# 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 numpy as np

import paddle
from paddle.distributed import fleet
from paddle.io import DataLoader, Dataset
from paddle.vision.models import ResNet
from paddle.vision.models.resnet import BottleneckBlock

base_lr = 0.1
momentum_rate = 0.9
l2_decay = 1e-4

epoch = 3
batch_num = 1
batch_size = 1
class_dim = 102


# define a random dataset
class RandomDataset(Dataset):
    def __init__(self, num_samples):
        self.num_samples = num_samples

    def __getitem__(self, idx):
        image = np.random.random([3, 224, 224]).astype('float32')
        label = np.random.randint(0, class_dim - 1, (1,)).astype('int64')
        return image, label

    def __len__(self):
        return self.num_samples


def optimizer_setting(parameter_list=None):
    optimizer = paddle.optimizer.Momentum(
        learning_rate=base_lr,
        momentum=momentum_rate,
        weight_decay=paddle.regularizer.L2Decay(l2_decay),
        parameters=parameter_list,
    )
    return optimizer


def train_resnet():
    fleet.init(is_collective=True)

    resnet = ResNet(BottleneckBlock, 18, num_classes=class_dim)
    optimizer = optimizer_setting(parameter_list=resnet.parameters())
    optimizer = fleet.distributed_optimizer(optimizer)
    resnet = fleet.distributed_model(resnet)

    dataset = RandomDataset(batch_num * batch_size)
    train_loader = DataLoader(
        dataset,
        batch_size=batch_size,
        shuffle=True,
        drop_last=True,
        num_workers=2,
    )

    print("Distributed training start...")
    for eop in range(epoch):
        resnet.train()

        for batch_id, data in enumerate(train_loader()):
            img, label = data
            label.stop_gradient = True

            out = resnet(img)
            loss = paddle.nn.functional.cross_entropy(input=out, label=label)
            avg_loss = paddle.mean(x=loss)
            acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1)
            acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5)

            avg_loss.backward()
            optimizer.step()
            resnet.clear_gradients()

            print(
                "[Epoch %d, batch %d] loss: %.5f, acc1: %.5f, acc5: %.5f"
                % (eop, batch_id, avg_loss, acc_top1, acc_top5)
            )

    print("Distributed training completed")


if __name__ == '__main__':
    import os

    nnodes = os.getenv('PADDLE_NNODES')
    cn = os.getenv('PADDLE_LOCAL_SIZE')
    print(f"Prepare distributed training with {nnodes} nodes {cn} cards")
    train_resnet()
