code stringlengths 3 6.57k |
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np.diag(cosine_similarity(student_logits, teacher_logits) |
print ("Average cosine similarity for these samples: ", np.mean(similarities) |
mean_squared_error(student_logits, teacher_logits) |
print ("Average mean squared error for these samples: ", mse_error) |
np.array(batch_counts) |
np.array(batch_n_dropped) |
np.array(batch_route_prob) |
print('SWITCH BEHAVIOUR:') |
print('Counts Shape: \n', switch_counts.shape) |
print('Counts: \n', switch_counts) |
print('N_dropped: \n', switch_n_dropped) |
print('Route Prob: \n', switch_route_prob) |
torch.Tensor([np.mean(similarities) |
generate_random_params(params) |
choice(params[param]) |
word_embedding_compression(word_embedding_module, d_model) |
word_embedding_module (type torch.Embedding) |
PCA(n_components = d_model) |
pca.fit_transform(word_embedding_matrix.detach() |
cpu() |
numpy() |
torch.from_numpy(compressed_word_embedding_matrix) |
torch.nn.parameter.Parameter(compressed_word_embedding_matrix) |
argparse.ArgumentParser() |
parser.add_argument('-p2', "--path", help="path to input video") |
parser.add_argument('-p1', "--spath", help="path where the images should be stored") |
parser.add_argument('-n', "--num", help="number from which image label naming starts", type=int) |
parser.parse_args() |
cv2.VideoCapture(args.path) |
print(args.num, args.path, args.spath) |
cap.read() |
cv2.imwrite(path+str(num) |
print(path+str(num) |
__init__(self, **kw) |
super() |
__init__(**kw) |
init_justs(self, justs) |
sum(x) |
zip(self.justs, os) |
calc_just(self, justs) |
information (joint angle, speed, and torque) |
_update_base_state(base_state, values) |
preallocate_episode_proto(episode_proto, max_num_steps) |
range(max_num_steps) |
episode_proto.state_action.add() |
range(NUM_MOTORS) |
step_log.motor_states.add() |
_update_base_state(step_log.base_position, [0, 0, 0]) |
_update_base_state(step_log.base_orientation, [0, 0, 0]) |
_update_base_state(step_log.base_angular_vel, [0, 0, 0]) |
update_episode_proto(episode_proto, minitaur, action, step) |
preallocated
(len(episode_proto.state_action) |
len(episode_proto.state_action) |
format(step, max_num_steps) |
minitaur.IsObservationValid() |
minitaur.GetTimeSinceReset() |
int(time_in_seconds) |
int((time_in_seconds - int(time_in_seconds) |
minitaur.GetMotorAngles() |
minitaur.GetMotorVelocities() |
minitaur.GetMotorTorques() |
range(minitaur.num_motors) |
_update_base_state(step_log.base_position, minitaur.GetBasePosition() |
_update_base_state(step_log.base_orientation, minitaur.GetBaseRollPitchYaw() |
minitaur.GetBaseRollPitchYawRate() |
MinitaurLogging(object) |
__init__(self, log_path=None) |
TODO(jietan) |
save_episode(self, episode_proto) |
tf.gfile.Exists(self._log_path) |
tf.gfile.MakeDirs(self._log_path) |
time.time() |
datetime.datetime.fromtimestamp(ts) |
format(time_stamp) |
tf.gfile.Open(log_path, "w") |
f.write(episode_proto.SerializeToString() |
restore_episode(self, log_path) |
tf.gfile.Open(log_path) |
f.read() |
minitaur_logging_pb2.MinitaurEpisode() |
episode_proto.ParseFromString(content) |
TestAreas(TestCase) |
test_empty_file_gives_no_areas(self) |
Popolo({}) |
len(popolo.areas) |
test_single_area_with_name(self) |
Popolo({"areas": [EXAMPLE_AREA]}) |
len(popolo.areas) |
test_area_id(self) |
Popolo({"areas": [EXAMPLE_AREA]}) |
test_area_type(self) |
Popolo({"areas": [EXAMPLE_AREA]}) |
test_area_identifiers(self) |
Popolo({"areas": [EXAMPLE_AREA]}) |
test_area_other_names(self) |
Popolo({"areas": [EXAMPLE_AREA]}) |
test_area_wikidata(self) |
Popolo({"areas": [EXAMPLE_AREA]}) |
test_area_repr(self) |
Popolo({"areas": [EXAMPLE_AREA]}) |
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