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vgg19.py
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#encoding=utf8
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
import numpy as np
__all__ = ["VGG11", "VGG13", "VGG16", "VGG19"]
# VGG19 模型，已经预训练好，用于计算生成器的 perceptual loss
class ConvBlock(nn.Layer):
def __init__(self, input_channels, output_channels, groups, name=None):
super(ConvBlock, self).__init__()
self.groups = groups
self._conv_1 = Conv2D(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(name=name + "1_weights"),
bias_attr=False)
if groups == 2 or groups == 3 or groups == 4:
self._conv_2 = Conv2D(
in_channels=output_channels,
out_channels=output_channels,
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(name=name + "2_weights"),
bias_attr=False)
if groups == 3 or groups == 4:
self._conv_3 = Conv2D(
in_channels=output_channels,
out_channels=output_channels,
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(name=name + "3_weights"),
bias_attr=False)
if groups == 4:
self._conv_4 = Conv2D(
in_channels=output_channels,
out_channels=output_channels,
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(name=name + "4_weights"),
bias_attr=False)
self._pool = MaxPool2D(kernel_size=2, stride=2, padding=0)
def forward(self, inputs):
x = self._conv_1(inputs)
x = F.relu(x)
if self.groups == 2 or self.groups == 3 or self.groups == 4:
x = self._conv_2(x)
x = F.relu(x)
if self.groups == 3 or self.groups == 4:
x = self._conv_3(x)
x = F.relu(x)
if self.groups == 4:
x = self._conv_4(x)
x = F.relu(x)
x = self._pool(x)
return x
class VGGNet(nn.Layer):
def __init__(self, layers=11, class_dim=1000):
super(VGGNet, self).__init__()
self.layers = layers
self.vgg_configure = {
11: [1, 1, 2, 2, 2],
13: [2, 2, 2, 2, 2],
16: [2, 2, 3, 3, 3],
19: [2, 2, 4, 4, 4]
}
assert self.layers in self.vgg_configure.keys(), \
"supported layers are {} but input layer is {}".format(vgg_configure.keys(), layers)
self.groups = self.vgg_configure[self.layers]
self._conv_block_1 = ConvBlock(3, 64, self.groups[0], name="conv1_")
self._conv_block_2 = ConvBlock(64, 128, self.groups[1], name="conv2_")
self._conv_block_3 = ConvBlock(128, 256, self.groups[2], name="conv3_")
self._conv_block_4 = ConvBlock(256, 512, self.groups[3], name="conv4_")
self._conv_block_5 = ConvBlock(512, 512, self.groups[4], name="conv5_")
self._drop = Dropout(p=0.5, mode="downscale_in_infer")
self._fc1 = Linear(
7 * 7 * 512,
4096,
weight_attr=ParamAttr(name="fc6_weights"),
bias_attr=ParamAttr(name="fc6_offset"))
self._fc2 = Linear(
4096,
4096,
weight_attr=ParamAttr(name="fc7_weights"),
bias_attr=ParamAttr(name="fc7_offset"))
self._out = Linear(
4096,
class_dim,
weight_attr=ParamAttr(name="fc8_weights"),
bias_attr=ParamAttr(name="fc8_offset"))
def forward(self, inputs):
features = []
features.append(inputs)
x = self._conv_block_1(inputs)
features.append(x)
x = self._conv_block_2(x)
features.append(x)
x = self._conv_block_3(x)
features.append(x)
x = self._conv_block_4(x)
features.append(x)
x = self._conv_block_5(x)
x = paddle.reshape(x, [0, -1])
x = self._fc1(x)
x = F.relu(x)
x = self._drop(x)
x = self._fc2(x)
x = F.relu(x)
x = self._drop(x)
x = self._out(x)
return x, features
def VGG11(**args):
model = VGGNet(layers=11, **args)
return model
def VGG13(**args):
model = VGGNet(layers=13, **args)
return model
def VGG16(**args):
model = VGGNet(layers=16, **args)
return model
def VGG19(**args):
model = VGGNet(layers=19, **args)
return model
# spade 模型输出尺寸是256*256，vgg 模型尺寸为224
def random_crop(img_tensor, opt, size=224):
left = np.random.randint(0, opt.crop_size - size + 1)
top = np.random.randint(0, opt.crop_size - size + 1)
img_tensor = img_tensor[:, :, top:top+size, left:left+size]
return img_tensor
def center_crop(img_tensor, opt, size=224):
left_top = (opt.crop_size - size) // 2
img_tensor = img_tensor[:, :, left_top:left_top+size, left_top:left_top+size]
return img_tensor
if __name__ == '__main__':
model = VGG19()
x = paddle.rand([1, 3, 224, 224])
out = model(x)
print(model._conv_block_1.parameters())