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import torch |
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from datasets.hkpoly_test import hktest |
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from utils import Prev_RetMetric, l2_norm, compute_recall_at_k |
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import numpy as np |
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from tqdm import tqdm |
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from model import SwinModel_Fusion as Model |
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from sklearn.metrics import roc_curve, auc |
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import json |
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def calculate_tar_at_far(fpr, tpr, target_fars): |
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tar_at_far = {} |
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for far in target_fars: |
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if far in fpr: |
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tar = tpr[np.where(fpr == far)][0] |
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else: |
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tar = np.interp(far, fpr, tpr) |
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tar_at_far[far] = tar |
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return tar_at_far |
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def get_fused_cross_score_matrix(model, cl_tokens, cb_tokens): |
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cl_tokens = torch.cat(cl_tokens) |
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cb_tokens = torch.cat(cb_tokens) |
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batch_size = cl_tokens.shape[0] |
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shard_size = 20 |
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similarity_matrix = torch.zeros((batch_size, batch_size)) |
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for i_start in tqdm(range(0, batch_size, shard_size)): |
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i_end = min(i_start + shard_size, batch_size) |
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shard_i = cl_tokens[i_start:i_end] |
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for j_start in range(0, batch_size, shard_size): |
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j_end = min(j_start + shard_size, batch_size) |
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shard_j = cb_tokens[j_start:j_end] |
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batch_i = shard_i.unsqueeze(1) |
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batch_j = shard_j.unsqueeze(0) |
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pairwise_i = batch_i.expand(-1, shard_size, -1, -1) |
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pairwise_j = batch_j.expand(shard_size, -1, -1, -1) |
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similarity_scores, distances = model.combine_features(pairwise_i.reshape(-1, 197, 1024), pairwise_j.reshape(-1, 197, 1024)) |
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scores = similarity_scores - 0.1 * distances |
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scores = scores.reshape(shard_size, shard_size) |
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similarity_matrix[i_start:i_end, j_start:j_end] = scores.cpu().detach() |
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return similarity_matrix |
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if __name__ == '__main__': |
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device = torch.device('cuda') |
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data = hktest(split = 'test') |
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dataloader = torch.utils.data.DataLoader(data,batch_size = 16, num_workers = 1, pin_memory = True) |
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model = Model().to(device) |
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checkpoint = torch.load("ridgeformer_checkpoints/phase2_ft_hkpoly.pt",map_location = torch.device('cpu')) |
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model.load_state_dict(checkpoint,strict=False) |
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model.eval() |
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cl_feats, cb_feats, cl_labels, cb_labels, cl_feats_unnormed, cb_feats_unnormed = list(),list(),list(),list(),list(),list() |
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with torch.no_grad(): |
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for (x_cl, x_cb, label) in tqdm(dataloader): |
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x_cl, x_cb, label = x_cl.to(device), x_cb.to(device), label.to(device) |
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x_cl_token = model.get_tokens(x_cl,'contactless') |
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x_cb_token = model.get_tokens(x_cb,'contactbased') |
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label = label.cpu().detach().numpy() |
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cl_feats.append(x_cl_token) |
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cb_feats.append(x_cb_token) |
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cl_labels.append(label) |
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cb_labels.append(label) |
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cl_label = torch.from_numpy(np.concatenate(cl_labels)) |
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cb_label = torch.from_numpy(np.concatenate(cb_labels)) |
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scores_mat = get_fused_cross_score_matrix(model, cl_feats, cb_feats) |
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scores = scores_mat.cpu().detach().numpy().flatten().tolist() |
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labels = torch.eq(cl_label.view(-1,1) - cb_label.view(1,-1),0.0).flatten().tolist() |
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ids_mod = list() |
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for i in labels: |
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if i==True: |
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ids_mod.append(1) |
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else: |
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ids_mod.append(0) |
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fpr,tpr,thresh = roc_curve(labels,scores,drop_intermediate=True) |
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lower_fpr_idx = max(i for i, val in enumerate(fpr) if val < 0.01) |
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upper_fpr_idx = min(i for i, val in enumerate(fpr) if val >= 0.01) |
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tar_far_102 = tpr[upper_fpr_idx] |
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lower_fpr_idx = max(i for i, val in enumerate(fpr) if val < 0.001) |
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upper_fpr_idx = min(i for i, val in enumerate(fpr) if val >= 0.001) |
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tar_far_103 = (tpr[lower_fpr_idx]+tpr[upper_fpr_idx])/2 |
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lower_fpr_idx = max(i for i, val in enumerate(fpr) if val < 0.0001) |
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upper_fpr_idx = min(i for i, val in enumerate(fpr) if val >= 0.0001) |
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tar_far_104 = (tpr[lower_fpr_idx]+tpr[upper_fpr_idx])/2 |
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fnr = 1 - tpr |
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EER = fpr[np.nanargmin(np.absolute((fnr - fpr)))] |
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roc_auc = auc(fpr, tpr) |
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print(f"ROCAUC for CB2CL: {roc_auc * 100} %") |
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print(f"EER for CB2CL: {EER * 100} %") |
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eer_cb2cl = EER * 100 |
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cbcltf102 = tar_far_102 * 100 |
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cbcltf103 = tar_far_103 * 100 |
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cbcltf104 = tar_far_104 * 100 |
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cl_label = cl_label.cpu().detach() |
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cb_label = cb_label.cpu().detach() |
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print(f"TAR@FAR=10^-2 for CB2CL: {tar_far_102 * 100} %") |
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print(f"TAR@FAR=10^-3 for CB2CL: {tar_far_103 * 100} %") |
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print(f"TAR@FAR=10^-4 for CB2CL: {tar_far_104 * 100} %") |
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recall_dict = dict() |
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for i in range(1,101): |
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recall_dict[i] = compute_recall_at_k(scores_mat, cl_label, cb_label, i) |
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print(f"R@1 for CB2CL: {compute_recall_at_k(scores_mat, cl_label, cb_label, 1) * 100} %") |
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print(f"R@10 for CB2CL: {compute_recall_at_k(scores_mat, cl_label, cb_label, 10) * 100} %") |
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print(f"R@50 for CB2CL: {compute_recall_at_k(scores_mat, cl_label, cb_label, 50) * 100} %") |
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print(f"R@100 for CB2CL: {compute_recall_at_k(scores_mat, cl_label, cb_label, 100) * 100} %") |
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