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import numpy as np
# import os
import paddle
import paddle.optimizer
import paddle.nn as nn
# from tqdm import tqdm
# from paddle.io import Dataset
# from paddle.io import DataLoader
import paddle.nn.functional as F
# import paddle.tensor as tensor
class VGG19(nn.Layer):
cfg = [
64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512,'M', 512, 512, 512, 512, 'M']
def __init__(self, output_index: int = 26) -> None:
super().__init__()
# arch = 'caffevgg19'
# weights_path = get_path_from_url(model_urls[arch][0],
# model_urls[arch][1])
data_dict: dict = np.load("./vgg19_no_fc.npy",
encoding='latin1',
allow_pickle=True).item()
self.features = self.make_layers(self.cfg, data_dict)
del data_dict
self.features = nn.Sequential(*self.features.sublayers()[:output_index])
mean = paddle.to_tensor([103.939, 116.779, 123.68])
self.mean = mean.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
def _process(self, x):
rgb = (x * 0.5 + 0.5) * 255 # value to 255
bgr = paddle.stack((rgb[:, 2, :, :], rgb[:, 1, :, :], rgb[:, 0, :, :]),
1) # rgb to bgr
return bgr - self.mean # vgg norm
def _forward_impl(self, x):
x = self._process(x)
# NOTE get output with out relu activation
x = self.features(x)
return x
def forward(self, x):
return self._forward_impl(x)
@staticmethod
def get_conv_filter(data_dict, name):
return data_dict[name][0]
@staticmethod
def get_bias(data_dict, name):
return data_dict[name][1]
@staticmethod
def get_fc_weight(data_dict, name):
return data_dict[name][0]
def make_layers(self, cfg, data_dict, batch_norm=False) -> nn.Sequential:
layers = []
in_channels = 3
block = 1
number = 1
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2D(kernel_size=2, stride=2)]
block += 1
number = 1
else:
conv2d = nn.Conv2D(in_channels, v, kernel_size=3, padding=1)
""" set value """
weight = paddle.to_tensor(
self.get_conv_filter(data_dict, f'conv{block}_{number}'))
weight = weight.transpose((3, 2, 0, 1))
bias = paddle.to_tensor(
self.get_bias(data_dict, f'conv{block}_{number}'))
conv2d.weight.set_value(weight)
conv2d.bias.set_value(bias)
number += 1
if batch_norm:
layers += [conv2d, nn.BatchNorm2D(v), nn.ReLU()]
else:
layers += [conv2d, nn.ReLU()]
in_channels = v
# print("number",block)
return nn.Sequential(*layers)
class InvertedresBlock(nn.Layer):
def __init__(self,
in_channels: int,
expansion: float,
out_channels: int,
bias_attr=False):
super().__init__()
self.in_channels = in_channels
self.expansion = expansion
self.out_channels = out_channels
self.bottle_channels = round(self.expansion * self.out_channels)
self.body = nn.Sequential(
# pw
Conv2DNormLReLU(self.in_channels,
self.bottle_channels,
kernel_size=1,
bias_attr=bias_attr),
# dw
nn.Conv2D(self.bottle_channels,
self.bottle_channels,
kernel_size=3,
stride=1,
padding=0,
groups=self.bottle_channels,
bias_attr=True),
nn.GroupNorm(1, self.bottle_channels),
nn.LeakyReLU(0.2),
# pw & linear
nn.Conv2D(self.bottle_channels,
self.out_channels,
kernel_size=1,
padding=0,
bias_attr=False),
nn.GroupNorm(1, self.out_channels),
)
def forward(self, x0):
x = self.body(x0)
if self.in_channels == self.out_channels:
out = paddle.add(x0, x)
else:
out = x
return x
class Conv2DNormLReLU(nn.Layer):
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: int = 3,
stride: int = 1,
padding: int = 1,
bias_attr=False) -> None:
super().__init__()
self.conv = nn.Conv2D(in_channels,
out_channels,
kernel_size,
stride,
padding,
bias_attr=bias_attr)
# NOTE layer norm is crucial for animegan!
self.norm = nn.GroupNorm(1, out_channels)
self.lrelu = nn.LeakyReLU(0.2)
def forward(self, x):
x = self.conv(x)
x = self.norm(x)
x = self.lrelu(x)
return x
class Generater(nn.Layer):
def __init__(self):
super().__init__()
self.VGG = VGG19()
self.A = nn.Sequential(InvertedresBlock(512, 2, 256),
InvertedresBlock(256, 2, 256),
InvertedresBlock(256, 2, 256),
InvertedresBlock(256, 2, 256),
Conv2DNormLReLU(256, 128))
self.B = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear'),
Conv2DNormLReLU(128, 128),
Conv2DNormLReLU(128, 128))
self.C = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear'),
Conv2DNormLReLU(128, 128),
Conv2DNormLReLU(128, 128))
self.D = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear'),
Conv2DNormLReLU(128, 64),
Conv2DNormLReLU(64, 64),
Conv2DNormLReLU(64, 32, 7, padding=3))
self.out = nn.Sequential(nn.Conv2D(32, 3, 1, bias_attr=False),
nn.Tanh())
# ,nn.Sigmoid())
def style_projection(self,content_feature,style_feature,alpha = 0.7):
def scatter_numpy(dim, index, src):
dst = src.copy()
idx_xsection_shape = index.shape[:dim] + index.shape[dim + 1:]
# print("idx_xsection_shape",idx_xsection_shape)#(b,c)
dst_xsection_shape = dst.shape[:dim] + dst.shape[dim + 1:]
def make_slice(arr, dim, i):
slc = [slice(None)] * arr.ndim
slc[dim] = i
return tuple(slc)
# We use index and dim parameters to create idx
# idx is in a form that can be used as a NumPy advanced index for scattering of src param.
idx = [[
*np.indices(idx_xsection_shape).reshape(index.ndim - 1, -1), index[make_slice(index, dim, i)].reshape(1, -1)[0]
] for i in range(index.shape[dim])]
idx = list(np.concatenate(idx, axis=1))
# print("idx",idx)
# idx.insert(dim, idx.pop())
if not np.isscalar(src):
src_idx = list(idx)#使idx和src_idx并不是同一个内存空间
src_idx.pop(dim)
src_idx.insert(dim, np.repeat(np.arange(index.shape[dim]), np.prod(idx_xsection_shape)))
dst[tuple(idx)] = src[tuple(src_idx)]
else:
dst[idx] = src
return dst
b,c,h,w = content_feature.shape
style_feature = F.interpolate(x=style_feature, size=content_feature.shape[-2:],mode="BILINEAR")
content_feat = content_feature.reshape([b,c,h*w]).numpy()
style_feat = style_feature.reshape([b,c,h*w]).numpy()
# print("content_feat",content_feat.shape,b,c)
# content_feat = np.reshape(content_feat, (b,c, -1))#(b,c,-1)
# style_feat = np.reshape(style_feat, (b,c, -1))#(b,c,-1)
# print(content_feat)
content_feat_index = np.argsort(content_feat, axis=2)
style_feat = np.sort(style_feat, axis=2)
# print("content_feat_index",content_feat_index)
# print("style_feat",style_feat)
fr_feat = scatter_numpy(dim=2, index=content_feat_index, src=style_feat)
fr_feat = fr_feat * alpha + content_feat * (1 - alpha)
fr_feat = np.reshape(fr_feat, (b,c,h,w))
fr_feat = paddle.to_tensor(fr_feat)
return fr_feat
# @paddle.jit.to_static
def forward(self,real_image,style_image,alpha):
alpha = alpha.numpy()[0]
# print("real_image",real_image.shape)
content_feature = self.VGG(real_image)
# print("content_feat",content_feature.shape)
style_feature = self.VGG(style_image)
fr_feat = self.style_projection(content_feature,style_feature,alpha)
a = self.A(fr_feat)
b = self.B(a)
c = self.C(b)
d = self.D(c)
out = self.out(d)
return out |