Style transfer algo gpu

src/custom_vgg19V2.py

@@ -0,0 +1,140 @@
+import os
+import tensorflow as tf
+import numpy as np
+import inspect
+import urllib.request
+
+VGG_MEAN = [103.939, 116.779, 123.68]
+data = None
+dir_path = os.path.dirname(os.path.realpath(__file__))
+weights_name = dir_path + "/../lib/weights/vgg19.npy"
+weights_url = "https://www.dropbox.com/s/68opci8420g7bcl/vgg19.npy?dl=1"
+
+
+class Vgg19:
+    def __init__(self, vgg19_npy_path=None):
+        global data
+
+        if vgg19_npy_path is None:
+            path = inspect.getfile(Vgg19)
+            path = os.path.abspath(os.path.join(path, os.pardir))
+            path = os.path.join(path, weights_name)
+
+            if os.path.exists(path):
+                vgg19_npy_path = path
+            else:
+                print("VGG19 weights were not found in the project directory")
+
+                answer = 0
+                while answer is not 'y' and answer is not 'N':
+                    answer = input("Would you like to download the 548 MB file? [y/N] ").replace(" ", "")
+
+                # Download weights if yes, else exit the program
+                if answer == 'y':
+                    print("Downloading. Please be patient...")
+                    urllib.request.urlretrieve(weights_url, weights_name)
+                    vgg19_npy_path = path
+                elif answer == 'N':
+                    print("Exiting the program..")
+                    exit(0)
+
+        if data is None:
+            data = np.load(vgg19_npy_path, encoding='latin1', allow_pickle=True)
+            self.data_dict = data.item()
+            print("VGG19 weights loaded")
+
+        else:
+            self.data_dict = data.item()
+
+    def build(self, rgb, shape):
+        rgb_scaled = rgb * 255.0
+        num_channels = shape[2]
+        channel_shape = shape
+        channel_shape[2] = 1
+
+        # Convert RGB to BGR
+        red, green, blue = tf.split(axis=3, num_or_size_splits=3, value=rgb_scaled)
+
+        assert red.get_shape().as_list()[1:] == channel_shape
+        assert green.get_shape().as_list()[1:] == channel_shape
+        assert blue.get_shape().as_list()[1:] == channel_shape
+
+        bgr = tf.concat(axis=3, values=[
+            blue - VGG_MEAN[0],
+            green - VGG_MEAN[1],
+            red - VGG_MEAN[2],
+        ])
+
+        shape[2] = num_channels
+        assert bgr.get_shape().as_list()[1:] == shape
+
+        self.conv1_1 = self.conv_layer(bgr, "conv1_1")
+        self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")
+        self.pool1 = self.avg_pool(self.conv1_2, 'pool1')
+
+        self.conv2_1 = self.conv_layer(self.pool1, "conv2_1")
+        self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2")
+        self.pool2 = self.avg_pool(self.conv2_2, 'pool2')
+
+        self.conv3_1 = self.conv_layer(self.pool2, "conv3_1")
+        self.conv3_2 = self.conv_layer(self.conv3_1, "conv3_2")
+        self.conv3_3 = self.conv_layer(self.conv3_2, "conv3_3")
+        self.conv3_4 = self.conv_layer(self.conv3_3, "conv3_4")
+        self.pool3 = self.avg_pool(self.conv3_4, 'pool3')
+
+        self.conv4_1 = self.conv_layer(self.pool3, "conv4_1")
+        self.conv4_2 = self.conv_layer(self.conv4_1, "conv4_2")
+        self.conv4_3 = self.conv_layer(self.conv4_2, "conv4_3")
+        self.conv4_4 = self.conv_layer(self.conv4_3, "conv4_4")
+        self.pool4 = self.avg_pool(self.conv4_4, 'pool4')
+
+        self.conv5_1 = self.conv_layer(self.pool4, "conv5_1")
+        self.conv5_2 = self.conv_layer(self.conv5_1, "conv5_2")
+        self.conv5_3 = self.conv_layer(self.conv5_2, "conv5_3")
+        self.conv5_4 = self.conv_layer(self.conv5_3, "conv5_4")
+
+        self.data_dict = None
+
+    def avg_pool(self, bottom, name):
+        return tf.nn.avg_pool2d(input=bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
+
+    def max_pool(self, bottom, name):
+        return tf.nn.max_pool2d(input=bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
+
+    def conv_layer(self, bottom, name):
+        with tf.compat.v1.variable_scope(name):
+            filt = self.get_conv_filter(name)
+
+            conv = tf.nn.conv2d(input=bottom, filters=filt, strides=[1, 1, 1, 1], padding='SAME')
+
+            conv_biases = self.get_bias(name)
+            bias = tf.nn.bias_add(conv, conv_biases)
+
+            relu = tf.nn.relu(bias)
+            return relu
+
+    def fc_layer(self, bottom, name):
+        with tf.compat.v1.variable_scope(name):
+            shape = bottom.get_shape().as_list()
+            dim = 1
+            for d in shape[1:]:
+                dim *= d
+            x = tf.reshape(bottom, [-1, dim])
+
+            weights = self.get_fc_weight(name)
+            biases = self.get_bias(name)
+
+            # Fully connected layer. Note that the '+' operation automatically
+            # broadcasts the biases.
+            fc = tf.nn.bias_add(tf.matmul(x, weights), biases)
+
+            return fc
+
+    def get_conv_filter(self, name):
+        return tf.constant(self.data_dict[name][0], name="filter")
+
+    def get_bias(self, name):
+        return tf.constant(self.data_dict[name][1], name="biases")
+
+    def get_fc_weight(self, name):
+        return tf.constant(self.data_dict[name][0], name="weights")

src/report.txt

@@ -0,0 +1,26 @@
+TensorFlow 2.0 Upgrade Script
+-----------------------------
+Converted 1 files
+Detected 0 issues that require attention
+--------------------------------------------------------------------------------
+================================================================================
+Detailed log follows:
+
+================================================================================
+--------------------------------------------------------------------------------
+Processing file 'custom_vgg19.py'
+ outputting to 'custom_vgg19V2.py'
+--------------------------------------------------------------------------------
+
+99:15: INFO: Added keywords to args of function 'tf.nn.avg_pool'
+99:15: INFO: Renamed keyword argument for tf.nn.avg_pool from value to input
+99:15: INFO: Renamed 'tf.nn.avg_pool' to 'tf.nn.avg_pool2d'
+102:15: INFO: Added keywords to args of function 'tf.nn.max_pool'
+102:15: INFO: Renamed keyword argument for tf.nn.max_pool from value to input
+102:15: INFO: Renamed 'tf.nn.max_pool' to 'tf.nn.max_pool2d'
+105:13: INFO: Renamed 'tf.variable_scope' to 'tf.compat.v1.variable_scope'
+108:19: INFO: Added keywords to args of function 'tf.nn.conv2d'
+108:19: INFO: Renamed keyword argument for tf.nn.conv2d from filter to filters
+117:13: INFO: Renamed 'tf.variable_scope' to 'tf.compat.v1.variable_scope'
+--------------------------------------------------------------------------------
+

src/style_transfer.py

@@ -8,6 +8,9 @@
 import numpy as np
 import os
 import tensorflow as tf
+
+# import tensorflow.compat.v1 as tf
+
 import time
 import utils
 from functools import reduce
@@ -134,8 +137,8 @@ def parse_args():
     STYLE_PATH = os.path.realpath(args.style)
     OUT_PATH = os.path.realpath(args.out)
 
-
-with tf.Session() as sess:
+with tf.compat.v1.Session() as sess:
+# with tf.Session() as sess:
     parse_args()
 
     # Initialize and process photo image to be used for our content