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import numpy as np |
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import torch |
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from scepter.modules.solver import LatentDiffusionSolver |
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from scepter.modules.solver.registry import SOLVERS |
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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 tqdm import tqdm |
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@SOLVERS.register_class() |
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class FormalACEPlusSolver(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.probe_prompt = cfg.get("PROBE_PROMPT", None) |
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self.probe_hw = cfg.get("PROBE_HW", []) |
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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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def run_step_val(self, batch_data, batch_idx=0, step=None, rank=None): |
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sample_id_list = batch_data['sample_id'] |
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loss_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.model.forward_train(**batch_data) |
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loss = results['loss'] |
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for sample_id in sample_id_list: |
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loss_dict[sample_id] = loss.detach().cpu().numpy() |
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return loss_dict |
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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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modify_image = result.get('modify_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((edit_img.permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)) |
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ret_labels.append(f'edit_image{i}; ') |
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ret_images.append((modify_image[i].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)) |
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ret_labels.append(f'modify_image{i}; ') |
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if edit_mask is not None: |
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ret_images.append((edit_mask[i].permute(1, 2, 0).cpu().numpy() * 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((target_image.permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)) |
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ret_labels.append(f'target_image; ') |
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if target_mask is not None: |
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ret_images.append((target_mask.permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)) |
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ret_labels.append(f'target_mask; ') |
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teacher_image = result.get('image', None) |
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if teacher_image is not None: |
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ret_images.append((teacher_image.permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)) |
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ret_labels.append(f"teacher_image") |
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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((reconstruct_image.permute(1, 2, 0).cpu().numpy() * 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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@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(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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batch_data.update(self.sample_args.get_lowercase_dict()) |
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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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if self.probe_prompt: |
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self.eval_mode() |
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all_results = [] |
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for prompt in self.probe_prompt: |
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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 = { |
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"prompt": [[prompt]], |
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"image": [torch.zeros(3, self.probe_hw[0], self.probe_hw[1])], |
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"image_mask": [torch.ones(1, self.probe_hw[0], self.probe_hw[1])], |
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"src_image_list": [[]], |
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"modify_image_list": [[]], |
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"src_mask_list": [[]], |
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"edit_id": [[]], |
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"height": self.probe_hw[0], |
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"width": self.probe_hw[1] |
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} |
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batch_data.update(self.sample_args.get_lowercase_dict()) |
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results = self.run_step_eval(batch_data) |
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all_results.extend(results) |
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self.train_mode() |
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log_data, log_label = self.save_results(all_results) |
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self.register_probe({ |
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'probe_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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return super(LatentDiffusionSolver, self).probe_data |
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