Simplify Paddle demo by showing vgg only and add comments

demo/README.md

@@ -6,7 +6,7 @@ there are several demos for different platforms.
 ## PaddlePaddle
 Locates in `./paddle`.
 
-This is a visualization for `resnet` on `cifar10` dataset, we visualize the CONV parameters,
+This is a visualization for `vgg` on `cifar10` dataset, we visualize the CONV parameters,
 and there are some interesting patterns.
 
 ## PyTorch GAN

demo/paddle/cifar10_image_classification_vgg.py → demo/paddle/paddle_cifar10.py

@@ -15,8 +15,6 @@
 
 from __future__ import print_function
 
-import sys
-
 import numpy as np
 from visualdl import LogWriter
 
@@ -26,64 +24,27 @@
 from paddle.v2.fluid.initializer import NormalInitializer
 from paddle.v2.fluid.param_attr import ParamAttr
 
+# create VisualDL logger and directory
 logdir = "./tmp"
 logwriter = LogWriter(logdir, sync_cycle=10)
 
+# create 'train' run
 with logwriter.mode("train") as writer:
+ # create 'loss' scalar tag to keep track of loss function
  loss_scalar = writer.scalar("loss")
 
 with logwriter.mode("train") as writer:
  acc_scalar = writer.scalar("acc")
 
 num_samples = 4
 with logwriter.mode("train") as writer:
- conv_image = writer.image("conv_image", num_samples, 1)
+ conv_image = writer.image("conv_image", num_samples,
+ 1) # show 4 samples for every 1 step
  input_image = writer.image("input_image", num_samples, 1)
 
 with logwriter.mode("train") as writer:
- param1_histgram = writer.histogram("param1", 100)
-
-
-def resnet_cifar10(input, depth=32):
- def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
- tmp = fluid.layers.conv2d(
- input=input,
- filter_size=filter_size,
- num_filters=ch_out,
- stride=stride,
- padding=padding,
- act=None,
- bias_attr=False)
- return fluid.layers.batch_norm(input=tmp, act=act)
-
- def shortcut(input, ch_in, ch_out, stride):
- if ch_in != ch_out:
- return conv_bn_layer(input, ch_out, 1, stride, 0, None)
- else:
- return input
-
- def basicblock(input, ch_in, ch_out, stride):
- tmp = conv_bn_layer(input, ch_out, 3, stride, 1)
- tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None)
- short = shortcut(input, ch_in, ch_out, stride)
- return fluid.layers.elementwise_add(x=tmp, y=short, act='relu')
-
- def layer_warp(block_func, input, ch_in, ch_out, count, stride):
- tmp = block_func(input, ch_in, ch_out, stride)
- for i in range(1, count):
- tmp = block_func(tmp, ch_out, ch_out, 1)
- return tmp
-
- assert (depth - 2) % 6 == 0
- n = (depth - 2) / 6
- conv1 = conv_bn_layer(
- input=input, ch_out=16, filter_size=3, stride=1, padding=1)
- res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
- res2 = layer_warp(basicblock, res1, 16, 32, n, 2)
- res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
- pool = fluid.layers.pool2d(
- input=res3, pool_size=8, pool_type='avg', pool_stride=1)
- return pool
+ param1_histgram = writer.histogram(
+ "param1", 100) # 100 buckets, e.g 100 data sets in a histograms
 
 
 def vgg16_bn_drop(input):
@@ -119,18 +80,7 @@ def conv_block(input, num_filter, groups, dropouts):
 images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
 label = fluid.layers.data(name='label', shape=[1], dtype='int64')
 
-net_type = "vgg"
-if len(sys.argv) >= 2:
- net_type = sys.argv[1]
-
-if net_type == "vgg":
- print("train vgg net")
- net, conv1 = vgg16_bn_drop(images)
-elif net_type == "resnet":
- print("train resnet")
- net = resnet_cifar10(images, 32)
-else:
- raise ValueError("%s network is not supported" % net_type)
+net, conv1 = vgg16_bn_drop(images)
 
 predict = fluid.layers.fc(
  input=net,
@@ -173,6 +123,9 @@ def conv_block(input, num_filter, groups, dropouts):
  fetch_list=[avg_cost, conv1, param1_var] + accuracy.metrics)
  pass_acc = accuracy.eval(exe)
 
+ # all code below is for VisualDL
+
+ # start picking sample from beginning
  if sample_num == 0:
  input_image.start_sampling()
  conv_image.start_sampling()
@@ -183,21 +136,26 @@ def conv_block(input, num_filter, groups, dropouts):
  idx = idx1
  if idx != -1:
  image_data = data[0][0]
+ # reshape the image to 32x32 and 3 channels
  input_image_data = np.transpose(
  image_data.reshape(data_shape), axes=[1, 2, 0])
+ # add sample to VisualDL Image Writer to view input image
  input_image.set_sample(idx, input_image_data.shape,
  input_image_data.flatten())
 
  conv_image_data = conv1_out[0][0]
+ # add sample to view conv image
  conv_image.set_sample(idx, conv_image_data.shape,
  conv_image_data.flatten())
 
  sample_num += 1
+ # when we have enough samples, call finish sampling()
  if sample_num % num_samples == 0:
  input_image.finish_sampling()
  conv_image.finish_sampling()
  sample_num = 0
 
+ # add record for loss and accuracy to scalar
  loss_scalar.add_record(step, loss)
  acc_scalar.add_record(step, acc)
  param1_histgram.add_record(step, param1.flatten())