You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
350 lines
12 KiB
350 lines
12 KiB
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import argparse
|
|
import random
|
|
import numpy
|
|
import cPickle
|
|
import sys, os
|
|
from PIL import Image
|
|
|
|
from paddle.trainer.config_parser import parse_config
|
|
from paddle.trainer.config_parser import logger
|
|
import py_paddle.swig_paddle as api
|
|
import matplotlib.pyplot as plt
|
|
|
|
|
|
def plot2DScatter(data, outputfile):
|
|
'''
|
|
Plot the data as a 2D scatter plot and save to outputfile
|
|
data needs to be two dimensinoal
|
|
'''
|
|
x = data[:, 0]
|
|
y = data[:, 1]
|
|
logger.info("The mean vector is %s" % numpy.mean(data, 0))
|
|
logger.info("The std vector is %s" % numpy.std(data, 0))
|
|
|
|
heatmap, xedges, yedges = numpy.histogram2d(x, y, bins=50)
|
|
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
|
|
|
|
plt.clf()
|
|
plt.scatter(x, y)
|
|
plt.savefig(outputfile, bbox_inches='tight')
|
|
|
|
|
|
def CHECK_EQ(a, b):
|
|
assert a == b, "a=%s, b=%s" % (a, b)
|
|
|
|
|
|
def copy_shared_parameters(src, dst):
|
|
'''
|
|
copy the parameters from src to dst
|
|
:param src: the source of the parameters
|
|
:type src: GradientMachine
|
|
:param dst: the destination of the parameters
|
|
:type dst: GradientMachine
|
|
'''
|
|
src_params = [src.getParameter(i) for i in xrange(src.getParameterSize())]
|
|
src_params = dict([(p.getName(), p) for p in src_params])
|
|
|
|
for i in xrange(dst.getParameterSize()):
|
|
dst_param = dst.getParameter(i)
|
|
src_param = src_params.get(dst_param.getName(), None)
|
|
if src_param is None:
|
|
continue
|
|
src_value = src_param.getBuf(api.PARAMETER_VALUE)
|
|
dst_value = dst_param.getBuf(api.PARAMETER_VALUE)
|
|
CHECK_EQ(len(src_value), len(dst_value))
|
|
dst_value.copyFrom(src_value)
|
|
dst_param.setValueUpdated()
|
|
|
|
|
|
def print_parameters(src):
|
|
src_params = [src.getParameter(i) for i in xrange(src.getParameterSize())]
|
|
|
|
print "***************"
|
|
for p in src_params:
|
|
print "Name is %s" % p.getName()
|
|
print "value is %s \n" % p.getBuf(api.PARAMETER_VALUE).copyToNumpyArray(
|
|
)
|
|
|
|
|
|
def load_mnist_data(imageFile):
|
|
f = open(imageFile, "rb")
|
|
f.read(16)
|
|
|
|
# Define number of samples for train/test
|
|
if "train" in imageFile:
|
|
n = 60000
|
|
else:
|
|
n = 10000
|
|
|
|
data = numpy.fromfile(f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28))
|
|
data = data / 255.0 * 2.0 - 1.0
|
|
|
|
f.close()
|
|
return data.astype('float32')
|
|
|
|
|
|
def load_cifar_data(cifar_path):
|
|
batch_size = 10000
|
|
data = numpy.zeros((5 * batch_size, 32 * 32 * 3), dtype="float32")
|
|
for i in range(1, 6):
|
|
file = cifar_path + "/data_batch_" + str(i)
|
|
fo = open(file, 'rb')
|
|
dict = cPickle.load(fo)
|
|
fo.close()
|
|
data[(i - 1) * batch_size:(i * batch_size), :] = dict["data"]
|
|
|
|
data = data / 255.0 * 2.0 - 1.0
|
|
return data
|
|
|
|
|
|
# synthesize 2-D uniform data
|
|
def load_uniform_data():
|
|
data = numpy.random.rand(1000000, 2).astype('float32')
|
|
return data
|
|
|
|
|
|
def merge(images, size):
|
|
if images.shape[1] == 28 * 28:
|
|
h, w, c = 28, 28, 1
|
|
else:
|
|
h, w, c = 32, 32, 3
|
|
img = numpy.zeros((h * size[0], w * size[1], c))
|
|
for idx in xrange(size[0] * size[1]):
|
|
i = idx % size[1]
|
|
j = idx // size[1]
|
|
img[j*h:j*h+h, i*w:i*w+w, :] = \
|
|
((images[idx, :].reshape((h, w, c), order="F").transpose(1, 0, 2) + 1.0) / 2.0 * 255.0)
|
|
return img.astype('uint8')
|
|
|
|
|
|
def save_images(images, path):
|
|
merged_img = merge(images, [8, 8])
|
|
if merged_img.shape[2] == 1:
|
|
im = Image.fromarray(numpy.squeeze(merged_img)).convert('RGB')
|
|
else:
|
|
im = Image.fromarray(merged_img, mode="RGB")
|
|
im.save(path)
|
|
|
|
|
|
def get_real_samples(batch_size, data_np):
|
|
return data_np[numpy.random.choice(
|
|
data_np.shape[0], batch_size, replace=False), :]
|
|
|
|
|
|
def get_noise(batch_size, noise_dim):
|
|
return numpy.random.normal(size=(batch_size, noise_dim)).astype('float32')
|
|
|
|
|
|
def get_fake_samples(generator_machine, batch_size, noise):
|
|
gen_inputs = api.Arguments.createArguments(1)
|
|
gen_inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(noise))
|
|
gen_outputs = api.Arguments.createArguments(0)
|
|
generator_machine.forward(gen_inputs, gen_outputs, api.PASS_TEST)
|
|
fake_samples = gen_outputs.getSlotValue(0).copyToNumpyMat()
|
|
return fake_samples
|
|
|
|
|
|
def get_training_loss(training_machine, inputs):
|
|
outputs = api.Arguments.createArguments(0)
|
|
training_machine.forward(inputs, outputs, api.PASS_TEST)
|
|
loss = outputs.getSlotValue(0).copyToNumpyMat()
|
|
return numpy.mean(loss)
|
|
|
|
|
|
def prepare_discriminator_data_batch_pos(batch_size, data_np):
|
|
real_samples = get_real_samples(batch_size, data_np)
|
|
labels = numpy.ones(batch_size, dtype='int32')
|
|
inputs = api.Arguments.createArguments(2)
|
|
inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(real_samples))
|
|
inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(labels))
|
|
return inputs
|
|
|
|
|
|
def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise):
|
|
fake_samples = get_fake_samples(generator_machine, batch_size, noise)
|
|
labels = numpy.zeros(batch_size, dtype='int32')
|
|
inputs = api.Arguments.createArguments(2)
|
|
inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(fake_samples))
|
|
inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(labels))
|
|
return inputs
|
|
|
|
|
|
def prepare_generator_data_batch(batch_size, noise):
|
|
label = numpy.ones(batch_size, dtype='int32')
|
|
inputs = api.Arguments.createArguments(2)
|
|
inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(noise))
|
|
inputs.setSlotIds(1, api.IVector.createVectorFromNumpy(label))
|
|
return inputs
|
|
|
|
|
|
def find(iterable, cond):
|
|
for item in iterable:
|
|
if cond(item):
|
|
return item
|
|
return None
|
|
|
|
|
|
def get_layer_size(model_conf, layer_name):
|
|
layer_conf = find(model_conf.layers, lambda x: x.name == layer_name)
|
|
assert layer_conf is not None, "Cannot find '%s' layer" % layer_name
|
|
return layer_conf.size
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("-d", "--data_source", help="mnist or cifar or uniform")
|
|
parser.add_argument(
|
|
"--use_gpu", default="1", help="1 means use gpu for training")
|
|
parser.add_argument("--gpu_id", default="0", help="the gpu_id parameter")
|
|
args = parser.parse_args()
|
|
data_source = args.data_source
|
|
use_gpu = args.use_gpu
|
|
assert data_source in ["mnist", "cifar", "uniform"]
|
|
assert use_gpu in ["0", "1"]
|
|
|
|
if not os.path.exists("./%s_samples/" % data_source):
|
|
os.makedirs("./%s_samples/" % data_source)
|
|
|
|
if not os.path.exists("./%s_params/" % data_source):
|
|
os.makedirs("./%s_params/" % data_source)
|
|
|
|
api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10',
|
|
'--log_period=100', '--gpu_id=' + args.gpu_id,
|
|
'--save_dir=' + "./%s_params/" % data_source)
|
|
|
|
if data_source == "uniform":
|
|
conf = "gan_conf.py"
|
|
num_iter = 10000
|
|
else:
|
|
conf = "gan_conf_image.py"
|
|
num_iter = 1000
|
|
|
|
gen_conf = parse_config(conf, "mode=generator_training,data=" + data_source)
|
|
dis_conf = parse_config(conf,
|
|
"mode=discriminator_training,data=" + data_source)
|
|
generator_conf = parse_config(conf, "mode=generator,data=" + data_source)
|
|
batch_size = dis_conf.opt_config.batch_size
|
|
noise_dim = get_layer_size(gen_conf.model_config, "noise")
|
|
|
|
if data_source == "mnist":
|
|
data_np = load_mnist_data("./data/mnist_data/train-images-idx3-ubyte")
|
|
elif data_source == "cifar":
|
|
data_np = load_cifar_data("./data/cifar-10-batches-py/")
|
|
else:
|
|
data_np = load_uniform_data()
|
|
|
|
# this creates a gradient machine for discriminator
|
|
dis_training_machine = api.GradientMachine.createFromConfigProto(
|
|
dis_conf.model_config)
|
|
# this create a gradient machine for generator
|
|
gen_training_machine = api.GradientMachine.createFromConfigProto(
|
|
gen_conf.model_config)
|
|
|
|
# generator_machine is used to generate data only, which is used for
|
|
# training discriminator
|
|
logger.info(str(generator_conf.model_config))
|
|
generator_machine = api.GradientMachine.createFromConfigProto(
|
|
generator_conf.model_config)
|
|
|
|
dis_trainer = api.Trainer.create(dis_conf, dis_training_machine)
|
|
|
|
gen_trainer = api.Trainer.create(gen_conf, gen_training_machine)
|
|
|
|
dis_trainer.startTrain()
|
|
gen_trainer.startTrain()
|
|
|
|
# Sync parameters between networks (GradientMachine) at the beginning
|
|
copy_shared_parameters(gen_training_machine, dis_training_machine)
|
|
copy_shared_parameters(gen_training_machine, generator_machine)
|
|
|
|
# constrain that either discriminator or generator can not be trained
|
|
# consecutively more than MAX_strike times
|
|
curr_train = "dis"
|
|
curr_strike = 0
|
|
MAX_strike = 5
|
|
|
|
for train_pass in xrange(100):
|
|
dis_trainer.startTrainPass()
|
|
gen_trainer.startTrainPass()
|
|
for i in xrange(num_iter):
|
|
# Do forward pass in discriminator to get the dis_loss
|
|
noise = get_noise(batch_size, noise_dim)
|
|
data_batch_dis_pos = prepare_discriminator_data_batch_pos(
|
|
batch_size, data_np)
|
|
dis_loss_pos = get_training_loss(dis_training_machine,
|
|
data_batch_dis_pos)
|
|
|
|
data_batch_dis_neg = prepare_discriminator_data_batch_neg(
|
|
generator_machine, batch_size, noise)
|
|
dis_loss_neg = get_training_loss(dis_training_machine,
|
|
data_batch_dis_neg)
|
|
|
|
dis_loss = (dis_loss_pos + dis_loss_neg) / 2.0
|
|
|
|
# Do forward pass in generator to get the gen_loss
|
|
data_batch_gen = prepare_generator_data_batch(batch_size, noise)
|
|
gen_loss = get_training_loss(gen_training_machine, data_batch_gen)
|
|
|
|
if i % 100 == 0:
|
|
print "d_pos_loss is %s d_neg_loss is %s" % (dis_loss_pos,
|
|
dis_loss_neg)
|
|
print "d_loss is %s g_loss is %s" % (dis_loss, gen_loss)
|
|
|
|
# Decide which network to train based on the training history
|
|
# And the relative size of the loss
|
|
if (not (curr_train == "dis" and curr_strike == MAX_strike)) and \
|
|
((curr_train == "gen" and curr_strike == MAX_strike) or dis_loss > gen_loss):
|
|
if curr_train == "dis":
|
|
curr_strike += 1
|
|
else:
|
|
curr_train = "dis"
|
|
curr_strike = 1
|
|
dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_neg)
|
|
dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_pos)
|
|
copy_shared_parameters(dis_training_machine,
|
|
gen_training_machine)
|
|
|
|
else:
|
|
if curr_train == "gen":
|
|
curr_strike += 1
|
|
else:
|
|
curr_train = "gen"
|
|
curr_strike = 1
|
|
gen_trainer.trainOneDataBatch(batch_size, data_batch_gen)
|
|
# TODO: add API for paddle to allow true parameter sharing between different GradientMachines
|
|
# so that we do not need to copy shared parameters.
|
|
copy_shared_parameters(gen_training_machine,
|
|
dis_training_machine)
|
|
copy_shared_parameters(gen_training_machine, generator_machine)
|
|
|
|
dis_trainer.finishTrainPass()
|
|
gen_trainer.finishTrainPass()
|
|
# At the end of each pass, save the generated samples/images
|
|
fake_samples = get_fake_samples(generator_machine, batch_size, noise)
|
|
if data_source == "uniform":
|
|
plot2DScatter(fake_samples, "./%s_samples/train_pass%s.png" %
|
|
(data_source, train_pass))
|
|
else:
|
|
save_images(fake_samples, "./%s_samples/train_pass%s.png" %
|
|
(data_source, train_pass))
|
|
dis_trainer.finishTrain()
|
|
gen_trainer.finishTrain()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|