From 3ebf6aaf9bb51cdd5e4c7de92cbb36af162c336d Mon Sep 17 00:00:00 2001 From: wangyang59 Date: Mon, 7 Nov 2016 09:40:31 -0800 Subject: [PATCH] fixed a gpu bug in trainer API to train gan using GPU --- demo/gan/.gitignore | 2 +- demo/gan/gan_conf_image.py | 132 +++++++++++++++------------------- demo/gan/gan_trainer.py | 18 ++--- demo/gan/gan_trainer_image.py | 38 +++++----- paddle/api/Paddle.swig | 3 +- 5 files changed, 92 insertions(+), 101 deletions(-) diff --git a/demo/gan/.gitignore b/demo/gan/.gitignore index 91ac27fe630..150fa0ab548 100644 --- a/demo/gan/.gitignore +++ b/demo/gan/.gitignore @@ -2,5 +2,5 @@ output/ *.png .pydevproject .project -trainLog.txt +train.log data/raw_data/ diff --git a/demo/gan/gan_conf_image.py b/demo/gan/gan_conf_image.py index 9a4f2a4ea49..00711730d56 100644 --- a/demo/gan/gan_conf_image.py +++ b/demo/gan/gan_conf_image.py @@ -41,39 +41,9 @@ settings( learning_method=AdamOptimizer() ) -def convTrans_bn(input, channels, output_x, num_filters, imgSize, stride, name, - param_attr, bias_attr, param_attr_bn): - tmp = imgSize - (output_x - 1) * stride - if tmp <= 1 or tmp > 5: - raise ValueError("convTrans input-output dimension does not fit") - elif tmp <= 3: - filter_size = tmp + 2 - padding = 1 - else: - filter_size = tmp - padding = 0 - - - convTrans = img_conv_layer(input, filter_size=filter_size, - num_filters=num_filters, - name=name + "_convt", num_channels=channels, - act=LinearActivation(), groups=1, stride=stride, - padding=padding, bias_attr=bias_attr, - param_attr=param_attr, shared_biases=True, layer_attr=None, - filter_size_y=None, stride_y=None, padding_y=None, - trans=True) - - convTrans_bn = batch_norm_layer(convTrans, - act=ReluActivation(), - name=name + "_convt_bn", - bias_attr=bias_attr, - param_attr=param_attr_bn, - use_global_stats=False) - - return convTrans_bn - def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name, - param_attr, bias_attr, param_attr_bn, bn): + param_attr, bias_attr, param_attr_bn, bn, trans=False, + act=ReluActivation()): tmp = imgSize - (output_x - 1) * stride if tmp <= 1 or tmp > 5: raise ValueError("conv input-output dimension does not fit") @@ -85,19 +55,25 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name, padding = 0 print (imgSize, output_x, stride, filter_size, padding) - + + if trans: + nameApx = "_conv" + else: + nameApx = "_convt" + if bn: conv = img_conv_layer(input, filter_size=filter_size, num_filters=num_filters, - name=name + "_conv", num_channels=channels, + name=name + nameApx, num_channels=channels, act=LinearActivation(), groups=1, stride=stride, padding=padding, bias_attr=bias_attr, param_attr=param_attr, shared_biases=True, layer_attr=None, - filter_size_y=None, stride_y=None, padding_y=None) + filter_size_y=None, stride_y=None, padding_y=None, + trans=trans) conv_bn = batch_norm_layer(conv, - act=ReluActivation(), - name=name + "_conv_bn", + act=act, + name=name + nameApx + "_bn", bias_attr=bias_attr, param_attr=param_attr_bn, use_global_stats=False) @@ -106,11 +82,12 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name, else: conv = img_conv_layer(input, filter_size=filter_size, num_filters=num_filters, - name=name + "_conv", num_channels=channels, - act=ReluActivation(), groups=1, stride=stride, + name=name + nameApx, num_channels=channels, + act=act, groups=1, stride=stride, padding=padding, bias_attr=bias_attr, param_attr=param_attr, shared_biases=True, layer_attr=None, - filter_size_y=None, stride_y=None, padding_y=None) + filter_size_y=None, stride_y=None, padding_y=None, + trans=trans) return conv def generator(noise): @@ -143,39 +120,46 @@ def generator(noise): param_attr=param_attr_bn, use_global_stats=False) - h2_bn = convTrans_bn(h1_bn, - channels=gf_dim*4, - output_x=s8, - num_filters=gf_dim*2, - imgSize=s4, - stride=2, - name="gen_layer_h2", - param_attr=param_attr, - bias_attr=bias_attr, - param_attr_bn=param_attr_bn) + h2_bn = conv_bn(h1_bn, + channels=gf_dim*4, + output_x=s8, + num_filters=gf_dim*2, + imgSize=s4, + stride=2, + name="gen_layer_h2", + param_attr=param_attr, + bias_attr=bias_attr, + param_attr_bn=param_attr_bn, + bn=True, + trans=True) - h3_bn = convTrans_bn(h2_bn, - channels=gf_dim*2, - output_x=s4, - num_filters=gf_dim, - imgSize=s2, - stride=2, - name="gen_layer_h3", - param_attr=param_attr, - bias_attr=bias_attr, - param_attr_bn=param_attr_bn) + h3_bn = conv_bn(h2_bn, + channels=gf_dim*2, + output_x=s4, + num_filters=gf_dim, + imgSize=s2, + stride=2, + name="gen_layer_h3", + param_attr=param_attr, + bias_attr=bias_attr, + param_attr_bn=param_attr_bn, + bn=True, + trans=True) - return convTrans_bn(h3_bn, - channels=gf_dim, - output_x=s2, - num_filters=c_dim, - imgSize=sample_dim, - stride=2, - name="gen_layer_h4", - param_attr=param_attr, - bias_attr=bias_attr, - param_attr_bn=param_attr_bn) + return conv_bn(h3_bn, + channels=gf_dim, + output_x=s2, + num_filters=c_dim, + imgSize=sample_dim, + stride=2, + name="gen_layer_h4", + param_attr=param_attr, + bias_attr=bias_attr, + param_attr_bn=param_attr_bn, + bn=False, + trans=True, + act=TanhActivation()) def discriminator(sample): @@ -186,10 +170,12 @@ def discriminator(sample): of the sample is from generator and dimension 1 is the probabblity of the sample is from real data. """ - param_attr = ParamAttr(is_static=is_generator_training) + param_attr = ParamAttr(is_static=is_generator_training, + initial_mean=0.0, + initial_std=0.02) bias_attr = ParamAttr(is_static=is_generator_training, - initial_mean=1.0, - initial_std=0) + initial_mean=0.0, + initial_std=0.0) param_attr_bn=ParamAttr(is_static=is_generator_training, initial_mean=1.0, diff --git a/demo/gan/gan_trainer.py b/demo/gan/gan_trainer.py index e64f0ffa0dc..6dc67e4b0d5 100644 --- a/demo/gan/gan_trainer.py +++ b/demo/gan/gan_trainer.py @@ -97,32 +97,32 @@ def prepare_discriminator_data_batch( (numpy.zeros(batch_size / 2, dtype='int32'), numpy.ones(batch_size / 2, dtype='int32')), 0) inputs = api.Arguments.createArguments(2) - inputs.setSlotValue(0, api.Matrix.createCpuDenseFromNumpy(all_samples)) - inputs.setSlotIds(1, api.IVector.createCpuVectorFromNumpy(all_labels)) + inputs.setSlotValue(0, api.Matrix.createGpuDenseFromNumpy(all_samples)) + inputs.setSlotIds(1, api.IVector.createGpuVectorFromNumy(all_labels)) return inputs def prepare_discriminator_data_batch_pos(batch_size, noise_dim, sample_dim): real_samples = get_real_samples(batch_size, sample_dim) labels = numpy.ones(batch_size, dtype='int32') inputs = api.Arguments.createArguments(2) - inputs.setSlotValue(0, api.Matrix.createCpuDenseFromNumpy(real_samples)) - inputs.setSlotIds(1, api.IVector.createCpuVectorFromNumpy(labels)) + inputs.setSlotValue(0, api.Matrix.createGpuDenseFromNumpy(real_samples)) + inputs.setSlotIds(1, api.IVector.createGpuVectorFromNumy(labels)) return inputs def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise_dim, sample_dim): fake_samples = get_fake_samples(generator_machine, batch_size, noise_dim, sample_dim) labels = numpy.zeros(batch_size, dtype='int32') inputs = api.Arguments.createArguments(2) - inputs.setSlotValue(0, api.Matrix.createCpuDenseFromNumpy(fake_samples)) - inputs.setSlotIds(1, api.IVector.createCpuVectorFromNumpy(labels)) + inputs.setSlotValue(0, api.Matrix.createGpuDenseFromNumpy(fake_samples)) + inputs.setSlotIds(1, api.IVector.createGpuVectorFromNumy(labels)) return inputs def prepare_generator_data_batch(batch_size, dim): noise = numpy.random.normal(size=(batch_size, dim)).astype('float32') label = numpy.ones(batch_size, dtype='int32') inputs = api.Arguments.createArguments(2) - inputs.setSlotValue(0, api.Matrix.createCpuDenseFromNumpy(noise)) - inputs.setSlotIds(1, api.IVector.createCpuVectorFromNumpy(label)) + inputs.setSlotValue(0, api.Matrix.createGpuDenseFromNumpy(noise)) + inputs.setSlotIds(1, api.IVector.createGpuVectorFromNumy(label)) return inputs @@ -140,7 +140,7 @@ def get_layer_size(model_conf, layer_name): def main(): - api.initPaddle('--use_gpu=0', '--dot_period=100', '--log_period=10000') + api.initPaddle('--use_gpu=1', '--dot_period=100', '--log_period=10000') gen_conf = parse_config("gan_conf.py", "mode=generator_training") dis_conf = parse_config("gan_conf.py", "mode=discriminator_training") generator_conf = parse_config("gan_conf.py", "mode=generator") diff --git a/demo/gan/gan_trainer_image.py b/demo/gan/gan_trainer_image.py index 536abab9210..8f1e17b9c74 100644 --- a/demo/gan/gan_trainer_image.py +++ b/demo/gan/gan_trainer_image.py @@ -16,7 +16,7 @@ import argparse import itertools import random import numpy -import sys,os +import sys,os,gc from PIL import Image from paddle.trainer.config_parser import parse_config @@ -94,10 +94,19 @@ def load_mnist_data(imageFile): f.close() return data +def merge(images, size): + h, w = 28, 28 + img = numpy.zeros((h * size[0], w * size[1])) + 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)) + 1.0) / 2.0 * 255.0 + return img + def saveImages(images, path): - for i in xrange(10): - im = Image.fromarray(images[i, :].reshape((28, 28)) * 255.0).convert('RGB') - im.save(path + "/image_" + str(i) + ".png") + merged_img = merge(images, [8, 8]) + im = Image.fromarray(merged_img).convert('RGB') + im.save(path) def get_real_samples(batch_size, data_np): return data_np[numpy.random.choice(data_np.shape[0], batch_size, @@ -124,8 +133,8 @@ 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.createCpuDenseFromNumpy(real_samples)) - inputs.setSlotIds(1, api.IVector.createCpuVectorFromNumpy(labels)) + inputs.setSlotValue(0, api.Matrix.createGpuDenseFromNumpy(real_samples)) + inputs.setSlotIds(1, api.IVector.createGpuVectorFromNumpy(labels)) return inputs def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise): @@ -133,16 +142,16 @@ def prepare_discriminator_data_batch_neg(generator_machine, batch_size, noise): #print fake_samples.shape labels = numpy.zeros(batch_size, dtype='int32') inputs = api.Arguments.createArguments(2) - inputs.setSlotValue(0, api.Matrix.createCpuDenseFromNumpy(fake_samples)) - inputs.setSlotIds(1, api.IVector.createCpuVectorFromNumpy(labels)) + inputs.setSlotValue(0, api.Matrix.createGpuDenseFromNumpy(fake_samples)) + inputs.setSlotIds(1, api.IVector.createGpuVectorFromNumpy(labels)) return inputs def prepare_generator_data_batch(batch_size, noise): label = numpy.ones(batch_size, dtype='int32') #label = numpy.zeros(batch_size, dtype='int32') inputs = api.Arguments.createArguments(2) - inputs.setSlotValue(0, api.Matrix.createCpuDenseFromNumpy(noise)) - inputs.setSlotIds(1, api.IVector.createCpuVectorFromNumpy(label)) + inputs.setSlotValue(0, api.Matrix.createGpuDenseFromNumpy(noise)) + inputs.setSlotIds(1, api.IVector.createGpuVectorFromNumpy(label)) return inputs @@ -160,7 +169,7 @@ def get_layer_size(model_conf, layer_name): def main(): - api.initPaddle('--use_gpu=0', '--dot_period=10', '--log_period=100') + api.initPaddle('--use_gpu=1', '--dot_period=10', '--log_period=100') gen_conf = parse_config("gan_conf_image.py", "mode=generator_training") dis_conf = parse_config("gan_conf_image.py", "mode=discriminator_training") generator_conf = parse_config("gan_conf_image.py", "mode=generator") @@ -169,7 +178,7 @@ def main(): sample_dim = get_layer_size(dis_conf.model_config, "sample") data_np = load_mnist_data("./data/raw_data/train-images-idx3-ubyte") - + # this create a gradient machine for discriminator dis_training_machine = api.GradientMachine.createFromConfigProto( dis_conf.model_config) @@ -252,10 +261,7 @@ def main(): fake_samples = get_fake_samples(generator_machine, batch_size, noise) - save_dir = "./pass_" + str(train_pass) - if not os.path.exists(save_dir): - os.makedirs(save_dir) - saveImages(fake_samples, save_dir) + saveImages(fake_samples, "train_pass%s.png" % train_pass) dis_trainer.finishTrain() gen_trainer.finishTrain() diff --git a/paddle/api/Paddle.swig b/paddle/api/Paddle.swig index 6a0fbc537d9..9194a6371be 100644 --- a/paddle/api/Paddle.swig +++ b/paddle/api/Paddle.swig @@ -193,5 +193,4 @@ namespace std { %ignore OptimizationConfigPrivate; %ignore ParameterTraverseCallbackPrivate; %include "utils/GlobalConstants.h" -%include "api/PaddleAPI.h" - +%include "api/PaddleAPI.h" \ No newline at end of file -- GitLab