import copy import logging from typing import Dict, Tuple import pandas as pd import pytorch_lightning as ptl import torch import torch.nn as nn import torch.nn.functional as F # from torch.utils.data import DistributedSampler # from annotator.lama.saicinpainting.evaluation import make_evaluator # from annotator.lama.saicinpainting.training.data.datasets import make_default_train_dataloader, make_default_val_dataloader # from annotator.lama.saicinpainting.training.losses.adversarial import make_discrim_loss # from annotator.lama.saicinpainting.training.losses.perceptual import PerceptualLoss, ResNetPL from annotator.lama.saicinpainting.training.modules import make_generator #, make_discriminator # from annotator.lama.saicinpainting.training.visualizers import make_visualizer from annotator.lama.saicinpainting.utils import add_prefix_to_keys, average_dicts, set_requires_grad, flatten_dict, \ get_has_ddp_rank LOGGER = logging.getLogger(__name__) def make_optimizer(parameters, kind='adamw', **kwargs): if kind == 'adam': optimizer_class = torch.optim.Adam elif kind == 'adamw': optimizer_class = torch.optim.AdamW else: raise ValueError(f'Unknown optimizer kind {kind}') return optimizer_class(parameters, **kwargs) def update_running_average(result: nn.Module, new_iterate_model: nn.Module, decay=0.999): with torch.no_grad(): res_params = dict(result.named_parameters()) new_params = dict(new_iterate_model.named_parameters()) for k in res_params.keys(): res_params[k].data.mul_(decay).add_(new_params[k].data, alpha=1 - decay) def make_multiscale_noise(base_tensor, scales=6, scale_mode='bilinear'): batch_size, _, height, width = base_tensor.shape cur_height, cur_width = height, width result = [] align_corners = False if scale_mode in ('bilinear', 'bicubic') else None for _ in range(scales): cur_sample = torch.randn(batch_size, 1, cur_height, cur_width, device=base_tensor.device) cur_sample_scaled = F.interpolate(cur_sample, size=(height, width), mode=scale_mode, align_corners=align_corners) result.append(cur_sample_scaled) cur_height //= 2 cur_width //= 2 return torch.cat(result, dim=1) class BaseInpaintingTrainingModule(ptl.LightningModule): def __init__(self, config, use_ddp, *args, predict_only=False, visualize_each_iters=100, average_generator=False, generator_avg_beta=0.999, average_generator_start_step=30000, average_generator_period=10, store_discr_outputs_for_vis=False, **kwargs): super().__init__(*args, **kwargs) LOGGER.info('BaseInpaintingTrainingModule init called') self.config = config self.generator = make_generator(config, **self.config.generator) self.use_ddp = use_ddp if not get_has_ddp_rank(): LOGGER.info(f'Generator\n{self.generator}') # if not predict_only: # self.save_hyperparameters(self.config) # self.discriminator = make_discriminator(**self.config.discriminator) # self.adversarial_loss = make_discrim_loss(**self.config.losses.adversarial) # self.visualizer = make_visualizer(**self.config.visualizer) # self.val_evaluator = make_evaluator(**self.config.evaluator) # self.test_evaluator = make_evaluator(**self.config.evaluator) # # if not get_has_ddp_rank(): # LOGGER.info(f'Discriminator\n{self.discriminator}') # # extra_val = self.config.data.get('extra_val', ()) # if extra_val: # self.extra_val_titles = list(extra_val) # self.extra_evaluators = nn.ModuleDict({k: make_evaluator(**self.config.evaluator) # for k in extra_val}) # else: # self.extra_evaluators = {} # # self.average_generator = average_generator # self.generator_avg_beta = generator_avg_beta # self.average_generator_start_step = average_generator_start_step # self.average_generator_period = average_generator_period # self.generator_average = None # self.last_generator_averaging_step = -1 # self.store_discr_outputs_for_vis = store_discr_outputs_for_vis # # if self.config.losses.get("l1", {"weight_known": 0})['weight_known'] > 0: # self.loss_l1 = nn.L1Loss(reduction='none') # # if self.config.losses.get("mse", {"weight": 0})['weight'] > 0: # self.loss_mse = nn.MSELoss(reduction='none') # # if self.config.losses.perceptual.weight > 0: # self.loss_pl = PerceptualLoss() # # # if self.config.losses.get("resnet_pl", {"weight": 0})['weight'] > 0: # # self.loss_resnet_pl = ResNetPL(**self.config.losses.resnet_pl) # # else: # # self.loss_resnet_pl = None # # self.loss_resnet_pl = None self.visualize_each_iters = visualize_each_iters LOGGER.info('BaseInpaintingTrainingModule init done') def configure_optimizers(self): discriminator_params = list(self.discriminator.parameters()) return [ dict(optimizer=make_optimizer(self.generator.parameters(), **self.config.optimizers.generator)), dict(optimizer=make_optimizer(discriminator_params, **self.config.optimizers.discriminator)), ] def train_dataloader(self): kwargs = dict(self.config.data.train) if self.use_ddp: kwargs['ddp_kwargs'] = dict(num_replicas=self.trainer.num_nodes * self.trainer.num_processes, rank=self.trainer.global_rank, shuffle=True) dataloader = make_default_train_dataloader(**self.config.data.train) return dataloader def val_dataloader(self): res = [make_default_val_dataloader(**self.config.data.val)] if self.config.data.visual_test is not None: res = res + [make_default_val_dataloader(**self.config.data.visual_test)] else: res = res + res extra_val = self.config.data.get('extra_val', ()) if extra_val: res += [make_default_val_dataloader(**extra_val[k]) for k in self.extra_val_titles] return res def training_step(self, batch, batch_idx, optimizer_idx=None): self._is_training_step = True return self._do_step(batch, batch_idx, mode='train', optimizer_idx=optimizer_idx) def validation_step(self, batch, batch_idx, dataloader_idx): extra_val_key = None if dataloader_idx == 0: mode = 'val' elif dataloader_idx == 1: mode = 'test' else: mode = 'extra_val' extra_val_key = self.extra_val_titles[dataloader_idx - 2] self._is_training_step = False return self._do_step(batch, batch_idx, mode=mode, extra_val_key=extra_val_key) def training_step_end(self, batch_parts_outputs): if self.training and self.average_generator \ and self.global_step >= self.average_generator_start_step \ and self.global_step >= self.last_generator_averaging_step + self.average_generator_period: if self.generator_average is None: self.generator_average = copy.deepcopy(self.generator) else: update_running_average(self.generator_average, self.generator, decay=self.generator_avg_beta) self.last_generator_averaging_step = self.global_step full_loss = (batch_parts_outputs['loss'].mean() if torch.is_tensor(batch_parts_outputs['loss']) # loss is not tensor when no discriminator used else torch.tensor(batch_parts_outputs['loss']).float().requires_grad_(True)) log_info = {k: v.mean() for k, v in batch_parts_outputs['log_info'].items()} self.log_dict(log_info, on_step=True, on_epoch=False) return full_loss def validation_epoch_end(self, outputs): outputs = [step_out for out_group in outputs for step_out in out_group] averaged_logs = average_dicts(step_out['log_info'] for step_out in outputs) self.log_dict({k: v.mean() for k, v in averaged_logs.items()}) pd.set_option('display.max_columns', 500) pd.set_option('display.width', 1000) # standard validation val_evaluator_states = [s['val_evaluator_state'] for s in outputs if 'val_evaluator_state' in s] val_evaluator_res = self.val_evaluator.evaluation_end(states=val_evaluator_states) val_evaluator_res_df = pd.DataFrame(val_evaluator_res).stack(1).unstack(0) val_evaluator_res_df.dropna(axis=1, how='all', inplace=True) LOGGER.info(f'Validation metrics after epoch #{self.current_epoch}, ' f'total {self.global_step} iterations:\n{val_evaluator_res_df}') for k, v in flatten_dict(val_evaluator_res).items(): self.log(f'val_{k}', v) # standard visual test test_evaluator_states = [s['test_evaluator_state'] for s in outputs if 'test_evaluator_state' in s] test_evaluator_res = self.test_evaluator.evaluation_end(states=test_evaluator_states) test_evaluator_res_df = pd.DataFrame(test_evaluator_res).stack(1).unstack(0) test_evaluator_res_df.dropna(axis=1, how='all', inplace=True) LOGGER.info(f'Test metrics after epoch #{self.current_epoch}, ' f'total {self.global_step} iterations:\n{test_evaluator_res_df}') for k, v in flatten_dict(test_evaluator_res).items(): self.log(f'test_{k}', v) # extra validations if self.extra_evaluators: for cur_eval_title, cur_evaluator in self.extra_evaluators.items(): cur_state_key = f'extra_val_{cur_eval_title}_evaluator_state' cur_states = [s[cur_state_key] for s in outputs if cur_state_key in s] cur_evaluator_res = cur_evaluator.evaluation_end(states=cur_states) cur_evaluator_res_df = pd.DataFrame(cur_evaluator_res).stack(1).unstack(0) cur_evaluator_res_df.dropna(axis=1, how='all', inplace=True) LOGGER.info(f'Extra val {cur_eval_title} metrics after epoch #{self.current_epoch}, ' f'total {self.global_step} iterations:\n{cur_evaluator_res_df}') for k, v in flatten_dict(cur_evaluator_res).items(): self.log(f'extra_val_{cur_eval_title}_{k}', v) def _do_step(self, batch, batch_idx, mode='train', optimizer_idx=None, extra_val_key=None): if optimizer_idx == 0: # step for generator set_requires_grad(self.generator, True) set_requires_grad(self.discriminator, False) elif optimizer_idx == 1: # step for discriminator set_requires_grad(self.generator, False) set_requires_grad(self.discriminator, True) batch = self(batch) total_loss = 0 metrics = {} if optimizer_idx is None or optimizer_idx == 0: # step for generator total_loss, metrics = self.generator_loss(batch) elif optimizer_idx is None or optimizer_idx == 1: # step for discriminator if self.config.losses.adversarial.weight > 0: total_loss, metrics = self.discriminator_loss(batch) if self.get_ddp_rank() in (None, 0) and (batch_idx % self.visualize_each_iters == 0 or mode == 'test'): if self.config.losses.adversarial.weight > 0: if self.store_discr_outputs_for_vis: with torch.no_grad(): self.store_discr_outputs(batch) vis_suffix = f'_{mode}' if mode == 'extra_val': vis_suffix += f'_{extra_val_key}' self.visualizer(self.current_epoch, batch_idx, batch, suffix=vis_suffix) metrics_prefix = f'{mode}_' if mode == 'extra_val': metrics_prefix += f'{extra_val_key}_' result = dict(loss=total_loss, log_info=add_prefix_to_keys(metrics, metrics_prefix)) if mode == 'val': result['val_evaluator_state'] = self.val_evaluator.process_batch(batch) elif mode == 'test': result['test_evaluator_state'] = self.test_evaluator.process_batch(batch) elif mode == 'extra_val': result[f'extra_val_{extra_val_key}_evaluator_state'] = self.extra_evaluators[extra_val_key].process_batch(batch) return result def get_current_generator(self, no_average=False): if not no_average and not self.training and self.average_generator and self.generator_average is not None: return self.generator_average return self.generator def forward(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: """Pass data through generator and obtain at leas 'predicted_image' and 'inpainted' keys""" raise NotImplementedError() def generator_loss(self, batch) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: raise NotImplementedError() def discriminator_loss(self, batch) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: raise NotImplementedError() def store_discr_outputs(self, batch): out_size = batch['image'].shape[2:] discr_real_out, _ = self.discriminator(batch['image']) discr_fake_out, _ = self.discriminator(batch['predicted_image']) batch['discr_output_real'] = F.interpolate(discr_real_out, size=out_size, mode='nearest') batch['discr_output_fake'] = F.interpolate(discr_fake_out, size=out_size, mode='nearest') batch['discr_output_diff'] = batch['discr_output_real'] - batch['discr_output_fake'] def get_ddp_rank(self): return self.trainer.global_rank if (self.trainer.num_nodes * self.trainer.num_processes) > 1 else None