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Eval_WiCo_GID.py
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Eval_WiCo_GID.py
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import os
import cv2
import time
import datetime
import numpy as np
import torch.autograd
from skimage import io
from scipy import stats
import torch.nn.functional as F
from torch.utils.data import DataLoader
from utils.loss import CrossEntropyLoss2d
from utils.utils import accuracy, intersectionAndUnion, AverageMeter, CaclTP
# Choose model and data
##################################################
from datasets import GID_WiCo as GID
from models.WiCoNet import WiCoNet as Net
NET_NAME = 'WiCoNet_3hw4L'
DATA_NAME = 'GID'
##################################################
# Change testing parameters here
working_path = os.path.abspath('.')
args = {
'gpu': True,
's_class': 0,
'val_batch_size': 1,
'size_local': 256,
'size_context': 256 * 3,
'data_dir': 'YOUR_DATA_DIR',
'load_path': 'YOUR_DATA_DIRSAVED_MODEL_CHECKPOINT.pth'
}
def norm_gray(x, out_range=(0, 255)):
# x=x*(x>0)
domain = np.min(x), np.max(x)
y = (x - (domain[1] + domain[0]) / 2) / (domain[1] - domain[0] + 1e-10)
y = y * (out_range[1] - out_range[0]) + (out_range[1] + out_range[0]) / 2
return y.astype('uint8')
def draw_rectangle(img, pos='boundary', color=(0, 255, 0), thick=2, text='context window'):
h, w, c = img.shape
if pos == 'boundary':
start_pos = (0, 0)
end_pos = (h - 1, w - 1)
elif pos == 'center':
start_pos = (h // 2 - 64, w // 2 - 64)
end_pos = (h // 2 + 64, w // 2 + 64)
cv2.rectangle(img, start_pos, end_pos, color, thick)
if pos == 'boundary':
cv2.putText(img, text, (start_pos[0] + 15, start_pos[1] + 15), cv2.FONT_HERSHEY_PLAIN, 1.2, color)
elif pos == 'center':
cv2.putText(img, text, (start_pos[0] - 24, start_pos[1] - 5), cv2.FONT_HERSHEY_PLAIN, 1.2, color)
return img
def main():
net = Net(4, num_classes=GID.num_classes + 1, size_context=args['size_context'], size_local=args['size_local']).cuda()
net.load_state_dict(torch.load(args['load_path'])) # , strict = False
net = net.cuda()
net.eval()
print(NET_NAME+' Model loaded.')
pred_path = os.path.join(args['data_dir'], 'Eval', NET_NAME)
if not os.path.exists(pred_path): os.makedirs(pred_path)
info_txt_path = os.path.join(pred_path, 'info.txt')
f = open(info_txt_path, 'w+')
val_set = GID.Loader(args['data_dir'], 'test', sliding_crop=True, size_context=args['size_context'], size_local=args['size_local'])
val_loader = DataLoader(val_set, batch_size=args['val_batch_size'], num_workers=4, shuffle=False)
predict(net, val_loader, pred_path, f)
f.close()
def predict(net, pred_loader, pred_path, f_out=None):
acc_meter = AverageMeter()
TP_meter = AverageMeter()
pred_meter = AverageMeter()
label_meter = AverageMeter()
Union_meter = AverageMeter()
output_info = f_out is not None
for vi, data in enumerate(pred_loader):
with torch.no_grad():
img_s, _, img, label = data
if args['gpu']:
img_s = img_s.cuda().float()
img = img.cuda().float()
label = label.cuda().float()
output, aux = net(img_s, img)
output = output.detach().cpu()
pred = torch.argmax(output, dim=1)
pred = pred.numpy().squeeze()
if args['s_class']:
class_map = F.softmax(output, dim=1)[:, args['s_class'], :, :]
class_map = class_map.numpy().squeeze()
class_map = norm_gray(class_map)
label = label.detach().cpu().numpy()
acc, _ = accuracy(pred, label)
acc_meter.update(acc)
pred_color = GID.Index2Color(pred)
img = img.detach().cpu().numpy().squeeze().transpose((1, 2, 0))
img = norm_gray(img)
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# img = draw_rectangle(img)
# img = draw_rectangle(img, pos='center', color=(0, 255, 255), thick=2, text='local window')
# label_color = GID.Index2Color(label).squeeze()
pred_name = os.path.join(pred_path, '%d_WiC.png' % vi)
io.imsave(pred_name, pred_color)
# img_name = os.path.join(pred_path, '%d_img.png'%vi)
# label_name = os.path.join(pred_path, '%d_label.png'%vi)
# io.imsave(label_name, label_color)
if args['s_class']:
saliency_map = cv2.applyColorMap(class_map, cv2.COLORMAP_JET)
pred_name = os.path.join(pred_path, '%d_saliency.png' % vi)
saliency_map = (img * 0.5 + saliency_map * 0.5).astype('uint8')
io.imsave(pred_name, saliency_map)
TP, pred_hist, label_hist, union_hist = CaclTP(pred, label, GID.num_classes)
TP_meter.update(TP)
pred_meter.update(pred_hist)
label_meter.update(label_hist)
Union_meter.update(union_hist)
print('Eval num %d/%d, Acc %.2f' % (vi, len(pred_loader), acc * 100))
if output_info:
f_out.write('Eval num %d/%d, Acc %.2f\n' % (vi, len(pred_loader), acc * 100))
precision = TP_meter.sum / (label_meter.sum + 1e-10) + 1e-10
recall = TP_meter.sum / (pred_meter.sum + 1e-10) + 1e-10
F1 = [stats.hmean([pre, rec]) for pre, rec in zip(precision, recall)]
F1 = np.array(F1)
IoU = TP_meter.sum / Union_meter.sum
IoU = np.array(IoU)
print(output.shape)
print('Acc %.2f' % (acc_meter.avg * 100))
avg_F = F1.mean()
mIoU = IoU.mean()
print('Avg F1 %.2f' % (avg_F * 100))
print(np.array2string(F1 * 100, precision=4, separator=', ', formatter={'float_kind': lambda x: "%.2f" % x}))
print('mIoU %.2f' % (mIoU * 100))
print(np.array2string(IoU * 100, precision=4, separator=', ', formatter={'float_kind': lambda x: "%.2f" % x}))
if output_info:
f_out.write('Acc %.2f\n' % (acc_meter.avg * 100))
f_out.write('Avg F1 %.2f\n' % (avg_F * 100))
f_out.write(
np.array2string(F1 * 100, precision=4, separator=', ', formatter={'float_kind': lambda x: "%.2f" % x}))
f_out.write('\nmIoU %.2f\n' % (mIoU * 100))
f_out.write(
np.array2string(IoU * 100, precision=4, separator=', ', formatter={'float_kind': lambda x: "%.2f" % x}))
return avg_F
if __name__ == '__main__':
main()