Captum is working without any error. But every time It is predicted as first class. Here is the latest snapshot of my code
import os
from captum.insights import AttributionVisualizer, Batch
from captum.insights.features import ImageFeature
from torchvision import datasets, transforms, models
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
test_transforms = transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor(),
#transforms.Normalize((0.4914, 0.4822, 0.4465),(0.2023, 0.1994, 0.2010))
])
def get_classes():
classes = [
"B",
"M"
]
return classes
def get_pretrained_model():
net= models.resnet18(pretrained=True)
num_ftrs = net.fc.in_features
net.fc = nn.Linear(num_ftrs, 2)
pt_path = os.path.abspath(
os.path.dirname(__file__) + "/models/ResnetBW.pt"
)
net.load_state_dict(torch.load(pt_path))
return net
def baseline_func(input):
return input * 0
def formatted_data_iter():
dataset = datasets.ImageFolder(
root="data/test", transform=test_transforms
)
dataloader = iter(
torch.utils.data.DataLoader(dataset, batch_size=4, shuffle=False, num_workers=2)
)
while True:
images, labels = next(dataloader)
yield Batch(inputs=images, labels=labels)
if __name__ == "__main__":
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
model = get_pretrained_model()
visualizer = AttributionVisualizer(
models=[model],
score_func=lambda o: torch.nn.functional.softmax(o, 1),
classes=get_classes(),
features=[
ImageFeature(
"Photo",
baseline_transforms=[baseline_func],
input_transforms=[normalize],
)
],
dataset=formatted_data_iter(),
)
visualizer.render(debug=True)
