From a4d047893ce31a28fdaaaa31cf7a68f25ac945fd Mon Sep 17 00:00:00 2001 From: "Mohamed K. Eid" Date: Tue, 4 Jul 2017 11:37:58 -0700 Subject: [PATCH] fixed overlapping convolutions and updated README --- README.md | 12 +++++----- lib/images/content/beach.jpg | Bin 1084907 -> 168777 bytes lib/images/examples/beach-alley.jpg | Bin 27718 -> 0 bytes lib/images/examples/beach-scream.jpg | Bin 0 -> 91838 bytes lib/images/examples/drawing-alley.jpg | Bin 27366 -> 0 bytes lib/images/examples/drawing-scream.jpg | Bin 0 -> 345706 bytes lib/images/examples/nyc-alley.jpg | Bin 27409 -> 0 bytes lib/images/examples/nyc-scream.jpg | Bin 0 -> 117419 bytes lib/images/style/alley-by-the-lake.jpg | Bin 200822 -> 0 bytes lib/images/style/scream.jpg | Bin 0 -> 91708 bytes lib/images/train2014 | 1 + src/generator.py | 8 +++---- src/helpers.py | 7 ++++++ src/train.py | 29 +++++++++++-------------- src/trainer.py | 22 +++++++++++++++---- 15 files changed, 49 insertions(+), 30 deletions(-) delete mode 100644 lib/images/examples/beach-alley.jpg create mode 100644 lib/images/examples/beach-scream.jpg delete mode 100644 lib/images/examples/drawing-alley.jpg create mode 100644 lib/images/examples/drawing-scream.jpg delete mode 100644 lib/images/examples/nyc-alley.jpg create mode 100644 lib/images/examples/nyc-scream.jpg delete mode 100644 lib/images/style/alley-by-the-lake.jpg create mode 100644 lib/images/style/scream.jpg create mode 120000 lib/images/train2014 diff --git a/README.md b/README.md index affe284..5899b8f 100644 --- a/README.md +++ b/README.md @@ -25,28 +25,28 @@ For description of the generator's output, a pretrained [VGG network](https://ar - + - + - + - + @@ -54,7 +54,7 @@ For description of the generator's output, a pretrained [VGG network](https://ar ## Prerequisites * [Python 3.5](https://www.python.org/downloads/release/python-350/) -* [TensorFlow](https://www.tensorflow.org/) (>= r1.0) +* [TensorFlow](https://www.tensorflow.org/) (>= r1.2) * [scikit-image](http://scikit-image.org/docs/dev/api/skimage.html) * [NumPy](http://www.numpy.org/) @@ -106,4 +106,4 @@ python test.py --styles Descriminative model trained on image classification used for gathering descriptive statistics for the loss measure. - The weights used by the VGG network. This file is not in this repository due to its size. You must download it and place in the working directory. The program will complain and ask for you to download it with a supplied link if it does not find it. \ No newline at end of file + The weights used by the VGG network. This file is not in this repository due to its size. You must download it and place in the working directory. 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zc0b<(E>39^S%=AF5vUv5xb?18&=I#y!f%rS;}xF1rmYKra?*_v5}Z8Hn84 z4)$e(rT+ljk@B#xj;V!#up1CK{{W0Xi!O6ftD+w|Z*^>IoBHE1n5j`&niSpORP%rI z@l2#-vyw!>*-3zLzZAlc)``L#&%qsPCbvkXitAcTDA?=>2N{gRsMecVWo<V^NRwaX==d*d3qkKY?s2_P<%GB zByTK{0$DQ?0%1X@5H}-9YhVG}Rx0x3rcgsDRtAk7uNjQSe&*)eH+261l9#YYPReFh ZFj65x#fJj#d}cElfs=PFQ+{3#|JflM<%R$N literal 0 HcmV?d00001 diff --git a/lib/images/train2014 b/lib/images/train2014 new file mode 120000 index 0000000..53201e4 --- /dev/null +++ b/lib/images/train2014 @@ -0,0 +1 @@ +/home/mo/Datasets/coco/train2014/ \ No newline at end of file diff --git a/src/generator.py b/src/generator.py index 0fd15f1..89fda99 100644 --- a/src/generator.py +++ b/src/generator.py @@ -18,8 +18,8 @@ def build(self, img): self.padded = self.__pad(img, 40) self.conv1 = self.__conv_block(self.padded, maps_shape=[9, 9, 3, 32], stride=1, name='conv1') - self.conv2 = self.__conv_block(self.conv1, maps_shape=[3, 3, 32, 64], stride=2, name='conv2') - self.conv3 = self.__conv_block(self.conv2, maps_shape=[3, 3, 64, 128], stride=2, name='conv3') + self.conv2 = self.__conv_block(self.conv1, maps_shape=[2, 2, 32, 64], stride=2, name='conv2') + self.conv3 = self.__conv_block(self.conv2, maps_shape=[2, 2, 64, 128], stride=2, name='conv3') self.resid1 = self.__residual_block(self.conv3, maps_shape=[3, 3, 128, 128], stride=1, name='resid1') self.resid2 = self.__residual_block(self.resid1, maps_shape=[3, 3, 128, 128], stride=1, name='resid2') @@ -27,8 +27,8 @@ def build(self, img): self.resid4 = self.__residual_block(self.resid3, maps_shape=[3, 3, 128, 128], stride=1, name='resid4') self.resid5 = self.__residual_block(self.resid4, maps_shape=[3, 3, 128, 128], stride=1, name='resid5') - self.conv4 = self.__upsample_block(self.resid5, maps_shape=[3, 3, 64, 128], stride=2, name='conv4') - self.conv5 = self.__upsample_block(self.conv4, maps_shape=[3, 3, 32, 64], stride=2, name='conv5') + self.conv4 = self.__upsample_block(self.resid5, maps_shape=[2, 2, 64, 128], stride=2, name='conv4') + self.conv5 = self.__upsample_block(self.conv4, maps_shape=[2, 2, 32, 64], stride=2, name='conv5') self.conv6 = self.__conv_block(self.conv5, maps_shape=[9, 9, 32, 3], stride=1, name='conv6', activation=None) self.output = tf.nn.sigmoid(self.conv6) diff --git a/src/helpers.py b/src/helpers.py index 0216ce9..7473e18 100644 --- a/src/helpers.py +++ b/src/helpers.py @@ -3,15 +3,22 @@ Description: helper class containing various methods with their functions ranging from image retrieval to auxiliary math helpers """ +import logging import numpy as np import tensorflow as tf from functools import reduce import skimage import skimage.io import skimage.transform +import sys from scipy.misc import toimage +# Configure the python logger +def config_logging(): + logging.basicConfig(level=logging.INFO, stream=sys.stdout) + + # Compute the content loss given a variable image (x) and a content image (c) def get_content_loss(x, c, layer): with tf.name_scope('get_content_loss'): diff --git a/src/train.py b/src/train.py index 4c76747..120be2f 100644 --- a/src/train.py +++ b/src/train.py @@ -10,6 +10,7 @@ import argparse import generator +import helpers import logging import os import tensorflow as tf @@ -19,13 +20,15 @@ # Model Hyper Params CONTENT_LAYER = 'conv3_3' STYLE_LAYERS = {'conv1_2': .25, 'conv2_2': .25, 'conv3_3': .25, 'conv4_3': .25} -EPOCHS = 250000 -LEARNING_RATE = .0001 +assert sum(STYLE_LAYERS.values()) == 1, "Style layer weights must up to 1" +EPOCHS = 30000 +LEARNING_RATE = .001 TRAINING_DIMS = {'height': 256, 'width': 256} +RETRAIN = False # Loss term weights CONTENT_WEIGHT = 1. -STYLE_WEIGHT = 3. +STYLE_WEIGHT = .3 TV_WEIGHT = .1 # Default image paths @@ -34,29 +37,23 @@ TRAINED_MODELS_PATH = DIR_PATH + '/../lib/generators/' TRAIN_PATH = None -# Logging params +# Logging params and config PRINT_TRAINING_STATUS = True -PRINT_EVERY_N = 100 - -# Logging config -log_dir = DIR_PATH + '/../log/' -if not os.path.isdir(log_dir): - os.makedirs(log_dir) - print('Directory "%s" was created for logging.' % log_dir) -log_path = ''.join([log_dir, str(time.time()), '.log']) -logging.basicConfig(filename=log_path, level=logging.INFO) -print("Printing log to %s" % log_path) +PRINT_EVERY_N = 10 +helpers.config_logging() # Parse arguments and assign them to their respective global variables def parse_args(): - global TRAIN_PATH + global TRAIN_PATH, RETRAIN # Create flags and assign values to their respective variables parser = argparse.ArgumentParser() parser.add_argument('train', help="path to image with style to learn") + parser.add_argument('--retrain', action="store_true", help="whether or not to retrain a model") args = parser.parse_args() TRAIN_PATH = os.path.abspath(args.train) + RETRAIN = args.retrain parse_args() @@ -64,5 +61,5 @@ def parse_args(): with tf.variable_scope('generator'): gen = generator.Generator() t = trainer.Trainer(sess, gen, TRAIN_PATH, TRAINING_DIMS, PRINT_TRAINING_STATUS, PRINT_EVERY_N) - t.train(EPOCHS, LEARNING_RATE, CONTENT_LAYER, CONTENT_WEIGHT, STYLE_LAYERS, STYLE_WEIGHT, TV_WEIGHT) + t.train(EPOCHS, LEARNING_RATE, CONTENT_LAYER, CONTENT_WEIGHT, STYLE_LAYERS, STYLE_WEIGHT, TV_WEIGHT, RETRAIN) sess.close() diff --git a/src/trainer.py b/src/trainer.py index fda81c6..a2f5613 100644 --- a/src/trainer.py +++ b/src/trainer.py @@ -22,7 +22,7 @@ def __init__(self, session, net, train_path, train_dims, print_training_status=T 'out_dir': self.current_path + '/../output/', 'style_file': train_path, 'trained_generators_dir': self.current_path + '/../lib/generators/', - 'training_dir': self.current_path + '/../lib/train2014/', + 'training_dir': self.current_path + '/../lib/images/train2014/', 'training_url': 'http://msvocds.blob.core.windows.net/coco2014/train2014.zip' } self.session = session @@ -31,7 +31,7 @@ def __init__(self, session, net, train_path, train_dims, print_training_status=T self.print_training_status = print_training_status self.train_n = print_every_n - def train(self, epochs, learning_rate, content_layer, content_weight, style_layers, style_weight, tv_weight): + def train(self, epochs, learning_rate, content_layer, content_weight, style_layers, style_weight, tv_weight, retrain=False): # Check if there is training data available and initialize generator network self.__check_for_examples() @@ -58,6 +58,13 @@ def train(self, epochs, learning_rate, content_layer, content_weight, style_laye with tf.name_scope('vgg_variable'): variable_model = vgg16.Vgg16() variable_model.build(variable_img, shape=art_shape[1:]) + + # Continue from a pretrained model + if retrain: + print("tryue") + name = os.path.basename(self.paths['style_file']).replace('.jpg', '') + saver = tf.train.Saver() + saver.restore(self.session, "%s/%s/%s" % (self.paths['trained_generators_dir'], name, name)) # Loss ops with tf.name_scope('loss'): @@ -88,8 +95,9 @@ def train(self, epochs, learning_rate, content_layer, content_weight, style_laye # Populate the training data logging.info("Initializing session and loading training images..") example = self.__next_example(height=art_shape[1], width=art_shape[2]) + self.session.run(tf.local_variables_initializer()) self.session.run(tf.global_variables_initializer()) - + # Initialize threads and begin training logging.info("Begining training..") coord = tf.train.Coordinator() @@ -109,6 +117,13 @@ def train(self, epochs, learning_rate, content_layer, content_weight, style_laye if self.print_training_status and i % self.train_n == 0: logging.info("Epoch %06d | Loss %.06f" % (i, loss)) + in_path = self.current_path + '/../lib/images/content/nyc.jpg' + input_img, input_shape = helpers.load_img_to(in_path, height=self.train_height, width=self.train_width) + input_img = input_img.reshape([1] + input_shape).astype(np.float32) + path_out = self.current_path + '/../output/' + str(start_time) + '.jpg' + img = self.session.run(variable_img, feed_dict={variable_placeholder: input_img}) + helpers.render(img, path_out=path_out) + # Alert that training has been completed and print the run time elapsed = time.time() - start_time @@ -170,7 +185,6 @@ def ask_to_unzip(path): if num_training_files <= 1: ask_to_download() - # Retrieves next example image from queue # Returns a new training example def __next_example(self, height, width):

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