Transformer-based Video Processing
Transformer models can also be adapted for video processing tasks, where the input is a sequence of 2D frames rather than a sequence of words. The basic idea is to use a 2D convolutional neural network (CNN) to extract features from each frame, and then feed the sequence of feature maps through a Transformer model to model the temporal dependencies between frames.
Here's how we can implement a Transformer model for video processing tasks:
We start by using a 2D CNN to extract features from each frame of the video. The output of the CNN will be a sequence of feature maps, where each feature map corresponds to one frame of the video.
We then reshape the sequence of feature maps to a sequence of flattened feature vectors, which we can feed through the Transformer model.
We use a positional encoding to add temporal information to the input sequence. The positional encoding can be similar to the one used for text, but instead of encoding the position of each word in the sentence, we encode the temporal position of each frame in the video.
We feed the sequence of flattened feature vectors through the Transformer model, which is designed to model the dependencies between the frames. The output of the Transformer model can be fed through a fully connected layer to obtain the final prediction for the video processing task.
Here's some sample code that illustrates this process:
import torch
import torch.nn as nn
import torch.nn.functional as F
class TransformerVideo(nn.Module):
def __init__(self, input_shape, num_layers, num_heads, hidden_dim):
super(TransformerVideo, self).__init__()
self.cnn = nn.Sequential(
nn.Conv2d(input_shape[0], 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.input_shape = input_shape
self.flatten = nn.Flatten()
self.positional_embedding = nn.Embedding(1000, hidden_dim)
self.transformer = nn.Transformer(d_model=hidden_dim, nhead=num_heads, num_encoder_layers=num_layers, num_decoder_layers=num_layers, dim_feedforward=hidden_dim)
self.output_layer = nn.Linear(hidden_dim, 1)
def forward(self, x):
batch_size = x.shape[0]
x = self.cnn(x)
x = x.reshape(batch_size, -1, self.input_shape[1]*self.input_shape[2])
seq_length = x.shape[1]
pos_embedding = self.positional_embedding(torch.arange(seq_length).to(x.device).unsqueeze(0).repeat(batch_size, 1))
x = x + pos_embedding
x = x.permute(1, 0, 2)
attn_mask = torch.zeros(seq_length, seq_length).to(x.device)
for i in range(seq_length):
attn_mask[i,:i+1] = 1
x = self.transformer(x, x, tgt_mask=attn_mask)
x = x.permute(1, 0, 2)
x = x[:,-1,:]
x = self.output_layer(x)
return x
In this example, we define a TransformerVideo class that inherits from nn.Module. The __init__ method of this class defines the architecture of the model. We first define a 2D CNN with several convolutional and pooling layers to extract features from each frame of the video. We then define a positional embedding layer, a Transformer layer with self-attention, and a linear output layer. The forward method of this class defines the computation that is performed by the model. The input to this method is a batch of videos represented as 4D tensors with dimensions `(batch_size, channels, height, width)
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