def make_variables(feats, labels, use_gpu):
# Create the input tensors and target label tensors
# transpose to batch x ch x depth x H x W
feats = feats.transpose(0, 4, 1, 2, 3)
feats = torch.from_numpy(np.float32(feats))
feats[feats == float("-Inf")] = 0
feats[feats == float("Inf")] = 0
# Form the target labels
target = []
# Append the sequence of labels of len (seq_size-1) to the target list for OF.
# Iterate over the batch labels, for each extract seq_size labels and extend
# in the target list
for i in range(labels[0].size(0)):
lbls = [y[i] for y in labels] # get labels of frames (size seq_size)
# for getting batch x 2 sized matrix, add vectors of size 2
if sum(lbls) >= 8:
target.append(1) # action is True
else:
target.append(0)
# Form a wrap into a tensor variable as B X S X I
# target is a vector of batchsize
return utils.create_variable(feats, use_gpu), \
utils.create_variable(torch.LongTensor(target), use_gpu)
def create_embedding(self):
"""
创建embedding variable
:return:
"""
# emb_profile_encode = create_variable('emb_profile_encode', shape=(66, 10))
emb_weekday = create_variable('emb_weekday', shape=(7, 2))
emb_hour = create_variable('emb_hour', shape=(24, 3))
return {'emb_weekday': emb_weekday, 'emb_hour': emb_hour}
def create_embedding(self):
"""
创建embedding variable
:return:
"""
# emb_profile_encode = create_variable('emb_profile_encode', shape=(66, 10))
# 'first_mode', 'max_dist_mode', 'min_dist_mode', 'max_price_mode', 'min_price_mode','max_eta_mode', 'min_eta_mode'
emb_first_mode = create_variable('emb_first_mode', shape=(12, 3))
emb_max_dist_mode = create_variable('emb_max_dist_mode', shape=(13, 3))
emb_min_dist_mode = create_variable('emb_min_dist_mode', shape=(13, 3))
emb_max_price_mode = create_variable('emb_max_price_mode', shape=(13, 3))
emb_min_price_mode = create_variable('emb_min_price_mode', shape=(13, 3))
emb_max_eta_mode = create_variable('emb_max_eta_mode', shape=(13, 3))
emb_min_eta_mode = create_variable('emb_min_eta_mode', shape=(13, 3))
# date
emb_weekday = create_variable('emb_weekday', shape=(7, 2))
emb_hour = create_variable('emb_hour', shape=(24, 3))
return {
'emb_first_mode': emb_first_mode,
'emb_max_dist_mode': emb_max_dist_mode,
'emb_min_dist_mode': emb_min_dist_mode,
'emb_max_price_mode': emb_max_price_mode,
'emb_min_price_mode': emb_min_price_mode,
'emb_max_eta_mode': emb_max_eta_mode,
'emb_min_eta_mode': emb_min_eta_mode,
'emb_weekday': emb_weekday,
'emb_hour': emb_hour}
def _init_hidden(self, batch_size):
#* self.n_directions
hidden = torch.zeros(self.n_layers * self.n_directions, batch_size, \
self.hidden_size)
return utils.create_variable(hidden, self.use_gpu)