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 saicinpainting.evaluation import make_evaluator
from saicinpainting.training.data.datasets import make_default_train_dataloader, make_default_val_dataloader
from saicinpainting.training.losses.adversarial import make_discrim_loss
from saicinpainting.training.losses.perceptual import PerceptualLoss, ResNetPL
from saicinpainting.training.modules import make_generator, make_discriminator
from saicinpainting.training.visualizers import make_visualizer
from 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.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