I’m completely new to PyTorch (have previously used tensor flow) and I’m stuck on something I’m working on. I’ve been tasked with using a pretrained model to extract the features from application documents and then compute similarity scores to identify duplicates. I have all of the pdf’s converted to .jpg’s, and I’ve loaded the pretrained model and modified the last layer to extract features.
The folder structure is like this:
root
Application 1
image 1
image 2…
Application 2
image 1
Image 2…
What I’m trying to do is extract features from the images in every sub-directory and calculate the euclidian distance between them and output a similarity matrix. Where I’m having an issue, and this may seem really basic, is actually making the predictions once the data is loaded. Below is the code I have so far, any help would be greatly appreciated.
def get_pretrained_model_notop(model_name): #pull the model and change last layer
pretrained_model = model_name(pretrained=True) #downloads pretrained model weights
for param in pretrained_model.parameters():
param.requires_grad = False #freezes layers
pretrained_model = nn.Sequential(*list(pretrained_model.children())[:-1]) #drops final layer, because we aren’t classifying 1000 imagenet classes
pretrained_model.fc = nn.Sequential(
nn.Flatten() #adds flatten layer at end of model
)
if torch.cuda.is_available(): #uses GPU if available
pretrained_model = pretrained_model.cuda()
return pretrained_model
def get_similarity(pretrained_model,train_imgs):   #function to extract features from the model and compute similarity scores
bottleneck_feature_example = pretrained_model(train_imgs)
similarity = euclidean_distances(bottleneck_feature_example)
similarity=similarity/similarity.max()
similarity_df = pd.DataFrame(similarity)
similarity_df=1-similarity_df
return np.round(similarity_df,4)
transforms =           transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
img_dir=‘path’
images = datasets.ImageFolder(img_dir,transform=transforms)
data_loader = torch.utils.data.DataLoader(images,
batch_size=32,
shuffle=True,
num_workers=4)
model_list=[models.densenet201]
model_name=[‘densenet201’]
pretrained_model=[get_pretrained_model_notop(selected_model) for selected_model in model_list]
for data in data_loader:
pred=[get_similarity(pretrained,data) for pretrained in pretrained_model]
pred_label_ensemble=sum(pred) / len(pred)
pred_label_ensemble.columns=page_numbers
prob_output_folder = unzipped.replace(‘MF_loan_document’, ‘MF_loan_document_results’)
pred_label_ensemble.to_csv(prob_output_folder+’/’+‘results.csv’,index=False)