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ACNet_eval_nyuv2.py
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ACNet_eval_nyuv2.py
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import argparse
import torch
import imageio
import skimage.transform
import torchvision
import numpy as np
import os
from torch.utils.data import DataLoader
import datetime
import cv2
import torch.optim
import ACNet_data_nyuv2_eval as ACNet_data
# import ACNet_models
import ACNet_models_V1
# import ACNet_models_V1_first as ACNet_models_V1
#import ACNet_models_V1_delA as ACNet_models_V1
from utils import utils
from utils.utils import load_ckpt, intersectionAndUnion, AverageMeter, accuracy, macc
parser = argparse.ArgumentParser(description='RGBD Sementic Segmentation')
parser.add_argument('--data-dir', default=None, metavar='DIR',
help='path to dataset')
parser.add_argument('-o', '--output', default='./result/', metavar='DIR',
help='path to output')
parser.add_argument('--cuda', action='store_true', default=False,
help='enables CUDA training')
parser.add_argument('--last-ckpt', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--num-class', default=40, type=int,
help='number of classes')
parser.add_argument('--visualize', default=False, action='store_true',
help='if output image')
args = parser.parse_args()
device = torch.device("cuda:0" if args.cuda and torch.cuda.is_available() else "cpu")
image_w = 640
image_h = 480
img_mean=[0.485, 0.456, 0.406]
img_std=[0.229, 0.224, 0.225]
# transform
class scaleNorm(object):
def __call__(self, sample):
image, depth, label = sample['image'], sample['depth'], sample['label']
# Bi-linear
image = skimage.transform.resize(image, (image_h, image_w), order=1,
mode='reflect', preserve_range=True)
# Nearest-neighbor
depth = skimage.transform.resize(depth, (image_h, image_w), order=0,
mode='reflect', preserve_range=True)
label = skimage.transform.resize(label, (image_h, image_w), order=0,
mode='reflect', preserve_range=True)
return {'image': image, 'depth': depth, 'label': label}
class ToTensor(object):
"""Convert ndarrays in sample to Tensors."""
def __call__(self, sample):
image, depth, label = sample['image'], sample['depth'], sample['label']
image = image.transpose((2, 0, 1))
depth = np.expand_dims(depth, 0).astype(np.float)
return {'image': torch.from_numpy(image).float(),
'depth': torch.from_numpy(depth).float(),
'label': torch.from_numpy(label).float()}
class Normalize(object):
def __call__(self, sample):
image, depth = sample['image'], sample['depth']
origin_image = image.clone()
origin_depth = depth.clone()
image = image / 255
image = torchvision.transforms.Normalize(mean=[0.4850042694973687, 0.41627756261047333, 0.3981809741523051],
std=[0.26415541082494515, 0.2728415392982039, 0.2831175140191598])(image)
depth = torchvision.transforms.Normalize(mean=[2.8424503515351494],
std=[0.9932836506164299])(depth)
sample['origin_image'] = origin_image
sample['origin_depth'] = origin_depth
sample['image'] = image
sample['depth'] = depth
return sample
# def visualize_result(img, label, preds, info, args):
# # segmentation
# img = img.squeeze(0).transpose(0, 2, 1)
# seg_color = utils.color_label_eval(label)
#
# # prediction
# pred_color = utils.color_label_eval(preds)
#
# # aggregate images and save
# im_vis = np.concatenate((img, seg_color, pred_color),
# axis=1).astype(np.uint8)
# im_vis = im_vis.transpose(2, 1, 0)
#
# img_name = str(info)
# # print('write check: ', im_vis.dtype)
# cv2.imwrite(os.path.join(args.output,
# img_name+'.png'), im_vis)
def visualize_result(img, depth, label, preds, info, args):
# segmentation
img = img.squeeze(0).transpose(0, 2, 1)
dep = depth.squeeze(0).squeeze(0)
dep = (dep*255/dep.max()).astype(np.uint8)
dep = cv2.applyColorMap(dep, cv2.COLORMAP_JET)
dep = dep.transpose(2,1,0)
seg_color = utils.color_label_eval(label)
# prediction
pred_color = utils.color_label_eval(preds)
# aggregate images and save
im_vis = np.concatenate((img, dep, seg_color, pred_color),
axis=1).astype(np.uint8)
im_vis = im_vis.transpose(2, 1, 0)
img_name = str(info)
# print('write check: ', im_vis.dtype)
cv2.imwrite(os.path.join(args.output,
img_name+'.png'), im_vis)
def inference():
model = ACNet_models_V1.ACNet(num_class=40, pretrained=False)
load_ckpt(model, None, args.last_ckpt, device)
model.eval()
model.to(device)
val_data = ACNet_data.SUNRGBD(transform=torchvision.transforms.Compose([scaleNorm(),
ToTensor(),
Normalize()]),
phase_train=False,
data_dir=args.data_dir
)
val_loader = DataLoader(val_data, batch_size=1, shuffle=False,num_workers=1, pin_memory=True)
acc_meter = AverageMeter()
intersection_meter = AverageMeter()
union_meter = AverageMeter()
a_meter = AverageMeter()
b_meter = AverageMeter()
with torch.no_grad():
for batch_idx, sample in enumerate(val_loader):
#todo batch=1，这里要查看sample的size，决定怎么填装image depth label，估计要用到for循环
origin_image = sample['origin_image'].numpy()
origin_depth = sample['origin_depth'].numpy()
image = sample['image'].to(device)
depth = sample['depth'].to(device)
label = sample['label'].numpy()
with torch.no_grad():
pred = model(image, depth)
output = torch.max(pred, 1)[1] + 1
output = output.squeeze(0).cpu().numpy()
acc, pix = accuracy(output, label)
intersection, union = intersectionAndUnion(output, label, args.num_class)
acc_meter.update(acc, pix)
a_m, b_m = macc(output, label, args.num_class)
intersection_meter.update(intersection)
union_meter.update(union)
a_meter.update(a_m)
b_meter.update(b_m)
print('[{}] iter {}, accuracy: {}'
.format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
batch_idx, acc))
# img = image.cpu().numpy()
# print('origin iamge: ', type(origin_image))
if args.visualize:
visualize_result(origin_image, origin_depth, label-1, output-1, batch_idx, args)
iou = intersection_meter.sum / (union_meter.sum + 1e-10)
for i, _iou in enumerate(iou):
print('class [{}], IoU: {}'.format(i, _iou))
mAcc = (a_meter.average() / (b_meter.average()+1e-10))
print(mAcc.mean())
print('[Eval Summary]:')
print('Mean IoU: {:.4}, Accuracy: {:.2f}%'
.format(iou.mean(), acc_meter.average() * 100))
# imageio.imsave(args.output, output.cpu().numpy().transpose((1, 2, 0)))
if __name__ == '__main__':
if not os.path.exists(args.output):
os.mkdir(args.output)
inference()