# Copyright (c) 2020 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 unittest import numpy as np import paddle.fluid as fluid import paddle import paddle.nn as nn class TestModelAverage(unittest.TestCase): def test_model_average_static(self): paddle.enable_static() place = fluid.CPUPlace() shape = [2, 3, 8, 8] exe = fluid.Executor(place) train_program = fluid.Program() startup = fluid.Program() test_program = fluid.Program() with fluid.program_guard(train_program, startup): with fluid.unique_name.guard(): data = fluid.data(name='X', shape=[None, 1], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) loss = paddle.mean(hidden) test_program = train_program.clone() optimizer = paddle.optimizer.Momentum(learning_rate=0.2, momentum=0.1) optimizer.minimize(loss) # build ModelAverage optimizer model_average = paddle.incubate.optimizer.ModelAverage( 0.15, min_average_window=2, max_average_window=10) exe.run(startup) for i in range(10): x = np.random.random(size=(10, 1)).astype('float32') latest_b, sum_1, sum_2, sum_3, num_accumulates, old_num_accumulates, num_updates = exe.run( program=train_program, feed={'X': x}, fetch_list=[ 'fc_0.b_0', 'fc_0.b_0_sum_1_0', 'fc_0.b_0_sum_2_0', 'fc_0.b_0_sum_3_0', 'fc_0.b_0_num_accumulates_0', 'fc_0.b_0_old_num_accumulates_0', 'fc_0.b_0_num_updates_0' ]) self.assertTrue( np.equal(sum_1, np.zeros(shape=[10], dtype='float32')).all()) self.assertTrue( np.equal(sum_2, np.zeros(shape=[10], dtype='float32')).all()) self.assertTrue( np.equal(num_accumulates, np.array([0], dtype='int64')).all()) self.assertTrue( np.equal(old_num_accumulates, np.array([2], dtype='int64')).all()) self.assertTrue( np.equal(num_updates, np.array([10], dtype='int64')).all()) average_b = (sum_1 + sum_2 + sum_3) / (num_accumulates + old_num_accumulates) # apply ModelAverage with model_average.apply(exe): x = np.random.random(size=(10, 1)).astype('float32') outs, b = exe.run(program=test_program, feed={'X': x}, fetch_list=[loss.name, 'fc_0.b_0']) self.assertAlmostEqual(np.mean(average_b), np.mean(b)) x = np.random.random(size=(10, 1)).astype('float32') outs, b = exe.run(program=test_program, feed={'X': x}, fetch_list=[loss.name, 'fc_0.b_0']) self.assertAlmostEqual(np.mean(latest_b), np.mean(b)) def test_model_average_dygraph(self): BATCH_SIZE = 16 BATCH_NUM = 4 EPOCH_NUM = 4 IMAGE_SIZE = 784 CLASS_NUM = 10 # define a random dataset class RandomDataset(paddle.io.Dataset): def __init__(self, num_samples): self.num_samples = num_samples def __getitem__(self, idx): image = np.random.random([IMAGE_SIZE]).astype('float32') label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64') return image, label def __len__(self): return self.num_samples class LinearNet(nn.Layer): def __init__(self): super(LinearNet, self).__init__() self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM) self.bias = self._linear.bias @paddle.jit.to_static def forward(self, x): return self._linear(x) def train(layer, loader, loss_fn, opt, model_average): for epoch_id in range(EPOCH_NUM): for batch_id, (image, label) in enumerate(loader()): out = layer(image) loss = loss_fn(out, label) loss.backward() opt.step() model_average.step() opt.clear_grad() model_average.clear_grad() # print("Train Epoch {} batch {}: loss = {}, bias = {}".format( # epoch_id, batch_id, np.mean(loss.numpy()), layer.bias.numpy())) sum_1 = model_average._get_accumulator('sum_1', layer.bias) sum_2 = model_average._get_accumulator('sum_2', layer.bias) sum_3 = model_average._get_accumulator('sum_3', layer.bias) num_accumulates = model_average._get_accumulator( 'num_accumulates', layer.bias) old_num_accumulates = model_average._get_accumulator( 'old_num_accumulates', layer.bias) num_updates = model_average._get_accumulator( 'num_updates', layer.bias) return ((sum_1 + sum_2 + sum_3) / (num_accumulates + old_num_accumulates)).numpy() def evaluate(layer, loader, loss_fn, check_param): for batch_id, (image, label) in enumerate(loader()): out = layer(image) loss = loss_fn(out, label) loss.backward() self.assertAlmostEqual(np.mean(layer.bias.numpy()), np.mean(check_param), delta=5e-3) # print("Evaluate batch {}: loss = {}, bias = {}".format( # batch_id, np.mean(loss.numpy()), layer.bias.numpy())) # create network layer = LinearNet() loss_fn = nn.CrossEntropyLoss() optimizer = paddle.optimizer.Momentum(learning_rate=0.2, momentum=0.1, parameters=layer.parameters()) # build ModelAverage optimizer model_average = paddle.incubate.optimizer.ModelAverage( 0.15, parameters=layer.parameters(), min_average_window=2, max_average_window=10) # create data loader dataset = RandomDataset(BATCH_NUM * BATCH_SIZE) loader = paddle.io.DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True, num_workers=2) eval_loader = paddle.io.DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True, num_workers=1) # train check_param = train(layer, loader, loss_fn, optimizer, model_average) # print(check_param) with model_average.apply(need_restore=False): evaluate(layer, eval_loader, loss_fn, check_param) check_param = (model_average._get_accumulator('restore', layer.bias)).numpy() # print(check_param) # print("\nEvaluate With Restored Paramters") model_average.restore() evaluate(layer, eval_loader, loss_fn, check_param) if __name__ == "__main__": unittest.main()