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Source code for mmedit.engine.hooks.pggan_fetch_data_hook

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence

import torch
from mmengine.dataset import DefaultSampler, InfiniteSampler, pseudo_collate
from mmengine.hooks import Hook
from mmengine.model import is_model_wrapper
from mmengine.runner import IterBasedTrainLoop
from mmengine.runner.loops import _InfiniteDataloaderIterator
from torch.utils.data.dataloader import DataLoader

from mmedit.registry import HOOKS

[docs]DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_module()
[docs]class PGGANFetchDataHook(Hook): """PGGAN Fetch Data Hook. Args: interval (int, optional): The interval of calling this hook. If set to -1, the visualization hook will not be called. Defaults to 1. """ def __init__(self): super().__init__()
[docs] def before_train_iter(self, runner, batch_idx: int, data_batch: DATA_BATCH = None) -> None: _module = runner.model.module if is_model_wrapper( runner.model) else runner.model _next_scale_int = _module._next_scale_int if isinstance(_next_scale_int, torch.Tensor): _next_scale_int = _next_scale_int.item() dataloader_orig = runner.train_loop.dataloader new_dataloader = self.update_dataloader(dataloader_orig, _next_scale_int) if new_dataloader is not None: runner.train_loop.dataloader = new_dataloader if isinstance(runner.train_loop, IterBasedTrainLoop): runner.train_loop.dataloader_iterator = \ _InfiniteDataloaderIterator(new_dataloader)
[docs] def update_dataloader(self, dataloader: DataLoader, curr_scale: int) -> Optional[DataLoader]: """Updata the data loader. Args: dataloader (DataLoader): The dataloader to be updated. curr_scale (int): The current scale of the generated image. Returns: Optional[DataLoader]: The updated dataloader. If the dataloader do not need to update, return None. """ if hasattr(dataloader.dataset, 'update_annotations'): update_flag = dataloader.dataset.update_annotations(curr_scale) else: update_flag = False if update_flag: dataset = dataloader.dataset # build new sampler sampler_orig = dataloader.sampler if isinstance(sampler_orig, DefaultSampler): shuffle = sampler_orig.shuffle seed = sampler_orig.seed round_up = sampler_orig.round_up sampler = DefaultSampler(dataset, shuffle, seed, round_up) elif isinstance(sampler_orig, InfiniteSampler): shuffle = sampler_orig.shuffle seed = sampler_orig.seed sampler = InfiniteSampler(dataset, shuffle, seed) else: raise ValueError( 'MMGeneration only support \'DefaultSampler\' and ' '\'InfiniteSampler\' as sampler. But receive ' f'\'{type(sampler_orig)}\'.') num_workers = dataloader.num_workers worker_init_fn = dataloader.worker_init_fn dataloader = DataLoader( dataset, batch_size=dataloader.dataset.samples_per_gpu, sampler=sampler, num_workers=num_workers, collate_fn=pseudo_collate, shuffle=False, worker_init_fn=worker_init_fn) return dataloader return None
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