def style_transfer(
content=None,
content_dir=None,
content_size=512,
style=None,
style_dir=None,
style_size=512,
crop=None,
preserve_color=None,
alpha=1.0,
style_interp_weights=None,
mask=None,
output_dir='/output',
save_ext='jpg',
gpu=0,
vgg_weights='/floyd_models/vgg19_weights_normalized.h5',
decoder_weights='/floyd_models/decoder_weights.h5',
tf_checkpoint_dir=None):
assert bool(content) != bool(content_dir), 'Either content or content_dir should be given'
assert bool(style) != bool(style_dir), 'Either style or style_dir should be given'
if not os.path.exists(output_dir):
print('Creating output dir at', output_dir)
os.mkdir(output_dir)
# Assume that it is either an h5 file or a name of a TensorFlow checkpoint
# NOTE: For now, artificially switching off pretrained h5 weights
decoder_in_h5 = False
# decoder_weights.endswith('.h5')
if content:
content_batch = [content]
else:
assert mask is None, 'For spatial control use the --content option'
content_batch = extract_image_names_recursive(content_dir)
if style:
style = style.split(',')
if mask:
assert len(style) == 2, 'For spatial control provide two style images'
style_batch = [style]
elif len(style) > 1: # Style blending
if not style_interp_weights:
# by default, all styles get equal weights
style_interp_weights = np.array([1.0/len(style)] * len(style))
else:
# normalize weights so that their sum equals to one
style_interp_weights = [float(w) for w in style_interp_weights.split(',')]
style_interp_weights = np.array(style_interp_weights)
style_interp_weights /= np.sum(style_interp_weights)
assert len(style) == len(style_interp_weights), """--style and --style_interp_weights must have the same number of elements"""
style_batch = [style]
else:
style_batch = style
else:
assert mask is None, 'For spatial control use the --style option'
style_batch = extract_image_names_recursive(style_dir)
print('Number of content images:', len(content_batch))
print('Number of style images:', len(style_batch))
if gpu >= 0:
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu)
data_format = 'channels_first'
else:
os.environ['CUDA_VISIBLE_DEVICES'] = ''
data_format = 'channels_last'
image, content, style, target, encoder, decoder, otherLayers = _build_graph(vgg_weights,
decoder_weights if decoder_in_h5 else None, alpha, data_format=data_format)
with tf.Session() as sess:
if decoder_in_h5:
sess.run(tf.global_variables_initializer())
elif tf_checkpoint_dir is not None: # Some checkpoint was provided
saver = tf.train.Saver()
saver.restore(sess, os.path.join(tf_checkpoint_dir, 'adain-final'))
for content_path, style_path in product(content_batch, style_batch):
content_name = get_filename(content_path)
content_image = load_image(content_path, content_size, crop=True)
if isinstance(style_path, list): # Style blending/Spatial control
style_paths = style_path
style_name = '_'.join(map(get_filename, style_paths))
# Gather all style images in one numpy array in order to get
# their activations in one pass
style_images = None
for i, style_path in enumerate(style_paths):
style_image = load_image(style_path, style_size, crop)
if preserve_color:
style_image = coral(style_image, content_image)
style_image = prepare_image(style_image)
if style_images is None:
shape = tuple([len(style_paths)]) + style_image.shape
style_images = np.empty(shape)
assert style_images.shape[1:] == style_image.shape, """Style images must have the same shape"""
style_images[i] = style_image
style_features = sess.run(encoder, feed_dict={
image: style_images
})
content_image = prepare_image(content_image)
content_feature = sess.run(encoder, feed_dict={
image: content_image[np.newaxis,:]
})
if mask:
# For spatial control, extract foreground and background
# parts of the content using the corresponding masks,
# run them individually through AdaIN then combine
if data_format == 'channels_first':
_, c, h, w = content_feature.shape
content_view_shape = (c, -1)
mask_shape = lambda mask: (c, len(mask), 1)
mask_slice = lambda mask: (slice(None),mask)
else:
_, h, w, c = content_feature.shape
content_view_shape = (-1, c)
mask_shape = lambda mask: (1, len(mask), c)
mask_slice = lambda mask: (mask,slice(None))
mask = load_mask(mask, h, w).reshape(-1)
fg_mask = np.flatnonzero(mask == 1)
bg_mask = np.flatnonzero(mask == 0)
content_feat_view = content_feature.reshape(content_view_shape)
content_feat_fg = content_feat_view[mask_slice(fg_mask)].reshape(mask_shape(fg_mask))
content_feat_bg = content_feat_view[mask_slice(bg_mask)].reshape(mask_shape(bg_mask))
style_feature_fg = style_features[0]
style_feature_bg = style_features[1]
target_feature_fg = sess.run(target, feed_dict={
content: content_feat_fg[np.newaxis,:],
style: style_feature_fg[np.newaxis,:]
})
target_feature_fg = np.squeeze(target_feature_fg)
target_feature_bg = sess.run(target, feed_dict={
content: content_feat_bg[np.newaxis,:],
style: style_feature_bg[np.newaxis,:]
})
target_feature_bg = np.squeeze(target_feature_bg)
target_feature = np.zeros_like(content_feat_view)
target_feature[mask_slice(fg_mask)] = target_feature_fg
target_feature[mask_slice(bg_mask)] = target_feature_bg
target_feature = target_feature.reshape(content_feature.shape)
else:
# For style blending, get activations for each style then
# take a weighted sum.
target_feature = np.zeros(content_feature.shape)
for style_feature, weight in zip(style_features, style_interp_weights):
target_feature += sess.run(target, feed_dict={
content: content_feature,
style: style_feature[np.newaxis,:]
}) * weight
else:
# NOTE: This is the part we care about, if only 1 style image is provided.
style_name = get_filename(style_path)
style_image = load_image(style_path, style_size, crop=True) # This only gives us square crop
style_image = center_crop_np(style_image) # Actually crop the center out
if preserve_color:
style_image = coral(style_image, content_image)
style_image = prepare_image(style_image, True, data_format)
content_image = prepare_image(content_image, True, data_format)
# Extract other layers
conv3_1_layer, conv4_1_layer = otherLayers
style_feature, conv3_1_out_style, conv4_1_out_style = sess.run([encoder, conv3_1_layer, conv4_1_layer], feed_dict={
image: style_image[np.newaxis,:]
})
content_feature = sess.run(encoder, feed_dict={
image: content_image[np.newaxis,:]
})
target_feature = sess.run(target, feed_dict={
content: content_feature,
style: style_feature
})
output = sess.run(decoder, feed_dict={
content: content_feature,
target: target_feature,
style: style_feature
})
# Grab the relevant layer outputs to see what's being minimized.
conv3_1_out_output, conv4_1_out_output = sess.run([conv3_1_layer, conv4_1_layer], feed_dict={
image: output
})
filename = '%s_stylized_%s.%s' % (content_name, style_name, save_ext)
filename = os.path.join(output_dir, filename)
save_image(filename, output[0], data_format=data_format)
print('Output image saved at', filename)
# TODO: Change these layers.
layersToViz = [conv3_1_out_style, conv4_1_out_style, conv3_1_out_output, conv4_1_out_output]
def initialize_model():
global vgg
global encoder
global decoder
global target
global weighted_target
global image
global content
global style
global persistent_session
global data_format
alpha = 1.0
graph = tf.Graph()
# build the detection model graph from the saved model protobuf
with graph.as_default():
image = tf.placeholder(shape=(None, 3, None, None), dtype=tf.float32)
content = tf.placeholder(shape=(1, 512, None, None), dtype=tf.float32)
style = tf.placeholder(shape=(1, 512, None, None), dtype=tf.float32)
target = adain(content, style, data_format=data_format)
weighted_target = target * alpha + (1 - alpha) * content
with open_weights('models/vgg19_weights_normalized.h5') as w:
vgg = build_vgg(image, w, data_format=data_format)
encoder = vgg['conv4_1']
with open_weights('models/decoder_weights.h5') as w:
decoder = build_decoder(weighted_target, w, trainable=False, data_format=data_format)
# the default session behavior is to consume the entire GPU RAM during inference!
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.12
# the persistent session across function calls exposed to external code interfaces
persistent_session = tf.Session(graph=graph, config=config)
persistent_session.run(tf.global_variables_initializer())
print('Initialized model')
while True:
with ai_integration.get_next_input(inputs_schema={
"style": {
"type": "image"
},
"content": {
"type": "image"
},
}) as inputs_dict:
# only update the negative fields if we reach the end of the function - then update successfully
result_data = {"content-type": 'text/plain',
"data": None,
"success": False,
"error": None}
print('Starting inference')
start = time.time()
content_size = 512
style_size = 512
crop = False
preserve_color = False
content_image = load_image(io.BytesIO(inputs_dict['content']), content_size, crop)
style_image = load_image(io.BytesIO(inputs_dict['style']), style_size, crop)
if preserve_color:
style_image = coral(style_image, content_image)
style_image = prepare_image(style_image)
content_image = prepare_image(content_image)
style_feature = persistent_session.run(encoder, feed_dict={
image: style_image[np.newaxis, :]
})
content_feature = persistent_session.run(encoder, feed_dict={
image: content_image[np.newaxis, :]
})
target_feature = persistent_session.run(target, feed_dict={
content: content_feature,
style: style_feature
})
output = persistent_session.run(decoder, feed_dict={
content: content_feature,
target: target_feature
})
output_img_bytes = save_image_in_memory(output[0], data_format=data_format)
result_data["content-type"] = 'image/jpeg'
result_data["data"] = output_img_bytes
result_data["success"] = True
result_data["error"] = None
print('Finished inference and it took ' + str(time.time() - start))
ai_integration.send_result(result_data)
