delete pynative lenet_model test

tests/ut/python/pynative_mode/ge/model/test_lenet_model.py

-# Copyright 2020 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.
-# ============================================================================
-""" test_lenet_model """
-import numpy as np
-import pytest
-
-import mindspore.nn as nn
-from mindspore.common.tensor import Tensor
-from mindspore.nn import WithGradCell
-from mindspore.ops import operations as P
-
-
-class LeNet5(nn.Cell):
-    """ LeNet5 definition """
-
-    def __init__(self):
-        super(LeNet5, self).__init__()
-        self.conv1 = nn.Conv2d(1, 6, 5, pad_mode='valid')
-        self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
-        self.fc1 = nn.Dense(16 * 5 * 5, 120)
-        self.fc2 = nn.Dense(120, 84)
-        self.fc3 = nn.Dense(84, 10)
-        self.relu = nn.ReLU()
-        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
-        self.flatten = P.Flatten()
-
-    def construct(self, x):
-        x = self.max_pool2d(self.relu(self.conv1(x)))
-        x = self.max_pool2d(self.relu(self.conv2(x)))
-        x = self.flatten(x)
-        x = self.relu(self.fc1(x))
-        x = self.relu(self.fc2(x))
-        x = self.fc3(x)
-        return x
-
-
-@pytest.mark.skip(reason="need ge backend")
-def test_lenet_pynative_train_net():
-    """ test_lenet_pynative_train_net """
-    data = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
-    label = Tensor(np.ones([1, 10]).astype(np.float32))
-    dout = Tensor(np.ones([1]).astype(np.float32))
-    iteration_num = 1
-    verification_step = 0
-
-    net = LeNet5()
-
-    for i in range(0, iteration_num):
-        # get the gradients
-        loss_fn = nn.SoftmaxCrossEntropyWithLogits(is_grad=False)
-        grad_fn = nn.SoftmaxCrossEntropyWithLogits()
-        grad_net = WithGradCell(net, grad_fn, sens=dout)
-
-
-def test_lenet_pynative_train_model():
-    """ test_lenet_pynative_train_model """
-    # get loss from model.compute_loss
-    return