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Port clip and clip_by_norm op to nn and change API.sepc

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revert-13637-optimize-opyreader
minqiyang 7 years ago
parent 0ff5d8b02a
commit b1448ded40
3 changed files with 98 additions and 121 deletions
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12
paddle/fluid/API.spec
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@ -170,6 +170,12 @@ paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'out', 'axis', 'use_
paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None)) paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None)) paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None)) paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
paddle.fluid.layers.logical_and ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.logical_or ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.logical_xor ArgSpec(args=['x', 'y', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.logical_not ArgSpec(args=['x', 'out', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.clip ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.clip_by_norm ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)) paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None)) paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None) paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
@ -235,12 +241,6 @@ paddle.fluid.layers.is_empty ArgSpec(args=['x', 'cond'], varargs=None, keywords=
paddle.fluid.layers.mean ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None) paddle.fluid.layers.mean ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.mul ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None) paddle.fluid.layers.mul ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None) paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.clip ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.clip_by_norm ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_and ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_or ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_xor ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.logical_not ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None) paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.gaussian_random ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None) paddle.fluid.layers.gaussian_random ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.sampling_id ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None) paddle.fluid.layers.sampling_id ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)

205
python/paddle/fluid/layers/nn.py
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@ -29,114 +29,29 @@ from .. import unique_name
from functools import reduce from functools import reduce
__all__ = [ __all__ = [
'fc', 'fc', 'embedding', 'dynamic_lstm', 'dynamic_lstmp', 'dynamic_gru',
'embedding', 'gru_unit', 'linear_chain_crf', 'crf_decoding', 'cos_sim', 'cross_entropy',
'dynamic_lstm', 'square_error_cost', 'chunk_eval', 'sequence_conv', 'conv2d', 'conv3d',
'dynamic_lstmp', 'sequence_pool', 'sequence_softmax', 'softmax', 'pool2d', 'pool3d',
'dynamic_gru', 'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'conv3d_transpose',
'gru_unit', 'sequence_expand', 'sequence_expand_as', 'sequence_pad', 'lstm_unit',
'linear_chain_crf', 'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min', 'reduce_prod',
'crf_decoding', 'sequence_first_step', 'sequence_last_step', 'dropout', 'split',
'cos_sim', 'ctc_greedy_decoder', 'edit_distance', 'l2_normalize', 'matmul', 'topk',
'cross_entropy', 'warpctc', 'sequence_reshape', 'transpose', 'im2sequence', 'nce',
'square_error_cost', 'hsigmoid', 'beam_search', 'row_conv', 'multiplex', 'layer_norm',
'chunk_eval', 'softmax_with_cross_entropy', 'smooth_l1', 'one_hot',
'sequence_conv', 'autoincreased_step_counter', 'reshape', 'squeeze', 'unsqueeze',
'conv2d', 'lod_reset', 'lrn', 'pad', 'pad_constant_like', 'label_smooth', 'roi_pool',
'conv3d', 'dice_loss', 'image_resize', 'image_resize_short', 'resize_bilinear',
'sequence_pool', 'gather', 'scatter', 'sequence_scatter', 'random_crop', 'mean_iou', 'relu',
'sequence_softmax', 'log', 'crop', 'rank_loss', 'elu', 'relu6', 'pow', 'stanh', 'hard_sigmoid',
'softmax', 'swish', 'prelu', 'brelu', 'leaky_relu', 'soft_relu', 'flatten',
'pool2d', 'sequence_mask', 'stack', 'pad2d', 'unstack', 'sequence_enumerate',
'pool3d', 'expand', 'sequence_concat', 'scale', 'elementwise_add', 'elementwise_div',
'batch_norm', 'elementwise_sub', 'elementwise_mul', 'elementwise_max', 'elementwise_min',
'beam_search_decode', 'elementwise_pow', 'logical_and', 'logical_or', 'logical_xor',
'conv2d_transpose', 'logical_not', 'clip', 'clip_by_norm'
'conv3d_transpose',
'sequence_expand',
'sequence_expand_as',
'sequence_pad',
'lstm_unit',
'reduce_sum',
'reduce_mean',
'reduce_max',
'reduce_min',
'reduce_prod',
'sequence_first_step',
'sequence_last_step',
'dropout',
'split',
'ctc_greedy_decoder',
'edit_distance',
'l2_normalize',
'matmul',
'topk',
'warpctc',
'sequence_reshape',
'transpose',
'im2sequence',
'nce',
'hsigmoid',
'beam_search',
'row_conv',
'multiplex',
'layer_norm',
'softmax_with_cross_entropy',
'smooth_l1',
'one_hot',
'autoincreased_step_counter',
'reshape',
'squeeze',
'unsqueeze',
'lod_reset',
'lrn',
'pad',
'pad_constant_like',
'label_smooth',
'roi_pool',
'dice_loss',
'image_resize',
'image_resize_short',
'resize_bilinear',
'gather',
'scatter',
'sequence_scatter',
'random_crop',
'mean_iou',
'relu',
'log',
'crop',
'rank_loss',
'elu',
'relu6',
'pow',
'stanh',
'hard_sigmoid',
'swish',
'prelu',
'brelu',
'leaky_relu',
'soft_relu',
'flatten',
'sequence_mask',
'stack',
'pad2d',
'unstack',
'sequence_enumerate',
'expand',
'sequence_concat',
'scale',
'elementwise_add',
'elementwise_div',
'elementwise_sub',
'elementwise_mul',
'elementwise_max',
'elementwise_min',
'elementwise_pow',
'logical_and',
'logical_or',
'logical_xor',
'logical_not',
] ]
@ -6622,7 +6537,7 @@ for func in [
]) ])
def _logical_op(op_name, x, y, name=None, out=None, binary_op=True): def _logical_op(op_name, x, y, out=None, name=None, binary_op=True):
helper = LayerHelper(op_name, **locals()) helper = LayerHelper(op_name, **locals())
assert x.dtype == y.dtype assert x.dtype == y.dtype
@ -6645,7 +6560,7 @@ def _logical_op(op_name, x, y, name=None, out=None, binary_op=True):
@templatedoc() @templatedoc()
def logical_and(x, y, name=None, out=None): def logical_and(x, y, out=None, name=None):
""" """
${comment} ${comment}
@ -6664,7 +6579,7 @@ def logical_and(x, y, name=None, out=None):
@templatedoc() @templatedoc()
def logical_or(x, y, name=None, out=None): def logical_or(x, y, out=None, name=None):
""" """
${comment} ${comment}
@ -6683,7 +6598,7 @@ def logical_or(x, y, name=None, out=None):
@templatedoc() @templatedoc()
def logical_xor(x, y, name=None, out=None): def logical_xor(x, y, out=None, name=None):
""" """
${comment} ${comment}
@ -6702,7 +6617,7 @@ def logical_xor(x, y, name=None, out=None):
@templatedoc() @templatedoc()
def logical_not(x, name=None, out=None): def logical_not(x, out=None, name=None):
""" """
${comment} ${comment}
@ -6717,3 +6632,67 @@ def logical_not(x, name=None, out=None):
return _logical_op( return _logical_op(
op_name="logical_not", x=x, y=None, name=name, out=out, binary_op=False) op_name="logical_not", x=x, y=None, name=name, out=out, binary_op=False)
@templatedoc()
def clip(x, min, max, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
min(${min_type}): ${min_comment}
max(${max_type}): ${max_comment}
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
helper = LayerHelper("clip", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
helper.append_op(
type="clip",
inputs={"X": x},
attrs={"min": min,
"max": max},
outputs={"Out": out})
return out
@templatedoc()
def clip_by_norm(x, max_norm, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
max_norm(${max_norm_type}): ${max_norm_comment}
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
helper = LayerHelper("clip_by_norm", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
helper.append_op(
type="clip_by_norm",
inputs={"X": x},
attrs={"max_norm": max_norm},
outputs={"Out": out})
return out

2
python/paddle/fluid/layers/ops.py
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@ -39,8 +39,6 @@ __all__ = [
'mean', 'mean',
'mul', 'mul',
'sigmoid_cross_entropy_with_logits', 'sigmoid_cross_entropy_with_logits',
'clip',
'clip_by_norm',
'uniform_random_batch_size_like', 'uniform_random_batch_size_like',
'gaussian_random', 'gaussian_random',
'sampling_id', 'sampling_id',

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