Add distribution implement of image classification. (#7687)

Add distribution implement of image classification

python/paddle/v2/fluid/tests/book_distribute/notest_dist_image_classification.py

+#Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+from __future__ import print_function
+
+import sys
+
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+import os
+import sys
+
+TRAINERS = 5
+BATCH_SIZE = 128
+PASS_NUM = 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
+
+
+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 = 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')
+cost = fluid.layers.cross_entropy(input=predict, label=label)
+avg_cost = fluid.layers.mean(x=cost)
+
+optimizer = fluid.optimizer.Adam(learning_rate=0.001)
+optimize_ops, params_grads = optimizer.minimize(avg_cost)
+
+accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
+
+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)
+
+t = fluid.DistributeTranspiler()
+# all parameter server endpoints list for spliting parameters
+pserver_endpoints = os.getenv("PSERVERS")
+# server endpoint for current node
+current_endpoint = os.getenv("SERVER_ENDPOINT")
+# run as trainer or parameter server
+training_role = os.getenv("TRAINING_ROLE",
+                          "TRAINER")  # get the training role: trainer/pserver
+t.transpile(
+    optimize_ops, params_grads, pservers=pserver_endpoints, trainers=TRAINERS)
+
+if training_role == "PSERVER":
+    if not current_endpoint:
+        print("need env SERVER_ENDPOINT")
+        exit(1)
+    print("start pserver at:", current_endpoint)
+    pserver_prog = t.get_pserver_program(current_endpoint)
+    pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
+    exe.run(pserver_startup)
+    exe.run(pserver_prog)
+    print("pserver run end")
+elif training_role == "TRAINER":
+    print("start trainer")
+    trainer_prog = t.get_trainer_program()
+    feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
+    exe.run(fluid.default_startup_program())
+    for pass_id in range(PASS_NUM):
+        accuracy.reset(exe)
+        for data in train_reader():
+            loss, acc = exe.run(trainer_prog,
+                                feed=feeder.feed(data),
+                                fetch_list=[avg_cost] + accuracy.metrics)
+            pass_acc = accuracy.eval(exe)
+            print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
+                pass_acc))
+            # this model is slow, so if we can train two mini batch, we think it works properly.
+    print("trainer run end")
+else:
+    print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
+exit(1)