I am using pretrained Resnet18 and I have two issues:

No matter what image I input I get the same class (463) as output
Docs say image must be at least 224,224 but for me anything above 224,224 throws size mismatch error while 200,200 and so on work.

I have normlaised manually with values given in docs.
resnet = models.resnet18(pretrained=True)
img=plt.imread('../input/bear.png')

normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])



img=scipy.misc.imresize(img,[244,244])
img=img/255.

img[:,:,0]=(img[:,:,0]-0.485)/0.299
img[:,:,1]=(img[:,:,1]-0.456)/0.224
img[:,:,2]=(img[:,:,2]-0.406)/0.225

img=torch.tensor(img)
img.transpose_(1,2)
img.transpose_(0,1)
img=img.unsqueeze_(0)

y=resnet(img[:,:,:220,:220].float())
print(y.max(),y.argmax(1))


y.argmax() always gives 463 with y.max() being around 2.0 . Vgg16 works fine on the otherhand. I am using pytorch on kaggle kernal.