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| from CLIP.clip import clip | |
| from CLIP.clip import model | |
| import torch | |
| def topk_overlap_loss(gt, pred, K=2, metric='l1'): | |
| idx = torch.argsort(gt, descending=True) | |
| # print(idx) | |
| idx = idx[:K] | |
| pred_TopK_1 = pred.gather(-1,idx) | |
| gt_Topk_1 = gt.gather(-1,idx) | |
| idx_pred = torch.argsort(pred, descending=True) | |
| idx_pred = idx_pred[:K] | |
| try: | |
| gt_TopK_2 = gt.gather(-1, idx_pred) | |
| except Exception as e: | |
| print(e) | |
| print(gt.shape) | |
| print(idx_pred.shape) | |
| pred_TopK_2 = pred.gather(-1, idx_pred) | |
| gt_Topk_1_normed = torch.nn.functional.softmax(gt_Topk_1, dim=-1) | |
| pred_TopK_1_normed = torch.nn.functional.softmax(pred_TopK_1, dim=-1) | |
| gt_TopK_2_normed = torch.nn.functional.softmax(gt_TopK_2, dim=-1) | |
| pred_TopK_2_normed = torch.nn.functional.softmax(pred_TopK_2, dim=-1) | |
| def kl(a,b): | |
| return torch.nn.functional.kl_div(a.log(), b, reduction="batchmean") | |
| def jsd(a,b): | |
| loss = kl(a,b) + kl(b,a) | |
| loss /= 2 | |
| return loss | |
| if metric == 'l1': | |
| loss = torch.abs((pred_TopK_1 - gt_Topk_1)) + torch.abs(gt_TopK_2 - pred_TopK_2) | |
| loss = loss/(2*K) | |
| elif metric == "l2": | |
| loss = torch.norm(pred_TopK_1 - gt_Topk_1, p=2) + torch.norm(gt_TopK_2 - pred_TopK_2, p=2) | |
| loss = loss/(2*K) | |
| elif metric == "kl-full": | |
| loss = kl(gt,pred) | |
| elif metric == "jsd-full": | |
| loss = jsd(gt,pred) | |
| elif metric == "kl-topk": | |
| loss = kl(gt_Topk_1_normed,pred_TopK_1_normed) + kl(gt_TopK_2_normed,pred_TopK_2_normed) | |
| loss /=2 | |
| elif metric == "jsd-topk": | |
| loss = jsd(gt_Topk_1_normed, pred_TopK_1_normed) + jsd(gt_TopK_2_normed, pred_TopK_2_normed) | |
| loss /= 2 | |
| return loss | |
| def topk_overlap_loss_batch(gt,pred,K=2,metric='l1'): | |
| idx = torch.argsort(gt,dim=1,descending=True) | |
| # print(idx) | |
| idx = idx[:,:K] | |
| pred_TopK_1 = pred.gather(1,idx) | |
| gt_Topk_1 = gt.gather(1,idx) | |
| idx_pred = torch.argsort(pred,dim=1,descending=True) | |
| idx_pred = idx_pred[:,:K] | |
| try: | |
| gt_TopK_2 = gt.gather(1, idx_pred) | |
| except Exception as e: | |
| print(e) | |
| print(gt.shape) | |
| print(idx_pred.shape) | |
| pred_TopK_2 = pred.gather(1, idx_pred) | |
| gt_Topk_1_normed = torch.nn.functional.softmax(gt_Topk_1,dim=-1) | |
| pred_TopK_1_normed = torch.nn.functional.softmax(pred_TopK_1,dim=-1) | |
| gt_TopK_2_normed = torch.nn.functional.softmax(gt_TopK_2,dim=-1) | |
| pred_TopK_2_normed = torch.nn.functional.softmax(pred_TopK_2,dim=-1) | |
| def kl(a,b): | |
| return torch.nn.functional.kl_div(a.log(), b, reduction="batchmean") | |
| def jsd(a,b): | |
| loss = kl(a,b) + kl(b,a) | |
| loss /= 2 | |
| return loss | |
| if metric == 'l1': | |
| loss = torch.abs((pred_TopK_1 - gt_Topk_1)) + torch.abs(gt_TopK_2 - pred_TopK_2) | |
| loss = loss/(2*K) | |
| elif metric == "l2": | |
| loss = torch.norm(pred_TopK_1 - gt_Topk_1, p=2) + torch.norm(gt_TopK_2 - pred_TopK_2, p=2) | |
| loss = loss/(2*K) | |
| elif metric == "kl-full": | |
| loss = kl(gt,pred) | |
| elif metric == "jsd-full": | |
| loss = jsd(gt,pred) | |
| elif metric == "kl-topk": | |
| loss = kl(gt_Topk_1_normed,pred_TopK_1_normed) + kl(gt_TopK_2_normed,pred_TopK_2_normed) | |
| loss /=2 | |
| elif metric == "jsd-topk": | |
| loss = jsd(gt_Topk_1_normed, pred_TopK_1_normed) + jsd(gt_TopK_2_normed, pred_TopK_2_normed) | |
| loss /= 2 | |
| return loss | |