unilm/beit2/visualize_attention.py

# --------------------------------------------------------
# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Based on DINO code bases
# https://github.com/facebookresearch/dino/blob/main/visualize_attention.py
# --------------------------------------------------------'
import os
import sys
import argparse
import cv2
import random
import colorsys
import requests
from io import BytesIO

import skimage.io
from skimage.measure import find_contours
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
import torch
import torch.nn as nn
import torchvision
from torchvision import transforms as pth_transforms
import numpy as np
from PIL import Image

import utils
from timm.models import create_model
import modeling_pretrain


def apply_mask(image, mask, color, alpha=0.5):
    for c in range(3):
        image[:, :, c] = image[:, :, c] * (1 - alpha * mask) + alpha * mask * color[c] * 255
    return image


def random_colors(N, bright=True):
    """
    Generate random colors.
    """
    brightness = 1.0 if bright else 0.7
    hsv = [(i / N, 1, brightness) for i in range(N)]
    colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))
    random.shuffle(colors)
    return colors


def display_instances(image, mask, fname="test", figsize=(5, 5), blur=False, contour=True, alpha=0.5):
    fig = plt.figure(figsize=figsize, frameon=False)
    ax = plt.Axes(fig, [0., 0., 1., 1.])
    ax.set_axis_off()
    fig.add_axes(ax)
    ax = plt.gca()

    N = 1
    mask = mask[None, :, :]
    # Generate random colors
    colors = random_colors(N)

    # Show area outside image boundaries.
    height, width = image.shape[:2]
    margin = 0
    ax.set_ylim(height + margin, -margin)
    ax.set_xlim(-margin, width + margin)
    ax.axis('off')
    masked_image = image.astype(np.uint32).copy()
    for i in range(N):
        color = colors[i]
        _mask = mask[i]
        if blur:
            _mask = cv2.blur(_mask,(10,10))
        # Mask
        masked_image = apply_mask(masked_image, _mask, color, alpha)
        # Mask Polygon
        # Pad to ensure proper polygons for masks that touch image edges.
        if contour:
            padded_mask = np.zeros((_mask.shape[0] + 2, _mask.shape[1] + 2))
            padded_mask[1:-1, 1:-1] = _mask
            contours = find_contours(padded_mask, 0.5)
            for verts in contours:
                # Subtract the padding and flip (y, x) to (x, y)
                verts = np.fliplr(verts) - 1
                p = Polygon(verts, facecolor="none", edgecolor=color)
                ax.add_patch(p)
    ax.imshow(masked_image.astype(np.uint8), aspect='auto')
    fig.savefig(fname)
    print(f"{fname} saved.")
    return


if __name__ == '__main__':
    parser = argparse.ArgumentParser('Visualize Self-Attention maps')
    parser.add_argument('--model', default='beit_base_patch16_224_8k_vocab', type=str, help='Architecture (support only ViT atm).')
    parser.add_argument('--rel_pos_bias', action='store_true')
    parser.add_argument('--disable_rel_pos_bias', action='store_false', dest='rel_pos_bias')
    parser.set_defaults(rel_pos_bias=True)
    parser.add_argument('--abs_pos_emb', action='store_true')
    parser.set_defaults(abs_pos_emb=False)
    parser.add_argument('--layer_scale_init_value', default=0.1, type=float, 
                        help="0.1 for base, 1e-5 for large. set 0 to disable layer scale")

    parser.add_argument('--input_size', default=480, type=int, help='Input resolution of the model.')
    parser.add_argument('--patch_size', default=16, type=int, help='Patch resolution of the model.')
    parser.add_argument('--pretrained_weights', default='', type=str,
        help="Path to pretrained weights to load.")
    parser.add_argument("--checkpoint_key", default="model", type=str,
        help='Key to use in the checkpoint (example: "teacher")')
    parser.add_argument("--image_path", default=None, type=str, help="Path of the image to load.")
    parser.add_argument('--output_dir', default='../visualization', help='Path where to save visualizations.')
    parser.add_argument("--threshold", type=float, default=0.6, help="""We visualize masks
        obtained by thresholding the self-attention maps to keep xx% of the mass.""")
    parser.add_argument('--selected_row', default=8, type=int)
    parser.add_argument('--selected_col', default=8, type=int)
    args = parser.parse_args()

    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    model = create_model(
        args.model,
        pretrained=False,
        drop_rate=0,
        drop_path_rate=0,
        attn_drop_rate=0,
        drop_block_rate=None,
        use_rel_pos_bias=args.rel_pos_bias,
        use_abs_pos_emb=args.abs_pos_emb,
        init_values=args.layer_scale_init_value,
    )

    for p in model.parameters():
        p.requires_grad = False
    model.eval()
    model.to(device)
    if os.path.isfile(args.pretrained_weights):
        state_dict = torch.load(args.pretrained_weights, map_location="cpu")
        if args.checkpoint_key is not None and args.checkpoint_key in state_dict:
            print(f"Take key {args.checkpoint_key} in provided checkpoint dict")
            state_dict = state_dict[args.checkpoint_key]
        # remove `module.` prefix
        state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
        # remove `backbone.` prefix induced by multicrop wrapper
        state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()}
        msg = model.load_state_dict(state_dict, strict=False)
        print('Pretrained weights found at {} and loaded with msg: {}'.format(args.pretrained_weights, msg))
    else:
        print("Please use the `--pretrained_weights` argument to indicate the path of the checkpoint to evaluate.")
        print("There is no reference weights available for this model => We use random weights.")

    # open image
    if args.image_path is None:
        # user has not specified any image - we use our own image
        print("Please use the `--image_path` argument to indicate the path of the image you wish to visualize.")
        print("Since no image path have been provided, we take the first image in our paper.")
        response = requests.get("https://dl.fbaipublicfiles.com/dino/img.png")
        img = Image.open(BytesIO(response.content))
        img = img.convert('RGB')
    elif os.path.isfile(args.image_path):
        with open(args.image_path, 'rb') as f:
            img = Image.open(f)
            img = img.convert('RGB')
    else:
        print(f"Provided image path {args.image_path} is non valid.")
        sys.exit(1)
    input_size = args.input_size
    transform = pth_transforms.Compose([
        pth_transforms.Resize(input_size),
        pth_transforms.CenterCrop(input_size),
        pth_transforms.ToTensor(),
        pth_transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
    ])
    img = transform(img)

    # make the image divisible by the patch size
    w, h = img.shape[1] - img.shape[1] % args.patch_size, img.shape[2] - img.shape[2] % args.patch_size
    img = img[:, :w, :h].unsqueeze(0)

    w_featmap = img.shape[-2] // args.patch_size
    h_featmap = img.shape[-1] // args.patch_size

    attentions = model.get_last_selfattention(img.to(device))

    bsz, nh, num_patches, _ = attentions.size()

    selected_row = args.selected_row
    selected_col = args.selected_col

    selected_index = selected_row * w_featmap + selected_col
    attentions = attentions[0, :, selected_index + 1, 1:]

    # we keep only a certain percentage of the mass
    val, idx = torch.sort(attentions)
    val /= torch.sum(val, dim=1, keepdim=True)
    cumval = torch.cumsum(val, dim=1)
    th_attn = cumval > (1 - args.threshold)
    idx2 = torch.argsort(idx)
    for head in range(nh):
        th_attn[head] = th_attn[head][idx2[head]]
    th_attn = th_attn.reshape(nh, w_featmap, h_featmap).float()
    # interpolate
    th_attn = nn.functional.interpolate(th_attn.unsqueeze(0), scale_factor=args.patch_size, mode="nearest")[0].cpu().numpy()

    attentions = attentions.reshape(nh, w_featmap, h_featmap)
    attentions = nn.functional.interpolate(attentions.unsqueeze(0), scale_factor=args.patch_size, mode="nearest")[0].cpu().numpy()

    # save attentions heatmaps
    os.makedirs(args.output_dir, exist_ok=True)
    torchvision.utils.save_image(torchvision.utils.make_grid(img, normalize=True, scale_each=True), os.path.join(args.output_dir, "img.png"))
    for j in range(nh):
        fname = os.path.join(args.output_dir, "attn-head" + str(j) + ".png")
        plt.imsave(fname=fname, arr=attentions[j], format='png')
        print(f"{fname} saved.")

    image = skimage.io.imread(os.path.join(args.output_dir, "img.png"))

    select_image = skimage.io.imread(os.path.join(args.output_dir, "img.png"))

    for _x in range(4, args.patch_size - 4):
        for _y in range(4, args.patch_size - 4):
            for _ in range(3):
                x = _x + selected_row * args.patch_size
                y = _y + selected_col * args.patch_size
                select_image[x, y, _] = select_image[x, y, _] * 0.5 + [1.0, 0, 0][_] * 255.0 * 0.5

    fname = os.path.join(args.output_dir, "select.png")
    plt.imsave(fname=fname, arr=select_image, format='png')

    if args.threshold < 1.0:

        for j in range(nh):
            display_instances(image, th_attn[j], fname=os.path.join(args.output_dir, "mask_th" + str(args.threshold) + "_head" + str(j) +".png"), blur=False)