def main():
if not os.path.exists(a.output_dir):
os.makedirs(a.output_dir)
input = tf.placeholder(tf.string, shape=[1])
key = tf.placeholder(tf.string, shape=[1])
in_data = tf.decode_base64(input[0])
img = tf.image.decode_png(in_data)
img = tf.image.rgb_to_grayscale(img)
out_data = tf.image.encode_png(img)
output = tf.convert_to_tensor([tf.encode_base64(out_data)])
variable_to_allow_model_saving = tf.Variable(1, dtype=tf.float32)
inputs = {
"key": key.name,
"input": input.name
}
tf.add_to_collection("inputs", json.dumps(inputs))
outputs = {
"key": tf.identity(key).name,
"output": output.name,
}
tf.add_to_collection("outputs", json.dumps(outputs))
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init_op)
saver = tf.train.Saver()
saver.export_meta_graph(filename=os.path.join(a.output_dir, "export.meta"))
saver.save(sess, os.path.join(a.output_dir, "export"), write_meta_graph=False)
print("exported example model to %s" % a.output_dir)
import tempfile
import tensorflow as tf
'''Tensors'''
tf.enable_eager_execution()
print(tf.add(1, 2))
print(tf.add([1, 2], [3, 4]))
print(tf.square(5))
print(tf.reduce_sum([1, 2, 3]))
print(tf.encode_base64("hello world"))
# Operator overloading is also supported
print(tf.square(2) + tf.square(3))
x = tf.matmul([[1]], [[2, 3]])
print(x)
print(x.shape)
print(x.dtype)
'''Numpy Compatability'''
import numpy as np
ndarray = np.ones([3, 3])
print("TensorFlow operations convert numpy arrays to Tensors automatically")
tensor = tf.multiply(ndarray, 42)
print(tensor)
def run(target, is_chief, job_name, a):
output_dir = "./export"
if tf.__version__.split('.')[0] != "1":
raise Exception("Tensorflow version 1 required")
if a.seed is None:
a.seed = random.randint(0, 2**31 - 1)
tf.set_random_seed(a.seed)
np.random.seed(a.seed)
random.seed(a.seed)
for k, v in a._get_kwargs():
print(k, "=", v)
if a.checkpoint is None:
raise Exception("checkpoint required for test mode")
# load some options from the checkpoint
# disable these features in test mode
a.scale_size = CROP_SIZE
a.flip = False
input = tf.placeholder(tf.string, shape=[1])
input_data = tf.decode_base64(input[0])
input_image = tf.image.decode_png(input_data)
# remove alpha channel if present
input_image = tf.cond(tf.equal(tf.shape(input_image)[2], 4),
lambda: input_image[:, :, :3], lambda: input_image)
# convert grayscale to RGB
input_image = tf.cond(tf.equal(tf.shape(input_image)[2], 1),
lambda: tf.image.grayscale_to_rgb(input_image),
lambda: input_image)
input_image = tf.image.convert_image_dtype(input_image, dtype=tf.float32)
input_image.set_shape([CROP_SIZE, CROP_SIZE, 3])
batch_input = tf.expand_dims(input_image, axis=0)
with tf.variable_scope("generator"):
batch_output = model.deprocess(
model.create_generator(a.num_generator_filters,
model.preprocess(batch_input), 3))
output_image = tf.image.convert_image_dtype(batch_output,
dtype=tf.uint8)[0]
if a.output_filetype == "jpeg":
output_data = tf.image.encode_jpeg(output_image, quality=80)
else:
output_data = tf.image.encode_png(output_image)
output = tf.convert_to_tensor([tf.encode_base64(output_data)])
key = tf.placeholder(tf.string, shape=[1])
inputs = {"key": key.name, "input": input.name}
tf.add_to_collection("inputs", json.dumps(inputs))
outputs = {
"key": tf.identity(key).name,
"output": output.name,
}
tf.add_to_collection("outputs", json.dumps(outputs))
init_op = tf.global_variables_initializer()
restore_saver = tf.train.Saver()
export_saver = tf.train.Saver()
with tf.Session() as sess:
print("monitored session created.")
sess.run(init_op)
print("loading model from checkpoint")
checkpoint = tf.train.latest_checkpoint(a.checkpoint)
restore_saver.restore(sess, checkpoint)
# ready to process image
print("exporting model")
export_saver.export_meta_graph(
filename=os.path.join(output_dir, "export.meta"))
export_saver.save(sess,
os.path.join(output_dir, "export"),
write_meta_graph=False)
import tensorflow as tf
tf.enable_eager_execution()
## Calculating tensors
# tf.Tensor(3, shape=(), dtype=int32)
print("tf.add(1, 2) =", tf.add(1, 2))
# tf.Tensor([4 6], shape=(2,), dtype=int32)
print("tf.add([1, 2], [3, 4]) =", tf.add([1, 2], [3, 4]))
# tf.Tensor(25, shape=(), dtype=int32)
print("tf.square(5) =", tf.square(5))
# tf.Tensor(6, shape=(), dtype=int32)
print("tf.reduce_sum([1, 2, 3]) =", tf.reduce_sum([1, 2, 3]))
# tf.Tensor(b'aGVsbG8gd29ybGQ', shape=(), dtype=string)
print("tf.encode_base64('hello world'):", tf.encode_base64('hello world'))
# Operator overloading
# tf.Tensor(13, shape=(), dtype=int32)
print("tf.square(2) + tf.square(3) =", tf.square(2) + tf.square(3))
# Show a shape and a datatype of tensor
print("x = tf.matmul([[1]], [[2, 3]])")
x = tf.matmul([[1]], [[2, 3]])
print("Shape of x:", x.shape)
print("Data type of x:", x.dtype)
## NumPy conpatibility
import numpy as np
ndarray = np.ones([3, 3])
# array([[1., 1., 1.],
# [1., 1., 1.],
import numpy as np
from cbrain.model_diagnostics import ModelDiagnostics
from numpy import linalg as LA
import matplotlib.pyplot as plt
# Otherwise tensorflow will use ALL your GPU RAM for no reason
limit_mem()
TRAINDIR = '/local/Tom.Beucler/SPCAM_PHYS/'
DATADIR = '/project/meteo/w2w/A6/S.Rasp/SP-CAM/fluxbypass_aqua/'
PREFIX = '8col009_01_'
NNs = 'NNA0.01' # NNA0.01
import os
os.chdir('/filer/z-sv-pool12c/t/Tom.Beucler/SPCAM/CBRAIN-CAM')
print(tf.encode_base64("Loading coordinates..."))
print('Loading coordinates...')
coor = xr.open_dataset("/project/meteo/w2w/A6/S.Rasp/SP-CAM/fluxbypass_aqua/AndKua_aqua_SPCAM3.0_sp_fbp_f4.cam2.h1.0000-01-01-00000.nc",\
decode_times=False)
lat = coor.lat
lon = coor.lon
coor.close()
config_fn = '/filer/z-sv-pool12c/t/Tom.Beucler/SPCAM/CBRAIN-CAM/pp_config/8col_rad_tbeucler_local_PostProc.yml'
data_fn = '/local/Tom.Beucler/SPCAM_PHYS/8col009_01_valid.nc'
dict_lay = {
'SurRadLayer': SurRadLayer,
'MassConsLayer': MassConsLayer,
'EntConsLayer': EntConsLayer
}
def build_graph(self, data_dir, batch_size, mode):
"""Builds the VAE-GAN network.
Args:
data_dir: Locations of input data.
batch_size: Batch size of input data.
mode: Mode of the graph (TRAINING, EVAL, or PREDICT)
Returns:
The tensors used in training the model.
"""
tensors = GraphReferences()
assert batch_size > 0
self.batch_size = batch_size
if mode is PREDICT_EMBED_IN:
# Input embeddings to send through decoder/generator network.
tensors.embeddings = tf.placeholder(tf.float32,
shape=(None,
EMBEDDING_DIMENSION),
name='input')
elif mode is PREDICT_IMAGE_IN:
tensors.prediction_image = tf.placeholder(tf.string,
shape=(None, ),
name='input')
tensors.image = tf.map_fn(self.process_image,
tensors.prediction_image,
dtype=tf.float32)
if mode in (TRAIN, EVAL):
mode_string = 'train'
if mode is EVAL:
mode_string = 'validation'
tensors.image = util.read_and_decode(data_dir, batch_size,
mode_string,
self.resized_image_size,
self.crop_image_dimension,
self.center_crop)
tensors.image = tf.reshape(
tensors.image,
[-1, self.resized_image_size, self.resized_image_size, 3])
tf.summary.image('original_images', tensors.image, 1)
tensors.embeddings, y_mean, y_stddev = self.encode(tensors.image)
if mode is PREDICT_IMAGE_IN:
tensors.image = tf.reshape(
tensors.image,
[-1, self.resized_image_size, self.resized_image_size, 3])
tensors.embeddings, y_mean, _ = self.encode(tensors.image, False)
tensors.predictions = tensors.embeddings
return tensors
decoded_images = self.decode(tensors.embeddings)
if mode is TRAIN:
tf.summary.image('decoded_images', decoded_images, 1)
if mode is PREDICT_EMBED_IN:
decoded_images = self.decode(tensors.embeddings, False, True)
output_images = (decoded_images + 1.0) / 2.0
output_img = tf.image.convert_image_dtype(output_images,
dtype=tf.uint8,
saturate=True)[0]
output_data = tf.image.encode_png(output_img)
output = tf.encode_base64(output_data)
tensors.predictions = output
return tensors
tensors.dis_fake = self.discriminate(decoded_images, self.dropout)
tensors.dis_real = self.discriminate(tensors.image,
self.dropout,
reuse=True)
tensors.cost_encoder = self.loss_encoder(tensors.image, decoded_images,
y_mean, y_stddev)
tensors.cost_generator = self.loss_generator(tensors.dis_fake)
tensors.cost_discriminator = self.loss_discriminator(
tensors.dis_real, tensors.dis_fake)
if mode in (TRAIN, EVAL):
tf.summary.scalar('cost_encoder', tensors.cost_encoder)
tf.summary.scalar('cost_generator', tensors.cost_generator)
tf.summary.scalar('cost_discriminator', tensors.cost_discriminator)
tf.summary.tensor_summary('disc_fake', tensors.dis_fake)
tf.summary.tensor_summary('disc_real', tensors.dis_real)
tf.summary.scalar('mean_disc_fake',
tf.reduce_mean(tensors.dis_fake))
tf.summary.scalar('mean_disc_real',
tf.reduce_mean(tensors.dis_real))
# Cost of Decoder/Generator is VAE network cost and cost of generator
# being detected by the discriminator.
enc_weight = 1
gen_weight = 1
tensors.cost_balance = (enc_weight * tensors.cost_encoder +
gen_weight * tensors.cost_generator)
tensors.global_step = tf.Variable(0,
name='global_step',
trainable=False)
t_vars = tf.trainable_variables()
with tf.variable_scope(tf.get_variable_scope(), reuse=None):
encoder_vars = [
var for var in t_vars if var.name.startswith('enc_')
]
generator_vars = [
var for var in t_vars if var.name.startswith('gen_')
]
discriminator_vars = [
var for var in t_vars if var.name.startswith('disc_')
]
vae_vars = encoder_vars + generator_vars
# Create optimizers for each network.
tensors.encoder_optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate,
beta1=self.beta1).minimize(tensors.cost_encoder,
var_list=vae_vars,
global_step=tensors.global_step)
tensors.generator_optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate,
beta1=self.beta1).minimize(tensors.cost_balance,
var_list=vae_vars,
global_step=tensors.global_step)
tensors.discriminator_optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate,
beta1=self.beta1).minimize(tensors.cost_discriminator,
var_list=discriminator_vars,
global_step=tensors.global_step)
return tensors
def export(checkpoint, img_shape):
if img_shape is None:
img_shape = [256, 256, 3]
# placeholder for base64 string decoded to an png image
input = tf.placeholder(tf.string, shape=[1])
input_data = tf.decode_base64(input[0])
input_image = tf.image.decode_png(input_data)
# remove alpha channel if present
input_image = tf.cond(tf.equal(tf.shape(input_image)[2], 4),
lambda: input_image[:, :, :3], lambda: input_image)
# convert grayscale to RGB
input_image = tf.cond(tf.equal(tf.shape(input_image)[2], 1),
lambda: tf.image.grayscale_to_rgb(input_image),
lambda: input_image)
input_image = tf.image.convert_image_dtype(input_image, dtype=tf.float32)
input_image.set_shape(img_shape)
# expected shape is (1, img_shape) because of batches
batch_input = tf.expand_dims(input_image, axis=0)
# create network
batch_output = transform.net(batch_input)
# clip RGB values to the allowed range and cast to uint8
batch_output = tf.clip_by_value(batch_output, 0, 255)
batch_output = tf.bitcast(tf.cast(batch_output, tf.int8), tf.uint8)
output_data = tf.image.encode_png(batch_output[0])
output = tf.convert_to_tensor([tf.encode_base64(output_data)])
# save inputs and outputs to collection
key = tf.placeholder(tf.string, shape=[1])
inputs = {"key": key.name, "input": input.name}
tf.add_to_collection("inputs", json.dumps(inputs))
outputs = {
"key": tf.identity(key).name,
"output": output.name,
}
tf.add_to_collection("outputs", json.dumps(outputs))
init_op = tf.global_variables_initializer()
restore_saver = tf.train.Saver()
export_saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
if os.path.isdir(checkpoint):
ckpt = tf.train.get_checkpoint_state(checkpoint)
if ckpt and ckpt.model_checkpoint_path:
restore_saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise Exception("No checkpoint found...")
else:
restore_saver.restore(sess, checkpoint)
print("exporting model")
export_saver.export_meta_graph(
filename=os.path.join(a.export, "export.meta"))
export_saver.save(sess,
os.path.join(a.export, "export"),
write_meta_graph=False)
return
# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
tf.enable_eager_execution()
print(tf.add(17, 2))
print(tf.add([11, 21, 3, 4],
[23, 41, 8, 6])) ###[11+23, 21+41, 3+8, 4+6]=[34, 62, 11, 10]
print(tf.square(8)) ##8*8=64
print(tf.reduce_sum([1, 2, 3, 4, 5])) ###it is summation of 1+2+3+4+5
print(tf.encode_base64("The first program is to print hello world"))
# Operator overloading is also supported
print(tf.square(4) - tf.square(3) + tf.abs(-5))
x = tf.matmul([[1, 2, 5]],
[[2, 5, 3], [2, 4, 7], [3, 6, 5]
]) ### 1x3 matrix multiple 3x3 matrix is 1x3 matrix
y = tf.matmul([[2, 4, 5], [1, 2, 5]],
[[12, 15, 13], [20, 4, 17], [13, 3, 8]
]) ### 2x3 matrix multiple 3x3 matrix is 2x3 matrix
print(x.shape)
print(x.dtype)
print(y.shape)
print(y.dtype)
ndarray = np.ones([4, 3])
print("A TensorFlow operation will convert numpy array to a tensor.")
def main():
#总的几时开始
mainStart = time.clock()
#add by Tony
step1Start = time.time()
if a.seed is None:
a.seed = random.randint(0, 2**31 - 1)
tf.set_random_seed(a.seed)
np.random.seed(a.seed)
random.seed(a.seed)
if not os.path.exists(a.output_dir):
os.makedirs(a.output_dir)
if a.mode == "test" or a.mode == "export":
if a.checkpoint is None:
raise Exception("checkpoint required for test mode")
# load some options from the checkpoint
#从checkpoint取一些选项
options = {"which_direction", "ngf", "ndf", "lab_colorization"}
with open(os.path.join(a.checkpoint, "options.json")) as f:
for key, val in json.loads(f.read()).items():
if key in options:
print("loaded", key, "=", val)
setattr(a, key, val)
# disable these features in test mode
a.scale_size = CROP_SIZE
a.flip = False
for k, v in a._get_kwargs():
print(k, "=", v)
with open(os.path.join(a.output_dir, "options.json"), "w") as f:
f.write(json.dumps(vars(a), sort_keys=True, indent=4))
#add by Tony
step1Stop = time.time()
if a.mode == "export":
# export the generator to a meta graph that can be imported later for standalone generation
if a.lab_colorization:
raise Exception("export not supported for lab_colorization")
input = tf.placeholder(tf.string, shape=[1])
input_data = tf.decode_base64(input[0])
input_image = tf.image.decode_png(input_data)
# remove alpha channel if present
input_image = tf.cond(tf.equal(tf.shape(input_image)[2], 4), lambda: input_image[:,:,:3], lambda: input_image)
# convert grayscale to RGB
input_image = tf.cond(tf.equal(tf.shape(input_image)[2], 1), lambda: tf.image.grayscale_to_rgb(input_image), lambda: input_image)
input_image = tf.image.convert_image_dtype(input_image, dtype=tf.float32)
input_image.set_shape([CROP_SIZE, CROP_SIZE, 3])
batch_input = tf.expand_dims(input_image, axis=0)
with tf.variable_scope("generator"):
batch_output = deprocess(create_generator(preprocess(batch_input), 3))
output_image = tf.image.convert_image_dtype(batch_output, dtype=tf.uint8)[0]
if a.output_filetype == "png":
output_data = tf.image.encode_png(output_image)
elif a.output_filetype == "jpeg":
output_data = tf.image.encode_jpeg(output_image, quality=80)
else:
raise Exception("invalid filetype")
output = tf.convert_to_tensor([tf.encode_base64(output_data)])
key = tf.placeholder(tf.string, shape=[1])
inputs = {
"key": key.name,
"input": input.name
}
tf.add_to_collection("inputs", json.dumps(inputs))
outputs = {
"key": tf.identity(key).name,
"output": output.name,
}
tf.add_to_collection("outputs", json.dumps(outputs))
init_op = tf.global_variables_initializer()
#创建一个Saver对象
restore_saver = tf.train.Saver()
#创建一个Saver对象
export_saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
print("loading model from checkpoint")
checkpoint = tf.train.latest_checkpoint(a.checkpoint)
restore_saver.restore(sess, checkpoint)
print("exporting model")
export_saver.export_meta_graph(filename=os.path.join(a.output_dir, "export.meta"))
export_saver.save(sess, os.path.join(a.output_dir, "export"), write_meta_graph=False)
return
#add by Tony
loadExamplesCreateModelStart = time.time()
#delete all files of folder
#test之前先删除掉facades/val和facades_test目录下的所有文件
def del_file(path):
ls = os.listdir(path)
for i in ls:
c_path = os.path.join(path, i)
if os.path.isdir(c_path):
del_file(c_path)
else:
os.remove(c_path)
del_file("facades/val")
del_file("facades_test")
examples = load_examples()
print("examples count = %d" % examples.count)
# inputs and targets are [batch_size, height, width, channels]
model = create_model(examples.inputs, examples.targets)
# undo colorization splitting on images that we use for display/output
if a.lab_colorization:
if a.which_direction == "AtoB":
# inputs is brightness, this will be handled fine as a grayscale image
# need to augment targets and outputs with brightness
targets = augment(examples.targets, examples.inputs)
outputs = augment(model.outputs, examples.inputs)
# inputs can be deprocessed normally and handled as if they are single channel
# grayscale images
inputs = deprocess(examples.inputs)
elif a.which_direction == "BtoA":
# inputs will be color channels only, get brightness from targets
inputs = augment(examples.inputs, examples.targets)
targets = deprocess(examples.targets)
outputs = deprocess(model.outputs)
else:
raise Exception("invalid direction")
else:
inputs = deprocess(examples.inputs)
targets = deprocess(examples.targets)
outputs = deprocess(model.outputs)
def convert(image):
if a.aspect_ratio != 1.0:
# upscale to correct aspect ratio
size = [CROP_SIZE, int(round(CROP_SIZE * a.aspect_ratio))]
image = tf.image.resize_images(image, size=size, method=tf.image.ResizeMethod.BICUBIC)
#改变图像数据的类型
return tf.image.convert_image_dtype(image, dtype=tf.uint8, saturate=True)
# reverse any processing on images so they can be written to disk or displayed to user
with tf.name_scope("convert_inputs"):
converted_inputs = convert(inputs)
with tf.name_scope("convert_targets"):
converted_targets = convert(targets)
with tf.name_scope("convert_outputs"):
converted_outputs = convert(outputs)
with tf.name_scope("encode_images"):
display_fetches = {
"paths": examples.paths,
"inputs": tf.map_fn(tf.image.encode_png, converted_inputs, dtype=tf.string, name="input_pngs"),
"targets": tf.map_fn(tf.image.encode_png, converted_targets, dtype=tf.string, name="target_pngs"),
"outputs": tf.map_fn(tf.image.encode_png, converted_outputs, dtype=tf.string, name="output_pngs"),
}
# summaries
with tf.name_scope("inputs_summary"):
tf.summary.image("inputs", converted_inputs)
with tf.name_scope("targets_summary"):
tf.summary.image("targets", converted_targets)
with tf.name_scope("outputs_summary"):
tf.summary.image("outputs", converted_outputs)
with tf.name_scope("predict_real_summary"):
tf.summary.image("predict_real", tf.image.convert_image_dtype(model.predict_real, dtype=tf.uint8))
with tf.name_scope("predict_fake_summary"):
tf.summary.image("predict_fake", tf.image.convert_image_dtype(model.predict_fake, dtype=tf.uint8))
tf.summary.scalar("discriminator_loss", model.discrim_loss)
tf.summary.scalar("generator_loss_GAN", model.gen_loss_GAN)
tf.summary.scalar("generator_loss_L1", model.gen_loss_L1)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name + "/values", var)
for grad, var in model.discrim_grads_and_vars + model.gen_grads_and_vars:
tf.summary.histogram(var.op.name + "/gradients", grad)
with tf.name_scope("parameter_count"):
parameter_count = tf.reduce_sum([tf.reduce_prod(tf.shape(v)) for v in tf.trainable_variables()])
#只保存最后一代的模型
saver = tf.train.Saver(max_to_keep=1)
logdir = a.output_dir if (a.trace_freq > 0 or a.summary_freq > 0) else None
sv = tf.train.Supervisor(logdir=logdir, save_summaries_secs=0, saver=None)
loadExamplesCreateModelStop = time.time()
#add by Tony
loadingModelStart = time.time()
with sv.managed_session() as sess:
print("parameter_count =", sess.run(parameter_count))
if a.checkpoint is not None:
print("loading model from checkpoint")
#可以使用tf.train.latest_checkpoint（）来自动获取最后一次保存的模型
checkpoint = tf.train.latest_checkpoint(a.checkpoint)
#模型的恢复用的是restore()函数，它需要两个参数restore(sess, save_path)，save_path指的是保存的模型路径
saver.restore(sess, checkpoint)
max_steps = 2**32
# a.max_epochs = number of training epochs
if a.max_epochs is not None:
max_steps = examples.steps_per_epoch * a.max_epochs
# a.max_steps = number of training steps
if a.max_steps is not None:
max_steps = a.max_steps
loadingModelStop = time.time()
if a.mode == "test":
# testing
# at most, process the test data once
#add by Tony
testStart = time.time()
start = time.time()
max_steps = min(examples.steps_per_epoch, max_steps)
for step in range(max_steps):
results = sess.run(display_fetches)
#把生成的图片放到相应的目录中
filesets = save_images(results)
for i, f in enumerate(filesets):
print("evaluated image", f["name"])
index_path = append_index(filesets)
print("wrote index at", index_path)
print("rate", (time.time() - start) / max_steps)
testStop = time.time()
#add by Tony : merge all small image to one
#把test生成的小的图片合成大的图片
mergeStart = time.time()
import mergeAllImages
if __name__ == "__main__":
mergeAllImages.main()
#把生成的图片文件复制到指定的output_file
shutil.copy("facades_test/merged3.png", a.output_file)
mergeStop = time.time()
#step1 time
#print("★★★step1 Time used :",str(step1Stop-step1Start) + "秒")
#loadExamplesCreateModelStop time
print("★★★loadExamplesCreateModel Time used :",str(loadExamplesCreateModelStop-loadExamplesCreateModelStart) + "秒")
#split time
print("(Split Time used :",str(splitStop-splitStart) + "秒")
#loading Model time
print("★★★loading Model time used :",str(loadingModelStop-loadingModelStart) + "秒")
#test time
print("★★★test Time used :",str(testStop-testStart) + "秒")
#merge time
print("★★★Merge Time used :",str(mergeStop-mergeStart) + "秒")
#all time
elapsed = (time.clock() - mainStart)
print("★★★Time used(Total) : ",str(elapsed) + "秒")
else:
# training
start = time.time()
for step in range(max_steps):
def should(freq):
return freq > 0 and ((step + 1) % freq == 0 or step == max_steps - 1)
options = None
run_metadata = None
if should(a.trace_freq):
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
fetches = {
"train": model.train,
"global_step": sv.global_step,
}
# display progress every 50 steps
if should(a.progress_freq):
fetches["discrim_loss"] = model.discrim_loss
fetches["gen_loss_GAN"] = model.gen_loss_GAN
fetches["gen_loss_L1"] = model.gen_loss_L1
# update summaries every 100 steps
if should(a.summary_freq):
fetches["summary"] = sv.summary_op
# write current training images every 0 steps
if should(a.display_freq):
fetches["display"] = display_fetches
results = sess.run(fetches, options=options, run_metadata=run_metadata)
if should(a.summary_freq):
print("recording summary")
sv.summary_writer.add_summary(results["summary"], results["global_step"])
if should(a.display_freq):
print("saving display images")
filesets = save_images(results["display"], step=results["global_step"])
append_index(filesets, step=True)
if should(a.trace_freq):
print("recording trace")
sv.summary_writer.add_run_metadata(run_metadata, "step_%d" % results["global_step"])
if should(a.progress_freq):
# global_step will have the correct step count if we resume from a checkpoint
train_epoch = math.ceil(results["global_step"] / examples.steps_per_epoch)
train_step = (results["global_step"] - 1) % examples.steps_per_epoch + 1
rate = (step + 1) * a.batch_size / (time.time() - start)
remaining = (max_steps - step) * a.batch_size / rate
print("progress epoch %d step %d image/sec %0.1f remaining %dm" % (train_epoch, train_step, rate, remaining / 60))
print("discrim_loss", results["discrim_loss"])
print("gen_loss_GAN", results["gen_loss_GAN"])
print("gen_loss_L1", results["gen_loss_L1"])
# 每隔save_freq（默认为5000）保存Model
if should(a.save_freq):
print("saving model")
# 保存训练好的模型 第二个参数设定保存的路径和名字 第三个参数将训练的次数作为后缀加入到模型名字中
# saver.save(sess, 'my-model', global_step=1000) ==> filename: 'my-model-1000'
saver.save(sess, os.path.join(a.output_dir, "model"), global_step=sv.global_step)
if sv.should_stop():
break
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--output_dir", required=True, help="where to put output files")
parser.add_argument("--checkpoint", required=True, help="directory with checkpoint to resume training from or use for testing")
parser.add_argument("--separable_conv", action="store_true", help="use separable convolutions in the generator")
parser.add_argument("--ngf", type=int, default=64, help="number of generator filters in first conv layer")
parser.add_argument('--disable_psf', action='store_true', help="disable the concatenation of the PSF as a channel")
a = parser.parse_args()
def load_data(dirty_path, psf_path):
# type: (str, str) -> tf.data.Dataset
def dataset_generator():
psf = fits_open(psf_path)[:, :, np.newaxis]
dirty = fits_open(dirty_path)[:, :, np.newaxis]
min_flux = dirty.min()
max_flux = dirty.max()
yield min_flux, max_flux, psf, dirty
ds = tf.data.Dataset.from_generator(dataset_generator,
output_shapes=((), ()) + ((256, 256, 1),) * 2,
output_types=(tf.float32, tf.float32) + (tf.float32,) * 2
)
ds = ds.batch(1)
return ds
dirty_path = tf.placeholder(tf.string, shape=[1])
psf_path = tf.placeholder(tf.string, shape=[1])
batch = load_data(dirty_path, psf_path)
iter = batch.make_one_shot_iterator()
min_flux, max_flux, psf, dirty = iter.get_next()
scaled_dirty = preprocess(dirty, min_flux, max_flux)
scaled_psf = preprocess(psf, min_flux, max_flux)
if a.disable_psf:
input_ = scaled_dirty
else:
input_ = tf.concat([scaled_dirty, scaled_psf], axis=3)
with tf.variable_scope("generator"):
generator = create_generator(input_, 1, ngf=a.ngf, separable_conv=a.separable_conv)
batch_output = deprocess(generator, min_flux, max_flux)
output_image = tf.image.convert_image_dtype(batch_output, dtype=tf.uint8)[0]
# lets just assume png for now
output_data = tf.image.encode_png(output_image)
output = tf.convert_to_tensor([tf.encode_base64(output_data)])
key = tf.placeholder(tf.string, shape=[1])
inputs = {
"key": key.name,
"input": dirty.name
}
tf.add_to_collection("inputs", json.dumps(inputs))
outputs = {
"key": tf.identity(key).name,
"output": output.name,
}
tf.add_to_collection("outputs", json.dumps(outputs))
init_op = tf.global_variables_initializer()
restore_saver = tf.train.Saver()
export_saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
print("loading model from checkpoint")
checkpoint = tf.train.latest_checkpoint(a.checkpoint)
restore_saver.restore(sess, checkpoint)
print("exporting model")
#export_saver.export_meta_graph(filename=os.path.join(a.output_dir, "export.meta"))
export_saver.save(sess, os.path.join(a.output_dir, "export"), write_meta_graph=True) #, save_relative_paths=True)
def main():
if arguments.seed is None:
arguments.seed = random.randint(0, 2**31 - 1)
tf.set_random_seed(arguments.seed)
np.random.seed(arguments.seed)
random.seed(arguments.seed)
if not os.path.exists(arguments.output_dir):
os.makedirs(arguments.output_dir)
if arguments.mode == "test" or arguments.mode == "export":
if arguments.checkpoint is None:
raise Exception("checkpoint required for test mode")
# load some options from the checkpoint
options = {"which_direction", "ngf", "ndf", "lab_colorization"}
with open(os.path.join(arguments.checkpoint, "options.json")) as f:
for key, val in json.loads(f.read()).items():
if key in options:
print("loaded", key, "=", val)
setattr(arguments, key, val)
# disable these features in test mode
arguments.scale_size = mu.CROP_SIZE
arguments.flip = False
for k, v in arguments._get_kwargs():
print(k, "=", v)
with open(os.path.join(arguments.output_dir, "options.json"), "w") as f:
f.write(json.dumps(vars(arguments), sort_keys=True, indent=4))
if arguments.mode == "export":
# export the generator to arguments meta graph that can be imported later for standalone generation
if arguments.lab_colorization:
raise Exception("export not supported for lab_colorization")
input = tf.placeholder(tf.string, shape=[1])
input_data = tf.decode_base64(input[0])
input_image = tf.image.decode_png(input_data)
# remove alpha channel if present
input_image = tf.cond(tf.equal(tf.shape(input_image)[2],
4), lambda: input_image[:, :, :3],
lambda: input_image)
# convert grayscale to RGB
input_image = tf.cond(tf.equal(tf.shape(input_image)[2], 1),
lambda: tf.image.grayscale_to_rgb(input_image),
lambda: input_image)
input_image = tf.image.convert_image_dtype(input_image,
dtype=tf.float32)
input_image.set_shape([mu.CROP_SIZE, mu.CROP_SIZE, 3])
batch_input = tf.expand_dims(input_image, axis=0)
with tf.variable_scope("generator"):
batch_output = de_process(
mu.create_generator(arguments, pre_process(batch_input), 3))
output_image = tf.image.convert_image_dtype(batch_output,
dtype=tf.uint8)[0]
if arguments.output_filetype == "png":
output_data = tf.image.encode_png(output_image)
elif arguments.output_filetype == "jpeg":
output_data = tf.image.encode_jpeg(output_image, quality=80)
else:
raise Exception("invalid filetype")
output = tf.convert_to_tensor([tf.encode_base64(output_data)])
key = tf.placeholder(tf.string, shape=[1])
inputs = {"key": key.name, "input": input.name}
tf.add_to_collection("inputs", json.dumps(inputs))
outputs = {
"key": tf.identity(key).name,
"output": output.name,
}
tf.add_to_collection("outputs", json.dumps(outputs))
init_op = tf.global_variables_initializer()
restore_saver = tf.train.Saver()
export_saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
print("loading model from checkpoint")
checkpoint = tf.train.latest_checkpoint(arguments.checkpoint)
restore_saver.restore(sess, checkpoint)
print("exporting model")
export_saver.export_meta_graph(
filename=os.path.join(arguments.output_dir, "export.meta"))
export_saver.save(sess,
os.path.join(arguments.output_dir, "export"),
write_meta_graph=False)
return
# load training data
# inputs and targets are [batch_size, height, width, channels]
source_placeholder = tf.placeholder(tf.float32, (None, 256, 256, 3),
"x_source")
target_placeholder = tf.placeholder(tf.float32, (None, 256, 256, 3),
"y_target")
model = mu.create_model(arguments, source_placeholder, target_placeholder)
outputs = de_process(model.outputs)
def convert(image):
if arguments.aspect_ratio != 1.0:
# upscale to correct aspect ratio
size = [
mu.CROP_SIZE,
int(round(mu.CROP_SIZE * arguments.aspect_ratio))
]
image = tf.image.resize_images(
image, size=size, method=tf.image.ResizeMethod.BICUBIC)
return tf.image.convert_image_dtype(image,
dtype=tf.uint8,
saturate=True)
# reverse any processing on images so they can be written to disk or displayed to user
with tf.name_scope("convert_outputs"):
converted_outputs = convert(outputs)
with tf.name_scope("encode_images"):
display_fetches = {
"outputs":
tf.map_fn(tf.image.encode_png,
converted_outputs,
dtype=tf.string,
name="output_pngs"),
}
# summaries
with tf.name_scope("outputs_summary"):
tf.summary.image("outputs", converted_outputs)
with tf.name_scope("predict_real_summary"):
tf.summary.image(
"predict_real",
tf.image.convert_image_dtype(model.predict_real, dtype=tf.uint8))
with tf.name_scope("predict_fake_summary"):
tf.summary.image(
"predict_fake",
tf.image.convert_image_dtype(model.predict_fake, dtype=tf.uint8))
tf.summary.scalar("discriminator_loss", model.discrim_loss)
tf.summary.scalar("generator_loss_GAN", model.gen_loss_GAN)
tf.summary.scalar("generator_loss_L1", model.gen_loss_L1)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name + "/values", var)
for grad, var in model.discrim_grads_and_vars + model.gen_grads_and_vars:
tf.summary.histogram(var.op.name + "/gradients", grad)
with tf.name_scope("parameter_count"):
parameter_count = tf.reduce_sum(
[tf.reduce_prod(tf.shape(v)) for v in tf.trainable_variables()])
saver = tf.train.Saver(max_to_keep=1)
log_dir = arguments.output_dir if (arguments.trace_freq > 0
or arguments.summary_freq > 0) else None
# todo: Supervisor is deprecated, use MonitoredTrainingSession
sv = tf.train.Supervisor(logdir=log_dir, save_summaries_secs=0, saver=None)
# training loop
with sv.managed_session() as sess:
images_filename_list = du.get_data_files_list(arguments.input_dir,
"jpg")
steps_per_epoch = int(
math.ceil(len(images_filename_list) / arguments.batch_size))
print("parameter_count =", sess.run(parameter_count))
print(f"Data count = {len(images_filename_list)}")
if arguments.checkpoint is not None:
print("loading model from checkpoint")
checkpoint = tf.train.latest_checkpoint(arguments.checkpoint)
saver.restore(sess, checkpoint)
max_steps = 2**32
if arguments.max_epochs is not None:
max_steps = steps_per_epoch * arguments.max_epochs
if arguments.max_steps is not None:
max_steps = arguments.max_steps
if arguments.mode == "test":
# testing
start = time.time()
for test_file in images_filename_list:
x_source, y_target = du.read_single_data_file(
test_file, arguments)
results = sess.run(display_fetches,
feed_dict={
source_placeholder:
np.expand_dims(x_source, 0),
target_placeholder:
np.expand_dims(y_target, 0)
})
fileset = save_image(test_file, results)
print("evaluated image", fileset["name"])
index_path = append_index(fileset)
print("wrote index at", index_path)
print(f"time: {(time.time() - start)}")
else:
# training
start = time.time()
for step in range(max_steps):
def should(freq):
return freq > 0 and ((step + 1) % freq == 0
or step == max_steps - 1)
options = None
run_metadata = None
if should(arguments.trace_freq):
options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
fetches = {
"train": model.train,
"global_step": sv.global_step,
}
if should(arguments.progress_freq):
fetches["discrim_loss"] = model.discrim_loss
fetches["gen_loss_GAN"] = model.gen_loss_GAN
fetches["gen_loss_L1"] = model.gen_loss_L1
if should(arguments.summary_freq):
fetches["summary"] = sv.summary_op
if should(arguments.display_freq):
fetches["display"] = display_fetches
x_source, y_target = du.generate_batch(images_filename_list,
arguments)
results = sess.run(fetches,
options=options,
run_metadata=run_metadata,
feed_dict={
source_placeholder: x_source,
target_placeholder: y_target
})
if should(arguments.summary_freq):
print("recording summary")
sv.summary_writer.add_summary(results["summary"],
results["global_step"])
if should(arguments.display_freq):
# todo: broken, need to fix
print("saving display images")
filesets = save_images(results["display"],
step=results["global_step"])
append_index(filesets, step=True)
if should(arguments.trace_freq):
print("recording trace")
sv.summary_writer.add_run_metadata(
run_metadata, "step_%d" % results["global_step"])
if should(arguments.progress_freq):
# global_step will have the correct step count if we resume from arguments checkpoint
train_epoch = math.ceil(results["global_step"] /
steps_per_epoch)
train_step = (results["global_step"] -
1) % steps_per_epoch + 1
rate = (step + 1) * arguments.batch_size / (time.time() -
start)
remaining = (max_steps -
step) * arguments.batch_size / rate
print(
"progress epoch %d step %d image/sec %0.1f remaining %dm"
% (train_epoch, train_step, rate, remaining / 60))
print("discrim_loss", results["discrim_loss"])
print("gen_loss_GAN", results["gen_loss_GAN"])
print("gen_loss_L1", results["gen_loss_L1"])
if should(arguments.save_freq):
print("saving model")
saver.save(sess,
os.path.join(arguments.output_dir, "model"),
global_step=sv.global_step)
if sv.should_stop():
break
# https://www.tensorflow.org/tutorials/eager/eager_basics
import tensorflow as tf
import numpy as np
tf.enable_eager_execution()
# Tensors
# dataType, shape
# able to exist in accelerator memory (GPU)
# immutable
print(tf.add(1, 2)) # tf.Tensor(3, shape=(), dtype=int32)
print(tf.add([1, 2], [3, 4])) # tf.Tensor([4 6], shape=(2,), dtype=int32)
print(tf.square(5)) # tf.Tensor(25, shape=(), dtype=int32)
print(tf.reduce_sum([1, 2, 3])) # tf.Tensor(6, shape=(), dtype=int32)
print(tf.encode_base64(
"hello world")) # tf.Tensor(b'aGVsbG8gd29ybGQ', shape=(), dtype=string)
print(tf.square(2) + tf.square(3)) # tf.Tensor(13, shape=(), dtype=int32)
ndarray = np.ones((3, 3))
tensor = tf.multiply(ndarray,
42) # automatically convert numpy array to tensor
print(tensor)
print(np.add(tensor, 1)) # automatically convert tensor to numpy array
print(tensor.numpy())
# GPU acceleration
x = tf.random_uniform(shape=(3, 3))
print(tf.test.is_gpu_available())
print(x.device.endswith("GPU:0")) # tensor.device. the 0 index of gpu
# tensorflow will automatically choose one device to run
def train():
if args.seed is None:
args.seed = random.randint(0, 2**31 - 1)
tf.set_random_seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
if args.mode == "test" or args.mode == "export":
if args.checkpoint_dir is None:
raise Exception("checkpoint required for test mode")
# load some options from the checkpoint
# options = {"which_direction", "ngf", "ndf", "lab_colorization"}
# with open(os.path.join(args.checkpoint_dir, "options.json"), 'r') as f:
# for key, val in json.loads(f.read()).items():
# if key in options:
# print("loaded", key, "=", val)
# setattr(args, key, val)
# disable these features in test mode
args.scale_size = CROP_SIZE
args.flip = False
for k, v in args._get_kwargs():
print(k, "=", v)
with open(os.path.join(args.output_dir, "options.json"), "w") as f:
f.write(json.dumps(vars(args), sort_keys=True, indent=4))
if args.mode == "export":
# export the generator to a meta graph that can be imported later for standalone generation
if args.lab_colorization:
raise Exception("export not supported for lab_colorization")
inputs = tf.placeholder(tf.string, shape=[1])
input_data = tf.decode_base64(inputs[0])
input_image = tf.image.decode_png(input_data)
# remove alpha channel if present
input_image = tf.cond(tf.equal(tf.shape(input_image)[2],
4), lambda: input_image[:, :, :3],
lambda: input_image)
# convert grayscale to RGB
input_image = tf.cond(tf.equal(tf.shape(input_image)[2], 1),
lambda: tf.image.grayscale_to_rgb(input_image),
lambda: input_image)
input_image = tf.image.convert_image_dtype(input_image,
dtype=tf.float32)
input_image.set_shape([CROP_SIZE, CROP_SIZE, 3])
batch_input = tf.expand_dims(input_image, axis=0)
model_ = Pix2Pix()
batch_output = deprocess(
model_.get_generator(preprocess(batch_input),
3,
ngf=args.ngf,
conv_type=args.conv_type,
channel_multiplier=args.channel_multiplier,
padding='SAME'))
# with tf.variable_scope("generator"):
# batch_output = deprocess(model.get_generator(preprocess(batch_input), 3))
output_image = tf.image.convert_image_dtype(batch_output,
dtype=tf.uint8)[0]
if args.output_filetype == "png":
output_data = tf.image.encode_png(output_image)
elif args.output_filetype == "jpeg":
output_data = tf.image.encode_jpeg(output_image, quality=80)
else:
raise Exception("invalid filetype")
output = tf.convert_to_tensor([tf.encode_base64(output_data)])
key = tf.placeholder(tf.string, shape=[1])
inputs = {"key": key.name, "input": inputs.name}
tf.add_to_collection("inputs", json.dumps(inputs))
outputs = {
"key": tf.identity(key).name,
"output": output.name,
}
tf.add_to_collection("outputs", json.dumps(outputs))
init_op = tf.global_variables_initializer()
restore_saver = tf.train.Saver()
export_saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
print("loading model from checkpoint")
checkpoint = tf.train.latest_checkpoint(args.checkpoint_dir)
restore_saver.restore(sess, checkpoint)
print("exporting model")
export_saver.export_meta_graph(
filename=os.path.join(args.output_dir, "export.meta"))
export_saver.save(sess,
os.path.join(args.output_dir, "export"),
write_meta_graph=False)
return
examples = load_examples()
print("examples count = %d" % examples.count)
max_steps = 2**32
if args.max_epochs is not None:
max_steps = examples.steps_per_epoch * args.max_epochs
if args.max_steps is not None:
max_steps = args.max_steps
# inputs and targets are [batch_size, height, width, channels]
modelNamedtuple = create_model(examples.inputs, examples.targets,
max_steps)
# undo colorization splitting on images that we use for display/output
if args.lab_colorization:
if args.which_direction == "AtoB":
# inputs is brightness, this will be handled fine as a grayscale image
# need to augment targets and outputs with brightness
targets = augment(examples.targets, examples.inputs)
outputs = augment(modelNamedtuple.outputs, examples.inputs)
# inputs can be deprocessed normally and handled as if they are single channel
# grayscale images
inputs = deprocess(examples.inputs)
elif args.which_direction == "BtoA":
# inputs will be color channels only, get brightness from targets
inputs = augment(examples.inputs, examples.targets)
targets = deprocess(examples.targets)
outputs = deprocess(modelNamedtuple.outputs)
else:
raise Exception("invalid direction")
else:
inputs = deprocess(examples.inputs)
targets = deprocess(examples.targets)
outputs = deprocess(modelNamedtuple.outputs)
def convert(image):
if args.aspect_ratio != 1.0:
# upscale to correct aspect ratio
size = [CROP_SIZE, int(round(CROP_SIZE * args.aspect_ratio))]
image = tf.image.resize_images(
image, size=size, method=tf.image.ResizeMethod.BICUBIC)
return tf.image.convert_image_dtype(image,
dtype=tf.uint8,
saturate=True)
# reverse any processing on images so they can be written to disk or displayed to user
with tf.name_scope("convert_inputs"):
converted_inputs = convert(inputs)
with tf.name_scope("convert_targets"):
converted_targets = convert(targets)
with tf.name_scope("convert_outputs"):
converted_outputs = convert(outputs)
with tf.name_scope("encode_images"):
if args.multiple_A:
# channels = converted_inputs.shape.as_list()[3]
converted_inputs = tf.split(converted_inputs, 2, 3)[1]
print('\n----642----: {}\n'.format(
converted_inputs.shape.as_list()))
display_fetches = {
"paths":
examples.paths,
"inputs":
tf.map_fn(tf.image.encode_png,
converted_inputs,
dtype=tf.string,
name="input_pngs"),
"targets":
tf.map_fn(tf.image.encode_png,
converted_targets,
dtype=tf.string,
name="target_pngs"),
"outputs":
tf.map_fn(tf.image.encode_png,
converted_outputs,
dtype=tf.string,
name="output_pngs"),
}
# summaries
# with tf.name_scope("inputs_summary"):
# tf.summary.image("inputs", converted_inputs)
with tf.name_scope("targets_summary"):
tf.summary.image("targets", converted_targets)
with tf.name_scope("outputs_summary"):
tf.summary.image("outputs", converted_outputs)
tf.summary.scalar("discriminator_loss", modelNamedtuple.discrim_loss)
tf.summary.scalar("generator_loss_GAN", modelNamedtuple.gen_loss_GAN)
tf.summary.scalar("generator_loss_L1", modelNamedtuple.gen_loss_L1)
# for var in tf.trainable_variables():
# tf.summary.histogram(var.op.name + "/values", var)
for grad, var in modelNamedtuple.discrim_grads_and_vars + modelNamedtuple.gen_grads_and_vars:
tf.summary.histogram(var.op.name + "/gradients", grad)
with tf.name_scope("parameter_count"):
parameter_count = tf.reduce_sum(
[tf.reduce_prod(tf.shape(v)) for v in tf.trainable_variables()])
summary_op = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=5)
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
summary_writer = tf.summary.FileWriter(args.output_dir, sess.graph)
sess.run(tf.global_variables_initializer())
print("parameter_count =", sess.run(parameter_count))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
if args.checkpoint_dir is not None:
print("loading model from checkpoint")
checkpoint = tf.train.latest_checkpoint(args.checkpoint_dir)
saver.restore(sess, checkpoint)
# max_steps = 2 ** 32
# if args.max_epochs is not None:
# max_steps = examples.steps_per_epoch * args.max_epochs
# if args.max_steps is not None:
# max_steps = args.max_steps
if args.mode == "test":
# testing
# at most, process the test data once
start = time.time()
max_steps = min(examples.steps_per_epoch, max_steps)
for step in range(max_steps):
results = sess.run(display_fetches)
filesets = save_images(results)
for i, f in enumerate(filesets):
print("evaluated image", f["name"])
index_path = append_index(filesets)
print("wrote index at", index_path)
print("rate", (time.time() - start) / max_steps)
else:
# training
start = time.time()
for step in range(max_steps):
def should(freq):
return freq > 0 and ((step + 1) % freq == 0
or step == max_steps - 1)
for i in range(args.n_dis):
sess.run(modelNamedtuple.d_train)
fetches = {
"g_train": modelNamedtuple.g_train,
"losses": modelNamedtuple.losses,
"global_step": modelNamedtuple.global_step,
}
if should(args.progress_freq):
fetches["discrim_loss"] = modelNamedtuple.discrim_loss
fetches["gen_loss_GAN"] = modelNamedtuple.gen_loss_GAN
fetches["gen_loss_L1"] = modelNamedtuple.gen_loss_L1
if should(args.summary_freq):
fetches["summary"] = summary_op
if should(args.display_freq):
fetches["display"] = display_fetches
# results = sess.run(fetches, options=options, run_metadata=run_metadata)
results = sess.run(fetches)
if should(args.summary_freq):
# print("recording summary")
summary_writer.add_summary(results["summary"],
results["global_step"])
if should(args.display_freq):
# print("saving display images")
filesets = save_images(results["display"],
step=results["global_step"])
append_index(filesets, step=True)
if should(args.progress_freq):
# global_step will have the correct step count if we resume from a checkpoint
train_epoch = math.ceil(results["global_step"] /
examples.steps_per_epoch)
train_step = (results["global_step"] -
1) % examples.steps_per_epoch + 1
rate = (step + 1) * args.batch_size / (time.time() - start)
remaining = (max_steps - step) * args.batch_size / rate
print(
"progress epoch %d step %d image/sec %0.1f remaining %dm"
% (train_epoch, train_step, rate, remaining / 60))
print("discrim_loss", results["discrim_loss"])
print("gen_loss_GAN", results["gen_loss_GAN"])
print("gen_loss_L1", results["gen_loss_L1"])
if should(args.save_freq):
print("saving model...")
saver.save(sess,
os.path.join(args.output_dir, "model"),
global_step=modelNamedtuple.global_step)
coord.request_stop()
coord.join(threads)
def main():
if tf.__version__.split('.')[0] != "1":
raise Exception("Tensorflow version 1 required")
if a.seed is None:
a.seed = random.randint(0, 2**31 - 1)
tf.set_random_seed(a.seed)
np.random.seed(a.seed)
random.seed(a.seed)
if not os.path.exists(a.output_dir):
os.makedirs(a.output_dir)
if a.mode == "test" or a.mode == "export":
if a.checkpoint is None:
raise Exception("checkpoint required for test mode")
# load some options from the checkpoint
options = {"which_direction", "ngf", "ndf", "lab_colorization"}
with open(os.path.join(a.checkpoint, "options.json")) as f:
for key, val in json.loads(f.read()).items():
if key in options:
print("loaded", key, "=", val)
setattr(a, key, val)
# disable these features in test mode
a.scale_size = CROP_SIZE
a.flip = False
for k, v in a._get_kwargs():
print(k, "=", v)
with open(os.path.join(a.output_dir, "options.json"), "w") as f:
f.write(json.dumps(vars(a), sort_keys=True, indent=4))
if a.mode == "export":
# export the generator to a meta graph that can be imported later for standalone generation
input = tf.placeholder(tf.string, shape=[1])
input_data = tf.decode_base64(input[0])
input_image = tf.image.decode_png(input_data)
# remove alpha channel if present
input_image = tf.cond(tf.equal(tf.shape(input_image)[2], 4), lambda: input_image[:,:,:3], lambda: input_image)
# convert grayscale to RGB
input_image = tf.cond(tf.equal(tf.shape(input_image)[2], 1), lambda: tf.image.grayscale_to_rgb(input_image), lambda: input_image)
input_image = tf.image.convert_image_dtype(input_image, dtype=tf.float32)
input_image.set_shape([CROP_SIZE, CROP_SIZE, 3])
batch_input = tf.expand_dims(input_image, axis=0)
with tf.variable_scope("generator"):
batch_output = deprocess(create_generator(preprocess(batch_input)[0], 3))
output_image = tf.image.convert_image_dtype(batch_output, dtype=tf.uint8)[0]
if a.output_filetype == "png":
output_data = tf.image.encode_png(output_image)
elif a.output_filetype == "jpeg":
output_data = tf.image.encode_jpeg(output_image, quality=80)
else:
raise Exception("invalid filetype")
output = tf.convert_to_tensor([tf.encode_base64(output_data)])
key = tf.placeholder(tf.string, shape=[1])
inputs = {
"key": key.name,
"input": input.name
}
tf.add_to_collection("inputs", json.dumps(inputs))
outputs = {
"key": tf.identity(key).name,
"output": output.name,
}
tf.add_to_collection("outputs", json.dumps(outputs))
init_op = tf.global_variables_initializer()
restore_saver = tf.train.Saver()
export_saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
print("loading model from checkpoint")
checkpoint = tf.train.latest_checkpoint(a.checkpoint)
restore_saver.restore(sess, checkpoint)
print("exporting model")
export_saver.export_meta_graph(filename=os.path.join(a.output_dir, "export.meta"))
export_saver.save(sess, os.path.join(a.output_dir, "export"), write_meta_graph=False)
return
gen_examples = load_examples_generator()
dis_examples = load_examples_discriminator()
print("examples count = %d" % gen_examples.count)
# inputs and targets are [batch_size, height, width, channels]
model_dis_train = create_model_dis_train(dis_examples.inputs, dis_examples.targets)
model_gen_train = create_model_gen_train(gen_examples.inputs, gen_examples.targets)
# model for D0
model_dis_old = create_model_dis_old(gen_examples.inputs, gen_examples.targets)
# undo colorization splitting on images that we use for display/output
inputs = deprocess(gen_examples.inputs)
targets = deprocess(gen_examples.targets)
outputs = deprocess(model_gen_train.outputs)
def convert(image):
if a.aspect_ratio != 1.0:
# upscale to correct aspect ratio
size = [CROP_SIZE, int(round(CROP_SIZE * a.aspect_ratio))]
image = tf.image.resize_images(image, size=size, method=tf.image.ResizeMethod.BICUBIC)
return tf.image.convert_image_dtype(image, dtype=tf.uint8, saturate=True)
# reverse any processing on images so they can be written to disk or displayed to user
with tf.name_scope("convert_inputs"):
converted_inputs = convert(inputs)
with tf.name_scope("convert_targets"):
converted_targets = convert(targets)
with tf.name_scope("convert_outputs"):
converted_outputs = convert(outputs)
with tf.name_scope("encode_images"):
display_fetches = {
"paths": gen_examples.paths,
"inputs": tf.map_fn(tf.image.encode_png, converted_inputs, dtype=tf.string, name="input_pngs"),
"targets": tf.map_fn(tf.image.encode_png, converted_targets, dtype=tf.string, name="target_pngs"),
"outputs": tf.map_fn(tf.image.encode_png, converted_outputs, dtype=tf.string, name="output_pngs"),
}
# summaries
with tf.name_scope("inputs_summary"):
tf.summary.image("inputs", converted_inputs)
with tf.name_scope("targets_summary"):
tf.summary.image("targets", converted_targets)
with tf.name_scope("outputs_summary"):
tf.summary.image("outputs", converted_outputs)
with tf.name_scope("predict_real_summary"):
tf.summary.image("predict_real", tf.image.convert_image_dtype(model_dis_train.predict_real, dtype=tf.uint8))
with tf.name_scope("predict_fake_summary"):
tf.summary.image("predict_fake", tf.image.convert_image_dtype(model_dis_train.predict_fake, dtype=tf.uint8))
tf.summary.scalar("discriminator_loss", model_dis_train.discrim_loss)
tf.summary.scalar("generator_loss_GAN", model_gen_train.gen_loss_GAN)
tf.summary.scalar("generator_loss_L1", model_gen_train.gen_loss_L1)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name + "/values", var)
#for grad, var in model_dis_train.discrim_grads_and_vars + model_gen_train.gen_grads_and_vars:
# tf.summary.histogram(var.op.name + "/gradients", grad)
with tf.name_scope("parameter_count"):
parameter_count = tf.reduce_sum([tf.reduce_prod(tf.shape(v)) for v in tf.trainable_variables()])
# create savers
d_vars = [var for var in tf.trainable_variables() if var.name.startswith("discriminator")]
d_saver = tf.train.Saver(d_vars, max_to_keep=2)
g_vars = [var for var in tf.trainable_variables() if var.name.startswith("generator")]
g_saver = tf.train.Saver(g_vars, max_to_keep=3)
saver = tf.train.Saver(max_to_keep=100)
logdir = a.output_dir if (a.trace_freq > 0 or a.summary_freq > 0) else None
sv = tf.train.Supervisor(logdir=logdir, save_summaries_secs=0)
with sv.managed_session() as sess:
print("parameter_count =", sess.run(parameter_count))
if a.checkpoint is not None:
print("loading model from checkpoint")
checkpoint = tf.train.latest_checkpoint(a.checkpoint)
saver.restore(sess, checkpoint)
max_steps = 2**32
if a.max_epochs is not None:
max_steps = gen_examples.steps_per_epoch * a.max_epochs
if a.max_steps is not None:
max_steps = a.max_steps
if a.mode == "test":
# testing
# at most, process the test data once
max_steps = min(gen_examples.steps_per_epoch, max_steps)
for step in range(max_steps):
results = sess.run(display_fetches)
filesets = save_images(results)
#print(results['inputs'])
for i, f in enumerate(filesets):
print("evaluated image", f["name"])
index_path = append_index(filesets)
print("wrote index at", index_path)
else:
# training
start = time.time()
for step in range(max_steps):
def should(freq):
return freq > 0 and ((step + 1) % freq == 0 or step == max_steps - 1)
def save_d(model_name):
# model_name [0,1]
# 0: old model
# 1: new model
d_saver.save(sess, os.path.join(a.output_dir+"/discriminator", "d"), global_step=model_name)
def load_d(model_name):
# model_name [0,1]
# 0: old model
# 1: new model
checkpoint = a.output_dir+"/discriminator/"+"d-"+str(model_name)
d_saver.restore(sess, checkpoint)
'''
if(model_name==1):
d_saver.restore(sess, checkpoint)
elif(model_name==0):
d_old_saver.restore(sess, checkpoint)
'''
def save_g(model_name):
# model_name [0,1,2]
# 0: old model
# 1: new model
# 2: temp model
g_saver.save(sess, os.path.join(a.output_dir+"/generator", "d"), global_step=model_name)
def load_g(model_name):
# model_name [0,1,2]
# 0: old model
# 1: new model
# 2: temp model
checkpoint = a.output_dir+"/generator/"+"d-"+str(model_name)
g_saver.restore(sess, checkpoint)
def update_g():
save_g(2)
load_g(1)
save_g(0)
load_g(2)
save_g(1)
options = None
run_metadata = None
if should(a.trace_freq):
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
# two-phase training, initiate with D training
if(step==0):
cur_train_phase = 'd'
fetches = {
"train": model_dis_train.train,
"global_step": sv.global_step,
}
predict_fake = np.mean(sess.run(model_dis_train.predict_fake))
d_old = False
g_old = False
first_G = True
else:
if(cur_train_phase=='g'):
# G training
fetches = {
"train": model_gen_train.train,
"global_step": sv.global_step,
}
predict_fake = np.mean(sess.run(model_gen_train.predict_fake))
gan_predict_fake = np.mean(sess.run(model_gen_train.gan_predict_fake))
if should(a.progress_freq):
fetches["discrim_loss"] = model_gen_train.discrim_loss
fetches["gen_loss_GAN"] = model_gen_train.gen_loss_GAN
fetches["gen_loss_L1"] = model_gen_train.gen_loss_L1
fetches["predict_fake"] = model_gen_train.predict_fake
fetches["predict_real"] = model_gen_train.predict_real
fetches["gan_predict_fake"] = model_gen_train.gan_predict_fake
fetches["gan_predict_real"] = model_gen_train.gan_predict_real
if(predict_fake>0.95 and gan_predict_fake>0.95):
if(first_G==True):
print("*** G is trained well @ steps %d***" %step)
os.system("echo G is trained well @ steps ^"+str(step)+">>"+a.output_dir+"/two_phase_training.txt")
cur_train_phase = 'd'
# save Cur_d as Old_d
save_d(0)
# save Cur_g as Old_g
save_g(0)
save_g(1)
first_G = False
d_old = True
g_old = True
else:
if(d_old==True):
# test G on D_old
load_d(0)
predict_fake = np.mean(sess.run(model_dis_old.predict_fake))
gan_predict_fake = np.mean(sess.run(model_gen_train.gan_predict_fake))
# if D_old is fooled,
if(predict_fake>0.9 and gan_predict_fake>0.9):
# copy Currend_d to D_old
load_d(1) # restore Cur_d
save_d(0) # save Cur_d to Old_d
# switch to next training phase
print("*** G is trained well @ steps %d***" %step)
update_g()
os.system("echo G is trained well @ steps ^"+str(step)+">>"+a.output_dir+"/two_phase_training.txt")
cur_train_phase = 'd'
# if D_old is not fooled
else:
# copy D_old to Currend_d
print("*** G cannot fool D0 @ steps %d, Copy D0 to HERE***" %step)
save_d(1)
os.system("echo G cannot fool D0 @ steps ^"+str(step)+" Copy D0 to HERE>>"+a.output_dir+"/two_phase_training.txt")
# set d_old = False
d_old = False
else:
print("*** G is trained well @ steps %d***" %step)
update_g()
os.system("echo G is trained well @ steps ^"+str(step)+">>"+a.output_dir+"/two_phase_training.txt")
cur_train_phase = 'd'
d_old = True
else:
# D training
if(g_old==True):
# random load G
ran = np.random.randint(10,size=1)
if(ran<3):
# train with old G
load_g(0)
fetches = {
"train": model_dis_train.train,
"global_step": sv.global_step,
}
load_g(1)
else:
# train with new G
fetches = {
"train": model_dis_train.train,
"global_step": sv.global_step,
}
predict_fake = np.mean(sess.run(model_dis_train.predict_fake))
predict_real = np.mean(sess.run(model_dis_train.predict_real))
if should(a.progress_freq):
fetches["discrim_loss"] = model_dis_train.discrim_loss
fetches["gen_loss_GAN"] = model_dis_train.gen_loss_GAN
fetches["gen_loss_L1"] = model_dis_train.gen_loss_L1
fetches["predict_fake"] = model_dis_train.predict_fake
fetches["predict_real"] = model_dis_train.predict_real
fetches["gan_predict_fake"] = model_dis_train.gan_predict_fake
fetches["gan_predict_real"] = model_dis_train.gan_predict_real
if(predict_fake<0.05 and predict_real>0.95):
print("*** D is trained well @ steps %d***" %step)
os.system("echo D is trained well @ steps ^"+str(step)+">>"+a.output_dir+"/two_phase_training.txt")
cur_train_phase = 'g'
save_d(1)
else:
fetches = {
"train": model_dis_train.train,
"global_step": sv.global_step,
}
predict_fake = np.mean(sess.run(model_dis_train.predict_fake))
predict_real = np.mean(sess.run(model_dis_train.predict_real))
gan_predict_fake = np.mean(sess.run(model_gen_train.gan_predict_fake))
gan_predict_real = np.mean(sess.run(model_gen_train.gan_predict_real))
if should(a.progress_freq):
fetches["discrim_loss"] = model_dis_train.discrim_loss
fetches["gen_loss_GAN"] = model_dis_train.gen_loss_GAN
fetches["gen_loss_L1"] = model_dis_train.gen_loss_L1
fetches["predict_fake"] = model_dis_train.predict_fake
fetches["predict_real"] = model_dis_train.predict_real
fetches["gan_predict_fake"] = model_dis_train.gan_predict_fake
fetches["gan_predict_real"] = model_dis_train.gan_predict_real
if(predict_fake<0.05 and predict_real>0.95 and gan_predict_fake<0.05 and gan_predict_real>0.95):
print("*** D is trained well @ steps %d***" %step)
os.system("echo D is trained well @ steps ^"+str(step)+">>"+a.output_dir+"/two_phase_training.txt")
cur_train_phase = 'g'
save_d(1)
if should(a.summary_freq):
fetches["summary"] = sv.summary_op
if should(a.display_freq):
fetches["display"] = display_fetches
results = sess.run(fetches, options=options, run_metadata=run_metadata)
if should(a.summary_freq):
print("recording summary")
sv.summary_writer.add_summary(results["summary"], results["global_step"])
if should(a.display_freq):
print("saving display images")
filesets = save_images(results["display"], step=results["global_step"])
append_index(filesets, step=True)
if should(a.trace_freq):
print("recording trace")
sv.summary_writer.add_run_metadata(run_metadata, "step_%d" % results["global_step"])
if should(a.progress_freq):
# global_step will have the correct step count if we resume from a checkpoint
train_epoch = math.ceil(results["global_step"] / gen_examples.steps_per_epoch)
train_step = (results["global_step"] - 1) % gen_examples.steps_per_epoch + 1
rate = (step + 1) * a.batch_size / (time.time() - start)
remaining = (max_steps - step) * a.batch_size / rate
print("progress epoch %d step %d image/sec %0.1f remaining %dm" % (train_epoch, train_step, rate, remaining / 60))
print("discrim_loss", results["discrim_loss"])
print("gen_loss_GAN", results["gen_loss_GAN"])
print("gen_loss_L1", results["gen_loss_L1"])
print("predict_fake", np.mean(results["predict_fake"]))
print("predict_real", np.mean(results["predict_real"]))
print("gan_predict_fake", np.mean(results["gan_predict_fake"]))
print("gan_predict_real", np.mean(results["gan_predict_real"]))
if should(a.save_freq):
print("saving model")
saver.save(sess, os.path.join(a.output_dir, "model"), global_step=sv.global_step)
if sv.should_stop():
break
def main():
if tf.__version__.split('.')[0] != "1":
raise Exception("Tensorflow version 1 required")
if a.seed is None:
a.seed = random.randint(0, 2**31 - 1)
tf.set_random_seed(a.seed)
np.random.seed(a.seed)
random.seed(a.seed)
if not os.path.exists(a.output_dir):
os.makedirs(a.output_dir)
if a.mode == "test" or a.mode == "export":
if a.checkpoint is None:
raise Exception("checkpoint required for test mode")
# load some options from the checkpoint
options = {"which_direction", "ngf", "ndf", "lab_colorization"}
with open(os.path.join(a.checkpoint, "options.json")) as f:
for key, val in json.loads(f.read()).items():
if key in options:
print("loaded", key, "=", val)
setattr(a, key, val)
# disable these features in test mode
a.scale_size = CROP_SIZE
a.flip = False
for k, v in a._get_kwargs():
print(k, "=", v)
with open(os.path.join(a.output_dir, "options.json"), "w") as f:
f.write(json.dumps(vars(a), sort_keys=True, indent=4))
if a.mode == "export":
# export the generator to a meta graph that can be imported later for standalone generation
if a.lab_colorization:
raise Exception("export not supported for lab_colorization")
input = tf.placeholder(tf.string, shape=[1])
input_data = tf.decode_base64(input[0])
input_image = tf.image.decode_png(input_data)
# remove alpha channel if present
input_image = tf.cond(tf.equal(tf.shape(input_image)[2], 4), lambda: input_image[:,:,:3], lambda: input_image)
# convert grayscale to RGB
input_image = tf.cond(tf.equal(tf.shape(input_image)[2], 1), lambda: tf.image.grayscale_to_rgb(input_image), lambda: input_image)
input_image = tf.image.convert_image_dtype(input_image, dtype=tf.float32)
input_image.set_shape([CROP_SIZE, CROP_SIZE, 3])
batch_input = tf.expand_dims(input_image, axis=0)
with tf.variable_scope("generator"):
batch_output = deprocess(create_generator(preprocess(batch_input), 3))
output_image = tf.image.convert_image_dtype(batch_output, dtype=tf.uint8)[0]
if a.output_filetype == "png":
output_data = tf.image.encode_png(output_image)
elif a.output_filetype == "jpeg":
output_data = tf.image.encode_jpeg(output_image, quality=80)
else:
raise Exception("invalid filetype")
output = tf.convert_to_tensor([tf.encode_base64(output_data)])
key = tf.placeholder(tf.string, shape=[1])
inputs = {
"key": key.name,
"input": input.name
}
tf.add_to_collection("inputs", json.dumps(inputs))
outputs = {
"key": tf.identity(key).name,
"output": output.name,
}
tf.add_to_collection("outputs", json.dumps(outputs))
init_op = tf.global_variables_initializer()
restore_saver = tf.train.Saver()
export_saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
print("loading model from checkpoint")
checkpoint = tf.train.latest_checkpoint(a.checkpoint)
restore_saver.restore(sess, checkpoint)
print("exporting model")
export_saver.export_meta_graph(filename=os.path.join(a.output_dir, "export.meta"))
export_saver.save(sess, os.path.join(a.output_dir, "export"), write_meta_graph=False)
return
examples = load_examples()
print("examples count = %d" % examples.count)
# inputs and targets are [batch_size, height, width, channels]
model = create_model(examples.inputs, examples.targets)
# undo colorization splitting on images that we use for display/output
if a.lab_colorization:
if a.which_direction == "AtoB":
# inputs is brightness, this will be handled fine as a grayscale image
# need to augment targets and outputs with brightness
targets = augment(examples.targets, examples.inputs)
outputs = augment(model.outputs, examples.inputs)
# inputs can be deprocessed normally and handled as if they are single channel
# grayscale images
inputs = deprocess(examples.inputs)
elif a.which_direction == "BtoA":
# inputs will be color channels only, get brightness from targets
inputs = augment(examples.inputs, examples.targets)
targets = deprocess(examples.targets)
outputs = deprocess(model.outputs)
else:
raise Exception("invalid direction")
else:
inputs = deprocess(examples.inputs)
targets = deprocess(examples.targets)
outputs = deprocess(model.outputs)
def convert(image):
if a.aspect_ratio != 1.0:
# upscale to correct aspect ratio
size = [CROP_SIZE, int(round(CROP_SIZE * a.aspect_ratio))]
image = tf.image.resize_images(image, size=size, method=tf.image.ResizeMethod.BICUBIC)
return tf.image.convert_image_dtype(image, dtype=tf.uint8, saturate=True)
# reverse any processing on images so they can be written to disk or displayed to user
with tf.name_scope("convert_inputs"):
converted_inputs = convert(inputs)
with tf.name_scope("convert_targets"):
converted_targets = convert(targets)
with tf.name_scope("convert_outputs"):
converted_outputs = convert(outputs)
with tf.name_scope("encode_images"):
display_fetches = {
"paths": examples.paths,
"inputs": tf.map_fn(tf.image.encode_png, converted_inputs, dtype=tf.string, name="input_pngs"),
"targets": tf.map_fn(tf.image.encode_png, converted_targets, dtype=tf.string, name="target_pngs"),
"outputs": tf.map_fn(tf.image.encode_png, converted_outputs, dtype=tf.string, name="output_pngs"),
}
# summaries
with tf.name_scope("inputs_summary"):
tf.summary.image("inputs", converted_inputs)
with tf.name_scope("targets_summary"):
tf.summary.image("targets", converted_targets)
with tf.name_scope("outputs_summary"):
tf.summary.image("outputs", converted_outputs)
with tf.name_scope("predict_real_summary"):
tf.summary.image("predict_real", tf.image.convert_image_dtype(model.predict_real, dtype=tf.uint8))
with tf.name_scope("predict_fake_summary"):
tf.summary.image("predict_fake", tf.image.convert_image_dtype(model.predict_fake, dtype=tf.uint8))
tf.summary.scalar("discriminator_loss", model.discrim_loss)
tf.summary.scalar("generator_loss_GAN", model.gen_loss_GAN)
tf.summary.scalar("generator_loss_L1", model.gen_loss_L1)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name + "/values", var)
for grad, var in model.discrim_grads_and_vars + model.gen_grads_and_vars:
tf.summary.histogram(var.op.name + "/gradients", grad)
with tf.name_scope("parameter_count"):
parameter_count = tf.reduce_sum([tf.reduce_prod(tf.shape(v)) for v in tf.trainable_variables()])
saver = tf.train.Saver(max_to_keep=1)
logdir = a.output_dir if (a.trace_freq > 0 or a.summary_freq > 0) else None
sv = tf.train.Supervisor(logdir=logdir, save_summaries_secs=0, saver=None)
with sv.managed_session() as sess:
print("parameter_count =", sess.run(parameter_count))
if a.checkpoint is not None:
print("loading model from checkpoint")
checkpoint = tf.train.latest_checkpoint(a.checkpoint)
saver.restore(sess, checkpoint)
max_steps = 2**32
if a.max_epochs is not None:
max_steps = examples.steps_per_epoch * a.max_epochs
if a.max_steps is not None:
max_steps = a.max_steps
if a.mode == "test":
# testing
# at most, process the test data once
max_steps = min(examples.steps_per_epoch, max_steps)
for step in range(max_steps):
results = sess.run(display_fetches)
filesets = save_images(results)
for i, f in enumerate(filesets):
print("evaluated image", f["name"])
index_path = append_index(filesets)
print("wrote index at", index_path)
else:
# training
start = time.time()
for step in range(max_steps):
def should(freq):
return freq > 0 and ((step + 1) % freq == 0 or step == max_steps - 1)
options = None
run_metadata = None
if should(a.trace_freq):
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
fetches = {
"train": model.train,
"global_step": sv.global_step,
}
if should(a.progress_freq):
fetches["discrim_loss"] = model.discrim_loss
fetches["gen_loss_GAN"] = model.gen_loss_GAN
fetches["gen_loss_L1"] = model.gen_loss_L1
if should(a.summary_freq):
fetches["summary"] = sv.summary_op
if should(a.display_freq):
fetches["display"] = display_fetches
results = sess.run(fetches, options=options, run_metadata=run_metadata)
if should(a.summary_freq):
print("recording summary")
sv.summary_writer.add_summary(results["summary"], results["global_step"])
if should(a.display_freq):
print("saving display images")
filesets = save_images(results["display"], step=results["global_step"])
append_index(filesets, step=True)
if should(a.trace_freq):
print("recording trace")
sv.summary_writer.add_run_metadata(run_metadata, "step_%d" % results["global_step"])
if should(a.progress_freq):
# global_step will have the correct step count if we resume from a checkpoint
train_epoch = math.ceil(results["global_step"] / examples.steps_per_epoch)
train_step = (results["global_step"] - 1) % examples.steps_per_epoch + 1
rate = (step + 1) * a.batch_size / (time.time() - start)
remaining = (max_steps - step) * a.batch_size / rate
print("progress epoch %d step %d image/sec %0.1f remaining %dm" % (train_epoch, train_step, rate, remaining / 60))
print("discrim_loss", results["discrim_loss"])
print("gen_loss_GAN", results["gen_loss_GAN"])
print("gen_loss_L1", results["gen_loss_L1"])
if should(a.save_freq):
print("saving model")
saver.save(sess, os.path.join(a.output_dir, "model"), global_step=sv.global_step)
if sv.should_stop():
break
def main():
if tf.__version__.split('.')[0] != "1":
raise Exception("Tensorflow version 1 required")
if a.seed is None:
a.seed = random.randint(0, 2**31 - 1)
tf.set_random_seed(a.seed)
np.random.seed(a.seed)
random.seed(a.seed)
if not os.path.exists(a.output_dir):
os.makedirs(a.output_dir)
if a.mode == "test" or a.mode == "export":
if a.checkpoint is None:
raise Exception("checkpoint required for test mode")
# load some options from the checkpoint
options = {"which_direction", "ngf", "ndf", "lab_colorization"}
with open(os.path.join(a.checkpoint, "options.json")) as f:
for key, val in json.loads(f.read()).items():
if key in options:
print("loaded", key, "=", val)
setattr(a, key, val)
# disable these features in test mode
a.scale_size = CROP_SIZE
a.flip = False
for k, v in a._get_kwargs():
print(k, "=", v)
with open(os.path.join(a.output_dir, "options.json"), "w") as f:
f.write(json.dumps(vars(a), sort_keys=True, indent=4))
if a.mode == "export":
# export the generator to a meta graph that can be imported later for standalone generation
if a.lab_colorization:
raise Exception("export not supported for lab_colorization")
input = tf.placeholder(tf.string, shape=[1])
input_data = tf.decode_base64(input[0])
input_image = tf.image.decode_png(input_data)
# remove alpha channel if present
input_image = tf.cond(tf.equal(tf.shape(input_image)[2], 4), lambda: input_image[:,:,:3], lambda: input_image)
# convert grayscale to RGB
input_image = tf.cond(tf.equal(tf.shape(input_image)[2], 1), lambda: tf.image.grayscale_to_rgb(input_image), lambda: input_image)
input_image = tf.image.convert_image_dtype(input_image, dtype=tf.float32)
input_image.set_shape([CROP_SIZE, CROP_SIZE, 3])
batch_input = tf.expand_dims(input_image, axis=0)
with tf.variable_scope("generator"):
batch_output = deprocess(create_generator(preprocess(batch_input), 3))
output_image = tf.image.convert_image_dtype(batch_output, dtype=tf.uint8)[0]
if a.output_filetype == "png":
output_data = tf.image.encode_png(output_image)
elif a.output_filetype == "jpeg":
output_data = tf.image.encode_jpeg(output_image, quality=80)
else:
raise Exception("invalid filetype")
output = tf.convert_to_tensor([tf.encode_base64(output_data)])
key = tf.placeholder(tf.string, shape=[1])
inputs = {
"key": key.name,
"input": input.name
}
tf.add_to_collection("inputs", json.dumps(inputs))
outputs = {
"key": tf.identity(key).name,
"output": output.name,
}
tf.add_to_collection("outputs", json.dumps(outputs))
init_op = tf.global_variables_initializer()
restore_saver = tf.train.Saver()
export_saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
print("loading model from checkpoint")
checkpoint = tf.train.latest_checkpoint(a.checkpoint)
restore_saver.restore(sess, checkpoint)
print("exporting model")
export_saver.export_meta_graph(filename=os.path.join(a.output_dir, "export.meta"))
export_saver.save(sess, os.path.join(a.output_dir, "export"), write_meta_graph=False)
return
examples = load_examples()
print("examples count = %d" % examples.count)
# inputs and targets are [batch_size, height, width, channels]
model = create_model(examples.inputs, examples.targets)
# undo colorization splitting on images that we use for display/output
if a.lab_colorization:
if a.which_direction == "AtoB":
# inputs is brightness, this will be handled fine as a grayscale image
# need to augment targets and outputs with brightness
targets = augment(examples.targets, examples.inputs)
outputs = augment(model.outputs, examples.inputs)
# inputs can be deprocessed normally and handled as if they are single channel
# grayscale images
inputs = deprocess(examples.inputs)
elif a.which_direction == "BtoA":
# inputs will be color channels only, get brightness from targets
inputs = augment(examples.inputs, examples.targets)
targets = deprocess(examples.targets)
outputs = deprocess(model.outputs)
else:
raise Exception("invalid direction")
else:
inputs = deprocess(examples.inputs)
targets = deprocess(examples.targets)
outputs = deprocess(model.outputs)
def convert(image):
if a.aspect_ratio != 1.0:
# upscale to correct aspect ratio
size = [CROP_SIZE, int(round(CROP_SIZE * a.aspect_ratio))]
image = tf.image.resize_images(image, size=size, method=tf.image.ResizeMethod.BICUBIC)
return tf.image.convert_image_dtype(image, dtype=tf.uint8, saturate=True)
# reverse any processing on images so they can be written to disk or displayed to user
with tf.name_scope("convert_inputs"):
converted_inputs = convert(inputs)
with tf.name_scope("convert_targets"):
converted_targets = convert(targets)
with tf.name_scope("convert_outputs"):
converted_outputs = convert(outputs)
with tf.name_scope("encode_images"):
display_fetches = {
"paths": examples.paths,
"inputs": tf.map_fn(tf.image.encode_png, converted_inputs, dtype=tf.string, name="input_pngs"),
"targets": tf.map_fn(tf.image.encode_png, converted_targets, dtype=tf.string, name="target_pngs"),
"outputs": tf.map_fn(tf.image.encode_png, converted_outputs, dtype=tf.string, name="output_pngs"),
}
# summaries
with tf.name_scope("inputs_summary"):
tf.summary.image("inputs", converted_inputs)
with tf.name_scope("targets_summary"):
tf.summary.image("targets", converted_targets)
with tf.name_scope("outputs_summary"):
tf.summary.image("outputs", converted_outputs)
with tf.name_scope("predict_real_summary"):
tf.summary.image("predict_real", tf.image.convert_image_dtype(model.predict_real, dtype=tf.uint8))
with tf.name_scope("predict_fake_summary"):
tf.summary.image("predict_fake", tf.image.convert_image_dtype(model.predict_fake, dtype=tf.uint8))
tf.summary.scalar("discriminator_loss", model.discrim_loss)
tf.summary.scalar("generator_loss_GAN", model.gen_loss_GAN)
tf.summary.scalar("generator_loss_L1", model.gen_loss_L1)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name + "/values", var)
for grad, var in model.discrim_grads_and_vars + model.gen_grads_and_vars:
tf.summary.histogram(var.op.name + "/gradients", grad)
with tf.name_scope("parameter_count"):
parameter_count = tf.reduce_sum([tf.reduce_prod(tf.shape(v)) for v in tf.trainable_variables()])
saver = tf.train.Saver(max_to_keep=1)
logdir = a.output_dir if (a.trace_freq > 0 or a.summary_freq > 0) else None
sv = tf.train.Supervisor(logdir=logdir, save_summaries_secs=0, saver=None)
with sv.managed_session() as sess:
print("parameter_count =", sess.run(parameter_count))
if a.checkpoint is not None:
print("loading model from checkpoint")
checkpoint = tf.train.latest_checkpoint(a.checkpoint)
saver.restore(sess, checkpoint)
max_steps = 2**32
if a.max_epochs is not None:
max_steps = examples.steps_per_epoch * a.max_epochs
if a.max_steps is not None:
max_steps = a.max_steps
if a.mode == "test":
# testing
# at most, process the test data once
max_steps = min(examples.steps_per_epoch, max_steps)
for step in range(max_steps):
results = sess.run(display_fetches)
filesets = save_images(results)
for i, f in enumerate(filesets):
print("evaluated image", f["name"])
index_path = append_index(filesets)
print("wrote index at", index_path)
else:
# training
start = time.time()
for step in range(max_steps):
def should(freq):
return freq > 0 and ((step + 1) % freq == 0 or step == max_steps - 1)
options = None
run_metadata = None
if should(a.trace_freq):
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
fetches = {
"train": model.train,
"global_step": sv.global_step,
}
if should(a.progress_freq):
fetches["discrim_loss"] = model.discrim_loss
fetches["gen_loss_GAN"] = model.gen_loss_GAN
fetches["gen_loss_L1"] = model.gen_loss_L1
if should(a.summary_freq):
fetches["summary"] = sv.summary_op
if should(a.display_freq):
fetches["display"] = display_fetches
results = sess.run(fetches, options=options, run_metadata=run_metadata)
if should(a.summary_freq):
print("recording summary")
sv.summary_writer.add_summary(results["summary"], results["global_step"])
if should(a.display_freq):
print("saving display images")
filesets = save_images(results["display"], step=results["global_step"])
append_index(filesets, step=True)
if should(a.trace_freq):
print("recording trace")
sv.summary_writer.add_run_metadata(run_metadata, "step_%d" % results["global_step"])
if should(a.progress_freq):
# global_step will have the correct step count if we resume from a checkpoint
train_epoch = math.ceil(results["global_step"] / examples.steps_per_epoch)
train_step = (results["global_step"] - 1) % examples.steps_per_epoch + 1
rate = (step + 1) * a.batch_size / (time.time() - start)
remaining = (max_steps - step) * a.batch_size / rate
print("progress epoch %d step %d image/sec %0.1f remaining %dm" % (train_epoch, train_step, rate, remaining / 60))
print("discrim_loss", results["discrim_loss"])
print("gen_loss_GAN", results["gen_loss_GAN"])
print("gen_loss_L1", results["gen_loss_L1"])
if should(a.save_freq):
print("saving model")
saver.save(sess, os.path.join(a.output_dir, "model"), global_step=sv.global_step)
if sv.should_stop():
break
# -*- coding: utf-8 -*-
import tensorflow as tf
import timeit
tf.enable_eager_execution()
print(tf.add(1, 2))
print(tf.add([1, 2], [3, 4]))
print(tf.square(5))
print(tf.reduce_sum([1, 2, 3]))
print(tf.encode_base64("Hello World!"))
# Operator overloading is also supported
print(tf.square(2) + tf.square(3))
a = tf.constant([[1]])
print(a)
b = tf.constant([[2, 3]])
print(b)
x = tf.matmul(a, b)
print(x)
print(x.shape)
print(x.dtype)
import numpy as np
nd_array = np.ones([3, 3])
print(nd_array)
print("Tensorflow operation convert numpy arrays to Tensors automatically")
ts_array = tf.multiply(nd_array, 42)
