| import torch | |
| import torch.nn.functional as F | |
| from models.cross_entropy_model import FBankCrossEntropyNet | |
| def get_cosine_distance(a, b): | |
| a = torch.from_numpy(a) | |
| b = torch.from_numpy(b) | |
| return (1 - F.cosine_similarity(a, b)).numpy() | |
| MODEL_PATH = 'weights/triplet_loss_trained_model.pth' | |
| model_instance = FBankCrossEntropyNet() | |
| model_instance.load_state_dict(torch.load(MODEL_PATH, map_location=lambda storage, loc: storage)) | |
| model_instance = model_instance.double() | |
| model_instance.eval() | |
| ### I think the instance model was train in stage 2 (constrative learning) ### | |
| def get_embeddings_instance(x): | |
| x = torch.from_numpy(x) | |
| with torch.no_grad(): | |
| embeddings = model_instance(x) | |
| return embeddings.numpy() | |
| def get_embeddings(x , model): | |
| model.double() | |
| x = torch.from_numpy(x) | |
| with torch.no_grad(): | |
| embeddings = model(x) | |
| return embeddings.numpy() |