image data distribution

[SeungyounShin]

Thanks for your sharing codes

I am trying to predict a low resolution image with pretrained ERFNet. Sadly, It doesn't work.
I think my preprocessing of image data is different from yours.
I would like to know mean/std normalization is correct.

Code I used is below.

import torch
import cv2
import utils.transforms as tf
import numpy as np
import models
import numpy as np
import matplotlib.pyplot as plt

PATH = '#/erfnet.pth'
test_image = "./test_images/test1.png"


#model load
model = models.ERFNet(37, partial_bn=False)
ckpt = torch.load(PATH)
torch.nn.Module.load_state_dict(model, ckpt['state_dict'], strict=False)
model = model.cuda()
model.eval()

#load image
img = plt.imread(test_image)[:,:,:3]
orgh,orgw = img.shape[0], img.shape[1]
zeros = np.zeros((448, 720,3))
xoffset = 2
yoffset = 10
zeros[xoffset:orgh+xoffset,yoffset:orgw+yoffset,:] = img
img = zeros
img = cv2.resize(img, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
plt.imshow(img)
print("image shape : ", img.shape)
#h,w = img.shape[0], img.shape[1]

#image preprocess
img *= 255.
img -= np.array([103.939, 116.779, 123.68])
img /= np.array([1.,1.,1.])
img /= 255.

h,w = img.shape[0], img.shape[1]

image = torch.from_numpy(img).permute(2, 0, 1).contiguous().float().view(1,3,h,w)
image = image.cuda()

#infer
out = model(image)
index = torch.max(out, 1)[1].squeeze().cpu().numpy()
plt.imshow(index, alpha= 0.5)

plt.show()

[cardwing]

Is the low-resolution image from ApolloScape? If yes, you may need to finetune the model. If no, you have to retrain the model from scratch.