def __init__(self, data_path, graph_args={}, train_val_test='train'):
'''
train_val_test: (train, val, test)
'''
self.data_path = data_path
self.path_list = sorted(glob.glob(os.path.join(self.data_path,'*.txt')))
self.all_feature=[]
self.all_adjacency=[]
self.all_mean_xy=[]
self.it = 0
#self.load_data()
#total_num = len(self.all_feature)
# equally choose validation set
self.feature_num=0
self.prev=0
#train_id_list = list(np.linspace(0, total_num-1, int(total_num*0.8)).astype(int))
#val_id_list = list(set(list(range(total_num))) - set(train_id_list))
# # last 20% data as validation set
self.train_val_test = train_val_test
"""
if train_val_test.lower() == 'train':
self.all_feature = self.all_feature[train_id_list]
self.all_adjacency = self.all_adjacency[train_id_list]
self.all_mean_xy = self.all_mean_xy[train_id_list]
elif train_val_test.lower() == 'val':
self.all_feature = self.all_feature[val_id_list]
self.all_adjacency = self.all_adjacency[val_id_list]
self.all_mean_xy = self.all_mean_xy[val_id_list]
"""
self.graph = Graph(**graph_args) #num_node = 120,max_hop = 1
def __init__(self, data_list, graph_args={}, train_val_test='train'):
'''
train_val_test: (train, val, test)
'''
self.data_list = data_list
# self.load_data()
self.loadDirectData()
total_num = len(self.all_feature)
# equally choose validation set
train_id_list = list(
np.linspace(0, total_num - 1, int(total_num * 0.8)).astype(int))
val_id_list = list(set(list(range(total_num))) - set(train_id_list))
# # last 20% data as validation set
self.train_val_test = train_val_test
if train_val_test.lower() == 'train':
self.all_feature = self.all_feature[train_id_list]
self.all_adjacency = self.all_adjacency[train_id_list]
self.all_mean_xy = self.all_mean_xy[train_id_list]
elif train_val_test.lower() == 'val':
self.all_feature = self.all_feature[val_id_list]
self.all_adjacency = self.all_adjacency[val_id_list]
self.all_mean_xy = self.all_mean_xy[val_id_list]
self.graph = Graph(**graph_args) #num_node = 120,max_hop = 1
def __init__(self, in_channels, graph_args, edge_importance_weighting, **kwargs): super().__init__() # load graph self.graph = Graph(**graph_args) A = np.ones((graph_args['max_hop']+1, graph_args['num_node'], graph_args['num_node'])) # build networks spatial_kernel_size = np.shape(A)[0] temporal_kernel_size = 5 #9 #5 # 3 kernel_size = (temporal_kernel_size, spatial_kernel_size) # best self.st_gcn_networks = nn.ModuleList(( nn.BatchNorm2d(in_channels), Graph_Conv_Block(in_channels, 64, kernel_size, 1, residual=True, **kwargs), Graph_Conv_Block(64, 64, kernel_size, 1, **kwargs), Graph_Conv_Block(64, 64, kernel_size, 1, **kwargs), )) # initialize parameters for edge importance weighting if edge_importance_weighting: self.edge_importance = nn.ParameterList( [nn.Parameter(torch.ones(np.shape(A))) for i in self.st_gcn_networks] ) else: self.edge_importance = [1] * len(self.st_gcn_networks) self.num_node = num_node = self.graph.num_node self.out_dim_per_node = out_dim_per_node = 2 #(x, y) coordinate self.seq2seq_car = Seq2Seq(input_size=(64), hidden_size=out_dim_per_node, num_layers=2, dropout=0.5, isCuda=False) self.seq2seq_human = Seq2Seq(input_size=(64), hidden_size=out_dim_per_node, num_layers=2, dropout=0.5, isCuda=False) self.seq2seq_bike = Seq2Seq(input_size=(64), hidden_size=out_dim_per_node, num_layers=2, dropout=0.5, isCuda=False)
class Feeder(torch.utils.data.Dataset):
""" Feeder for skeleton-based action recognition
Arguments:
data_path: the path to '.npy' data, the shape of data should be (N, C, T, V, M)
"""
def __init__(self, data_path, graph_args={}, train_val_test='train'):
'''
train_val_test: (train, val, test)
'''
self.data_path = data_path
self.path_list = sorted(glob.glob(os.path.join(self.data_path,'*.txt')))
self.all_feature=[]
self.all_adjacency=[]
self.all_mean_xy=[]
self.it = 0
#self.load_data()
#total_num = len(self.all_feature)
# equally choose validation set
self.feature_num=0
self.prev=0
#train_id_list = list(np.linspace(0, total_num-1, int(total_num*0.8)).astype(int))
#val_id_list = list(set(list(range(total_num))) - set(train_id_list))
# # last 20% data as validation set
self.train_val_test = train_val_test
"""
if train_val_test.lower() == 'train':
self.all_feature = self.all_feature[train_id_list]
self.all_adjacency = self.all_adjacency[train_id_list]
self.all_mean_xy = self.all_mean_xy[train_id_list]
elif train_val_test.lower() == 'val':
self.all_feature = self.all_feature[val_id_list]
self.all_adjacency = self.all_adjacency[val_id_list]
self.all_mean_xy = self.all_mean_xy[val_id_list]
"""
self.graph = Graph(**graph_args) #num_node = 120,max_hop = 1
#def load_data(self,path):
# all_feature, all_adjacency, all_mean_xy = generate_data(path)
#with open(self.data_path, 'rb') as reader:
# Training (N, C, T, V)=(5010, 11, 12, 120), (5010, 120, 120), (5010, 2)
# [self.all_feature, self.all_adjacency, self.all_mean_xy]= pickle.load(reader)
def __len__(self):
return 24*(len(self.path_list))
def __getitem__(self, idx):
# C = 11: [frame_id, object_id, object_type, position_x, position_y, position_z, object_length, pbject_width, pbject_height, heading] + [mask]
#if(idx>=self.feature_num):
try:
now_feature = self.all_feature[idx-self.prev].copy()
except:
path = self.path_list[self.it]
self.it = self.it+1
#self.all_feature, self.all_adjacency, self.all_mean_xy = generate_data(path)
while True:
try:
self.all_feature =[]
self.all_feature, self.all_adjacency, self.all_mean_xy = generate_data(path)
if(len(self.all_feature)>0):
break
except:
self.it = self.it+1
path = self.path_list[self.it]
self.prev = self.feature_num
self.feature_num = self.feature_num+len(self.all_feature)
now_feature = self.all_feature[idx-self.prev].copy()
# (C, T, V) = (11, 12, 120)
now_mean_xy = self.all_mean_xy[idx-self.prev].copy() # (2,) = (x, y)
if self.train_val_test.lower() == 'train' and np.random.random()>0.5:
angle = 2 * np.pi * np.random.random()
sin_angle = np.sin(angle)
cos_angle = np.cos(angle)
angle_mat = np.array(
[[cos_angle, -sin_angle],
[sin_angle, cos_angle]])
xy = now_feature[3:5, :, :]
num_xy = np.sum(xy.sum(axis=0).sum(axis=0) != 0) # get the number of valid data
# angle_mat: (2, 2), xy: (2, 12, 120)
out_xy = np.einsum('ab,btv->atv', angle_mat, xy)
now_mean_xy = np.matmul(angle_mat, now_mean_xy)
xy[:,:,:num_xy] = out_xy[:,:,:num_xy]
now_feature[3:5, :, :] = xy
now_adjacency = self.graph.get_adjacency(self.all_adjacency[idx-self.prev])
now_A = self.graph.normalize_adjacency(now_adjacency)
return now_feature, now_A, now_mean_xy