PyTorch: The size of tensor a (28) must match the size of tensor b (4) at non-singleton dimension 3 about iba

[berleon]

Hi unnir,

I was not able to reproduce your error from your snippet. I adapted your snippet to use the CIFAR10 dataset (just for sanity checking and I did not had an ImageNet copy arround). It now runs through without any problem:

from IBA.pytorch import IBA, tensor_to_np_img, get_imagenet_folder, imagenet_transform
from IBA.utils import plot_saliency_map, to_unit_interval, load_monkeys

from torch.utils.data import DataLoader
from torchvision.models import vgg16, resnet34
from torchvision.datasets import CIFAR10
from torchvision.transforms import Resize, ToTensor, Compose
import torch
from torch import nn

# imagenet_dir = /path/to/imagenet/validation

# Load model
dev = 'cuda:0' if  torch.cuda.is_available() else 'cpu'
model = resnet34(pretrained=True)
model.fc = nn.Linear(512, 10)
model.to(dev)
model.eval()

# Add a Per-Sample Bottleneck at layer conv4_1
iba = IBA(model.layer2)

# Estimate the mean and variance of the feature map at this layer.
val_set = CIFAR10('/tmp/cifar', transform=Compose([Resize(224), ToTensor()]), download=True)
val_loader = DataLoader(val_set, batch_size=1, shuffle=True, num_workers=0)
iba.estimate(model, val_loader, n_samples=1, progbar=True)

# Load Image
monkeys, target = load_monkeys(pil=True)
monkeys_transform = imagenet_transform()(monkeys)

# fix target
target = 5

# Closure that returns the loss for one batch
model_loss_closure = lambda x: -torch.log_softmax(model(x), dim=1)[:, target].mean()

# Explain class target for the given image
saliency_map = iba.analyze(monkeys_transform.unsqueeze(0).to(dev), model_loss_closure, beta=10)

# display result
model_loss_closure = lambda x: -torch.log_softmax(model(x), 1)[:, target].mean()
heatmap = iba.analyze(monkeys_transform[None].to(dev), model_loss_closure )
plot_saliency_map(heatmap, tensor_to_np_img(monkeys_transform))

Please, let me know if this version does not work for you.

Best,
Leon

[unnir]

it was my mistake. Sorry and thank you for your answer.

[berleon]

No Problem, let me know if you encounter another problem.