test_imperative_resnet.py

# Copyright (c) 2018 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 contextlib
import unittest
import numpy as np
import six

import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.imperative.nn import Conv2D, Pool2D, BatchNorm, FC
from paddle.fluid.imperative.base import to_variable
from test_imperative_base import new_program_scope

batch_size = 8
train_parameters = {
    "input_size": [3, 224, 224],
    "input_mean": [0.485, 0.456, 0.406],
    "input_std": [0.229, 0.224, 0.225],
    "learning_strategy": {
        "name": "piecewise_decay",
        "batch_size": batch_size,
        "epochs": [30, 60, 90],
        "steps": [0.1, 0.01, 0.001, 0.0001]
    },
    "batch_size": batch_size,
    "lr": 0.1,
    "total_images": 1281164,
}


def optimizer_setting(params):
    ls = params["learning_strategy"]
    if ls["name"] == "piecewise_decay":
        if "total_images" not in params:
            total_images = 1281167
        else:
            total_images = params["total_images"]
        batch_size = ls["batch_size"]
        step = int(total_images / batch_size + 1)

        bd = [step * e for e in ls["epochs"]]
        base_lr = params["lr"]
        lr = []
        lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
        optimizer = fluid.optimizer.SGD(learning_rate=0.01)
        # TODO(minqiyang): Add learning rate scheduler support to imperative mode
        #  optimizer = fluid.optimizer.Momentum(
    #  learning_rate=params["lr"],
    #  learning_rate=fluid.layers.piecewise_decay(
    #  boundaries=bd, values=lr),
    #  momentum=0.9,
    #  regularization=fluid.regularizer.L2Decay(1e-4))

    return optimizer


class ConvBNLayer(fluid.imperative.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 stride=1,
                 groups=1,
                 act=None):
        super(ConvBNLayer, self).__init__()

        self._conv = Conv2D(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            act=None,
            bias_attr=None)

        self._batch_norm = BatchNorm(num_filters, act=act)

    def forward(self, inputs):
        y = self._conv(inputs)
        y = self._batch_norm(y)

        return y


class BottleneckBlock(fluid.imperative.Layer):
    def __init__(self, num_channels, num_filters, stride, shortcut=True):
        super(BottleneckBlock, self).__init__()

        self.conv0 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=1,
            act='relu')
        self.conv1 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            act='relu')
        self.conv2 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters * 4,
            filter_size=1,
            act=None)

        if not shortcut:
            self.short = ConvBNLayer(
                num_channels=num_channels,
                num_filters=num_filters * 4,
                filter_size=1,
                stride=stride)

        self.shortcut = shortcut

        self._num_channels_out = num_filters * 4

    def forward(self, inputs):
        y = self.conv0(inputs)
        conv1 = self.conv1(y)
        conv2 = self.conv2(conv1)

        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)

        y = fluid.layers.elementwise_add(x=short, y=conv2)

        layer_helper = LayerHelper('elementwise_add_activation', act='relu')
        return layer_helper.append_activation(y)


class ResNet(fluid.imperative.Layer):
    def __init__(self, layers=50, class_dim=102):
        super(ResNet, self).__init__()

        self.layers = layers
        supported_layers = [50, 101, 152]
        assert layers in supported_layers, \
            "supported layers are {} but input layer is {}".format(supported_layers, layers)

        if layers == 50:
            depth = [3, 4, 6, 3]
        elif layers == 101:
            depth = [3, 4, 23, 3]
        elif layers == 152:
            depth = [3, 8, 36, 3]
        num_filters = [64, 128, 256, 512]

        self.conv = ConvBNLayer(
            num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu')
        self.pool2d_max = Pool2D(
            pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')

        self.bottleneck_block_list = []
        num_channels = 64
        for block in range(len(depth)):
            shortcut = False
            for i in range(depth[block]):
                bottleneck_block = BottleneckBlock(
                    num_channels=num_channels,
                    num_filters=num_filters[block],
                    stride=2 if i == 0 and block != 0 else 1,
                    shortcut=shortcut)
                num_channels = bottleneck_block._num_channels_out
                self.bottleneck_block_list.append(bottleneck_block)
                shortcut = True

        self.pool2d_avg = Pool2D(
            pool_size=7, pool_type='avg', global_pooling=True)

        import math
        stdv = 1.0 / math.sqrt(2048 * 1.0)

        self.out = FC(size=class_dim,
                      act='softmax',
                      param_attr=fluid.param_attr.ParamAttr(
                          initializer=fluid.initializer.Uniform(-stdv, stdv)))

    def forward(self, inputs):
        y = self.conv(inputs)
        y = self.pool2d_max(y)
        for bottleneck_block in self.bottleneck_block_list:
            y = bottleneck_block(y)
        y = self.pool2d_avg(y)
        y = self.out(y)
        return y


class TestImperativeResnet(unittest.TestCase):
    def test_resnet_float32(self):
        seed = 90

        batch_size = train_parameters["batch_size"]
        batch_num = 2
        with fluid.imperative.guard():
            fluid.default_startup_program().random_seed = seed
            fluid.default_main_program().random_seed = seed

            resnet = ResNet()
            optimizer = optimizer_setting(train_parameters)
            np.random.seed(seed)
            import random
            random.seed = seed
            train_reader = paddle.batch(
                paddle.dataset.flowers.train(use_xmap=False),
                batch_size=batch_size)

            dy_param_init_value = {}
            for param in resnet.parameters():
                dy_param_init_value[param.name] = param._numpy()

            for batch_id, data in enumerate(train_reader()):
                if batch_id >= batch_num:
                    break

                dy_x_data = np.array(
                    [x[0].reshape(3, 224, 224) for x in data]).astype('float32')
                y_data = np.array([x[1] for x in data]).astype('int64').reshape(
                    batch_size, 1)

                img = to_variable(dy_x_data)
                label = to_variable(y_data)
                label._stop_gradient = True

                out = resnet(img)
                loss = fluid.layers.cross_entropy(input=out, label=label)
                avg_loss = fluid.layers.mean(x=loss)

                dy_out = avg_loss._numpy()

                if batch_id == 0:
                    for param in resnet.parameters():
                        if param.name not in dy_param_init_value:
                            dy_param_init_value[param.name] = param._numpy()

                avg_loss._backward()

                dy_grad_value = {}
                for param in resnet.parameters():
                    if not param.stop_gradient:
                        np_array = np.array(param._ivar._grad_ivar().value()
                                            .get_tensor())
                        dy_grad_value[param.name + core.grad_var_suffix(
                        )] = np_array

                optimizer.minimize(avg_loss)
                resnet.clear_gradients()

                fluid.default_main_program().global_block()._clear_block()

                dy_param_value = {}
                for param in resnet.parameters():
                    dy_param_value[param.name] = param._numpy()

        with new_program_scope():
            fluid.default_startup_program().random_seed = seed
            fluid.default_main_program().random_seed = seed

            exe = fluid.Executor(fluid.CPUPlace(
            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))

            resnet = ResNet()
            optimizer = optimizer_setting(train_parameters)

            np.random.seed(seed)
            import random
            random.seed = seed
            train_reader = paddle.batch(
                paddle.dataset.flowers.train(use_xmap=False),
                batch_size=batch_size)

            img = fluid.layers.data(
                name='pixel', shape=[3, 224, 224], dtype='float32')
            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
            out = resnet(img)
            loss = fluid.layers.cross_entropy(input=out, label=label)
            avg_loss = fluid.layers.mean(x=loss)
            optimizer.minimize(avg_loss)

            # initialize params and fetch them
            static_param_init_value = {}
            static_param_name_list = []
            static_grad_name_list = []
            for param in resnet.parameters():
                static_param_name_list.append(param.name)
            for param in resnet.parameters():
                if not param.stop_gradient:
                    static_grad_name_list.append(param.name +
                                                 core.grad_var_suffix())

            out = exe.run(fluid.default_startup_program(),
                          fetch_list=static_param_name_list)

            for i in range(len(static_param_name_list)):
                static_param_init_value[static_param_name_list[i]] = out[i]

            for batch_id, data in enumerate(train_reader()):
                if batch_id >= batch_num:
                    break

                static_x_data = np.array(
                    [x[0].reshape(3, 224, 224) for x in data]).astype('float32')
                y_data = np.array([x[1] for x in data]).astype('int64').reshape(
                    [batch_size, 1])

                fetch_list = [avg_loss.name]
                fetch_list.extend(static_param_name_list)
                fetch_list.extend(static_grad_name_list)
                out = exe.run(fluid.default_main_program(),
                              feed={"pixel": static_x_data,
                                    "label": y_data},
                              fetch_list=fetch_list)

                static_param_value = {}
                static_grad_value = {}
                static_out = out[0]
                param_start_pos = 1
                grad_start_pos = len(static_param_name_list) + param_start_pos
                for i in range(param_start_pos,
                               len(static_param_name_list) + param_start_pos):
                    static_param_value[static_param_name_list[
                        i - param_start_pos]] = out[i]
                for i in range(grad_start_pos,
                               len(static_grad_name_list) + grad_start_pos):
                    static_grad_value[static_grad_name_list[
                        i - grad_start_pos]] = out[i]

        self.assertTrue(np.allclose(static_out, dy_out))

        self.assertEqual(len(dy_param_init_value), len(static_param_init_value))
        for key, value in six.iteritems(static_param_init_value):
            self.assertTrue(np.allclose(value, dy_param_init_value[key]))
            self.assertTrue(np.isfinite(value.all()))
            self.assertFalse(np.isnan(value.any()))

        self.assertEqual(len(dy_grad_value), len(static_grad_value))
        for key, value in six.iteritems(static_grad_value):
            self.assertTrue(np.allclose(value, dy_grad_value[key]))
            self.assertTrue(np.isfinite(value.all()))
            self.assertFalse(np.isnan(value.any()))

        self.assertEqual(len(dy_param_value), len(static_param_value))
        for key, value in six.iteritems(static_param_value):
            self.assertTrue(np.allclose(value, dy_param_value[key]))
            self.assertTrue(np.isfinite(value.all()))
            self.assertFalse(np.isnan(value.any()))


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