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import torch
import torch.nn as nn
import torch.nn.functional as F
from .Attention import Block, CrossBlock
from util.util import PositionalEncoding, PosCNN
from .blocks import Conv2dBlock, ResBlocks, ActFirstResBlock
from .Unifront import UnifontModule
from params import *
class Generator(nn.Module):
def __init__(
self,
arg = None,
embed_dim=256,
depth=3,
num_heads=4,
mlp_ratio=4,
drop=0.0,
norm_layer=nn.LayerNorm,
max_num_patch=100,
):
super().__init__()
# --------------------------------------------------------------------------
# MAE encoder specifics
self.layer_norm = None
self.grid_size = None
self.embed_dim = [256, 256,128, 128, 64, 32, 16]
num_block = 4
self.pos_enc = PositionalEncoding(embed_dim, drop, max_num_patch)
self.query_embed = UnifontModule(
embed_dim,
ALPHABET,
input_type="unifont",
linear=True,
)
"""Block 1"""
index = 1
self.blocks_2 = nn.ModuleList(
[
CrossBlock(
self.embed_dim[index],
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=True,
norm_layer=norm_layer,
)
for i in range(depth+2)
]
)
self.layer_norm2 = nn.LayerNorm(self.embed_dim[index])
self.tRGB_1 = nn.Sequential(
nn.Conv2d(self.embed_dim[index], self.embed_dim[num_block], 3, 1, 1)
)
self.conv_1 = self._make_upsample_block(self.embed_dim[index], self.embed_dim[index+1])
"""Block 2"""
index+=1
self.blocks_3 = nn.ModuleList(
[
Block(
dim=self.embed_dim[index],
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=True,
norm_layer=norm_layer,
)
for i in range(depth)
]
)
self.layer_norm3 = nn.LayerNorm(self.embed_dim[index])
self.tRGB_2 = nn.Sequential(
nn.Conv2d(self.embed_dim[index], self.embed_dim[num_block], 3, 1, 1)
)
self.conv_2 = self._make_upsample_block(self.embed_dim[index], self.embed_dim[index+1])
"""Block 3"""
index+=1
self.blocks_4 = nn.ModuleList(
[
Block(
dim=self.embed_dim[index],
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=True,
norm_layer=norm_layer,
)
for i in range(depth)
]
)
self.layer_norm4 = nn.LayerNorm(self.embed_dim[index])
self.tRGB_3 = nn.Sequential(
nn.Conv2d(self.embed_dim[index], self.embed_dim[num_block], 3, 1, 1)
)
self.conv_3 = self._make_upsample_block(self.embed_dim[index], self.embed_dim[index+1])
"""Block 4"""
index+=1
self.blocks_5 = nn.ModuleList(
[
Block(
dim=self.embed_dim[index],
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=True,
norm_layer=norm_layer,
)
for i in range(depth)
]
)
self.layer_norm5 = nn.LayerNorm(self.embed_dim[index])
self.pos_block = nn.ModuleList([PosCNN(i, i) for i in self.embed_dim])
self.norm = norm_layer(embed_dim, elementwise_affine=True)
self.noise = torch.distributions.Normal(
loc=torch.tensor([0.0]), scale=torch.tensor([1.0])
)
self.deconv = nn.Sequential(
ResBlocks(
2, self.embed_dim[index], norm="in", activation="relu", pad_type="reflect"
),
nn.Upsample(scale_factor=2),
Conv2dBlock(
self.embed_dim[index],
self.embed_dim[index + 1],
3,
1,
1,
norm="in",
activation="none",
pad_type="reflect",
),
Conv2dBlock(
self.embed_dim[5],
self.embed_dim[5],
5,
1,
2,
norm="in",
activation="relu",
pad_type="reflect",
),
Conv2dBlock(
self.embed_dim[5],
1,
7,
1,
3,
norm="none",
activation="tanh",
pad_type="reflect",
),
)
self.initialize_weights()
def _make_upsample_block(self, in_dim, out_dim):
return nn.Sequential(
nn.Upsample(scale_factor=2),
Conv2dBlock(in_dim, out_dim, 3, 1, 1, norm="in", activation="none", pad_type="reflect"),
Conv2dBlock(out_dim, out_dim, 3, 1, 1, norm="in", activation="relu", pad_type="reflect"),
)
def initialize_weights(self):
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def _generate_features(self, src, tgt):
b = src.size(0)
start_h = 2
start_w = tgt.size(1)
src = src
tmp = self.query_embed(tgt.clone())
tgt = self.pos_enc(self.query_embed(tgt))
stack_output = []
for blk in self.blocks_2:
tgt = blk(tgt, src)
stack_output.append(tgt)
h2 = stack_output[-1]
tgt = torch.cat([h2, tmp], dim=1)
tgt = self.layer_norm2(tgt)
tgt = tgt.permute(0, 2, 1).view(b, self.embed_dim[1], start_h, start_w)
x_1 = self.tRGB_1(tgt)
tgt = self.conv_1(tgt)
b, c, h, w = tgt.shape
tgt = tgt.view(b, c, -1).permute(0, 2, 1)
for j, blk in enumerate(self.blocks_3):
tgt = blk(tgt)
if j == 0:
tgt = self.pos_block[2](tgt, h, w)
tgt = self.layer_norm3(tgt).permute(0, 2, 1).view(b, self.embed_dim[2], h, w)
x_2 = self.tRGB_2(tgt)
tgt = self.conv_2(tgt)
b, c, h, w = tgt.shape
tgt = tgt.view(b, c, -1).permute(0, 2, 1)
for j, blk in enumerate(self.blocks_4):
tgt = blk(tgt)
if j == 0:
tgt = self.pos_block[3](tgt, h, w)
tgt = self.layer_norm4(tgt).permute(0, 2, 1).view(b, self.embed_dim[3], h, w)
x_3 = self.tRGB_3(tgt)
tgt = self.conv_3(tgt)
b, c, h, w = tgt.shape
tgt = tgt.view(b, c, -1).permute(0, 2, 1)
for j, blk in enumerate(self.blocks_5):
tgt = blk(tgt)
if j == 0:
tgt = self.pos_block[4](tgt, h, w)
tgt = self.layer_norm5(tgt).permute(0, 2, 1).view(b, self.embed_dim[4], h, w)
fused = (
F.interpolate(x_1, scale_factor=8)
+ F.interpolate(x_2, scale_factor=4)
+ F.interpolate(x_3, scale_factor=2)
+ tgt
)
noise = self.noise.sample(fused.size()).squeeze(-1).to(fused.device)
return fused + noise
def forward(self, src_w, tgt):
features = self._generate_features(src_w, tgt)
return self.deconv(features)
def Eval(self, xw, QRS):
outputs = []
for i in range(QRS.shape[1]):
tgt = QRS[:, i, :].squeeze(1)
features = self._generate_features(xw, tgt)
outputs.append(self.deconv(features).detach())
return outputs |