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eval.py
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eval.py
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import os
from PIL import Image
import time
import logging
from argparse import ArgumentParser
import numpy as np
import yaml
import torch
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.nn.functional as F
from semseg.models.model_helper import ModelBuilder
from semseg.utils.utils import AverageMeter, intersectionAndUnion
from semseg.utils.utils import check_makedirs, convert_state_dict, colorize
frequency = [21,20,21,7,9,21,12,20,20,12,19,17,7,20,3,2,1,4,12]
freq_weight = np.array([0.94939928, 0.95166245, 0.94939928, 1.04426393, 1.01163338,
0.94939928, 0.98391915, 0.95166245, 0.95166245, 0.98391915,
0.95417012, 0.96009662, 1.04426393, 0.95166245, 1.2633772 ,
1.49250381, 2.46072278, 1.16236314, 0.98391915])
freq_weight = np.array([0.81474634, 0.82578303, 0.81609603, 0.93735321, 0.90806335,
0.88318648, 1.33970274, 0.95993896, 0.81763352, 0.88318648,
0.83263326, 0.88318648, 2.2087964 , 0.82409642, 1.13403389,
1.33970274, 1.33970274, 2.2087964 , 1.04336154])
# Setup Parser
def get_parser():
parser = ArgumentParser(description='PyTorch Evaluation')
parser.add_argument(
'--base_size', type=int,
default=2048, help='based size for scaling')
parser.add_argument(
'--scales', type=float,
default=[0.5,0.75,1.0,1.25,1.5,2.0], nargs='+', help='evaluation scales')
parser.add_argument(
"--config", type=str, default="config.yaml")
parser.add_argument(
'--model_path', type=str,
default='checkpoints/psp_best.pth', help='evaluation model path')
parser.add_argument(
'--save_folder', type=str,
default='checkpoints/results/', help='results save folder')
parser.add_argument(
'--names_path', type=str,
default='../../vis_meta/cityscapes/cityscapesnames.mat',
help='path of dataset category names')
parser.add_argument(
'--crop', action="store_true", default=False, help="whether use crop evaluation"
)
return parser
def get_logger():
logger_name = "main-logger"
logger = logging.getLogger(logger_name)
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
fmt = "[%(asctime)s %(levelname)s %(filename)s line %(lineno)d %(process)d] %(message)s"
handler.setFormatter(logging.Formatter(fmt))
logger.addHandler(handler)
return logger
def main():
global args, logger, cfg
args = get_parser().parse_args()
cfg = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
logger = get_logger()
logger.info(args)
cfg_dset = cfg['dataset']
mean, std = cfg_dset['mean'], cfg_dset['std']
num_classes = cfg['net']['num_classes']
crop_size = cfg_dset['val']['crop']['size']
crop_h, crop_w = crop_size
assert num_classes > 1
gray_folder = os.path.join(args.save_folder, 'gray')
color_folder = os.path.join(args.save_folder, 'color')
cfg_dset = cfg['dataset']
data_root, f_data_list = cfg_dset['val']['data_root'], cfg_dset['val']['data_list']
data_list = []
for line in open(f_data_list, 'r'):
arr = line.strip().split(" ")
arr = [os.path.join(data_root, item) for item in arr]
data_list.append(arr)
# Create network.
args.use_auxloss = True if cfg['net'].get('aux_loss', False) else False
logger.info("=> creating model ...")
cfg['net']['sync_bn'] = False
model = ModelBuilder(cfg['net'])
saved_state_dict = convert_state_dict(torch.load(args.model_path)['model_state'])
model.load_state_dict(saved_state_dict)
model.cuda()
logger.info("Load Model Done: {}".format(model))
if "cityscapes" in cfg['dataset']['type']:
validate_city(model, num_classes, data_list, mean, std, args.base_size,
crop_h, crop_w, args.scales, gray_folder, color_folder)
else:
valiadte_whole(model, num_classes, data_list, mean, std, args.scales, gray_folder, color_folder)
cal_acc(data_list, gray_folder, num_classes)
def net_process(model, image):
input = image.cuda()
input_var = torch.autograd.Variable(input)
output = model(input_var)[0] if args.use_auxloss else model(input_var)
output = F.softmax(output, dim=1)
return output
def scale_crop_process(model, image, classes, crop_h, crop_w, h, w, stride_rate=2/3):
ori_h, ori_w = image.size()[-2:]
pad_h = max(crop_h - ori_h, 0)
pad_w = max(crop_w - ori_w, 0)
pad_h_half = int(pad_h / 2)
pad_w_half = int(pad_w / 2)
if pad_h > 0 or pad_w > 0:
border = (pad_w_half, pad_w - pad_w_half, pad_h_half, pad_h - pad_h_half)
image = F.pad(image, border, mode='constant', value=0.)
new_h, new_w = image.size()[-2:]
stride_h = int(np.ceil(crop_h*stride_rate))
stride_w = int(np.ceil(crop_w*stride_rate))
grid_h = int(np.ceil(float(new_h-crop_h)/stride_h) + 1)
grid_w = int(np.ceil(float(new_w-crop_w)/stride_w) + 1)
prediction_crop = torch.zeros((1, classes, new_h, new_w), dtype=torch.float).cuda()
count_crop = torch.zeros((new_h, new_w), dtype=torch.float).cuda()
for index_h in range(0, grid_h):
for index_w in range(0, grid_w):
s_h = index_h * stride_h
e_h = min(s_h + crop_h, new_h)
s_h = e_h - crop_h
s_w = index_w * stride_w
e_w = min(s_w + crop_w, new_w)
s_w = e_w - crop_w
image_crop = image[:, :, s_h:e_h, s_w:e_w].contiguous()
count_crop[s_h:e_h, s_w:e_w] += 1
#with torch.no_grad():
# image_flip = flip_image(image_crop)
# flip_out = flip_image(net_process(model,image_flip))
# nor_out = net_process(model,image_crop)
# out = flip_out+nor_out
# prediction_crop[:, :, s_h:e_h, s_w:e_w] += out
with torch.no_grad():
prediction_crop[:, :, s_h:e_h, s_w:e_w] += net_process(model, image_crop)
prediction_crop /= count_crop
prediction_crop = prediction_crop[:, :, pad_h_half:pad_h_half+ori_h, pad_w_half:pad_w_half+ori_w]
prediction = F.interpolate(prediction_crop, size=(h, w), mode='bilinear', align_corners=True)
return prediction[0]
def scale_whole_process(model, image, h, w):
with torch.no_grad():
prediction = net_process(model, image)
prediction = F.interpolate(prediction, size=(h, w), mode='bilinear', align_corners=True)
return prediction[0]
def validate_city(model, classes, data_list, mean, std, base_size, crop_h, crop_w, scales, gray_folder, color_folder):
logger.info('>>>>>>>>>>>>>>>> Start Crop Evaluation >>>>>>>>>>>>>>>>')
data_time = AverageMeter()
batch_time = AverageMeter()
model.eval()
end = time.time()
for i, (input_pth, _) in enumerate(data_list):
data_time.update(time.time() - end)
image = Image.open(input_pth).convert('RGB')
image = np.asarray(image).astype(np.float32)
image = (image - mean) / std
image = torch.Tensor(image).permute(2, 0, 1)
image = image.contiguous().unsqueeze(dim=0)
h, w = image.size()[-2:]
prediction = torch.zeros((classes, h, w), dtype=torch.float).cuda()
for scale in scales:
long_size = round(scale * base_size)
new_h = long_size
new_w = long_size
if h > w:
new_w = round(long_size/float(h)*w)
else:
new_h = round(long_size/float(w)*h)
image_scale = F.interpolate(image, size=(new_h, new_w), mode='bilinear', align_corners=True)
prediction += scale_crop_process(model, image_scale, classes, crop_h, crop_w, h, w)
prediction = torch.max(prediction, dim=0)[1].cpu().numpy()
batch_time.update(time.time() - end)
end = time.time()
if (i + 1) % 10 == 0:
logger.info('Test: [{}/{}] '
'Data {data_time.val:.3f} ({data_time.avg:.3f}) '
'Batch {batch_time.val:.3f} ({batch_time.avg:.3f}).'.format(i + 1, len(data_list),
data_time=data_time,
batch_time=batch_time))
check_makedirs(gray_folder)
check_makedirs(color_folder)
gray = np.uint8(prediction)
color = colorize(gray)
image_path, _ = data_list[i]
image_name = image_path.split('/')[-1].split('.')[0]
gray_path = os.path.join(gray_folder, image_name + '.png')
color_path = os.path.join(color_folder, image_name + '.png')
gray = Image.fromarray(gray)
gray.save(gray_path)
color.save(color_path)
logger.info('<<<<<<<<<<<<<<<<< End Crop Evaluation <<<<<<<<<<<<<<<<<')
def valiadte_whole(model, classes, data_list, mean, std, scales, gray_folder, color_folder):
logger.info('>>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>')
data_time = AverageMeter()
batch_time = AverageMeter()
model.eval()
end = time.time()
for i, (input_pth, _) in enumerate(data_list):
data_time.update(time.time() - end)
image = Image.open(input_pth).convert('RGB')
image = np.asarray(image).astype(np.float32)
image = (image - mean) / std
image = torch.Tensor(image).permute(2, 0, 1)
image = image.contiguous().unsqueeze(dim=0)
h, w = image.size()[-2:]
prediction = torch.zeros((classes, h, w), dtype=torch.float).cuda()
for scale in scales:
new_h = round(h * scale)
new_w = round(w * scale)
image_scale = F.interpolate(image, size=(new_h, new_w), mode='bilinear', align_corners=True)
prediction += scale_whole_process(model, image_scale, h, w)
prediction = torch.max(prediction, dim=0)[1].cpu().numpy() ##############attention###############
batch_time.update(time.time() - end)
end = time.time()
if (i + 1) % 10 == 0:
logger.info('Test: [{}/{}] '
'Data {data_time.val:.3f} ({data_time.avg:.3f}) '
'Batch {batch_time.val:.3f} ({batch_time.avg:.3f}).'.format(i + 1, len(data_list),
data_time=data_time,
batch_time=batch_time))
check_makedirs(gray_folder)
check_makedirs(color_folder)
gray = np.uint8(prediction)
color = colorize(gray)
image_path, _ = data_list[i]
image_name = image_path.split('/')[-1].split('.')[0]
gray_path = os.path.join(gray_folder, image_name + '.png')
color_path = os.path.join(color_folder, image_name + '.png')
gray = Image.fromarray(gray)
gray.save(gray_path)
color.save(color_path)
logger.info('<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<')
def cal_acc(data_list, pred_folder, classes):
intersection_meter = AverageMeter()
union_meter = AverageMeter()
target_meter = AverageMeter()
for i, (image_path, target_path) in enumerate(data_list):
image_name = image_path.split('/')[-1].split('.')[0]
pred = np.asarray(Image.open(os.path.join(pred_folder, image_name+'.png')))
target = np.asarray(Image.open(os.path.join('/home/cityscapes', target_path)))
intersection, union, target = intersectionAndUnion(pred, target, classes)
intersection_meter.update(intersection)
union_meter.update(union)
target_meter.update(target)
accuracy = sum(intersection_meter.val) / (sum(target_meter.val) + 1e-10)
print('Evaluating {0}/{1} on image {2}, accuracy {3:.4f}.'
.format(i + 1, len(data_list), image_name+'.png', accuracy))
iou_class = intersection_meter.sum / (union_meter.sum + 1e-10)
accuracy_class = intersection_meter.sum / (target_meter.sum + 1e-10)
mIoU = np.mean(iou_class)
mAcc = np.mean(accuracy_class)
allAcc = sum(intersection_meter.sum) / (sum(target_meter.sum) + 1e-10)
torch.save(mIoU, 'eval_metric.pth.tar')
print('Eval result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}.'.format(mIoU, mAcc, allAcc))
for i in range(classes):
print('Class_{} result: iou/accuracy {:.4f}/{:.4f}'.format(i, iou_class[i], accuracy_class[i]))
def flip_image(img):
assert(img.dim()==4)
with torch.cuda.device_of(img):
idx = torch.arange(img.size(3)-1, -1, -1).type_as(img).long()
return img.index_select(3, idx)
if __name__ == '__main__':
main()