=False, **kwargs):
"""Constructs a DPN-68b model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet-1K
test_time_pool (bool): If True, pools features for input resolution beyond
standard 224x224 input with avg+max at inference/validation time
**kwargs : Keyword args passed to model __init__
num_classes (int): Number of classes for classifier linear layer, default=1000
"""
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),
test_time_pool=test_time_pool, **kwargs)
if pretrained:
model.load_state_dict(load_state_dict_from_url(model_urls['dpn68b-extra']))
return model
def dpn92(pretrained=False, test_time_pool=False, **kwargs):
"""Constructs a DPN-92 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet-1K
test_time_pool (bool): If True, pools features for input resolution beyond
standard 224x224 input with avg+max at inference/validation time
**kwargs : Keyword args passed to model __init__
num_classes (int): Number of classes for classifier linear layer, default=1000
"""
model = DPN(
num_init_features=64, k_r=96, groups=32,
k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128),
test_time_pool=test_time_pool, **kwargs)
if pretrained:
model.load_state_dict(load_state_dict_from_url(model_urls['dpn92-extra']))
return model
def dpn98(pretrained=False, test_time_pool=False, **kwargs):
"""Constructs a DPN-98 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet-1K
test_time_pool (bool): If True, pools features for input resolution beyond
standard 224x224 input with avg+max at inference/validation time
**kwargs : Keyword args passed to model __init__
num_classes (int): Number of classes for classifier linear layer, default=1000
"""
model = DPN(
num_init_features=96, k_r=160, groups=40,
k_sec=(3, 6, 20, 3), inc_sec=(16, 32, 32, 128),
test_time_pool=test_time_pool, **kwargs)
if pretrained:
model.load_state_dict(load_state_dict_from_url(model_urls['dpn98']))
return model
def dpn131(pretrained=False, test_time_pool=False, **kwargs):
"""Constructs a DPN-131 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet-1K
test_time_pool (bool): If True, pools features for input resolution beyond
standard 224x224 input with avg+max at inference/validation time
**kwargs : Keyword args passed to model __init__
num_classes (int): Number of classes for classifier linear layer, default=1000
"""
model = DPN(
num_init_features=128, k_r=160, groups=40,
k_sec=(4, 8, 28, 3), inc_sec=(16, 32, 32, 128),
test_time_pool=test_time_pool, **kwargs)
if pretrained:
model.load_state_dict(load_state_dict_from_url(model_urls['dpn131']))
return model
def dpn107(pretrained=False, test_time_pool=False, **kwargs):
"""Constructs a DPN-107 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet-1K
test_time_pool (bool): If True, pools features for input resolution beyond
standard 224x224 input with avg+max at inference/validation time
**kwargs : Keyword args passed to model __init__
num_classes (int): Number of classes for classifier linear layer, default=1000
"""
model = DPN(
num_init_features=128, k_r=200, groups=50,
k_sec=(4, 8, 20, 3), inc_sec=(20, 64, 64, 128),
test_time_pool=test_time_pool, **kwargs)
if pretrained:
model.load_state_dict(load_state_dict_from_url(model_urls['dpn107-extra']))
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 == 'proj':
self.key_stride = 1
self.has_proj = True
elif block_type == 'down':
self.key_stride = 2
self.has_proj = True
else:
assert block_type == '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
blocks = OrderedDict()
# conv1
if small:
blocks['conv1_1'] = InputBlock(num_init_features, kernel_size=3, padding=1)
else:
blocks['conv1_1'] = InputBlock(num_init_features, kernel_size=7, padding=3)
# conv2
bw = 64 * bw_factor
inc = inc_sec[0]
r = (k_r * bw) // (64 * bw_factor)
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):
blocks['conv2_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
in_chs += inc
# conv3
bw = 128 * bw_factor
inc = inc_sec[1]
r = (k_r * bw) // (64 * bw_factor)
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):
blocks['conv3_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
in_chs += inc
# conv4
bw = 256 * bw_factor
inc = inc_sec[2]
r = (k_r * bw) // (64 * bw_factor)
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):
blocks['conv4_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
in_chs += inc
# conv5
bw = 512 * bw_factor
inc = inc_sec[3]
r = (k_r * bw) // (64 * bw_factor)
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):
blocks['conv5_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', b)
in_chs += inc
blocks['conv5_bn_ac'] = CatBnAct(in_chs)
self.features = nn.Sequential(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 forward(self, x):
x = self.features(x)
if not self.training and self.test_time_pool:
x = F.avg_pool2d(x, 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(x, pool_type='avg')
out = self.classifier(x)
return out.view(out.size(0), -1)