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from collections import OrderedDict
import torch
import torch.nn as nn
from torch.nn import DataParallel
from torch.nn.parallel import DistributedDataParallel
from torchvision.models import vgg as vgg
NAMES = {
'vgg11': [
'conv1_1', 'relu1_1', 'pool1', 'conv2_1', 'relu2_1', 'pool2',
'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'pool3', 'conv4_1',
'relu4_1', 'conv4_2', 'relu4_2', 'pool4', 'conv5_1', 'relu5_1',
'conv5_2', 'relu5_2', 'pool5'
],
'vgg13': [
'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1',
'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1',
'conv3_2', 'relu3_2', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2',
'relu4_2', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'pool5'
],
'vgg16': [
'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1',
'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1',
'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1',
'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'pool4',
'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3',
'pool5'
],
'vgg19': [
'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1',
'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1',
'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4',
'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 'conv5_1', 'relu5_1',
'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4',
'pool5'
]
}
# MODEL_PATH = {
# 'vgg19': 'losses/pretrained/vgg19-dcbb9e9d.pth'
# }
def load_model(model, model_path, strict=True, cpu=False):
if isinstance(model, DataParallel) or isinstance(model, DistributedDataParallel):
model = model.module
if cpu:
loaded_model = torch.load(model_path, map_location='cpu')
else:
loaded_model = torch.load(model_path)
model.load_state_dict(loaded_model, strict=strict)
def insert_bn(names):
"""Insert bn layer after each conv.
Args:
names (list): The list of layer names.
Returns:
list: The list of layer names with bn layers.
"""
names_bn = []
for name in names:
names_bn.append(name)
if 'conv' in name:
position = name.replace('conv', '')
names_bn.append('bn' + position)
return names_bn
class VGGFeatureExtractor(nn.Module):
"""VGG network for feature extraction.
In this implementation, we allow users to choose whether use normalization
in the input feature and the type of vgg network. Note that the pretrained
path must fit the vgg type.
Args:
layer_name_list (list[str]): Forward function returns the corresponding
features according to the layer_name_list.
Example: {'relu1_1', 'relu2_1', 'relu3_1'}.
vgg_type (str): Set the type of vgg network. Default: 'vgg19'.
use_input_norm (bool): If True, normalize the input image. Importantly,
the input feature must in the range [0, 1]. Default: True.
requires_grad (bool): If true, the parameters of VGG network will be
optimized. Default: False.
remove_pooling (bool): If true, the max pooling operations in VGG net
will be removed. Default: False.
pooling_stride (int): The stride of max pooling operation. Default: 2.
"""
def __init__(self,
layer_name_list,
vgg_type='vgg19',
use_input_norm=True,
requires_grad=False,
remove_pooling=False,
pooling_stride=2):
super(VGGFeatureExtractor, self).__init__()
self.layer_name_list = layer_name_list
self.use_input_norm = use_input_norm
self.names = NAMES[vgg_type.replace('_bn', '')]
if 'bn' in vgg_type:
self.names = insert_bn(self.names)
# only borrow layers that will be used to avoid unused params
max_idx = 0
for v in layer_name_list:
idx = self.names.index(v)
if idx > max_idx:
max_idx = idx
features = getattr(vgg, vgg_type)(pretrained=True).features[:max_idx + 1]
# vgg_model = getattr(vgg, vgg_type)(pretrained=False)
# load_model(vgg_model, MODEL_PATH[vgg_type], strict=True)
# features = vgg_model.features[:max_idx + 1]
modified_net = OrderedDict()
for k, v in zip(self.names, features):
if 'pool' in k:
# if remove_pooling is true, pooling operation will be removed
if remove_pooling:
continue
else:
# in some cases, we may want to change the default stride
modified_net[k] = nn.MaxPool2d(
kernel_size=2, stride=pooling_stride)
else:
modified_net[k] = v
self.vgg_net = nn.Sequential(modified_net)
if not requires_grad:
self.vgg_net.eval()
for param in self.parameters():
param.requires_grad = False
else:
self.vgg_net.train()
for param in self.parameters():
param.requires_grad = True
if self.use_input_norm:
# the mean is for image with range [0, 1]
self.register_buffer(
'mean',
torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
# the std is for image with range [0, 1]
self.register_buffer(
'std',
torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
def forward(self, x):
"""Forward function.
Args:
x (Tensor): Input tensor with shape (n, c, h, w).
Returns:
Tensor: Forward results.
"""
if self.use_input_norm:
x = (x - self.mean) / self.std
output = {}
for key, layer in self.vgg_net._modules.items():
x = layer(x)
if key in self.layer_name_list:
output[key] = x.clone()
return output
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