def inference_conv(image): # incomplete :/ image_reshaped = tf.reshape(image, [-1, IMAGE_SIZE, IMAGE_SIZE, 1]) with tf.name_scope("conv1") as scope: W_conv1 = utils.weight_variable([3, 3, 1, 32], name="W_conv1") b_conv1 = utils.bias_variable([32], name="b_conv1") add_to_reg_loss_and_summary(W_conv1, b_conv1) h_conv1 = tf.nn.tanh( utils.conv2d_basic(image_reshaped, W_conv1, b_conv1)) with tf.name_scope("conv2") as scope: W_conv2 = utils.weight_variable([3, 3, 32, 64], name="W_conv2") b_conv2 = utils.bias_variable([64], name="b_conv2") add_to_reg_loss_and_summary(W_conv2, b_conv2) h_conv2 = tf.nn.tanh(utils.conv2d_strided(h_conv1, W_conv2, b_conv2)) with tf.name_scope("conv3") as scope: W_conv3 = utils.weight_variable([3, 3, 64, 128], name="W_conv3") b_conv3 = utils.bias_variable([128], name="b_conv3") add_to_reg_loss_and_summary(W_conv3, b_conv3) h_conv3 = tf.nn.tanh(utils.conv2d_strided(h_conv2, W_conv3, b_conv3)) with tf.name_scope("conv4") as scope: W_conv4 = utils.weight_variable([3, 3, 128, 256], name="W_conv4") b_conv4 = utils.bias_variable([256], name="b_conv4") add_to_reg_loss_and_summary(W_conv4, b_conv4) h_conv4 = tf.nn.tanh(utils.conv2d_strided(h_conv3, W_conv4, b_conv4))
def encoder_conv(image): with tf.name_scope("enc_conv1") as scope: W_conv1 = utils.weight_variable([3, 3, 3, 32], name="W_conv1") b_conv1 = utils.bias_variable([32], name="b_conv1") h_conv1 = tf.nn.tanh(utils.conv2d_strided(image, W_conv1, b_conv1)) with tf.name_scope("enc_conv2") as scope: W_conv2 = utils.weight_variable([3, 3, 32, 64], name="W_conv2") b_conv2 = utils.bias_variable([64], name="b_conv2") h_conv2 = tf.nn.tanh(utils.conv2d_strided(h_conv1, W_conv2, b_conv2)) with tf.name_scope("enc_conv3") as scope: W_conv3 = utils.weight_variable([3, 3, 64, 128], name="W_conv3") b_conv3 = utils.bias_variable([128], name="b_conv3") h_conv3 = tf.nn.tanh(utils.conv2d_strided(h_conv2, W_conv3, b_conv3)) with tf.name_scope("enc_conv4") as scope: W_conv4 = utils.weight_variable([3, 3, 128, 256], name="W_conv4") b_conv4 = utils.bias_variable([256], name="b_conv4") h_conv4 = tf.nn.tanh(utils.conv2d_strided(h_conv3, W_conv4, b_conv4)) with tf.name_scope("enc_fc") as scope: image_size = IMAGE_SIZE // 16 h_conv4_flatten = tf.reshape(h_conv4, [-1, image_size * image_size * 256]) W_fc5 = utils.weight_variable([image_size * image_size * 256, 512], name="W_fc5") b_fc5 = utils.bias_variable([512], name="b_fc5") encoder_val = tf.matmul(h_conv4_flatten, W_fc5) + b_fc5 return encoder_val
def inference_conv(image): # incomplete :/ image_reshaped = tf.reshape(image, [-1, IMAGE_SIZE, IMAGE_SIZE, 1]) with tf.name_scope("conv1") as scope: W_conv1 = utils.weight_variable([3, 3, 1, 32], name="W_conv1") b_conv1 = utils.bias_variable([32], name="b_conv1") add_to_reg_loss_and_summary(W_conv1, b_conv1) h_conv1 = tf.nn.tanh(utils.conv2d_basic(image_reshaped, W_conv1, b_conv1)) with tf.name_scope("conv2") as scope: W_conv2 = utils.weight_variable([3, 3, 32, 64], name="W_conv2") b_conv2 = utils.bias_variable([64], name="b_conv2") add_to_reg_loss_and_summary(W_conv2, b_conv2) h_conv2 = tf.nn.tanh(utils.conv2d_strided(h_conv1, W_conv2, b_conv2)) with tf.name_scope("conv3") as scope: W_conv3 = utils.weight_variable([3, 3, 64, 128], name="W_conv3") b_conv3 = utils.bias_variable([128], name="b_conv3") add_to_reg_loss_and_summary(W_conv3, b_conv3) h_conv3 = tf.nn.tanh(utils.conv2d_strided(h_conv2, W_conv3, b_conv3)) with tf.name_scope("conv4") as scope: W_conv4 = utils.weight_variable([3, 3, 128, 256], name="W_conv4") b_conv4 = utils.bias_variable([256], name="b_conv4") add_to_reg_loss_and_summary(W_conv4, b_conv4) h_conv4 = tf.nn.tanh(utils.conv2d_strided(h_conv3, W_conv4, b_conv4))
def conv2d_layer(x, name, W_s, pool_, if_relu=False, stride=2, stddev=0.02, if_dropout=False, keep_prob_=1): '''Conv2d operator Args: pool_: if pool_==0:not pooling else pooling ''' W = utils.weight_variable(W_s, stddev=stddev, name='W' + name) b = utils.bias_variable([W_s[3]], name='b' + name) #conv = utils.conv2d_strided_valid(x, W, b, stride) conv = utils.conv2d_strided(x, W, b, stride) print('shape after conv: ', conv.shape) print('--------------------------------') if if_dropout: conv = tf.nn.dropout(conv, keep_prob_) if if_relu: conv = tf.nn.relu(conv, name=name + '_relu') if pool_: conv = utils.max_pool(conv, pool_, 2) print('shape after pool: ', conv.shape) return conv
def inference_fully_convolutional(dataset): ''' Fully convolutional inference on notMNIST dataset :param datset: [batch_size, 28*28*1] tensor :return: logits ''' dataset_reshaped = tf.reshape(dataset, [-1, 28, 28, 1]) with tf.name_scope("conv1") as scope: W_conv1 = utils.weight_variable_xavier_initialized([3, 3, 1, 32], name="W_conv1") b_conv1 = utils.bias_variable([32], name="b_conv1") h_conv1 = tf.nn.relu( utils.conv2d_strided(dataset_reshaped, W_conv1, b_conv1)) with tf.name_scope("conv2") as scope: W_conv2 = utils.weight_variable_xavier_initialized([3, 3, 32, 64], name="W_conv2") b_conv2 = utils.bias_variable([64], name="b_conv2") h_conv2 = tf.nn.relu(utils.conv2d_strided(h_conv1, W_conv2, b_conv2)) with tf.name_scope("conv3") as scope: W_conv3 = utils.weight_variable_xavier_initialized([3, 3, 64, 128], name="W_conv3") b_conv3 = utils.bias_variable([128], name="b_conv3") h_conv3 = tf.nn.relu(utils.conv2d_strided(h_conv2, W_conv3, b_conv3)) with tf.name_scope("conv4") as scope: W_conv4 = utils.weight_variable_xavier_initialized([3, 3, 128, 256], name="W_conv4") b_conv4 = utils.bias_variable([256], name="b_conv4") h_conv4 = tf.nn.relu(utils.conv2d_strided(h_conv3, W_conv4, b_conv4)) with tf.name_scope("conv5") as scope: # W_conv5 = utils.weight_variable_xavier_initialized([2, 2, 256, 512], name="W_conv5") # b_conv5 = utils.bias_variable([512], name="b_conv5") # h_conv5 = tf.nn.relu(utils.conv2d_strided(h_conv4, W_conv5, b_conv5)) h_conv5 = utils.avg_pool_2x2(h_conv4) with tf.name_scope("conv6") as scope: W_conv6 = utils.weight_variable_xavier_initialized([1, 1, 256, 10], name="W_conv6") b_conv6 = utils.bias_variable([10], name="b_conv6") logits = tf.nn.relu(utils.conv2d_basic(h_conv5, W_conv6, b_conv6)) print logits.get_shape() logits = tf.reshape(logits, [-1, 10]) return logits
def encoder(dataset, train_mode): with tf.variable_scope("Encoder"): with tf.name_scope("enc_conv1") as scope: W_conv1 = utils.weight_variable_xavier_initialized([3, 3, 3, 32], name="W_conv1") b_conv1 = utils.bias_variable([32], name="b_conv1") h_conv1 = utils.conv2d_strided(dataset, W_conv1, b_conv1) h_bn1 = utils.batch_norm(h_conv1, 32, train_mode, scope="conv1_bn") h_relu1 = tf.nn.relu(h_bn1) with tf.name_scope("enc_conv2") as scope: W_conv2 = utils.weight_variable_xavier_initialized([3, 3, 32, 64], name="W_conv2") b_conv2 = utils.bias_variable([64], name="b_conv2") h_conv2 = utils.conv2d_strided(h_relu1, W_conv2, b_conv2) h_bn2 = utils.batch_norm(h_conv2, 64, train_mode, scope="conv2_bn") h_relu2 = tf.nn.relu(h_bn2) with tf.name_scope("enc_conv3") as scope: W_conv3 = utils.weight_variable_xavier_initialized([3, 3, 64, 128], name="W_conv3") b_conv3 = utils.bias_variable([128], name="b_conv3") h_conv3 = utils.conv2d_strided(h_relu2, W_conv3, b_conv3) h_bn3 = utils.batch_norm(h_conv3, 128, train_mode, scope="conv3_bn") h_relu3 = tf.nn.relu(h_bn3) with tf.name_scope("enc_conv4") as scope: W_conv4 = utils.weight_variable_xavier_initialized([3, 3, 128, 256], name="W_conv4") b_conv4 = utils.bias_variable([256], name="b_conv4") h_conv4 = utils.conv2d_strided(h_relu3, W_conv4, b_conv4) h_bn4 = utils.batch_norm(h_conv4, 256, train_mode, scope="conv4_bn") h_relu4 = tf.nn.relu(h_bn4) with tf.name_scope("enc_conv5") as scope: W_conv5 = utils.weight_variable_xavier_initialized([3, 3, 256, 512], name="W_conv5") b_conv5 = utils.bias_variable([512], name="b_conv5") h_conv5 = utils.conv2d_strided(h_relu4, W_conv5, b_conv5) h_bn5 = utils.batch_norm(h_conv5, 512, train_mode, scope="conv5_bn") h_relu5 = tf.nn.relu(h_bn5) with tf.name_scope("enc_fc") as scope: image_size = IMAGE_SIZE // 32 h_relu5_flatten = tf.reshape(h_relu5, [-1, image_size * image_size * 512]) W_fc = utils.weight_variable([image_size * image_size * 512, 1024], name="W_fc") b_fc = utils.bias_variable([1024], name="b_fc") encoder_val = tf.matmul(h_relu5_flatten, W_fc) + b_fc return encoder_val
def discriminator(input_images, train_mode): # dropout_prob = 1.0 # if train_mode: # dropout_prob = 0.5 W_conv0 = utils.weight_variable([5, 5, NUM_OF_CHANNELS, 64 * 1], name="W_conv0") b_conv0 = utils.bias_variable([64 * 1], name="b_conv0") h_conv0 = utils.conv2d_strided(input_images, W_conv0, b_conv0) h_bn0 = h_conv0 # utils.batch_norm(h_conv0, 64 * 1, train_mode, scope="disc_bn0") h_relu0 = utils.leaky_relu(h_bn0, 0.2, name="h_relu0") utils.add_activation_summary(h_relu0) W_conv1 = utils.weight_variable([5, 5, 64 * 1, 64 * 2], name="W_conv1") b_conv1 = utils.bias_variable([64 * 2], name="b_conv1") h_conv1 = utils.conv2d_strided(h_relu0, W_conv1, b_conv1) h_bn1 = utils.batch_norm(h_conv1, 64 * 2, train_mode, scope="disc_bn1") h_relu1 = utils.leaky_relu(h_bn1, 0.2, name="h_relu1") utils.add_activation_summary(h_relu1) W_conv2 = utils.weight_variable([5, 5, 64 * 2, 64 * 4], name="W_conv2") b_conv2 = utils.bias_variable([64 * 4], name="b_conv2") h_conv2 = utils.conv2d_strided(h_relu1, W_conv2, b_conv2) h_bn2 = utils.batch_norm(h_conv2, 64 * 4, train_mode, scope="disc_bn2") h_relu2 = utils.leaky_relu(h_bn2, 0.2, name="h_relu2") utils.add_activation_summary(h_relu2) W_conv3 = utils.weight_variable([5, 5, 64 * 4, 64 * 8], name="W_conv3") b_conv3 = utils.bias_variable([64 * 8], name="b_conv3") h_conv3 = utils.conv2d_strided(h_relu2, W_conv3, b_conv3) h_bn3 = utils.batch_norm(h_conv3, 64 * 8, train_mode, scope="disc_bn3") h_relu3 = utils.leaky_relu(h_bn3, 0.2, name="h_relu3") utils.add_activation_summary(h_relu3) shape = h_relu3.get_shape().as_list() h_3 = tf.reshape( h_relu3, [FLAGS.batch_size, (IMAGE_SIZE // 16) * (IMAGE_SIZE // 16) * shape[3]]) W_4 = utils.weight_variable([h_3.get_shape().as_list()[1], 1], name="W_4") b_4 = utils.bias_variable([1], name="b_4") h_4 = tf.matmul(h_3, W_4) + b_4 return tf.nn.sigmoid(h_4), h_4, h_relu3
def inference_strided(input_image): W1 = utils.weight_variable([9, 9, 3, 32]) b1 = utils.bias_variable([32]) tf.histogram_summary("W1", W1) tf.histogram_summary("b1", b1) h_conv1 = tf.nn.relu(utils.conv2d_basic(input_image, W1, b1)) W2 = utils.weight_variable([3, 3, 32, 64]) b2 = utils.bias_variable([64]) tf.histogram_summary("W2", W2) tf.histogram_summary("b2", b2) h_conv2 = tf.nn.relu(utils.conv2d_strided(h_conv1, W2, b2)) W3 = utils.weight_variable([3, 3, 64, 128]) b3 = utils.bias_variable([128]) tf.histogram_summary("W3", W3) tf.histogram_summary("b3", b3) h_conv3 = tf.nn.relu(utils.conv2d_strided(h_conv2, W3, b3)) # upstrides W4 = utils.weight_variable([3, 3, 64, 128]) b4 = utils.bias_variable([64]) tf.histogram_summary("W4", W4) tf.histogram_summary("b4", b4) # print h_conv3.get_shape() # print W4.get_shape() h_conv4 = tf.nn.relu(utils.conv2d_transpose_strided(h_conv3, W4, b4)) W5 = utils.weight_variable([3, 3, 32, 64]) b5 = utils.bias_variable([32]) tf.histogram_summary("W5", W5) tf.histogram_summary("b5", b5) h_conv5 = tf.nn.relu(utils.conv2d_transpose_strided(h_conv4, W5, b5)) W6 = utils.weight_variable([9, 9, 32, 3]) b6 = utils.bias_variable([3]) tf.histogram_summary("W6", W6) tf.histogram_summary("b6", b6) pred_image = tf.nn.tanh(utils.conv2d_basic(h_conv5, W6, b6)) return pred_image
def inference_fully_convolutional(dataset): ''' Fully convolutional inference on notMNIST dataset :param datset: [batch_size, 28*28*1] tensor :return: logits ''' dataset_reshaped = tf.reshape(dataset, [-1, 28, 28, 1]) with tf.name_scope("conv1") as scope: W_conv1 = utils.weight_variable_xavier_initialized([3, 3, 1, 32], name="W_conv1") b_conv1 = utils.bias_variable([32], name="b_conv1") h_conv1 = tf.nn.relu(utils.conv2d_strided(dataset_reshaped, W_conv1, b_conv1)) with tf.name_scope("conv2") as scope: W_conv2 = utils.weight_variable_xavier_initialized([3, 3, 32, 64], name="W_conv2") b_conv2 = utils.bias_variable([64], name="b_conv2") h_conv2 = tf.nn.relu(utils.conv2d_strided(h_conv1, W_conv2, b_conv2)) with tf.name_scope("conv3") as scope: W_conv3 = utils.weight_variable_xavier_initialized([3, 3, 64, 128], name="W_conv3") b_conv3 = utils.bias_variable([128], name="b_conv3") h_conv3 = tf.nn.relu(utils.conv2d_strided(h_conv2, W_conv3, b_conv3)) with tf.name_scope("conv4") as scope: W_conv4 = utils.weight_variable_xavier_initialized([3, 3, 128, 256], name="W_conv4") b_conv4 = utils.bias_variable([256], name="b_conv4") h_conv4 = tf.nn.relu(utils.conv2d_strided(h_conv3, W_conv4, b_conv4)) with tf.name_scope("conv5") as scope: # W_conv5 = utils.weight_variable_xavier_initialized([2, 2, 256, 512], name="W_conv5") # b_conv5 = utils.bias_variable([512], name="b_conv5") # h_conv5 = tf.nn.relu(utils.conv2d_strided(h_conv4, W_conv5, b_conv5)) h_conv5 = utils.avg_pool_2x2(h_conv4) with tf.name_scope("conv6") as scope: W_conv6 = utils.weight_variable_xavier_initialized([1, 1, 256, 10], name="W_conv6") b_conv6 = utils.bias_variable([10], name="b_conv6") logits = tf.nn.relu(utils.conv2d_basic(h_conv5, W_conv6, b_conv6)) print logits.get_shape() logits = tf.reshape(logits, [-1, 10]) return logits
def discriminator(input_images, train_mode): # dropout_prob = 1.0 # if train_mode: # dropout_prob = 0.5 W_conv0 = utils.weight_variable([5, 5, NUM_OF_CHANNELS, 64 * 1], name="W_conv0") b_conv0 = utils.bias_variable([64 * 1], name="b_conv0") h_conv0 = utils.conv2d_strided(input_images, W_conv0, b_conv0) h_bn0 = h_conv0 # utils.batch_norm(h_conv0, 64 * 1, train_mode, scope="disc_bn0") h_relu0 = utils.leaky_relu(h_bn0, 0.2, name="h_relu0") utils.add_activation_summary(h_relu0) W_conv1 = utils.weight_variable([5, 5, 64 * 1, 64 * 2], name="W_conv1") b_conv1 = utils.bias_variable([64 * 2], name="b_conv1") h_conv1 = utils.conv2d_strided(h_relu0, W_conv1, b_conv1) h_bn1 = utils.batch_norm(h_conv1, 64 * 2, train_mode, scope="disc_bn1") h_relu1 = utils.leaky_relu(h_bn1, 0.2, name="h_relu1") utils.add_activation_summary(h_relu1) W_conv2 = utils.weight_variable([5, 5, 64 * 2, 64 * 4], name="W_conv2") b_conv2 = utils.bias_variable([64 * 4], name="b_conv2") h_conv2 = utils.conv2d_strided(h_relu1, W_conv2, b_conv2) h_bn2 = utils.batch_norm(h_conv2, 64 * 4, train_mode, scope="disc_bn2") h_relu2 = utils.leaky_relu(h_bn2, 0.2, name="h_relu2") utils.add_activation_summary(h_relu2) W_conv3 = utils.weight_variable([5, 5, 64 * 4, 64 * 8], name="W_conv3") b_conv3 = utils.bias_variable([64 * 8], name="b_conv3") h_conv3 = utils.conv2d_strided(h_relu2, W_conv3, b_conv3) h_bn3 = utils.batch_norm(h_conv3, 64 * 8, train_mode, scope="disc_bn3") h_relu3 = utils.leaky_relu(h_bn3, 0.2, name="h_relu3") utils.add_activation_summary(h_relu3) shape = h_relu3.get_shape().as_list() h_3 = tf.reshape(h_relu3, [FLAGS.batch_size, (IMAGE_SIZE // 16) * (IMAGE_SIZE // 16) * shape[3]]) W_4 = utils.weight_variable([h_3.get_shape().as_list()[1], 1], name="W_4") b_4 = utils.bias_variable([1], name="b_4") h_4 = tf.matmul(h_3, W_4) + b_4 return tf.nn.sigmoid(h_4), h_4, h_relu3
def conv2d_layer(x, name, W_s, pool_, if_relu=False, stride=2): '''Conv2d operator Args: pool_: if pool_==0:not pooling else pooling ''' W = utils.weight_variable(W_s, name='W'+name) b = utils.bias_variable([W_s[3]], name='b'+name) conv = utils.conv2d_strided(x, W, b, stride) if if_relu: conv = tf.nn.relu(conv, name=name+'_relu') if pool_: conv = utils.max_pool(conv, pool_, 2) return conv
def inference(image, keep_prob): """ Semantic segmentation network definition # 语义分割网络定义 :param image: input image. Should have values in range 0-255 :param keep_prob: :return: """ # 获取预训练网络VGG print("setting up vgg initialized conv layers ...") # model_dir Model_zoo/ # MODEL_URL 下载VGG19网址 model_data = utils.get_model_data(FLAGS.model_dir, MODEL_URL) # 返回VGG19模型中内容 mean = model_data['normalization'][0][0][0] # 获得图像均值 mean_pixel = np.mean(mean, axis=(0, 1)) # RGB weights = np.squeeze(model_data['layers']) # 压缩VGG网络中参数,把维度是1的维度去掉 剩下的就是权重 processed_image = utils.process_image(image, mean_pixel) # 图像减均值 with tf.variable_scope("inference"): # 命名作用域 是inference image_net = vgg_net(weights, processed_image) # 传入权重参数和预测图像,获得所有层输出结果 # conv_final_layer = image_net["conv5_3"] # 获得输出结果 conv_final_layer = image_net["relu4_4"] w5_0 = utils.weight_variable([3, 3, 512, 512], name="W5_0") #取消pool4降采样操作,改成3*3/s1 b5_0 = utils.bias_variable([512], name="b5_0") conv5_0 = utils.conv2d_strided(conv_final_layer, w5_0, b5_0) w5_1 = utils.weight_variable([3, 3, 512, 512], name="W5_1") b5_1 = utils.bias_variable([512], name="b5_1") conv5_1 = utils.conv2d_atrous_2(conv5_0, w5_1, b5_1) w5_2 = utils.weight_variable([3, 3, 512, 512], name="W5_2") #将第五层的conv5_1,2,3改成2-空洞卷积 b5_2 = utils.bias_variable([512], name="b5_2") conv5_2 = utils.conv2d_atrous_2(conv5_1, w5_2, b5_2) w5_3 = utils.weight_variable([3, 3, 512, 512], name="W5_3") b5_3 = utils.bias_variable([512], name="b5_3") conv5_3 = utils.conv2d_atrous_2(conv5_2, w5_3, b5_3) # pool5 = utils.max_pool_2x2(conv_final_layer) # /32 缩小32倍 # W6 = utils.weight_variable([7, 7, 512, 4096], name="W6") # 初始化第6层的w b # b6 = utils.bias_variable([4096], name="b6") # conv6 = utils.conv2d_basic(pool5, W6, b6) # relu6 = tf.nn.relu(conv6, name="relu6") # if FLAGS.debug: # utils.add_activation_summary(relu6) # relu_dropout6 = tf.nn.dropout(relu6, keep_prob=keep_prob) w6_0 = utils.weight_variable([3, 3, 512, 4096], name="W6_0") # 取消pool5降采样操作,改成3*3/s1 b6_0 = utils.bias_variable([4096], name="b6_0") conv6_0 = utils.conv2d_strided(conv5_3, w6_0, b6_0) w6 = utils.weight_variable([3, 3, 4096, 4096], name="W7") b6 = utils.bias_variable([4096], name="b6") #第6层为4-空洞卷积 conv6 = utils.conv2d_atrous_4(conv6_0, w6, b6) relu6 = tf.nn.relu(conv6, name="relu6") if FLAGS.debug: utils.add_activation_summary(relu6) relu_dropout6 = tf.nn.dropout(relu6, keep_prob=keep_prob) # W7 = utils.weight_variable([1, 1, 4096, 4096], name="W7") # 第7层卷积层 # b7 = utils.bias_variable([4096], name="b7") # conv7 = utils.conv2d_basic(relu_dropout6, W7, b7) # relu7 = tf.nn.relu(conv7, name="relu7") # if FLAGS.debug: # utils.add_activation_summary(relu7) # relu_dropout7 = tf.nn.dropout(relu7, keep_prob=keep_prob) w7 = utils.weight_variable([1, 1, 4096, 4096], name="w7") b7 = utils.bias_variable([4096], name="b7") #第7层为4—空洞卷积 conv7 = utils.conv2d_atrous_4(relu_dropout6, w7, b7) relu7 = tf.nn.relu(conv7, name="relu6") if FLAGS.debug: utils.add_activation_summary(relu7) relu_dropout7 = tf.nn.dropout(relu7, keep_prob=keep_prob) # W8 = utils.weight_variable([1, 1, 4096, NUM_OF_CLASSESS], name="W8") # b8 = utils.bias_variable([NUM_OF_CLASSESS], name="b8") # conv8 = utils.conv2d_basic(relu_dropout7, W8, b8) # 第8层卷积层 分类151类 # # annotation_pred1 = tf.argmax(conv8, dimension=3, name="prediction1") w8 = utils.weight_variable([1, 1, 4096, NUM_OF_CLASSESS], name="W8") b8 = utils.bias_variable([NUM_OF_CLASSESS], name="b8") # 第8层为4—空洞卷积 conv8 = utils.conv2d_atrous_4(relu_dropout7, w8, b8) # conv8 = utils.max_pool_2x2(conv8) print(conv8.shape) # now to upscale to actual image size # deconv_shape1 = image_net["pool4"].get_shape() # 将pool4 1/16结果尺寸拿出来 做融合 [b,h,w,c] # # 定义反卷积层的 W,B [H, W, OUTC, INC] 输出个数为pool4层通道个数,输入为conv8通道个数 # # 扩大两倍 所以stride = 2 kernel_size = 4 # W_t1 = utils.weight_variable([4, 4, deconv_shape1[3].value, NUM_OF_CLASSESS], name="W_t1") # b_t1 = utils.bias_variable([deconv_shape1[3].value], name="b_t1") # # 输入为conv8特征图,使得其特征图大小扩大两倍,并且特征图个数变为pool4的通道数 # conv_t1 = utils.conv2d_transpose_strided(conv8, W_t1, b_t1, output_shape=tf.shape(image_net["pool4"])) # fuse_1 = tf.add(conv_t1, image_net["pool4"], name="fuse_1") # 进行融合 逐像素相加 deconv_shape1 = image_net["pool4"].get_shape( ) # 将pool4 1/16结果尺寸拿出来 做融合 [b,h,w,c] # 定义反卷积层的 W,B [H, W, OUTC, INC] 输出个数为pool4层通道个数,输入为conv8通道个数 # 扩大两倍 所以stride = 2 kernel_size = 4 W_t1 = utils.weight_variable( [4, 4, deconv_shape1[3].value, NUM_OF_CLASSESS], name="W_t1") b_t1 = utils.bias_variable([deconv_shape1[3].value], name="b_t1") # 输入为conv8特征图,使得其特征图大小扩大两倍,并且特征图个数变为pool4的通道数 conv_t1 = utils.conv2d_transpose_strided(conv8, W_t1, b_t1, output_shape=tf.shape( image_net["pool4"])) fuse_1 = tf.add(conv_t1, image_net["pool4"], name="fuse_1") # 进行融合 逐像素相加 # 获得pool3尺寸 是原图大小的1/8 deconv_shape2 = image_net["pool3"].get_shape() # 输出通道数为pool3通道数, 输入通道数为pool4通道数 W_t2 = utils.weight_variable( [4, 4, deconv_shape2[3].value, deconv_shape1[3].value], name="W_t2") b_t2 = utils.bias_variable([deconv_shape2[3].value], name="b_t2") # 将上一层融合结果fuse_1在扩大两倍,输出尺寸和pool3相同 conv_t2 = utils.conv2d_transpose_strided(fuse_1, W_t2, b_t2, output_shape=tf.shape( image_net["pool3"])) # 融合操作deconv(fuse_1) + pool3 fuse_2 = tf.add(conv_t2, image_net["pool3"], name="fuse_2") shape = tf.shape(image) # 获得原始图像大小 # 堆叠列表,反卷积输出尺寸,[b,原图H,原图W,类别个数] deconv_shape3 = tf.stack( [shape[0], shape[1], shape[2], NUM_OF_CLASSESS]) # 建立反卷积w[8倍扩大需要ks=16, 输出通道数为类别个数, 输入通道数pool3通道数] W_t3 = utils.weight_variable( [16, 16, deconv_shape2[3].value, NUM_OF_CLASSESS], name="W_t3") b_t3 = utils.bias_variable([NUM_OF_CLASSESS], name="b_t3") # 反卷积,fuse_2反卷积,输出尺寸为 [b,原图H,原图W,类别个数] conv_t3 = utils.conv2d_transpose_strided(fuse_2, W_t3, b_t3, output_shape=deconv_shape3, stride=8) # deconv_shape3 = tf.stack([shape[0], shape[1], shape[2], NUM_OF_CLASSESS]) # # 建立反卷积w[8倍扩大需要ks=16, 输出通道数为类别个数, 输入通道数pool3通道数] # W_t1 = utils.weight_variable([16, 16, NUM_OF_CLASSESS, NUM_OF_CLASSESS], name="W_t1") ##反卷积生成原图大小 # b_t1 = utils.bias_variable([NUM_OF_CLASSESS], name="b_t1") # # 反卷积,fuse_2反卷积,输出尺寸为 [b,原图H,原图W,类别个数] # conv_t1 = utils.conv2d_transpose_strided(conv8, W_t1, b_t1, output_shape=deconv_shape3, stride=8) # 目前conv_t3的形式为size为和原始图像相同的size,通道数与分类数相同 # 这句我的理解是对于每个像素位置,根据第3维度(通道数)通过argmax能计算出这个像素点属于哪个分类 # 也就是对于每个像素而言,NUM_OF_CLASSESS个通道中哪个数值最大,这个像素就属于哪个分类 # 每个像素点有21个值,哪个值最大就属于那一类 # 返回一张图,每一个点对于其来别信息shape=[b,h,w] # annotation_pred = tf.argmax(conv_t1, dimension=3, name="prediction") annotation_pred = tf.argmax(conv_t3, axis=3, name="prediction") # 从第三维度扩展 形成[b,h,w,c] 其中c=1, conv_t3最后具有21深度的特征图 # return tf.expand_dims(annotation_pred, dim=3), conv_t1 return tf.expand_dims(annotation_pred, axis=3), conv_t3
def res_net(weights, image): layers = ('conv1', 'res2a', 'res2b', 'res2c', 'res3a', 'res3b1', 'res3b2', 'res3b3', 'res4a', 'res4b1', 'res4b2', 'res4b3', 'res4b4', 'res4b5', 'res4b6', 'res4b7', 'res4b8', 'res4b9', 'res4b10', 'res4b11', 'res4b12', 'res4b13', 'res4b14', 'res4b15', 'res4b16', 'res4b17', 'res4b18', 'res4b19', 'res4b20', 'res4b21', 'res4b22', 'res5a', 'res5b', 'res5c') res_branch1_parm = { #branch1:padding(if 0 then 'VALID' else 'SMAE'),stride 'res2a': [0, 1, 0, 1, 1, 1, 0, 1], 'res3a': [0, 2, 0, 2, 1, 1, 0, 1], 'res4a': [0, 2, 0, 2, 1, 1, 0, 1], 'res5a': [0, 2, 0, 2, 1, 1, 0, 1] } net = {} current = image n = 0 for i, name in enumerate(layers): kind = name[:3] print('-----------------layer: %s-----------------------' % name) print('Input: ', current.shape) #convolutional if kind == 'con': kernels, bias, n = get_kernel_bias(n, weights, name) print('kernel size: ', kernels.shape, 'bias size', bias.shape) # matconvnet: weights are [width, height, in_channels, out_channels] # tensorflow: weights are [height, width, in_channels, out_channels] current = utils.conv2d_strided(current, kernels, bias) current = utils.max_pool_2x2(current, 3) #resnet elif kind == 'res': sub_kind = name[4] #not blockneck if sub_kind == 'a': res_param = res_branch1_parm[name] branch1_name = '%s_%s' % (name, 'branch1') branch1_w, branch1_b, out_chan_t, n = get_kernel_bias_res( n, weights, branch1_name, 2) print('branch1:kernel size: ', branch1_w.shape, 'bias size', branch1_b.shape) else: res_param = None branch1_w = None branch2a_name = '%s_%s' % (name, 'branch2a') branch2a_w, branch2a_b, out_chan_t2, n = get_kernel_bias_res( n, weights, branch2a_name, 0) print('branch2a:kernel size: ', branch2a_w.shape, 'bias size', branch2a_b.shape) branch2b_name = '%s_%s' % (name, 'branch2b') branch2b_w, branch2b_b, _, n = get_kernel_bias_res( n, weights, branch2b_name, 0) print('branch2b:kernel size: ', branch2b_w.shape, 'bias size', branch2b_b.shape) branch2c_name = '%s_%s' % (name, 'branch2c') branch2c_w, branch2c_b, out_chan2, n = get_kernel_bias_res( n, weights, branch2c_name, 3) print('branch2c:kernel size: ', branch2c_w.shape, 'bias size', branch2c_b.shape) if sub_kind == 'a': out_chan1 = out_chan_t else: out_chan1 = out_chan_t2 current = utils.bottleneck_unit(current, res_param, branch1_w, branch1_b, branch2a_w, branch2a_b, branch2b_w, branch2b_b, branch2c_w, branch2c_b, out_chan1, out_chan2, False, False, name) print('layer output ', current.shape) net[name] = current current = utils.avg_pool(current, 7, 1) print('resnet final sz ', current.shape) #return net return current
def encoder(dataset, train_mode): with tf.variable_scope("Encoder"): with tf.name_scope("enc_conv1") as scope: W_conv1 = utils.weight_variable_xavier_initialized([3, 3, 3, 32], name="W_conv1") b_conv1 = utils.bias_variable([32], name="b_conv1") h_conv1 = utils.conv2d_strided(dataset, W_conv1, b_conv1) h_bn1 = utils.batch_norm(h_conv1, 32, train_mode, scope="conv1_bn") h_relu1 = tf.nn.relu(h_bn1) with tf.name_scope("enc_conv2") as scope: W_conv2 = utils.weight_variable_xavier_initialized([3, 3, 32, 64], name="W_conv2") b_conv2 = utils.bias_variable([64], name="b_conv2") h_conv2 = utils.conv2d_strided(h_relu1, W_conv2, b_conv2) h_bn2 = utils.batch_norm(h_conv2, 64, train_mode, scope="conv2_bn") h_relu2 = tf.nn.relu(h_bn2) with tf.name_scope("enc_conv3") as scope: W_conv3 = utils.weight_variable_xavier_initialized([3, 3, 64, 128], name="W_conv3") b_conv3 = utils.bias_variable([128], name="b_conv3") h_conv3 = utils.conv2d_strided(h_relu2, W_conv3, b_conv3) h_bn3 = utils.batch_norm(h_conv3, 128, train_mode, scope="conv3_bn") h_relu3 = tf.nn.relu(h_bn3) with tf.name_scope("enc_conv4") as scope: W_conv4 = utils.weight_variable_xavier_initialized( [3, 3, 128, 256], name="W_conv4") b_conv4 = utils.bias_variable([256], name="b_conv4") h_conv4 = utils.conv2d_strided(h_relu3, W_conv4, b_conv4) h_bn4 = utils.batch_norm(h_conv4, 256, train_mode, scope="conv4_bn") h_relu4 = tf.nn.relu(h_bn4) with tf.name_scope("enc_conv5") as scope: W_conv5 = utils.weight_variable_xavier_initialized( [3, 3, 256, 512], name="W_conv5") b_conv5 = utils.bias_variable([512], name="b_conv5") h_conv5 = utils.conv2d_strided(h_relu4, W_conv5, b_conv5) h_bn5 = utils.batch_norm(h_conv5, 512, train_mode, scope="conv5_bn") h_relu5 = tf.nn.relu(h_bn5) with tf.name_scope("enc_fc") as scope: image_size = IMAGE_SIZE // 32 h_relu5_flatten = tf.reshape(h_relu5, [-1, image_size * image_size * 512]) W_fc = utils.weight_variable([image_size * image_size * 512, 1024], name="W_fc") b_fc = utils.bias_variable([1024], name="b_fc") encoder_val = tf.matmul(h_relu5_flatten, W_fc) + b_fc return encoder_val