File size: 2,732 Bytes
32da3e8 | 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 | from collections import OrderedDict
from typing import Tuple
import numpy as np
import torch
from omegaconf import DictConfig
from PIL import Image
def parse_configs(config: DictConfig) -> Tuple[DictConfig, DictConfig, DictConfig, DictConfig, DictConfig, DictConfig, DictConfig, DictConfig, DictConfig]:
"""Load a config file and return component sections as DictConfigs."""
rae_config = config.get("stage_1", None)
stage2_config = config.get("stage_2", None)
transport_config = config.get("transport", None)
sampler_config = config.get("sampler", None)
guidance_config = config.get("guidance", None)
misc = config.get("misc", None)
training_config = config.get("training", None)
eval_config = config.get("eval", None)
dataset_config = config.get("dataset", None)
return rae_config, stage2_config, transport_config, sampler_config, guidance_config, misc, training_config, eval_config, dataset_config
def center_crop_arr(pil_image, image_size):
"""
Center cropping implementation from ADM.
https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126
"""
while min(*pil_image.size) >= 2 * image_size:
pil_image = pil_image.resize(
tuple(x // 2 for x in pil_image.size), resample=Image.BOX
)
scale = image_size / min(*pil_image.size)
pil_image = pil_image.resize(
tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
)
arr = np.array(pil_image)
crop_y = (arr.shape[0] - image_size) // 2
crop_x = (arr.shape[1] - image_size) // 2
return Image.fromarray(arr[crop_y: crop_y + image_size, crop_x: crop_x + image_size])
#################################################################################
# Training Helper Functions #
#################################################################################
def requires_grad(model, flag=True):
"""
Set requires_grad flag for all parameters in a model.
"""
for p in model.parameters():
p.requires_grad = flag
@torch.no_grad()
def update_ema(ema_model, model, decay=0.9999):
"""Step the EMA model towards the current model."""
ema_params = OrderedDict(ema_model.named_parameters())
model_params = OrderedDict(model.named_parameters())
for name, param in model_params.items():
ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
def get_autocast_kwargs(args) -> dict:
"""Get autocast kwargs for bf16 or fp32 precision."""
if args.precision == "bf16":
return dict(enabled=True, dtype=torch.bfloat16)
return dict(enabled=False)
|