tensorflow使用训练好的VGG模型进行测试

心已赠人 2023-07-11 14:00 13阅读 0赞

import scipy.io
    import numpy as np 
    import os 
    import scipy.misc 
    import matplotlib.pyplot as plt 
    import tensorflow as tf

def _conv_layer(input, weights, bias):
        conv = tf.nn.conv2d(input, tf.constant(weights), strides=(1, 1, 1, 1),
                padding='SAME')
        return tf.nn.bias_add(conv, bias)
    def _pool_layer(input):
        return tf.nn.max_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1),
                padding='SAME')
    def preprocess(image, mean_pixel):
        return image - mean_pixel
    def unprocess(image, mean_pixel):
        return image + mean_pixel
    def imread(path):
        return scipy.misc.imread(path).astype(np.float)
    def imsave(path, img):
        img = np.clip(img, 0, 255).astype(np.uint8)
        scipy.misc.imsave(path, img)
    print ("Functions for VGG ready")

def net(data_path, input_image):
        layers = (
            'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',
            'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
            'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',
            'relu3_3', 'conv3_4', 'relu3_4', 'pool3',
            'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
            'relu4_3', 'conv4_4', 'relu4_4', 'pool4',
            'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',
            'relu5_3', 'conv5_4', 'relu5_4'
        )
        data = scipy.io.loadmat(data_path)
        mean = data['normalization'][0][0][0]
        mean_pixel = np.mean(mean, axis=(0, 1))
        weights = data['layers'][0]
        net = { }
        current = input_image
        for i, name in enumerate(layers):
            kind = name[:4]
            if kind == 'conv':
                kernels, bias = weights[i][0][0][0][0]
                # matconvnet: weights are [width, height, in_channels, out_channels]
                # tensorflow: weights are [height, width, in_channels, out_channels]
                kernels = np.transpose(kernels, (1, 0, 2, 3))
                bias = bias.reshape(-1)
                current = _conv_layer(current, kernels, bias)
            elif kind == 'relu':
                current = tf.nn.relu(current)
            elif kind == 'pool':
                current = _pool_layer(current)
            net[name] = current
        assert len(net) == len(layers)
        return net, mean_pixel, layers
    print ("Network for VGG ready")

cwd  = os.getcwd()
    VGG_PATH = cwd + "/data/imagenet-vgg-verydeep-19.mat"
    IMG_PATH = cwd + "/data/cat.jpg"
    input_image = imread(IMG_PATH)
    shape = (1,input_image.shape[0],input_image.shape[1],input_image.shape[2]) 
    with tf.Session() as sess:
        image = tf.placeholder('float', shape=shape)
        nets, mean_pixel, all_layers = net(VGG_PATH, image)
        input_image_pre = np.array([preprocess(input_image, mean_pixel)])
        layers = all_layers # For all layers 
        # layers = ('relu2_1', 'relu3_1', 'relu4_1')
        for i, layer in enumerate(layers):
            print ("[%d/%d] %s" % (i+1, len(layers), layer))
            features = nets[layer].eval(feed_dict={ image: input_image_pre})
            
            print (" Type of 'features' is ", type(features))
            print (" Shape of 'features' is %s" % (features.shape,))
            # Plot response 
            if 1:
                plt.figure(i+1, figsize=(10, 5))
                plt.matshow(features[0, :, :, 0], cmap=plt.cm.gray, fignum=i+1)
                plt.title("" + layer)
                plt.colorbar()
                plt.show()

运行结果：  
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