Source code for mmagic.evaluation.metrics.equivariance

# Copyright (c) OpenMMLab. All rights reserved.
from collections import defaultdict
from copy import deepcopy
from typing import Iterator, List, Optional, Sequence

import numpy as np
import torch
import torch.nn as nn
from mmengine.dist import all_gather
from import DataLoader

from mmagic.registry import METRICS
from .base_gen_metric import GenerativeMetric

[docs]class Equivariance(GenerativeMetric):
[docs] name = 'Equivariance'
def __init__(self, fake_nums: int, real_nums: int = 0, fake_key: Optional[str] = None, real_key: Optional[str] = 'gt_img', need_cond_input: bool = False, sample_mode: str = 'ema', sample_kwargs: dict = dict(), collect_device: str = 'cpu', prefix: Optional[str] = None, eq_cfg=dict()): super().__init__(fake_nums, real_nums, fake_key, real_key, need_cond_input, sample_mode, collect_device, prefix) # set default sampler config self._eq_cfg = deepcopy(eq_cfg) self._eq_cfg.setdefault('compute_eqt_int', False) self._eq_cfg.setdefault('compute_eqt_frac', False) self._eq_cfg.setdefault('compute_eqr', False) self._eq_cfg.setdefault('translate_max', 0.125) self._eq_cfg.setdefault('rotate_max', 1) self.SAMPLER_MODE = 'EqSampler' self.sample_kwargs = sample_kwargs # compute numbers of eq self.n_sub_metric = 0 if self._eq_cfg['compute_eqt_int']: self.n_sub_metric += 1 if self._eq_cfg['compute_eqt_frac']: self.n_sub_metric += 1 if self._eq_cfg['compute_eqr']: self.n_sub_metric += 1 self.fake_results = defaultdict(list) @torch.no_grad()
[docs] def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None: """Process one batch of data samples and predictions. The processed results should be stored in ``self.fake_results``, which will be used to compute the metrics when all batches have been processed. Args: data_batch (dict): A batch of data from the dataloader. data_samples (Sequence[dict]): A batch of outputs from the model. """ cfg_key_list = ['compute_eqt_int', 'compute_eqt_frac', 'compute_eqr'] sample_key_list = ['eqt_int', 'eqt_frac', 'eqr'] for pred in data_samples: for cfg_key, sample_key in zip(cfg_key_list, sample_key_list): if self._eq_cfg[cfg_key]: assert sample_key in pred # assert hasattr(pred, sample_key) eq_sample = pred[sample_key] diff = eq_sample['diff'].to(torch.float64).sum() mask = eq_sample['mask'].to(torch.float64).sum() self.fake_results[sample_key] += [diff, mask]
[docs] def get_metric_sampler(self, model: nn.Module, dataloader: DataLoader, metrics: List[GenerativeMetric]): """Get sampler for generative metrics. Returns a dummy iterator, whose return value of each iteration is a dict containing batch size and sample mode to generate images. Args: model (nn.Module): Model to evaluate. dataloader (DataLoader): Dataloader for real images. Used to get batch size during generate fake images. metrics (List['GenerativeMetric']): Metrics with the same sampler mode. Returns: :class:`dummy_iterator`: Sampler for generative metrics. """ batch_size = dataloader.batch_size sample_model = metrics[0].sample_model assert all([metric.sample_model == sample_model for metric in metrics ]), ('\'sample_model\' between metrics is inconsistency.') return eq_iterator( batch_size=batch_size, max_length=max([metric.fake_nums_per_device for metric in metrics]), sample_mode=sample_model, eq_cfg=self._eq_cfg, sample_kwargs=self.sample_kwargs)
[docs] def compute_metrics(self, results) -> dict: """Compute the metrics from processed results. Args: results (list): The processed results of each batch. Returns: dict: The computed metrics. The keys are the names of the metrics, and the values are corresponding results. """ results = dict() for key in ['eqt_int', 'eqt_frac', 'eqr']: if key not in self.fake_results: continue sums = torch.stack(self.fake_results[key], dim=0) mses = (sums[0::2] / sums[1::2]).mean() psnrs = np.log10(2) * 20 - mses.log10() * 10 psnrs = psnrs.cpu().numpy() results[key] = psnrs return results
[docs] def _collect_target_results(self, target: str) -> Optional[list]: """Collect function for Eq metric. This function support collect results typing as Dict[List[Tensor]]`. Args: target (str): Target results to collect. Returns: Optional[list]: The collected results. """ if target == 'real': return results = getattr(self, f'{target}_results') results_collected = [] results_collected = dict() for key, result in results.items(): result_collected = torch.stack(result) result_collected =, dim=0) results_collected[key] = torch.split(result_collected, len(result_collected)) return results_collected
[docs]class eq_iterator: def __init__(self, batch_size, max_length, sample_mode, eq_cfg, sample_kwargs) -> None: self.batch_size = batch_size self.max_length = max_length self.sample_mode = sample_mode self.eq_cfg = deepcopy(eq_cfg) self.sample_kwargs = sample_kwargs
[docs] def __iter__(self) -> Iterator: self.idx = 0 return self
[docs] def __len__(self) -> int: return self.max_length // self.batch_size
[docs] def __next__(self) -> dict: if self.idx >= self.max_length: raise StopIteration self.idx += self.batch_size mode = dict( sample_mode=self.sample_mode, eq_cfg=self.eq_cfg, sample_kwargs=self.sample_kwargs) # StyleGAN3 forward will receive eq config from mode return dict(inputs=dict(mode=mode, num_batches=self.batch_size))
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