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vgg_before.py
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from collections import namedtuple
import tensorflow as tf
import numpy as np
from PIL import Image
from tensorflow.keras.layers import Input
class vgg16_bn(tf.keras.Model):
def __init__(self):
super(vgg16_bn, self).__init__()
self.VGG_MEAN = [103.939, 116.779, 123.68]
self.input_layer = tf.keras.layers.Input(shape=(768,768,3))
# Block 1
self.conv1_1 = tf.keras.layers.Conv2D(filters=64,
kernel_size=(3, 3),
activation='relu',
padding='same',strides=[1, 1],
name='conv1_1')
self.conv1_2 = tf.keras.layers.Conv2D(filters=64,
kernel_size=(3, 3),
activation='relu',
padding='same',
name='conv1_2')
self.pool1_1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool1_1')
# Block 2
self.conv2_1 = tf.keras.layers.Conv2D(filters=128,
kernel_size=(3, 3),
activation='relu',
padding='same',
name='conv2_1')
self.conv2_2 = tf.keras.layers.Conv2D(filters=128,
kernel_size=(3, 3),
activation='relu',
padding='same',
name='conv2_2')
self.pool2_1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool2_1')
# Block 3
self.conv3_1 = tf.keras.layers.Conv2D(filters=256,
kernel_size=(3, 3),
activation='relu',
padding='same',
name='conv3_1')
self.conv3_2 = tf.keras.layers.Conv2D(filters=256,
kernel_size=(3, 3),
activation='relu',
padding='same',
name='conv3_2')
self.conv3_3 = tf.keras.layers.Conv2D(filters=256,
kernel_size=(3, 3),
activation='relu',
padding='same',
name='conv3_3')
self.pool3_1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool3_1')
# Block 4
self.conv4_1 = tf.keras.layers.Conv2D(filters=512,
kernel_size=(3, 3),
activation='relu',
padding='same',
name='conv4_1')
self.conv4_2 = tf.keras.layers.Conv2D(filters=512,
kernel_size=(3, 3),
activation='relu',
padding='same',
name='conv4_2')
self.conv4_3 = tf.keras.layers.Conv2D(filters=512,
kernel_size=(3, 3),
activation='relu',
padding='same',
name='conv4_3')
self.pool4_1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool4_1')
# Block 5
self.conv5_1 = tf.keras.layers.Conv2D(filters=512,
kernel_size=(3, 3),
activation='relu',
padding='same',
name='conv5_1')
self.conv5_2 = tf.keras.layers.Conv2D(filters=512,
kernel_size=(3, 3),
activation='relu',
padding='same',
name='conv5_2')
self.conv5_3 = tf.keras.layers.Conv2D(filters=512,
kernel_size=(3, 3),
activation='relu',
padding='same',
name='conv5_3')
#self.pool5_1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool5_1')
# fc6, fc7 without atrous conv
self.pool6_1 = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(1, 1),padding="same", name='pool6_1')
self.conv6_1 = tf.keras.layers.Conv2D(filters=1024,
kernel_size=(3, 3),
padding="same",
dilation_rate=6,
name='conv6_1')
self.conv6_2 = tf.keras.layers.Conv2D(filters=1024,
kernel_size=(1, 1),
name='conv6_2')
#self.flatten = tf.keras.layers.Flatten()
#self.fc6 = tf.keras.layers.Dense(units=4096, use_bias=True, name='fc6', activation='relu')
#self.fc7 = tf.keras.layers.Dense(units=4096, use_bias=True, name='fc7', activation='relu')
#self.fc8 = tf.keras.layers.Dense(units=1000, use_bias=True, name='fc8', activation=None)
def call(self, inputs):
# define input layer
# print(self.pool1_1)
#x = self.input_layer(inputs)
#red, green, blue = tf.split(axis=3, num_or_size_splits=3, value=inputs)
#bgr = tf.concat(axis=3, values=[blue - self.VGG_MEAN[0], green - self.VGG_MEAN[1], red - self.VGG_MEAN[2]])
x = self.conv1_1(inputs)
x = self.conv1_2(x)
x = self.pool1_1(x)
x = self.conv2_1(x)
x = self.conv2_2(x)
x_relu2_2 = x
x = self.pool2_1(x)
x = self.conv3_1(x)
x = self.conv3_2(x)
x = self.conv3_3(x)
x_relu3_2 = x
x = self.pool3_1(x)
x = self.conv4_1(x)
x = self.conv4_2(x)
x = self.conv4_3(x)
x_relu4_3 = x
x = self.pool4_1(x)
x = self.conv5_1(x)
x = self.conv5_2(x)
x = self.conv5_3(x)
x_relu5_3 = x
#x = self.pool5_1(x)
x = self.pool6_1(x)
x = self.conv6_1(x)
x = self.conv6_2(x)
x_fc7 = x
#x = self.fc8(x)
# #prob = tf.nn.softmax(x)
# print(x_relu2_2.shape)
# print(x_relu3_2.shape)
# print(x_relu4_3.shape)
# print(x_relu5_3.shape)
# print(x_fc7.shape)
vgg_outputs = namedtuple("VggOutputs", ['fc7', 'relu5_3', 'relu4_3', 'relu3_2', 'relu2_2'])
out = vgg_outputs(x_fc7, x_relu5_3, x_relu4_3, x_relu3_2, x_relu2_2)
return out
if __name__ == '__main__':
image = Image.open("./data/test.jpg")
image = image.resize([768, 768])
image = np.array(image)
image_data = image.astype(np.float32)
model = vgg16_bn()
inputs = tf.random.normal(shape=[1, 768, 768, 3])
print(image_data.shape)
x_1 = model(inputs=inputs)
#x_1 = model(inputs=np.expand_dims(image_data, 0))
# Load weighs
weighs = np.load("./pretrain/vgg16.npy", encoding='latin1',allow_pickle=True).item()
for layer_name in weighs.keys():
#print(layer_name)
layer = model.get_layer(layer_name)
#print(type(weighs[layer_name]))
layer.set_weights(weighs[layer_name])
print(x_1.shape)
print("##")
#print(x_2.shape)
model.summary()