| import torch |
| import torchvision |
| import numpy as np |
| import math |
| import random |
| import time |
|
|
|
|
| class Bucketeer: |
| def __init__( |
| self, dataloader, |
| sizes=[(256, 256), (192, 384), (192, 320), (384, 192), (320, 192)], |
| is_infinite=True, epoch=0, |
| ): |
| |
| self.sizes = sizes |
| self.batch_size = dataloader.batch_size |
| self._dataloader = dataloader |
| self.iterator = iter(dataloader) |
| self.sampler = dataloader.sampler |
| self.buckets = {s: [] for s in self.sizes} |
| self.is_infinite = is_infinite |
| self._epoch = epoch |
|
|
| def get_available_batch(self): |
| available_size = [] |
| for b in self.buckets: |
| if len(self.buckets[b]) >= self.batch_size: |
| available_size.append(b) |
|
|
| if len(available_size) == 0: |
| return None |
| else: |
| b = random.choice(available_size) |
| batch = self.buckets[b][:self.batch_size] |
| self.buckets[b] = self.buckets[b][self.batch_size:] |
| return batch |
|
|
| def __next__(self): |
| batch = self.get_available_batch() |
| while batch is None: |
| try: |
| elements = next(self.iterator) |
| except StopIteration: |
| |
| if self.is_infinite: |
| self._epoch += 1 |
| if hasattr(self._dataloader.sampler, "set_epoch"): |
| self._dataloader.sampler.set_epoch(self._epoch) |
| time.sleep(2) |
| self.iterator = iter(self._dataloader) |
| elements = next(self.iterator) |
| else: |
| raise StopIteration |
|
|
| for dct in elements: |
| try: |
| img = dct['video'] |
| size = (img.shape[-1], img.shape[-2]) |
| self.buckets[size].append({**{'video': img}, **{k:dct[k] for k in dct if k != 'video'}}) |
| except Exception as e: |
| continue |
|
|
| batch = self.get_available_batch() |
|
|
| out = {k:[batch[i][k] for i in range(len(batch))] for k in batch[0]} |
| return {k: torch.stack(o, dim=0) if isinstance(o[0], torch.Tensor) else o for k, o in out.items()} |
|
|
| def __iter__(self): |
| return self |
|
|
| def __len__(self): |
| return len(self.iterator) |
|
|
|
|
| class TemporalLengthBucketeer: |
| def __init__( |
| self, dataloader, max_frames=16, epoch=0, |
| ): |
| self.batch_size = dataloader.batch_size |
| self._dataloader = dataloader |
| self.iterator = iter(dataloader) |
| self.buckets = {temp: [] for temp in range(1, max_frames + 1)} |
| self._epoch = epoch |
|
|
| def get_available_batch(self): |
| available_size = [] |
| for b in self.buckets: |
| if len(self.buckets[b]) >= self.batch_size: |
| available_size.append(b) |
|
|
| if len(available_size) == 0: |
| return None |
| else: |
| b = random.choice(available_size) |
| batch = self.buckets[b][:self.batch_size] |
| self.buckets[b] = self.buckets[b][self.batch_size:] |
| return batch |
|
|
| def __next__(self): |
| batch = self.get_available_batch() |
| while batch is None: |
| try: |
| elements = next(self.iterator) |
| except StopIteration: |
| |
| self._epoch += 1 |
| if hasattr(self._dataloader.sampler, "set_epoch"): |
| self._dataloader.sampler.set_epoch(self._epoch) |
| time.sleep(2) |
| self.iterator = iter(self._dataloader) |
| elements = next(self.iterator) |
|
|
| for dct in elements: |
| try: |
| video_latent = dct['video'] |
| temp = video_latent.shape[2] |
| self.buckets[temp].append({**{'video': video_latent}, **{k:dct[k] for k in dct if k != 'video'}}) |
| except Exception as e: |
| continue |
|
|
| batch = self.get_available_batch() |
|
|
| out = {k:[batch[i][k] for i in range(len(batch))] for k in batch[0]} |
| out = {k: torch.cat(o, dim=0) if isinstance(o[0], torch.Tensor) else o for k, o in out.items()} |
|
|
| if 'prompt_embed' in out: |
| |
| prompt_embeds = out['prompt_embed'].clone() |
| del out['prompt_embed'] |
| prompt_attention_mask = out['prompt_attention_mask'].clone() |
| del out['prompt_attention_mask'] |
| pooled_prompt_embeds = out['pooled_prompt_embed'].clone() |
| del out['pooled_prompt_embed'] |
|
|
| out['text'] = { |
| 'prompt_embeds' : prompt_embeds, |
| 'prompt_attention_mask': prompt_attention_mask, |
| 'pooled_prompt_embeds': pooled_prompt_embeds, |
| } |
|
|
| return out |
|
|
| def __iter__(self): |
| return self |
|
|
| def __len__(self): |
| return len(self.iterator) |
