app.py

# Copyright (C) 2020 * Ltd. All rights reserved.
# author : Sanghyeon Jo <[email protected]>

import gradio as gr

import os
import sys
import copy
import shutil
import random
import argparse
import numpy as np

import torch
import torch.nn as nn
import torch.nn.functional as F

from torchvision import transforms
from torch.utils.tensorboard import SummaryWriter

from torch.utils.data import DataLoader

from core.puzzle_utils import *
from core.networks import *
from core.datasets import *

from tools.general.io_utils import *
from tools.general.time_utils import *
from tools.general.json_utils import *

from tools.ai.log_utils import *
from tools.ai.demo_utils import *
from tools.ai.optim_utils import *
from tools.ai.torch_utils import *
from tools.ai.evaluate_utils import *

from tools.ai.augment_utils import *
from tools.ai.randaugment import *

import PIL.Image

parser = argparse.ArgumentParser()

###############################################################################
# Dataset
###############################################################################
parser.add_argument('--seed', default=2606, type=int)
parser.add_argument('--num_workers', default=4, type=int)

###############################################################################
# Network
###############################################################################
parser.add_argument('--architecture', default='DeepLabv3+', type=str)
parser.add_argument('--backbone', default='resnet50', type=str)
parser.add_argument('--mode', default='fix', type=str)
parser.add_argument('--use_gn', default=True, type=str2bool)

###############################################################################
# Inference parameters
###############################################################################
parser.add_argument('--tag', default='', type=str)

parser.add_argument('--domain', default='val', type=str)

parser.add_argument('--scales', default='0.5,1.0,1.5,2.0', type=str)
parser.add_argument('--iteration', default=10, type=int)

class_names = [
    "aeroplane",
    "bicycle",
    "bird",
    "boat",
    "bottle",
    "bus",
    "car",
    "cat",
    "chair",
    "cow",
    "diningtable",
    "dog",
    "horse",
    "motorbike",
    "person",
    "pottedplant",
    "sheep",
    "sofa",
    "train",
    "tvmonitor"
]

cmap_dic = {
    "background": [
        0,
        0,
        0
    ],
    "aeroplane": [
        128,
        0,
        0
    ],
    "bicycle": [
        0,
        128,
        0
    ],
    "bird": [
        128,
        128,
        0
    ],
    "boat": [
        0,
        0,
        128
    ],
    "bottle": [
        128,
        0,
        128
    ],
    "bus": [
        0,
        128,
        128
    ],
    "car": [
        128,
        128,
        128
    ],
    "cat": [
        64,
        0,
        0
    ],
    "chair": [
        192,
        0,
        0
    ],
    "cow": [
        64,
        128,
        0
    ],
    "diningtable": [
        192,
        128,
        0
    ],
    "dog": [
        64,
        0,
        128
    ],
    "horse": [
        192,
        0,
        128
    ],
    "motorbike": [
        64,
        128,
        128
    ],
    "person": [
        192,
        128,
        128
    ],
    "pottedplant": [
        0,
        64,
        0
    ],
    "sheep": [
        128,
        64,
        0
    ],
    "sofa": [
        0,
        192,
        0
    ],
    "train": [
        128,
        192,
        0
    ],
    "tvmonitor": [
        0,
        64,
        128
    ]
}

colors = np.asarray([cmap_dic[class_name] for class_name in class_names])

if __name__ == '__main__':
    ###################################################################################
    # Arguments
    ###################################################################################
    args = parser.parse_args()

    model_dir = create_directory('./experiments/models/')
    model_path = model_dir + f'DeepLabv3+@ResNet-50@[email protected]'

    if 'train' in args.domain:
        args.tag += '@train'
    else:
        args.tag += '@' + args.domain

    args.tag += '@scale=%s' % args.scales
    args.tag += '@iteration=%d' % args.iteration

    set_seed(args.seed)
    log_func = lambda string='': print(string)

    ###################################################################################
    # Transform, Dataset, DataLoader
    ###################################################################################
    imagenet_mean = [0.485, 0.456, 0.406]
    imagenet_std = [0.229, 0.224, 0.225]

    normalize_fn = Normalize(imagenet_mean, imagenet_std)

    # for mIoU
    meta_dic = read_json('./data/VOC_2012.json')

    ###################################################################################
    # Network
    ###################################################################################
    if args.architecture == 'DeepLabv3+':
        model = DeepLabv3_Plus(args.backbone, num_classes=meta_dic['classes'] + 1, mode=args.mode,
                               use_group_norm=args.use_gn)
    elif args.architecture == 'Seg_Model':
        model = Seg_Model(args.backbone, num_classes=meta_dic['classes'] + 1)
    elif args.architecture == 'CSeg_Model':
        model = CSeg_Model(args.backbone, num_classes=meta_dic['classes'] + 1)

    model.eval()

    log_func('[i] Architecture is {}'.format(args.architecture))
    log_func('[i] Total Params: %.2fM' % (calculate_parameters(model)))
    log_func()

    load_model(model, model_path, parallel=False)

    #################################################################################################
    # Evaluation
    #################################################################################################
    eval_timer = Timer()
    scales = [float(scale) for scale in args.scales.split(',')]

    model.eval()
    eval_timer.tik()


    def inference(images, image_size):
        logits = model(images)
        logits = resize_for_tensors(logits, image_size)

        logits = logits[0] + logits[1].flip(-1)
        logits = get_numpy_from_tensor(logits).transpose((1, 2, 0))
        return logits


    def predict_image(ori_image):
        ori_image = PIL.Image.fromarray(ori_image)
        with torch.no_grad():
            ori_w, ori_h = ori_image.size

            cams_list = []

            for scale in scales:
                image = copy.deepcopy(ori_image)
                image = image.resize((round(ori_w * scale), round(ori_h * scale)), resample=PIL.Image.BICUBIC)

                image = normalize_fn(image)
                image = image.transpose((2, 0, 1))

                image = torch.from_numpy(image)
                flipped_image = image.flip(-1)

                images = torch.stack([image, flipped_image])

                cams = inference(images, (ori_h, ori_w))
                cams_list.append(cams)

            preds = np.sum(cams_list, axis=0)
            preds = F.softmax(torch.from_numpy(preds), dim=-1).numpy()

            if args.iteration > 0:
                preds = crf_inference(np.asarray(ori_image), preds.transpose((2, 0, 1)), t=args.iteration)
                pred_mask = np.argmax(preds, axis=0)
            else:
                pred_mask = np.argmax(preds, axis=-1)

            pred_mask = decode_from_colormap(pred_mask, colors)[..., ::-1]

            return Image.fromarray(pred_mask.astype(np.uint8)).convert("RGB")
             

    demo = gr.Interface(
        fn=predict_image,
        inputs="image",
        outputs="image"
    )

    demo.launch()