# Source: https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/wideresnet.py (License: BSD-3-Clause)
"""
Implementation of WideResNet as described in: `Wide Residual Networks <https://arxiv.org/abs/1605.07146>`_.
"""
import re
import os
from os.path import expanduser
# import hickle as hkl
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
__all__ = ['WideResNet', 'wideresnet50']
model_urls = {
'wideresnet50': 'https://s3.amazonaws.com/pytorch/h5models/wide-resnet-50-2-export.hkl'
}
def define_model(params):
def conv2d(input_, params, base, stride=1, pad=0):
return F.conv2d(input_, params[base + '.weight'],
params[base + '.bias'], stride, pad)
def group(input_, params, base, stride, n):
o = input_
for i in range(0,n):
b_base = ('%s.block%d.conv') % (base, i)
x = o
o = conv2d(x, params, b_base + '0')
o = F.relu(o)
o = conv2d(o, params, b_base + '1', stride=i==0 and stride or 1, pad=1)
o = F.relu(o)
o = conv2d(o, params, b_base + '2')
if i == 0:
o += conv2d(x, params, b_base + '_dim', stride=stride)
else:
o += x
o = F.relu(o)
return o
# determine network size by parameters
blocks = [sum([re.match('group%d.block\d+.conv0.weight'%j, k) is not None
for k in params.keys()]) for j in range(4)]
def f(input_, params, pooling_classif=True):
o = F.conv2d(input_, params['conv0.weight'], params['conv0.bias'], 2, 3)
o = F.relu(o)
o = F.max_pool2d(o, 3, 2, 1)
o_g0 = group(o, params, 'group0', 1, blocks[0])
o_g1 = group(o_g0, params, 'group1', 2, blocks[1])
o_g2 = group(o_g1, params, 'group2', 2, blocks[2])
o_g3 = group(o_g2, params, 'group3', 2, blocks[3])
if pooling_classif:
o = F.avg_pool2d(o_g3, 7, 1, 0)
o = o.view(o.size(0), -1)
o = F.linear(o, params['fc.weight'], params['fc.bias'])
return o
return f
[docs]class WideResNet(nn.Module):
def __init__(self, pooling, f, params):
super(WideResNet, self).__init__()
self.pooling = pooling
self.f = f
self.params = params
def forward(self, x):
x = self.f(x, self.params, self.pooling)
return x
[docs]def wideresnet50(pooling):
pass
"""Pretrained WideResnet50 model"""
# dir_models = os.path.join(expanduser("~"), '.torch/wideresnet')
# path_hkl = os.path.join(dir_models, 'wideresnet50.hkl')
# if os.path.isfile(path_hkl):
# params = hkl.load(path_hkl)
# # convert numpy arrays to torch Variables
# for k,v in sorted(params.items()):
# print(k, v.shape)
# params[k] = Variable(torch.from_numpy(v), requires_grad=True)
# else:
# os.system('mkdir -p ' + dir_models)
# os.system('wget {} -O {}'.format(model_urls['wideresnet50'], path_hkl))
# f = define_model(params)
# model = WideResNet(pooling, f, params)
# return model