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sigmoid and exponential pool performance, accelerate with numpy #1

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sigmoid and exponential pool performance, accelerate with numpy #1

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16 changes: 8 additions & 8 deletions data_processing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -212,28 +212,28 @@ def _process_feats(self, output_reshaped, mask):
# descriptors:
def sigmoid(value):
"""Return the sigmoid of the input."""
return 1.0 / (1.0 + math.exp(-value))
return 1.0 / (1.0 + np.exp(-value))

def exponential(value):
"""Return the exponential of the input."""
return math.exp(value)
return np.exp(value)

# Vectorized calculation of above two functions:
sigmoid_v = np.vectorize(sigmoid)
exponential_v = np.vectorize(exponential)
#sigmoid_v = np.vectorize(sigmoid)
#exponential_v = np.vectorize(exponential)

grid_h, grid_w, _, _ = output_reshaped.shape

anchors = [self.anchors[i] for i in mask]

# Reshape to N, height, width, num_anchors, box_params:
anchors_tensor = np.reshape(anchors, [1, 1, len(anchors), 2])
box_xy = sigmoid_v(output_reshaped[..., :2])
box_wh = exponential_v(output_reshaped[..., 2:4]) * anchors_tensor
box_confidence = sigmoid_v(output_reshaped[..., 4])
box_xy = sigmoid(output_reshaped[..., :2])
box_wh = exponential(output_reshaped[..., 2:4]) * anchors_tensor
box_confidence = sigmoid(output_reshaped[..., 4])

box_confidence = np.expand_dims(box_confidence, axis=-1)
box_class_probs = sigmoid_v(output_reshaped[..., 5:])
box_class_probs = sigmoid(output_reshaped[..., 5:])

col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)
row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)
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