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未验证 提交 a508e725 编写于 作者: Z zhiboniu 提交者： GitHub
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unset fluid api in nn.functional (#34114)

上级 1c95631f
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Showing with 92 addition and 92 deletion
python/paddle/nn/functional/common.py
浏览文件 @ a508e725
...@@ -16,18 +16,21 @@ import warnings ...@@ -16,18 +16,21 @@ import warnings
import paddle import paddle
from ...fluid.framework import in_dygraph_mode, default_main_program from ...fluid.framework import in_dygraph_mode, default_main_program
from paddle.fluid.layer_helper import LayerHelper from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.layers.tensor import Variable, fill_constant, zeros, concat from paddle.fluid.layers.tensor import fill_constant
from ...tensor import concat
from ...tensor.creation import zeros
from paddle.static import Variable
from ...fluid.layers import core from ...fluid.layers import core
from ...fluid import dygraph_utils from ...fluid import dygraph_utils
# TODO: define the common functions to build a neural network # TODO: define the common functions to build a neural network
from ...fluid.layers import unfold # noqa: F401 from ...fluid.layers import unfold # noqa: F401
from ...fluid.layers import squeeze from ...tensor.manipulation import squeeze
from ...fluid.layers import unsqueeze from ...tensor.manipulation import unsqueeze
from ...tensor import clip from ...tensor import clip
from ...tensor import sum from ...tensor import sum
from ...tensor import sqrt from ...tensor import sqrt
from ...fluid.data_feeder import check_variable_and_dtype, check_dtype from ...fluid.data_feeder import check_variable_and_dtype, check_dtype
from ...fluid.framework import Variable, in_dygraph_mode, _varbase_creator from ...fluid.framework import in_dygraph_mode, _varbase_creator
from ...fluid.framework import in_dygraph_mode from ...fluid.framework import in_dygraph_mode
from ...fluid import core, dygraph_utils from ...fluid import core, dygraph_utils
...@@ -927,9 +930,9 @@ def dropout(x, ...@@ -927,9 +930,9 @@ def dropout(x,
keep_prob = 1 - p keep_prob = 1 - p
if training: if training:
if p == 1.: if p == 1.:
return layers.scale(x, scale=0.) return paddle.scale(x, scale=0.)
scale_input = layers.scale( scale_input = paddle.scale(
x, scale=1 / keep_prob) if mode == 'upscale_in_train' else x x, scale=1 / keep_prob) if mode == 'upscale_in_train' else x
#get mask shape #get mask shape
...@@ -947,17 +950,17 @@ def dropout(x, ...@@ -947,17 +950,17 @@ def dropout(x,
mask_shape[i] = input_shape[i] mask_shape[i] = input_shape[i]
#get mask #get mask
random_tensor = layers.uniform_random( random_tensor = paddle.uniform(
mask_shape, dtype='float32', min=0., max=1.0) mask_shape, dtype='float32', min=0., max=1.0)
p = layers.fill_constant(shape=[1], dtype='float32', value=p) p = layers.fill_constant(shape=[1], dtype='float32', value=p)
keep_mask = layers.greater_equal(random_tensor, p) keep_mask = paddle.greater_equal(random_tensor, p)
scale_input = layers.cast(scale_input, dtype) scale_input = paddle.cast(scale_input, dtype)
keep_mask = layers.cast(keep_mask, dtype) keep_mask = paddle.cast(keep_mask, dtype)
ret = paddle.multiply(scale_input, keep_mask, name=name) ret = paddle.multiply(scale_input, keep_mask, name=name)
return ret return ret
else: # test else: # test
ret = layers.scale( ret = paddle.scale(
x, scale=keep_prob) if mode == 'downscale_in_infer' else x x, scale=keep_prob) if mode == 'downscale_in_infer' else x
return ret return ret
...@@ -1113,7 +1116,7 @@ def alpha_dropout(x, p=0.5, training=True, name=None): ...@@ -1113,7 +1116,7 @@ def alpha_dropout(x, p=0.5, training=True, name=None):
if training: if training:
if p == 1: if p == 1:
return layers.scale(x, scale=0.) return paddle.scale(x, scale=0.)
#get transformation params #get transformation params
alpha = 1.6732632423543772848170429916717 alpha = 1.6732632423543772848170429916717
scale = 1.0507009873554804934193349852946 scale = 1.0507009873554804934193349852946
...@@ -1125,23 +1128,22 @@ def alpha_dropout(x, p=0.5, training=True, name=None): ...@@ -1125,23 +1128,22 @@ def alpha_dropout(x, p=0.5, training=True, name=None):
input_shape = x.shape input_shape = x.shape
#get mask #get mask
random_tensor = layers.uniform_random( random_tensor = paddle.uniform(
input_shape, dtype='float32', min=0., max=1.0) input_shape, dtype='float32', min=0., max=1.0)
p = layers.fill_constant(shape=[1], dtype='float32', value=p) p = layers.fill_constant(shape=[1], dtype='float32', value=p)
keep_mask = layers.greater_equal(random_tensor, p) keep_mask = paddle.greater_equal(random_tensor, p)
keep_mask = layers.cast(keep_mask, dtype) keep_mask = paddle.cast(keep_mask, dtype)
drop_mask = layers.elementwise_sub( drop_mask = paddle.subtract(
layers.fill_constant( layers.fill_constant(
shape=input_shape, dtype=dtype, value=1.), shape=input_shape, dtype=dtype, value=1.),
keep_mask) keep_mask)
#apply mask #apply mask
b = layers.fill_constant(shape=[1], dtype=dtype, value=b) b = layers.fill_constant(shape=[1], dtype=dtype, value=b)
y = layers.elementwise_add( y = paddle.add(paddle.multiply(x, keep_mask),
paddle.multiply(x, keep_mask), paddle.scale(
layers.scale(
drop_mask, scale=alpha_p)) drop_mask, scale=alpha_p))
res = layers.elementwise_add(layers.scale(y, scale=a), b, name=name) res = paddle.add(paddle.scale(y, scale=a), b, name=name)
return res return res
else: # test else: # test
return x return x
...@@ -1277,42 +1279,42 @@ def pad(x, pad, mode='constant', value=0, data_format="NCHW", name=None): ...@@ -1277,42 +1279,42 @@ def pad(x, pad, mode='constant', value=0, data_format="NCHW", name=None):
if x_dim == 3: if x_dim == 3:
pad = concat([zeros((4, ), dtype="int32"), pad], axis=0) pad = concat([zeros((4, ), dtype="int32"), pad], axis=0)
unsqueezed_dim = [3, 4] unsqueezed_dim = [3, 4]
x = unsqueeze(x, axes=unsqueezed_dim) x = unsqueeze(x, axis=unsqueezed_dim)
elif x_dim == 4: elif x_dim == 4:
pad = concat([pad, zeros((2, ), dtype="int32")], axis=0) pad = concat([pad, zeros((2, ), dtype="int32")], axis=0)
unsqueezed_dim = [2] unsqueezed_dim = [2]
x = unsqueeze(x, axes=unsqueezed_dim) x = unsqueeze(x, axis=unsqueezed_dim)
elif data_format in ["NLC", "NHWC", "NDHWC"]: elif data_format in ["NLC", "NHWC", "NDHWC"]:
data_format = "NDHWC" data_format = "NDHWC"
if x_dim == 3: if x_dim == 3:
pad = concat([zeros((4, ), dtype="int32"), pad], axis=0) pad = concat([zeros((4, ), dtype="int32"), pad], axis=0)
unsqueezed_dim = [2, 3] unsqueezed_dim = [2, 3]
x = unsqueeze(x, axes=unsqueezed_dim) x = unsqueeze(x, axis=unsqueezed_dim)
elif x_dim == 4: elif x_dim == 4:
pad = concat([pad, zeros((2, ), dtype="int32")], axis=0) pad = concat([pad, zeros((2, ), dtype="int32")], axis=0)
unsqueezed_dim = [1] unsqueezed_dim = [1]
x = unsqueeze(x, axes=unsqueezed_dim) x = unsqueeze(x, axis=unsqueezed_dim)
else: else:
if data_format in ["NCL", "NCHW", "NCDHW"]: if data_format in ["NCL", "NCHW", "NCDHW"]:
data_format = "NCDHW" data_format = "NCDHW"
if x_dim == 3: if x_dim == 3:
pad = [0, 0, 0, 0] + pad pad = [0, 0, 0, 0] + pad
unsqueezed_dim = [3, 4] unsqueezed_dim = [3, 4]
x = unsqueeze(x, axes=unsqueezed_dim) x = unsqueeze(x, axis=unsqueezed_dim)
elif x_dim == 4: elif x_dim == 4:
pad = pad + [0, 0] pad = pad + [0, 0]
unsqueezed_dim = [2] unsqueezed_dim = [2]
x = unsqueeze(x, axes=unsqueezed_dim) x = unsqueeze(x, axis=unsqueezed_dim)
elif data_format in ["NLC", "NHWC", "NDHWC"]: elif data_format in ["NLC", "NHWC", "NDHWC"]:
data_format = "NDHWC" data_format = "NDHWC"
if x_dim == 3: if x_dim == 3:
pad = [0, 0, 0, 0] + pad pad = [0, 0, 0, 0] + pad
unsqueezed_dim = [2, 3] unsqueezed_dim = [2, 3]
x = unsqueeze(x, axes=unsqueezed_dim) x = unsqueeze(x, axis=unsqueezed_dim)
elif x_dim == 4: elif x_dim == 4:
pad = pad + [0, 0] pad = pad + [0, 0]
unsqueezed_dim = [1] unsqueezed_dim = [1]
x = unsqueeze(x, axes=unsqueezed_dim) x = unsqueeze(x, axis=unsqueezed_dim)
if in_dygraph_mode(): if in_dygraph_mode():
if isinstance(pad, Variable): if isinstance(pad, Variable):
...@@ -1336,7 +1338,7 @@ def pad(x, pad, mode='constant', value=0, data_format="NCHW", name=None): ...@@ -1336,7 +1338,7 @@ def pad(x, pad, mode='constant', value=0, data_format="NCHW", name=None):
type='pad3d', inputs=inputs, outputs={"Out": out}, attrs=attrs) type='pad3d', inputs=inputs, outputs={"Out": out}, attrs=attrs)
if len(unsqueezed_dim) != 0: if len(unsqueezed_dim) != 0:
out = squeeze(out, axes=unsqueezed_dim) out = squeeze(out, axis=unsqueezed_dim)
return out return out
......
python/paddle/nn/functional/conv.py
浏览文件 @ a508e725
...@@ -16,13 +16,17 @@ from paddle.fluid.framework import _global_flags ...@@ -16,13 +16,17 @@ from paddle.fluid.framework import _global_flags
import numpy as np import numpy as np
from ...device import get_cudnn_version from ...device import get_cudnn_version
from ...fluid.framework import Variable, in_dygraph_mode from ...fluid.framework import in_dygraph_mode
from ...static import Variable
from ...fluid import core, dygraph_utils, get_flags from ...fluid import core, dygraph_utils, get_flags
from ...fluid.layers import nn, utils from ...fluid.layers.utils import convert_to_list, _is_symmetric_padding
from ...fluid.data_feeder import check_variable_and_dtype from ...fluid.data_feeder import check_variable_and_dtype
from ...fluid.param_attr import ParamAttr from ...framework import ParamAttr
from ...fluid.layer_helper import LayerHelper from ...fluid.layer_helper import LayerHelper
from paddle import _C_ops from paddle import _C_ops
from ...tensor.manipulation import unsqueeze, squeeze
from ...tensor.math import add
from ...fluid.layers import nn
__all__ = [] __all__ = []
...@@ -69,24 +73,24 @@ def _update_padding_nd(padding, channel_last, num_dims): ...@@ -69,24 +73,24 @@ def _update_padding_nd(padding, channel_last, num_dims):
padding_algorithm = "EXPLICIT" padding_algorithm = "EXPLICIT"
padding = _exclude_padding_in_batch_and_channel(padding, padding = _exclude_padding_in_batch_and_channel(padding,
channel_last) channel_last)
if utils._is_symmetric_padding(padding, num_dims): if _is_symmetric_padding(padding, num_dims):
padding = padding[0::2] padding = padding[0::2]
# for padding like [pad_before, pad_after, pad_before, pad_after, ...] # for padding like [pad_before, pad_after, pad_before, pad_after, ...]
elif len(padding) == 2 * num_dims and isinstance(padding[0], int): elif len(padding) == 2 * num_dims and isinstance(padding[0], int):
padding_algorithm = "EXPLICIT" padding_algorithm = "EXPLICIT"
padding = utils.convert_to_list(padding, 2 * num_dims, 'padding') padding = convert_to_list(padding, 2 * num_dims, 'padding')
if utils._is_symmetric_padding(padding, num_dims): if _is_symmetric_padding(padding, num_dims):
padding = padding[0::2] padding = padding[0::2]
# for padding like [pad_d1, pad_d2, ...] # for padding like [pad_d1, pad_d2, ...]
elif len(padding) == num_dims and isinstance(padding[0], int): elif len(padding) == num_dims and isinstance(padding[0], int):
padding_algorithm = "EXPLICIT" padding_algorithm = "EXPLICIT"
padding = utils.convert_to_list(padding, num_dims, 'padding') padding = convert_to_list(padding, num_dims, 'padding')
else: else:
raise ValueError("In valid padding: {}".format(padding)) raise ValueError("In valid padding: {}".format(padding))
# for integer padding # for integer padding
else: else:
padding_algorithm = "EXPLICIT" padding_algorithm = "EXPLICIT"
padding = utils.convert_to_list(padding, num_dims, 'padding') padding = convert_to_list(padding, num_dims, 'padding')
if not all([p >= 0 for p in padding]): if not all([p >= 0 for p in padding]):
raise ValueError( raise ValueError(
"Invalid padding, all value should be larger than or equal to 0, but received: {}". "Invalid padding, all value should be larger than or equal to 0, but received: {}".
...@@ -323,8 +327,8 @@ def conv1d(x, ...@@ -323,8 +327,8 @@ def conv1d(x,
"The size of padding's dimension should be 1 or 2. But got padding={}". "The size of padding's dimension should be 1 or 2. But got padding={}".
format(padding)) format(padding))
stride = utils.convert_to_list(stride, 1, 'stride') + [1] stride = convert_to_list(stride, 1, 'stride') + [1]
dilation = utils.convert_to_list(dilation, 1, 'dilation') + [1] dilation = convert_to_list(dilation, 1, 'dilation') + [1]
l_type = "conv2d" l_type = "conv2d"
if (num_channels == groups and num_channels != 1 and if (num_channels == groups and num_channels != 1 and
...@@ -333,8 +337,8 @@ def conv1d(x, ...@@ -333,8 +337,8 @@ def conv1d(x,
use_cudnn = False use_cudnn = False
squeeze_aixs = -2 if channel_last else -1 squeeze_aixs = -2 if channel_last else -1
x = nn.unsqueeze(input=x, axes=[squeeze_aixs]) x = unsqueeze(x, axis=[squeeze_aixs])
weight = nn.unsqueeze(input=weight, axes=[-1]) weight = unsqueeze(weight, axis=[-1])
if in_dygraph_mode(): if in_dygraph_mode():
attrs = ('strides', stride, 'paddings', padding, 'dilations', dilation, attrs = ('strides', stride, 'paddings', padding, 'dilations', dilation,
'groups', groups, 'use_cudnn', use_cudnn, 'use_mkldnn', False, 'groups', groups, 'use_cudnn', use_cudnn, 'use_mkldnn', False,
...@@ -366,7 +370,7 @@ def conv1d(x, ...@@ -366,7 +370,7 @@ def conv1d(x,
type=l_type, inputs=inputs, outputs=outputs, attrs=attrs) type=l_type, inputs=inputs, outputs=outputs, attrs=attrs)
if bias is not None: if bias is not None:
out = nn.elementwise_add(out, bias, axis=channel_dim) out = nn.elementwise_add(out, bias, axis=channel_dim)
out = nn.squeeze(input=out, axes=[squeeze_aixs]) out = squeeze(out, axis=[squeeze_aixs])
return out return out
...@@ -530,8 +534,8 @@ def conv2d(x, ...@@ -530,8 +534,8 @@ def conv2d(x,
# update attrs # update attrs
padding, padding_algorithm = _update_padding_nd(padding, channel_last, 2) padding, padding_algorithm = _update_padding_nd(padding, channel_last, 2)
stride = utils.convert_to_list(stride, 2, 'stride') stride = convert_to_list(stride, 2, 'stride')
dilation = utils.convert_to_list(dilation, 2, 'dilation') dilation = convert_to_list(dilation, 2, 'dilation')
l_type = "conv2d" l_type = "conv2d"
if (num_channels == groups and num_channels != 1 and if (num_channels == groups and num_channels != 1 and
...@@ -730,8 +734,8 @@ def conv1d_transpose(x, ...@@ -730,8 +734,8 @@ def conv1d_transpose(x,
"The size of padding's dimension should 1 or 2. But got padding={}". "The size of padding's dimension should 1 or 2. But got padding={}".
format(padding)) format(padding))
stride = utils.convert_to_list(stride, 1, 'stride') + [1] stride = convert_to_list(stride, 1, 'stride') + [1]
dilation = utils.convert_to_list(dilation, 1, 'dilation') + [1] dilation = convert_to_list(dilation, 1, 'dilation') + [1]
if output_size is None: if output_size is None:
output_size = [] output_size = []
...@@ -740,8 +744,7 @@ def conv1d_transpose(x, ...@@ -740,8 +744,7 @@ def conv1d_transpose(x,
raise ValueError('output_padding option is mutually exclusive with ' raise ValueError('output_padding option is mutually exclusive with '
'output_size') 'output_size')
if isinstance(output_size, (list, tuple, int)): if isinstance(output_size, (list, tuple, int)):
output_size = utils.convert_to_list(output_size, 1, output_size = convert_to_list(output_size, 1, 'output_size') + [1]
'output_size') + [1]
else: else:
raise ValueError( raise ValueError(
"output_size should be int, or list, tuple of ints") "output_size should be int, or list, tuple of ints")
...@@ -749,7 +752,7 @@ def conv1d_transpose(x, ...@@ -749,7 +752,7 @@ def conv1d_transpose(x,
if output_padding == 0: if output_padding == 0:
output_padding = [] output_padding = []
else: else:
output_padding = utils.convert_to_list(output_padding, 1, output_padding = convert_to_list(output_padding, 1,
'output_padding') + [0] 'output_padding') + [0]
if len(output_padding) > 0 and output_padding[0] > stride[0]: if len(output_padding) > 0 and output_padding[0] > stride[0]:
...@@ -768,8 +771,8 @@ def conv1d_transpose(x, ...@@ -768,8 +771,8 @@ def conv1d_transpose(x,
squeeze_axis = -2 if channel_last else -1 squeeze_axis = -2 if channel_last else -1
conv2d_data_format = "NHWC" if channel_last else "NCHW" conv2d_data_format = "NHWC" if channel_last else "NCHW"
x = nn.unsqueeze(input=x, axes=[squeeze_axis]) x = unsqueeze(x, axis=[squeeze_axis])
weight = nn.unsqueeze(input=weight, axes=[-1]) weight = unsqueeze(weight, axis=[-1])
if in_dygraph_mode(): if in_dygraph_mode():
attrs = ('output_padding', output_padding, 'output_size', output_size, attrs = ('output_padding', output_padding, 'output_size', output_size,
...@@ -803,7 +806,7 @@ def conv1d_transpose(x, ...@@ -803,7 +806,7 @@ def conv1d_transpose(x,
if bias is not None: if bias is not None:
out = nn.elementwise_add(out, bias, axis=channel_dim) out = nn.elementwise_add(out, bias, axis=channel_dim)
out = nn.squeeze(input=out, axes=[squeeze_axis]) out = squeeze(out, axis=[squeeze_axis])
return out return out
...@@ -979,8 +982,8 @@ def conv2d_transpose(x, ...@@ -979,8 +982,8 @@ def conv2d_transpose(x,
# update attrs # update attrs
padding, padding_algorithm = _update_padding_nd(padding, channel_last, 2) padding, padding_algorithm = _update_padding_nd(padding, channel_last, 2)
stride = utils.convert_to_list(stride, 2, 'stride') stride = convert_to_list(stride, 2, 'stride')
dilation = utils.convert_to_list(dilation, 2, 'dilation') dilation = convert_to_list(dilation, 2, 'dilation')
if output_size is None: if output_size is None:
output_size = [] output_size = []
...@@ -989,7 +992,7 @@ def conv2d_transpose(x, ...@@ -989,7 +992,7 @@ def conv2d_transpose(x,
raise ValueError('output_padding option is mutually exclusive with ' raise ValueError('output_padding option is mutually exclusive with '
'output_size') 'output_size')
if isinstance(output_size, (list, tuple, int)): if isinstance(output_size, (list, tuple, int)):
output_size = utils.convert_to_list(output_size, 2, 'output_size') output_size = convert_to_list(output_size, 2, 'output_size')
else: else:
raise ValueError( raise ValueError(
"output_size should be int, or list, tuple of ints") "output_size should be int, or list, tuple of ints")
...@@ -997,8 +1000,7 @@ def conv2d_transpose(x, ...@@ -997,8 +1000,7 @@ def conv2d_transpose(x,
if output_padding == 0: if output_padding == 0:
output_padding = [] output_padding = []
else: else:
output_padding = utils.convert_to_list(output_padding, 2, output_padding = convert_to_list(output_padding, 2, 'output_padding')
'output_padding')
op_type = 'conv2d_transpose' op_type = 'conv2d_transpose'
num_filters = weight.shape[1] num_filters = weight.shape[1]
...@@ -1187,8 +1189,8 @@ def conv3d(x, ...@@ -1187,8 +1189,8 @@ def conv3d(x,
cudnn_version is not None) else False cudnn_version is not None) else False
padding, padding_algorithm = _update_padding_nd(padding, channel_last, 3) padding, padding_algorithm = _update_padding_nd(padding, channel_last, 3)
stride = utils.convert_to_list(stride, 3, 'stride') stride = convert_to_list(stride, 3, 'stride')
dilation = utils.convert_to_list(dilation, 3, 'dilation') dilation = convert_to_list(dilation, 3, 'dilation')
op_type = "conv3d" op_type = "conv3d"
return _conv_nd(x, weight, bias, stride, padding, padding_algorithm, return _conv_nd(x, weight, bias, stride, padding, padding_algorithm,
...@@ -1369,8 +1371,8 @@ def conv3d_transpose(x, ...@@ -1369,8 +1371,8 @@ def conv3d_transpose(x,
groups)) groups))
padding, padding_algorithm = _update_padding_nd(padding, channel_last, 3) padding, padding_algorithm = _update_padding_nd(padding, channel_last, 3)
stride = utils.convert_to_list(stride, 3, 'stride') stride = convert_to_list(stride, 3, 'stride')
dilation = utils.convert_to_list(dilation, 3, 'dilation') dilation = convert_to_list(dilation, 3, 'dilation')
if output_size is None: if output_size is None:
output_size = [] output_size = []
else: else:
...@@ -1378,7 +1380,7 @@ def conv3d_transpose(x, ...@@ -1378,7 +1380,7 @@ def conv3d_transpose(x,
raise ValueError('output_padding option is mutually exclusive with ' raise ValueError('output_padding option is mutually exclusive with '
'output_size') 'output_size')
if isinstance(output_size, (list, tuple, int)): if isinstance(output_size, (list, tuple, int)):
output_size = utils.convert_to_list(output_size, 3, 'output_size') output_size = convert_to_list(output_size, 3, 'output_size')
else: else:
raise ValueError( raise ValueError(
"output_size should be int, or list, tuple of ints") "output_size should be int, or list, tuple of ints")
...@@ -1386,8 +1388,7 @@ def conv3d_transpose(x, ...@@ -1386,8 +1388,7 @@ def conv3d_transpose(x,
if output_padding == 0: if output_padding == 0:
output_padding = [] output_padding = []
else: else:
output_padding = utils.convert_to_list(output_padding, 3, output_padding = convert_to_list(output_padding, 3, 'output_padding')
'output_padding')
cudnn_version = get_cudnn_version() cudnn_version = get_cudnn_version()
......
python/paddle/nn/functional/extension.py
浏览文件 @ a508e725
...@@ -17,8 +17,9 @@ ...@@ -17,8 +17,9 @@
import numpy as np import numpy as np
from ...fluid.data_feeder import check_dtype from ...fluid.data_feeder import check_dtype
from ...fluid.layer_helper import LayerHelper from ...fluid.layer_helper import LayerHelper
from ...fluid.framework import Variable, in_dygraph_mode from ...fluid.framework import in_dygraph_mode
from ...fluid.layers.tensor import assign from ...static import Variable
from ...tensor.creation import assign
from ...fluid import core, dygraph_utils from ...fluid import core, dygraph_utils
from ...fluid.layers.layer_function_generator import templatedoc from ...fluid.layers.layer_function_generator import templatedoc
from ...fluid.layers.sequence_lod import sequence_mask from ...fluid.layers.sequence_lod import sequence_mask
......
python/paddle/nn/functional/input.py
浏览文件 @ a508e725
...@@ -14,7 +14,8 @@ ...@@ -14,7 +14,8 @@
from __future__ import print_function from __future__ import print_function
import warnings import warnings
from ...fluid.framework import Variable, in_dygraph_mode from ...fluid.framework import in_dygraph_mode
from ...static import Variable
from ...fluid.layer_helper import LayerHelper from ...fluid.layer_helper import LayerHelper
from ...fluid.layers import core from ...fluid.layers import core
from ...fluid.data_feeder import check_variable_and_dtype, check_dtype from ...fluid.data_feeder import check_variable_and_dtype, check_dtype
......
python/paddle/nn/functional/loss.py
浏览文件 @ a508e725
...@@ -27,7 +27,7 @@ from ...fluid.layers.nn import _elementwise_op_in_dygraph ...@@ -27,7 +27,7 @@ from ...fluid.layers.nn import _elementwise_op_in_dygraph
from ...fluid.layers import dice_loss # noqa: F401 from ...fluid.layers import dice_loss # noqa: F401
from ...fluid.layers import log_loss # noqa: F401 from ...fluid.layers import log_loss # noqa: F401
from ...fluid.layers import npair_loss # noqa: F401 from ...fluid.layers import npair_loss # noqa: F401
from ...fluid.layers import reshape from ...tensor.manipulation import reshape
from ...fluid.layers import softmax_with_cross_entropy as fluid_softmax_with_cross_entropy from ...fluid.layers import softmax_with_cross_entropy as fluid_softmax_with_cross_entropy
from ...fluid.layers import square_error_cost # noqa: F401 from ...fluid.layers import square_error_cost # noqa: F401
...@@ -36,7 +36,7 @@ from ...fluid.layers import huber_loss ...@@ -36,7 +36,7 @@ from ...fluid.layers import huber_loss
from ...fluid.layer_helper import LayerHelper from ...fluid.layer_helper import LayerHelper
from ...fluid.framework import in_dygraph_mode from ...fluid.framework import in_dygraph_mode
from ...fluid.framework import _varbase_creator from ...fluid.framework import _varbase_creator
from ...fluid.framework import Variable from ...static import Variable
from paddle.utils import deprecated from paddle.utils import deprecated
from paddle import _C_ops from paddle import _C_ops
...@@ -291,9 +291,7 @@ def binary_cross_entropy_with_logits(logit, ...@@ -291,9 +291,7 @@ def binary_cross_entropy_with_logits(logit,
pos_weight, 'pos_weight', ['float32', 'float64'], pos_weight, 'pos_weight', ['float32', 'float64'],
'binary_cross_entropy_with_logits') 'binary_cross_entropy_with_logits')
log_weight = paddle.add( log_weight = paddle.add(
paddle.multiply( paddle.multiply(label, paddle.subtract(pos_weight, one)), one)
label, paddle.fluid.layers.elementwise_sub(pos_weight, one)),
one)
pos_weight_name = name if reduction == 'none' and weight is None else None pos_weight_name = name if reduction == 'none' and weight is None else None
out = paddle.multiply(out, log_weight, name=pos_weight_name) out = paddle.multiply(out, log_weight, name=pos_weight_name)
...@@ -515,9 +513,9 @@ def smooth_l1_loss(input, label, reduction='mean', delta=1.0, name=None): ...@@ -515,9 +513,9 @@ def smooth_l1_loss(input, label, reduction='mean', delta=1.0, name=None):
if reduction == 'none': if reduction == 'none':
return out return out
elif reduction == 'mean': elif reduction == 'mean':
return fluid.layers.reduce_mean(out) return paddle.mean(out)
elif reduction == 'sum': elif reduction == 'sum':
return fluid.layers.reduce_sum(out) return paddle.sum(out)
def margin_ranking_loss(input, def margin_ranking_loss(input,
...@@ -592,7 +590,7 @@ def margin_ranking_loss(input, ...@@ -592,7 +590,7 @@ def margin_ranking_loss(input,
fluid.data_feeder.check_variable_and_dtype( fluid.data_feeder.check_variable_and_dtype(
label, 'label', ['float32', 'float64'], 'margin_rank_loss') label, 'label', ['float32', 'float64'], 'margin_rank_loss')
out = paddle.fluid.layers.elementwise_sub(other, input) out = paddle.subtract(other, input)
out = paddle.multiply(out, label) out = paddle.multiply(out, label)
if margin != 0.0: if margin != 0.0:
...@@ -898,11 +896,11 @@ def kl_div(input, label, reduction='mean', name=None): ...@@ -898,11 +896,11 @@ def kl_div(input, label, reduction='mean', name=None):
if fluid.data_feeder.convert_dtype( if fluid.data_feeder.convert_dtype(
input.dtype) == 'float32' and fluid.data_feeder.convert_dtype( input.dtype) == 'float32' and fluid.data_feeder.convert_dtype(
label.dtype) == 'float64': label.dtype) == 'float64':
input = fluid.layers.cast(input, 'float64') input = paddle.cast(input, 'float64')
elif fluid.data_feeder.convert_dtype( elif fluid.data_feeder.convert_dtype(
input.dtype) == 'float64' and fluid.data_feeder.convert_dtype( input.dtype) == 'float64' and fluid.data_feeder.convert_dtype(
label.dtype) == 'float32': label.dtype) == 'float32':
label = fluid.layers.cast(label, 'float64') label = paddle.cast(label, 'float64')
if paddle.in_dynamic_mode(): if paddle.in_dynamic_mode():
out = _C_ops.kldiv_loss(input, label, 'reduction', reduction) out = _C_ops.kldiv_loss(input, label, 'reduction', reduction)
...@@ -988,16 +986,12 @@ def mse_loss(input, label, reduction='mean', name=None): ...@@ -988,16 +986,12 @@ def mse_loss(input, label, reduction='mean', name=None):
label, 'label', ['float32', 'float64'], 'mse_loss') label, 'label', ['float32', 'float64'], 'mse_loss')
if reduction == 'none': if reduction == 'none':
return paddle.fluid.layers.square( return paddle.square(paddle.subtract(input, label), name=name)
paddle.fluid.layers.elementwise_sub(input, label), name=name)
elif reduction == 'mean': elif reduction == 'mean':
return paddle.mean( return paddle.mean(
paddle.fluid.layers.square( paddle.square(paddle.subtract(input, label)), name=name)
paddle.fluid.layers.elementwise_sub(input, label)),
name=name)
else: else:
return paddle.sum(paddle.fluid.layers.square( return paddle.sum(paddle.square(paddle.subtract(input, label)),
paddle.fluid.layers.elementwise_sub(input, label)),
name=name) name=name)
......
python/paddle/nn/functional/norm.py
浏览文件 @ a508e725
...@@ -19,8 +19,8 @@ from ...fluid.data_feeder import check_variable_and_dtype, check_type ...@@ -19,8 +19,8 @@ from ...fluid.data_feeder import check_variable_and_dtype, check_type
from ...fluid.layer_helper import LayerHelper from ...fluid.layer_helper import LayerHelper
from ...fluid.framework import in_dygraph_mode, core from ...fluid.framework import in_dygraph_mode, core
from ...framework import create_parameter from ...framework import create_parameter
from ...fluid.initializer import Constant from ..initializer import Constant
from ...fluid.param_attr import ParamAttr from ...framework import ParamAttr
from ...fluid import core, dygraph_utils from ...fluid import core, dygraph_utils
import numbers import numbers
from paddle import _C_ops from paddle import _C_ops
...@@ -104,8 +104,7 @@ def normalize(x, p=2, axis=1, epsilon=1e-12, name=None): ...@@ -104,8 +104,7 @@ def normalize(x, p=2, axis=1, epsilon=1e-12, name=None):
type='p_norm', inputs={'X': x}, outputs={'Out': out}, attrs=attrs) type='p_norm', inputs={'X': x}, outputs={'Out': out}, attrs=attrs)
eps = out.block.create_var(dtype=out.dtype) eps = out.block.create_var(dtype=out.dtype)
paddle.fluid.layers.fill_constant([1], out.dtype, epsilon, out=eps) paddle.fluid.layers.fill_constant([1], out.dtype, epsilon, out=eps)
return paddle.fluid.layers.elementwise_div( return paddle.divide(x, paddle.maximum(out, eps), name=name)
x, paddle.maximum(out, eps), name=name)
def batch_norm(x, def batch_norm(x,
......
python/paddle/nn/functional/pooling.py
浏览文件 @ a508e725
...@@ -15,7 +15,8 @@ ...@@ -15,7 +15,8 @@
# TODO: define pooling functions # TODO: define pooling functions
from ...fluid import core from ...fluid import core
from ...fluid.framework import in_dygraph_mode from ...fluid.framework import in_dygraph_mode
from ...fluid.layers import utils, LayerHelper, unsqueeze, squeeze from ...fluid.layers import utils, LayerHelper
from ...tensor.manipulation import unsqueeze, squeeze
from ...fluid.data_feeder import check_type, check_variable_and_dtype from ...fluid.data_feeder import check_type, check_variable_and_dtype
from paddle import _C_ops from paddle import _C_ops
from paddle import _C_ops from paddle import _C_ops
......
python/paddle/nn/functional/vision.py
浏览文件 @ a508e725
...@@ -13,7 +13,8 @@ ...@@ -13,7 +13,8 @@
# limitations under the License. # limitations under the License.
from ...device import get_cudnn_version from ...device import get_cudnn_version
from ...fluid.framework import core, in_dygraph_mode, Variable from ...fluid.framework import core, in_dygraph_mode
from ...static import Variable
from ...fluid.layer_helper import LayerHelper from ...fluid.layer_helper import LayerHelper
from ...fluid.data_feeder import check_variable_and_dtype from ...fluid.data_feeder import check_variable_and_dtype
from ...fluid import dygraph_utils from ...fluid import dygraph_utils
......
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