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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,
):
# Ratios and Sizes : (w h)
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:
# To make it infinity
if self.is_infinite:
self._epoch += 1
if hasattr(self._dataloader.sampler, "set_epoch"):
self._dataloader.sampler.set_epoch(self._epoch)
time.sleep(2) # Prevent possible deadlock during epoch transition
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:
# To make it infinity
self._epoch += 1
if hasattr(self._dataloader.sampler, "set_epoch"):
self._dataloader.sampler.set_epoch(self._epoch)
time.sleep(2) # Prevent possible deadlock during epoch transition
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:
# Loading the pre-extrcted textual features
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) |