wehub-resource-sync
127 lines
3.5 KiB
Python
127 lines
3.5 KiB
Python
# Copyright (c) 2022 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
|
|
from op_test_ipu import IPUOpTest
|
|
|
|
import paddle
|
|
import paddle.static
|
|
|
|
|
|
class TestBase(IPUOpTest):
|
|
def setUp(self):
|
|
self.set_atol()
|
|
self.set_data_feed()
|
|
self.set_feed_attr()
|
|
self.set_attrs()
|
|
self.set_training()
|
|
|
|
@property
|
|
def fp16_enabled(self):
|
|
return False
|
|
|
|
def set_atol(self):
|
|
super().set_atol()
|
|
self.atol = 1e-6
|
|
self.rtol = 1e-5
|
|
|
|
def set_data_feed(self):
|
|
self.feed_fp32 = {
|
|
"image": np.random.uniform(size=[1, 3, 10, 10]).astype('float32'),
|
|
}
|
|
|
|
def set_feed_attr(self):
|
|
self.feed_shape = [x.shape for x in self.feed_fp32.values()]
|
|
self.feed_list = list(self.feed_fp32.keys())
|
|
self.feed_dtype = [x.dtype for x in self.feed_fp32.values()]
|
|
|
|
def set_attrs(self):
|
|
self.attrs = {
|
|
"optimizer": 'sgd',
|
|
"weight_decay": 0.0,
|
|
}
|
|
|
|
def set_training(self):
|
|
self.is_training = True
|
|
self.epoch = 100
|
|
|
|
@IPUOpTest.static_graph
|
|
def build_model(self):
|
|
image = paddle.static.data(
|
|
name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32'
|
|
)
|
|
conv1 = paddle.nn.Conv2D(
|
|
in_channels=image.shape[1],
|
|
out_channels=3,
|
|
kernel_size=3,
|
|
bias_attr=False,
|
|
)(image)
|
|
loss = paddle.mean(conv1)
|
|
self.fetch_list = [loss]
|
|
|
|
weight_decay = self.attrs['weight_decay']
|
|
# Only support ClipGradByGlobalNorm
|
|
clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0)
|
|
if self.attrs['optimizer'] == 'sgd':
|
|
opt = paddle.optimizer.SGD(
|
|
learning_rate=1e-1, weight_decay=weight_decay, grad_clip=clip
|
|
)
|
|
elif self.attrs['optimizer'] == 'adam':
|
|
opt = paddle.optimizer.Adam(
|
|
learning_rate=1e-1, weight_decay=weight_decay, grad_clip=clip
|
|
)
|
|
elif self.attrs['optimizer'] == 'lamb':
|
|
opt = paddle.optimizer.Lamb(
|
|
learning_rate=1e-1,
|
|
lamb_weight_decay=weight_decay,
|
|
grad_clip=clip,
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
f"Not supported optimizer {self.attrs['optimizer']} for test"
|
|
)
|
|
opt.minimize(loss)
|
|
|
|
def run_model(self, exec_mode):
|
|
self.run_op_test(exec_mode)
|
|
|
|
def test(self):
|
|
for m in IPUOpTest.ExecutionMode:
|
|
if not self.skip_mode(m):
|
|
self.build_model()
|
|
self.run_model(m)
|
|
self.check()
|
|
|
|
|
|
class TestAdam(TestBase):
|
|
def set_attrs(self):
|
|
self.attrs = {
|
|
"optimizer": 'adam',
|
|
"weight_decay": 0.0,
|
|
}
|
|
|
|
|
|
class TestLamb(TestBase):
|
|
def set_attrs(self):
|
|
self.attrs = {
|
|
"optimizer": 'lamb',
|
|
"weight_decay": 0.1,
|
|
}
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|