add PaddlePaddle demo (#91)

* init paddle

* add image writer

* change sample numble to 4

* add image and conv image

* add histogram

* rename test file

demo/paddle/cifar10_image_classification_vgg.py

@@ -0,0 +1,188 @@
+from __future__ import print_function
+
+import sys
+
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.framework as framework
+from paddle.v2.fluid.param_attr import ParamAttr
+from paddle.v2.fluid.initializer import NormalInitializer
+from visualdl import LogWriter
+import numpy as np
+
+logdir = "./tmp"
+logwriter = LogWriter(logdir, sync_cycle=10)
+
+with logwriter.mode("train") as writer:
+    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)
+    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
+
+
+def vgg16_bn_drop(input):
+    def conv_block(input, num_filter, groups, dropouts):
+        return fluid.nets.img_conv_group(
+            input=input,
+            pool_size=2,
+            pool_stride=2,
+            conv_num_filter=[num_filter] * groups,
+            conv_filter_size=3,
+            conv_act='relu',
+            conv_with_batchnorm=True,
+            conv_batchnorm_drop_rate=dropouts,
+            pool_type='max')
+
+    conv1 = conv_block(input, 64, 2, [0.3, 0])
+    conv2 = conv_block(conv1, 128, 2, [0.4, 0])
+    conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
+    conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
+    conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
+
+    drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
+    fc1 = fluid.layers.fc(input=drop, size=512, act=None)
+    bn = fluid.layers.batch_norm(input=fc1, act='relu')
+    drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
+    fc2 = fluid.layers.fc(input=drop2, size=512, act=None)
+    return fc2, conv1
+
+
+classdim = 10
+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)
+
+predict = fluid.layers.fc(input=net, size=classdim, act='softmax',
+                          param_attr=ParamAttr(name="param1", initializer=NormalInitializer()))
+cost = fluid.layers.cross_entropy(input=predict, label=label)
+avg_cost = fluid.layers.mean(x=cost)
+
+optimizer = fluid.optimizer.Adam(learning_rate=0.001)
+opts = optimizer.minimize(avg_cost)
+
+accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
+
+BATCH_SIZE = 16
+PASS_NUM = 1
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.cifar.train10(), buf_size=128 * 10),
+    batch_size=BATCH_SIZE)
+
+place = fluid.CPUPlace()
+exe = fluid.Executor(place)
+feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
+exe.run(fluid.default_startup_program())
+
+step = 0
+sample_num = 0
+
+start_up_program = framework.default_startup_program()
+
+param1_var = start_up_program.global_block().var("param1")
+
+for pass_id in range(PASS_NUM):
+    accuracy.reset(exe)
+    for data in train_reader():
+        loss, conv1_out, param1, acc = exe.run(fluid.default_main_program(),
+                            feed=feeder.feed(data),
+                            fetch_list=[avg_cost, conv1, param1_var] + accuracy.metrics)
+        pass_acc = accuracy.eval(exe)
+
+        if sample_num == 0:
+            input_image.start_sampling()
+            conv_image.start_sampling()
+
+        idx1 = input_image.is_sample_taken()
+        idx2 = conv_image.is_sample_taken()
+        assert idx1 == idx2
+        idx = idx1
+        if idx != -1:
+            image_data = data[0][0]
+            input_image_data = np.transpose(image_data.reshape(data_shape), axes=[1, 2, 0])
+            input_image.set_sample(idx, input_image_data.shape, input_image_data.flatten())
+
+            conv_image_data = conv1_out[0][0]
+            conv_image.set_sample(idx, conv_image_data.shape, conv_image_data.flatten())
+
+            sample_num += 1
+            if sample_num % num_samples == 0:
+                input_image.finish_sampling()
+                conv_image.finish_sampling()
+                sample_num = 0
+
+        loss_scalar.add_record(step, loss)
+        acc_scalar.add_record(step, acc)
+        param1_histgram.add_record(step, param1.flatten())
+
+        print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
+            pass_acc))
+        step += 1
+        # this model is slow, so if we can train two mini batch, we think it works properly.
+        # exit(0)
+exit(1)

visualdl/logic/sdk.cc

@@ -98,14 +98,18 @@ struct is_same_type<T, T> {
 void Image::SetSample(int index,
                       const std::vector<shape_t>& shape,
                       const std::vector<value_t>& data) {
+  std::vector<shape_t> new_shape = shape;
+  if (shape.size() == 2) {
+    new_shape.emplace_back(1);
+  }
   // production
   int size = std::accumulate(
-      shape.begin(), shape.end(), 1., [](int a, int b) { return a * b; });
+          new_shape.begin(), new_shape.end(), 1., [](int a, int b) { return a * b; });
   CHECK_GT(size, 0);
-  CHECK_EQ(shape.size(), 3)
+  CHECK_LE(new_shape.size(), 3)
       << "shape should be something like (width, height, num_channel)";
-  CHECK_LE(shape.back(), 3);
-  CHECK_GE(shape.back(), 2);
+  CHECK_LE(new_shape.back(), 3);
+  CHECK_GE(new_shape.back(), 1);
   CHECK_EQ(size, data.size()) << "image's shape not match data";
   CHECK_LT(index, num_samples_);
   CHECK_LE(index, num_records_);
@@ -116,8 +120,8 @@ void Image::SetSample(int index,
   for (int i = 0; i < data.size(); i++) {
     data_str[i] = data[i];
   }
-  Uint8Image image(shape[2], shape[0] * shape[1]);
-  NormalizeImage(&image, &data[0], shape[0] * shape[1], shape[2]);
+  Uint8Image image(new_shape[2], new_shape[0] * new_shape[1]);
+  NormalizeImage(&image, &data[0], new_shape[0] * new_shape[1], new_shape[2]);
   // entry.SetRaw(std::string(data_str.begin(), data_str.end()));
   entry.SetRaw(
       std::string(image.data(), image.data() + image.rows() * image.cols()));
@@ -127,7 +131,7 @@ void Image::SetSample(int index,
       "value_t should not use int64_t field, this type is used to store shape");
 
   // set meta.
-  entry.SetMulti(shape);
+  entry.SetMulti(new_shape);
 
   // // set meta with hack
   // Entry<shape_t> meta;

visualdl/server/lib.py

@@ -132,7 +132,9 @@ def get_invididual_image(storage, mode, tag, step_index, max_size=80):
 
         shape = record.shape()
 
-        data = np.array(record.data(), dtype='uint8').reshape(record.shape())
+        if shape[2] == 1:
+          shape = [shape[0], shape[1]]
+        data = np.array(record.data(), dtype='uint8').reshape(shape)
         tempfile = NamedTemporaryFile(mode='w+b', suffix='.png')
         with Image.fromarray(data) as im:
             size = max(shape[0], shape[1])

visualdl/server/storage_mock.py

@@ -35,6 +35,7 @@ def add_image(writer,
                     image_writer.set_sample(index, shape, list(data))
             image_writer.finish_sampling()
 
+
 def add_histogram(writer, mode, tag, num_buckets):
     with writer.mode(mode) as writer:
         histogram = writer.histogram(tag, num_buckets)