Pytorch implementation This code is based on the Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning
- Python 3.6
- PyTorch (version >= 0.2.0)
- h5py, scikit-learn, metric-learn, tqdm
- MARS: [Google Drive] [BaiduYun].
- Move the downloaded zip files to
./data/and unzip here.
For the MARS datasaet:
python run.py --dataset mars --logs_dir logs/mars_EF_20/ --EF 20 --mode Dissimilarity --max_frames 256
def get_Dissimilarity_result_rank(self):
# extract feature
u_feas = self.get_feature(self.u_data)
l_feas = self.get_feature(self.l_data)
print("u_features", u_feas.shape, "l_features", l_feas.shape)
u_l_dist = np.dot(u_feas,np.transpose(l_feas))
u_u_dist = np.dot(u_feas,np.transpose(u_feas))
l_l_dist = np.dot(l_feas,np.transpose(l_feas))
dist = re_ranking(u_l_dist,u_u_dist,l_l_dist) # distance rerank (https://github.com/zhunzhong07/person-re-ranking)
scores = np.zeros((u_feas.shape[0]))
labels = np.zeros((u_feas.shape[0]))
num_correct_pred = 0
for idx, u_fea in enumerate(dist):
index_min = np.argmin(u_fea)
scores[idx] = - u_fea[index_min] # "- dist" : more dist means less score
labels[idx] = self.l_label[index_min] # take the nearest labled neighbor as the prediction label
# count the correct number of Nearest Neighbor prediction
if self.u_label[idx] == labels[idx]:
num_correct_pred += 1
print("{} predictions on all the unlabeled data: {} of {} is correct, accuracy = {:0.3f}".format(
self.mode, num_correct_pred, u_feas.shape[0], num_correct_pred/u_feas.shape[0]))
return labels, scores
def get_Dissimilarity_result(self):
# extract feature
u_feas = self.get_feature(self.u_data)
l_feas = self.get_feature(self.l_data)
print("u_features", u_feas.shape, "l_features", l_feas.shape)
scores = np.zeros((u_feas.shape[0]))
labels = np.zeros((u_feas.shape[0]))
num_correct_pred = 0
for idx, u_fea in enumerate(u_feas):
diffs = l_feas - u_fea
dist = np.linalg.norm(diffs,axis=1)
index_min = np.argmin(dist)
scores[idx] = - dist[index_min] # "- dist" : more dist means less score
labels[idx] = self.l_label[index_min] # take the nearest labled neighbor as the prediction label
# count the correct number of Nearest Neighbor prediction
if self.u_label[idx] == labels[idx]:
num_correct_pred +=1
print("{} predictions on all the unlabeled data: {} of {} is correct, accuracy = {:0.3f}".format(
self.mode, num_correct_pred, u_feas.shape[0], num_correct_pred/u_feas.shape[0]))
return labels, scores