pytorch_generate_unitsegments.py

#!/usr/bin/env python
# coding=utf-8
# the example script to generate the unit segmentation visualization using pyTorch
# Bolei Zhou

import torch
from torch.autograd import Variable as V
import torchvision.models as models
from torchvision import transforms as trn
from torch.nn import functional as F
import os
import pdb
import numpy as np
from scipy.misc import imresize as imresize
import cv2
from PIL import Image
from dataset import Dataset
import torch.utils.data as data
import torchvision.models as models

# visualization setup
img_size = (224, 224)       # input image size
segment_size = (120,120)    # the unit segmentaiton size
num_top = 12                # how many top activated images to extract
margin = 3                  # pixels between two segments
threshold_scale = 0.2       # the scale used to segment the feature map. Smaller the segmentation will be tighter.
flag_crop = 1               # whether to generate tight crop for the unit visualiation.
flag_classspecific = 1      # whether to generate the class specific unit for each category (only works for network with global average pooling at the end)

# dataset setup
batch_size = 64
num_workers = 6


"""
using old version of pytorch to load new torch models
"""
import torch._utils
try:
    torch._utils._rebuild_tensor_v2
except AttributeError:
    def _rebuild_tensor_v2(storage, storage_offset, size, stride, requires_grad, backward_hooks):
        tensor = torch._utils._rebuild_tensor(storage, storage_offset, size, stride)
        tensor.requires_grad = requires_grad
        tensor._backward_hooks = backward_hooks
        return tensor
    torch._utils._rebuild_tensor_v2 = _rebuild_tensor_v2


# load the pre-trained weights
id_model = 1
if id_model == 1:
    model_name = 'wideresnet_places365'
    model_file = 'whole_wideresnet18_places365.pth.tar' # download it from https://github.com/CSAILVision/places365/blob/master/run_placesCNN_unified.py
    if not os.path.exists(model_file):
        os.system('wget http://places2.csail.mit.edu/models_places365/' + model_file)
        os.system('wget https://raw.githubusercontent.com/csailvision/places365/master/wideresnet.py')    
elif id_model == 2:
    model_name = 'resnet18_imagenet'
    model = models.resnet18(pretrained=True)
    features_names = ['layer4']
elif id_model == 3:
    model_name = 'squeezenet_imagenet'
    model = models.squeezenet1_0(pretrained=True)
    features_names = ['features']

# config the class list file here
class_file = 'categories_places365.txt'
if id_model == 1:
    if not os.path.exists(class_file):
        synset_url = 'https://raw.githubusercontent.com/csailvision/places365/master/categories_places365.txt'
        os.system('wget ' + synset_url)

if not os.path.exists(class_file):
    print('Your category list does not exist')
    # raise FileNotFoundError
classes = list()
with open(class_file) as f:
    for line in f:
            classes.append(line.strip().split(' ')[0][3:])
classes = tuple(classes)
model = torch.load(model_file)


# feature extraction layer setup

features_names = ['layer4']

model.eval()
model.cuda()


# image datasest to be processed
name_dataset = 'sun+imagenetval'
root_image = 'images'
with open('images/imagelist.txt') as f:
    lines = f.readlines()
imglist = []
# 删除字符串尾部的符号
for line in lines:
    line = line.rstrip()
    imglist.append(root_image + '/' + line)

features_blobs = []
def hook_feature(module, input, output):
    # hook the feature extractor
    features_blobs.append(np.squeeze(output.data.cpu().numpy()))

for name in features_names:
    model._modules.get(name).register_forward_hook(hook_feature)

# image transformer
tf = trn.Compose([
        trn.Scale(img_size),
        trn.ToTensor(),
        trn.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

dataset = Dataset(imglist, tf)
loader = data.DataLoader(
        dataset,
        batch_size=batch_size,
        num_workers=num_workers,
        shuffle=False)

# extract the max value activaiton for each image
imglist_results = []
maxfeatures = [None] * len(features_names)
num_batches = len(dataset) / batch_size
for batch_idx, (input, paths) in enumerate(loader):
    del features_blobs[:]
    print('%d / %d' % (batch_idx+1, num_batches))
    input = input.cuda()
    input_var = V(input, volatile=True)
    logit = model.forward(input_var)
    # 字符串相加,每次存入64个
    imglist_results = imglist_results + list(paths)
    if maxfeatures[0] is None:
        # initialize the feature variable
        for i, feat_batch in enumerate(features_blobs):
            # num_imglist*512
            size_features = (len(dataset), feat_batch.shape[1])
            maxfeatures[i] = np.zeros(size_features)
    start_idx = batch_idx*batch_size
    end_idx = min((batch_idx+1)*batch_size, len(dataset))
    for i, feat_batch in enumerate(features_blobs):
        #feat_batch:batch*14*14*512,找到feature map中最大的那个点,maxfeatures输出为52189*512
        maxfeatures[i][start_idx:end_idx] = np.max(np.max(feat_batch,3),2)

# generate the top activated images
output_folder = 'result_segments/%s' % model_name
if not os.path.exists(output_folder):
    os.makedirs(output_folder + '/image')

# output the html first
for layerID, layer in enumerate(features_names):
    file_html = os.path.join(output_folder, layer + '.html')
    with open(file_html, 'w') as f:
        # 神经元的表示:512
        num_units = maxfeatures[layerID].shape[1]
        lines_units = ['%s-unit%03d.jpg' % (layer, unitID) for unitID in range(num_units)]
        lines_units = ['unit%03d<br><img src="image/%s">'%(unitID, lines_units[unitID]) for unitID in range(num_units)]
        f.write('\n<br>'.join(lines_units))

    # it contains the cropped regions
    if flag_crop == 1:
        file_html_crop = os.path.join(output_folder, layer + '_crop.html')
        with open(file_html_crop, 'w') as f:
            num_units = maxfeatures[layerID].shape[1]
            lines_units = ['%s-unit%03d_crop.jpg' % (layer, unitID) for unitID in range(num_units)]
            lines_units = ['unit%03d<br><img src="image/%s">'%(unitID, lines_units[unitID]) for unitID in range(num_units)]
            f.write('\n<br>'.join(lines_units))

if flag_classspecific == 1:
    num_topunit_class = 3
    layer_lastconv = features_names[-1]
    # get the softmax weight
    params = list(model.parameters())
    # size:365*512
    weight_softmax = np.squeeze(params[-2].data.cpu().numpy())

    file_html = os.path.join(output_folder, 'class_specific_unit.html')
    output_lines = []
    for classID in range(len(classes)):
        line = '<h2>%s</h2>' % classes[classID]
        #沿着axis排序,按照权重从大到小的下标输出
        #取出权重值top3
        idx_units_sorted = np.argsort(np.squeeze(weight_softmax[classID]))[::-1]
        for sortID in range(num_topunit_class):
            unitID = idx_units_sorted[sortID]
            weight_unit = weight_softmax[classID][unitID]
            line += 'weight=%.3f %s<br>' % (weight_unit, lines_units[unitID])
        line = '<p>%s</p>' % line
        output_lines.append(line)

    with open(file_html, 'w') as f:
        f.write('\n'.join(output_lines))


# generate the unit visualization,每个图片在layer4上有512个units
for layerID, layer in enumerate(features_names):
    num_units = maxfeatures[layerID].shape[1]
    imglist_sorted = []
    # load the top actiatied image list into one list,找出前12个激活值最大的图像
    for unitID in range(num_units):
        #[:, unitID]！！找出所有图片的第一个神经元
        activations_unit = np.squeeze(maxfeatures[layerID][:, unitID])
        #将第一个神经元，按大小排列，并且找出top12.比较的是每个feature map最大的那个值的大小
        idx_sorted = np.argsort(activations_unit)[::-1]
        imglist_sorted += [imglist[item] for item in idx_sorted[:num_top]]

    # data loader for the top activated images
    loader_top = data.DataLoader(
        Dataset(imglist_sorted, tf),
        batch_size=num_top,
        num_workers=num_workers,
        shuffle=False)
    # 依次生成512个神经元top12的图像
    for unitID, (input, paths) in enumerate(loader_top):
        del features_blobs[:]
        print('%d / %d' % (unitID+1, num_units))
        input = input.cuda()
        input_var = V(input, volatile=True)
        logit = model.forward(input_var)
        #得到top12 feature map
        feature_maps = features_blobs[layerID]

        images_input = input.cpu().numpy()
        max_value = 0
        output_unit = []
        # 可视化,得到一个二值mask，然后在原图上显示
        for i in range(num_top):
            feature_map = feature_maps[i][unitID]
            if max_value == 0:
                max_value = np.max(feature_map)
            feature_map = feature_map / max_value
            mask = cv2.resize(feature_map, segment_size)
            #大于0.2设置为1,小于0.2设置为0
            mask[mask < threshold_scale] = 0.0 # binarize the mask
            mask[mask > threshold_scale] = 1.0

            img = cv2.imread(paths[i])
            img = cv2.resize(img, segment_size)
            img = cv2.normalize(img.astype('float'), None, 0.0, 1.0, cv2.NORM_MINMAX)
            #两者相乘得到图像
            img_mask = np.multiply(img, mask[:,:, np.newaxis])
            img_mask = np.uint8(img_mask * 255)
            output_unit.append(img_mask)
            output_unit.append(np.uint8(np.ones((segment_size[0],margin,3))*255))
        #将top12个合成一个图片保存。
        montage_unit = np.concatenate(output_unit, axis=1)
        cv2.imwrite(os.path.join(output_folder, 'image', '%s-unit%03d.jpg'%(layer, unitID)), montage_unit)
        if flag_crop == 1:
            # load the library to crop image
            import tightcrop
            montage_unit_crop = tightcrop.crop_tiled_image(montage_unit, margin)
            cv2.imwrite(os.path.join(output_folder, 'image', '%s-unit%03d_crop.jpg'%(layer, unitID)), montage_unit_crop)
print('done check results in ' + output_folder)