ViT for Masked Image Modeling

This class is designed for the task of masked image modeling, it reconstructs masked portions of images, which is a form of self-supervised learning where the model learns to predict the parts of the image that have been masked.

ViTForMaskedImageModeling Class Exploration

Initializing the ViT Model

self.vit = ViTModel(config, add_pooling_layer=False, use_mask_token=True)

This line creates an instance of ViTModel. It specifies not to add an additional pooling layer (add_pooling_layer=False) and to use a mask token (use_mask_token=True). The mask token is crucial for masked image modeling.

Decoder Initialization

self.decoder = nn.Sequential(
    nn.Conv2d(
        in_channels=config.hidden_size,
        out_channels=config.encoder_stride2 * config.num_channels,
        kernel_size=1,
    ),
    nn.PixelShuffle(config.encoder_stride),
)

Initializes a sequential decoder consisting of a 2D convolutional layer and a pixel shuffle operation. This decoder is responsible for reconstructing the original image from the encoded representations.

Weights Initialization and Final Processing

self.post_init()

Calls the post_init method to initialize the weights and apply any final configurations to the model.

Forward Method

def forward(
    self,
    pixel_values: Optional[torch.Tensor] = None,
    bool_masked_pos: Optional[torch.BoolTensor] = None,
    head_mask: Optional[torch.Tensor] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    interpolate_pos_encoding: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[tuple, MaskedImageModelingOutput]:

The forward method defines the processing of input images (pixel_values) through the ViT model and the decoder for masked image modeling.

Validating Configuration

if bool_masked_pos is not None and (self.config.patch_size != self.config.encoder_stride):
    raise ValueError(...)

Ensures that if a boolean mask for the positions (bool_masked_pos) is provided, the patch size and the encoder stride are equal. This is required for the reconstruction phase to align correctly with the input image.

Processing through ViTModel

outputs = self.vit(
    pixel_values,
    bool_masked_pos=bool_masked_pos,
    head_mask=head_mask,
    ...
)

The input pixel values, along with optional parameters like the boolean masked positions, head mask, etc., are processed through the ViTModel.

Extracting the Sequence Output

sequence_output = outputs[0]

Retrieves the sequence output from the ViT model's output, which contains the encoded representations of the input image.

Reshaping for Reconstruction

sequence_output = sequence_output[:, 1:]
batch_size, sequence_length, num_channels = sequence_output.shape
height = width = math.floor(sequence_length0.5)
sequence_output = sequence_output.permute(0, 2, 1).reshape(batch_size, num_channels, height, width)

The sequence output is reshaped to prepare for the reconstruction of the original image. The first token (usually the [CLS] token) is removed as it's not needed for reconstruction.

Reconstructing Pixel Values

reconstructed_pixel_values = self.decoder(sequence_output)

The decoder is used to reconstruct the pixel values from the encoded sequence output.

Computing Reconstruction Loss

if bool_masked_pos is not None:
    ...

If a mask is provided, this section computes the reconstruction loss, which quantifies how well the model reconstructs the masked parts of the image.

Returning the Output

The method returns the reconstruction loss and the reconstructed pixel values, along with other optional outputs like hidden states and attentions, based on specified flags.