fixed a gpu bug in trainer API to train gan using GPU

demo/gan/.gitignore

@@ -2,5 +2,5 @@ output/
 *.png
 .pydevproject
 .project
-trainLog.txt
+train.log
 data/raw_data/

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")
@@ -86,18 +56,24 @@ def conv_bn(input, channels, imgSize, num_filters, output_x, stride, name,
 
     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):
@@ -143,7 +120,7 @@ def generator(noise):
                      param_attr=param_attr_bn,
                      use_global_stats=False)
     
-    h2_bn = convTrans_bn(h1_bn, 
+    h2_bn = conv_bn(h1_bn, 
                     channels=gf_dim*4, 
                     output_x=s8,
                     num_filters=gf_dim*2, 
@@ -152,9 +129,11 @@ def generator(noise):
                     name="gen_layer_h2", 
                     param_attr=param_attr, 
                     bias_attr=bias_attr, 
-                        param_attr_bn=param_attr_bn)
+                    param_attr_bn=param_attr_bn,
+                    bn=True,
+                    trans=True)
     
-    h3_bn = convTrans_bn(h2_bn, 
+    h3_bn = conv_bn(h2_bn, 
                     channels=gf_dim*2, 
                     output_x=s4,
                     num_filters=gf_dim, 
@@ -163,10 +142,12 @@ def generator(noise):
                     name="gen_layer_h3", 
                     param_attr=param_attr, 
                     bias_attr=bias_attr, 
-                        param_attr_bn=param_attr_bn)
+                    param_attr_bn=param_attr_bn,
+                    bn=True,
+                    trans=True)
      
     
-    return convTrans_bn(h3_bn,
+    return conv_bn(h3_bn,
                    channels=gf_dim, 
                    output_x=s2,
                    num_filters=c_dim, 
@@ -175,7 +156,10 @@ def generator(noise):
                    name="gen_layer_h4", 
                    param_attr=param_attr, 
                    bias_attr=bias_attr, 
-                        param_attr_bn=param_attr_bn)
+                   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,

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")

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")
@@ -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()
 

paddle/api/Paddle.swig

@@ -194,4 +194,3 @@ namespace std {
 %ignore ParameterTraverseCallbackPrivate;
 %include "utils/GlobalConstants.h"
 %include "api/PaddleAPI.h"
-