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from abc import abstractmethod
from functools import partial
from typing import Iterable
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .SSN import Conv, Conv2DMod, Decoder, Up
from .attention import AttentionBlock
from .blocks import ResBlock, Res_Type, get_activation
class Attention_Encoder(nn.Module):
def __init__(self, in_channels=3, mid_act='gelu', dropout=0.0, num_heads=8, resnet=True):
super(Attention_Encoder, self).__init__()
self.in_conv = Conv(in_channels, 32-in_channels, stride=1, activation=mid_act, resnet=resnet)
self.down_32_64 = Conv(32, 64, stride=2, activation=mid_act, resnet=resnet)
self.down_64_64_1 = Conv(64, 64, activation=mid_act, resnet=resnet)
self.down_64_128 = Conv(64, 128, stride=2, activation=mid_act, resnet=resnet)
self.down_128_128_1 = Conv(128, 128, activation=mid_act, resnet=resnet)
self.down_128_256 = Conv(128, 256, stride=2, activation=mid_act, resnet=resnet)
self.down_256_256_1 = Conv(256, 256, activation=mid_act, resnet=resnet)
self.down_256_256_1_attn = AttentionBlock(256, num_heads)
self.down_256_512 = Conv(256, 512, stride=2, activation=mid_act, resnet=resnet)
self.down_512_512_1 = Conv(512, 512, activation=mid_act, resnet=resnet)
self.down_512_512_1_attn = AttentionBlock(512, num_heads)
self.down_512_512_2 = Conv(512, 512, activation=mid_act, resnet=resnet)
self.down_512_512_2_attn = AttentionBlock(512, num_heads)
self.down_512_512_3 = Conv(512, 512, activation=mid_act, resnet=resnet)
self.down_512_512_3_attn = AttentionBlock(512, num_heads)
def forward(self, x):
x1 = self.in_conv(x) # 32 x 256 x 256
x1 = torch.cat((x, x1), dim=1)
x2 = self.down_32_64(x1)
x3 = self.down_64_64_1(x2)
x4 = self.down_64_128(x3)
x5 = self.down_128_128_1(x4)
x6 = self.down_128_256(x5)
x7 = self.down_256_256_1(x6)
x7 = self.down_256_256_1_attn(x7)
x8 = self.down_256_512(x7)
x9 = self.down_512_512_1(x8)
x9 = self.down_512_512_1_attn(x9)
x10 = self.down_512_512_2(x9)
x10 = self.down_512_512_2_attn(x10)
x11 = self.down_512_512_3(x10)
x11 = self.down_512_512_3_attn(x11)
return x11, x10, x9, x8, x7, x6, x5, x4, x3, x2, x1
class Attention_Decoder(nn.Module):
def __init__(self, out_channels=3, mid_act='gelu', out_act='sigmoid', resnet = True, num_heads=8):
super(Attention_Decoder, self).__init__()
input_channel = 512
fea_dim = 100
self.to_style1 = nn.Linear(in_features=fea_dim, out_features=input_channel)
self.up_16_16_1 = Conv(input_channel, 256, activation=mid_act, style=True, resnet=resnet)
self.up_16_16_1_attn = AttentionBlock(256, num_heads=num_heads)
self.up_16_16_2 = Conv(768, 512, activation=mid_act, resnet=resnet)
self.up_16_16_2_attn = AttentionBlock(512, num_heads=num_heads)
self.up_16_16_3 = Conv(1024, 512, activation=mid_act, resnet=resnet)
self.up_16_16_3_attn = AttentionBlock(512, num_heads=num_heads)
self.up_16_32 = Up(1024, 256, activation=mid_act, resnet=resnet)
self.to_style2 = nn.Linear(in_features=fea_dim, out_features=512)
self.up_32_32_1 = Conv(512, 256, activation=mid_act, style=True, resnet=resnet)
self.up_32_32_1_attn = AttentionBlock(256, num_heads=num_heads)
self.up_32_64 = Up(512, 128, activation=mid_act, resnet=resnet)
self.to_style3 = nn.Linear(in_features=fea_dim, out_features=256)
self.up_64_64_1 = Conv(256, 128, activation=mid_act, style=True, resnet=resnet)
self.up_64_128 = Up(256, 64, activation=mid_act, resnet=resnet)
self.to_style4 = nn.Linear(in_features=fea_dim, out_features=128)
self.up_128_128_1 = Conv(128, 64, activation=mid_act, style=True, resnet=resnet)
self.up_128_256 = Up(128, 32, activation=mid_act, resnet=resnet)
self.out_conv = Conv(64, out_channels, activation=out_act)
self.out_act = get_activation(out_act)
def forward(self, x, style):
x11, x10, x9, x8, x7, x6, x5, x4, x3, x2, x1 = x
style1 = self.to_style1(style)
y = self.up_16_16_1(x11, style1) # 256 x 16 x 16
y = self.up_16_16_1_attn(y)
y = torch.cat((x10, y), dim=1) # 768 x 16 x 16
y = self.up_16_16_2(y, y) # 512 x 16 x 16
y = self.up_16_16_2_attn(y)
y = torch.cat((x9, y), dim=1) # 1024 x 16 x 16
y = self.up_16_16_3(y, y) # 512 x 16 x 16
y = self.up_16_16_3_attn(y)
y = torch.cat((x8, y), dim=1) # 1024 x 16 x 16
y = self.up_16_32(y, y) # 256 x 32 x 32
y = torch.cat((x7, y), dim=1)
style2 = self.to_style2(style)
y = self.up_32_32_1(y, style2) # 256 x 32 x 32
y = self.up_32_32_1_attn(y)
y = torch.cat((x6, y), dim=1)
y = self.up_32_64(y, y)
y = torch.cat((x5, y), dim=1)
style3 = self.to_style3(style)
y = self.up_64_64_1(y, style3) # 128 x 64 x 64
y = torch.cat((x4, y), dim=1)
y = self.up_64_128(y, y)
y = torch.cat((x3, y), dim=1)
style4 = self.to_style4(style)
y = self.up_128_128_1(y, style4) # 64 x 128 x 128
y = torch.cat((x2, y), dim=1)
y = self.up_128_256(y, y) # 32 x 256 x 256
y = torch.cat((x1, y), dim=1)
y = self.out_conv(y, y) # 3 x 256 x 256
y = self.out_act(y)
return y
class Attention_SSN(nn.Module):
def __init__(self, in_channels, out_channels, num_heads=8, resnet=True, mid_act='gelu', out_act='gelu'):
super(Attention_SSN, self).__init__()
self.encoder = Attention_Encoder(in_channels, mid_act, num_heads, resnet)
self.decoder = Attention_Decoder(out_channels, mid_act, out_act, resnet)
def forward(self, x, softness):
latent = self.encoder(x)
pred = self.decoder(latent, softness)
return pred
def get_model_size(model):
param_size = 0
import pdb; pdb.set_trace()
for param in model.parameters():
param_size += param.nelement() * param.element_size()
buffer_size = 0
for buffer in model.buffers():
buffer_size += buffer.nelement() * buffer.element_size()
size_all_mb = (param_size + buffer_size) / 1024 ** 2
print('model size: {:.3f}MB'.format(size_all_mb))
# return param_size + buffer_size
return size_all_mb
if __name__ == '__main__':
model = AttentionBlock(in_channels=256, num_heads=8)
x = torch.randn(5, 256, 64, 64)
y = model(x)
print('{}, {}'.format(x.shape, y.shape))
# ------------------------------------------------------------------ #
in_channels = 3
out_channels = 1
num_heads = 8
resnet = True
mid_act = 'gelu'
out_act = 'gelu'
model = Attention_SSN(in_channels=in_channels,
out_channels=out_channels,
num_heads=num_heads,
resnet=resnet,
mid_act=mid_act,
out_act=out_act)
x = torch.randn(5, 3, 256, 256)
softness = torch.randn(5, 100)
y = model(x, softness)
print('x: {}, y: {}'.format(x.shape, y.shape))
get_model_size(model)
# ------------------------------------------------------------------ #
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