def test_subpixel_map():
subpixel_map_estimator = estimator.SubpixelMap(
half_support_window=2, disparity_step=1)
similarities = autograd.Variable(th.Tensor([0.1, 0.4, 0.3, 0.2,
0.3])).view(1, 5, 1, 1)
disparity = subpixel_map_estimator(similarities).squeeze().item()
expected_disparity = 1.52
assert np.isclose(expected_disparity, disparity, atol=1e-4)
subpixel_map_estimator = estimator.SubpixelMap(
half_support_window=2, disparity_step=2)
disparity = subpixel_map_estimator(similarities).squeeze().item()
expected_disparity = 2.124
assert np.isclose(expected_disparity, disparity, atol=1e-4)
def default_with_continuous_fully_connected():
"""Returns default network with temporal convolutions."""
stereo_network = ShallowEventStereo(
temporal_aggregation_module=temporal_aggregation.
ContinuousFullyConnected(),
spatial_aggregation_module=_shallow_spatial_aggregation(),
matching_module=_shallow_matching_module(),
estimator_module=estimator.SubpixelMap(half_support_window=2,
disparity_step=1))
stereo_network.set_maximum_disparity(maximum_disparity=38)
return stereo_network
def default_with_hand_crafted():
"""Returns default network with hand crafted temporal aggregation."""
stereo_network = ShallowEventStereo(
temporal_aggregation_module=Dummy(),
spatial_aggregation_module=_shallow_spatial_aggregation(
number_of_input_features=4),
matching_module=_shallow_matching_module(),
estimator_module=estimator.SubpixelMap(half_support_window=2,
disparity_step=1))
stereo_network.set_maximum_disparity(maximum_disparity=38)
return stereo_network
def default(maximum_disparity=255):
"""Returns network with default parameters."""
network = PdsNetwork(
size_adapter_module=size_adapter.SizeAdapter(),
embedding_module=embedding.Embedding(),
matching_module=matching.Matching(
operation=matching.MatchingOperation(), maximum_disparity=0),
regularization_module=regularization.Regularization(),
estimator_module=estimator.SubpixelMap())
network.set_maximum_disparity(maximum_disparity)
return network
def default_with_hand_crafted(maximum_disparity=63):
"""Returns default network with continuous fully connected."""
stereo_network = DenseDeepEventStereo(
size_adapter_module=size_adapter.SizeAdapter(),
temporal_aggregation_module=Dummy(),
spatial_aggregation_module=embedding.Embedding(
number_of_input_features=4),
matching_module=matching.Matching(
operation=matching.MatchingOperation(), maximum_disparity=0),
regularization_module=regularization.Regularization(),
estimator_module=estimator.SubpixelMap())
stereo_network.set_maximum_disparity(maximum_disparity)
return stereo_network
def default_with_temporal_convolutions(maximum_disparity=63):
"""Returns default network with temporal convolutions."""
stereo_network = DenseDeepEventStereo(
size_adapter_module=size_adapter.SizeAdapter(),
temporal_aggregation_module=temporal_aggregation.
TemporalConvolutional(),
spatial_aggregation_module=embedding.Embedding(
number_of_input_features=64),
matching_module=matching.Matching(
operation=matching.MatchingOperation(), maximum_disparity=0),
regularization_module=regularization.Regularization(),
estimator_module=estimator.SubpixelMap())
stereo_network.set_maximum_disparity(maximum_disparity)
return stereo_network