"""
Copyright 2020 The OneFlow 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 argparse

from oneflow.compatible import single_client as flow

DATA_DIR = "/dataset/imagenet_1k/oneflow/30/train"
parser = argparse.ArgumentParser(description="flags for multi-node and resource")
parser.add_argument("-i", "--iter_num", type=int, default=10, required=False)
parser.add_argument("-g", "--gpu_num_per_node", type=int, default=1, required=False)
parser.add_argument(
    "-m", "--multinode", default=False, action="store_true", required=False
)
parser.add_argument("-n", "--node_list", type=str, default=None, required=False)
parser.add_argument("-e", "--eval_dir", type=str, default=DATA_DIR, required=False)
parser.add_argument("-t", "--train_dir", type=str, default=DATA_DIR, required=False)
parser.add_argument("-load", "--model_load_dir", type=str, default="", required=False)
parser.add_argument(
    "-save", "--model_save_dir", type=str, default="./checkpoints", required=False
)
args = parser.parse_args()


def _data_load_layer(data_dir):
    rgb_mean = [123.68, 116.78, 103.94]
    ofrecord = flow.data.ofrecord_reader(
        data_dir, batch_size=12, data_part_num=8, name="decode"
    )
    image = flow.data.ofrecord_image_decoder(ofrecord, "encoded", color_space="RGB")
    label = flow.data.ofrecord_raw_decoder(
        ofrecord, "class/label", shape=(), dtype=flow.int32
    )
    rsz = flow.image.resize(image, resize_x=227, resize_y=227, color_space="RGB")
    normal = flow.image.crop_mirror_normalize(
        rsz,
        color_space="RGB",
        output_layout="NCHW",
        mean=rgb_mean,
        output_dtype=flow.float,
    )
    return (label, normal)


def _conv2d_layer(
    name,
    input,
    filters,
    kernel_size=3,
    strides=1,
    padding="SAME",
    data_format="NCHW",
    dilation_rate=1,
    activation="Relu",
    use_bias=False,
    weight_initializer=flow.random_uniform_initializer(),
    bias_initializer=None,
):
    weight_shape = (filters, input.shape[1], kernel_size, kernel_size)
    weight = flow.get_variable(
        name + "-weight",
        shape=weight_shape,
        dtype=input.dtype,
        initializer=weight_initializer,
    )
    output = flow.nn.conv2d(
        input, weight, strides, padding, None, data_format, dilation_rate, name=name
    )
    if use_bias:
        bias = flow.get_variable(
            name + "-bias",
            shape=(filters,),
            dtype=input.dtype,
            initializer=bias_initializer,
        )
        output = flow.nn.bias_add(output, bias, data_format)
    if activation is not None:
        if activation == "Relu":
            output = flow.math.relu(output)
        else:
            raise NotImplementedError
    return output


def alexnet(images, labels):
    conv1 = _conv2d_layer(
        "conv1", images, filters=64, kernel_size=11, strides=4, padding="VALID"
    )
    pool1 = flow.nn.avg_pool2d(conv1, 3, 2, "VALID", "NCHW", name="pool1")
    conv2 = _conv2d_layer("conv2", pool1, filters=192, kernel_size=5)
    pool2 = flow.nn.avg_pool2d(conv2, 3, 2, "VALID", "NCHW", name="pool2")
    conv3 = _conv2d_layer("conv3", pool2, filters=384)
    conv4 = _conv2d_layer("conv4", conv3, filters=384)
    conv5 = _conv2d_layer("conv5", conv4, filters=256)
    pool5 = flow.nn.avg_pool2d(conv5, 3, 2, "VALID", "NCHW", name="pool5")
    if len(pool5.shape) > 2:
        pool5 = flow.reshape(pool5, shape=(pool5.shape[0], -1))
    fc1 = flow.layers.dense(
        inputs=pool5,
        units=4096,
        activation=flow.math.relu,
        use_bias=False,
        kernel_initializer=flow.random_uniform_initializer(),
        bias_initializer=False,
        trainable=True,
        name="fc1",
    )
    dropout1 = flow.nn.dropout(fc1, rate=0.5)
    fc2 = flow.layers.dense(
        inputs=dropout1,
        units=4096,
        activation=flow.math.relu,
        use_bias=False,
        kernel_initializer=flow.random_uniform_initializer(),
        bias_initializer=False,
        trainable=True,
        name="fc2",
    )
    dropout2 = flow.nn.dropout(fc2, rate=0.5)
    fc3 = flow.layers.dense(
        inputs=dropout2,
        units=1001,
        activation=None,
        use_bias=False,
        kernel_initializer=flow.random_uniform_initializer(),
        bias_initializer=False,
        trainable=True,
        name="fc3",
    )
    loss = flow.nn.sparse_softmax_cross_entropy_with_logits(
        labels, fc3, name="softmax_loss"
    )
    return loss


@flow.global_function
def alexnet_train_job():
    flow.config.train.primary_lr(1e-05)
    flow.config.train.model_update_conf(dict(naive_conf={}))
    (labels, images) = _data_load_layer(args.train_dir)
    loss = alexnet(images, labels)
    flow.losses.add_loss(loss)
    return loss


@flow.global_function
def alexnet_eval_job():
    (labels, images) = _data_load_layer(args.eval_dir)
    loss = alexnet(images, labels)
    return loss


def main():
    flow.config.gpu_device_num(args.gpu_num_per_node)
    flow.config.ctrl_port(9788)
    flow.config.default_data_type(flow.float)
    if args.multinode:
        flow.config.ctrl_port(12138)
        nodes = []
        for n in args.node_list.strip().split(","):
            addr_dict = {}
            addr_dict["addr"] = n
            nodes.append(addr_dict)
        flow.config.machine(nodes)
    check_point = flow.train.CheckPoint()
    if not args.model_load_dir:
        check_point.init()
    else:
        check_point.load(args.model_load_dir)
    print("{:>12}  {:>12}  {:>12}".format("iter", "loss type", "loss value"))
    for i in range(args.iter_num):
        fmt_str = "{:>12}  {:>12}  {:>12.10f}"
        train_loss = alexnet_train_job().get().mean()
        print(fmt_str.format(i, "train loss:", train_loss))
        if (i + 1) % 10 == 0:
            eval_loss = alexnet_eval_job().get().mean()
            print(fmt_str.format(i, "eval loss:", eval_loss))
        if (i + 1) % 100 == 0:
            check_point.save(args.model_save_dir + str(i))


if __name__ == "__main__":
    main()
