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################################################## PACKAGES ############################################################
################################################# PACKAGES #############################################################
# PyTorch for deep learning operations
import torch
import torch.nn as nn
# PyTorch data loading and utilities
import torch.multiprocessing
# Additional PyTorch modules and libraries
import numpy as np
# Hugging Face Transformers library for BERT models
from transformers import BertModel, BertTokenizer, AutoImageProcessor, VideoMAEModel
# Visualization and progress tracking
from datasets import load_dataset
import av # pip install av
# Additional utility for iterating over combinations
import pandas as pd
from configs import CFG
from text_image import OneEncoder as TextImageEncoder
def read_video_pyav(container, indices):
"""
Decode the video with PyAV decoder.
Args:
container (`av.container.input.InputContainer`): PyAV container.
indices (`List[int]`): List of frame indices to decode.
Returns:
result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
"""
frames = []
container.seek(0)
start_index = indices[0]
end_index = indices[-1]
for i, frame in enumerate(container.decode(video=0)):
if i > end_index:
break
if i >= start_index and i in indices:
frames.append(frame)
return np.stack([x.to_ndarray(format="rgb24") for x in frames])
def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
"""
Sample a given number of frame indices from the video.
Args:
clip_len (`int`): Total number of frames to sample.
frame_sample_rate (`int`): Sample every n-th frame.
seg_len (`int`): Maximum allowed index of sample's last frame.
Returns:
indices (`List[int]`): List of sampled frame indices
"""
converted_len = int(clip_len * frame_sample_rate)
end_idx = np.random.randint(converted_len, seg_len)
start_idx = end_idx - converted_len
indices = np.linspace(start_idx, end_idx, num=clip_len)
indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
return indices
class AlignmentLayer(nn.Module):
def __init__(self, input_dim=768, projection_dim=CFG.projection_dim, dropout_rate=CFG.dropout_rate, *args, **kwargs):
super(AlignmentLayer, self).__init__(*args, **kwargs)
# Attributes
self.input_dim = input_dim
self.projection_dim = projection_dim
self.dropout_rate = dropout_rate
# Layers
self.linear_layer1 = nn.Linear(self.input_dim, self.projection_dim)
self.gelu = nn.GELU()
self.linear_layer2 = nn.Linear(self.projection_dim, self.projection_dim)
self.dropout = nn.Dropout(self.dropout_rate)
self.normalization_layer = nn.LayerNorm(self.projection_dim)
def forward(self, inputs):
x = inputs
x = self.linear_layer1(x)
x = self.gelu(x)
x = self.linear_layer2(x)
x = self.dropout(x)
x = self.normalization_layer(x)
return x
def __call__(self, inputs):
return self.forward(inputs)
class VideoEncoder(nn.Module):
def __init__(self, model_name=CFG.video_name, projection_dim=CFG.projection_dim,
trainable=False, dropout_rate=CFG.dropout_rate, *args, **kwargs):
super(VideoEncoder, self).__init__(*args, **kwargs)
# Attributes
self.model_name = model_name
self.projection_dim = projection_dim
self.dropout_rate = dropout_rate
self.trainable = trainable
# Models
self.pretrained_encoder = VideoMAEModel.from_pretrained(self.model_name)
self.alignment_layer = AlignmentLayer(
input_dim=self.pretrained_encoder.config.hidden_size,
projection_dim=self.projection_dim,
dropout_rate=self.dropout_rate)
# Freeze VideoMAE
for parameter in self.pretrained_encoder.parameters():
parameter.requires_grad = self.trainable
def forward(self, inputs):
x = self.pretrained_encoder(inputs).last_hidden_state
x = self.alignment_layer(x)
return x
def __call__(self, inputs):
return self.forward(inputs)
class ModalityTokenEncoder(nn.Module):
def __init__(self, projection_dim=CFG.projection_dim, token_size=CFG.token_size, device='cpu', *args, **kwargs):
super(ModalityTokenEncoder, self).__init__(*args, **kwargs)
# Attributes
self.projection_dim = projection_dim
self.device = device
self.token_size = token_size
# Models
video_variance = torch.rand(1) * 0.5 + 0.1
self.video_token = nn.Parameter(torch.normal(mean=0, std=video_variance.item(),
size=(self.token_size, self.projection_dim)).to(self.device))
def forward(self):
return self.video_token
def __call__(self):
return self.forward()
class OneEncoder(nn.Module):
def __init__(self, device='cpu', modality_token_encoder=ModalityTokenEncoder(), checkpoint="bilalfaye/OneEncoder-text-image",
video_processor=AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base"),
video_encoder=VideoEncoder(), *args, **kwargs):
super(OneEncoder, self).__init__(*args, **kwargs)
self.device = device
self.checkpoint = checkpoint
self.modality_token_encoder = modality_token_encoder
self.modality_token_encoder.device = self.device
self.text_image_encoder = TextImageEncoder(device=self.device)
self.text_image_encoder.from_pretrained(self.checkpoint)
self.video_processor = video_processor
self.video_encoder = video_encoder
self.temperature = nn.Parameter(torch.tensor(0.07).to(self.device))
# Freeze
for parameter in self.text_image_encoder.parameters():
parameter.requires_grad = False
@classmethod
def load_video(cls, video_path):
container = av.open(video_path)
return container
@classmethod
def read_video_pyav(cls, container, indices):
"""
Decode the video with PyAV decoder.
Args:
container (`av.container.input.InputContainer`): PyAV container.
indices (`List[int]`): List of frame indices to decode.
Returns:
result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
"""
frames = []
container.seek(0)
start_index = indices[0]
end_index = indices[-1]
for i, frame in enumerate(container.decode(video=0)):
if i > end_index:
break
if i >= start_index and i in indices:
frames.append(frame)
return np.stack([x.to_ndarray(format="rgb24") for x in frames])
@classmethod
def sample_frame_indices(cls, clip_len, frame_sample_rate, seg_len):
"""
Sample a given number of frame indices from the video.
Args:
clip_len (`int`): Total number of frames to sample.
frame_sample_rate (`int`): Sample every n-th frame.
seg_len (`int`): Maximum allowed index of sample's last frame.
Returns:
indices (`List[int]`): List of sampled frame indices
"""
converted_len = int(clip_len * frame_sample_rate)
end_idx = np.random.randint(converted_len, seg_len)
start_idx = end_idx - converted_len
indices = np.linspace(start_idx, end_idx, num=clip_len)
indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
return indices
def encode_video(self, videos):
"""
:param videos: torch.Size([batch, 16, 3, 224, 224])
:return: torch.Size([batch, 1568, 768])
"""
video_features = self.video_encoder(videos.to(self.device))
modality_token_features = self.modality_token_encoder()
outputs = self.text_image_encoder.universal_projection_encoder([video_features, modality_token_features]).last_hidden_state
return outputs
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