File size: 9,738 Bytes
f9567e5 |
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 |
"""
This file contains some tools
"""
import torch
import torch.nn as nn
import numpy as np
import os
from tqdm import tqdm
from torchvision import transforms
from torchvision.utils import save_image
from absl import logging
from PIL import Image, ImageDraw, ImageFont
import textwrap
def save_image_with_caption(image_tensor, caption, filename, font_size=20, font_path='/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf'):
"""
Save an image with a caption
"""
image_tensor = image_tensor.clone().detach()
image_tensor = torch.clamp(image_tensor, min=0, max=1)
image_pil = transforms.ToPILImage()(image_tensor)
draw = ImageDraw.Draw(image_pil)
font = ImageFont.truetype(font_path, font_size)
wrap_text = textwrap.wrap(caption, width=len(caption)//4 + 1)
text_sizes = [draw.textsize(line, font=font) for line in wrap_text]
max_text_width = max(size[0] for size in text_sizes)
total_text_height = sum(size[1] for size in text_sizes) + 15
new_height = image_pil.height + total_text_height + 25
new_image = Image.new('RGB', (image_pil.width, new_height), 'white')
new_image.paste(image_pil, (0, 0))
current_y = image_pil.height + 5
draw = ImageDraw.Draw(new_image)
for line, size in zip(wrap_text, text_sizes):
x = (new_image.width - size[0]) / 2
draw.text((x, current_y), line, font=font, fill='black')
current_y += size[1] + 5
new_image.save(filename)
def set_logger(log_level='info', fname=None):
import logging as _logging
handler = logging.get_absl_handler()
formatter = _logging.Formatter('%(asctime)s - %(filename)s - %(message)s')
handler.setFormatter(formatter)
logging.set_verbosity(log_level)
if fname is not None:
handler = _logging.FileHandler(fname)
handler.setFormatter(formatter)
logging.get_absl_logger().addHandler(handler)
def dct2str(dct):
return str({k: f'{v:.6g}' for k, v in dct.items()})
def get_nnet(name, **kwargs):
if name == 'dimr':
from libs.model.dimr_t2i import MRModel
return MRModel(kwargs["model_args"])
elif name == 'dit':
from libs.model.dit_t2i import DiT_H_2
return DiT_H_2(kwargs["model_args"])
else:
raise NotImplementedError(name)
def set_seed(seed: int):
if seed is not None:
torch.manual_seed(seed)
np.random.seed(seed)
def get_optimizer(params, name, **kwargs):
if name == 'adam':
from torch.optim import Adam
return Adam(params, **kwargs)
elif name == 'adamw':
from torch.optim import AdamW
return AdamW(params, **kwargs)
else:
raise NotImplementedError(name)
def customized_lr_scheduler(optimizer, warmup_steps=-1):
from torch.optim.lr_scheduler import LambdaLR
def fn(step):
if warmup_steps > 0:
return min(step / warmup_steps, 1)
else:
return 1
return LambdaLR(optimizer, fn)
def get_lr_scheduler(optimizer, name, **kwargs):
if name == 'customized':
return customized_lr_scheduler(optimizer, **kwargs)
elif name == 'cosine':
from torch.optim.lr_scheduler import CosineAnnealingLR
return CosineAnnealingLR(optimizer, **kwargs)
else:
raise NotImplementedError(name)
def ema(model_dest: nn.Module, model_src: nn.Module, rate):
param_dict_src = dict(model_src.named_parameters())
for p_name, p_dest in model_dest.named_parameters():
p_src = param_dict_src[p_name]
assert p_src is not p_dest
p_dest.data.mul_(rate).add_((1 - rate) * p_src.data)
class TrainState(object):
def __init__(self, optimizer, lr_scheduler, step, nnet=None, nnet_ema=None):
self.optimizer = optimizer
self.lr_scheduler = lr_scheduler
self.step = step
self.nnet = nnet
self.nnet_ema = nnet_ema
def ema_update(self, rate=0.9999):
if self.nnet_ema is not None:
ema(self.nnet_ema, self.nnet, rate)
def save(self, path):
os.makedirs(path, exist_ok=True)
torch.save(self.step, os.path.join(path, 'step.pth'))
for key, val in self.__dict__.items():
if key != 'step' and val is not None:
torch.save(val.state_dict(), os.path.join(path, f'{key}.pth'))
def load(self, path):
logging.info(f'load from {path}')
self.step = torch.load(os.path.join(path, 'step.pth'))
for key, val in self.__dict__.items():
if key != 'step' and val is not None:
val.load_state_dict(torch.load(os.path.join(path, f'{key}.pth'), map_location='cpu'))
def resume(self, ckpt_root, step=None):
if not os.path.exists(ckpt_root):
return
if step is None:
ckpts = list(filter(lambda x: '.ckpt' in x, os.listdir(ckpt_root)))
if not ckpts:
return
steps = map(lambda x: int(x.split(".")[0]), ckpts)
step = max(steps)
ckpt_path = os.path.join(ckpt_root, f'{step}.ckpt')
logging.info(f'resume from {ckpt_path}')
self.load(ckpt_path)
def to(self, device):
for key, val in self.__dict__.items():
if isinstance(val, nn.Module):
val.to(device)
def trainable_parameters(nnet):
params_decay = []
params_nodecay = []
for name, param in nnet.named_parameters():
if name.endswith(".nodecay_weight") or name.endswith(".nodecay_bias"):
params_nodecay.append(param)
else:
params_decay.append(param)
print("params_decay", len(params_decay))
print("params_nodecay", len(params_nodecay))
params = [
{'params': params_decay},
{'params': params_nodecay, 'weight_decay': 0.0}
]
return params
def initialize_train_state(config, device):
nnet = get_nnet(**config.nnet)
nnet_ema = get_nnet(**config.nnet)
nnet_ema.eval()
optimizer = get_optimizer(trainable_parameters(nnet), **config.optimizer)
lr_scheduler = get_lr_scheduler(optimizer, **config.lr_scheduler)
train_state = TrainState(optimizer=optimizer, lr_scheduler=lr_scheduler, step=0,
nnet=nnet, nnet_ema=nnet_ema)
train_state.ema_update(0)
train_state.to(device)
return train_state
def amortize(n_samples, batch_size):
k = n_samples // batch_size
r = n_samples % batch_size
return k * [batch_size] if r == 0 else k * [batch_size] + [r]
def sample2dir(accelerator, path, n_samples, mini_batch_size, sample_fn, unpreprocess_fn=None, return_clipScore=False, ClipSocre_model=None, config=None):
os.makedirs(path, exist_ok=True)
idx = 0
batch_size = mini_batch_size * accelerator.num_processes
clip_score_list = []
if return_clipScore:
assert ClipSocre_model is not None
for _batch_size in tqdm(amortize(n_samples, batch_size), disable=not accelerator.is_main_process, desc='sample2dir'):
samples, clip_score = sample_fn(mini_batch_size, return_clipScore=return_clipScore, ClipSocre_model=ClipSocre_model, config=config)
samples = unpreprocess_fn(samples)
samples = accelerator.gather(samples.contiguous())[:_batch_size]
clip_score_list.append(accelerator.gather(clip_score)[:_batch_size])
if accelerator.is_main_process:
for sample in samples:
save_image(sample, os.path.join(path, f"{idx}.png"))
idx += 1
if return_clipScore:
return clip_score_list
else:
return None
def sample2dir_wCLIP(accelerator, path, n_samples, mini_batch_size, sample_fn, unpreprocess_fn=None, return_clipScore=False, ClipSocre_model=None, config=None):
os.makedirs(path, exist_ok=True)
idx = 0
batch_size = mini_batch_size * accelerator.num_processes
clip_score_list = []
if return_clipScore:
assert ClipSocre_model is not None
for _batch_size in amortize(n_samples, batch_size):
samples, clip_score = sample_fn(mini_batch_size, return_clipScore=return_clipScore, ClipSocre_model=ClipSocre_model, config=config)
samples = unpreprocess_fn(samples)
samples = accelerator.gather(samples.contiguous())[:_batch_size]
clip_score_list.append(accelerator.gather(clip_score)[:_batch_size])
if accelerator.is_main_process:
for sample in samples:
save_image(sample, os.path.join(path, f"{idx}.png"))
idx += 1
break
if return_clipScore:
return clip_score_list
else:
return None
def sample2dir_wPrompt(accelerator, path, n_samples, mini_batch_size, sample_fn, unpreprocess_fn=None, config=None):
os.makedirs(path, exist_ok=True)
idx = 0
batch_size = mini_batch_size * accelerator.num_processes
for _batch_size in tqdm(amortize(n_samples, batch_size), disable=not accelerator.is_main_process, desc='sample2dir'):
samples, samples_caption = sample_fn(mini_batch_size, return_caption=True, config=config)
samples = unpreprocess_fn(samples)
samples = accelerator.gather(samples.contiguous())[:_batch_size]
if accelerator.is_main_process:
for sample, caption in zip(samples,samples_caption):
try:
save_image_with_caption(sample, caption, os.path.join(path, f"{idx}.png"))
except:
save_image(sample, os.path.join(path, f"{idx}.png"))
idx += 1
def grad_norm(model):
total_norm = 0.
for p in model.parameters():
param_norm = p.grad.data.norm(2)
total_norm += param_norm.item() ** 2
total_norm = total_norm ** (1. / 2)
return total_norm
|