107 lines
4.3 KiB
Python
107 lines
4.3 KiB
Python
import math
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import decord
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from torch.nn import functional as F
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import torch
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IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG']
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def is_image_file(filename):
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return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
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class DecordInit(object):
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"""Using Decord(https://github.com/dmlc/decord) to initialize the video_reader."""
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def __init__(self, num_threads=1):
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self.num_threads = num_threads
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self.ctx = decord.cpu(0)
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def __call__(self, filename):
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"""Perform the Decord initialization.
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Args:
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results (dict): The resulting dict to be modified and passed
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to the next transform in pipeline.
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"""
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reader = decord.VideoReader(filename,
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ctx=self.ctx,
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num_threads=self.num_threads)
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return reader
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def __repr__(self):
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repr_str = (f'{self.__class__.__name__}('
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f'sr={self.sr},'
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f'num_threads={self.num_threads})')
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return repr_str
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def pad_to_multiple(number, ds_stride):
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remainder = number % ds_stride
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if remainder == 0:
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return number
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else:
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padding = ds_stride - remainder
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return number + padding
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class Collate:
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def __init__(self, args):
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self.max_image_size = args.max_image_size
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self.ae_stride = args.ae_stride
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self.ae_stride_t = args.ae_stride_t
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self.patch_size = args.patch_size
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self.patch_size_t = args.patch_size_t
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self.num_frames = args.num_frames
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def __call__(self, batch):
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unzip = tuple(zip(*batch))
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if len(unzip) == 2:
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batch_tubes, labels = unzip
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labels = torch.as_tensor(labels).to(torch.long)
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elif len(unzip) == 3:
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batch_tubes, input_ids, cond_mask = unzip
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input_ids = torch.stack(input_ids).squeeze(1)
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cond_mask = torch.stack(cond_mask).squeeze(1)
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else:
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raise NotImplementedError
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ds_stride = self.ae_stride * self.patch_size
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t_ds_stride = self.ae_stride_t * self.patch_size_t
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# pad to max multiple of ds_stride
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batch_input_size = [i.shape for i in batch_tubes]
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max_t, max_h, max_w = self.num_frames, \
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self.max_image_size, \
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self.max_image_size
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pad_max_t, pad_max_h, pad_max_w = pad_to_multiple(max_t, t_ds_stride), \
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pad_to_multiple(max_h, ds_stride), \
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pad_to_multiple(max_w, ds_stride)
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each_pad_t_h_w = [[pad_max_t - i.shape[1],
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pad_max_h - i.shape[2],
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pad_max_w - i.shape[3]] for i in batch_tubes]
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pad_batch_tubes = [F.pad(im,
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(0, pad_w,
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0, pad_h,
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0, pad_t), value=0) for (pad_t, pad_h, pad_w), im in zip(each_pad_t_h_w, batch_tubes)]
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pad_batch_tubes = torch.stack(pad_batch_tubes, dim=0)
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# make attention_mask
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max_tube_size = [pad_max_t, pad_max_h, pad_max_w]
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max_latent_size = [max_tube_size[0] // self.ae_stride_t,
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max_tube_size[1] // self.ae_stride,
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max_tube_size[2] // self.ae_stride]
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max_patchify_latent_size = [max_latent_size[0] // self.patch_size_t,
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max_latent_size[1] // self.patch_size,
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max_latent_size[2] // self.patch_size]
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valid_patchify_latent_size = [[int(math.ceil(i[1] / t_ds_stride)),
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int(math.ceil(i[2] / ds_stride)),
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int(math.ceil(i[3] / ds_stride))] for i in batch_input_size]
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attention_mask = [F.pad(torch.ones(i),
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(0, max_patchify_latent_size[2] - i[2],
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0, max_patchify_latent_size[1] - i[1],
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0, max_patchify_latent_size[0] - i[0]), value=0) for i in valid_patchify_latent_size]
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attention_mask = torch.stack(attention_mask)
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if len(unzip) == 2:
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return pad_batch_tubes, labels, attention_mask
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elif len(unzip) == 3:
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return pad_batch_tubes, attention_mask, input_ids, cond_mask
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