Simplify Paddle demo by showing vgg only and add comments (#425)

* Simplify Paddle demo by showing vgg only and add comments

* update python format

* fix format issue

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
 
@@ -26,10 +24,13 @@ import paddle.v2.fluid.framework as framework
 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:
@@ -37,53 +38,13 @@ with logwriter.mode("train") as writer:
 
 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 @@ data_shape = [3, 32, 32]
 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 @@ for pass_id in range(PASS_NUM):
             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 @@ for pass_id in range(PASS_NUM):
         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())