# Copyright 2019 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.
# ============================================================================
import numpy as np
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import Momentum
from mindspore.ops import operations as P
import ms_service_pb2


class LeNet(nn.Cell):
    def __init__(self):
        super(LeNet, self).__init__()
        self.relu = P.ReLU()
        self.batch_size = 32

        self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.reshape = P.Reshape()
        self.fc1 = nn.Dense(400, 120)
        self.fc2 = nn.Dense(120, 84)
        self.fc3 = nn.Dense(84, 10)

    def construct(self, input_x):
        output = self.conv1(input_x)
        output = self.relu(output)
        output = self.pool(output)
        output = self.conv2(output)
        output = self.relu(output)
        output = self.pool(output)
        output = self.reshape(output, (self.batch_size, -1))
        output = self.fc1(output)
        output = self.relu(output)
        output = self.fc2(output)
        output = self.relu(output)
        output = self.fc3(output)
        return output


def train(net, data, label):
    learning_rate = 0.01
    momentum = 0.9

    optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum)
    criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
    net_with_criterion = WithLossCell(net, criterion)
    train_network = TrainOneStepCell(net_with_criterion, optimizer)  # optimizer
    train_network.set_train()
    res = train_network(data, label)
    print("+++++++++Loss+++++++++++++")
    print(res)
    print("+++++++++++++++++++++++++++")
    assert res
    return res

def test_lenet(data, label):
    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
    net = LeNet()
    return train(net, data, label)

if __name__ == '__main__':
    tensor = ms_service_pb2.Tensor()
    tensor.tensor_shape.dim.extend([32, 1, 32, 32])
    # tensor.tensor_shape.dim.add() = 1
    # tensor.tensor_shape.dim.add() = 32
    # tensor.tensor_shape.dim.add() = 32
    tensor.tensor_type = ms_service_pb2.MS_FLOAT32
    tensor.data = np.ones([32, 1, 32, 32]).astype(np.float32).tobytes()

    data_from_buffer = np.frombuffer(tensor.data, dtype=np.float32)
    print(tensor.tensor_shape.dim)
    data_from_buffer = data_from_buffer.reshape(tensor.tensor_shape.dim)
    print(data_from_buffer.shape)
    input_data = Tensor(data_from_buffer * 0.01)
    input_label = Tensor(np.ones([32]).astype(np.int32))
    test_lenet(input_data, input_label)