74 lines
3.1 KiB
Python
74 lines
3.1 KiB
Python
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import torch
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def accuracy(output, target, topk=(1,), max_traj_len=0):
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with torch.no_grad():
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maxk = max(topk)
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batch_size = target.size(0)
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_, pred = output.topk(maxk, 1, True, True)
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pred = pred.t() # [k, bs*T]
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#print(pred, target.view(1, -1).expand_as(pred))
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correct = pred.eq(target.view(1, -1).expand_as(pred)) # [k, bs*T]
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correct_a = correct[:1].view(-1, max_traj_len) # [bs, T]
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correct_a0 = correct_a[:, 0].reshape(-1).float().mean().mul_(100.0)
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correct_aT = correct_a[:, -1].reshape(-1).float().mean().mul_(100.0)
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res = []
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for k in topk:
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correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
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res.append(correct_k.mul_(100.0 / batch_size))
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correct_1 = correct[:1] # (1, bs*T)
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# Success Rate
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trajectory_success = torch.all(correct_1.view(correct_1.shape[1] // max_traj_len, -1), dim=1)
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trajectory_success_rate = trajectory_success.sum() * 100.0 / trajectory_success.shape[0]
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# MIoU
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_, pred_token = output.topk(1, 1, True, True) # [bs*T, 1]
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pred_inst = pred_token.view(correct_1.shape[1], -1) # [bs*T, 1]
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pred_inst_set = set()
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target_inst = target.view(correct_1.shape[1], -1) # [bs*T, 1]
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target_inst_set = set()
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for i in range(pred_inst.shape[0]):
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# print(pred_inst[i], target_inst[i])
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pred_inst_set.add(tuple(pred_inst[i].tolist()))
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target_inst_set.add(tuple(target_inst[i].tolist()))
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MIoU1 = 100.0 * len(pred_inst_set.intersection(target_inst_set)) / len(pred_inst_set.union(target_inst_set))
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batch_size = batch_size // max_traj_len
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pred_inst = pred_token.view(batch_size, -1) # [bs, T]
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pred_inst_set = set()
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target_inst = target.view(batch_size, -1) # [bs, T]
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target_inst_set = set()
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MIoU_sum = 0
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for i in range(pred_inst.shape[0]):
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# print(pred_inst[i], target_inst[i])
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pred_inst_set.update(pred_inst[i].tolist())
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target_inst_set.update(target_inst[i].tolist())
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MIoU_current = 100.0 * len(pred_inst_set.intersection(target_inst_set)) / len(
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pred_inst_set.union(target_inst_set))
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MIoU_sum += MIoU_current
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pred_inst_set.clear()
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target_inst_set.clear()
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MIoU2 = MIoU_sum / batch_size
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return res, trajectory_success_rate, MIoU1, MIoU2, correct_a0, correct_aT
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def similarity_score(pred, act_emb, metric='cos'):
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# pred shape: [bs*t, 512] 512 is action embedding shape
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sim_score = torch.zeros((pred.shape[0], act_emb.shape[0])).cuda()
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if metric == 'cos':
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cos = torch.nn.CosineSimilarity(dim=0, eps=1e-8)
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#print(pred[0].shape, sim_score.shape, act_emb[0].shape)
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for i in range(sim_score.shape[0]):
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for j in range(act_emb.shape[0]):
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sim_score[i][j] = cos(pred[i],act_emb[j].cuda())
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sim_score = torch.abs(sim_score)
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print('sim_score', sim_score, torch.max(sim_score, 1))
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return sim_score.cpu()
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