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# Copyright (C) 2020 * Ltd. All rights reserved.
# author : Sanghyeon Jo <josanghyeokn@gmail.com>
import gradio as gr
import os
import sys
import copy
import shutil
import random
import argparse
import numpy as np
import imageio
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 *
parser = argparse.ArgumentParser()
###############################################################################
# Dataset
###############################################################################
parser.add_argument('--seed', default=2606, type=int)
parser.add_argument('--num_workers', default=4, type=int)
parser.add_argument('--data_dir', default='../VOCtrainval_11-May-2012/', type=str)
###############################################################################
# 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)
if __name__ == '__main__':
###################################################################################
# Arguments
###################################################################################
args = parser.parse_args()
model_dir = create_directory('./experiments/models/')
model_path = model_dir + f'DeepLabv3+@ResNet-50@Fix@GN.pth'
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 = model.cuda()
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):
images = images.cuda()
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):
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)
return pred_mask.astype(np.uint8)
demo = gr.Interface(
fn=predict_image,
inputs="image",
outputs="image"
)
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