# 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.
# ============================================================================
"""Lenet Tutorial
The sample can be run on CPU, GPU and Ascend 910 AI processor.
"""
import os
import urllib.request
from urllib.parse import urlparse
import gzip
import argparse
import mindspore.dataset as ds
import mindspore.nn as nn
from mindspore import context
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train import Model
from mindspore.common.initializer import TruncatedNormal
import mindspore.dataset.transforms.vision.c_transforms as CV
import mindspore.dataset.transforms.c_transforms as C
from mindspore.dataset.transforms.vision import Inter
from mindspore.nn.metrics import Accuracy
from mindspore.common import dtype as mstype
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits


def unzipfile(gzip_path):
    """unzip dataset file
    Args:
        gzip_path: dataset file path
    """
    open_file = open(gzip_path.replace('.gz',''), 'wb')
    gz_file = gzip.GzipFile(gzip_path)
    open_file.write(gz_file.read())
    gz_file.close()


def download_dataset():
    """Download the dataset from http://yann.lecun.com/exdb/mnist/."""
    print("******Downloading the MNIST dataset******")
    train_path = "./MNIST_Data/train/"
    test_path = "./MNIST_Data/test/"
    train_path_check = os.path.exists(train_path)
    test_path_check = os.path.exists(test_path)
    if train_path_check == False and test_path_check ==False:
        os.makedirs(train_path)
        os.makedirs(test_path)
    train_url = {"http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz", "http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz"}
    test_url = {"http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz", "http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz"}
    for url in train_url:
        url_parse = urlparse(url)
        # split the file name from url
        file_name = os.path.join(train_path,url_parse.path.split('/')[-1])
        if not os.path.exists(file_name.replace('.gz','')):
            file = urllib.request.urlretrieve(url, file_name)
            unzipfile(file_name)
            os.remove(file_name)
    for url in test_url:
        url_parse = urlparse(url)
        # split the file name from url
        file_name = os.path.join(test_path,url_parse.path.split('/')[-1])
        if not os.path.exists(file_name.replace('.gz','')):
            file = urllib.request.urlretrieve(url, file_name)
            unzipfile(file_name)
            os.remove(file_name)


def create_dataset(data_path, batch_size=32, repeat_size=1,
                   num_parallel_workers=1):
    """ create dataset for train or test
    Args:
        data_path: Data path
        batch_size: The number of data records in each group
        repeat_size: The number of replicated data records
        num_parallel_workers: The number of parallel workers
    """
    # define dataset
    mnist_ds = ds.MnistDataset(data_path)

    # define operation parameters
    resize_height, resize_width = 32, 32
    rescale = 1.0 / 255.0
    shift = 0.0
    rescale_nml = 1 / 0.3081
    shift_nml = -1 * 0.1307 / 0.3081

    # define map operations
    resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # Resize images to (32, 32)
    rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) # normalize images
    rescale_op = CV.Rescale(rescale, shift) # rescale images
    hwc2chw_op = CV.HWC2CHW() # change shape from (height, width, channel) to (channel, height, width) to fit network.
    type_cast_op = C.TypeCast(mstype.int32) # change data type of label to int32 to fit network

    # apply map operations on images
    mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
    mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)

    # apply DatasetOps
    buffer_size = 10000
    mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 10000 as in LeNet train script
    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
    mnist_ds = mnist_ds.repeat(repeat_size)

    return mnist_ds


def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
    """Conv layer weight initial."""
    weight = weight_variable()
    return nn.Conv2d(in_channels, out_channels,
                     kernel_size=kernel_size, stride=stride, padding=padding,
                     weight_init=weight, has_bias=False, pad_mode="valid")


def fc_with_initialize(input_channels, out_channels):
    """Fc layer weight initial."""
    weight = weight_variable()
    bias = weight_variable()
    return nn.Dense(input_channels, out_channels, weight, bias)


def weight_variable():
    """Weight initial."""
    return TruncatedNormal(0.02)


class LeNet5(nn.Cell):
    """Lenet network structure."""
    # define the operator required
    def __init__(self):
        super(LeNet5, self).__init__()
        self.batch_size = 32
        self.conv1 = conv(1, 6, 5)
        self.conv2 = conv(6, 16, 5)
        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
        self.fc2 = fc_with_initialize(120, 84)
        self.fc3 = fc_with_initialize(84, 10)
        self.relu = nn.ReLU()
        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
        self.flatten = nn.Flatten()

    # use the preceding operators to construct networks
    def construct(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.max_pool2d(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.max_pool2d(x)
        x = self.flatten(x)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.relu(x)
        x = self.fc3(x)
        return x


def train_net(args, model, epoch_size, mnist_path, repeat_size, ckpoint_cb):
    """Define the training method."""
    print("============== Starting Training ==============")
    # load training dataset
    ds_train = create_dataset(os.path.join(mnist_path, "train"), 32, repeat_size)
    model.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor()], dataset_sink_mode=False)


def test_net(args, network, model, mnist_path):
    """Define the evaluation method."""
    print("============== Starting Testing ==============")
    # load the saved model for evaluation
    param_dict = load_checkpoint("checkpoint_lenet-1_1875.ckpt")
    # load parameter to the network
    load_param_into_net(network, param_dict)
    # load testing dataset
    ds_eval = create_dataset(os.path.join(mnist_path, "test"))
    acc = model.eval(ds_eval, dataset_sink_mode=False)
    print("============== Accuracy:{} ==============".format(acc))


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='MindSpore LeNet Example')
    parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
                        help='device where the code will be implemented (default: Ascend)')
    args = parser.parse_args()
    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target,
                        enable_mem_reuse=False)
    # download mnist dataset
    download_dataset()
    # learning rate setting
    lr = 0.01
    momentum = 0.9
    epoch_size = 1
    mnist_path = "./MNIST_Data"
    # define the loss function
    net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
    repeat_size = epoch_size
    # create the network
    network = LeNet5()
    # define the optimizer
    net_opt = nn.Momentum(network.trainable_params(), lr, momentum)
    config_ck = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=10)
    # save the network model and parameters for subsequence fine-tuning
    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
    # group layers into an object with training and evaluation features
    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})

    train_net(args, model, epoch_size, mnist_path, repeat_size, ckpoint_cb)
    test_net(args, network, model, mnist_path)
