def test_plot_topn_connections_forward(self):
""" Forward top 2 connections """
weights = np.array([[[[-1, 0, 1], [+0, 0, 1]], [[1, 1, 2], [2, 3, 3]],
[[7, 1, 2], [2, 3, 4]]],
[[[+1, 1, 2], [-1, 1, 2]], [[1, 0, 6], [4, 1, 8]],
[[6, 1, 2], [2, 0, 2]]],
[[[-3, 0, 5], [+0, 1, 2]], [[0, 2, 4], [5, 1, 4]],
[[9, 1, 2], [4, 6, 6]]]])
layer = Convo2D('test_2', '', 3, weights, weights)
layer.set_coordinates(10, 0)
prev_layer = Dense('test_prev', '', 2, np.ones((3, 2)), np.ones(
(3, 2))) # weights/grads of other do no matter
prev_layer.set_coordinates(0, 0)
strongest_idx, shapes = layer.plot_topn_connections_forward(
prev_layer, 2, [1])
assert strongest_idx.shape == (2, )
assert (strongest_idx == np.array([1, 2])).all()
assert len(
shapes) == 2 # For each active Dense to each Convolution top 2
assert isinstance(shapes[0], dict)
assert shapes[0]['type'] == 'path'
def test_plot_topn_connections_backward(self):
""" Backward top 2 connections """
# def plot_topn_connections(self, backward_layer, topn, active_units, True)
# 3 x 3 shape filters x 2 with 3 inputs => weights array of shape (3, 3, 2, 3)
weights = np.array([[[[-1, 0, 1], [+0, 0, 1]], [[1, 1, 2], [2, 3, 3]],
[[7, 1, 2], [2, 3, 4]]],
[[[+1, 1, 2], [-1, 1, 2]], [[1, 0, 6], [4, 1, 8]],
[[6, 1, 2], [2, 0, 2]]],
[[[-3, 0, 5], [+0, 1, 2]], [[0, 2, 4], [5, 1, 4]],
[[9, 1, 2], [4, 6, 6]]]])
# Max on convolution filters
# [[9, 2, 6], [5, 6, 8]]
layer = Convo2D('test_1', 3, weights, weights)
layer.set_xoffset(10)
prev_layer = Dense('test_prev', 2, np.ones((3, 2)), np.ones(
(3, 2))) # weights/grads of other do no matter
prev_layer.set_xoffset(0)
strongest_idx, shapes = layer.plot_topn_connections(
prev_layer, 2, [1, 2], True)
assert strongest_idx.shape == (2, )
assert (strongest_idx == np.array([0, 1])).all()
assert len(shapes) == 4 # Each Convolution to the top Dense
assert isinstance(shapes[0], dict)
assert shapes[0]['type'] == 'path'
def test_plot_topn_connections_backward(self):
""" Backward top 2 connections """
# 3 x 3 shape filters x 2 with 3 inputs => weights array of shape (3, 3, 2, 3)
weights = self.weights_convo_3_3_4_3
layer = Convo2D('test_1', '', 3, weights, weights)
layer.set_coordinates(10, 0)
prev_layer = Dense('test_prev', '', 4, np.ones((6, 4)), np.ones(
(6, 4))) # weights/grads of other do no matter
prev_layer.set_coordinates(0, 0)
strongest_idx, shapes = layer.plot_topn_connections_backward(
prev_layer, 2, [1, 2])
assert strongest_idx.shape == (2, )
assert (strongest_idx == np.array([1, 3])).all()
assert len(shapes) == 4 # Each Convolution to the top Dense
assert isinstance(shapes[0], dict)
assert shapes[0]['type'] == 'path'
def test_plot_topn_connections_backward_flatten_stride2d(self):
""" Backward top 2 connections with stride on 2D and flatten """
# 3 x 3 shape filters x 4 with 3 inputs => weights array of shape (3, 3, 4, 3)
weights = self.weights_convo_3_3_4_3
layer = Convo2D('test_1', '', 3, weights, weights,
flatten_output=True) # <--
layer.set_coordinates(10, 0)
# Stride or pooling induce a fractional sampling factor (down-sampling)
layer.append_sampling_factor(1 / np.array([2, 2, 1, 1])) # <--
prev_layer = Dense('test_prev', '', 4, np.ones((6, 4)), np.ones(
(6, 4))) # weights/grads of other do no matter
prev_layer.set_coordinates(0, 0)
strongest_idx, shapes = layer.plot_topn_connections_backward(
prev_layer, 2, [1, 2])
assert strongest_idx.shape == (2, )
assert (strongest_idx == np.array([1, 3])).all() # <--
assert len(shapes) == 4 # Each Convolution to the top Dense
assert isinstance(shapes[0], dict)
assert shapes[0]['type'] == 'path'