main_LPBNN.py

from tqdm import tqdm
import network
import utils
import os
import random
import argparse
import numpy as np
import time

from torch.utils import data
from datasets import VOCSegmentation, Cityscapes
from utils import ext_transforms as et
from metrics import StreamSegMetrics
from network.deeplab3_VAE import loss_latent_from_nn
import torch
import torch.nn as nn
from utils.visualizer import Visualizer

from PIL import Image
import matplotlib
import matplotlib.pyplot as plt

mu_div=1.0
num_models=4
def tile(x,n_tile):
    return torch.cat([x for i in range(n_tile)], dim=0)
def get_argparser():
    parser = argparse.ArgumentParser()

    # Datset Options
    parser.add_argument("--data_root", type=str, default='./datasets/data',
                        help="path to Dataset")
    parser.add_argument("--dataset", type=str, default='voc',
                        choices=['voc', 'cityscapes'], help='Name of dataset')
    parser.add_argument("--num_classes", type=int, default=None,
                        help="num classes (default: None)")

    # Deeplab Options
    parser.add_argument("--model", type=str, default='deeplabv3plus_VAE_resnet101',
                        choices=['deeplabv3_resnet50',  'deeplabv3plus_resnet50','deeplabv3plus_BE_resnet101','deeplabv3plus_VAE_resnet101',
                                 'deeplabv3_resnet101', 'deeplabv3plus_resnet18', 'deeplabv3plus_resnet101',
                                 'deeplabv3_mobilenet', 'deeplabv3plus_mobilenet'], help='model name')
    parser.add_argument("--separable_conv", action='store_true', default=False,
                        help="apply separable conv to decoder and aspp")
    parser.add_argument("--output_stride", type=int, default=16, choices=[8, 16])

    # Train Options
    parser.add_argument("--test_only", action='store_true', default=False)
    parser.add_argument("--save_val_results", action='store_true', default=False,
                        help="save segmentation results to \"./results\"")
    parser.add_argument("--total_itrs", type=int, default=30e3,
                        help="epoch number (default: 30k)")
    parser.add_argument("--lr", type=float, default=0.01,
                        help="learning rate (default: 0.01)")
    parser.add_argument("--lr_policy", type=str, default='poly', choices=['poly', 'step'],
                        help="learning rate scheduler policy")
    parser.add_argument("--step_size", type=int, default=10000)
    parser.add_argument("--crop_val", action='store_true', default=False,
                        help='crop validation (default: False)')
    parser.add_argument("--batch_size", type=int, default=16,
                        help='batch size (default: 16)')
    parser.add_argument("--val_batch_size", type=int, default=1,
                        help='batch size for validation (default: 1)')
    parser.add_argument("--crop_size", type=int, default=513)
    
    parser.add_argument("--ckpt", default=None, type=str,
                        help="restore from checkpoint")
    parser.add_argument("--continue_training", action='store_true', default=False)

    parser.add_argument("--loss_type", type=str, default='cross_entropy',
                        choices=['cross_entropy', 'focal_loss'], help="loss type (default: False)")
    parser.add_argument("--gpu_id", type=str, default='0',
                        help="GPU ID")
    parser.add_argument("--weight_decay", type=float, default=1e-4,
                        help='weight decay (default: 1e-4)')
    parser.add_argument("--random_seed", type=int, default=1,
                        help="random seed (default: 1)")
    parser.add_argument("--print_interval", type=int, default=10,
                        help="print interval of loss (default: 10)")
    parser.add_argument("--val_interval", type=int, default=100,
                        help="epoch interval for eval (default: 100)")
    parser.add_argument("--download", action='store_true', default=False,
                        help="download datasets")

    # PASCAL VOC Options
    parser.add_argument("--year", type=str, default='2012',
                        choices=['2012_aug', '2012', '2011', '2009', '2008', '2007'], help='year of VOC')

    # Visdom options
    parser.add_argument("--enable_vis", action='store_true', default=False,
                        help="use visdom for visualization")
    parser.add_argument("--vis_port", type=str, default='13570',
                        help='port for visdom')
    parser.add_argument("--vis_env", type=str, default='main',
                        help='env for visdom')
    parser.add_argument("--vis_num_samples", type=int, default=8,
                        help='number of samples for visualization (default: 8)')
    return parser


def get_dataset(opts):
    """ Dataset And Augmentation
    """
    if opts.dataset == 'voc':
        train_transform = et.ExtCompose([
            #et.ExtResize(size=opts.crop_size),
            et.ExtRandomScale((0.5, 2.0)),
            et.ExtRandomCrop(size=(opts.crop_size, opts.crop_size), pad_if_needed=True),
            et.ExtRandomHorizontalFlip(),
            et.ExtToTensor(),
            et.ExtNormalize(mean=[0.485, 0.456, 0.406],
                            std=[0.229, 0.224, 0.225]),
        ])
        if opts.crop_val:
            val_transform = et.ExtCompose([
                et.ExtResize(opts.crop_size),
                et.ExtCenterCrop(opts.crop_size),
                et.ExtToTensor(),
                et.ExtNormalize(mean=[0.485, 0.456, 0.406],
                                std=[0.229, 0.224, 0.225]),
            ])
        else:
            val_transform = et.ExtCompose([
                et.ExtToTensor(),
                et.ExtNormalize(mean=[0.485, 0.456, 0.406],
                                std=[0.229, 0.224, 0.225]),
            ])
        train_dst = VOCSegmentation(root=opts.data_root, year=opts.year,
                                    image_set='train', download=opts.download, transform=train_transform)
        val_dst = VOCSegmentation(root=opts.data_root, year=opts.year,
                                  image_set='val', download=False, transform=val_transform)

    if opts.dataset == 'cityscapes':
        train_transform = et.ExtCompose([
            #et.ExtResize( 512 ),
            et.ExtRandomCrop(size=(opts.crop_size, opts.crop_size)),
            et.ExtColorJitter( brightness=0.5, contrast=0.5, saturation=0.5 ),
            et.ExtRandomHorizontalFlip(),
            et.ExtToTensor(),
            et.ExtNormalize(mean=[0.485, 0.456, 0.406],
                            std=[0.229, 0.224, 0.225]),
        ])

        val_transform = et.ExtCompose([
            #et.ExtResize( 512 ),
            et.ExtToTensor(),
            et.ExtNormalize(mean=[0.485, 0.456, 0.406],
                            std=[0.229, 0.224, 0.225]),
        ])

        train_dst = Cityscapes(root=opts.data_root,
                               split='train', transform=train_transform)
        val_dst = Cityscapes(root=opts.data_root,
                             split='val', transform=val_transform)
    return train_dst, val_dst


def validate(opts, model, loader, device, metrics, ret_samples_ids=None):
    """Do validation and return specified samples"""
    metrics.reset()
    ret_samples = []
    softmax = nn.Softmax2d()
    if opts.save_val_results:
        if not os.path.exists('results'):
            os.mkdir('results')
        denorm = utils.Denormalize(mean=[0.485, 0.456, 0.406], 
                                   std=[0.229, 0.224, 0.225])
        img_id = 0

    with torch.no_grad():
        for i, (images, labels) in tqdm(enumerate(loader)):

            images =tile(images, num_models)

            images = images.to(device, dtype=torch.float32)
            labels = labels.to(device, dtype=torch.long)

            outputs = model(images)
            outputs= softmax(outputs)

            pred_list = torch.chunk(outputs, num_models)

            outputs = sum(i for i in pred_list) / num_models

            preds = outputs.detach().max(dim=1)[1].cpu().numpy()
            targets = labels.cpu().numpy()

            metrics.update(targets, preds)
            if ret_samples_ids is not None and i in ret_samples_ids:  # get vis samples
                ret_samples.append(
                    (images[0].detach().cpu().numpy(), targets[0], preds[0]))

            if opts.save_val_results:
                for i in range(len(images)):
                    image = images[i].detach().cpu().numpy()
                    target = targets[i]
                    pred = preds[i]

                    image = (denorm(image) * 255).transpose(1, 2, 0).astype(np.uint8)
                    target = loader.dataset.decode_target(target).astype(np.uint8)
                    pred = loader.dataset.decode_target(pred).astype(np.uint8)

                    Image.fromarray(image).save('results/%d_image.png' % img_id)
                    Image.fromarray(target).save('results/%d_target.png' % img_id)
                    Image.fromarray(pred).save('results/%d_pred.png' % img_id)

                    fig = plt.figure()
                    plt.imshow(image)
                    plt.axis('off')
                    plt.imshow(pred, alpha=0.7)
                    ax = plt.gca()
                    ax.xaxis.set_major_locator(matplotlib.ticker.NullLocator())
                    ax.yaxis.set_major_locator(matplotlib.ticker.NullLocator())
                    plt.savefig('results/%d_overlay.png' % img_id, bbox_inches='tight', pad_inches=0)
                    plt.close()
                    img_id += 1

        score = metrics.get_results()
    return score, ret_samples


def main():
    opts = get_argparser().parse_args()
    if opts.dataset.lower() == 'voc':
        opts.num_classes = 21
    elif opts.dataset.lower() == 'cityscapes':
        opts.num_classes = 19

    # Setup visualization
    vis = Visualizer(port=opts.vis_port,
                     env=opts.vis_env) if opts.enable_vis else None
    if vis is not None:  # display options
        vis.vis_table("Options", vars(opts))

    os.environ['CUDA_VISIBLE_DEVICES'] = opts.gpu_id
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print("Device: %s" % device)

    # Setup random seed
    torch.manual_seed(opts.random_seed)
    np.random.seed(opts.random_seed)
    random.seed(opts.random_seed)

    # Setup dataloader
    if opts.dataset=='voc' and not opts.crop_val:
        opts.val_batch_size = 1
    
    train_dst, val_dst = get_dataset(opts)
    train_loader = data.DataLoader(
        train_dst, batch_size=opts.batch_size, shuffle=True, num_workers=2)
    val_loader = data.DataLoader(
        val_dst, batch_size=opts.val_batch_size, shuffle=True, num_workers=2)
    print("Dataset: %s, Train set: %d, Val set: %d" %
          (opts.dataset, len(train_dst), len(val_dst)))

    # Set up model
    model_map = {
        'deeplabv3_resnet50': network.deeplabv3_resnet50,
        'deeplabv3plus_resnet50': network.deeplabv3plus_resnet50,
        'deeplabv3_resnet101': network.deeplabv3_resnet101,
        'deeplabv3plus_resnet101': network.deeplabv3plus_resnet101,
        'deeplabv3_mobilenet': network.deeplabv3_mobilenet,
        'deeplabv3plus_mobilenet': network.deeplabv3plus_mobilenet,
        'deeplabv3plus_resnet18' : network.deeplabv3plus_resnet18,
        'deeplabv3plus_VAE_resnet101' : network.deeplabv3plus_VAE_resnet101,
        'deeplabv3plus_BE_resnet101': network.deeplabv3plus_BE_resnet101
    }

    model = model_map[opts.model](num_classes=opts.num_classes, output_stride=opts.output_stride)
    if opts.separable_conv and 'plus' in opts.model:
        network.convert_to_separable_conv(model.classifier)
    utils.set_bn_momentum(model.backbone, momentum=0.01)
    
    # Set up metrics
    metrics = StreamSegMetrics(opts.num_classes)

    # Set up optimizer
    optimizer = torch.optim.SGD(params=[
        {'params': model.backbone.parameters(), 'lr': 0.1*opts.lr},
        {'params': model.classifier.parameters(), 'lr': opts.lr},
    ], lr=opts.lr, momentum=0.9, weight_decay=opts.weight_decay)
    #optimizer = torch.optim.SGD(params=model.parameters(), lr=opts.lr, momentum=0.9, weight_decay=opts.weight_decay)
    #torch.optim.lr_scheduler.StepLR(optimizer, step_size=opts.lr_decay_step, gamma=opts.lr_decay_factor)
    if opts.lr_policy=='poly':
        scheduler = utils.PolyLR(optimizer, opts.total_itrs, power=0.9)
    elif opts.lr_policy=='step':
        scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=opts.step_size, gamma=0.1)

    # Set up criterion
    #criterion = utils.get_loss(opts.loss_type)
    if opts.loss_type == 'focal_loss':
        criterion = utils.FocalLoss(ignore_index=255, size_average=True)
    elif opts.loss_type == 'cross_entropy':
        criterion = nn.CrossEntropyLoss(ignore_index=255, reduction='mean')

    def save_ckpt(path):
        """ save current model
        """
        torch.save({
            "cur_itrs": cur_itrs,
            "model_state": model.module.state_dict(),
            "optimizer_state": optimizer.state_dict(),
            "scheduler_state": scheduler.state_dict(),
            "best_score": best_score,
        }, path)
        print("Model saved as %s" % path)
    
    utils.mkdir('checkpoints')
    # Restore
    best_score = 0.0
    cur_itrs = 0
    cur_epochs = 0
    if opts.ckpt is not None and os.path.isfile(opts.ckpt):
        # https://github.com/VainF/DeepLabV3Plus-Pytorch/issues/8#issuecomment-605601402, @PytaichukBohdan
        checkpoint = torch.load(opts.ckpt, map_location=torch.device('cpu'))
        model.load_state_dict(checkpoint["model_state"])
        model = nn.DataParallel(model)
        model.to(device)
        if opts.continue_training:
            optimizer.load_state_dict(checkpoint["optimizer_state"])
            scheduler.load_state_dict(checkpoint["scheduler_state"])
            cur_itrs = checkpoint["cur_itrs"]
            best_score = checkpoint['best_score']
            print("Training state restored from %s" % opts.ckpt)
        print("Model restored from %s" % opts.ckpt)
        del checkpoint  # free memory
    else:
        print("[!] Retrain")
        model = nn.DataParallel(model)
        model.to(device)

    #==========   Train Loop   ==========#
    vis_sample_id = np.random.randint(0, len(val_loader), opts.vis_num_samples,
                                      np.int32) if opts.enable_vis else None  # sample idxs for visualization
    denorm = utils.Denormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # denormalization for ori images

    if opts.test_only:
        model.eval()
        val_score, ret_samples = validate(
            opts=opts, model=model, loader=val_loader, device=device, metrics=metrics, ret_samples_ids=vis_sample_id)
        print(metrics.to_str(val_score))
        return

    #scaler = torch.cuda.amp.GradScaler()
    scaler = torch.cuda.amp.GradScaler()

    torch.cuda.empty_cache()

    interval_loss = 0
    train_losses = []
    while cur_epochs < 80: #cur_itrs < opts.total_itrs:
        # =====  Train  =====
        model.train()
        cur_epochs += 1
        torch.cuda.synchronize()
        since = int(round(time.time()*1000))

        for (images, labels) in train_loader:
            cur_itrs += 1

            #images = images.to(device, dtype=torch.float32)
            images = images.to(device, dtype=torch.float16)
            labels = labels.to(device, dtype=torch.long)

            optimizer.zero_grad()
            # Casts operations to mixed precision
            with torch.cuda.amp.autocast():
                outputs = model(images)
                loss = criterion(outputs, labels)+ mu_div * loss_latent_from_nn(model)
                
            scaler.scale(loss).backward()
            scaler.step(optimizer)
            scaler.update()

            np_loss = loss.detach().cpu().numpy()
            interval_loss += np_loss
            if vis is not None:
                vis.vis_scalar('Loss', cur_itrs, np_loss)

            if (cur_itrs) % 10 == 0:
                interval_loss = interval_loss/10
                print("Epoch %d, Itrs %d/%d, Loss=%f" %
                      (cur_epochs, cur_itrs, opts.total_itrs, interval_loss))
                interval_loss = 0.0

            if (cur_itrs) % opts.val_interval == 0:
                train_losses.append(loss.item())
                plt.figure(figsize=(20,10))
                plt.plot(train_losses)
                plt.xlabel("Time")
                plt.ylabel("Loss")
                plt.title("Loss function evolution")
                plt.savefig('loss_train.png')
                save_ckpt('checkpoints/latest_%s_%s_os%d.pth' %
                          (opts.model, opts.dataset, opts.output_stride))
                print("validation...")
                model.eval()
                val_score, ret_samples = validate(
                    opts=opts, model=model, loader=val_loader, device=device, metrics=metrics, ret_samples_ids=vis_sample_id)
                print(metrics.to_str(val_score))
                if val_score['Mean IoU'] > best_score:  # save best model
                    best_score = val_score['Mean IoU']
                    save_ckpt('checkpoints/best_%s_%s_os%d.pth' %
                              (opts.model, opts.dataset,opts.output_stride))

                if vis is not None:  # visualize validation score and samples
                    vis.vis_scalar("[Val] Overall Acc", cur_itrs, val_score['Overall Acc'])
                    vis.vis_scalar("[Val] Mean IoU", cur_itrs, val_score['Mean IoU'])
                    vis.vis_table("[Val] Class IoU", val_score['Class IoU'])

                    for k, (img, target, lbl) in enumerate(ret_samples):
                        img = (denorm(img) * 255).astype(np.uint8)
                        target = train_dst.decode_target(target).transpose(2, 0, 1).astype(np.uint8)
                        lbl = train_dst.decode_target(lbl).transpose(2, 0, 1).astype(np.uint8)
                        concat_img = np.concatenate((img, target, lbl), axis=2)  # concat along width
                        vis.vis_image('Sample %d' % k, concat_img)
                model.train()
            scheduler.step()  

            if cur_itrs >=  opts.total_itrs:
                return

        torch.cuda.synchronize()
        time_elapsed = int(round(time.time()*1000)) - since
        print ('training time elapsed {}ms'.format(time_elapsed))

        
if __name__ == '__main__':
    main()