When I make predictions with the .pth model, I get 1000-sized logits arrays

[systemosc]

Hi, hope you're fine

I ran your code and it worked quite well : first run.sh then ResNet18_224.ipynb and finally ResNet18_FineTune.ipynb
I get the path files
The problem I have is that when I load the model weights, and I do y = model_ft(x) on an image from Tiny ImageNet, I get a probabilities/logits array of size 1000, and not 200 as expected.
Here is my code (strongly inspired by yours) :

model_ft = models.resnet18()
model_ft.conv1 = nn.Conv2d(3,64, kernel_size=(3,3), stride=(1,1), padding=(1,1), bias=False)
model_ft.maxpool = nn.Sequential()
model_ft.avgpool = nn.AdaptiveAvgPool2d(1)
model_ft.fc.out_features = 200

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_ft = model_ft.to(device)

model_ft.load_state_dict(torch.load('./models/finetuned2/model_18_epoch.pt',
                                    map_location=torch.device('cpu')))

path = 'tiny-imagenet-200/val/images/val_2342.JPEG'
x = plt.imread(path)
plt.imshow(x)

x = np.transpose(x, (2, 0, 1))
x.reshape((1, 3, 64, 64))

x = Variable(torch.from_numpy(x))
x = x.float()
x = x.view(1, 3, 64, 64)

model_ft.eval()

y = model_ft(x)
z = y.detach().numpy()

Any help is welcome, thanks a lot.

[tjmoon0104]

I updated the code to re-initialize the final layer instead of overriding out_features. New one should be working.