import sys
sys.path.append('/e/dengnc')
__package__ = "deeplightfield"

import os
import torch
import torch.optim
import torchvision
from tensorboardX import SummaryWriter
from .loss.loss import PerceptionReconstructionLoss
from .my import netio
from .my import util
from .my.simple_perf import SimplePerf
from .data.lf_syn import LightFieldSynDataset
from .trans_unet import TransUnet


device = torch.device("cuda:2")
DATA_DIR = os.path.dirname(__file__) + '/data/lf_syn_2020.12.23'
TRAIN_DATA_DESC_FILE = DATA_DIR + '/train.json'
OUTPUT_DIR = DATA_DIR + '/output_low_lr'
RUN_DIR = DATA_DIR + '/run_low_lr'
BATCH_SIZE = 1
TEST_BATCH_SIZE = 10
NUM_EPOCH = 1000
MODE = "Silence"  # "Perf"
EPOCH_BEGIN = 500


def train():
    # 1. Initialize data loader
    print("Load dataset: " + TRAIN_DATA_DESC_FILE)
    train_dataset = LightFieldSynDataset(TRAIN_DATA_DESC_FILE)
    train_data_loader = torch.utils.data.DataLoader(
        dataset=train_dataset,
        batch_size=BATCH_SIZE,
        pin_memory=True,
        shuffle=True,
        drop_last=False)
    print(len(train_data_loader))

    # 2. Initialize components
    model = TransUnet(cam_params=train_dataset.cam_params,
                      view_images=train_dataset.sparse_view_images,
                      view_depths=train_dataset.sparse_view_depths,
                      view_positions=train_dataset.sparse_view_positions,
                      diopter_of_layers=train_dataset.diopter_of_layers).to(device)
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
    loss = PerceptionReconstructionLoss()

    if EPOCH_BEGIN > 0:
        netio.LoadNet('%s/model-epoch_%d.pth' % (RUN_DIR, EPOCH_BEGIN), model,
                      solver=optimizer)

    # 3. Train
    model.train()
    epoch = EPOCH_BEGIN
    iters = EPOCH_BEGIN * len(train_data_loader)

    util.CreateDirIfNeed(RUN_DIR)

    perf = SimplePerf(enable=(MODE == "Perf"), start=True)
    writer = SummaryWriter(RUN_DIR)

    print("Begin training...")
    for epoch in range(EPOCH_BEGIN, NUM_EPOCH):
        for _, view_images, _, view_positions in train_data_loader:

            view_images = view_images.to(device)

            perf.Checkpoint("Load")

            out_view_images = model(view_positions)

            perf.Checkpoint("Forward")

            optimizer.zero_grad()
            loss_value = loss(out_view_images, view_images)

            perf.Checkpoint("Compute loss")

            loss_value.backward()

            perf.Checkpoint("Backward")

            optimizer.step()

            perf.Checkpoint("Update")

            print("Epoch: ", epoch, ", Iter: ", iters,
                  ", Loss: ", loss_value.item())

            iters = iters + BATCH_SIZE

            # Write tensorboard logs.
            writer.add_scalar("loss", loss_value, iters)
            if iters % len(train_data_loader) == 0:
                output_vs_gt = torch.cat([out_view_images, view_images], dim=0)
                writer.add_image("Output_vs_gt", torchvision.utils.make_grid(
                    output_vs_gt, scale_each=True, normalize=False)
                    .cpu().detach().numpy(), iters)

        # Save checkpoint
        if ((epoch + 1) % 50 == 0):
            netio.SaveNet('%s/model-epoch_%d.pth' % (RUN_DIR, epoch + 1), model,
                          solver=optimizer)

    print("Train finished")
    netio.SaveNet('%s/model-epoch_%d.pth' % (RUN_DIR, epoch + 1), model)


def test(net_file: str):
    # 1. Load train dataset
    print("Load dataset: " + TRAIN_DATA_DESC_FILE)
    train_dataset = LightFieldSynDataset(TRAIN_DATA_DESC_FILE)
    train_data_loader = torch.utils.data.DataLoader(
        dataset=train_dataset,
        batch_size=TEST_BATCH_SIZE,
        pin_memory=True,
        shuffle=False,
        drop_last=False)

    # 2. Load trained model
    model = TransUnet(cam_params=train_dataset.cam_params,
                      view_images=train_dataset.sparse_view_images,
                      view_depths=train_dataset.sparse_view_depths,
                      view_positions=train_dataset.sparse_view_positions,
                      diopter_of_layers=train_dataset.diopter_of_layers).to(device)
    netio.LoadNet(net_file, model)

    # 3. Test on train dataset
    print("Begin test on train dataset...")
    util.CreateDirIfNeed(OUTPUT_DIR)
    for view_idxs, view_images, _, view_positions in train_data_loader:
        out_view_images = model(view_positions)
        util.WriteImageTensor(
            view_images,
            ['%s/gt_view%02d.png' % (OUTPUT_DIR, i) for i in view_idxs])
        util.WriteImageTensor(
            out_view_images,
            ['%s/out_view%02d.png' % (OUTPUT_DIR, i) for i in view_idxs])


if __name__ == "__main__":
    #train()
    test(RUN_DIR + '/model-epoch_1000.pth')