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210 lines
8.1 KiB
210 lines
8.1 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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from paddle.fluid import core
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from paddle.fluid.op import Operator
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import paddle.fluid as fluid
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import paddle
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import paddle.nn as nn
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class TestModelAverage(unittest.TestCase):
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def test_model_average_static(self):
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paddle.enable_static()
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place = fluid.CPUPlace()
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shape = [2, 3, 8, 8]
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exe = fluid.Executor(place)
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train_program = fluid.Program()
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startup = fluid.Program()
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test_program = fluid.Program()
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with fluid.program_guard(train_program, startup):
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with fluid.unique_name.guard():
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data = fluid.data(name='X', shape=[None, 1], dtype='float32')
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hidden = fluid.layers.fc(input=data, size=10)
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loss = fluid.layers.mean(hidden)
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test_program = train_program.clone()
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optimizer = paddle.optimizer.Momentum(
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learning_rate=0.2, momentum=0.1)
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optimizer.minimize(loss)
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# build ModelAverage optimizer
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model_average = paddle.incubate.optimizer.ModelAverage(
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0.15, min_average_window=2, max_average_window=10)
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exe.run(startup)
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for i in range(10):
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x = np.random.random(size=(10, 1)).astype('float32')
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latest_b, sum_1, sum_2, sum_3, num_accumulates, old_num_accumulates, num_updates = exe.run(
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program=train_program,
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feed={'X': x},
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fetch_list=[
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'fc_0.b_0', 'fc_0.b_0_sum_1_0', 'fc_0.b_0_sum_2_0',
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'fc_0.b_0_sum_3_0', 'fc_0.b_0_num_accumulates_0',
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'fc_0.b_0_old_num_accumulates_0', 'fc_0.b_0_num_updates_0'
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])
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self.assertTrue(
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np.equal(
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sum_1, np.zeros(
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shape=[10], dtype='float32')).all())
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self.assertTrue(
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np.equal(
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sum_2, np.zeros(
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shape=[10], dtype='float32')).all())
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self.assertTrue(
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np.equal(
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num_accumulates, np.array(
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[0], dtype='int64')).all())
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self.assertTrue(
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np.equal(
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old_num_accumulates, np.array(
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[2], dtype='int64')).all())
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self.assertTrue(
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np.equal(
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num_updates, np.array(
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[10], dtype='int64')).all())
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average_b = (sum_1 + sum_2 + sum_3) / (
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num_accumulates + old_num_accumulates)
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# apply ModelAverage
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with model_average.apply(exe):
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x = np.random.random(size=(10, 1)).astype('float32')
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outs, b = exe.run(program=test_program,
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feed={'X': x},
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fetch_list=[loss.name, 'fc_0.b_0'])
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self.assertAlmostEqual(np.mean(average_b), np.mean(b))
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x = np.random.random(size=(10, 1)).astype('float32')
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outs, b = exe.run(program=test_program,
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feed={'X': x},
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fetch_list=[loss.name, 'fc_0.b_0'])
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self.assertAlmostEqual(np.mean(latest_b), np.mean(b))
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def test_model_average_dygraph(self):
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BATCH_SIZE = 16
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BATCH_NUM = 4
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EPOCH_NUM = 4
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IMAGE_SIZE = 784
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CLASS_NUM = 10
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# define a random dataset
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class RandomDataset(paddle.io.Dataset):
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def __init__(self, num_samples):
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self.num_samples = num_samples
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def __getitem__(self, idx):
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image = np.random.random([IMAGE_SIZE]).astype('float32')
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label = np.random.randint(0, CLASS_NUM - 1,
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(1, )).astype('int64')
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return image, label
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def __len__(self):
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return self.num_samples
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class LinearNet(nn.Layer):
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def __init__(self):
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super(LinearNet, self).__init__()
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self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM)
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self.bias = self._linear.bias
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@paddle.jit.to_static
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def forward(self, x):
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return self._linear(x)
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def train(layer, loader, loss_fn, opt, model_average):
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for epoch_id in range(EPOCH_NUM):
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for batch_id, (image, label) in enumerate(loader()):
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out = layer(image)
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loss = loss_fn(out, label)
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loss.backward()
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opt.step()
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model_average.step()
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opt.clear_grad()
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model_average.clear_grad()
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# print("Train Epoch {} batch {}: loss = {}, bias = {}".format(
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# epoch_id, batch_id, np.mean(loss.numpy()), layer.bias.numpy()))
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sum_1 = model_average._get_accumulator('sum_1', layer.bias)
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sum_2 = model_average._get_accumulator('sum_2', layer.bias)
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sum_3 = model_average._get_accumulator('sum_3', layer.bias)
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num_accumulates = model_average._get_accumulator('num_accumulates',
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layer.bias)
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old_num_accumulates = model_average._get_accumulator(
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'old_num_accumulates', layer.bias)
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num_updates = model_average._get_accumulator('num_updates',
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layer.bias)
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return ((sum_1 + sum_2 + sum_3) /
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(num_accumulates + old_num_accumulates)).numpy()
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def evaluate(layer, loader, loss_fn, check_param):
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for batch_id, (image, label) in enumerate(loader()):
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out = layer(image)
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loss = loss_fn(out, label)
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loss.backward()
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self.assertAlmostEqual(
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np.mean(layer.bias.numpy()),
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np.mean(check_param),
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delta=5e-3)
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# print("Evaluate batch {}: loss = {}, bias = {}".format(
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# batch_id, np.mean(loss.numpy()), layer.bias.numpy()))
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# create network
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layer = LinearNet()
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loss_fn = nn.CrossEntropyLoss()
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optimizer = paddle.optimizer.Momentum(
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learning_rate=0.2, momentum=0.1, parameters=layer.parameters())
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# build ModelAverage optimizer
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model_average = paddle.incubate.optimizer.ModelAverage(
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0.15,
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parameters=layer.parameters(),
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min_average_window=2,
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max_average_window=10)
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# create data loader
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dataset = RandomDataset(BATCH_NUM * BATCH_SIZE)
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loader = paddle.io.DataLoader(
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dataset,
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batch_size=BATCH_SIZE,
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shuffle=True,
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drop_last=True,
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num_workers=2)
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eval_loader = paddle.io.DataLoader(
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dataset,
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batch_size=BATCH_SIZE,
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shuffle=True,
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drop_last=True,
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num_workers=1)
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# train
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check_param = train(layer, loader, loss_fn, optimizer, model_average)
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# print(check_param)
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with model_average.apply(need_restore=False):
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evaluate(layer, eval_loader, loss_fn, check_param)
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check_param = (model_average._get_accumulator('restore',
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layer.bias)).numpy()
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# print(check_param)
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# print("\nEvaluate With Restored Paramters")
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model_average.restore()
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evaluate(layer, eval_loader, loss_fn, check_param)
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if __name__ == "__main__":
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unittest.main()
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