# 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 = 1 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 fluid.default_main_program().global_block( ).all_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 fluid.default_main_program().global_block( ).all_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 fluid.default_main_program().global_block( ).all_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) dy_param_value = {} for param in fluid.default_main_program().global_block( ).all_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 fluid.default_startup_program().global_block( ).all_parameters(): static_param_name_list.append(param.name) for param in fluid.default_main_program().global_block( ).all_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()