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Use batches for SmoothGrad #78

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Use batches for SmoothGrad #78

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efa62f2
get gradients for noisy samples in single batch
lenbrocki Jan 19, 2022
a43d45f
allow GetMask to return batched shape
lenbrocki Jan 19, 2022
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22 changes: 13 additions & 9 deletions saliency/core/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -110,18 +110,22 @@ def GetSmoothedMask(self,
magnitude: If true, computes the sum of squares of gradients instead of
just the sum. Defaults to true.
"""
stdev = stdev_spread * (np.max(x_value) - np.min(x_value))

stdev = stdev_spread * (np.max(x_value) - np.min(x_value))
total_gradients = np.zeros_like(x_value, dtype=np.float32)
for _ in range(nsamples):
noise = np.random.normal(0, stdev, x_value.shape)
x_plus_noise = x_value + noise
grad = self.GetMask(x_plus_noise, call_model_function, call_model_args,
shape = (nsamples,) + x_value.shape
noisy_samples = np.zeros(shape)
for i in range(nsamples):
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You could get rid of the loop completely if you used np.repeat(...) and generating the noise mask for the batched shape.

noise = np.random.normal(0, stdev, x_value.shape)
x_plus_noise = x_value + noise
noisy_samples[i] = x_plus_noise

grads = self.GetMask(noisy_samples, call_model_function, call_model_args,
**kwargs)
if magnitude:
total_gradients += (grad * grad)
else:
total_gradients += grad
if magnitude:
total_gradients = np.sum((grads * grads), axis = 0)
else:
total_gradients = np.sum(grads, axis = 0)

return total_gradients / nsamples

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10 changes: 8 additions & 2 deletions saliency/core/gradients.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,7 +47,10 @@ def GetMask(self, x_value, call_model_function, call_model_args=None):
call_model_args: The arguments that will be passed to the call model
function, for every call of the model.
"""
x_value_batched = np.expand_dims(x_value, axis=0)
if(len(x_value.shape) == 3):
x_value_batched = np.expand_dims(x_value, axis=0)
else:
x_value_batched = x_value
call_model_output = call_model_function(
x_value_batched,
call_model_args=call_model_args,
Expand All @@ -57,4 +60,7 @@ def GetMask(self, x_value, call_model_function, call_model_args=None):
x_value_batched.shape,
self.expected_keys)

return call_model_output[INPUT_OUTPUT_GRADIENTS][0]
if(x_value_batched.shape[0] == 1):
return call_model_output[INPUT_OUTPUT_GRADIENTS][0]
else:
return call_model_output[INPUT_OUTPUT_GRADIENTS]