test_image_classification_fp16.py

#   Copyright (c) 2019 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.

import paddle
import paddle.fluid as fluid
import contextlib
import math
import sys
import numpy
import unittest
import os
import copy
import numpy as np
import tempfile
from paddle.static.amp import decorate

paddle.enable_static()


def resnet_cifar10(input, depth=32):

    def conv_bn_layer(input,
                      ch_out,
                      filter_size,
                      stride,
                      padding,
                      act='relu',
                      bias_attr=False):
        tmp = fluid.layers.conv2d(input=input,
                                  filter_size=filter_size,
                                  num_filters=ch_out,
                                  stride=stride,
                                  padding=padding,
                                  act=None,
                                  bias_attr=bias_attr)
        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, bias_attr=True)
        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=4096, 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=4096, act=None)
    return fc2


def train(net_type, use_cuda, save_dirname, is_local):
    classdim = 10
    data_shape = [3, 32, 32]

    train_program = fluid.Program()
    startup_prog = fluid.Program()
    train_program.random_seed = 123
    startup_prog.random_seed = 456
    with fluid.program_guard(train_program, startup_prog):
        images = fluid.layers.data(name='pixel',
                                   shape=data_shape,
                                   dtype='float32')
        label = fluid.layers.data(name='label', shape=[1], dtype='int64')

        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)

        logits = fluid.layers.fc(input=net, size=classdim, act="softmax")
        cost, predict = fluid.layers.softmax_with_cross_entropy(
            logits, label, return_softmax=True)
        avg_cost = paddle.mean(cost)
        acc = fluid.layers.accuracy(input=predict, label=label)

        # Test program
        test_program = train_program.clone(for_test=True)

        optimizer = fluid.optimizer.Lamb(learning_rate=0.001)

        amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
            custom_black_varnames={"loss", "conv2d_0.w_0"})
        mp_optimizer = decorate(optimizer=optimizer,
                                amp_lists=amp_lists,
                                init_loss_scaling=8.0,
                                use_dynamic_loss_scaling=True)

        mp_optimizer.minimize(avg_cost)
        loss_scaling = mp_optimizer.get_loss_scaling()
        scaled_loss = mp_optimizer.get_scaled_loss()

    BATCH_SIZE = 128
    PASS_NUM = 1

    # no shuffle for unit test
    train_reader = paddle.batch(paddle.dataset.cifar.train10(),
                                batch_size=BATCH_SIZE)

    test_reader = paddle.batch(paddle.dataset.cifar.test10(),
                               batch_size=BATCH_SIZE)

    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
    exe = fluid.Executor(place)
    feeder = fluid.DataFeeder(place=place, feed_list=[images, label])

    def train_loop(main_program):
        exe.run(startup_prog)
        loss = 0.0
        for pass_id in range(PASS_NUM):
            for batch_id, data in enumerate(train_reader()):
                np_scaled_loss, loss = exe.run(
                    main_program,
                    feed=feeder.feed(data),
                    fetch_list=[scaled_loss, avg_cost])
                print(
                    'PassID {0:1}, BatchID {1:04}, train loss {2:2.4}, scaled train closs {3:2.4}'
                    .format(pass_id, batch_id + 1, float(loss),
                            float(np_scaled_loss)))
                if (batch_id % 10) == 0:
                    acc_list = []
                    avg_loss_list = []
                    for tid, test_data in enumerate(test_reader()):
                        loss_t, acc_t = exe.run(program=test_program,
                                                feed=feeder.feed(test_data),
                                                fetch_list=[avg_cost, acc])
                        if math.isnan(float(loss_t)):
                            sys.exit("got NaN loss, training failed.")
                        acc_list.append(float(acc_t))
                        avg_loss_list.append(float(loss_t))
                        break  # Use 1 segment for speeding up CI

                    acc_value = numpy.array(acc_list).mean()
                    avg_loss_value = numpy.array(avg_loss_list).mean()

                    print(
                        'PassID {0:1}, BatchID {1:04}, test loss {2:2.2}, acc {3:2.2}'
                        .format(pass_id, batch_id + 1, float(avg_loss_value),
                                float(acc_value)))

                    if acc_value > 0.08:  # Low threshold for speeding up CI
                        fluid.io.save_inference_model(
                            save_dirname, ["pixel"], [predict],
                            exe,
                            main_program=train_program,
                            clip_extra=True)
                        return

    if is_local:
        train_loop(train_program)
    else:
        port = os.getenv("PADDLE_PSERVER_PORT", "6174")
        pserver_ips = os.getenv("PADDLE_PSERVER_IPS")  # ip,ip...
        eplist = []
        for ip in pserver_ips.split(","):
            eplist.append(':'.join([ip, port]))
        pserver_endpoints = ",".join(eplist)  # ip:port,ip:port...
        trainers = int(os.getenv("PADDLE_TRAINERS"))
        current_endpoint = os.getenv("POD_IP") + ":" + port
        trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
        training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
        t = fluid.DistributeTranspiler()
        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
        if training_role == "PSERVER":
            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)
        elif training_role == "TRAINER":
            train_loop(t.get_trainer_program())


def infer(use_cuda, save_dirname=None):
    if save_dirname is None:
        return

    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
    exe = fluid.Executor(place)

    inference_scope = fluid.core.Scope()
    with fluid.scope_guard(inference_scope):
        # Use fluid.io.load_inference_model to obtain the inference program desc,
        # the feed_target_names (the names of variables that will be fed
        # data using feed operators), and the fetch_targets (variables that
        # we want to obtain data from using fetch operators).
        [inference_program, feed_target_names,
         fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)

        # The input's dimension of conv should be 4-D or 5-D.
        # Use normilized image pixels as input data, which should be in the range [0, 1.0].
        batch_size = 1
        tensor_img = numpy.random.rand(batch_size, 3, 32, 32).astype("float32")

        # Construct feed as a dictionary of {feed_target_name: feed_target_data}
        # and results will contain a list of data corresponding to fetch_targets.
        results = exe.run(inference_program,
                          feed={feed_target_names[0]: tensor_img},
                          fetch_list=fetch_targets)

        print("infer results: ", results[0])

        fluid.io.save_inference_model(save_dirname,
                                      feed_target_names,
                                      fetch_targets,
                                      exe,
                                      inference_program,
                                      clip_extra=True)


class TestImageClassification(unittest.TestCase):

    def setUp(self):
        self.temp_dir = tempfile.TemporaryDirectory()

    def tearDown(self):
        self.temp_dir.cleanup()

    def main(self, net_type, use_cuda, is_local=True):
        if use_cuda and not fluid.core.is_compiled_with_cuda():
            return

        # Directory for saving the trained model
        save_dirname = os.path.join(
            self.temp_dir.name,
            "image_classification_" + net_type + ".inference.model")

        train(net_type, use_cuda, save_dirname, is_local)
        #infer(use_cuda, save_dirname)

    def test_amp_lists(self):
        white_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.white_list)
        black_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.black_list)
        gray_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.gray_list)

        amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists()
        self.assertEqual(amp_lists.white_list, white_list)
        self.assertEqual(amp_lists.black_list, black_list)
        self.assertEqual(amp_lists.gray_list, gray_list)

    def test_amp_lists_1(self):
        white_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.white_list)
        black_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.black_list)
        gray_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.gray_list)

        # 1. w={'exp}, b=None
        white_list.add('exp')
        black_list.remove('exp')

        amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
            {'exp'})
        self.assertEqual(amp_lists.white_list, white_list)
        self.assertEqual(amp_lists.black_list, black_list)
        self.assertEqual(amp_lists.gray_list, gray_list)

    def test_amp_lists_2(self):
        white_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.white_list)
        black_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.black_list)
        gray_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.gray_list)

        # 2. w={'tanh'}, b=None
        white_list.add('tanh')
        gray_list.remove('tanh')

        amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
            {'tanh'})
        self.assertEqual(amp_lists.white_list, white_list)
        self.assertEqual(amp_lists.black_list, black_list)
        self.assertEqual(amp_lists.gray_list, gray_list)

    def test_amp_lists_3(self):
        white_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.white_list)
        black_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.black_list)
        gray_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.gray_list)

        # 3. w={'lstm'}, b=None
        white_list.add('lstm')

        amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
            {'lstm'})
        self.assertEqual(amp_lists.white_list, white_list)
        self.assertEqual(amp_lists.black_list, black_list)
        self.assertEqual(amp_lists.gray_list, gray_list)

    def test_amp_lists_4(self):
        white_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.white_list)
        black_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.black_list)
        gray_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.gray_list)

        # 4. w=None, b={'conv2d'}
        white_list.remove('conv2d')
        black_list.add('conv2d')

        amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
            custom_black_list={'conv2d'})
        self.assertEqual(amp_lists.white_list, white_list)
        self.assertEqual(amp_lists.black_list, black_list)
        self.assertEqual(amp_lists.gray_list, gray_list)

    def test_amp_lists_5(self):
        white_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.white_list)
        black_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.black_list)
        gray_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.gray_list)

        # 5. w=None, b={'tanh'}
        black_list.add('tanh')
        gray_list.remove('tanh')

        amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
            custom_black_list={'tanh'})
        self.assertEqual(amp_lists.white_list, white_list)
        self.assertEqual(amp_lists.black_list, black_list)
        self.assertEqual(amp_lists.gray_list, gray_list)

    def test_amp_lists_6(self):
        white_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.white_list)
        black_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.black_list)
        gray_list = copy.copy(
            fluid.contrib.mixed_precision.fp16_lists.gray_list)

        # 6. w=None, b={'lstm'}
        black_list.add('lstm')

        amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
            custom_black_list={'lstm'})
        self.assertEqual(amp_lists.white_list, white_list)
        self.assertEqual(amp_lists.black_list, black_list)
        self.assertEqual(amp_lists.gray_list, gray_list)

    def test_amp_lists_7(self):
        # 7. w={'lstm'} b={'lstm'}
        # raise ValueError
        self.assertRaises(ValueError,
                          fluid.contrib.mixed_precision.AutoMixedPrecisionLists,
                          {'lstm'}, {'lstm'})

    def test_vgg_cuda(self):
        with self.scope_prog_guard():
            self.main('vgg', use_cuda=True)

    def test_resnet_cuda(self):
        with self.scope_prog_guard():
            self.main('resnet', use_cuda=True)

    @contextlib.contextmanager
    def scope_prog_guard(self):
        prog = fluid.Program()
        startup_prog = fluid.Program()
        scope = fluid.core.Scope()
        with fluid.scope_guard(scope):
            with fluid.program_guard(prog, startup_prog):
                yield


class TestAmpWithNonIterableDataLoader(unittest.TestCase):

    def decorate_with_data_loader(self):
        main_prog = paddle.static.Program()
        start_prog = paddle.static.Program()
        with paddle.static.program_guard(main_prog, start_prog):
            with paddle.fluid.unique_name.guard():
                image = fluid.layers.data(name='image',
                                          shape=[3, 224, 224],
                                          dtype='float32')
                label = fluid.layers.data(name='label',
                                          shape=[1],
                                          dtype='int64')
                py_reader = fluid.io.DataLoader.from_generator(
                    feed_list=[image, label],
                    capacity=4,
                    iterable=False,
                    use_double_buffer=False)

                net = vgg16_bn_drop(image)
                logits = fluid.layers.fc(input=net, size=10, act="softmax")
                cost, predict = fluid.layers.softmax_with_cross_entropy(
                    logits, label, return_softmax=True)
                avg_cost = paddle.mean(cost)

                optimizer = fluid.optimizer.Lamb(learning_rate=0.001)
                amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
                    custom_black_varnames={"loss", "conv2d_0.w_0"})
                mp_optimizer = decorate(optimizer=optimizer,
                                        amp_lists=amp_lists,
                                        init_loss_scaling=8.0,
                                        use_dynamic_loss_scaling=True)

                mp_optimizer.minimize(avg_cost)

    def test_non_iterable_dataloader(self):
        self.decorate_with_data_loader()


if __name__ == '__main__':
    unittest.main()