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import torch
from torch import nn
from model.lightning.base_modules import BaseModule
from torch.utils.data import DataLoader, Dataset
from models.volumetric_avatar.img2vol_enc import LocalEncoder
from models.volumetric_avatar.warp_generator import WarpGenerator
from models.volumetric_avatar.warped_vol_dec import Decoder_stage2
class HeadImitationModule(BaseModule):
def __init__(self, encoder, warp_generator, decoder, config):
super().__init__(config)
self.encoder = encoder
self.warp_generator = warp_generator
self.decoder = decoder
self.config = config
self.criterion = nn.MSELoss() # TODO:loss
self.learning_rate = config.get("learning_rate", 1e-4)
def forward(self, source_img):
latent_volume = self.encoder(source_img)
warped_volume, deltas = self.warp_generator({"orig": latent_volume})
output_img, _, _, _ = self.decoder({}, {}, warped_volume)
return output_img
def _step(self, batch):
source_img, target_img = batch
predicted_img = self.forward(source_img)
loss = self.criterion(predicted_img, target_img)
return loss
def training_step(self, batch, batch_idx):
loss = self._step(batch)
self.log("train_loss", loss, prog_bar=True)
return loss
def validation_step(self, batch, batch_idx):
loss = self._step(batch)
self.log("val_loss", loss, prog_bar=True)
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)
return optimizer
class CustomDataset(Dataset):
def __init__(self, source_images, target_images):
self.source_images = source_images
self.target_images = target_images
def __len__(self):
return len(self.source_images)
def __getitem__(self, idx):
return self.source_images[idx], self.target_images[idx]
def create_data_loaders(source_images, target_images, batch_size=16):
dataset = CustomDataset(source_images, target_images)
return DataLoader(dataset, batch_size=batch_size, shuffle=True)
if __name__ == "__main__":
# TODO:config
config = {
"learning_rate": 1e-4,
"batch_size": 16,
"num_epochs": 10,
}
encoder = LocalEncoder(
use_amp_autocast=True,
gen_upsampling_type="nearest",
gen_downsampling_type="bilinear",
gen_input_image_size=256,
gen_latent_texture_size=64,
gen_latent_texture_depth=8,
gen_latent_texture_channels=64,
warp_norm_grad=True,
gen_num_channels=32,
enc_channel_mult=1,
norm_layer_type="bn",
num_gpus=1,
gen_max_channels=256,
enc_block_type="res",
gen_activation_type="relu",
in_channels=3,
)
warp_generator = WarpGenerator(WarpGenerator.Config(
eps=1e-8,
num_gpus=1,
gen_adaptive_conv_type="conv",
gen_activation_type="relu",
gen_upsampling_type="nearest",
gen_downsampling_type="bilinear",
gen_dummy_input_size=64,
gen_latent_texture_depth=8,
gen_latent_texture_size=64,
gen_max_channels=256,
gen_num_channels=32,
gen_use_adaconv=False,
gen_adaptive_kernel=False,
gen_embed_size=32,
warp_output_size=64,
warp_channel_mult=1,
warp_block_type="res",
norm_layer_type="bn",
input_channels=64,
))
decoder = Decoder_stage2(
eps=1e-8,
image_size=256,
use_amp_autocast=True,
gen_embed_size=32,
gen_adaptive_kernel=False,
gen_adaptive_conv_type="conv",
gen_latent_texture_size=64,
in_channels=64,
gen_num_channels=32,
dec_max_channels=256,
gen_use_adanorm=False,
gen_activation_type="relu",
gen_use_adaconv=False,
dec_channel_mult=1,
dec_num_blocks=4,
dec_up_block_type="res",
dec_pred_seg=False,
dec_seg_channel_mult=1,
dec_pred_conf=False,
dec_conf_ms_names="",
dec_conf_names="",
dec_conf_ms_scales=4,
dec_conf_channel_mult=1,
gen_downsampling_type="bilinear",
num_gpus=1,
norm_layer_type="bn",
)
# TODO:data
source_images = torch.randn(100, 3, 256, 256)
target_images = torch.randn(100, 3, 256, 256)
train_loader = create_data_loaders(source_images, target_images, batch_size=config["batch_size"])
model = LightningModel(encoder, warp_generator, decoder, config)
# training
from lightning.pytorch import Trainer
trainer = Trainer(max_epochs=config["num_epochs"], devices="auto", accelerator="gpu")
trainer.fit(model, train_loader) |