├── Alexnet.py ├── Figure_1.png ├── Model.py ├── README.md ├── Resnet_50_101_152.py ├── VGG_19.py ├── data ├── 001 │ ├── 000679_001.jpg │ ├── 000686_001.jpg │ ├── 000696_001.jpg │ ├── 000703_001.jpg │ ├── 000705_001.jpg │ ├── 000711_001.jpg │ ├── 000712_001.jpg │ ├── 000713_001.jpg │ ├── 000715_001.jpg │ └── 000718_001.jpg ├── 002 │ ├── 000009_001.jpg │ ├── 000018_001.jpg │ ├── 000020_001.jpg │ ├── 000024_001.jpg │ ├── 000026_001.jpg │ ├── 000029_001.jpg │ ├── 000031_001.jpg │ ├── 000037_001.jpg │ ├── 000062_001.jpg │ └── 000063_001.jpg ├── 003 │ ├── 000285_001.jpg │ ├── 000287_001.jpg │ ├── 000293_001.jpg │ ├── 000294_001.jpg │ ├── 000295_001.jpg │ ├── 000296_001.jpg │ ├── 000299_001.jpg │ ├── 000300_001.jpg │ ├── 000302_001.jpg │ └── 000304_001.jpg ├── 004 │ ├── 000385_001.jpg │ ├── 000392_001.jpg │ ├── 000394_001.jpg │ ├── 000395_001.jpg │ ├── 000402_001.jpg │ ├── 000418_001.jpg │ ├── 000419_001.jpg │ ├── 000422_001.jpg │ ├── 000423_001.jpg │ └── 000424_001.jpg ├── 005 │ ├── 000500_001.jpg │ ├── 000502_001.jpg │ ├── 000504_001.jpg │ ├── 000505_001.jpg │ ├── 000506_001.jpg │ ├── 000508_001.jpg │ ├── 000511_001.jpg │ ├── 000515_001.jpg │ ├── 000518_001.jpg │ └── 000519_001.jpg ├── 006 │ ├── 000523_001.jpg │ ├── 000524_001.jpg │ ├── 000525_001.jpg │ ├── 000530_001.jpg │ ├── 000533_001.jpg │ ├── 000539_001.jpg │ ├── 000540_001.jpg │ ├── 000542_001.jpg │ ├── 000544_001.jpg │ └── 000549_001.jpg ├── 007 │ ├── 000523_001.jpg │ ├── 000524_001.jpg │ ├── 000525_001.jpg │ ├── 000530_001.jpg │ ├── 000533_001.jpg │ ├── 000539_001.jpg │ ├── 000540_001.jpg │ ├── 000542_001.jpg │ ├── 000544_001.jpg │ └── 000549_001.jpg ├── 008 │ ├── 000523_001.jpg │ ├── 000524_001.jpg │ ├── 000525_001.jpg │ ├── 000530_001.jpg │ ├── 000533_001.jpg │ ├── 000539_001.jpg │ ├── 000540_001.jpg │ ├── 000542_001.jpg │ ├── 000544_001.jpg │ └── 000549_001.jpg └── 009 │ ├── 000160_001.jpg │ ├── 000162_001.jpg │ ├── 000168_001.jpg │ ├── 000175_001.jpg │ ├── 000176_001.jpg │ ├── 000178_001.jpg │ ├── 000181_001.jpg │ ├── 000183_001.jpg │ ├── 000190_001.jpg │ └── 000195_001.jpg ├── dataload.py ├── inception_V4.py └── main.py /Alexnet.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @Time : 2020/3/3 19:59 3 | # @Author : YYLin 4 | # @Email : 854280599@qq.com 5 | # @File : Alexnet.py 6 | import tensorflow as tf 7 | 8 | 9 | # 定义一个最大池化层 10 | def maxPoolLayer(x, kHeight, kWidth, strideX, strideY, name, padding="SAME"): 11 | return tf.nn.max_pool(x, ksize=[1, kHeight, kWidth, 1], 12 | strides=[1, strideX, strideY, 1], padding=padding, name=name) 13 | 14 | 15 | # 使用dropout 随机丢包 16 | def dropout(x, keepPro, name=None): 17 | return tf.nn.dropout(x, keepPro, name) 18 | 19 | 20 | # 定义一个局部正则化 21 | def LRN(x, r, alpha, beta, name=None, bias=1.0): 22 | return tf.nn.local_response_normalization(x, depth_radius=r, alpha=alpha, beta=beta, bias=bias, name=name) 23 | 24 | 25 | # 定义一个全连接层的函数 其实可以修改成之前我写的那个 26 | def fcLayer(x, inputD, outputD, reluFlag, name): 27 | with tf.variable_scope(name) as scope: 28 | w = tf.get_variable("w", shape=[inputD, outputD], dtype="float") 29 | b = tf.get_variable("b", [outputD], dtype="float") 30 | out = tf.nn.xw_plus_b(x, w, b, name=scope.name) 31 | 32 | if reluFlag: 33 | return tf.nn.relu(out) 34 | else: 35 | return out 36 | 37 | 38 | def convLayer(x, kHeight, kWidth, strideX, strideY, featureNum, name, padding="SAME", groups=1): 39 | channel = int(x.get_shape()[-1]) 40 | conv = lambda a, b: tf.nn.conv2d(a, b, strides=[1, strideY, strideX, 1], padding=padding) 41 | with tf.variable_scope(name) as scope: 42 | w = tf.get_variable("w", shape=[kHeight, kWidth, channel / groups, featureNum]) 43 | b = tf.get_variable("b", shape=[featureNum]) 44 | 45 | xNew = tf.split(value=x, num_or_size_splits=groups, axis=3) 46 | wNew = tf.split(value=w, num_or_size_splits=groups, axis=3) 47 | 48 | featureMap = [conv(t1, t2) for t1, t2 in zip(xNew, wNew)] 49 | mergeFeatureMap = tf.concat(axis=3, values=featureMap) 50 | # print mergeFeatureMap.shape 51 | out = tf.nn.bias_add(mergeFeatureMap, b) 52 | return tf.nn.relu(tf.reshape(out, mergeFeatureMap.get_shape().as_list()), name=scope.name) 53 | 54 | 55 | # 定义一个alexNet类别 方便之后调用 56 | class AlexNet(object): 57 | def __init__(self, x, classNum, keepPro): 58 | self.X = x 59 | self.KEEPPRO = keepPro 60 | self.CLASSNUM = classNum 61 | self.begin_alexNet() 62 | 63 | def begin_alexNet(self): 64 | conv1 = convLayer(self.X, 11, 11, 4, 4, 96, "conv1", "VALID") 65 | lrn1 = LRN(conv1, 2, 2e-05, 0.75, "norm1") 66 | pool1 = maxPoolLayer(lrn1, 3, 3, 2, 2, "pool1", "VALID") 67 | 68 | conv2 = convLayer(pool1, 5, 5, 1, 1, 256, "conv2", groups=2) 69 | lrn2 = LRN(conv2, 2, 2e-05, 0.75, "lrn2") 70 | pool2 = maxPoolLayer(lrn2, 3, 3, 2, 2, "pool2", "VALID") 71 | 72 | conv3 = convLayer(pool2, 3, 3, 1, 1, 384, "conv3") 73 | 74 | conv4 = convLayer(conv3, 3, 3, 1, 1, 384, "conv4", groups=2) 75 | 76 | conv5 = convLayer(conv4, 3, 3, 1, 1, 256, "conv5", groups=2) 77 | pool5 = maxPoolLayer(conv5, 3, 3, 2, 2, "pool5", "VALID") 78 | 79 | fcIn = tf.reshape(pool5, [-1, 256 * 2 * 2]) 80 | fc1 = fcLayer(fcIn, 256 * 2 * 2, 4096, True, "fc6") 81 | dropout1 = dropout(fc1, self.KEEPPRO) 82 | 83 | fc2 = fcLayer(dropout1, 4096, 4096, True, "fc7") 84 | dropout2 = dropout(fc2, self.KEEPPRO) 85 | 86 | self.fc3 = fcLayer(dropout2, 4096, self.CLASSNUM, True, "fc8") 87 | 88 | -------------------------------------------------------------------------------- /Figure_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YYlin/Collection-of-Image-Recognition-Models-in-Tensorflow/38eb48df5fca5a0027b364b29c055bb5805f83a2/Figure_1.png -------------------------------------------------------------------------------- /Model.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @Time : 2020/3/3 20:07 3 | # @Author : YYLin 4 | # @Email : 854280599@qq.com 5 | # @File : Model.py 6 | from dataload import * 7 | import tensorflow as tf 8 | from Alexnet import AlexNet 9 | from sklearn.model_selection import train_test_split 10 | import Resnet_50_101_152 11 | from VGG_19 import VGG19 12 | import inception_V4 13 | import matplotlib.pyplot as plt 14 | 15 | class Model(object): 16 | def __init__(self, sess, type_of_model, epoch, dataset_name, batch_size, img_size, y_dim, resnet_type): 17 | self.sess = sess 18 | self.type_of_model = type_of_model 19 | self.epoch = epoch 20 | self.dataset_name = dataset_name 21 | self.batch_size = batch_size 22 | self.img_size = img_size 23 | self.y_dim = y_dim 24 | self.resnet_type = resnet_type 25 | 26 | # load dataset 27 | if self.dataset_name == 'satellite': 28 | print('loading satellite .............') 29 | self.data_X, self.data_Y = load_satetile_image(self.img_size, y_dim=self.y_dim) 30 | # split the data into train set and valid set 31 | self.X_train, self.X_val, self.y_train, self.y_val = train_test_split(self.data_X, self.data_Y, 32 | shuffle=True, test_size=0.1, 33 | random_state=2019) 34 | print('self.X_train, self.y_train:',self.X_train.shape, self.y_train.shape, self.X_val.shape, self.y_val.shape) 35 | 36 | def build_model(self): 37 | 38 | # the placeholder of image and label 39 | self.y = tf.placeholder(tf.float32, [self.batch_size, self.y_dim], name='y') 40 | self.inputs = tf.placeholder(tf.float32, [self.batch_size, self.img_size, self.img_size, 3], name='img') 41 | self.keep_prob = tf.placeholder("float") 42 | 43 | # chose the model 44 | if self.type_of_model == 'Alexnet': 45 | network = AlexNet(self.inputs, self.y_dim, self.keep_prob) 46 | score = network.fc3 47 | elif self.type_of_model == 'ResNet': 48 | network = Resnet_50_101_152.resnet(self.inputs, self.resnet_type, self.y_dim) 49 | score = tf.squeeze(network, axis=(1, 2)) 50 | elif self.type_of_model == 'VGG19': 51 | network = VGG19(self.inputs, self.keep_prob, self.y_dim) 52 | score = network.fc8 53 | elif self.type_of_model == 'inception_V4': 54 | score = inception_V4.inference(self.inputs, self.batch_size, self.y_dim) 55 | else: 56 | print('these is no %s'%self.type_of_model) 57 | 58 | softmax_result = tf.nn.softmax(score) 59 | 60 | # 定义损失函数 以及相对应的优化器 61 | cross_entropy = -tf.reduce_sum(self.y * tf.log(softmax_result)) 62 | self.Optimizer = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) 63 | 64 | # 用于判断验证集的结果是否正确 65 | correct_prediction = tf.equal(tf.argmax(softmax_result, 1), tf.argmax(self.y, 1)) 66 | self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) 67 | 68 | def plot_acc(self, train_acc, val_acc): 69 | fig, ax = plt.subplots() 70 | plt.xlabel('Iter') 71 | plt.ylabel('Accuracy') 72 | 73 | x = [i for i in range(len(train_acc))] 74 | plt.plot(x, train_acc, "x-", label="Train Accuracy") 75 | plt.plot(x, val_acc, "+-", label="Valid Accuracy") 76 | 77 | """open the grid""" 78 | plt.grid(True) 79 | 80 | plt.legend(bbox_to_anchor=(1.0, 1), loc=1, borderaxespad=0.) 81 | 82 | plt.show() 83 | 84 | def train(self): 85 | print('begin training ...........') 86 | tf.global_variables_initializer().run() 87 | train_acc_list = [] 88 | valid_acc_list = [] 89 | 90 | for k in range(self.epoch): 91 | for i in range(len(self.X_train) // self.batch_size): 92 | if (i+1) == (len(self.X_train) // self.batch_size): 93 | print('Reload Imaging') 94 | self.data_X, self.data_Y = load_satetile_image(self.img_size, y_dim=self.y_dim) 95 | # split the data into train set and valid set 96 | self.X_train, self.X_val, self.y_train, self.y_val = train_test_split(self.data_X, self.data_Y, shuffle=True, test_size=0.1, 97 | random_state=2019) 98 | 99 | dropout_rate = 0.5 100 | _, train_acc = self.sess.run([self.Optimizer, self.accuracy], feed_dict={self.inputs: self.X_train[i*self.batch_size:(i+1)*self.batch_size], 101 | self.y: self.y_train[i*self.batch_size:(i+1)*self.batch_size], self.keep_prob: dropout_rate}) 102 | 103 | # 输出验证集上的结果 104 | all_val_acc = 0 105 | for j in range((len(self.X_val) // self.batch_size) - 1): 106 | dropout_rate = 0 107 | val_acc = self.sess.run(self.accuracy, feed_dict={ 108 | self.inputs: self.X_val[j * self.batch_size:(j + 1) * self.batch_size], 109 | self.y: self.y_val[j * self.batch_size:(j + 1) * self.batch_size], 110 | self.keep_prob: dropout_rate}) 111 | all_val_acc += val_acc 112 | 113 | print("step %d, train accuracy %f valid accuracy %f:" % (i,train_acc, all_val_acc / (j + 1))) 114 | train_acc_list.append(train_acc) 115 | valid_acc_list.append(all_val_acc) 116 | self.plot_acc(train_acc_list, valid_acc_list) 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Image recognition model collection 2 | Collection of classic image recognition models, e.g.ResNet, Alexnet, VGG19, inception_V4 in Tensorflow. 3 | 4 | 5 | ## The format of dataset 6 | Firstly, Use unzip data.zip to unzipped it, and then images belonging to different categories are placed in different folders. 7 | 8 | 9 | ## Training networks 10 | | models | paper | commands | introduction | 11 | | ---------- | :-----------: | :-----------: | :-----------: | 12 | | Alexnet | [Alexnet](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)| `python main.py --type_of_model Alexnet` | [Alexnet](https://blog.csdn.net/qq_41776781/article/details/94437671) 13 | | | | | 14 | | VGG19 | [VGG19](https://arxiv.org/abs/1801.01401)| `python main.py --type_of_model VGG19` | [VGG19](https://blog.csdn.net/qq_41776781/article/details/94452085) 15 | | | | | 16 | |Inception_V4 | [Inception_V4](https://arxiv.org/abs/1606.03498) | `python main.py --type_of_model inception_V4` | [Inception_V4](https://blog.csdn.net/qq_41776781/article/details/94476538) 17 | | | | | 18 | | ResNet | [ResNet](https://arxiv.org/abs/1606.03498) | `python main.py --type_of_model ResNet --resnet_type 50` | [ResNet](https://blog.csdn.net/qq_41776781/article/details/94459299) 19 | 20 | ## The Accuracy of training and validing images 21 | ![](https://github.com/YYlin/Collection-of-Image-Recognition-Models-in-Tensorflow/blob/master/Figure_1.png) 22 | 23 | ## An explanation 24 | It's easy to use the above recognition models with kears, but by reading papers and analyzing source code, which could help in improving programming ability. If you want to know basic something about these models, you can visit my [CSDN](https://blog.csdn.net/qq_41776781/category_9291732.html), 25 | 26 | -------------------------------------------------------------------------------- /Resnet_50_101_152.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @Time : 2020/3/3 22:30 3 | # @Author : YYLin 4 | # @Email : 854280599@qq.com 5 | # @File : Resnet_50_101_152.py 6 | import tensorflow as tf 7 | 8 | UNITS = {'50': [3, 4, 6, 3], '101': [3, 4, 23, 3], 9 | '152': [3, 8, 36, 3]} 10 | CHANNELS = [64, 128, 256, 512] 11 | 12 | 13 | def bottleneck(net, channel, holes=1, c_name='pretrain', stride=1, 14 | shortcut_conv=False, key=tf.GraphKeys.GLOBAL_VARIABLES): 15 | with tf.variable_scope('bottleneck_v2', reuse=tf.AUTO_REUSE): 16 | # define initializer for weights and biases 17 | w_initializer = tf.contrib.layers.xavier_initializer() 18 | b_initializer = tf.zeros_initializer() 19 | regularizer = tf.contrib.layers.l2_regularizer(scale=0.0001) 20 | # batch normalization 21 | net = tf.layers.batch_normalization(inputs=net, axis=-1, name='preact') 22 | net = tf.nn.relu(net) 23 | 24 | # shortcut 25 | if shortcut_conv: 26 | with tf.variable_scope('shortcut', reuse=tf.AUTO_REUSE): 27 | kernel = tf.get_variable(initializer=w_initializer, 28 | shape=[1, 1, net.shape[-1], 29 | channel * 4], 30 | name='weights', 31 | regularizer=regularizer, 32 | collections=['pretrain', key]) 33 | # convolution for shortcut in order to output size 34 | shortcut = tf.nn.conv2d(input=net, filter=kernel, 35 | strides=[1, stride, stride, 1], 36 | padding='SAME') 37 | biases = tf.get_variable(initializer=b_initializer, 38 | shape=channel * 4, name='biases', 39 | regularizer=regularizer, 40 | collections=['pretrain', key]) 41 | shortcut = tf.nn.bias_add(shortcut, biases) 42 | else: 43 | # shortcut 44 | shortcut = net 45 | 46 | # convolution 1 47 | with tf.variable_scope('conv1', reuse=tf.AUTO_REUSE): 48 | kernel = tf.get_variable(initializer=w_initializer, 49 | shape=[1, 1, net.shape[-1], channel], 50 | name='weights', regularizer=regularizer, 51 | collections=['pretrain', key]) 52 | net = tf.nn.atrous_conv2d(value=net, filters=kernel, rate=holes, 53 | padding='SAME') 54 | biases = tf.get_variable(initializer=b_initializer, 55 | shape=channel, name='biases', 56 | regularizer=regularizer, 57 | collections=['non_pretrain', key]) 58 | net = tf.nn.bias_add(net, biases) 59 | # batch normalization 60 | net = tf.layers.batch_normalization(inputs=net, axis=-1, 61 | name='preact') 62 | net = tf.nn.relu(net) 63 | 64 | # convolution 2 65 | with tf.variable_scope('conv2', reuse=tf.AUTO_REUSE): 66 | kernel = tf.get_variable(initializer=w_initializer, 67 | shape=[3, 3, channel, channel], 68 | name='weights', regularizer=regularizer, 69 | collections=['pretrain', key]) 70 | net = tf.nn.conv2d(input=net, filter=kernel, 71 | strides=[1, stride, stride, 1], padding='SAME') 72 | biases = tf.get_variable(initializer=b_initializer, 73 | shape=channel, name='biases', 74 | regularizer=regularizer, 75 | collections=['non_pretrain', key]) 76 | net = tf.nn.bias_add(net, biases) 77 | # batch normalization 78 | net = tf.layers.batch_normalization(inputs=net, axis=-1, 79 | name='preact') 80 | net = tf.nn.relu(net) 81 | 82 | # convolution 3 83 | with tf.variable_scope('conv3', reuse=tf.AUTO_REUSE): 84 | kernel = tf.get_variable(initializer=w_initializer, 85 | shape=[1, 1, channel, channel * 4], 86 | name='weights', regularizer=regularizer, 87 | collections=['pretrain', key]) 88 | net = tf.nn.atrous_conv2d(value=net, filters=kernel, rate=holes, 89 | padding='SAME') 90 | biases = tf.get_variable(initializer=b_initializer, 91 | shape=channel * 4, name='biases', 92 | regularizer=regularizer, 93 | collections=['pretrain', key]) 94 | net = tf.nn.bias_add(net, biases) 95 | 96 | return net, shortcut 97 | 98 | 99 | def block(net, name, unit, channel): 100 | with tf.variable_scope(name, reuse=tf.AUTO_REUSE): 101 | for i in range(unit): 102 | with tf.variable_scope('unit_' + str(i + 1), reuse=tf.AUTO_REUSE): 103 | # block1 i=0 stride=1 104 | if i == 0: 105 | if name != 'block1': 106 | net, shortcut = bottleneck(net, channel, 107 | stride=2, 108 | shortcut_conv=True) 109 | else: 110 | net, shortcut = bottleneck(net, channel, 111 | stride=1, 112 | shortcut_conv=True) 113 | else: 114 | net, shortcut = bottleneck(net, channel) 115 | net = tf.add(net, shortcut) 116 | 117 | return net 118 | 119 | 120 | def resnet(input_, resnet_v2, classes): 121 | key = tf.GraphKeys.GLOBAL_VARIABLES 122 | with tf.variable_scope(resnet_v2, reuse=tf.AUTO_REUSE): 123 | # define initializer for weights and biases 124 | w_initializer = tf.contrib.layers.xavier_initializer() 125 | b_initializer = tf.zeros_initializer() 126 | regularizer = tf.contrib.layers.l2_regularizer(scale=0.0001) 127 | # convolution 1 128 | with tf.variable_scope('conv1', reuse=tf.AUTO_REUSE): 129 | kernel = tf.get_variable(initializer=w_initializer, 130 | shape=[7, 7, 3, 64], 131 | name='weights', regularizer=regularizer, 132 | collections=['pretrain', key]) 133 | net = tf.nn.conv2d(input=input_, filter=kernel, 134 | strides=[1, 2, 2, 1], padding='SAME') 135 | biases = tf.get_variable(initializer=b_initializer, shape=64, 136 | name='biases', regularizer=regularizer, 137 | collections=['pretrain', key]) 138 | net = tf.nn.bias_add(net, biases) 139 | net = tf.nn.max_pool(value=net, ksize=[1, 3, 3, 1], 140 | strides=[1, 2, 2, 1], padding='SAME') 141 | 142 | for i in range(4): 143 | net = block(net, 'block' + str(i + 1), UNITS[resnet_v2][i], 144 | CHANNELS[i]) 145 | 146 | net = tf.layers.batch_normalization(inputs=net, axis=-1, name='postnorm') 147 | net = tf.nn.relu(net) 148 | 149 | h, w = net.shape[1:3] 150 | net = tf.nn.avg_pool(value=net, ksize=[1, h, w, 1], 151 | strides=[1, 1, 1, 1], padding='VALID') 152 | 153 | # logits is not in scope 'resnet_v2' in order to fine-tune 154 | with tf.variable_scope('logits', reuse=tf.AUTO_REUSE): 155 | kernel = tf.get_variable(initializer=w_initializer, 156 | shape=[1, 1, 2048, classes], name='weights', 157 | regularizer=regularizer, 158 | collections=['non_pretrain', key]) 159 | net = tf.nn.conv2d(input=net, filter=kernel, 160 | strides=[1, 1, 1, 1], padding='VALID') 161 | biases = tf.get_variable(initializer=b_initializer, shape=classes, 162 | name='biases', regularizer=regularizer, 163 | collections=['non_pretrain', key]) 164 | net = tf.nn.bias_add(net, biases) 165 | return net 166 | 167 | -------------------------------------------------------------------------------- /VGG_19.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @Time : 2020/3/3 22:52 3 | # @Author : YYLin 4 | # @Email : 854280599@qq.com 5 | # @File : VGG_19.py 6 | # -*- coding: utf-8 -*- 7 | import tensorflow as tf 8 | 9 | 10 | def maxPoolLayer(x, kHeight, kWidth, strideX, strideY, name, padding="SAME"): 11 | return tf.nn.max_pool(x, ksize=[1, kHeight, kWidth, 1], 12 | strides=[1, strideX, strideY, 1], padding=padding, name=name) 13 | 14 | 15 | def dropout(x, keepPro, name=None): 16 | return tf.nn.dropout(x, keepPro, name) 17 | 18 | 19 | def fcLayer(x, inputD, outputD, reluFlag, name): 20 | with tf.variable_scope(name) as scope: 21 | w = tf.get_variable("w", shape=[inputD, outputD], dtype="float") 22 | b = tf.get_variable("b", [outputD], dtype="float") 23 | out = tf.nn.xw_plus_b(x, w, b, name=scope.name) 24 | if reluFlag: 25 | return tf.nn.relu(out) 26 | else: 27 | return out 28 | 29 | 30 | def convLayer(x, kHeight, kWidth, strideX, strideY, featureNum, name, padding="SAME"): 31 | channel = int(x.get_shape()[-1]) 32 | with tf.variable_scope(name) as scope: 33 | w = tf.get_variable("w", shape=[kHeight, kWidth, channel, featureNum]) 34 | b = tf.get_variable("b", shape=[featureNum]) 35 | featureMap = tf.nn.conv2d(x, w, strides=[1, strideY, strideX, 1], padding=padding) 36 | out = tf.nn.bias_add(featureMap, b) 37 | return tf.nn.relu(tf.reshape(out, featureMap.get_shape().as_list()), name=scope.name) 38 | 39 | 40 | class VGG19(object): 41 | def __init__(self, x, keepPro, classNum): 42 | self.X = x 43 | self.KEEPPRO = keepPro 44 | self.CLASSNUM = classNum 45 | self.begin_VGG_19() 46 | 47 | def begin_VGG_19(self): 48 | """build model""" 49 | conv1_1 = convLayer(self.X, 3, 3, 1, 1, 64, "conv1_1") 50 | conv1_2 = convLayer(conv1_1, 3, 3, 1, 1, 64, "conv1_2") 51 | pool1 = maxPoolLayer(conv1_2, 2, 2, 2, 2, "pool1") 52 | 53 | conv2_1 = convLayer(pool1, 3, 3, 1, 1, 128, "conv2_1") 54 | conv2_2 = convLayer(conv2_1, 3, 3, 1, 1, 128, "conv2_2") 55 | pool2 = maxPoolLayer(conv2_2, 2, 2, 2, 2, "pool2") 56 | 57 | conv3_1 = convLayer(pool2, 3, 3, 1, 1, 256, "conv3_1") 58 | conv3_2 = convLayer(conv3_1, 3, 3, 1, 1, 256, "conv3_2") 59 | conv3_3 = convLayer(conv3_2, 3, 3, 1, 1, 256, "conv3_3") 60 | conv3_4 = convLayer(conv3_3, 3, 3, 1, 1, 256, "conv3_4") 61 | pool3 = maxPoolLayer(conv3_4, 2, 2, 2, 2, "pool3") 62 | 63 | conv4_1 = convLayer(pool3, 3, 3, 1, 1, 512, "conv4_1") 64 | conv4_2 = convLayer(conv4_1, 3, 3, 1, 1, 512, "conv4_2") 65 | conv4_3 = convLayer(conv4_2, 3, 3, 1, 1, 512, "conv4_3") 66 | conv4_4 = convLayer(conv4_3, 3, 3, 1, 1, 512, "conv4_4") 67 | pool4 = maxPoolLayer(conv4_4, 2, 2, 2, 2, "pool4") 68 | 69 | conv5_1 = convLayer(pool4, 3, 3, 1, 1, 512, "conv5_1") 70 | conv5_2 = convLayer(conv5_1, 3, 3, 1, 1, 512, "conv5_2") 71 | conv5_3 = convLayer(conv5_2, 3, 3, 1, 1, 512, "conv5_3") 72 | conv5_4 = convLayer(conv5_3, 3, 3, 1, 1, 512, "conv5_4") 73 | pool5 = maxPoolLayer(conv5_4, 2, 2, 2, 2, "pool5") 74 | 75 | fcIn = tf.reshape(pool5, [-1, 4 * 4 * 512]) 76 | fc6 = fcLayer(fcIn, 4 * 4 * 512, 4096, True, "fc6") 77 | dropout1 = dropout(fc6, self.KEEPPRO) 78 | 79 | fc7 = fcLayer(dropout1, 4096, 4096, True, "fc7") 80 | dropout2 = dropout(fc7, self.KEEPPRO) 81 | 82 | self.fc8 = fcLayer(dropout2, 4096, self.CLASSNUM, True, "fc8") 83 | 84 | 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dataload.py 6 | import os 7 | import cv2 8 | import numpy as np 9 | from keras.utils import to_categorical 10 | 11 | 12 | def load_satetile_image(img_size=100, y_dim=10): 13 | img_list = [] 14 | label_list = [] 15 | dir_counter = 0 16 | 17 | path = 'data' 18 | 19 | # 对路径下的所有子文件夹中的所有jpg文件进行读取并存入到一个list中 20 | for child_dir in os.listdir(path): 21 | child_path = os.path.join(path, child_dir) 22 | for dir_image in os.listdir(child_path): 23 | img = cv2.imread(os.path.join(child_path, dir_image)) 24 | img = cv2.resize(img, (img_size, img_size), interpolation=cv2.INTER_LANCZOS4) 25 | img = img / 255.0 26 | img_list.append(img) 27 | label_list.append(dir_counter) 28 | 29 | dir_counter += 1 30 | 31 | X_train = np.array(img_list) 32 | Y_train = to_categorical(label_list, y_dim) 33 | return X_train, Y_train 34 | -------------------------------------------------------------------------------- /inception_V4.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @Time : 2020/3/3 22:57 3 | # @Author : YYLin 4 | # @Email : 854280599@qq.com 5 | # @File : inception_V4.py 6 | import tensorflow as tf 7 | 8 | 9 | # 定义变量函数初始化函数 10 | def define_variable(shape, name): 11 | return tf.Variable(tf.truncated_normal(shape, stddev=0.1), name) 12 | 13 | 14 | # 最大下采样操作 15 | def max_pool(name, l_input, k1, k2): 16 | return tf.nn.max_pool(l_input, ksize=[1, k1, k1, 1], strides=[1, k2, k2, 1], padding='SAME', name=name) 17 | 18 | 19 | # network structure: inception_A 20 | def inception_A(input): 21 | p1f11 = 96 22 | p2f11 = 64 23 | p2f22 = 96 24 | p3f11 = 64 25 | p3f22 = 96 26 | p3f33 = 96 27 | p4f11 = 96 28 | path1 = tf.layers.conv2d(input, p1f11, 1, padding='same', activation=tf.nn.relu) 29 | path2 = tf.layers.conv2d(input, p2f11, 1, padding='same', activation=tf.nn.relu) 30 | path2 = tf.layers.conv2d(path2, p2f22, 3, padding='same', activation=tf.nn.relu) 31 | path3 = tf.layers.conv2d(input, p3f11, 1, padding='same', activation=tf.nn.relu) 32 | path3 = tf.layers.conv2d(path3, p3f22, 3, padding='same', activation=tf.nn.relu) 33 | path3 = tf.layers.conv2d(path3, p3f33, 3, padding='same', activation=tf.nn.relu) 34 | path4 = tf.layers.average_pooling2d(input, pool_size=3, strides=1, padding='same') 35 | path4 = tf.layers.conv2d(path4, p4f11, 1, padding='same', activation=tf.nn.relu) 36 | out = tf.concat((path1, path2, path3, path4), axis=-1) 37 | return out 38 | 39 | 40 | # network structure: Reduction_A 41 | def reduction_A(input): 42 | channel = 384 43 | path1 = tf.layers.max_pooling2d(input, pool_size=3, strides=2, padding='same') 44 | path2 = tf.layers.conv2d(input, channel, 3, strides=2, padding='same', activation=tf.nn.relu) 45 | path3 = tf.layers.conv2d(input, channel, 1, padding='same', activation=tf.nn.relu) 46 | path3 = tf.layers.conv2d(path3, channel, 3, padding='same', activation=tf.nn.relu) 47 | path3 = tf.layers.conv2d(path3, channel, 3, strides=2, padding='same', activation=tf.nn.relu) 48 | out = tf.concat((path1, path2, path3), axis=-1) 49 | return out 50 | 51 | 52 | # network structure: inception_B 53 | def inception_B(input): 54 | p1f11 = 384 55 | p2f11 = 192 56 | p2f22 = 224 57 | p2f33 = 256 58 | p3f11 = 192 59 | p3f22 = 192 60 | p3f33 = 224 61 | p3f44 = 224 62 | p3f55 = 256 63 | p4f11 = 128 64 | path1 = tf.layers.conv2d(input, p1f11, 1, padding='same', activation=tf.nn.relu) 65 | path2 = tf.layers.conv2d(input, p2f11, 1, padding='same', activation=tf.nn.relu) 66 | path2 = tf.layers.conv2d(path2, p2f22, [1, 7], padding='same', activation=tf.nn.relu) 67 | path2 = tf.layers.conv2d(path2, p2f33, [7, 1], padding='same', activation=tf.nn.relu) 68 | path3 = tf.layers.conv2d(input, p3f11, 1, padding='same', activation=tf.nn.relu) 69 | path3 = tf.layers.conv2d(path3, p3f22, [1, 7], padding='same', activation=tf.nn.relu) 70 | path3 = tf.layers.conv2d(path3, p3f33, [7, 1], padding='same', activation=tf.nn.relu) 71 | path3 = tf.layers.conv2d(path3, p3f44, [1, 7], padding='same', activation=tf.nn.relu) 72 | path3 = tf.layers.conv2d(path3, p3f55, [7, 1], padding='same', activation=tf.nn.relu) 73 | path4 = tf.layers.average_pooling2d(input, pool_size=3, strides=1, padding='same') 74 | path4 = tf.layers.conv2d(path4, p4f11, 1, padding='same', activation=tf.nn.relu) 75 | out = tf.concat((path1, path2, path3, path4), axis=-1) 76 | return out 77 | 78 | 79 | # network structure: Reduction_B 80 | def reduction_B(input): 81 | p2f11 = 192 82 | p2f22 = 192 83 | p3f11 = 256 84 | p3f22 = 256 85 | p3f33 = 320 86 | p3f44 = 320 87 | path1 = tf.layers.max_pooling2d(input, pool_size=3, strides=2, padding='same') 88 | path2 = tf.layers.conv2d(input, p2f11, 1, padding='same', activation=tf.nn.relu) 89 | path2 = tf.layers.conv2d(path2, p2f22, 3, strides=2, padding='same', activation=tf.nn.relu) 90 | path3 = tf.layers.conv2d(input, p3f11, 1, padding='same', activation=tf.nn.relu) 91 | path3 = tf.layers.conv2d(path3, p3f22, [1, 7], padding='same', activation=tf.nn.relu) 92 | path3 = tf.layers.conv2d(path3, p3f33, [7, 1], padding='same', activation=tf.nn.relu) 93 | path3 = tf.layers.conv2d(path3, p3f44, 3, strides=2, padding='same', activation=tf.nn.relu) 94 | out = tf.concat((path1, path2, path3), axis=-1) 95 | return out 96 | 97 | 98 | # network structure: inception_C 99 | def inception_C(input): 100 | p1f11 = 256 101 | p2f11 = 384 102 | p2f11_1 = 256 103 | p2f11_2 = 256 104 | p3f11 = 384 105 | p3f22 = 448 106 | p3f33 = 512 107 | p3f33_1 = 256 108 | p3f33_2 = 256 109 | p4f11 = 256 110 | path1 = tf.layers.conv2d(input, p1f11, 1, padding='same', activation=tf.nn.relu) 111 | path2 = tf.layers.conv2d(input, p2f11, 1, padding='same', activation=tf.nn.relu) 112 | path2_1 = tf.layers.conv2d(path2, p2f11_1, [1, 3], padding='same', activation=tf.nn.relu) 113 | path2_2 = tf.layers.conv2d(path2, p2f11_2, [3, 1], padding='same', activation=tf.nn.relu) 114 | path3 = tf.layers.conv2d(input, p3f11, 1, padding='same', activation=tf.nn.relu) 115 | path3 = tf.layers.conv2d(path3, p3f22, [1, 3], padding='same', activation=tf.nn.relu) 116 | path3 = tf.layers.conv2d(path3, p3f33, [3, 1], padding='same', activation=tf.nn.relu) 117 | path3_1 = tf.layers.conv2d(path3, p3f33_1, [3, 1], padding='same', activation=tf.nn.relu) 118 | path3_2 = tf.layers.conv2d(path3, p3f33_2, [1, 3], padding='same', activation=tf.nn.relu) 119 | path4 = tf.layers.average_pooling2d(input, pool_size=3, strides=1, padding='same') 120 | path4 = tf.layers.conv2d(path4, p4f11, 1, padding='same', activation=tf.nn.relu) 121 | out = tf.concat((path1, path2_1, path2_2, path3_1, path3_2, path4), axis=-1) 122 | return out 123 | 124 | 125 | # 输入参数：images，image batch、4D tensor、tf.float32、[batch_size, width, height, channels] 126 | # 返回参数：logits, float、 [batch_size, n_classes] 127 | def inference(images, batch_size, n_classes): 128 | w_conv1 = define_variable([3, 3, 3, 192], name="W") 129 | b_conv1 = define_variable([192], name="B") 130 | conv1 = tf.nn.conv2d(images, w_conv1, strides=[1, 1, 1, 1], padding='SAME') 131 | relu1 = tf.nn.relu(conv1 + b_conv1) 132 | 133 | pool1 = tf.nn.max_pool(relu1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME') 134 | 135 | # inception_A 136 | # 35*35 grid 137 | inception_a1 = inception_A(pool1) 138 | inception_a2 = inception_A(inception_a1) 139 | inception_a3 = inception_A(inception_a2) 140 | inception_a4 = inception_A(inception_a3) 141 | 142 | # reduction_A 143 | # from 35*35 to 17*17 144 | reduction_a = reduction_A(inception_a4) 145 | 146 | # inception_B 147 | # 17*17 grid 148 | inception_b1 = inception_B(reduction_a) 149 | inception_b2 = inception_B(inception_b1) 150 | inception_b3 = inception_B(inception_b2) 151 | inception_b4 = inception_B(inception_b3) 152 | inception_b5 = inception_B(inception_b4) 153 | inception_b6 = inception_B(inception_b5) 154 | inception_b7 = inception_B(inception_b6) 155 | 156 | # reduction_B 157 | # from 17*17 to 8*8 158 | reduction_b = reduction_B(inception_b7) 159 | 160 | # inception_C 161 | # 8*8 grid 162 | inception_c1 = inception_C(reduction_b) 163 | inception_c2 = inception_C(inception_c1) 164 | inception_c3 = inception_C(inception_c2) 165 | 166 | net = tf.layers.average_pooling2d(inception_c3, 7, 1, name="avgpool") # -> [batch, 1, 1, 768] 167 | 168 | # dropout层 169 | with tf.variable_scope('dropout') as scope: 170 | drop_out = tf.nn.dropout(net, 0.8) 171 | 172 | reshape = tf.reshape(drop_out, shape=[batch_size, -1]) 173 | dim = reshape.get_shape()[1].value 174 | weights1 = tf.Variable(tf.truncated_normal(shape=[dim, 1024], stddev=0.005, dtype=tf.float32), name='weights', 175 | dtype=tf.float32) 176 | biases1 = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[1024]), name='biases', dtype=tf.float32) 177 | local6 = tf.nn.relu(tf.matmul(reshape, weights1) + biases1) 178 | 179 | weights = tf.Variable(tf.truncated_normal(shape=[1024, n_classes], stddev=0.005, dtype=tf.float32), 180 | name='softmax_linear', dtype=tf.float32) 181 | biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[n_classes]), name='biases', dtype=tf.float32) 182 | logits = tf.add(tf.matmul(local6, weights), biases, name='softmax_linear') 183 | 184 | return logits 185 | 186 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @Time : 2020/3/3 20:02 3 | # @Author : YYLin 4 | # @Email : 854280599@qq.com 5 | # @File : main.py 6 | import argparse 7 | import sys 8 | import tensorflow as tf 9 | from Model import Model 10 | 11 | 12 | # parsing and configuration 13 | def parse_args(): 14 | desc = "Tensorflow implementation of StarGAN" 15 | parser = argparse.ArgumentParser(description=desc) 16 | parser.add_argument('--dataset', type=str, default='satellite', help='dataset_name') 17 | parser.add_argument('--type_of_model', type=str, default='inception_V4', help='ResNet Alexnet VGG19 inception_V4') 18 | 19 | parser.add_argument('--epoch', type=int, default=20, help='The number of epochs to run') 20 | parser.add_argument('--img_size', type=int, default=100, help='The size of image') 21 | parser.add_argument('--batch_size', type=int, default=4, help='The size of batch size') 22 | parser.add_argument('--y_dim', type=int, default=9, help='The classification of dataset') 23 | 24 | parser.add_argument('--resnet_type', type=str, default='50', help='The 50 101 152 layer can be chose') 25 | 26 | return parser.parse_args() 27 | 28 | 29 | def main(): 30 | # parse arguments 31 | args = parse_args() 32 | if args is None: 33 | print('The args is None') 34 | sys.exit() 35 | else: 36 | print('args:', args) 37 | 38 | with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess: 39 | if args.type_of_model == 'Alexnet': 40 | print('****** Using Alexnet ******') 41 | model = Model(sess, type_of_model=args.type_of_model, epoch=args.epoch, dataset_name=args.dataset, batch_size=args.batch_size, 42 | img_size=args.img_size, y_dim=args.y_dim, resnet_type=args.resnet_type) 43 | elif args.type_of_model == 'ResNet': 44 | print('****** Using ResNet ******') 45 | model = Model(sess, type_of_model=args.type_of_model, epoch=args.epoch, dataset_name=args.dataset, 46 | batch_size=args.batch_size, 47 | img_size=args.img_size, y_dim=args.y_dim, resnet_type=args.resnet_type) 48 | elif args.type_of_model == 'VGG19': 49 | print('****** Using VGG19 ******') 50 | model = Model(sess, type_of_model=args.type_of_model, epoch=args.epoch, dataset_name=args.dataset, 51 | batch_size=args.batch_size, 52 | img_size=args.img_size, y_dim=args.y_dim, resnet_type=args.resnet_type) 53 | elif args.type_of_model == 'inception_V4': 54 | print('****** Using inception_V4 ******') 55 | model = Model(sess, type_of_model=args.type_of_model, epoch=args.epoch, dataset_name=args.dataset, 56 | batch_size=args.batch_size, 57 | img_size=args.img_size, y_dim=args.y_dim, resnet_type=args.resnet_type) 58 | else: 59 | print('these is no %s'%args.type_of_model) 60 | sys.exit() 61 | 62 | # build graph 63 | model.build_model() 64 | 65 | model.train() 66 | 67 | 68 | if __name__ == '__main__': 69 | main() 70 | 71 | --------------------------------------------------------------------------------