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291 lines
11 KiB
291 lines
11 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import paddle
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import paddle.fluid as fluid
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import contextlib
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import math
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import sys
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import numpy
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import unittest
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import os
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import numpy as np
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def resnet_cifar10(input, depth=32):
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def conv_bn_layer(input,
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ch_out,
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filter_size,
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stride,
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padding,
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act='relu',
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bias_attr=False):
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tmp = fluid.layers.conv2d(
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input=input,
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filter_size=filter_size,
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num_filters=ch_out,
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stride=stride,
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padding=padding,
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act=None,
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bias_attr=bias_attr)
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return fluid.layers.batch_norm(input=tmp, act=act)
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def shortcut(input, ch_in, ch_out, stride):
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if ch_in != ch_out:
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return conv_bn_layer(input, ch_out, 1, stride, 0, None)
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else:
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return input
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def basicblock(input, ch_in, ch_out, stride):
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tmp = conv_bn_layer(input, ch_out, 3, stride, 1)
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tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None, bias_attr=True)
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short = shortcut(input, ch_in, ch_out, stride)
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return fluid.layers.elementwise_add(x=tmp, y=short, act='relu')
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def layer_warp(block_func, input, ch_in, ch_out, count, stride):
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tmp = block_func(input, ch_in, ch_out, stride)
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for i in range(1, count):
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tmp = block_func(tmp, ch_out, ch_out, 1)
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return tmp
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assert (depth - 2) % 6 == 0
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n = (depth - 2) / 6
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conv1 = conv_bn_layer(
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input=input, ch_out=16, filter_size=3, stride=1, padding=1)
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res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
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res2 = layer_warp(basicblock, res1, 16, 32, n, 2)
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res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
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pool = fluid.layers.pool2d(
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input=res3, pool_size=8, pool_type='avg', pool_stride=1)
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return pool
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def vgg16_bn_drop(input):
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def conv_block(input, num_filter, groups, dropouts):
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return fluid.nets.img_conv_group(
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input=input,
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pool_size=2,
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pool_stride=2,
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conv_num_filter=[num_filter] * groups,
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conv_filter_size=3,
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conv_act='relu',
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conv_with_batchnorm=True,
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conv_batchnorm_drop_rate=dropouts,
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pool_type='max')
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conv1 = conv_block(input, 64, 2, [0.3, 0])
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conv2 = conv_block(conv1, 128, 2, [0.4, 0])
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conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
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conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
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conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
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drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
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fc1 = fluid.layers.fc(input=drop, size=4096, act=None)
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bn = fluid.layers.batch_norm(input=fc1, act='relu')
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drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
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fc2 = fluid.layers.fc(input=drop2, size=4096, act=None)
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return fc2
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def train(net_type, use_cuda, save_dirname, is_local):
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classdim = 10
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data_shape = [3, 32, 32]
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images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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if net_type == "vgg":
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print("train vgg net")
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net = vgg16_bn_drop(images)
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elif net_type == "resnet":
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print("train resnet")
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net = resnet_cifar10(images, 32)
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else:
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raise ValueError("%s network is not supported" % net_type)
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predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
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cost = fluid.layers.cross_entropy(input=predict, label=label)
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avg_cost = fluid.layers.mean(cost)
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acc = fluid.layers.accuracy(input=predict, label=label)
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# Test program
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test_program = fluid.default_main_program().clone(for_test=True)
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optimizer = fluid.optimizer.Adam(learning_rate=0.001)
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optimizer.minimize(avg_cost)
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BATCH_SIZE = 128
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PASS_NUM = 1
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train_reader = paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.cifar.train10(), buf_size=128 * 10),
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batch_size=BATCH_SIZE)
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test_reader = paddle.batch(
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paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE)
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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exe = fluid.Executor(place)
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feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
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def train_loop(main_program):
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exe.run(fluid.default_startup_program())
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loss = 0.0
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for pass_id in range(PASS_NUM):
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for batch_id, data in enumerate(train_reader()):
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exe.run(main_program, feed=feeder.feed(data))
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if (batch_id % 10) == 0:
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acc_list = []
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avg_loss_list = []
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for tid, test_data in enumerate(test_reader()):
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loss_t, acc_t = exe.run(program=test_program,
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feed=feeder.feed(test_data),
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fetch_list=[avg_cost, acc])
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if math.isnan(float(loss_t)):
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sys.exit("got NaN loss, training failed.")
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acc_list.append(float(acc_t))
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avg_loss_list.append(float(loss_t))
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break # Use 1 segment for speeding up CI
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acc_value = numpy.array(acc_list).mean()
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avg_loss_value = numpy.array(avg_loss_list).mean()
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print(
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'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'.
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format(pass_id, batch_id + 1,
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float(avg_loss_value), float(acc_value)))
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if acc_value > 0.01: # Low threshold for speeding up CI
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fluid.io.save_inference_model(save_dirname, ["pixel"],
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[predict], exe)
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return
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if is_local:
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train_loop(fluid.default_main_program())
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else:
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port = os.getenv("PADDLE_INIT_PORT", "6174")
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pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
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eplist = []
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for ip in pserver_ips.split(","):
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eplist.append(':'.join([ip, port]))
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pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
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trainers = int(os.getenv("TRAINERS"))
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current_endpoint = os.getenv("POD_IP") + ":" + port
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trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
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training_role = os.getenv("TRAINING_ROLE", "TRAINER")
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t = fluid.DistributeTranspiler()
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t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
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if training_role == "PSERVER":
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pserver_prog = t.get_pserver_program(current_endpoint)
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pserver_startup = t.get_startup_program(current_endpoint,
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pserver_prog)
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exe.run(pserver_startup)
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exe.run(pserver_prog)
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elif training_role == "TRAINER":
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train_loop(t.get_trainer_program())
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def infer(use_cuda, save_dirname=None):
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if save_dirname is None:
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return
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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exe = fluid.Executor(place)
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inference_scope = fluid.core.Scope()
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with fluid.scope_guard(inference_scope):
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# Use fluid.io.load_inference_model to obtain the inference program desc,
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# the feed_target_names (the names of variables that will be feeded
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# data using feed operators), and the fetch_targets (variables that
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# we want to obtain data from using fetch operators).
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[inference_program, feed_target_names,
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fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
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# The input's dimension of conv should be 4-D or 5-D.
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# Use normilized image pixels as input data, which should be in the range [0, 1.0].
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batch_size = 1
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tensor_img = numpy.random.rand(batch_size, 3, 32, 32).astype("float32")
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# Use inference_transpiler to speedup
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inference_transpiler_program = inference_program.clone()
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t = fluid.InferenceTranspiler()
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t.transpile(inference_transpiler_program, place)
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# Construct feed as a dictionary of {feed_target_name: feed_target_data}
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# and results will contain a list of data corresponding to fetch_targets.
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results = exe.run(inference_program,
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feed={feed_target_names[0]: tensor_img},
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fetch_list=fetch_targets)
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transpiler_results = exe.run(inference_transpiler_program,
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feed={feed_target_names[0]: tensor_img},
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fetch_list=fetch_targets)
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assert len(results[0]) == len(transpiler_results[0])
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for i in range(len(results[0])):
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np.testing.assert_almost_equal(
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results[0][i], transpiler_results[0][i], decimal=5)
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print("infer results: ", results[0])
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fluid.io.save_inference_model(save_dirname, feed_target_names,
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fetch_targets, exe,
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inference_transpiler_program)
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def main(net_type, use_cuda, is_local=True):
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if use_cuda and not fluid.core.is_compiled_with_cuda():
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return
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# Directory for saving the trained model
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save_dirname = "image_classification_" + net_type + ".inference.model"
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train(net_type, use_cuda, save_dirname, is_local)
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infer(use_cuda, save_dirname)
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class TestImageClassification(unittest.TestCase):
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def test_vgg_cuda(self):
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with self.scope_prog_guard():
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main('vgg', use_cuda=True)
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def test_resnet_cuda(self):
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with self.scope_prog_guard():
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main('resnet', use_cuda=True)
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def test_vgg_cpu(self):
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with self.scope_prog_guard():
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main('vgg', use_cuda=False)
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def test_resnet_cpu(self):
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with self.scope_prog_guard():
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main('resnet', use_cuda=False)
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@contextlib.contextmanager
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def scope_prog_guard(self):
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prog = fluid.Program()
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startup_prog = fluid.Program()
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scope = fluid.core.Scope()
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with fluid.scope_guard(scope):
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with fluid.program_guard(prog, startup_prog):
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yield
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if __name__ == '__main__':
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unittest.main()
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