diff options
Diffstat (limited to 'tensorflow/contrib/model_pruning/python')
| -rw-r--r-- | tensorflow/contrib/model_pruning/python/pruning.py | 21 | ||||
| -rw-r--r-- | tensorflow/contrib/model_pruning/python/pruning_test.py | 17 |
2 files changed, 30 insertions, 8 deletions
diff --git a/tensorflow/contrib/model_pruning/python/pruning.py b/tensorflow/contrib/model_pruning/python/pruning.py index d16af9da19..86963be4b8 100644 --- a/tensorflow/contrib/model_pruning/python/pruning.py +++ b/tensorflow/contrib/model_pruning/python/pruning.py @@ -523,7 +523,8 @@ class Pruning(object): """Performs block-granular masking of the weights. Block pruning occurs only if the block_height or block_width is > 1 and - if the weight tensor has ndims = 2. Otherwise, elementwise pruning occurs. + if the weight tensor, when squeezed, has ndims = 2. Otherwise, elementwise + pruning occurs. Args: weights: The weight tensor that needs to be masked. threshold: The current threshold value. The function will compute a new @@ -540,7 +541,8 @@ class Pruning(object): Raises: ValueError: if block pooling function is not AVG or MAX """ - if weights.get_shape().ndims != 2 or self._block_dim == [1, 1]: + squeezed_weights = array_ops.squeeze(weights) + if squeezed_weights.get_shape().ndims != 2 or self._block_dim == [1, 1]: return self._update_mask(weights, threshold) if self._block_pooling_function not in ['AVG', 'MAX']: @@ -549,9 +551,11 @@ class Pruning(object): with ops.name_scope(weights.op.name + '_pruning_ops'): abs_weights = math_ops.abs( - array_ops.reshape( - weights, [1, weights.get_shape()[0], - weights.get_shape()[1], 1])) + array_ops.reshape(weights, [ + 1, + squeezed_weights.get_shape()[0], + squeezed_weights.get_shape()[1], 1 + ])) pool_window = [self._block_dim[0], self._block_dim[1]] pooled_weights = nn_ops.pool( abs_weights, @@ -572,9 +576,10 @@ class Pruning(object): array_ops.ones(self._block_dim)) sliced_mask = array_ops.slice( updated_mask, [0, 0], - [weights.get_shape()[0], - weights.get_shape()[1]]) - return smoothed_threshold, sliced_mask + [squeezed_weights.get_shape()[0], + squeezed_weights.get_shape()[1]]) + return smoothed_threshold, array_ops.reshape(sliced_mask, + array_ops.shape(weights)) def _get_mask_assign_ops(self): # Make sure the assignment ops have not already been added to the list diff --git a/tensorflow/contrib/model_pruning/python/pruning_test.py b/tensorflow/contrib/model_pruning/python/pruning_test.py index 1767b4bb94..89e6571319 100644 --- a/tensorflow/contrib/model_pruning/python/pruning_test.py +++ b/tensorflow/contrib/model_pruning/python/pruning_test.py @@ -142,6 +142,23 @@ class PruningTest(test.TestCase): self._blockMasking(param_list + ["block_pooling_function=MAX"], weights_max, expected_mask) + self._blockMasking(param_list + ["block_pooling_function=AVG"], weights_avg, + expected_mask) + + def testBlockMaskingWithHigherDimensions(self): + param_list = ["block_height=2", "block_width=2", "threshold_decay=0"] + + # Weights as in testBlockMasking, but with one extra dimension. + weights_avg = constant_op.constant( + [[[0.1, 0.1, 0.2, 0.2], [0.1, 0.1, 0.2, 0.2], [0.3, 0.3, 0.4, 0.4], + [0.3, 0.3, 0.4, 0.4]]]) + weights_max = constant_op.constant( + [[[0.1, 0.0, 0.2, 0.0], [0.0, -0.1, 0.0, -0.2], [0.3, 0.0, 0.4, 0.0], + [0.0, -0.3, 0.0, -0.4]]]) + expected_mask = [[[0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1], [1, 1, 1, 1]]] + + self._blockMasking(param_list + ["block_pooling_function=MAX"], weights_max, + expected_mask) self._blockMasking(param_list + ["block_pooling_function=AVG"], weights_avg, expected_mask) |