File size: 12,536 Bytes
9844a09 5495955 9844a09 5495955 9844a09 5495955 9844a09 5495955 9844a09 5495955 9844a09 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 |
"""PyTorch MLE (Mnaga Line Extraction) model"""
from dataclasses import dataclass
import torch
import torch.nn as nn
from transformers import PreTrainedModel
from transformers.modeling_outputs import ModelOutput, BaseModelOutput
from transformers.activations import ACT2FN
from .configuration_mle import MLEConfig
@dataclass
class MLEModelOutput(ModelOutput):
last_hidden_state: torch.FloatTensor | None = None
@dataclass
class MLEForAnimeLineExtractionOutput(ModelOutput):
last_hidden_state: torch.FloatTensor | None = None
pixel_values: torch.Tensor | None = None
class MLEBatchNorm(nn.Module):
def __init__(
self,
config: MLEConfig,
in_features: int,
):
super().__init__()
self.norm = nn.BatchNorm2d(in_features, eps=config.batch_norm_eps)
# the original model uses leaky_relu
if config.hidden_act == "leaky_relu":
self.act_fn = nn.LeakyReLU(negative_slope=config.negative_slope)
else:
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.norm(hidden_states)
hidden_states = self.act_fn(hidden_states)
return hidden_states
class MLEResBlock(nn.Module):
def __init__(
self,
config: MLEConfig,
in_channels: int,
out_channels: int,
stride_size: int,
):
super().__init__()
self.norm1 = MLEBatchNorm(config, in_channels)
self.conv1 = nn.Conv2d(
in_channels,
out_channels,
config.block_kernel_size,
stride=stride_size,
padding=config.block_kernel_size // 2,
)
self.norm2 = MLEBatchNorm(config, out_channels)
self.conv2 = nn.Conv2d(
out_channels,
out_channels,
config.block_kernel_size,
stride=1,
padding=config.block_kernel_size // 2,
)
if in_channels != out_channels or stride_size != 1:
self.resize = nn.Conv2d(
in_channels,
out_channels,
kernel_size=1,
stride=stride_size,
)
else:
self.resize = None
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
output = self.norm1(hidden_states)
output = self.conv1(output)
output = self.norm2(output)
output = self.conv2(output)
if self.resize is not None:
resized_input = self.resize(hidden_states)
output += resized_input
else:
output += hidden_states
return output
class MLEEncoderLayer(nn.Module):
def __init__(
self,
config: MLEConfig,
in_features: int,
out_features: int,
num_layers: int,
stride_sizes: list[int],
):
super().__init__()
self.blocks = nn.ModuleList(
[
MLEResBlock(
config,
in_channels=in_features if i == 0 else out_features,
out_channels=out_features,
stride_size=stride_sizes[i],
)
for i in range(num_layers)
]
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
for block in self.blocks:
hidden_states = block(hidden_states)
return hidden_states
class MLEEncoder(nn.Module):
def __init__(
self,
config: MLEConfig,
):
super().__init__()
self.layers = nn.ModuleList(
[
MLEEncoderLayer(
config,
in_features=(
config.in_channels
if i == 0
else config.in_channels
* config.block_patch_size
* (config.upsample_ratio ** (i - 1))
),
out_features=config.in_channels
* config.block_patch_size
* (config.upsample_ratio**i),
num_layers=num_layers,
stride_sizes=(
[
1 if i_layer < num_layers - 1 else 2
for i_layer in range(num_layers)
]
if i > 0
else [1 for _ in range(num_layers)]
),
)
for i, num_layers in enumerate(config.num_encoder_layers)
]
)
def forward(
self, hidden_states: torch.Tensor
) -> tuple[torch.Tensor, tuple[torch.Tensor, ...]]:
all_hidden_states: tuple[torch.Tensor, ...] = ()
for layer in self.layers:
hidden_states = layer(hidden_states)
all_hidden_states += (hidden_states,)
return hidden_states, all_hidden_states
class MLEUpsampleBlock(nn.Module):
def __init__(self, config: MLEConfig, in_features: int, out_features: int):
super().__init__()
self.norm = MLEBatchNorm(config, in_features=in_features)
self.conv = nn.Conv2d(
in_features,
out_features,
config.block_kernel_size,
stride=1,
padding=config.block_kernel_size // 2,
)
self.upsample = nn.Upsample(scale_factor=config.upsample_ratio)
def forward(self, hidden_states: torch.Tensor):
output = self.norm(hidden_states)
output = self.conv(output)
output = self.upsample(output)
return output
class MLEUpsampleResBlock(nn.Module):
def __init__(self, config: MLEConfig, in_features: int, out_features: int):
super().__init__()
self.upsample = MLEUpsampleBlock(
config, in_features=in_features, out_features=out_features
)
self.norm = MLEBatchNorm(config, in_features=out_features)
self.conv = nn.Conv2d(
out_features,
out_features,
config.block_kernel_size,
stride=1,
padding=config.block_kernel_size // 2,
)
if in_features != out_features:
self.resize = nn.Sequential(
nn.Conv2d(
in_features,
out_features,
kernel_size=1,
stride=1,
),
nn.Upsample(scale_factor=config.upsample_ratio),
)
else:
self.resize = None
def forward(self, hidden_states: torch.Tensor):
output = self.upsample(hidden_states)
output = self.norm(output)
output = self.conv(output)
if self.resize is not None:
output += self.resize(hidden_states)
return output
class MLEDecoderLayer(nn.Module):
def __init__(
self,
config: MLEConfig,
in_features: int,
out_features: int,
num_layers: int,
):
super().__init__()
self.blocks = nn.ModuleList(
[
(
MLEResBlock(
config,
in_channels=out_features,
out_channels=out_features,
stride_size=1,
)
if i > 0
else MLEUpsampleResBlock(
config,
in_features=in_features,
out_features=out_features,
)
)
for i in range(num_layers)
]
)
def forward(
self, hidden_states: torch.Tensor, shortcut_states: torch.Tensor
) -> torch.Tensor:
for block in self.blocks:
hidden_states = block(hidden_states)
hidden_states += shortcut_states
return hidden_states
class MLEDecoderHead(nn.Module):
def __init__(self, config: MLEConfig, num_layers: int):
super().__init__()
self.layer = MLEEncoderLayer(
config,
in_features=config.block_patch_size,
out_features=config.last_hidden_channels,
stride_sizes=[1 for _ in range(num_layers)],
num_layers=num_layers,
)
self.norm = MLEBatchNorm(config, in_features=config.last_hidden_channels)
self.conv = nn.Conv2d(
config.last_hidden_channels,
out_channels=1,
kernel_size=1,
stride=1,
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.layer(hidden_states)
hidden_states = self.norm(hidden_states)
pixel_values = self.conv(hidden_states)
return pixel_values
class MLEDecoder(nn.Module):
def __init__(
self,
config: MLEConfig,
):
super().__init__()
encoder_output_channels = (
config.in_channels
* config.block_patch_size
* (config.upsample_ratio ** (len(config.num_encoder_layers) - 1))
)
upsample_ratio = config.upsample_ratio
num_decoder_layers = config.num_decoder_layers
self.layers = nn.ModuleList(
[
(
MLEDecoderLayer(
config,
in_features=encoder_output_channels // (upsample_ratio**i),
out_features=encoder_output_channels
// (upsample_ratio ** (i + 1)),
num_layers=num_layers,
)
if i < len(num_decoder_layers) - 1
else MLEDecoderHead(
config,
num_layers=num_layers,
)
)
for i, num_layers in enumerate(num_decoder_layers)
]
)
def forward(
self,
last_hidden_states: torch.Tensor,
encoder_hidden_states: tuple[torch.Tensor, ...],
) -> torch.Tensor:
hidden_states = last_hidden_states
num_encoder_hidden_states = len(encoder_hidden_states) # 5
for i, layer in enumerate(self.layers):
if i < len(self.layers) - 1:
hidden_states = layer(
hidden_states,
# 0, 1, 2, 3, 4
# ↓ ↓ ↓ ↓ ↓
# 8, 7, 6, 5, 5
encoder_hidden_states[num_encoder_hidden_states - 2 - i],
)
else:
# decoder head
hidden_states = layer(hidden_states)
return hidden_states
class MLEPretrainedModel(PreTrainedModel):
config_class = MLEConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
class MLEModel(MLEPretrainedModel):
def __init__(self, config: MLEConfig):
super().__init__(config)
self.config = config
self.encoder = MLEEncoder(config)
self.decoder = MLEDecoder(config)
# Initialize weights and apply final processing
self.post_init()
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
encoder_output, all_hidden_states = self.encoder(pixel_values)
decoder_output = self.decoder(encoder_output, all_hidden_states)
return decoder_output
class MLEForAnimeLineExtraction(MLEPretrainedModel):
def __init__(self, config: MLEConfig):
super().__init__(config)
self.model = MLEModel(config)
def postprocess(self, output_tensor: torch.Tensor, input_shape: tuple[int, int]):
pixel_values = output_tensor[:, 0, :, :]
pixel_values = torch.clip(pixel_values, 0, 255)
pixel_values = pixel_values[:, 0 : input_shape[0], 0 : input_shape[1]]
return pixel_values
def forward(
self, pixel_values: torch.Tensor, return_dict: bool = True
) -> tuple[torch.Tensor, ...] | MLEForAnimeLineExtractionOutput:
# height, width
input_image_size = (pixel_values.shape[2], pixel_values.shape[3])
model_output = self.model(pixel_values)
if not return_dict:
return (model_output, self.postprocess(model_output, input_image_size))
else:
return MLEForAnimeLineExtractionOutput(
last_hidden_state=model_output,
pixel_values=self.postprocess(model_output, input_image_size),
)
|