mask[-1] = 1
# train_loader a generator: (data, label)
(data, label) = next(train_loader)
# Return DataTuple(!) and an empty (aux) tuple.
return DataTuple(data, label), MaskAuxTuple(mask.type(torch.uint8))
if __name__ == "__main__":
""" Tests sequence generator - generates and displays a random sample"""
# "Loaded parameters".
from utils.param_interface import ParamInterface
params = ParamInterface()
params.add_default_params({'batch_size': 3, 'start_index': 0, 'stop_index': 54999,
'use_train_data': True, 'mnist_folder': '~/data/mnist'})
# Create problem object.
problem = SequentialPixelMNIST(params)
# Get generator
generator = problem.return_generator()
# Get batch.
num_rows = 28
num_columns = 28
sample_num = 0
data_tuple, _ = next(generator)
x, y = data_tuple
print(x.size())
# Display single sample (0) from batch.
:num_bits] = query_matrix[:self.NUM_QUESTIONS, :num_bits]
return Q
if __name__ == "__main__":
""" Tests Shape-Color-Query - generates and displays a sample"""
# "Loaded parameters".
from utils.param_interface import ParamInterface
params = ParamInterface()
params.add_default_params({
'batch_size': 10,
'data_folder': '~/data/shape-color-query/',
'data_filename': 'training.hy',
'shuffle': True,
"regenerate": True,
'use_train_data': True,
'dataset_size': 100,
'img_size': 224})
# Configure logger.
logging.basicConfig(level=logging.DEBUG)
logger.debug("params: {}".format(params))
# Create problem object.
problem = ShapeColorQuery(params)
# Get generator
generator = problem.return_generator()
# "Loaded parameters".
from utils.param_interface import ParamInterface
params = ParamInterface()
params.add_default_params({
'num_control_bits': 3,
'num_data_bits': 8, # input and output size
'encoding_bit': 0,
'solving_bit': 1,
# controller parameters
'controller': {
'name': 'rnn',
'hidden_state_size': 20,
'num_layers': 1,
'non_linearity': 'sigmoid'
},
'mae_interface': {
'shift_size': 3
}, # encoder interface parameters
'mas_interface': {
'shift_size': 3
}, # solver interface parameters
# memory parameters
'memory': {
'num_addresses': -1,
'num_content_bits': 11
},
'visualization_mode': 2
})
logger.debug("params: {}".format(params))
input_size = params["num_control_bits"] + params["num_data_bits"]
# "Loaded parameters".
from utils.param_interface import ParamInterface
params = ParamInterface()
params.add_default_params({
'num_control_bits': 2,
'num_data_bits': 8, # input and output size
# controller parameters
'controller': {
'name': 'ffgru',
'hidden_state_size': 5,
'num_layers': 1,
'non_linearity': 'none',
'ff_output_size': 5
},
# interface parameters
'interface': {
'num_read_heads': 2,
'shift_size': 3
},
# memory parameters
'memory': {
'num_addresses': 4,
'num_content_bits': 7
},
'visualization_mode': 2
})
logger.debug("params: {}".format(params))
input_size = params["num_control_bits"] + params["num_data_bits"]
output_size = params["num_data_bits"]
# Return DataTuple(!) and an empty (aux) tuple.
return DataTuple(data_padded, label), LabelAuxTuple(class_names)
if __name__ == "__main__":
""" Tests sequence generator - generates and displays a random sample"""
# "Loaded parameters".
from utils.param_interface import ParamInterface
params = ParamInterface()
params.add_default_params({
'batch_size': 2,
'start_index': 0,
'stop_index': 54999,
'use_train_data': True,
'mnist_folder': '~/data/mnist',
'padding': [4, 4, 3, 3],
'up_scaling': False
})
# Create problem object.
problem = MNIST(params)
# Get generator
generator = problem.return_generator()
# Get batch.
dt, at = next(generator)
# Display single sample (0) from batch.
problem.show_sample(dt, at, 0)
return DataTuple(data_padded, label), LabelAuxTuple(class_names)
if __name__ == "__main__":
""" Tests sequence generator - generates and displays a random sample"""
np.random.seed(0)
torch.manual_seed(0)
# "Loaded parameters".
from utils.param_interface import ParamInterface
params = ParamInterface()
params.add_default_params({
'batch_size': 2,
'start_index': 0,
'stop_index': 40000,
'use_train_data': True,
'folder': '~/data/cifar10',
'padding': [0, 0, 0, 0],
'up_scaling': True
})
# Create problem object.
problem = CIFAR10(params)
# Get generator
generator = problem.return_generator()
# Get batch.
dt, at = next(generator)
# Display single sample (0) from batch.
problem.show_sample(dt, at, 0)
