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!48276 [Golden-Stick] remove useless quant testcase 

Merge pull request !48276 from yangruoqi713/master
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i-robot 2023-02-01 07:10:57 +00:00 committed by Gitee
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tests/st/quantization/ops/test_Conv2dBnFoldQuant.py
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# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
train Conv2dBnFoldQuant Cell
"""
import pytest
import numpy as np
from mindspore import nn
from mindspore import context
from mindspore import Tensor
from mindspore.common import set_seed
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.conv = nn.Conv2dBnFoldQuant(2, 3, kernel_size=(2, 2), stride=(1, 1), pad_mode='valid')
def construct(self, x):
return self.conv(x)
def test_conv2d_bn_fold_quant():
set_seed(1)
network = Net()
inputs = Tensor(np.ones([1, 2, 5, 5]).astype(np.float32))
label = Tensor(np.ones([1, 3, 4, 4]).astype(np.int32))
opt = nn.Momentum(filter(lambda x: x.requires_grad, network.get_parameters()), learning_rate=0.1, momentum=0.9)
loss = nn.MSELoss()
net_with_loss = nn.WithLossCell(network, loss)
train_network = nn.TrainOneStepCell(net_with_loss, opt)
train_network.set_train()
out_loss = train_network(inputs, label)
print("------------------", out_loss.asnumpy())
expect_loss = np.array([0.940427])
error = np.array([0.1])
diff = out_loss.asnumpy() - expect_loss
assert np.all(abs(diff) < error)
@pytest.mark.level1
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_conv2d_bn_fold_quant_ascend():
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
test_conv2d_bn_fold_quant()