# 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.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=params["lr"]) # 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) return fluid.layers.elementwise_add(x=short, y=conv2, act='relu') class ResNet(fluid.imperative.Layer): def __init__(self, layers=50, class_dim=1000): 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_gpu_float32(self): seed = 90 batch_size = train_parameters["batch_size"] 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 >= 1: 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.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 >= 1: 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.assertEqual(len(dy_grad_value), len(static_grad_value)) # TODO(minqiyang): find a way to align the gradient # for key, value in six.iteritems(static_grad_value): # self.assertTrue( # np.allclose(value, dy_grad_value[key])) 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])) if __name__ == '__main__': unittest.main()