main_polypGen.py

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

from torch.utils import data


from datasets import VOCSegmentation_polypGen2021 as polyGenSeg

from utils import ext_transforms as et
from metrics import StreamSegMetrics

import torch
import torch.nn as nn
from utils.visualizer import Visualizer

from PIL import Image
import matplotlib
import matplotlib.pyplot as plt
from skimage.transform import resize

def plotInference( imgs, depth):
    f =  plt.figure()
    f.add_subplot(1,2, 1)
    plt.imshow(resize((imgs), (256, 256)))
    f.add_subplot(1,2, 2)
    depth_masked = resize(depth, (256, 256))
    plt.imshow(depth_masked)
    # plt.show(block=True)
    return f


def get_argparser():
    parser = argparse.ArgumentParser()

    # Datset Options
    parser.add_argument("--data_root", type=str, default='./trainData_polypGen/',
                        help="path to Dataset")
    parser.add_argument("--dataType", type=str, default='polypGen',
                        help="path to Dataset")
    parser.add_argument("--dataset", type=str, default='polypGen',
                        choices=['polypGen'], help='Name of dataset')
    parser.add_argument("--num_classes", type=int, default=2,
                        help="num classes (default: None)")
    parser.add_argument("--download", action='store_true', default=False,
                        help="download datasets")
    # Deeplab Options
    parser.add_argument("--model", type=str, default='resnet-Unet',
                        choices=['deeplabv3_resnet50',  'deeplabv3plus_resnet50',
                                 'deeplabv3_resnet101', 'deeplabv3plus_resnet101',
                                 'deeplabv3_mobilenet', 'deeplabv3plus_mobilenet', 'pspNet', 'segNet', 'FCN8', 'resnet-Unet', 'axial', 'unet'], help='model name')

    # if unet backbone
    parser.add_argument("--backbone", type=str, default='resnet101',
                        choices=['vgg19',  'resnet34' , 'resnet50',
                                 'resnet101', 'densenet121', 'none'], 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=True,
                        help="save segmentation results to \"./results_polypGen\"")
    
    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=16,
                        help='batch size for validation (default: 4)')
    parser.add_argument("--crop_size", type=int, default=512)
    
    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='2',
                        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)")


    # 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 == 'polypGen':
        train_transform = et.ExtCompose([
             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 = polyGenSeg(root=opts.data_root, image_set='train_polypGen', download=opts.download, transform=train_transform)
    val_dst = polyGenSeg(root=opts.data_root, 
                                  image_set='val_polypGen', download=False, 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 = []
    directoryName = 'results_%s_%s'%(opts.dataType, opts.model)
    if opts.save_val_results:
        if not os.path.exists(directoryName):
            os.mkdir(directoryName)
        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 = images.to(device, dtype=torch.float32)
            labels = labels.to(device, dtype=torch.long)

            outputs = model(images)
            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 =(target).astype(np.uint8)
                    pred = (pred).astype(np.uint8)

                    Image.fromarray(image).save(directoryName + '/%d_image.png' % img_id)
                    Image.fromarray(target).save(directoryName +'/%d_target.png' % img_id)
                    Image.fromarray(pred).save(directoryName+'/%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(directoryName+'/%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 = 2 # foreground + background

    # 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
    if opts.model == 'pspNet':
        from network.network import PSPNet
        #,FCN8,SegNet
        model = PSPNet(num_classes=opts.num_classes, pretrained=True)
    elif opts.model == 'FCN8':
        from network.network import FCN8
        model = FCN8(num_classes=opts.num_classes)

    elif opts.model =='resnet-Unet':
        from backboned_unet import Unet
        # net = Unet(backbone_name='densenet121', classes=2)
        # model = Unet(backbone_name=opts.backbone, pretrained=True, classes=opts.num_classes, decoder_filters=(512, 256, 128, 64, 32))
        
        # for VGG
        model = Unet(backbone_name=opts.backbone, pretrained=True, classes=opts.num_classes)
        pytorch_total_params = sum(p.numel() for p in model.parameters())
        print('number of trainable parameters:', pytorch_total_params)   

    elif opts.model =='axial':
        from network.axialNet import axial50s
        model = axial50s(pretrained=True)
        pytorch_total_params = sum(p.numel() for p in model.parameters())
        print('number of trainable parameters:', pytorch_total_params)
        
    elif opts.model =='unet':
        from network.unet import UNet
        model = UNet(n_channels=3, n_classes=1, bilinear=True)

    else:
        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
        }
    
        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)
    if (opts.model != 'pspNet') and (opts.model != 'segNet') and (opts.model != 'FCN8'):
        utils.set_bn_momentum(model.backbone, momentum=0.01)
    
    # Set up metrics
    metrics = StreamSegMetrics(opts.num_classes)

    # Set up optimizer
    if opts.model == 'pspNet' or 'segNet' or 'FCN8':
        optimizer = torch.optim.SGD(params=model.parameters(), lr=opts.lr, momentum=0.9, weight_decay=opts.weight_decay) 

    elif opts.model == 'resnet-Unet':
        optimizer = torch.optim.Adam(params=model.parameters(), lr=opts.lr) 

    elif opts.model == 'unet':
        optimizer = torch.optim.RMSprop(params=model.parameters(), lr=opts.lr, weight_decay=1e-8, momentum=0.9)
        
    elif opts.model == 'axial':
        optimizer = torch.optim.Adam(model.parameters(), lr=opts.lr)

    else:
        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):
        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 
    denorm = utils.Denormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  

    interval_loss = 0
    while True: 
        # =====  Train  =====
        model.train()
        cur_epochs += 1
        for (images, labels) in train_loader:
            cur_itrs += 1

            images = images.to(device, dtype=torch.float32)
            labels = labels.to(device, dtype=torch.long)
     
            optimizer.zero_grad()
            outputs = model(images)
            loss = criterion(outputs, labels) 
            loss.backward()
            optimizer.step()

            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:
                save_ckpt('checkpoints_polypGen/latest_%s_%s_os%d_%s_%s.pth' %
                          (opts.model, opts.dataset, opts.output_stride, opts.dataType, opts.backbone))
                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_polypGen/best_%s_%s_os%d_%s_%s.pth' %
                              (opts.model, opts.dataset,opts.output_stride, opts.dataType, opts.backbone))

                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
        
if __name__ == '__main__':
    main()