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import glob
import os
from typing import TYPE_CHECKING, Union
import numpy as np
import torch
import torch.nn as nn
from safetensors.torch import load_file, save_file
from toolkit.losses import get_gradient_penalty
from toolkit.metadata import get_meta_for_safetensors
from toolkit.optimizer import get_optimizer
from toolkit.train_tools import get_torch_dtype
class MeanReduce(nn.Module):
def __init__(self):
super().__init__()
def forward(self, inputs):
# global mean over spatial dims (keeps channel/batch)
return torch.mean(inputs, dim=(2, 3), keepdim=True)
class SelfAttention2d(nn.Module):
"""
Lightweight self-attention layer (SAGAN-style) that keeps spatial
resolution unchanged. Adds minimal params / compute but improves
long-range modelling – helpful for variable-sized inputs.
"""
def __init__(self, in_channels: int):
super().__init__()
self.query = nn.Conv1d(in_channels, in_channels // 8, 1)
self.key = nn.Conv1d(in_channels, in_channels // 8, 1)
self.value = nn.Conv1d(in_channels, in_channels, 1)
self.gamma = nn.Parameter(torch.zeros(1))
def forward(self, x):
B, C, H, W = x.shape
flat = x.view(B, C, H * W) # (B,C,N)
q = self.query(flat).permute(0, 2, 1) # (B,N,C//8)
k = self.key(flat) # (B,C//8,N)
attn = torch.bmm(q, k) # (B,N,N)
attn = attn.softmax(dim=-1) # softmax along last dim
v = self.value(flat) # (B,C,N)
out = torch.bmm(v, attn.permute(0, 2, 1)) # (B,C,N)
out = out.view(B, C, H, W) # restore spatial dims
return self.gamma * out + x # residual
class CriticModel(nn.Module):
def __init__(self, base_channels: int = 64):
super().__init__()
def sn_conv(in_c, out_c, k, s, p):
return nn.utils.spectral_norm(
nn.Conv2d(in_c, out_c, kernel_size=k, stride=s, padding=p)
)
layers = [
# initial down-sample
sn_conv(3, base_channels, 3, 2, 1),
nn.LeakyReLU(0.2, inplace=True),
]
in_c = base_channels
# progressive downsamples ×3 (64→128→256→512)
for _ in range(3):
out_c = min(in_c * 2, 1024)
layers += [
sn_conv(in_c, out_c, 3, 2, 1),
nn.LeakyReLU(0.2, inplace=True),
]
# single attention block after reaching 256 channels
if out_c == 256:
layers += [SelfAttention2d(out_c)]
in_c = out_c
# extra depth (keeps spatial size)
layers += [
sn_conv(in_c, 1024, 3, 1, 1),
nn.LeakyReLU(0.2, inplace=True),
# final 1-channel prediction map
sn_conv(1024, 1, 3, 1, 1),
MeanReduce(), # → (B,1,1,1)
nn.Flatten(), # → (B,1)
]
self.main = nn.Sequential(*layers)
def forward(self, inputs):
# force full-precision inside AMP ctx for stability
with torch.cuda.amp.autocast(False):
return self.main(inputs.float())
if TYPE_CHECKING:
from jobs.process.TrainVAEProcess import TrainVAEProcess
from jobs.process.TrainESRGANProcess import TrainESRGANProcess
class Critic:
process: Union['TrainVAEProcess', 'TrainESRGANProcess']
def __init__(
self,
learning_rate=1e-5,
device='cpu',
optimizer='adam',
num_critic_per_gen=1,
dtype='float32',
lambda_gp=10,
start_step=0,
warmup_steps=1000,
process=None,
optimizer_params=None,
):
self.learning_rate = learning_rate
self.device = device
self.optimizer_type = optimizer
self.num_critic_per_gen = num_critic_per_gen
self.dtype = dtype
self.torch_dtype = get_torch_dtype(self.dtype)
self.process = process
self.model = None
self.optimizer = None
self.scheduler = None
self.warmup_steps = warmup_steps
self.start_step = start_step
self.lambda_gp = lambda_gp
if optimizer_params is None:
optimizer_params = {}
self.optimizer_params = optimizer_params
self.print = self.process.print
print(f" Critic config: {self.__dict__}")
def setup(self):
self.model = CriticModel().to(self.device)
self.load_weights()
self.model.train()
self.model.requires_grad_(True)
params = self.model.parameters()
self.optimizer = get_optimizer(
params,
self.optimizer_type,
self.learning_rate,
optimizer_params=self.optimizer_params,
)
self.scheduler = torch.optim.lr_scheduler.ConstantLR(
self.optimizer,
total_iters=self.process.max_steps * self.num_critic_per_gen,
factor=1,
verbose=False,
)
def load_weights(self):
path_to_load = None
self.print(f"Critic: Looking for latest checkpoint in {self.process.save_root}")
files = glob.glob(os.path.join(self.process.save_root, f"CRITIC_{self.process.job.name}*.safetensors"))
if files:
latest_file = max(files, key=os.path.getmtime)
print(f" - Latest checkpoint is: {latest_file}")
path_to_load = latest_file
else:
self.print(" - No checkpoint found, starting from scratch")
if path_to_load:
self.model.load_state_dict(load_file(path_to_load))
def save(self, step=None):
self.process.update_training_metadata()
save_meta = get_meta_for_safetensors(self.process.meta, self.process.job.name)
step_num = f"_{str(step).zfill(9)}" if step is not None else ''
save_path = os.path.join(
self.process.save_root, f"CRITIC_{self.process.job.name}{step_num}.safetensors"
)
save_file(self.model.state_dict(), save_path, save_meta)
self.print(f"Saved critic to {save_path}")
def get_critic_loss(self, vgg_output):
# (caller still passes combined [pred|target] images)
if self.start_step > self.process.step_num:
return torch.tensor(0.0, dtype=self.torch_dtype, device=self.device)
warmup_scaler = 1.0
if self.process.step_num < self.start_step + self.warmup_steps:
warmup_scaler = (self.process.step_num - self.start_step) / self.warmup_steps
self.model.eval()
self.model.requires_grad_(False)
vgg_pred, _ = torch.chunk(vgg_output.float(), 2, dim=0)
stacked_output = self.model(vgg_pred)
return (-torch.mean(stacked_output)) * warmup_scaler
def step(self, vgg_output):
self.model.train()
self.model.requires_grad_(True)
self.optimizer.zero_grad()
critic_losses = []
inputs = vgg_output.detach().to(self.device, dtype=torch.float32)
vgg_pred, vgg_target = torch.chunk(inputs, 2, dim=0)
stacked_output = self.model(inputs).float()
out_pred, out_target = torch.chunk(stacked_output, 2, dim=0)
# hinge loss + gradient penalty
loss_real = torch.relu(1.0 - out_target).mean()
loss_fake = torch.relu(1.0 + out_pred).mean()
gradient_penalty = get_gradient_penalty(self.model, vgg_target, vgg_pred, self.device)
critic_loss = loss_real + loss_fake + self.lambda_gp * gradient_penalty
critic_loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
self.optimizer.step()
self.scheduler.step()
critic_losses.append(critic_loss.item())
return float(np.mean(critic_losses))
def get_lr(self):
if hasattr(self.optimizer, 'get_avg_learning_rate'):
learning_rate = self.optimizer.get_avg_learning_rate()
elif self.optimizer_type.startswith('dadaptation') or \
self.optimizer_type.lower().startswith('prodigy'):
learning_rate = (
self.optimizer.param_groups[0]["d"] *
self.optimizer.param_groups[0]["lr"]
)
else:
learning_rate = self.optimizer.param_groups[0]['lr']
return learning_rate |