def init_net(self):
## dropout 的保留率
self.keep_probability = tf.placeholder(tf.float32,
name="keep_probabilty")
## 原始图像的向量
self.image = tf.placeholder(tf.float32,
shape=[None, None, None, 3],
name="input_image") # 1.batch大小，2，H,3,W，4，
## 原始图像对应的标注图像的向量
self.annotation = tf.placeholder(tf.int32,
shape=[None, None, None, 1],
name="annotation")
## 输入原始图像向量、保留率，得到预测的标注图像和随后一层的网络输出 logits：未归一化的概率
self.pred_annotation, self.logits = inference(self.image,
self.keep_probability)
self.trainable_var = tf.trainable_variables()
self.sess = tf.Session()
print("Setting up Saver...")
self.saver = tf.train.Saver() # 保存
self.sess.run(tf.global_variables_initializer()) # 初始化所有变量
## 加载之前的checkpoint
MODEL_PATH = utils.get_config('MODEL_PATH')
self.ckpt = tf.train.get_checkpoint_state(MODEL_PATH)
if self.ckpt and self.ckpt.model_checkpoint_path:
self.saver.restore(self.sess, self.ckpt.model_checkpoint_path)
print("Model restored...")
def inference(image, keep_prob): #预测
"""
Semantic segmentation network definition
:param image: input image. Should have values in range 0-255
:param keep_prob:
:return:
"""
## 获取训练好的vgg部分的model
print("setting up vgg initialized conv layers ...")
model_data = scipy.io.loadmat(utils.get_config('VGG_PATH'))
mean = model_data['normalization'][0][0][0]
mean_pixel = np.mean(mean, axis=(0, 1))
weights = np.squeeze(model_data['layers']) #压缩维度
## 将图像的向量值都减去平均像素值，进行 normalization
processed_image = utils.process_image(image, mean_pixel) #预处理图像
with tf.variable_scope("inference"):
## 计算前5层vgg网络的输出结果
image_net = vgg_net(weights, processed_image)
conv_final_layer = image_net["conv5_3"]
## pool1 size缩小2倍
## pool2 size缩小4倍
## pool3 size缩小8倍
## pool4 size缩小16倍
## pool5 size缩小32倍
pool5 = utils.max_pool_2x2(conv_final_layer)
## 初始化第6层的w、b
## 7*7 卷积核的视野很大
W6 = utils.weight_variable([7, 7, 512, 4096], name="W6")
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)
## 在第6层没有进行池化，所以经过第6层后 size缩小仍为32倍
## 初始化第7层的w、b
W7 = utils.weight_variable([1, 1, 4096, 4096], name="W7")
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)
## 在第7层没有进行池化，所以经过第7层后 size缩小仍为32倍
## 初始化第8层的w、b
## 输出维度为NUM_OF_CLASSESS
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)
# annotation_pred1 = tf.argmax(conv8, dimension=3, name="prediction1")
# now to upscale to actual image size
## 开始将size提升为图像原始尺寸
deconv_shape1 = image_net["pool4"].get_shape()
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")
## 对第8层的结果进行反卷积(上采样),通道数也由NUM_OF_CLASSESS变为第4层的通道数
conv_t1 = utils.conv2d_transpose_strided(conv8,
W_t1,
b_t1,
output_shape=tf.shape(
image_net["pool4"]))
## 对应论文原文中的"2× upsampled prediction + pool4 prediction"
fuse_1 = tf.add(conv_t1, image_net["pool4"], name="fuse_1")
deconv_shape2 = image_net["pool3"].get_shape()
## 对上一层上采样的结果进行反卷积(上采样),通道数也由上一层的通道数变为第3层的通道数
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")
conv_t2 = utils.conv2d_transpose_strided(fuse_1,
W_t2,
b_t2,
output_shape=tf.shape(
image_net["pool3"]))
## 对应论文原文中的"2× upsampled prediction + pool3 prediction"
fuse_2 = tf.add(conv_t2, image_net["pool3"], name="fuse_2")
## 原始图像的height、width和通道数
shape = tf.shape(image)
## 既形成一个列表，形式为[height, width, in_channels, out_channels]
deconv_shape3 = tf.stack(
[shape[0], shape[1], shape[2], NUM_OF_CLASSESS])
W_t3 = utils.weight_variable(
[16, 16, NUM_OF_CLASSESS, deconv_shape2[3].value], name="W_t3")
b_t3 = utils.bias_variable([NUM_OF_CLASSESS], name="b_t3")
## 再进行一次反卷积，将上一层的结果转化为和原始图像相同size、通道数为分类数的形式数据
conv_t3 = utils.conv2d_transpose_strided(fuse_2,
W_t3,
b_t3,
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
annotation_pred = tf.argmax(conv_t3, dimension=3, name="prediction")
return tf.expand_dims(annotation_pred, dim=3), conv_t3
