Source code for mmpretrain.models.selfsup.maskfeat

# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Dict, List, Optional, Sequence, Union

import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmengine.model import BaseModule

from mmpretrain.models import VisionTransformer
from mmpretrain.registry import MODELS
from mmpretrain.structures import DataSample
from .base import BaseSelfSupervisor

[docs]@MODELS.register_module() class HOGGenerator(BaseModule): """Generate HOG feature for images. This module is used in MaskFeat to generate HOG feature. The code is modified from file `slowfast/models/ <>`_. Here is the link of `HOG wikipedia <>`_. Args: nbins (int): Number of bin. Defaults to 9. pool (float): Number of cell. Defaults to 8. gaussian_window (int): Size of gaussian kernel. Defaults to 16. """ def __init__(self, nbins: int = 9, pool: int = 8, gaussian_window: int = 16) -> None: super().__init__() self.nbins = nbins self.pool = pool self.pi = math.pi weight_x = torch.FloatTensor([[1, 0, -1], [2, 0, -2], [1, 0, -1]]) weight_x = weight_x.view(1, 1, 3, 3).repeat(3, 1, 1, 1).contiguous() weight_y = weight_x.transpose(2, 3).contiguous() self.register_buffer('weight_x', weight_x) self.register_buffer('weight_y', weight_y) self.gaussian_window = gaussian_window if gaussian_window: gaussian_kernel = self.get_gaussian_kernel(gaussian_window, gaussian_window // 2) self.register_buffer('gaussian_kernel', gaussian_kernel)
[docs] def get_gaussian_kernel(self, kernlen: int, std: int) -> torch.Tensor: """Returns a 2D Gaussian kernel array.""" def _gaussian_fn(kernlen: int, std: int) -> torch.Tensor: n = torch.arange(0, kernlen).float() n -= n.mean() n /= std w = torch.exp(-0.5 * n**2) return w kernel_1d = _gaussian_fn(kernlen, std) kernel_2d = kernel_1d[:, None] * kernel_1d[None, :] return kernel_2d / kernel_2d.sum()
def _reshape(self, hog_feat: torch.Tensor) -> torch.Tensor: """Reshape HOG Features for output.""" hog_feat = hog_feat.flatten(1, 2) self.unfold_size = hog_feat.shape[-1] // 14 hog_feat = hog_feat.permute(0, 2, 3, 1) hog_feat = hog_feat.unfold(1, self.unfold_size, self.unfold_size).unfold( 2, self.unfold_size, self.unfold_size) hog_feat = hog_feat.flatten(1, 2).flatten(2) return hog_feat
[docs] @torch.no_grad() def forward(self, x: torch.Tensor) -> torch.Tensor: """Generate hog feature for each batch images. Args: x (torch.Tensor): Input images of shape (N, 3, H, W). Returns: torch.Tensor: Hog features. """ # input is RGB image with shape [B 3 H W] self.h, self.w = x.size(-2), x.size(-1) x = F.pad(x, pad=(1, 1, 1, 1), mode='reflect') gx_rgb = F.conv2d( x, self.weight_x, bias=None, stride=1, padding=0, groups=3) gy_rgb = F.conv2d( x, self.weight_y, bias=None, stride=1, padding=0, groups=3) norm_rgb = torch.stack([gx_rgb, gy_rgb], dim=-1).norm(dim=-1) phase = torch.atan2(gx_rgb, gy_rgb) phase = phase / self.pi * self.nbins # [-9, 9] b, c, h, w = norm_rgb.shape out = torch.zeros((b, c, self.nbins, h, w), dtype=torch.float, device=x.device) phase = phase.view(b, c, 1, h, w) norm_rgb = norm_rgb.view(b, c, 1, h, w) if self.gaussian_window: if h != self.gaussian_window: assert h % self.gaussian_window == 0, 'h {} gw {}'.format( h, self.gaussian_window) repeat_rate = h // self.gaussian_window temp_gaussian_kernel = self.gaussian_kernel.repeat( [repeat_rate, repeat_rate]) else: temp_gaussian_kernel = self.gaussian_kernel norm_rgb *= temp_gaussian_kernel out.scatter_add_(2, phase.floor().long() % self.nbins, norm_rgb) out = out.unfold(3, self.pool, self.pool) out = out.unfold(4, self.pool, self.pool) out = out.sum(dim=[-1, -2]) self.out = F.normalize(out, p=2, dim=2) return self._reshape(self.out)
[docs] def generate_hog_image(self, hog_out: torch.Tensor) -> np.ndarray: """Generate HOG image according to HOG features.""" assert hog_out.size(0) == 1 and hog_out.size(1) == 3, \ 'Check the input batch size and the channcel number, only support'\ '"batch_size = 1".' hog_image = np.zeros([self.h, self.w]) cell_gradient = np.array(hog_out.mean(dim=1).squeeze().detach().cpu()) cell_width = self.pool / 2 max_mag = np.array(cell_gradient).max() angle_gap = 360 / self.nbins for x in range(cell_gradient.shape[1]): for y in range(cell_gradient.shape[2]): cell_grad = cell_gradient[:, x, y] cell_grad /= max_mag angle = 0 for magnitude in cell_grad: angle_radian = math.radians(angle) x1 = int(x * self.pool + magnitude * cell_width * math.cos(angle_radian)) y1 = int(y * self.pool + magnitude * cell_width * math.sin(angle_radian)) x2 = int(x * self.pool - magnitude * cell_width * math.cos(angle_radian)) y2 = int(y * self.pool - magnitude * cell_width * math.sin(angle_radian)) magnitude = 0 if magnitude < 0 else magnitude cv2.line(hog_image, (y1, x1), (y2, x2), int(255 * math.sqrt(magnitude))) angle += angle_gap return hog_image
[docs]@MODELS.register_module() class MaskFeatViT(VisionTransformer): """Vision Transformer for MaskFeat pre-training. A PyTorch implement of: `Masked Feature Prediction for Self-Supervised Visual Pre-Training <>`_. Args: arch (str | dict): Vision Transformer architecture Default: 'b' img_size (int | tuple): Input image size patch_size (int | tuple): The patch size out_indices (Sequence | int): Output from which stages. Defaults to -1, means the last stage. drop_rate (float): Probability of an element to be zeroed. Defaults to 0. drop_path_rate (float): stochastic depth rate. Defaults to 0. norm_cfg (dict): Config dict for normalization layer. Defaults to ``dict(type='LN')``. final_norm (bool): Whether to add a additional layer to normalize final feature map. Defaults to True. out_type (str): The type of output features. Please choose from - ``"cls_token"``: The class token tensor with shape (B, C). - ``"featmap"``: The feature map tensor from the patch tokens with shape (B, C, H, W). - ``"avg_featmap"``: The global averaged feature map tensor with shape (B, C). - ``"raw"``: The raw feature tensor includes patch tokens and class tokens with shape (B, L, C). It only works without input mask. Defaults to ``"avg_featmap"``. interpolate_mode (str): Select the interpolate mode for position embeding vector resize. Defaults to "bicubic". patch_cfg (dict): Configs of patch embeding. Defaults to an empty dict. layer_cfgs (Sequence | dict): Configs of each transformer layer in encoder. Defaults to an empty dict. init_cfg (dict, optional): Initialization config dict. Defaults to None. """ def __init__(self, arch: Union[str, dict] = 'b', img_size: int = 224, patch_size: int = 16, out_indices: Union[Sequence, int] = -1, drop_rate: float = 0, drop_path_rate: float = 0, norm_cfg: dict = dict(type='LN', eps=1e-6), final_norm: bool = True, out_type: str = 'raw', interpolate_mode: str = 'bicubic', patch_cfg: dict = dict(), layer_cfgs: dict = dict(), init_cfg: Optional[Union[List[dict], dict]] = None) -> None: super().__init__( arch=arch, img_size=img_size, patch_size=patch_size, out_indices=out_indices, drop_rate=drop_rate, drop_path_rate=drop_path_rate, norm_cfg=norm_cfg, final_norm=final_norm, out_type=out_type, with_cls_token=True, interpolate_mode=interpolate_mode, patch_cfg=patch_cfg, layer_cfgs=layer_cfgs, init_cfg=init_cfg) self.mask_token = nn.parameter.Parameter( torch.zeros(1, 1, self.embed_dims), requires_grad=True) self.num_patches = self.patch_resolution[0] * self.patch_resolution[1]
[docs] def init_weights(self) -> None: """Initialize position embedding, mask token and cls token.""" super().init_weights() if not (isinstance(self.init_cfg, dict) and self.init_cfg['type'] == 'Pretrained'): nn.init.trunc_normal_(self.cls_token, std=.02) nn.init.trunc_normal_(self.mask_token, std=.02) nn.init.trunc_normal_(self.pos_embed, std=.02) self.apply(self._init_weights)
def _init_weights(self, m: torch.nn.Module) -> None: if isinstance(m, (nn.Linear, nn.Conv2d, nn.Conv3d)): nn.init.trunc_normal_(m.weight, std=0.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0)
[docs] def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor]) -> torch.Tensor: """Generate features for masked images. The function supports two kind of forward behaviors. If the ``mask`` is not ``None``, the forward function will be executed as masked image modeling pre-training; if the ``mask`` is ``None``, the forward function will call ``super().forward()``, which extract features from images without mask. Args: x (torch.Tensor): Input images. mask (torch.Tensor, optional): Input masks. Returns: torch.Tensor: Features with cls_tokens. """ if mask is None: return super().forward(x) else: B = x.shape[0] x = self.patch_embed(x)[0] # masking: length -> length * mask_ratio B, L, _ = x.shape mask_tokens = self.mask_token.expand(B, L, -1) mask = mask.unsqueeze(-1) x = x * (1 - + mask_tokens * mask # append cls token cls_tokens = self.cls_token.expand(B, -1, -1) x =, x), dim=1) x = x + self.pos_embed x = self.drop_after_pos(x) for i, layer in enumerate(self.layers): x = layer(x) if i == len(self.layers) - 1 and self.final_norm: x = self.norm1(x) return x
[docs]@MODELS.register_module() class MaskFeat(BaseSelfSupervisor): """MaskFeat. Implementation of `Masked Feature Prediction for Self-Supervised Visual Pre-Training <>`_. """ def extract_feat(self, inputs: torch.Tensor): return self.backbone(inputs, mask=None)
[docs] def loss(self, inputs: torch.Tensor, data_samples: List[DataSample], **kwargs) -> Dict[str, torch.Tensor]: """The forward function in training. Args: inputs (torch.Tensor): The input images. data_samples (List[DataSample]): All elements required during the forward function. Returns: Dict[str, torch.Tensor]: A dictionary of loss components. """ mask = torch.stack([data_sample.mask for data_sample in data_samples]) mask = mask.flatten(1).bool() latent = self.backbone(inputs, mask) B, L, C = latent.shape pred = self.neck((latent.view(B * L, C), )) pred = pred[0].view(B, L, -1) hog = self.target_generator(inputs) # remove cls_token before compute loss loss = self.head.loss(pred[:, 1:], hog, mask) losses = dict(loss=loss) return losses
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