167 lines
6.5 KiB
Python
167 lines
6.5 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import argparse
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import os
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import cv2
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import numpy as np
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import torch
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from scepter.modules.solver.hooks.checkpoint import CheckpointHook
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from scepter.modules.solver.registry import SOLVERS
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from scepter.modules.utils.config import Config
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from scepter.modules.utils.distribute import we
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from scepter.modules.utils.file_system import FS
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from scepter.modules.utils.logger import get_logger
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def run_task(cfg):
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# torch.cuda.set_per_process_memory_fraction(0.4, we.device_id)
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# torch.cuda.empty_cache()
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std_logger = get_logger(name='scepter')
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std_logger.info(f'Pytorch version: {torch.__version__}')
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# std_logger.info(f"Os environment: {os.environ}")
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if cfg.args.stage == 'train':
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solver = SOLVERS.build(cfg.SOLVER, logger=std_logger)
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save_config(cfg)
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solver.set_up_pre()
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solver.set_up()
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ori_steps = solver.max_steps
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if 'train' in solver.datas:
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dataset = solver.datas['train'].dataset
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if hasattr(dataset, 'real_number'):
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solver.max_steps = int(
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cfg.SOLVER.MAX_EPOCHS * dataset.real_number /
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(solver.datas['train'].batch_size * we.world_size))
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std_logger.info(
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f'max step is changed from {ori_steps} to {solver.max_steps} '
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f'according to the setting epoches {cfg.SOLVER.MAX_EPOCHS} '
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f'and dataset size {dataset.real_number}')
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if 'train' in solver.hooks_dict:
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for hook in solver.hooks_dict['train']:
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if isinstance(hook, CheckpointHook):
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ori_interval = hook.interval
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hook.interval = int(hook.interval *
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solver.max_steps /
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cfg.SOLVER.MAX_EPOCHS)
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std_logger.info(
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f'checkpoint save interval is changed from {ori_interval} '
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f'to {hook.interval} according to the setting epoches '
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f'interval {ori_interval}')
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# size 为无限的时候，使用默认值。
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solver.solve()
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def save_image(image, save_path, backend='cv2'):
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if backend == 'cv2':
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image = image.copy()
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cv2.cvtColor(image, cv2.COLOR_RGB2BGR, image)
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cv2.imwrite(save_path, image)
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def concatenate_images(images):
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heights = [img.shape[0] for img in images]
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max_width = sum([img.shape[1] for img in images])
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concatenated_image = np.zeros((max(heights), max_width, 3), dtype=np.uint8)
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x_offset = 0
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for img in images:
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concatenated_image[0:img.shape[0],
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x_offset:x_offset + img.shape[1], :] = img
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x_offset += img.shape[1]
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return concatenated_image
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def save_config(cfg):
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from scepter.modules.utils.distribute import get_dist_info
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rank, _ = get_dist_info()
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if rank == 0:
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config_path = os.path.join(cfg.SOLVER.WORK_DIR,
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cfg.args.cfg_file.split('/')[-1])
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with FS.put_to(config_path) as local_config_path:
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with open(local_config_path, 'w') as f_out:
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f_out.write(cfg.dump())
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def update_config(cfg):
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if cfg.args.work_dir and cfg.args.work_dir != '':
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cfg.SOLVER.WORK_DIR = cfg.args.work_dir
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return cfg
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(
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description='Argparser for Cate process:\n')
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parser.add_argument(
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'--stage',
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dest='stage',
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help='Running stage!',
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default='train',
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choices=['train', 'inference', 'upsampler_inference', 'control'])
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parser.add_argument('--base_model',
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dest='base_model',
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help='Base model name!',
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default='sd')
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parser.add_argument(
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'--prompt',
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dest='prompt',
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help='Prompt sentence!',
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default='a woman is walking on the street in a rainy day.')
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parser.add_argument('--n_prompt',
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dest='n_prompt',
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help='Add Prompt sentence!',
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default='')
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parser.add_argument('--image',
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dest='image',
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help='Image to be upsampled!',
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default='')
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parser.add_argument('--num_samples',
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dest='num_samples',
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help="Output image's number!",
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default=4,
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type=int)
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parser.add_argument('--sampler',
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dest='sampler',
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help='sampler',
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default='ddim',
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type=str)
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parser.add_argument('--sample_steps',
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dest='sample_steps',
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help='sample_steps',
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default=50,
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type=int)
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parser.add_argument('--inference_resolution',
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dest='inference_resolution',
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help='inference resolution',
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default=1024,
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type=int)
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parser.add_argument('--seed',
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dest='seed',
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help='seed',
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default=2023,
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type=int)
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parser.add_argument('--save_folder',
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dest='save_folder',
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help="Output image's save folder!",
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default='test_images')
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parser.add_argument('--pretrained_model',
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dest='pretrained_model',
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help='The pretrained model for our network!',
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default='')
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parser.add_argument('--learning_rate',
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dest='learning_rate',
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help='The learning rate for our network!',
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default=None)
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parser.add_argument('--max_steps',
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dest='max_steps',
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help='The max steps for our network!',
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default=None)
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parser.add_argument('--control_mode',
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dest='control_mode',
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help='',
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default=None)
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parser.add_argument('--work_dir', dest='work_dir', help='', default=None)
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cfg = Config(load=True, parser_ins=parser)
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cfg = update_config(cfg)
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we.init_env(cfg, logger=None, fn=run_task)
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