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test.py
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# -*- coding: UTF-8 -*-
'''=================================================
@Project -> File shloc -> test
@IDE PyCharm
@Author fx221@cam.ac.uk
@Date 2021-05-31 15:09
=================================================='''
import argparse
import json
import torchvision.transforms as tvf
import torch
import torch.nn.functional as F
from tqdm import tqdm
import cv2
import os
import numpy as np
import os.path as osp
from net.segnet import get_segnet
from tools.common import torch_set_gpu
from tools.seg_tools import read_seg_map_without_group, label_to_bgr
val_transform = tvf.Compose(
(
# tvf.ToPILImage(),
# tvf.Resize(224),
tvf.ToTensor(),
tvf.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
)
)
def predict(net, img):
img_tensor = val_transform(img)
img_tensor = img_tensor.cuda().unsqueeze(0)
with torch.no_grad():
prediction = net(img_tensor)
return prediction
def inference_rec(output, img, fn, map_gid_rgb, save_dir=None):
cv2.namedWindow("out", cv2.WINDOW_NORMAL)
with torch.no_grad():
# output = predict(net=model, img=img)
pred_mask = output["masks"][0]
pred_label = torch.softmax(pred_mask, dim=1).max(1)[1].cpu().numpy()
pred_conf = torch.softmax(pred_mask, dim=1).max(1)[0].cpu().numpy()
last_feat = output['feats'][-1]
pred_feat_max = F.adaptive_max_pool2d(last_feat, output_size=(1, 1))
pred_feat_avg = F.adaptive_avg_pool2d(last_feat, output_size=(1, 1))
if pred_label.shape[0] == 1:
pred_label = pred_label[0]
pred_conf = pred_conf[0]
uids = np.unique(pred_label).tolist()
pred_seg = label_to_bgr(label=pred_label, maps=map_gid_rgb)
pred_conf_img = np.uint8(pred_conf * 255)
pred_conf_img = cv2.applyColorMap(src=pred_conf_img, colormap=cv2.COLORMAP_PARULA)
H = args.R # img.shape[0]
W = args.R # img.shape[1]
pred_seg = cv2.resize(pred_seg, dsize=(W, H), interpolation=cv2.INTER_NEAREST)
pred_conf_img = cv2.resize(pred_conf_img, dsize=(W, H), interpolation=cv2.INTER_NEAREST)
img = cv2.resize(img, dsize=(W, H))
img_seg = (0.5 * img + 0.5 * pred_seg).astype(np.uint8)
cat_img = np.hstack([img_seg, pred_seg, pred_conf_img])
cv2.imshow("out", cat_img)
key = cv2.waitKey()
if key in (27, ord('q')): # exit by pressing key esc or q
cv2.destroyAllWindows()
exit(0)
# return
if save_dir is not None:
conf_fn = osp.join(save_dir, "confidence", fn.split('.')[0] + ".npy")
mask_fn = osp.join(save_dir, "masks", fn.replace("jpg", "png"))
vis_fn = osp.join(save_dir, "vis", fn.replace("jpg", "png"))
if not osp.exists(osp.dirname(conf_fn)):
os.makedirs(osp.dirname(conf_fn), exist_ok=True)
if not osp.exists(osp.dirname(vis_fn)):
os.makedirs(osp.dirname(vis_fn), exist_ok=True)
if not osp.exists(osp.dirname(mask_fn)):
os.makedirs(osp.dirname(mask_fn), exist_ok=True)
pred_confidence, pred_ids = torch.topk(torch.softmax(pred_mask, dim=1), k=10, largest=True, dim=1)
conf_data = {"confidence": pred_confidence[0].cpu().numpy(),
"ids": pred_ids[0].cpu().numpy(),
'feat_max': pred_feat_max.squeeze().cpu().numpy(),
'feat_avg': pred_feat_avg.squeeze().cpu().numpy(),
}
np.save(conf_fn, conf_data)
cv2.imwrite(vis_fn, cat_img)
cv2.imwrite(mask_fn, pred_seg)
def main(args):
map_gid_rgb = read_seg_map_without_group(args.grgb_gid_file)
model = get_segnet(network=args.network,
n_classes=args.classes,
encoder_name=args.encoder_name,
encoder_weights=args.encoder_weights,
encoder_depth=args.encoder_depth,
upsampling=args.upsampling,
out_channels=args.out_channels,
classification=args.classification,
segmentation=args.segmentation, )
print("model: ", model)
if args.pretrained_weight is not None:
model.load_state_dict(torch.load(args.pretrained_weight), strict=True)
print("Load weight from {:s}".format(args.pretrained_weight))
model.eval().cuda()
img_path = args.image_path
save_dir = args.save_dir
print('Save results to ', save_dir)
imglist = []
with open(args.image_list, "r") as f:
lines = f.readlines()
for l in lines:
imglist.append(l.strip())
for fn in tqdm(imglist, total=len(imglist)):
if fn.find('left') >= 0 or fn.find('right') >= 0:
continue
img = cv2.imread(osp.join(img_path, fn))
img = cv2.resize(img, dsize=(args.R, args.R))
with torch.no_grad():
output = predict(net=model, img=img)
inference_rec(output=output, img=img, fn=fn, map_gid_rgb=map_gid_rgb, save_dir=save_dir)
if __name__ == '__main__':
parser = argparse.ArgumentParser("Test Semantic localization Network")
parser.add_argument("--config", type=str, required=True, help="configuration file")
parser.add_argument("--pretrained_weight", type=str, default=None)
parser.add_argument("--save_root", type=str, default="/home/mifs/fx221/fx221/exp/shloc/aachen")
parser.add_argument("--image_path", type=str, default=None)
parser.add_argument("--network", type=str, default="pspf")
parser.add_argument("--save_dir", type=str, default=None)
parser.add_argument("--encoder_name", type=str, default='timm-resnest50d')
parser.add_argument("--encoder_weights", type=str, default='imagenet')
parser.add_argument("--out_channels", type=int, default='2048')
parser.add_argument("--upsampling", type=int, default='8')
parser.add_argument("--gpu", type=int, nargs='+', default=[0], help='-1 for CPU')
parser.add_argument("--R", type=int, default=256)
args = parser.parse_args()
with open(args.config, 'rt') as f:
t_args = argparse.Namespace()
t_args.__dict__.update(json.load(f))
args = parser.parse_args(namespace=t_args)
torch_set_gpu(gpus=args.gpu)
main(args=args)