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
