137 lines
7.5 KiB
Python
137 lines
7.5 KiB
Python
import math
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from einops import rearrange
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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.ae_stride_thw = (self.ae_stride_t, self.ae_stride, self.ae_stride)
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self.ae_stride_1hw = (1, self.ae_stride, self.ae_stride)
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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.patch_size_thw = (self.patch_size_t, self.patch_size, self.patch_size)
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self.patch_size_1hw = (1, self.patch_size, self.patch_size)
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self.num_frames = args.num_frames
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self.use_image_num = args.use_image_num
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self.max_thw = (self.num_frames, self.max_image_size, self.max_image_size)
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self.max_1hw = (1, self.max_image_size, self.max_image_size)
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def package(self, batch):
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batch_tubes_vid = [i['video_data']['video'] for i in batch] # b [c t h w]
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input_ids_vid = torch.stack([i['video_data']['input_ids'] for i in batch]) # b 1 l
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cond_mask_vid = torch.stack([i['video_data']['cond_mask'] for i in batch]) # b 1 l
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batch_tubes_img, input_ids_img, cond_mask_img = None, None, None
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if self.use_image_num != 0:
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import ipdb;ipdb.set_trace()
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batch_tubes_img = [j for i in batch for j in i['image_data']['image']] # b*num_img [c 1 h w]
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input_ids_img = torch.stack([i['image_data']['input_ids'] for i in batch]) # b image_num l
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cond_mask_img = torch.stack([i['image_data']['cond_mask'] for i in batch]) # b image_num l
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return batch_tubes_vid, input_ids_vid, cond_mask_vid, batch_tubes_img, input_ids_img, cond_mask_img
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def __call__(self, batch):
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batch_tubes_vid, input_ids_vid, cond_mask_vid, batch_tubes_img, input_ids_img, cond_mask_img = self.package(batch)
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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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if self.use_image_num == 0:
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pad_batch_tubes, attention_mask = process(batch_tubes_vid, t_ds_stride, ds_stride,
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self.max_thw, self.ae_stride_thw, self.patch_size_thw, extra_1=True)
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# attention_mask: b t hw, use first frame
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attention_mask = attention_mask[:, 0] # b hw
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input_ids, cond_mask = input_ids_vid.squeeze(1), cond_mask_vid.squeeze(1) # b 1 l -> b l
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else:
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pad_batch_tubes_vid, attention_mask_vid = self.process(batch_tubes_vid, t_ds_stride, ds_stride,
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self.max_thw, self.ae_stride_thw, self.patch_size_thw, extra_1=True)
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# attention_mask_vid: b t hw, use first frame
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attention_mask_vid = attention_mask_vid[:, :1] # b 1 hw
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pad_batch_tubes_img, attention_mask_img = self.process(batch_tubes_img, 1, ds_stride,
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self.max_1hw, self.ae_stride_1hw, self.patch_size_1hw, extra_1=False)
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pad_batch_tubes_img = rearrange(pad_batch_tubes_img, '(b i) c 1 h w -> b c i h w', i=self.use_image_num)
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attention_mask_img = rearrange(attention_mask_img, '(b i) 1 hw -> b i hw', i=self.use_image_num)
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pad_batch_tubes = torch.cat([pad_batch_tubes_vid, pad_batch_tubes_img], dim=2) # concat at temporal, video first
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# attention_mask_img: b num_img hw
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attention_mask = torch.cat([attention_mask_vid, attention_mask_img], dim=1) # b 1+num_img hw
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input_ids = torch.cat([input_ids_vid, input_ids_img], dim=1) # b 1+num_img hw
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cond_mask = torch.cat([cond_mask_vid, cond_mask_img], dim=1) # b 1+num_img hw
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return pad_batch_tubes, attention_mask, input_ids, cond_mask
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def process(self, batch_tubes, t_ds_stride, ds_stride, max_thw, ae_stride_thw, patch_size_thw, extra_1):
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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] # [(c t h w), (c t h w)]
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max_t, max_h, max_w = max_thw
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pad_max_t, pad_max_h, pad_max_w = pad_to_multiple(max_t-1 if extra_1 else 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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pad_max_t = pad_max_t + 1 if extra_1 else pad_max_t
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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]-1) // ae_stride_thw[0] + 1) if extra_1 else (max_tube_size[0] // ae_stride_thw[0]),
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max_tube_size[1] // ae_stride_thw[1],
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max_tube_size[2] // ae_stride_thw[2]]
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max_patchify_latent_size = [((max_latent_size[0]-1) // patch_size_thw[0] + 1) if extra_1 else (max_latent_size[0] // patch_size_thw[0]),
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max_latent_size[1] // patch_size_thw[1],
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max_latent_size[2] // patch_size_thw[2]]
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valid_patchify_latent_size = [[int(math.ceil((i[1]-1) / t_ds_stride)) + 1 if extra_1 else 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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attention_mask = attention_mask.flatten(-2) # b t h w -> b t hw
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return pad_batch_tubes, attention_mask
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