Spaces:
Paused
Paused
File size: 6,819 Bytes
1c72248 |
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 |
import glob
import os
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
from typing import TYPE_CHECKING, Union
class MeanReduce(nn.Module):
def __init__(self):
super(MeanReduce, self).__init__()
def forward(self, inputs):
return torch.mean(inputs, dim=(1, 2, 3), keepdim=True)
class Vgg19Critic(nn.Module):
def __init__(self):
# vgg19 input (bs, 3, 512, 512)
# pool1 (bs, 64, 256, 256)
# pool2 (bs, 128, 128, 128)
# pool3 (bs, 256, 64, 64)
# pool4 (bs, 512, 32, 32) <- take this input
super(Vgg19Critic, self).__init__()
self.main = nn.Sequential(
# input (bs, 512, 32, 32)
nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1),
nn.LeakyReLU(0.2), # (bs, 512, 16, 16)
nn.Conv2d(1024, 1024, kernel_size=3, stride=2, padding=1),
nn.LeakyReLU(0.2), # (bs, 512, 8, 8)
nn.Conv2d(1024, 1024, kernel_size=3, stride=2, padding=1),
# (bs, 1, 4, 4)
MeanReduce(), # (bs, 1, 1, 1)
nn.Flatten(), # (bs, 1)
# nn.Flatten(), # (128*8*8) = 8192
# nn.Linear(128 * 8 * 8, 1)
)
def forward(self, inputs):
return self.main(inputs)
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 = Vgg19Critic().to(self.device, dtype=self.torch_dtype)
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 and len(files) > 0:
latest_file = max(files, key=os.path.getmtime)
print(f" - Latest checkpoint is: {latest_file}")
path_to_load = latest_file
else:
self.print(f" - 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 = ''
if step is not None:
# zeropad 9 digits
step_num = f"_{str(step).zfill(9)}"
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):
if self.start_step > self.process.step_num:
return torch.tensor(0.0, dtype=self.torch_dtype, device=self.device)
warmup_scaler = 1.0
# we need a warmup when we come on of 1000 steps
# we want to scale the loss by 0.0 at self.start_step steps and 1.0 at self.start_step + warmup_steps
if self.process.step_num < self.start_step + self.warmup_steps:
warmup_scaler = (self.process.step_num - self.start_step) / self.warmup_steps
# set model to not train for generator loss
self.model.eval()
self.model.requires_grad_(False)
vgg_pred, vgg_target = torch.chunk(vgg_output, 2, dim=0)
# run model
stacked_output = self.model(vgg_pred)
return (-torch.mean(stacked_output)) * warmup_scaler
def step(self, vgg_output):
# train critic here
self.model.train()
self.model.requires_grad_(True)
self.optimizer.zero_grad()
critic_losses = []
inputs = vgg_output.detach()
inputs = inputs.to(self.device, dtype=self.torch_dtype)
self.optimizer.zero_grad()
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)
# Compute gradient penalty
gradient_penalty = get_gradient_penalty(self.model, vgg_target, vgg_pred, self.device)
# Compute WGAN-GP critic loss
critic_loss = -(torch.mean(out_target) - torch.mean(out_pred)) + 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())
# avg loss
loss = np.mean(critic_losses)
return loss
def get_lr(self):
if self.optimizer_type.startswith('dadaptation'):
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
|