wehub-resource-sync
142 lines
4.7 KiB
Python
142 lines
4.7 KiB
Python
# 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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import time
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import unittest
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import numpy as np
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from dygraph_to_static_utils import (
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Dy2StTestBase,
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enable_to_static_guard,
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test_default_mode_only,
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)
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from test_resnet import SEED, ResNet, optimizer_setting
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import paddle
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from paddle.base import core
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# NOTE: Reduce batch_size from 8 to 2 to avoid unittest timeout.
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batch_size = 2
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epoch_num = 1
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place = (
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paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda() else paddle.CPUPlace()
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)
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if paddle.is_compiled_with_cuda():
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paddle.set_flags({'FLAGS_cudnn_deterministic': True})
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def train(build_strategy=None):
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"""
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Tests model decorated by `dygraph_to_static_output` in static graph mode. For users, the model is defined in dygraph mode and trained in static graph mode.
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"""
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np.random.seed(SEED)
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paddle.seed(SEED)
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paddle.framework.random._manual_program_seed(SEED)
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resnet = paddle.jit.to_static(ResNet(), build_strategy=build_strategy)
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optimizer = optimizer_setting(parameter_list=resnet.parameters())
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scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
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for epoch in range(epoch_num):
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total_loss = 0.0
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total_acc1 = 0.0
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total_acc5 = 0.0
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total_sample = 0
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for batch_id in range(100):
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start_time = time.time()
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img = paddle.to_tensor(
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np.random.random([batch_size, 3, 224, 224]).astype('float32')
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)
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label = paddle.to_tensor(
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np.random.randint(0, 100, [batch_size, 1], dtype='int64')
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)
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img.stop_gradient = True
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label.stop_gradient = True
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with paddle.amp.auto_cast():
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pred = resnet(img)
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# FIXME(Aurelius84): The following cross_entropy seems to bring out a
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# precision problem, need to figure out the underlying reason.
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# If we remove it, the loss between dygraph and dy2stat is exactly same.
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loss = paddle.nn.functional.cross_entropy(
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input=pred,
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label=label,
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reduction='none',
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use_softmax=False,
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)
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avg_loss = paddle.mean(x=pred)
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acc_top1 = paddle.static.accuracy(input=pred, label=label, k=1)
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acc_top5 = paddle.static.accuracy(input=pred, label=label, k=5)
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scaled = scaler.scale(avg_loss)
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scaled.backward()
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scaler.minimize(optimizer, scaled)
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resnet.clear_gradients()
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total_loss += avg_loss
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total_acc1 += acc_top1
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total_acc5 += acc_top5
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total_sample += 1
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end_time = time.time()
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if batch_id % 2 == 0:
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print(
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f"epoch {epoch} | batch step {batch_id}, "
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f"loss {total_loss.numpy() / total_sample:0.3f}, "
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f"acc1 {total_acc1.numpy() / total_sample:0.3f}, "
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f"acc5 {total_acc5.numpy() / total_sample:0.3f}, "
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f"time {end_time - start_time:f}"
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)
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if batch_id == 10:
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break
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return total_loss.numpy()
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class TestResnet(Dy2StTestBase):
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def train(self, to_static: bool):
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with enable_to_static_guard(to_static):
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return train()
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@test_default_mode_only
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def test_resnet(self):
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static_loss = self.train(to_static=True)
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dygraph_loss = self.train(to_static=False)
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np.testing.assert_allclose(
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static_loss,
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dygraph_loss,
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rtol=1e-05,
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err_msg=f'static_loss: {static_loss} \n dygraph_loss: {dygraph_loss}',
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)
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@test_default_mode_only
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def test_resnet_composite(self):
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core._set_prim_backward_enabled(True)
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static_loss = self.train(to_static=True)
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core._set_prim_backward_enabled(False)
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dygraph_loss = self.train(to_static=False)
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np.testing.assert_allclose(
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static_loss,
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dygraph_loss,
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rtol=1e-05,
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err_msg=f'static_loss: {static_loss} \n dygraph_loss: {dygraph_loss}',
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)
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if __name__ == '__main__':
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unittest.main()
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