| import torch |
| from datasets.rb_loader_cl import RB_loader_cl |
| from datasets.rb_loader_cb import RB_loader_cb |
| from utils import Prev_RetMetric, l2_norm, compute_recall_at_k |
| import numpy as np |
| from tqdm import tqdm |
| from model import SwinModel_Fusion as Model |
| from sklearn.metrics import roc_curve, auc |
| import json |
| import torch.nn.functional as F |
|
|
| def get_fused_cross_score_matrix(model, cl_tokens, cb_tokens): |
| cl_tokens = torch.cat(cl_tokens) |
| cb_tokens = torch.cat(cb_tokens) |
| |
| batch_size_cl = cl_tokens.shape[0] |
| batch_size_cb = cb_tokens.shape[0] |
| shard_size = 20 |
| similarity_matrix = torch.zeros((batch_size_cl, batch_size_cb)) |
| for i_start in tqdm(range(0, batch_size_cl, shard_size)): |
| i_end = min(i_start + shard_size, batch_size_cl) |
| shard_i = cl_tokens[i_start:i_end] |
| for j_start in range(0, batch_size_cb, shard_size): |
| j_end = min(j_start + shard_size, batch_size_cb) |
| shard_j = cb_tokens[j_start:j_end] |
| batch_i = shard_i.unsqueeze(1) |
| batch_j = shard_j.unsqueeze(0) |
|
|
| pairwise_i = batch_i.expand(-1, shard_j.shape[0], -1, -1) |
| pairwise_j = batch_j.expand(shard_i.shape[0], -1, -1, -1) |
| |
| similarity_scores, distances = model.combine_features( |
| pairwise_i.reshape(-1, 197, shard_i.shape[-1]), |
| pairwise_j.reshape(-1, 197, shard_j.shape[-1]) |
| ) |
| scores = similarity_scores - 0.1 * distances |
| scores = scores.reshape(shard_i.shape[0], shard_j.shape[0]) |
| similarity_matrix[i_start:i_end, j_start:j_end] = scores.cpu().detach() |
| return similarity_matrix |
|
|
| device = torch.device('cuda') |
| data_cl = RB_loader_cl(split="test") |
| data_cb = RB_loader_cb(split="test") |
| dataloader_cb = torch.utils.data.DataLoader(data_cb,batch_size = 16, num_workers = 1, pin_memory = True) |
| dataloader_cl = torch.utils.data.DataLoader(data_cl,batch_size = 16, num_workers = 1, pin_memory = True) |
| model = Model().to(device) |
| checkpoint = torch.load("ridgeformer_checkpoints/phase2_scratch.pt",map_location = torch.device('cpu')) |
| model.load_state_dict(checkpoint,strict=False) |
|
|
| model.eval() |
| cl_feats, cb_feats, cl_labels, cb_labels, cl_fnames, cb_fnames, cl_feats_unnormed, cb_feats_unnormed = list(),list(),list(),list(),list(),list(),list(),list() |
| print("Computing Test Recall") |
|
|
| with torch.no_grad(): |
| for (x_cb, target) in tqdm(dataloader_cb): |
| x_cb, label = x_cb.to(device), target.to(device) |
| x_cb_token = model.get_tokens(x_cb,'contactbased') |
| label = label.cpu().detach().numpy() |
| cb_feats.append(x_cb_token) |
| cb_labels.append(label) |
|
|
| with torch.no_grad(): |
| for (x_cl, target) in tqdm(dataloader_cl): |
| x_cl, label = x_cl.to(device), target.to(device) |
| x_cl_token = model.get_tokens(x_cl,'contactless') |
| label = label.cpu().detach().numpy() |
| cl_feats.append(x_cl_token) |
| cl_labels.append(label) |
|
|
| cl_label = torch.from_numpy(np.concatenate(cl_labels)) |
| cb_label = torch.from_numpy(np.concatenate(cb_labels)) |
|
|
| |
| scores_mat = get_fused_cross_score_matrix(model, cl_feats, cb_feats) |
| scores = scores_mat.cpu().detach().numpy().flatten().tolist() |
| labels = torch.eq(cl_label.view(-1,1) - cb_label.view(1,-1),0.0).flatten().tolist() |
| ids_mod = list() |
| for i in labels: |
| if i==True: |
| ids_mod.append(1) |
| else: |
| ids_mod.append(0) |
| fpr,tpr,thresh = roc_curve(labels,scores,drop_intermediate=True) |
| lower_fpr_idx = max(i for i, val in enumerate(fpr) if val < 0.01) |
| upper_fpr_idx = min(i for i, val in enumerate(fpr) if val >= 0.01) |
| tar_far_102 = tpr[upper_fpr_idx] |
|
|
| lower_fpr_idx = max(i for i, val in enumerate(fpr) if val < 0.001) |
| upper_fpr_idx = min(i for i, val in enumerate(fpr) if val >= 0.001) |
| tar_far_103 = (tpr[lower_fpr_idx]+tpr[upper_fpr_idx])/2 |
|
|
| lower_fpr_idx = max(i for i, val in enumerate(fpr) if val < 0.0001) |
| upper_fpr_idx = min(i for i, val in enumerate(fpr) if val >= 0.0001) |
| tar_far_104 = (tpr[lower_fpr_idx]+tpr[upper_fpr_idx])/2 |
|
|
| fnr = 1 - tpr |
|
|
| EER = fpr[np.nanargmin(np.absolute((fnr - fpr)))] |
| roc_auc = auc(fpr, tpr) |
| print(f"ROCAUC for CB2CL: {roc_auc * 100} %") |
| print(f"EER for CB2CL: {EER * 100} %") |
| eer_cb2cl = EER * 100 |
| cbcltf102 = tar_far_102 * 100 |
| cbcltf103 = tar_far_103 * 100 |
| cbcltf104 = tar_far_104 * 100 |
| cl_label = cl_label.cpu().detach() |
| cb_label = cb_label.cpu().detach() |
| print(f"TAR@FAR=10^-2 for CB2CL: {tar_far_102 * 100} %") |
| print(f"TAR@FAR=10^-3 for CB2CL: {tar_far_103 * 100} %") |
| print(f"TAR@FAR=10^-4 for CB2CL: {tar_far_104 * 100} %") |
|
|
| print(f"R@1 for CB2CL: {compute_recall_at_k(scores_mat, cl_label, cb_label, 1) * 100} %") |
| print(f"R@10 for CB2CL: {compute_recall_at_k(scores_mat, cl_label, cb_label, 10) * 100} %") |
| print(f"R@50 for CB2CL: {compute_recall_at_k(scores_mat, cl_label, cb_label, 50) * 100} %") |
| print(f"R@100 for CB2CL: {compute_recall_at_k(scores_mat, cl_label, cb_label, 100) * 100} %") |
|
|
| |
| scores = get_fused_cross_score_matrix(model, cl_feats, cl_feats) |
| scores_mat = scores |
| row, col = torch.triu_indices(row=scores.size(0), col=scores.size(1), offset=1) |
| scores = scores[row, col] |
| labels = torch.eq(cl_label.view(-1,1) - cl_label.view(1,-1),0.0).float().cuda() |
| labels = labels[torch.triu(torch.ones(labels.shape),diagonal = 1) == 1] |
| scores = scores.cpu().detach().numpy().flatten().tolist() |
| labels = labels.flatten().tolist() |
| ids_mod = list() |
| for i in labels: |
| if i==True: |
| ids_mod.append(1) |
| else: |
| ids_mod.append(0) |
| fpr,tpr,thresh = roc_curve(labels,scores,drop_intermediate=True) |
|
|
| lower_fpr_idx = max(i for i, val in enumerate(fpr) if val < 0.01) |
| upper_fpr_idx = min(i for i, val in enumerate(fpr) if val >= 0.01) |
| tar_far_102 = tpr[upper_fpr_idx] |
|
|
| lower_fpr_idx = max(i for i, val in enumerate(fpr) if val < 0.001) |
| upper_fpr_idx = min(i for i, val in enumerate(fpr) if val >= 0.001) |
| tar_far_103 = (tpr[lower_fpr_idx]+tpr[upper_fpr_idx])/2 |
|
|
| lower_fpr_idx = max(i for i, val in enumerate(fpr) if val < 0.0001) |
| upper_fpr_idx = min(i for i, val in enumerate(fpr) if val >= 0.0001) |
| tar_far_104 = (tpr[lower_fpr_idx]+tpr[upper_fpr_idx])/2 |
|
|
| fnr = 1 - tpr |
|
|
| EER = fpr[np.nanargmin(np.absolute((fnr - fpr)))] |
| roc_auc = auc(fpr, tpr) |
| print(f"ROCAUC for CL2CL: {roc_auc * 100} %") |
| print(f"EER for CL2CL: {EER * 100} %") |
| eer_cb2cl = EER * 100 |
| cbcltf102 = tar_far_102 * 100 |
| cbcltf103 = tar_far_103 * 100 |
| cbcltf104 = tar_far_104 * 100 |
| cl_label = cl_label.cpu().detach() |
| print(f"TAR@FAR=10^-2 for CL2CL: {tar_far_102 * 100} %") |
| print(f"TAR@FAR=10^-3 for CL2CL: {tar_far_103 * 100} %") |
| print(f"TAR@FAR=10^-4 for CL2CL: {tar_far_104 * 100} %") |
|
|
| print(f"R@1 for CL2CL: {compute_recall_at_k(scores_mat, cl_label, cl_label, 1) * 100} %") |
| print(f"R@10 for CL2CL: {compute_recall_at_k(scores_mat, cl_label, cl_label, 10) * 100} %") |
| print(f"R@50 for CL2CL: {compute_recall_at_k(scores_mat, cl_label, cl_label, 50) * 100} %") |
| print(f"R@100 for CL2CL: {compute_recall_at_k(scores_mat, cl_label, cl_label, 100) * 100} %") |
|
|
