add Gait3D support
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darkliang
@@ -3,7 +3,7 @@ from time import strftime, localtime
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import numpy as np
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from utils import get_msg_mgr, mkdir
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from .metric import mean_iou, cuda_dist, compute_ACC_mAP
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from .metric import mean_iou, cuda_dist, compute_ACC_mAP, evaluate_rank
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from .re_rank import re_ranking
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@@ -225,3 +225,43 @@ def evaluate_segmentation(data, dataset):
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miou = mean_iou(pred, labels)
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get_msg_mgr().log_info('mIOU: %.3f' % (miou.mean()))
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return {"scalar/test_accuracy/mIOU": miou}
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def evaluate_Gait3D(data, conf, metric='euc'):
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msg_mgr = get_msg_mgr()
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features, labels, cams, time_seqs = data['embeddings'], data['labels'], data['types'], data['views']
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import json
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probe_sets = json.load(
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open('./datasets/Gait3D/Gait3D.json', 'rb'))['PROBE_SET']
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probe_mask = []
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for id, ty, sq in zip(labels, cams, time_seqs):
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if '-'.join([id, ty, sq]) in probe_sets:
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probe_mask.append(True)
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else:
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probe_mask.append(False)
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probe_mask = np.array(probe_mask)
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# probe_features = features[:probe_num]
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probe_features = features[probe_mask]
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# gallery_features = features[probe_num:]
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gallery_features = features[~probe_mask]
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# probe_lbls = np.asarray(labels[:probe_num])
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# gallery_lbls = np.asarray(labels[probe_num:])
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probe_lbls = np.asarray(labels)[probe_mask]
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gallery_lbls = np.asarray(labels)[~probe_mask]
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results = {}
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msg_mgr.log_info(f"The test metric you choose is {metric}.")
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dist = cuda_dist(probe_features, gallery_features, metric).cpu().numpy()
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cmc, all_AP, all_INP = evaluate_rank(dist, probe_lbls, gallery_lbls)
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mAP = np.mean(all_AP)
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mINP = np.mean(all_INP)
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for r in [1, 5, 10]:
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results['scalar/test_accuracy/Rank-{}'.format(r)] = cmc[r - 1] * 100
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results['scalar/test_accuracy/mAP'] = mAP * 100
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results['scalar/test_accuracy/mINP'] = mINP * 100
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# print_csv_format(dataset_name, results)
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msg_mgr.log_info(results)
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return results
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@@ -86,3 +86,73 @@ def compute_ACC_mAP(distmat, q_pids, g_pids, q_views=None, g_views=None, rank=1)
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mAP = np.mean(all_AP)
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return ACC, mAP
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def evaluate_rank(distmat, p_lbls, g_lbls, max_rank=50):
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'''
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Copy from https://github.com/Gait3D/Gait3D-Benchmark/blob/72beab994c137b902d826f4b9f9e95b107bebd78/lib/utils/rank.py#L12-L63
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'''
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num_p, num_g = distmat.shape
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if num_g < max_rank:
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max_rank = num_g
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print('Note: number of gallery samples is quite small, got {}'.format(num_g))
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indices = np.argsort(distmat, axis=1)
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matches = (g_lbls[indices] == p_lbls[:, np.newaxis]).astype(np.int32)
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# compute cmc curve for each probe
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all_cmc = []
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all_AP = []
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all_INP = []
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num_valid_p = 0. # number of valid probe
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for p_idx in range(num_p):
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# compute cmc curve
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# binary vector, positions with value 1 are correct matches
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raw_cmc = matches[p_idx]
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if not np.any(raw_cmc):
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# this condition is true when probe identity does not appear in gallery
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continue
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cmc = raw_cmc.cumsum()
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pos_idx = np.where(raw_cmc == 1) # 返回坐标，此处raw_cmc为一维矩阵，所以返回相当于index
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max_pos_idx = np.max(pos_idx)
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inp = cmc[max_pos_idx] / (max_pos_idx + 1.0)
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all_INP.append(inp)
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cmc[cmc > 1] = 1
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all_cmc.append(cmc[:max_rank])
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num_valid_p += 1.
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# compute average precision
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# reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
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num_rel = raw_cmc.sum()
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pos_idx = np.where(raw_cmc == 1) # 返回坐标，此处raw_cmc为一维矩阵，所以返回相当于index
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max_pos_idx = np.max(pos_idx)
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inp = cmc[max_pos_idx] / (max_pos_idx + 1.0)
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all_INP.append(inp)
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cmc[cmc > 1] = 1
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all_cmc.append(cmc[:max_rank])
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num_valid_p += 1.
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# compute average precision
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# reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
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num_rel = raw_cmc.sum()
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tmp_cmc = raw_cmc.cumsum()
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tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)]
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tmp_cmc = np.asarray(tmp_cmc) * raw_cmc
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AP = tmp_cmc.sum() / num_rel
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all_AP.append(AP)
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assert num_valid_p > 0, 'Error: all probe identities do not appear in gallery'
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all_cmc = np.asarray(all_cmc).astype(np.float32)
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all_cmc = all_cmc.sum(0) / num_valid_p
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return all_cmc, all_AP, all_INP
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@@ -0,0 +1,101 @@
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'''
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Modifed from https://github.com/Gait3D/Gait3D-Benchmark/blob/72beab994c137b902d826f4b9f9e95b107bebd78/lib/modeling/models/smplgait.py
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'''
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.autograd import Variable
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from ..base_model import BaseModel
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from ..modules import SetBlockWrapper, HorizontalPoolingPyramid, PackSequenceWrapper, SeparateFCs, SeparateBNNecks
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class SMPLGait(BaseModel):
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def __init__(self, cfgs, is_training):
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super().__init__(cfgs, is_training)
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def build_network(self, model_cfg):
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# Baseline
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self.Backbone = self.get_backbone(model_cfg['backbone_cfg'])
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self.Backbone = SetBlockWrapper(self.Backbone)
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self.FCs = SeparateFCs(**model_cfg['SeparateFCs'])
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self.BNNecks = SeparateBNNecks(**model_cfg['SeparateBNNecks'])
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self.TP = PackSequenceWrapper(torch.max)
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self.HPP = HorizontalPoolingPyramid(bin_num=model_cfg['bin_num'])
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# for SMPL
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self.fc1 = nn.Linear(85, 128)
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self.fc2 = nn.Linear(128, 256)
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self.fc3 = nn.Linear(256, 256)
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self.bn1 = nn.BatchNorm1d(128)
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self.bn2 = nn.BatchNorm1d(256)
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self.bn3 = nn.BatchNorm1d(256)
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self.dropout2 = nn.Dropout(p=0.2)
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self.dropout3 = nn.Dropout(p=0.2)
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def forward(self, inputs):
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ipts, labs, _, _, seqL = inputs
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sils = ipts[0] # [n, s, h, w]
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smpls = ipts[1] # [n, s, d]
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# extract SMPL features
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n, s, d = smpls.size()
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sps = smpls.view(-1, d)
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del smpls
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sps = F.relu(self.bn1(self.fc1(sps)))
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sps = F.relu(self.bn2(self.dropout2(self.fc2(sps)))) # (B, 256)
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sps = F.relu(self.bn3(self.dropout3(self.fc3(sps)))) # (B, 256)
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sps = sps.reshape(n, 1, s, 16, 16)
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iden = Variable(torch.eye(16)).unsqueeze(
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0).repeat(n, 1, s, 1, 1) # [n, 1, s, 16, 16]
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if sps.is_cuda:
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iden = iden.cuda()
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sps_trans = sps + iden # [n, 1, s, 16, 16]
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if len(sils.size()) == 4:
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sils = sils.unsqueeze(1)
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del ipts
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outs = self.Backbone(sils) # [n, c, s, h, w]
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outs_n, outs_c, outs_s, outs_h, outs_w = outs.size()
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zero_tensor = Variable(torch.zeros(
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(outs_n, outs_c, outs_s, outs_h, outs_h-outs_w)))
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if outs.is_cuda:
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zero_tensor = zero_tensor.cuda()
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# [n, s, c, h, h] [n, s, c, 16, 16]
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outs = torch.cat([outs, zero_tensor], -1)
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outs = outs.reshape(outs_n*outs_c*outs_s, outs_h,
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outs_h) # [n*c*s, 16, 16]
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sps = sps_trans.repeat(1, outs_c, 1, 1, 1).reshape(
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outs_n * outs_c * outs_s, 16, 16)
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outs_trans = torch.bmm(outs, sps)
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outs_trans = outs_trans.reshape(outs_n, outs_c, outs_s, outs_h, outs_h)
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# Temporal Pooling, TP
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outs_trans = self.TP(outs_trans, seqL, options={"dim": 2})[
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0] # [n, c, h, w]
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# Horizontal Pooling Matching, HPM
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feat = self.HPP(outs_trans) # [n, c, p]
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embed_1 = self.FCs(feat) # [n, c, p]
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embed_2, logits = self.BNNecks(embed_1) # [n, c, p]
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n, _, s, h, w = sils.size()
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retval = {
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'training_feat': {
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'triplet': {'embeddings': embed_1, 'labels': labs},
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'softmax': {'logits': logits, 'labels': labs}
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},
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'visual_summary': {
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'image/sils': sils.view(n*s, 1, h, w)
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},
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'inference_feat': {
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'embeddings': embed_1
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}
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}
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return retval
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