147 lines
6.1 KiB
Python
147 lines
6.1 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import numpy as np
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import torch
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from tqdm import tqdm
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from scepter.modules.utils.data import transfer_data_to_cuda
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from scepter.modules.utils.distribute import we
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from scepter.modules.utils.probe import ProbeData
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from .diffusion_solver import LatentDiffusionSolver
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from .registry import SOLVERS
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@SOLVERS.register_class()
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class ACESolver(LatentDiffusionSolver):
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def __init__(self, cfg, logger=None):
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super().__init__(cfg, logger=logger)
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self.log_train_num = cfg.get('LOG_TRAIN_NUM', -1)
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def save_results(self, results):
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log_data, log_label = [], []
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for result in results:
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ret_images, ret_labels = [], []
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edit_image = result.get('edit_image', None)
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edit_mask = result.get('edit_mask', None)
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if edit_image is not None:
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for i, edit_img in enumerate(result['edit_image']):
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if edit_img is None:
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continue
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ret_images.append(
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(edit_img.permute(1, 2, 0).cpu().numpy() * 255).astype(
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np.uint8))
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ret_labels.append(f'edit_image{i}; ')
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if edit_mask is not None:
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ret_images.append(
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(edit_mask[i].permute(1, 2, 0).cpu().numpy() *
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255).astype(np.uint8))
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ret_labels.append(f'edit_mask{i}; ')
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target_image = result.get('target_image', None)
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target_mask = result.get('target_mask', None)
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if target_image is not None:
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ret_images.append(
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(target_image.permute(1, 2, 0).cpu().numpy() * 255).astype(
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np.uint8))
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ret_labels.append('target_image; ')
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if target_mask is not None:
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ret_images.append(
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(target_mask.permute(1, 2, 0).cpu().numpy() *
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255).astype(np.uint8))
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ret_labels.append('target_mask; ')
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reconstruct_image = result.get('reconstruct_image', None)
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if reconstruct_image is not None:
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ret_images.append(
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(reconstruct_image.permute(1, 2, 0).cpu().numpy() *
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255).astype(np.uint8))
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ret_labels.append(f"{result['instruction']}")
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log_data.append(ret_images)
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log_label.append(ret_labels)
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return log_data, log_label
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@torch.no_grad()
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def run_eval(self):
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self.eval_mode()
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self.before_all_iter(self.hooks_dict[self._mode])
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all_results = []
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for batch_idx, batch_data in tqdm(
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enumerate(self.datas[self._mode].dataloader)):
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self.before_iter(self.hooks_dict[self._mode])
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if self.sample_args:
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batch_data.update(self.sample_args.get_lowercase_dict())
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with torch.autocast(device_type='cuda',
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enabled=self.use_amp,
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dtype=self.dtype):
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results = self.run_step_eval(transfer_data_to_cuda(batch_data),
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batch_idx,
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step=self.total_iter,
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rank=we.rank)
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all_results.extend(results)
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self.after_iter(self.hooks_dict[self._mode])
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log_data, log_label = self.save_results(all_results)
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self.register_probe({'eval_label': log_label})
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self.register_probe({
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'eval_image':
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ProbeData(log_data,
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is_image=True,
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build_html=True,
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build_label=log_label)
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})
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self.after_all_iter(self.hooks_dict[self._mode])
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@torch.no_grad()
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def run_test(self):
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self.test_mode()
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self.before_all_iter(self.hooks_dict[self._mode])
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all_results = []
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for batch_idx, batch_data in tqdm(
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enumerate(self.datas[self._mode].dataloader)):
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self.before_iter(self.hooks_dict[self._mode])
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if self.sample_args:
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batch_data.update(self.sample_args.get_lowercase_dict())
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with torch.autocast(device_type='cuda',
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enabled=self.use_amp,
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dtype=self.dtype):
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results = self.run_step_eval(transfer_data_to_cuda(batch_data),
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batch_idx,
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step=self.total_iter,
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rank=we.rank)
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all_results.extend(results)
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self.after_iter(self.hooks_dict[self._mode])
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log_data, log_label = self.save_results(all_results)
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self.register_probe({'test_label': log_label})
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self.register_probe({
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'test_image':
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ProbeData(log_data,
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is_image=True,
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build_html=True,
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build_label=log_label)
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})
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self.after_all_iter(self.hooks_dict[self._mode])
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@property
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def probe_data(self):
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if not we.debug and self.mode == 'train':
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batch_data = transfer_data_to_cuda(
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self.current_batch_data[self.mode])
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self.eval_mode()
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with torch.autocast(device_type='cuda',
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enabled=self.use_amp,
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dtype=self.dtype):
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batch_data['log_num'] = self.log_train_num
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results = self.run_step_eval(batch_data)
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self.train_mode()
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log_data, log_label = self.save_results(results)
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self.register_probe({
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'train_image':
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ProbeData(log_data,
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is_image=True,
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build_html=True,
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build_label=log_label)
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})
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self.register_probe({'train_label': log_label})
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return super(LatentDiffusionSolver, self).probe_data
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