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未验证 提交 7bfd799d 编写于 作者: C cnn 提交者： GitHub
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Release 2.0rc cherry pick api rename #28108 (#28184) 

* rename count_include_pad-->exclusive  return_indices-->return_mask

* remove track_running_stats

* fix typo.

* rename xxxd-->xxxxD

* solve conflicts
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python/paddle/fluid/tests/unittests/parallel_dygraph_sync_batch_norm.py
浏览文件 @ 7bfd799d
......@@ -63,10 +63,7 @@ class TestLayer(fluid.dygraph.Layer):
bias_attr=False)
self._sync_batch_norm2 = SyncBatchNorm(
num_filters,
weight_attr=False,
bias_attr=False,
track_running_stats=False)
num_filters, weight_attr=False, bias_attr=False)
def forward(self, inputs):
y = self._conv(inputs)
......
python/paddle/fluid/tests/unittests/test_adaptive_max_pool2d.py
浏览文件 @ 7bfd799d
......@@ -150,7 +150,7 @@ class TestAdaptiveMaxPool2DAPI(unittest.TestCase):
x = paddle.to_tensor(self.x_np)
out_1 = paddle.nn.functional.adaptive_max_pool2d(
x=x, return_indices=False, output_size=[3, 3])
x=x, return_mask=False, output_size=[3, 3])
out_2 = paddle.nn.functional.adaptive_max_pool2d(x=x, output_size=5)
......
python/paddle/fluid/tests/unittests/test_pool1d_api.py
浏览文件 @ 7bfd799d
......@@ -148,11 +148,7 @@ class TestPool1D_API(unittest.TestCase):
input_np = np.random.random([2, 3, 32]).astype("float32")
input = fluid.dygraph.to_variable(input_np)
result = F.avg_pool1d(
input,
kernel_size=2,
stride=2,
padding=[1],
count_include_pad=True)
input, kernel_size=2, stride=2, padding=[1], exclusive=True)
result_np = avg_pool1D_forward_naive(
input_np, ksize=[2], strides=[2], paddings=[1], exclusive=False)
......@@ -160,7 +156,8 @@ class TestPool1D_API(unittest.TestCase):
self.assertTrue(np.allclose(result.numpy(), result_np))
avg_pool1d_dg = paddle.nn.AvgPool1D(
kernel_size=2, stride=None, padding=1, count_include_pad=True)
kernel_size=2, stride=None, padding=1, exclusive=True)
result = avg_pool1d_dg(input)
self.assertTrue(np.allclose(result.numpy(), result_np))
......@@ -200,7 +197,7 @@ class TestPool1D_API(unittest.TestCase):
input_np = np.random.random([2, 3, 32]).astype("float32")
input = fluid.dygraph.to_variable(input_np)
result, index = F.max_pool1d(
input, kernel_size=2, stride=2, padding=0, return_indices=True)
input, kernel_size=2, stride=2, padding=0, return_mask=True)
result_np = max_pool1D_forward_naive(
input_np, ksize=[2], strides=[2], paddings=[0])
......
python/paddle/fluid/tests/unittests/test_pool2d_api.py
浏览文件 @ 7bfd799d
......@@ -134,7 +134,7 @@ class TestPool2D_API(unittest.TestCase):
input_np = np.random.random([2, 3, 32, 32]).astype("float32")
input = fluid.dygraph.to_variable(input_np)
result = max_pool2d(
input, kernel_size=2, stride=2, padding=0, return_indices=False)
input, kernel_size=2, stride=2, padding=0, return_mask=False)
result_np = pool2D_forward_naive(
input_np,
......@@ -159,7 +159,7 @@ class TestPool2D_API(unittest.TestCase):
kernel_size=2,
stride=2,
padding=0,
return_indices=False,
return_mask=False,
data_format="NHWC")
result_np = pool2D_forward_naive(
......@@ -222,7 +222,7 @@ class TestPool2D_API(unittest.TestCase):
kernel_size=2,
stride=None,
padding="SAME",
return_indices=True)
return_mask=True)
result_np = pool2D_forward_naive(
input_np,
......@@ -269,7 +269,7 @@ class TestPool2D_API(unittest.TestCase):
kernel_size=2,
stride=2,
padding=padding,
return_indices=False)
return_mask=False)
result_np = pool2D_forward_naive(
input_np,
......@@ -490,7 +490,7 @@ class TestPool2DError_API(unittest.TestCase):
padding=0,
ceil_mode=False,
data_format='NHWC',
return_indices=True)
return_mask=True)
self.assertRaises(ValueError, run9)
......
python/paddle/fluid/tests/unittests/test_pool3d_api.py
浏览文件 @ 7bfd799d
......@@ -83,7 +83,7 @@ class TestPool3D_API(unittest.TestCase):
stride=2,
padding=1,
ceil_mode=False,
count_include_pad=True)
exclusive=True)
result_np = avg_pool3D_forward_naive(
input_np,
......@@ -100,7 +100,7 @@ class TestPool3D_API(unittest.TestCase):
stride=None,
padding=1,
ceil_mode=False,
count_include_pad=True)
exclusive=True)
result = avg_pool3d_dg(input)
self.assertTrue(np.allclose(result.numpy(), result_np))
......@@ -175,7 +175,7 @@ class TestPool3D_API(unittest.TestCase):
stride=2,
padding=0,
data_format="NDHWC",
return_indices=False)
return_mask=False)
result_np = pool3D_forward_naive(
input_np,
......@@ -239,7 +239,7 @@ class TestPool3D_API(unittest.TestCase):
kernel_size=2,
stride=None,
padding="SAME",
return_indices=True)
return_mask=True)
result_np = pool3D_forward_naive(
input_np,
......@@ -467,7 +467,7 @@ class TestPool3DError_API(unittest.TestCase):
stride=2,
padding=0,
data_format='NDHWC',
return_indices=True)
return_mask=True)
self.assertRaises(ValueError, run10)
......
python/paddle/nn/functional/pooling.py
浏览文件 @ 7bfd799d
......@@ -157,7 +157,7 @@ def avg_pool1d(x,
kernel_size,
stride=None,
padding=0,
count_include_pad=True,
exclusive=True,
ceil_mode=False,
name=None):
"""
......@@ -179,7 +179,7 @@ def avg_pool1d(x,
4. A list[int] or tuple(int) whose length is 2. It has the form [pad_before, pad_after].
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
count_include_pad (bool): Whether to exclude padding points in average pooling
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is `True`.
ceil_mode (bool): ${ceil_mode_comment}Whether to use the ceil function to calculate output height and width.
If it is set to False, the floor function will be used. The default value is False.
......@@ -230,8 +230,8 @@ def avg_pool1d(x,
x, 'pooling_type', 'avg', 'ksize', kernel_size, 'global_pooling',
False, 'strides', stride, 'paddings', padding, 'padding_algorithm',
padding_algorithm, 'use_cudnn', True, 'ceil_mode', ceil_mode,
'use_mkldnn', False, 'exclusive', not count_include_pad,
'data_format', data_format)
'use_mkldnn', False, 'exclusive', not exclusive, 'data_format',
data_format)
return squeeze(output, [2])
op_type = 'pool2d'
......@@ -253,7 +253,7 @@ def avg_pool1d(x,
"use_cudnn": True,
"ceil_mode": ceil_mode,
"use_mkldnn": False,
"exclusive": not count_include_pad,
"exclusive": not exclusive,
"data_format": data_format,
})
......@@ -265,7 +265,7 @@ def avg_pool2d(x,
stride=None,
padding=0,
ceil_mode=False,
count_include_pad=True,
exclusive=True,
divisor_override=None,
data_format="NCHW",
name=None):
......@@ -294,7 +294,7 @@ def avg_pool2d(x,
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
ceil_mode (bool): when True, will use `ceil` instead of `floor` to compute the output shape
count_include_pad (bool): Whether to exclude padding points in average pooling
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is `true`.
divisor_override (float): if specified, it will be used as divisor, otherwise kernel_size will be used. Default None.
data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`.
......@@ -338,8 +338,8 @@ def avg_pool2d(x,
x, 'pooling_type', 'avg', 'ksize', kernel_size, 'global_pooling',
False, 'padding_algorithm', padding_algorithm, 'strides', stride,
'paddings', padding, 'use_cudnn', True, 'ceil_mode', ceil_mode,
'use_mkldnn', False, 'exclusive', not count_include_pad,
'data_format', data_format)
'use_mkldnn', False, 'exclusive', not exclusive, 'data_format',
data_format)
if divisor_override is None:
return output
else:
......@@ -365,7 +365,7 @@ def avg_pool2d(x,
"use_cudnn": True,
"ceil_mode": ceil_mode,
"use_mkldnn": False,
"exclusive": not count_include_pad,
"exclusive": not exclusive,
"data_format": data_format,
})
......@@ -381,7 +381,7 @@ def avg_pool3d(x,
stride=None,
padding=0,
ceil_mode=False,
count_include_pad=True,
exclusive=True,
divisor_override=None,
data_format="NCDHW",
name=None):
......@@ -408,7 +408,7 @@ def avg_pool3d(x,
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
ceil_mode (bool): ${ceil_mode_comment}
count_include_pad (bool): Whether to exclude padding points in average pooling
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is True.
divisor_override (int|float) if specified, it will be used as divisor, otherwise kernel_size will be used. Default None.
data_format (string): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
......@@ -452,8 +452,8 @@ def avg_pool3d(x,
x, 'pooling_type', 'avg', 'ksize', kernel_size, 'strides', stride,
'paddings', padding, 'global_pooling', False, 'padding_algorithm',
padding_algorithm, 'use_cudnn', True, 'ceil_mode', ceil_mode,
'use_mkldnn', False, 'exclusive', not count_include_pad,
'data_format', data_format)
'use_mkldnn', False, 'exclusive', not exclusive, 'data_format',
data_format)
if divisor_override is None:
return output
else:
......@@ -481,7 +481,7 @@ def avg_pool3d(x,
"use_cudnn": True,
"ceil_mode": ceil_mode,
"use_mkldnn": False,
"exclusive": not count_include_pad,
"exclusive": not exclusive,
"data_format": data_format,
})
......@@ -497,7 +497,7 @@ def max_pool1d(x,
kernel_size,
stride=None,
padding=0,
return_indices=False,
return_mask=False,
ceil_mode=False,
name=None):
"""
......@@ -519,7 +519,7 @@ def max_pool1d(x,
4. A list[int] or tuple(int) whose length is 2. It has the form [pad_before, pad_after].
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
return_indices (bool): Whether return the max indices along with the outputs. default is `False`.
return_mask (bool): Whether return the max indices along with the outputs. default is `False`.
ceil_mode (bool): Whether to use the ceil function to calculate output height and width. False is the default.
If it is set to False, the floor function will be used. Default False.
name(str, optional): For detailed information, please refer
......@@ -542,7 +542,7 @@ def max_pool1d(x,
data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32))
pool_out = F.max_pool1d(data, kernel_size=2, stride=2, padding=0)
# pool_out shape: [1, 3, 16]
pool_out, indices = F.max_pool1d(data, kernel_size=2, stride=2, padding=0, return_indices=True)
pool_out, indices = F.max_pool1d(data, kernel_size=2, stride=2, padding=0, return_mask=True)
# pool_out shape: [1, 3, 16], indices shape: [1, 3, 16]
"""
"""NCL to NCHW"""
......@@ -563,16 +563,16 @@ def max_pool1d(x,
padding = _expand_low_nd_padding(padding)
if in_dygraph_mode():
if return_indices:
if return_mask:
pool_out = core.ops.max_pool2d_with_index(
x, 'ksize', kernel_size, 'global_pooling', False, 'strides',
stride, 'paddings', padding, 'padding_algorithm',
padding_algorithm, 'use_cudnn', True, 'ceil_mode', ceil_mode,
'use_mkldnn', False, 'exclusive', True, 'data_format',
data_format)
return (squeeze(pool_out[0], [2]), squeeze(
pool_out[1],
[2])) if return_indices else squeeze(pool_out[0], [2])
return (squeeze(pool_out[0], [2]),
squeeze(pool_out[1],
[2])) if return_mask else squeeze(pool_out[0], [2])
else:
pool_out = core.ops.pool2d(
x, 'pooling_type', 'max', 'ksize', kernel_size,
......@@ -582,7 +582,7 @@ def max_pool1d(x,
'data_format', data_format)
return squeeze(pool_out, [2])
op_type = 'max_pool2d_with_index' if return_indices else "pool2d"
op_type = 'max_pool2d_with_index' if return_mask else "pool2d"
helper = LayerHelper(op_type, **locals())
dtype = helper.input_dtype()
pool_out = helper.create_variable_for_type_inference(dtype)
......@@ -608,14 +608,14 @@ def max_pool1d(x,
})
return (squeeze(pool_out, [2]),
squeeze(mask, [2])) if return_indices else squeeze(pool_out, [2])
squeeze(mask, [2])) if return_mask else squeeze(pool_out, [2])
def max_pool2d(x,
kernel_size,
stride=None,
padding=0,
return_indices=False,
return_mask=False,
ceil_mode=False,
data_format="NCHW",
name=None):
......@@ -643,7 +643,7 @@ def max_pool2d(x,
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
ceil_mode (bool): when True, will use `ceil` instead of `floor` to compute the output shape
return_indices (bool): Whether to return the max indices along with the outputs. Default False, only support `"NCHW"` data format
return_mask (bool): Whether to return the max indices along with the outputs. Default False, only support `"NCHW"` data format
data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
......@@ -668,12 +668,12 @@ def max_pool2d(x,
kernel_size=2,
stride=2, padding=0)
# output.shape [1, 3, 16, 16]
# for return_indices=True
# for return_mask=True
out, max_indices = F.max_pool2d(x,
kernel_size=2,
stride=2,
padding=0,
return_indices=True)
return_mask=True)
# out.shape [1, 3, 16, 16], max_indices.shape [1, 3, 16, 16],
"""
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'max_pool2d')
......@@ -693,20 +693,20 @@ def max_pool2d(x,
padding, padding_algorithm = _update_padding_nd(
padding, num_dims=2, channel_last=channel_last, ceil_mode=ceil_mode)
if data_format == "NHWC" and return_indices:
if data_format == "NHWC" and return_mask:
raise ValueError(
"When setting return_indices to true, data_format must be set to NCHW in API:max_pool2d"
"When setting return_mask to true, data_format must be set to NCHW in API:max_pool2d"
)
if in_dygraph_mode():
if return_indices:
if return_mask:
output = core.ops.max_pool2d_with_index(
x, 'ksize', kernel_size, 'global_pooling', False, 'strides',
stride, 'paddings', padding, 'padding_algorithm',
padding_algorithm, 'use_cudnn', True, 'ceil_mode', ceil_mode,
'use_mkldnn', False, 'exclusive', True, 'data_format',
data_format)
return output if return_indices else output[0]
return output if return_mask else output[0]
else:
output = core.ops.pool2d(
x, 'pooling_type', 'max', 'ksize', kernel_size,
......@@ -716,7 +716,7 @@ def max_pool2d(x,
'data_format', data_format)
return output
op_type = 'max_pool2d_with_index' if return_indices else "pool2d"
op_type = 'max_pool2d_with_index' if return_mask else "pool2d"
helper = LayerHelper(op_type, **locals())
dtype = helper.input_dtype()
pool_out = helper.create_variable_for_type_inference(dtype)
......@@ -741,14 +741,14 @@ def max_pool2d(x,
"data_format": data_format,
})
return (pool_out, mask) if return_indices else pool_out
return (pool_out, mask) if return_mask else pool_out
def max_pool3d(x,
kernel_size,
stride=None,
padding=0,
return_indices=False,
return_mask=False,
ceil_mode=False,
data_format="NCDHW",
name=None):
......@@ -773,7 +773,7 @@ def max_pool3d(x,
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
ceil_mode (bool): ${ceil_mode_comment}
return_indices (bool): Whether to return the max indices along with the outputs. Default False. Only support "NDCHW" data_format.
return_mask (bool): Whether to return the max indices along with the outputs. Default False. Only support "NDCHW" data_format.
data_format (string): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_depth, input_height, input_width]`.
......@@ -798,13 +798,13 @@ def max_pool3d(x,
kernel_size=2,
stride=2, padding=0)
output.shape [1, 3, 16, 16, 16]
# for return_indices=True
# for return_mask=True
x = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32, 32, 32]).astype(np.float32))
output, max_indices = paddle.nn.functional.max_pool3d(x,
kernel_size = 2,
stride = 2,
padding=0,
return_indices=True)
return_mask=True)
# output.shape [None, 3, 16, 16, 16], max_indices.shape [None, 3, 16, 16, 16],
"""
check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'max_pool3d')
......@@ -819,20 +819,20 @@ def max_pool3d(x,
padding, padding_algorithm = _update_padding_nd(
padding, 3, channel_last=channel_last, ceil_mode=ceil_mode)
if data_format == "NDHWC" and return_indices:
if data_format == "NDHWC" and return_mask:
raise ValueError(
"When setting return_indices to true, data_format must be set to NCDHW in API:max_pool3d"
"When setting return_mask to true, data_format must be set to NCDHW in API:max_pool3d"
)
if in_dygraph_mode():
if return_indices:
if return_mask:
output = core.ops.max_pool3d_with_index(
x, 'pooling_type', 'max', 'ksize', kernel_size, 'strides',
stride, 'paddings', padding, 'global_pooling', False,
'padding_algorithm', padding_algorithm, 'use_cudnn', True,
'ceil_mode', ceil_mode, 'use_mkldnn', False, 'exclusive', True,
'data_format', data_format)
return output if return_indices else output[0]
return output if return_mask else output[0]
else:
output = core.ops.pool3d(
x, 'pooling_type', 'max', 'ksize', kernel_size,
......@@ -842,7 +842,7 @@ def max_pool3d(x,
'data_format', data_format)
return output
op_type = "max_pool3d_with_index" if return_indices else "pool3d"
op_type = "max_pool3d_with_index" if return_mask else "pool3d"
helper = LayerHelper(op_type, **locals())
dtype = helper.input_dtype()
pool_out = helper.create_variable_for_type_inference(dtype)
......@@ -867,7 +867,7 @@ def max_pool3d(x,
"data_format": data_format,
})
return (pool_out, mask) if return_indices else pool_out
return (pool_out, mask) if return_mask else pool_out
def adaptive_avg_pool1d(x, output_size, name=None):
......@@ -1148,7 +1148,7 @@ def adaptive_avg_pool3d(x, output_size, data_format='NCDHW', name=None):
return pool_out
def adaptive_max_pool1d(x, output_size, return_indices=False, name=None):
def adaptive_max_pool1d(x, output_size, return_mask=False, name=None):
"""
This API implements adaptive max pooling 1d operation.
See more details in :ref:`api_nn_pooling_AdaptiveMaxPool1d` .
......@@ -1159,7 +1159,7 @@ def adaptive_max_pool1d(x, output_size, return_indices=False, name=None):
where N is batch size, C is the number of channels, L is the
length of the feature. The data type is float32 or float64.
output_size (int): The pool kernel size. The value should be an integer.
return_indices (bool): If true, the index of max pooling point will be returned along
return_mask (bool): If true, the index of max pooling point will be returned along
with outputs. It cannot be set in average pooling type. Default False.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
......@@ -1190,7 +1190,7 @@ def adaptive_max_pool1d(x, output_size, return_indices=False, name=None):
data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32))
pool_out = F.adaptive_max_pool1d(data, output_size=16)
# pool_out shape: [1, 3, 16])
pool_out, indices = F.adaptive_max_pool1d(data, output_size=16, return_indices=True)
pool_out, indices = F.adaptive_max_pool1d(data, output_size=16, return_mask=True)
# pool_out shape: [1, 3, 16] indices shape: [1, 3, 16]
"""
pool_type = 'max'
......@@ -1198,7 +1198,7 @@ def adaptive_max_pool1d(x, output_size, return_indices=False, name=None):
'adaptive_max_pool1d')
_check_input(x, 3)
check_type(output_size, 'pool_size', int, 'adaptive_max_pool1d')
check_type(return_indices, 'return_indices', bool, 'adaptive_max_pool1d')
check_type(return_mask, 'return_mask', bool, 'adaptive_max_pool1d')
pool_size = [1] + utils.convert_to_list(output_size, 1, 'pool_size')
......@@ -1209,7 +1209,7 @@ def adaptive_max_pool1d(x, output_size, return_indices=False, name=None):
pool_out = core.ops.max_pool2d_with_index(
x, 'pooling_type', pool_type, 'ksize', pool_size, 'adaptive', True)
return (squeeze(pool_out[0], [2]), squeeze(
pool_out[1], [2])) if return_indices else squeeze(pool_out[0], [2])
pool_out[1], [2])) if return_mask else squeeze(pool_out[0], [2])
helper = LayerHelper(l_type, **locals())
dtype = helper.input_dtype()
......@@ -1229,10 +1229,10 @@ def adaptive_max_pool1d(x, output_size, return_indices=False, name=None):
})
return (squeeze(pool_out, [2]),
squeeze(mask, [2])) if return_indices else squeeze(pool_out, [2])
squeeze(mask, [2])) if return_mask else squeeze(pool_out, [2])
def adaptive_max_pool2d(x, output_size, return_indices=False, name=None):
def adaptive_max_pool2d(x, output_size, return_mask=False, name=None):
"""
This operation applies a 2D adaptive max pooling on input tensor.
See more details in :ref:`api_nn_pooling_AdaptiveMaxPool2d` .
......@@ -1240,7 +1240,7 @@ def adaptive_max_pool2d(x, output_size, return_indices=False, name=None):
Args:
x (Tensor): The input tensor of adaptive max pool2d operator, which is a 4-D tensor. The data type can be float16, float32, float64, int32 or int64.
output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain two elements, (H, W). H and W can be either a int, or None which means the size will be the same as that of the input.
return_indices (bool): If true, the index of max pooling point will be returned along with outputs. Default False.
return_mask (bool): If true, the index of max pooling point will be returned along with outputs. Default False.
name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default.
Returns:
......@@ -1280,7 +1280,7 @@ def adaptive_max_pool2d(x, output_size, return_indices=False, name=None):
'adaptive_max_pool2d')
_check_input(x, 4)
#check_type(output_size, 'pool_size', (int), 'adaptive_max_pool2d')
check_type(return_indices, 'return_indices', bool, 'adaptive_max_pool2d')
check_type(return_mask, 'return_mask', bool, 'adaptive_max_pool2d')
in_h, in_w = x.shape[2:4]
if isinstance(output_size, int):
......@@ -1295,7 +1295,7 @@ def adaptive_max_pool2d(x, output_size, return_indices=False, name=None):
if in_dygraph_mode():
pool_out = core.ops.max_pool2d_with_index(
x, 'pooling_type', 'max', 'ksize', output_size, 'adaptive', True)
return pool_out if return_indices else pool_out[0]
return pool_out if return_mask else pool_out[0]
l_type = 'max_pool2d_with_index'
......@@ -1315,11 +1315,11 @@ def adaptive_max_pool2d(x, output_size, return_indices=False, name=None):
"ksize": output_size,
"adaptive": True,
})
#return (pool_out, mask) if return_indices else pool_out
#return (pool_out, mask) if return_mask else pool_out
return pool_out
def adaptive_max_pool3d(x, output_size, return_indices=False, name=None):
def adaptive_max_pool3d(x, output_size, return_mask=False, name=None):
"""
This operation applies a 3D adaptive max pooling on input tensor.
See more details in :ref:`api_nn_pooling_AdaptiveMaxPool3d` .
......@@ -1327,7 +1327,7 @@ def adaptive_max_pool3d(x, output_size, return_indices=False, name=None):
Args:
x (Tensor): The input tensor of adaptive max pool3d operator, which is a 5-D tensor. The data type can be float32, float64.
output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain three elements, (D, H, W). D, H and W can be either a int, or None which means the size will be the same as that of the input.
return_indices (bool): If true, the index of max pooling point will be returned along with outputs. Default False.
return_mask (bool): If true, the index of max pooling point will be returned along with outputs. Default False.
name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default.
Returns:
......@@ -1371,7 +1371,7 @@ def adaptive_max_pool3d(x, output_size, return_indices=False, name=None):
'adaptive_max_pool3d')
_check_input(x, 5)
#check_type(output_size, 'pool_size', (int), 'adaptive_max_pool3d')
check_type(return_indices, 'return_indices', bool, 'adaptive_max_pool3d')
check_type(return_mask, 'return_mask', bool, 'adaptive_max_pool3d')
in_l, in_h, in_w = x.shape[2:5]
if isinstance(output_size, int):
......@@ -1388,7 +1388,7 @@ def adaptive_max_pool3d(x, output_size, return_indices=False, name=None):
if in_dygraph_mode():
pool_out = core.ops.max_pool3d_with_index(
x, 'pooling_type', 'max', 'ksize', output_size, 'adaptive', True)
return pool_out if return_indices else pool_out[0]
return pool_out if return_mask else pool_out[0]
l_type = 'max_pool3d_with_index'
......@@ -1409,4 +1409,4 @@ def adaptive_max_pool3d(x, output_size, return_indices=False, name=None):
"adaptive": True,
})
return (pool_out, mask) if return_indices else pool_out
return (pool_out, mask) if return_mask else pool_out
python/paddle/nn/layer/norm.py
浏览文件 @ 7bfd799d
......@@ -73,7 +73,6 @@ class _InstanceNormBase(layers.Layer):
momentum=0.9,
weight_attr=None,
bias_attr=None,
track_running_stats=False,
data_format="NCHW",
name=None):
super(_InstanceNormBase, self).__init__()
......@@ -135,9 +134,6 @@ class InstanceNorm1D(_InstanceNormBase):
epsilon(float, optional): A value added to the denominator for
numerical stability. Default is 1e-5.
momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9.
track_running_stats(bool, optional): Whether to use global mean and
variance. In train mode, when setting track_running_stats True, the global mean
and variance are also used during train period. Default: False.
weight_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale`
of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm
will create ParamAttr as weight_attr, the name of scale can be set in ParamAttr.
......@@ -159,9 +155,6 @@ class InstanceNorm1D(_InstanceNormBase):
Returns:
None.
**Note**:
Momentum and track_running_stats is not effective. The next version will fix the problem .
Examples:
......@@ -214,9 +207,6 @@ class InstanceNorm2D(_InstanceNormBase):
epsilon(float, optional): A value added to the denominator for
numerical stability. Default is 1e-5.
momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9.
track_running_stats(bool, optional): Whether to use global mean and
variance. In train mode, when setting track_running_stats True, the global mean
and variance are also used during train period. Default: False.
weight_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale`
of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm
will create ParamAttr as weight_attr, the name of scale can be set in ParamAttr.
......@@ -237,8 +227,6 @@ class InstanceNorm2D(_InstanceNormBase):
Returns:
None.
**Note**:
Momentum and track_running_stats is not effective. The next version will fix the problem .
Examples:
......@@ -290,9 +278,6 @@ class InstanceNorm3D(_InstanceNormBase):
epsilon(float, optional): A value added to the denominator for
numerical stability. Default is 1e-5.
momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9.
track_running_stats(bool, optional): Whether to use global mean and
variance. In train mode, when setting track_running_stats True, the global mean
and variance are also used during train period. Default: False.
weight_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale`
of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm
will create ParamAttr as weight_attr, the name of scale can be set in ParamAttr.
......@@ -313,8 +298,6 @@ class InstanceNorm3D(_InstanceNormBase):
Returns:
None.
**Note**:
Momentum and track_running_stats is not effective. The next version will fix the problem .
Examples:
......@@ -570,7 +553,6 @@ class _BatchNormBase(layers.Layer):
weight_attr=None,
bias_attr=None,
data_format='NCHW',
track_running_stats=True,
name=None):
super(_BatchNormBase, self).__init__()
self._num_features = num_features
......@@ -636,7 +618,6 @@ class _BatchNormBase(layers.Layer):
self._momentum = momentum
self._epsilon = epsilon
self._fuse_with_relu = False
self._track_running_stats = track_running_stats
self._name = name
def _check_input_dim(self, input):
......@@ -651,11 +632,7 @@ class _BatchNormBase(layers.Layer):
self._check_input_dim(input)
if not self.training and not self._track_running_stats:
raise ValueError(
'When inference, expected track_running_stats is True.')
if self.training and not self._track_running_stats:
if self.training:
warnings.warn(
"When training, we now always track global mean and variance.")
......@@ -720,9 +697,6 @@ class BatchNorm1D(_BatchNormBase):
will create ParamAttr as bias_attr. If it is set to Fasle, the weight is not learnable.
If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None.
data_format(str, optional): Specify the input data format, may be "NC", "NCL" or "NLC". Defalut "NCL".
track_running_stats(bool, optional): Whether to use global mean and variance. In train period,
True will track global mean and variance used for inference. When inference, track_running_stats must be
True. Default: True.
name(str, optional): Name for the BatchNorm, default is None. For more information, please refer to :ref:`api_guide_Name`..
Shape:
......@@ -733,9 +707,6 @@ class BatchNorm1D(_BatchNormBase):
Returns:
None.
**Note**:
Now track_running_stats is actucal always true. The next version will fix the problem .
Examples:
.. code-block:: python
......@@ -817,9 +788,6 @@ class BatchNorm2D(_BatchNormBase):
will create ParamAttr as bias_attr. If it is set to Fasle, the weight is not learnable.
If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None.
data_format(str, optional): Specify the input data format, the data format can be "NCHW" or "NHWC". Default: NCHW.
track_running_stats(bool, optional): Whether to use global mean and variance. In train period,
True will track global mean and variance used for inference. When inference, track_running_stats must be
True. Default: True.
name(str, optional): Name for the BatchNorm, default is None. For more information, please refer to :ref:`api_guide_Name`..
Shape:
......@@ -830,9 +798,6 @@ class BatchNorm2D(_BatchNormBase):
Returns:
None
**Note**:
Now track_running_stats is actucal always true. The next version will fix the problem .
Examples:
.. code-block:: python
......@@ -912,9 +877,6 @@ class BatchNorm3D(_BatchNormBase):
will create ParamAttr as bias_attr. If it is set to Fasle, the weight is not learnable.
If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None.
data_format(str, optional): Specify the input data format, the data format can be "NCDHW" or "NDHWC. Default: NCDHW.
track_running_stats(bool, optional): Whether to use global mean and variance. In train period,
True will track global mean and variance used for inference. When inference, track_running_stats must be
True. Default: True.
name(str, optional): Name for the BatchNorm, default is None. For more information, please refer to :ref:`api_guide_Name`..
Shape:
......@@ -925,9 +887,6 @@ class BatchNorm3D(_BatchNormBase):
Returns:
None
**Note**:
Now track_running_stats is actucal always true. The next version will fix the problem .
Examples:
.. code-block:: python
......@@ -1024,8 +983,6 @@ class SyncBatchNorm(_BatchNormBase):
will create ParamAttr as bias_attr. If the Initializer of the bias_attr
is not set, the bias is initialized zero. If it is set to False, this layer will not
have trainable bias parameter. Default: None.
track_running_stats(bool, optional): Whether to compute global stats, which including running mean and
running variance. Default: True.
Shapes:
input: Tensor that the dimension from 2 to 5.
......@@ -1055,11 +1012,10 @@ class SyncBatchNorm(_BatchNormBase):
weight_attr=None,
bias_attr=None,
data_format='NCHW',
track_running_stats=True,
name=None):
super(SyncBatchNorm,
self).__init__(num_features, momentum, epsilon, weight_attr,
bias_attr, data_format, track_running_stats, name)
bias_attr, data_format, name)
def forward(self, x):
# create output
......@@ -1147,10 +1103,10 @@ class SyncBatchNorm(_BatchNormBase):
"""
layer_output = layer
if isinstance(layer, _BatchNormBase):
layer_output = SyncBatchNorm(
layer._num_features, layer._momentum, layer._epsilon,
layer._weight_attr, layer._bias_attr, layer._data_format,
layer._track_running_stats, layer._name)
layer_output = SyncBatchNorm(layer._num_features, layer._momentum,
layer._epsilon, layer._weight_attr,
layer._bias_attr, layer._data_format,
layer._name)
if layer._weight_attr != False and layer._bias_attr != False:
with no_grad():
......
python/paddle/nn/layer/pooling.py
浏览文件 @ 7bfd799d
......@@ -35,7 +35,7 @@ __all__ = [
class AvgPool1D(layers.Layer):
"""
This operation applies a 1D average pooling over an input signal composed
of several input planes, based on the input, output_size, return_indices parameters.
of several input planes, based on the input, output_size, return_mask parameters.
Input(X) and output(Out) are in NCL format, where N is batch
size, C is the number of channels, L is the length of the feature.
The output tensor shape will be [N, C, output_size].
......@@ -61,7 +61,7 @@ class AvgPool1D(layers.Layer):
4. A list[int] or tuple(int) whose length is 2. It has the form [pad_before, pad_after].
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
count_include_pad (bool): Whether to exclude padding points in average pooling
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is `True`.
ceil_mode (bool): ${ceil_mode_comment}Whether to use the ceil function to calculate output height and width.
If it is set to False, the floor function will be used. The default value is False.
......@@ -103,7 +103,7 @@ class AvgPool1D(layers.Layer):
kernel_size,
stride=None,
padding=0,
count_include_pad=True,
exclusive=True,
ceil_mode=False,
name=None):
super(AvgPool1D, self).__init__()
......@@ -111,12 +111,12 @@ class AvgPool1D(layers.Layer):
self.stride = stride
self.padding = padding
self.ceil_mode = ceil_mode
self.count_include_pad = count_include_pad
self.exclusive = exclusive
self.name = name
def forward(self, x):
out = F.avg_pool1d(x, self.kernel_size, self.stride, self.padding,
self.count_include_pad, self.ceil_mode, self.name)
self.exclusive, self.ceil_mode, self.name)
return out
......@@ -156,7 +156,7 @@ class AvgPool2D(layers.Layer):
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
ceil_mode (bool): when True, will use `ceil` instead of `floor` to compute the output shape
count_include_pad (bool): Whether to exclude padding points in average pooling
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is `true`.
divisor_override (float): if specified, it will be used as divisor, otherwise kernel_size will be used. Default None.
data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NDHW"`.
......@@ -197,7 +197,7 @@ class AvgPool2D(layers.Layer):
stride=None,
padding=0,
ceil_mode=False,
count_include_pad=True,
exclusive=True,
divisor_override=None,
data_format="NCHW",
name=None):
......@@ -206,7 +206,7 @@ class AvgPool2D(layers.Layer):
self.stride = stride
self.padding = padding
self.ceil_mode = ceil_mode
self.count_include_pad = count_include_pad
self.exclusive = exclusive
self.divisor = divisor_override
self.data_format = data_format
self.name = name
......@@ -218,7 +218,7 @@ class AvgPool2D(layers.Layer):
stride=self.stride,
padding=self.padding,
ceil_mode=self.ceil_mode,
count_include_pad=self.count_include_pad,
exclusive=self.exclusive,
divisor_override=self.divisor,
data_format=self.data_format,
name=self.name)
......@@ -247,7 +247,7 @@ class AvgPool3D(layers.Layer):
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
ceil_mode (bool): ${ceil_mode_comment}
count_include_pad (bool): Whether to exclude padding points in average pooling
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is True.
divisor_override (int|float) if specified, it will be used as divisor, otherwise kernel_size will be used. Default None.
data_format (string): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
......@@ -289,7 +289,7 @@ class AvgPool3D(layers.Layer):
stride,
padding=0,
ceil_mode=False,
count_include_pad=True,
exclusive=True,
divisor_override=None,
data_format="NCDHW",
name=None):
......@@ -298,7 +298,7 @@ class AvgPool3D(layers.Layer):
self.stride = stride
self.padding = padding
self.ceil_mode = ceil_mode
self.count_include_pad = count_include_pad
self.exclusive = exclusive
self.divisor = divisor_override
self.data_format = data_format
self.name = name
......@@ -310,7 +310,7 @@ class AvgPool3D(layers.Layer):
stride=self.stride,
padding=self.padding,
ceil_mode=self.ceil_mode,
count_include_pad=self.count_include_pad,
exclusive=self.exclusive,
divisor_override=self.divisor,
data_format=self.data_format,
name=self.name)
......@@ -319,7 +319,7 @@ class AvgPool3D(layers.Layer):
class MaxPool1D(layers.Layer):
"""
Applies a 1D max pooling over an input signal composed of several input planes based
on the input, output_size, return_indices parameters.
on the input, output_size, return_mask parameters.
Input(X) and output(Out) are in NCL format, where N is batch
size, C is the number of channels, L is the length of the feature.
......@@ -343,7 +343,7 @@ class MaxPool1D(layers.Layer):
4. A list[int] or tuple(int) whose length is 2. It has the form [pad_before, pad_after].
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
return_indices (bool): Whether return the max indices along with the outputs. default is `False`.
return_mask (bool): Whether return the max indices along with the outputs. default is `False`.
ceil_mode (bool): Whether to use the ceil function to calculate output height and width. False is the default.
If it is set to False, the floor function will be used. Default False.
name(str, optional): For detailed information, please refer
......@@ -377,7 +377,7 @@ class MaxPool1D(layers.Layer):
pool_out = MaxPool1D(data)
# pool_out shape: [1, 3, 16]
MaxPool1D = nn.MaxPool1D(kernel_size=2, stride=2, padding=0, return_indices=True)
MaxPool1D = nn.MaxPool1D(kernel_size=2, stride=2, padding=0, return_mask=True)
pool_out, indices = MaxPool1D(data)
# pool_out shape: [1, 3, 16], indices shape: [1, 3, 16]
......@@ -387,7 +387,7 @@ class MaxPool1D(layers.Layer):
kernel_size,
stride=None,
padding=0,
return_indices=False,
return_mask=False,
ceil_mode=False,
name=None):
super(MaxPool1D, self).__init__()
......@@ -395,12 +395,12 @@ class MaxPool1D(layers.Layer):
self.stride = stride
self.padding = padding
self.ceil_mode = ceil_mode
self.return_indices = return_indices
self.return_mask = return_mask
self.name = name
def forward(self, input):
out = F.max_pool1d(input, self.kernel_size, self.stride, self.padding,
self.return_indices, self.ceil_mode, self.name)
self.return_mask, self.ceil_mode, self.name)
return out
......@@ -440,7 +440,7 @@ class MaxPool2D(layers.Layer):
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
ceil_mode (bool): when True, will use `ceil` instead of `floor` to compute the output shape
return_indices (bool): Whether to return the max indices along with the outputs.
return_mask (bool): Whether to return the max indices along with the outputs.
data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NDHW"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
......@@ -473,8 +473,8 @@ class MaxPool2D(layers.Layer):
output = MaxPool2D(input)
# output.shape [1, 3, 16, 16]
# for return_indices=True
MaxPool2D = nn.MaxPool2D(kernel_size=2,stride=2, padding=0, return_indices=True)
# for return_mask=True
MaxPool2D = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, return_mask=True)
output, max_indices = MaxPool2D(input)
# output.shape [1, 3, 16, 16], max_indices.shape [1, 3, 16, 16],
"""
......@@ -483,7 +483,7 @@ class MaxPool2D(layers.Layer):
kernel_size,
stride=None,
padding=0,
return_indices=False,
return_mask=False,
ceil_mode=False,
data_format="NCHW",
name=None):
......@@ -491,7 +491,7 @@ class MaxPool2D(layers.Layer):
self.ksize = kernel_size
self.stride = stride
self.padding = padding
self.return_indices = return_indices
self.return_mask = return_mask
self.ceil_mode = ceil_mode
self.data_format = data_format
self.name = name
......@@ -502,7 +502,7 @@ class MaxPool2D(layers.Layer):
kernel_size=self.ksize,
stride=self.stride,
padding=self.padding,
return_indices=self.return_indices,
return_mask=self.return_mask,
data_format=self.data_format,
name=self.name)
......@@ -530,7 +530,7 @@ class MaxPool3D(layers.Layer):
5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
The default value is 0.
ceil_mode (bool): ${ceil_mode_comment}
return_indices (bool): Whether to return the max indices along with the outputs.
return_mask (bool): Whether to return the max indices along with the outputs.
data_format (string): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_depth, input_height, input_width]`.
......@@ -564,8 +564,8 @@ class MaxPool3D(layers.Layer):
output = MaxPool3D(input)
# output.shape [1, 2, 3, 16, 16]
# for return_indices=True
MaxPool3D = nn.MaxPool3D(kernel_size=2,stride=2, padding=0, return_indices=True)
# for return_mask=True
MaxPool3D = nn.MaxPool3D(kernel_size=2, stride=2, padding=0, return_mask=True)
output, max_indices = MaxPool3D(input)
# output.shape [1, 2, 3, 16, 16], max_indices.shape [1, 2, 3, 16, 16],
"""
......@@ -574,7 +574,7 @@ class MaxPool3D(layers.Layer):
kernel_size,
stride,
padding,
return_indices=False,
return_mask=False,
ceil_mode=False,
data_format="NCDHW",
name=None):
......@@ -582,7 +582,7 @@ class MaxPool3D(layers.Layer):
self.ksize = kernel_size
self.stride = stride
self.padding = padding
self.return_indices = return_indices
self.return_mask = return_mask
self.ceil_mode = ceil_mode
self.data_format = data_format
self.name = name
......@@ -593,7 +593,7 @@ class MaxPool3D(layers.Layer):
kernel_size=self.ksize,
stride=self.stride,
padding=self.padding,
return_indices=self.return_indices,
return_mask=self.return_mask,
data_format=self.data_format,
name=self.name)
......@@ -602,7 +602,7 @@ class AdaptiveAvgPool1D(layers.Layer):
"""
This operation applies a 1D adaptive average pooling over an input signal composed
of several input planes, based on the input, output_size, return_indices parameters.
of several input planes, based on the input, output_size, return_mask parameters.
Input(X) and output(Out) are in NCL format, where N is batch
size, C is the number of channels, L is the length of the feature.
The output tensor shape will be [N, C, output_size].
......@@ -841,7 +841,7 @@ class AdaptiveMaxPool1D(layers.Layer):
"""
This operation applies a 1D adaptive max pooling over an input signal composed
of several input planes, based on the input, output_size, return_indices parameters.
of several input planes, based on the input, output_size, return_mask parameters.
Input(X) and output(Out) are in NCL format, where N is batch
size, C is the number of channels, L is the length of the feature.
The output tensor shape will be [N, C, output_size].
......@@ -859,7 +859,7 @@ class AdaptiveMaxPool1D(layers.Layer):
Args:
output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain one int.
return_indices (bool): If true, the index of max pooling point will be returned along
return_mask (bool): If true, the index of max pooling point will be returned along
with outputs. It cannot be set in average pooling type. Default False.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
......@@ -898,22 +898,22 @@ class AdaptiveMaxPool1D(layers.Layer):
pool_out = AdaptiveMaxPool1D(data)
# pool_out shape: [1, 3, 16]
# for return_indices = true
AdaptiveMaxPool1D = nn.AdaptiveMaxPool1D(output_size=16, return_indices=True)
# for return_mask = true
AdaptiveMaxPool1D = nn.AdaptiveMaxPool1D(output_size=16, return_mask=True)
pool_out, indices = AdaptiveMaxPool1D(data)
# pool_out shape: [1, 3, 16], indices shape: [1, 3, 16]
"""
def __init__(self, output_size, return_indices=False, name=None):
def __init__(self, output_size, return_mask=False, name=None):
super(AdaptiveMaxPool1D, self).__init__()
self.output_size = output_size
self.return_indices = return_indices
self.return_mask = return_mask
self.name = name
def forward(self, input):
return F.adaptive_max_pool1d(input, self.output_size,
self.return_indices, self.name)
return F.adaptive_max_pool1d(input, self.output_size, self.return_mask,
self.name)
class AdaptiveMaxPool2D(layers.Layer):
......@@ -932,7 +932,7 @@ class AdaptiveMaxPool2D(layers.Layer):
Output(i ,j) &= max(Input[hstart:hend, wstart:wend])
Parameters:
output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain two element, (H, W). H and W can be either a int, or None which means the size will be the same as that of the input.
return_indices (bool): If true, the index of max pooling point will be returned along with outputs. It cannot be set in average pooling type. Default False.
return_mask (bool): If true, the index of max pooling point will be returned along with outputs. It cannot be set in average pooling type. Default False.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
......@@ -965,21 +965,21 @@ class AdaptiveMaxPool2D(layers.Layer):
paddle.disable_static()
input_data = np.random.rand(2, 3, 32, 32)
x = paddle.to_tensor(input_data)
adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=3, return_indices=True)
adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=3, return_mask=True)
pool_out, indices = adaptive_max_pool(x = x)
"""
def __init__(self, output_size, return_indices=False, name=None):
def __init__(self, output_size, return_mask=False, name=None):
super(AdaptiveMaxPool2D, self).__init__()
self._output_size = output_size
self._return_indices = return_indices
self._return_mask = return_mask
self._name = name
def forward(self, x):
return F.adaptive_max_pool2d(
x,
output_size=self._output_size,
return_indices=self._return_indices,
return_mask=self._return_mask,
name=self._name)
......@@ -1002,7 +1002,7 @@ class AdaptiveMaxPool3D(layers.Layer):
Parameters:
output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain three elements, (D, H, W). D, H and W can be either a int, or None which means the size will be the same as that of the input.
return_indices (bool): If true, the index of max pooling point will be returned along with outputs. Default False.
return_mask (bool): If true, the index of max pooling point will be returned along with outputs. Default False.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
......@@ -1040,21 +1040,21 @@ class AdaptiveMaxPool3D(layers.Layer):
pool = paddle.nn.AdaptiveMaxPool3D(output_size=4)
out = pool(x)
# out shape: [2, 3, 4, 4, 4]
pool = paddle.nn.AdaptiveMaxPool3D(output_size=3, return_indices=True)
pool = paddle.nn.AdaptiveMaxPool3D(output_size=3, return_mask=True)
out, indices = pool(x)
# out shape: [2, 3, 4, 4, 4], indices shape: [2, 3, 4, 4, 4]
"""
def __init__(self, output_size, return_indices=False, name=None):
def __init__(self, output_size, return_mask=False, name=None):
super(AdaptiveMaxPool3D, self).__init__()
self._output_size = output_size
self._return_indices = return_indices
self._return_mask = return_mask
self._name = name
def forward(self, x):
return F.adaptive_max_pool3d(
x,
output_size=self._output_size,
return_indices=self._return_indices,
return_mask=self._return_mask,
name=self._name)
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