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未验证 提交 350d20fe 编写于 作者: F FDInSky 提交者： GitHub
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[cherry-pick] interpolate api doc (#24685) 

* test=develop update interpolate & upsample api
上级 a9050465
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Showing with 314 addition and 357 deletion
python/paddle/fluid/layers/nn.py
浏览文件 @ 350d20fe
......@@ -7050,6 +7050,8 @@ def image_resize(input,
'TRILINEAR' : Trilinear interpolation
'NEAREST' : Nearest neighbor interpolation
'BICUBIC' : Bicubic interpolation
Linear interpolation is the method of using a line connecting two known quantities
to determine the value of an unknown quantity between the two known quantities.
......@@ -7068,6 +7070,11 @@ def image_resize(input,
interpolating functions of three variables (e.g. D-direction,
H-direction and W-direction in this op) on a rectilinear 3D grid.
The linear interpolation is performed on three directions.
Bicubic interpolation is an extension of cubic interpolation for interpolating
data points on a two-dimensional regular grid. The interpolated surface is
smoother than corresponding surfaces obtained by bilinear interpolation or
nearest-neighbor interpolation.
Align_corners and align_mode are optional parameters,the calculation method
of interpolation can be selected by them.
......@@ -7162,27 +7169,45 @@ def image_resize(input,
output: (N,C,D_out,H_out,W_out) where:
D_out = D_{in} * scale_{factor}
Trilinear interpolation:
if:
align_corners = False , align_mode = 0
input : (N,C,D_in,H_in,W_in)
output: (N,C,D_out,H_out,W_out) where:
D_out = (D_{in}+0.5) * scale_{factor} - 0.5
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,D_in,H_in,W_in)
output: (N,C,D_out,H_out,W_out) where:
D_out = D_{in} * scale_{factor}
H_out = H_{in} * scale_{factor}
W_out = W_{in} * scale_{factor}
For details of linear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Linear_interpolation.
For details of nearest neighbor interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation.
For details of bilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bilinear_interpolation.
For details of trilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Trilinear_interpolation.
For details of bicubic interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bicubic_interpolation
Parameters:
input (Variable): 4-D or 5-D Tensor, its data type is float32, float64, or uint8,
input (Variable): 3-D, 4-D or 5-D Tensor, its data type is float32, float64, or uint8,
its data format is specified by :attr:`data_format`.
out_shape(list|tuple|Variable|None): Output shape of image resize
layer, the shape is (out_h, out_w) when input is a 4-D Tensor and is
(out_d, out_h, out_w) when input is a 5-D Tensor. Default: None. If
a list, each element can be an integer or a Tensor Variable of shape: [1].
out_shape (list|tuple|Variable|None): Output shape of image resize
layer, the shape is (out_w, ) when input is a 3-D Tensor, the shape is (out_h, out_w)
when input is a 4-D Tensor and is (out_d, out_h, out_w) when input is a 5-D Tensor.
Default: None. If a list, each element can be an integer or a Tensor Variable of shape: [1].
If a Tensor Variable, its dimensions size should be a 1.
scale(float|Variable|None): The multiplier for the input height or width. At
least one of :attr:`out_shape` or :attr:`scale` must be set.
......@@ -7190,7 +7215,7 @@ def image_resize(input,
Default: None.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
resample(str): The resample method. It supports 'BILINEAR', 'TRILINEAR'
resample(str): The resample method. It supports 'LINEAR', 'BICUBIC', 'BILINEAR', 'TRILINEAR'
and 'NEAREST' currently. Default: 'BILINEAR'
actual_shape(Variable): An optional input to specify output shape
dynamically. If provided, image resize
......@@ -7209,26 +7234,27 @@ def image_resize(input,
input and output tensors are aligned, preserving the values at the
corner pixels.
Default: True
align_mode(int) : An optional for bilinear interpolation. can be \'0\'
for src_idx = scale*(dst_indx+0.5)-0.5 , can be \'1\' for
src_idx = scale*dst_index.
align_mode(int) : An optional for linear/bilinear/trilinear interpolation. Refer to the fomula in the
the example code above, it can be \'0\' for src_idx = scale*(dst_indx+0.5)-0.5 ,
can be \'1\' for src_idx = scale*dst_index.
data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`, `"NCDHW"`,
will be consistent with that of the input. An optional string from:`NCW`, `NWC`, `"NCHW"`, `"NHWC"`, `"NCDHW"`,
`"NDHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. When it is `"NCHW"`, the data is stored
in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`.
Returns:
A 3-D Tensor of the shape (num_batches, channels, out_w) or (num_batches, out_w, channels),
A 4-D Tensor of the shape (num_batches, channels, out_h, out_w) or (num_batches, out_h, out_w, channels),
or 5-D Tensor of the shape (num_batches, channels, out_d, out_h, out_w) or (num_batches, out_d, out_h, out_w, channels).
Raises:
TypeError: out_shape should be a list or tuple or Variable.
TypeError: actual_shape should either be Variable or None.
ValueError: The 'resample' of image_resize can only be 'BILINEAR',
'TRILINEAR' or 'NEAREST' currently.
ValueError: The 'resample' of image_resize can only be 'LINEAR', 'BILINEAR',
'TRILINEAR', 'BICUBIC' or 'NEAREST' currently.
ValueError: 'LINEAR' only support 3-D tensor.
ValueError: 'BILINEAR' and 'NEAREST' only support 4-D tensor.
ValueError: 'BICUBIC', 'BILINEAR' and 'NEAREST' only support 4-D tensor.
ValueError: 'TRILINEAR' only support 5-D tensor.
ValueError: One of out_shape and scale must not be None.
ValueError: out_shape length should be 1 for input 3-D tensor.
......@@ -7237,7 +7263,7 @@ def image_resize(input,
ValueError: scale should be greater than zero.
TypeError: align_corners should be a bool value
ValueError: align_mode can only be '0' or '1'
ValueError: data_format can only be 'NCW', 'NCHW', 'NHWC', 'NCDHW' or 'NDHWC'.
ValueError: data_format can only be 'NCW', 'NWC', 'NCHW', 'NHWC', 'NCDHW' or 'NDHWC'.
Examples:
.. code-block:: python
......@@ -7332,10 +7358,10 @@ def image_resize(input,
helper = LayerHelper('{}_interp'.format(resample_type), **locals())
dtype = helper.input_dtype()
if len(input.shape) == 3 and data_format not in ['NCHW', 'NHWC']:
if len(input.shape) == 3 and data_format not in ['NCW', 'NWC']:
raise ValueError(
"Got wrong value for param `data_format`: " + data_format +
" received but only `NCHW` or `NHWC` supported for 3-D input.")
" received but only `NCW` or `NWC` supported for 3-D input.")
elif len(input.shape) == 4 and data_format not in ['NCHW', 'NHWC']:
raise ValueError(
"Got wrong value for param `data_format`: " + data_format +
......@@ -7348,9 +7374,9 @@ def image_resize(input,
def _is_list_or_turple_(data):
return (isinstance(data, list) or isinstance(data, tuple))
if data_format == 'NCHW' or data_format == 'NCDHW':
if data_format == 'NCHW' or data_format == 'NCDHW' or data_format == 'NCW':
data_layout = 'NCHW'
if data_format == 'NHWC' or data_format == 'NDHWC':
if data_format == 'NHWC' or data_format == 'NDHWC' or data_format == 'NWC':
data_layout = 'NHWC'
inputs = {"X": input}
......@@ -7472,7 +7498,7 @@ def resize_linear(input,
actual_shape=None,
align_corners=True,
align_mode=1,
data_format='NCHW'):
data_format='NCW'):
"""
This op resizes the input by performing linear interpolation based on given
output shape which specified by actual_shape, out_shape and scale
......@@ -7515,7 +7541,7 @@ def resize_linear(input,
W_out = W_{in} * scale_{factor}
Parameters:
input(Variable): 3-D Tensor(NCHW), its data type is float32, float64, or uint8,
input(Variable): 3-D Tensor(NCW), its data type is float32, float64, or uint8,
its data format is specified by :attr:`data_format`.
out_shape(list|tuple|Variable|None): Output shape of resize linear
layer, the shape is (out_w,). Default: None. If a list, each
......@@ -7541,13 +7567,14 @@ def resize_linear(input,
align_corners(bool): ${align_corners_comment}
align_mode(bool): ${align_mode_comment}
data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. 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]`.
name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`
will be consistent with that of the input. An optional string from: `"NCW"`, `"NWC"`.
The default is `"NCW"`. When it is `"NCW"`, the data is stored in the order of:
`[batch_size, input_channels, input_width]`.
name(str, optional): The default value is None. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`
Returns:
Variable: 3-D tensor(NCHW or NHWC).
Variable: 3-D tensor(NCW or NWC).
Examples:
.. code-block:: python
......
python/paddle/fluid/tests/unittests/test_bicubic_interp_op.py
浏览文件 @ 350d20fe
......@@ -290,22 +290,18 @@ class TestBicubicInterpOpAPI(unittest.TestCase):
name="scale_tensor", shape=[1], dtype="float32")
out1 = interpolate(
x, out_shape=[12, 12], resample='BICUBIC', align_corners=False)
x, size=[12, 12], mode='bicubic', align_corners=False)
out2 = interpolate(
x, out_shape=[12, dim], resample='BICUBIC', align_corners=False)
x, size=[12, dim], mode='bicubic', align_corners=False)
out3 = interpolate(
x,
out_shape=shape_tensor,
resample='BICUBIC',
align_corners=False)
x, size=shape_tensor, mode='bicubic', align_corners=False)
out4 = interpolate(
x, size=[12, 12], mode='bicubic', align_corners=False)
out5 = interpolate(
x,
out_shape=[4, 4],
actual_shape=actual_size,
resample='BICUBIC',
scale_factor=scale_tensor,
mode='bicubic',
align_corners=False)
out5 = interpolate(
x, scale=scale_tensor, resample='BICUBIC', align_corners=False)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
......@@ -328,7 +324,7 @@ class TestBicubicInterpOpAPI(unittest.TestCase):
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(x_data)
interp = interpolate(
x, out_shape=[12, 12], resample='BICUBIC', align_corners=False)
x, size=[12, 12], mode='bicubic', align_corners=False)
dy_result = interp.numpy()
expect = bicubic_interp_np(
x_data, out_h=12, out_w=12, align_corners=False)
......@@ -348,28 +344,21 @@ class TestBicubicOpError(unittest.TestCase):
x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
out = interpolate(
x,
out_shape=[12, 12],
resample='UNKONWN',
align_corners=False)
x, size=[12, 12], mode='UNKONWN', align_corners=False)
def test_input_shape():
x = fluid.data(name="x", shape=[2], dtype="float32")
out = interpolate(
x,
out_shape=[12, 12],
resample='BICUBIC',
align_corners=False)
x, size=[12, 12], mode='BICUBIC', align_corners=False)
def test_align_corcers():
x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
interpolate(
x, out_shape=[12, 12], resample='BICUBIC', align_corners=3)
interpolate(x, size=[12, 12], mode='BICUBIC', align_corners=3)
def test_out_shape():
x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
out = interpolate(
x, out_shape=[12], resample='BICUBIC', align_corners=False)
x, size=[12], mode='bicubic', align_corners=False)
def test_attr_data_format():
# for 5-D input, data_format only can be NCDHW or NDHWC
......@@ -377,8 +366,8 @@ class TestBicubicOpError(unittest.TestCase):
name="input", shape=[2, 3, 6, 9, 4], dtype="float32")
out = interpolate(
input,
out_shape=[4, 8, 4, 5],
resample='TRILINEAR',
size=[4, 8, 4, 5],
mode='trilinear',
data_format='NHWC')
def test_actual_shape():
......@@ -386,20 +375,17 @@ class TestBicubicOpError(unittest.TestCase):
x = fluid.create_lod_tensor(
np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace())
out = interpolate(
x,
out_shape=[12, 12],
resample='BICUBIC',
align_corners=False)
x, size=[12, 12], mode='BICUBIC', align_corners=False)
def test_scale_value():
# the scale must be greater than zero.
x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
out = interpolate(
x,
out_shape=None,
resample='BICUBIC',
size=None,
mode='BICUBIC',
align_corners=False,
scale=-2.0)
scale_factor=-2.0)
def test_attr_5D_input():
# for 5-D input, data_format only can be NCDHW or NDHWC
......@@ -407,8 +393,8 @@ class TestBicubicOpError(unittest.TestCase):
name="input", shape=[2, 3, 6, 9, 4], dtype="float32")
out = interpolate(
input,
out_shape=[4, 8, 4, 5],
resample='TRILINEAR',
size=[4, 8, 4, 5],
mode='trilinear',
data_format='NDHWC')
def test_scale_type():
......@@ -418,29 +404,29 @@ class TestBicubicOpError(unittest.TestCase):
np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace())
out = interpolate(
x,
out_shape=None,
resample='BICUBIC',
size=None,
mode='bicubic',
align_corners=False,
scale=scale)
scale_factor=scale)
def test_align_mode():
x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
out = interpolate(
x,
out_shape=None,
resample='NEAREST',
size=None,
mode='nearest',
align_corners=False,
align_mode=2,
scale=1.0)
scale_factor=1.0)
def test_outshape_and_scale():
x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
out = interpolate(
x,
out_shape=None,
resample='BICUBIC',
size=None,
mode='bicubic',
align_corners=False,
scale=None)
scale_factor=None)
self.assertRaises(ValueError, test_mode_type)
self.assertRaises(ValueError, test_input_shape)
......
python/paddle/fluid/tests/unittests/test_linear_interp_op.py
浏览文件 @ 350d20fe
......@@ -211,106 +211,58 @@ class TestLinearInterpOpSizeTensor(TestLinearInterpOp):
self.outputs = {'Out': output_np}
class TestLinearInterpOpAPI(unittest.TestCase):
class TestResizeLinearAPI(unittest.TestCase):
def test_case(self):
x = fluid.data(name="x", shape=[1, 3, 128], dtype="float32")
x = fluid.data(name="x", shape=[1, 3, 64], dtype="float32")
dim = fluid.data(name="dim", shape=[1], dtype="int32")
shape_tensor = fluid.data(name="shape_tensor", shape=[1], dtype="int32")
actual_size = fluid.data(name="actual_size", shape=[1], dtype="int32")
scale_tensor = fluid.data(
name="scale_tensor", shape=[1], dtype="float32")
dim = fluid.data(name="dim", shape=[1], dtype="int32")
actual_size = fluid.data(name='actual_size', shape=[1], dtype='int32')
out1 = fluid.layers.resize_linear(
x, out_shape=[256, ], align_mode=1, align_corners=False)
x, out_shape=[128, ], align_mode=1, align_corners=False)
out2 = fluid.layers.resize_linear(
x, out_shape=shape_tensor, align_mode=1, align_corners=False)
x, out_shape=[128], align_mode=1, align_corners=False)
out3 = fluid.layers.resize_linear(
x, scale=scale_tensor, align_mode=1, align_corners=False)
x, out_shape=shape_tensor, align_mode=1, align_corners=False)
out4 = fluid.layers.resize_linear(
x, out_shape=[dim, ], align_mode=1, align_corners=False)
out5 = fluid.layers.resize_linear(
x,
out_shape=[256, ],
out_shape=[128, ],
actual_shape=actual_size,
align_mode=1,
align_corners=False)
out5 = fluid.layers.resize_linear(
x, scale=scale_tensor, align_mode=1, align_corners=False)
x_data = np.random.random((1, 3, 128)).astype("float32")
shape_data = np.array([256, ]).astype("int32")
scale_data = np.array([2.0, ]).astype("float32")
dim_data = np.array([256, ]).astype("int32")
actual_size_data = np.array([256, ]).astype("int32")
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
results = exe.run(fluid.default_main_program(),
feed={
"x": x_data,
"shape_tensor": shape_data,
"scale_tensor": scale_data,
"dim": dim_data,
'actual_size': actual_size_data,
},
fetch_list=[out1, out2, out3, out4, out5],
return_numpy=True)
expect_res = linear_interp_np(
x_data, out_w=256, align_mode=1, align_corners=False)
for res in results:
self.assertTrue(np.allclose(res, expect_res))
class TestLinearInterpOpAPI2_Func(unittest.TestCase):
def test_case(self):
x = fluid.data(name="x", shape=[1, 3, 128], dtype="float32")
shape_tensor = fluid.data(name="shape_tensor", shape=[1], dtype="int32")
scale_tensor = fluid.data(
name="scale_tensor", shape=[1], dtype="float32")
dim = fluid.data(name="dim", shape=[1], dtype="int32")
actual_size = fluid.data(name='actual_size', shape=[1], dtype='int32')
out1 = interpolate(
x,
out_shape=[256, ],
align_mode=1,
align_corners=False,
resample='LINEAR')
out2 = interpolate(
x,
out_shape=shape_tensor,
align_mode=1,
align_corners=False,
resample='LINEAR')
out3 = interpolate(
out6 = interpolate(
x,
scale=scale_tensor,
scale_factor=scale_tensor,
mode='linear',
align_mode=1,
align_corners=False,
resample='LINEAR')
out4 = interpolate(
data_format='NCW')
out7 = interpolate(
x,
out_shape=[dim, ],
size=[128, ],
mode='linear',
align_mode=1,
align_corners=False,
resample='LINEAR')
out5 = interpolate(
data_format='NCW')
out8 = interpolate(
x,
out_shape=[256, ],
actual_shape=actual_size,
size=shape_tensor,
mode='linear',
align_mode=1,
align_corners=False,
resample='LINEAR')
data_format='NCW')
x_data = np.random.random((1, 3, 128)).astype("float32")
shape_data = np.array([256, ]).astype("int32")
scale_data = np.array([2.0, ]).astype("float32")
dim_data = np.array([256, ]).astype("int32")
actual_size_data = np.array([256, ]).astype("int32")
x_data = np.random.random((1, 3, 64)).astype("float32")
dim_data = np.array([128]).astype("int32")
shape_data = np.array([128, ]).astype("int32")
actual_size_data = np.array([128, ]).astype("int32")
scale_data = np.array([2.0]).astype("float32")
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
......@@ -318,20 +270,20 @@ class TestLinearInterpOpAPI2_Func(unittest.TestCase):
place = core.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
results = exe.run(fluid.default_main_program(),
feed={
"x": x_data,
"shape_tensor": shape_data,
"scale_tensor": scale_data,
"dim": dim_data,
'actual_size': actual_size_data,
},
fetch_list=[out1, out2, out3, out4, out5],
return_numpy=True)
results = exe.run(
fluid.default_main_program(),
feed={
"x": x_data,
"dim": dim_data,
"shape_tensor": shape_data,
"actual_size": actual_size_data,
"scale_tensor": scale_data
},
fetch_list=[out1, out2, out3, out4, out5, out6, out7, out8],
return_numpy=True)
expect_res = linear_interp_np(
x_data, out_w=256, align_mode=1, align_corners=False)
x_data, out_w=128, align_mode=1, align_corners=False)
for res in results:
self.assertTrue(np.allclose(res, expect_res))
......@@ -342,10 +294,11 @@ class TestLinearInterpOpAPI2_0(unittest.TestCase):
# dygraph
x_data = np.random.random((1, 3, 128)).astype("float32")
us_1 = paddle.nn.UpSample(
out_shape=[64, ],
resample='LINEAR',
size=[64, ],
mode='linear',
align_mode=1,
align_corners=False)
align_corners=False,
data_format='NCW')
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(x_data)
interp = us_1(x)
......@@ -356,7 +309,7 @@ class TestLinearInterpOpAPI2_0(unittest.TestCase):
self.assertTrue(np.allclose(interp.numpy(), expect))
class TestLinearInterpOpUint8(OpTest):
class TestResizeLinearOpUint8(OpTest):
def setUp(self):
self.out_size = None
self.actual_shape = None
......@@ -433,24 +386,22 @@ class TestLinearInterpOpError(unittest.TestCase):
def input_shape_error():
x1 = fluid.data(name="x1", shape=[1], dtype="float32")
out1 = paddle.nn.UpSample(
out_shape=[256, ], data_format='NCW', resample='LINEAR')
size=[256, ], data_format='NCW', mode='linear')
out1_res = out1(x1)
def data_format_error():
x2 = fluid.data(name="x2", shape=[1, 3, 128], dtype="float32")
out2 = paddle.nn.UpSample(
out_shape=[256, ], data_format='NHWCD', resample='LINEAR')
size=[256, ], data_format='NHWCD', mode='linear')
out2_res = out2(x2)
def out_shape_error():
x3 = fluid.data(name="x3", shape=[1, 3, 128], dtype="float32")
out3 = paddle.nn.UpSample(
out_shape=[
size=[
256,
256,
],
data_format='NHWC',
resample='LINEAR')
], data_format='NHWC', mode='linear')
out3_res = out3(x3)
self.assertRaises(ValueError, input_shape_error)
......
python/paddle/fluid/tests/unittests/test_trilinear_interp_op.py
浏览文件 @ 350d20fe
......@@ -588,14 +588,11 @@ class TestTrilinearInterpAPI(unittest.TestCase):
x, out_shape=[4, 4, 8], actual_shape=actual_size)
out5 = fluid.layers.resize_trilinear(x, scale=scale_tensor)
out6 = interpolate(
x, scale=scale_tensor, resample='TRILINEAR', data_format="NCDHW")
x, scale_factor=scale_tensor, mode='trilinear', data_format="NCDHW")
out7 = interpolate(
x, out_shape=[4, 4, 8], resample='TRILINEAR', data_format="NCDHW")
x, size=[4, 4, 8], mode='trilinear', data_format="NCDHW")
out8 = interpolate(
x,
out_shape=shape_tensor,
resample='TRILINEAR',
data_format="NCDHW")
x, size=shape_tensor, mode='trilinear', data_format="NCDHW")
x_data = np.random.random((2, 3, 6, 9, 4)).astype("float32")
dim_data = np.array([18]).astype("int32")
......
python/paddle/nn/functional/common.py
浏览文件 @ 350d20fe
......@@ -45,31 +45,34 @@ __all__ = [
def interpolate(input,
out_shape=None,
scale=None,
name=None,
resample='BILINEAR',
actual_shape=None,
align_corners=True,
size=None,
scale_factor=None,
mode='nearest',
align_corners=False,
align_mode=1,
data_format='NCHW'):
data_format='NCHW',
name=None):
"""
:alias_main: paddle.nn.functional.interpolate
:alias: paddle.nn.functional.interpolate,paddle.nn.functional.common.interpolate
This op resizes a batch of images.
The input must be a 4-D Tensor of the shape (num_batches, channels, in_h, in_w)
or (num_batches, in_h, in_w, channels), or a 5-D Tensor of the shape
The input must be a 3-D Tensor of the shape (num_batches, channels, in_w)
or 4-D (num_batches, channels, in_h, in_w), or a 5-D Tensor of the shape
(num_batches, channels, in_d, in_h, in_w) or (num_batches, in_d, in_h, in_w, channels),
and the resizing only applies on the three dimensions(depth, height and width).
**Warning:** the parameter :attr:`actual_shape` will be deprecated in the
future and only use :attr:`out_shape` instead.
Supporting resample methods:
'BILINEAR' : Bilinear interpolation
'TRILINEAR' : Trilinear interpolation
'NEAREST' : Nearest neighbor interpolation
'BICUBIC' : Bicubic interpolation
'linear' : Linear interpolation
'bilinear' : Bilinear interpolation
'trilinear' : Trilinear interpolation
'nearest' : Nearest neighbor interpolation
'bicubic' : Bicubic interpolation
Linear interpolation is the method of using a line connecting two known quantities
to determine the value of an unknown quantity between the two known quantities.
Nearest neighbor interpolation is to perform nearest neighbor interpolation
in both the 3rd dimension(in height direction) and the 4th dimension(in width
direction) on input tensor.
......@@ -96,18 +99,24 @@ def interpolate(input,
.. code-block:: text
For scale:
For scale_factor:
if align_corners = True && out_size > 1 :
scale_factor = (in_size-1.0)/(out_size-1.0)
else:
scale_factor = float(in_size/out_size)
Linear interpolation:
if:
align_corners = False , align_mode = 0
input : (N,C,W_in)
output: (N,C,W_out) where:
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,W_in)
output: (N,C,W_out) where:
W_out = W_{in} * scale_{factor}
Nearest neighbor interpolation:
if:
align_corners = False
input : (N,C,H_in,W_in)
......@@ -120,35 +129,28 @@ def interpolate(input,
output: (N,C,H_out,W_out) where:
H_out = round(H_{in} * scale_{factor})
W_out = round(W_{in} * scale_{factor})
Bilinear interpolation:
if:
align_corners = False , align_mode = 0
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = H_{in} * scale_{factor}
W_out = W_{in} * scale_{factor}
Bicubic interpolation:
if:
align_corners = False
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = H_{in} * scale_{factor}
......@@ -157,87 +159,82 @@ def interpolate(input,
Trilinear interpolation:
if:
align_corners = False , align_mode = 0
input : (N,C,D_in,H_in,W_in)
output: (N,C,D_out,H_out,W_out) where:
D_out = (D_{in}+0.5) * scale_{factor} - 0.5
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,D_in,H_in,W_in)
output: (N,C,D_out,H_out,W_out) where:
D_out = D_{in} * scale_{factor}
H_out = H_{in} * scale_{factor}
W_out = W_{in} * scale_{factor}
For details of linear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Linear_interpolation.
For details of nearest neighbor interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation.
For details of bilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bilinear_interpolation.
For details of trilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Trilinear_interpolation.
For details of bicubic interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bicubic_interpolation
Parameters:
input (Variable): 4-D or 5-D Tensor, its data type is float32, float64, or uint8,
input (Variable): 3-D, 4-D or 5-D Tensor, its data type is float32, float64, or uint8,
its data format is specified by :attr:`data_format`.
out_shape(list|tuple|Variable|None): Output shape of image resize
layer, the shape is (out_h, out_w) when input is a 4-D Tensor and is
(out_d, out_h, out_w) when input is a 5-D Tensor. Default: None. If
a list, each element can be an integer or a Tensor Variable of shape: [1].
size (list|tuple|Variable|None): Output shape of image resize
layer, the shape is (out_w, ) when input is a 3-D Tensor, the shape is (out_h, out_w)
when input is a 4-D Tensor and is (out_d, out_h, out_w) when input is a 5-D Tensor.
Default: None. If a list, each element can be an integer or a Tensor Variable of shape: [1].
If a Tensor Variable, its dimensions size should be a 1.
scale(float|Variable|None): The multiplier for the input height or width. At
least one of :attr:`out_shape` or :attr:`scale` must be set.
And :attr:`out_shape` has a higher priority than :attr:`scale`.
scale_factor (float|Variable|None): The multiplier for the input height or width. At
least one of :attr:`out_shape` or :attr:`scale_factor` must be set.
And :attr:`out_shape` has a higher priority than :attr:`scale_factor`.
Default: None.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
resample(str): The resample method. It supports 'BILINEAR', 'TRILINEAR' ,
'BICUBIC' and 'NEAREST' currently. Default: 'BILINEAR'
actual_shape(Variable): An optional input to specify output shape
dynamically. If provided, image resize
according to this given shape rather than
:attr:`out_shape` and :attr:`scale` specifying
shape. That is to say actual_shape has the
highest priority. It is recommended to use
:attr:`out_shape` if you want to specify output
shape dynamically, because :attr:`actual_shape`
will be deprecated. When using actual_shape to
specify output shape, one of :attr:`out_shape`
and :attr:`scale` should also be set, otherwise
errors would be occurred in graph constructing stage.
Default: None
mode (str): The resample method. It supports 'linear', 'nearest', 'bilinear',
'bicubic' and 'trilinear' currently. Default: 'nearest'
align_corners(bool) : An optional bool, If True, the centers of the 4 corner pixels of the
input and output tensors are aligned, preserving the values at the
corner pixels.
Default: True
align_mode(int) : An optional for bilinear interpolation. can be \'0\'
for src_idx = scale*(dst_indx+0.5)-0.5 , can be \'1\' for
src_idx = scale*dst_index.
Default: False
align_mode(int) : An optional for linear/bilinear/trilinear interpolation. Refer to the formula in the example above,
it can be \'0\' for src_idx = scale_factor*(dst_indx+0.5)-0.5 , can be \'1\' for
src_idx = scale_factor*dst_index.
data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`, `"NCDHW"`,
will be consistent with that of the input. An optional string from:`NCW`, `NWC`, `"NCHW"`, `"NHWC"`, `"NCDHW"`,
`"NDHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. When it is `"NCHW"`, the data is stored
in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`.
name(str, optional): The default value is None.
Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`
Returns:
A 3-D Tensor of the shape (num_batches, channels, out_w) or (num_batches, out_w, channels),
A 4-D Tensor of the shape (num_batches, channels, out_h, out_w) or (num_batches, out_h, out_w, channels),
or 5-D Tensor of the shape (num_batches, channels, out_d, out_h, out_w) or (num_batches, out_d, out_h, out_w, channels).
Raises:
TypeError: out_shape should be a list or tuple or Variable.
TypeError: actual_shape should either be Variable or None.
ValueError: The 'resample' of image_resize can only be 'BILINEAR',
'TRILINEAR', 'BICUBIC', or 'NEAREST' currently.
ValueError: 'BILINEAR', 'BICUBIC' and 'NEAREST' only support 4-D tensor.
ValueError: 'TRILINEAR' only support 5-D tensor.
ValueError: One of out_shape and scale must not be None.
ValueError: out_shape length should be 2 for input 4-D tensor.
ValueError: out_shape length should be 3 for input 5-D tensor.
ValueError: scale should be greater than zero.
TypeError: size should be a list or tuple or Variable.
ValueError: The 'mode' of image_resize can only be 'linear', 'bilinear',
'trilinear', 'bicubic', or 'nearest' currently.
ValueError: 'linear' only support 3-D tensor.
ValueError: 'bilinear', 'bicubic' and 'nearest' only support 4-D tensor.
ValueError: 'trilinear' only support 5-D tensor.
ValueError: One of size and scale_factor must not be None.
ValueError: size length should be 1 for input 3-D tensor.
ValueError: size length should be 2 for input 4-D tensor.
ValueError: size length should be 3 for input 5-D tensor.
ValueError: scale_factor should be greater than zero.
TypeError: align_corners should be a bool value
ValueError: align_mode can only be '0' or '1'
ValueError: data_format can only be 'NCHW', 'NHWC', 'NCDHW' or 'NDHWC'.
ValueError: data_format can only be 'NCW', 'NWC', 'NCHW', 'NHWC', 'NCDHW' or 'NDHWC'.
Examples:
.. code-block:: python
......@@ -246,22 +243,22 @@ def interpolate(input,
import numpy as np
input = fluid.data(name="input", shape=[None,3,6,10])
#1
output = paddle.nn.functional.interpolate(input=input,out_shape=[12,12])
output = paddle.nn.functional.interpolate(input=input, size=[12,12])
#2
#x = np.array([2]).astype("int32")
#dim1 = fluid.data(name="dim1", shape=[1], dtype="int32")
#fluid.layers.assign(input=x, output=dim1)
#output = paddle.nn.functional.interpolate(input=input,out_shape=[12,dim1])
#output = paddle.nn.functional.interpolate(input=input, size=[12,dim1])
#3
#x = np.array([3,12]).astype("int32")
#shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
#fluid.layers.assign(input=x, output=shape_tensor)
#output = paddle.nn.functional.interpolate(input=input,out_shape=shape_tensor)
#output = paddle.nn.functional.interpolate(input=input, size=shape_tensor)
#4
#x = np.array([0.5]).astype("float32")
#scale_tensor = fluid.data(name="scale", shape=[1], dtype="float32")
#fluid.layers.assign(x,scale_tensor)
#output = paddle.nn.functional.interpolate(input=input,scale=scale_tensor)
#output = paddle.nn.functional.interpolate(input=input, scale_factor=scale_tensor)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
......@@ -285,46 +282,51 @@ def interpolate(input,
import paddle.fluid.dygraph as dg
with dg.guard(place) as g:
input = dg.to_variable(input_data)
output = paddle.nn.functional.interpolate(input=input, out_shape=[12,12])
output = paddle.nn.functional.interpolate(input=input, size=[12,12])
print(output.shape)
# [2L, 3L, 12L, 12L]
"""
resample_methods = {
'LINEAR': 'linear',
'BILINEAR': 'bilinear',
'TRILINEAR': 'trilinear',
'NEAREST': 'nearest',
'BICUBIC': 'bicubic',
}
data_format = data_format.upper()
resample = mode.upper()
resample_type = mode.lower()
resample_methods = [
'LINEAR',
'BILINEAR',
'TRILINEAR',
'NEAREST',
'BICUBIC',
]
if resample not in resample_methods:
raise ValueError(
"The 'resample' of image_resize can only be 'LINEAR', 'BILINEAR', 'TRILINEAR', "
" 'BICUBIC' or 'NEAREST' currently.")
resample_type = resample_methods[resample]
"The 'resample' of image_resize can only be 'linaer', 'bilinear', 'trilinear', "
" 'bicubic' or 'nearest' currently.")
if resample in ['LINEAR'] and len(input.shape) != 3:
raise ValueError("'LINEAR' only support 3-D tensor.")
raise ValueError("'linear' only support 3-D tensor.")
if resample in ['BILINEAR', 'NEAREST', 'BICUBIC'] and len(input.shape) != 4:
raise ValueError(
"'BILINEAR', 'BICUBIC' and 'NEAREST' only support 4-D tensor.")
"'bilinear', 'bicubic' and 'nearest' only support 4-D tensor.")
if resample == 'TRILINEAR' and len(input.shape) != 5:
raise ValueError("'TRILINEAR'only support 5-D tensor.")
raise ValueError("'trilinear'only support 5-D tensor.")
if size is None and scale_factor is None:
raise ValueError("One of size and scale_factor must not be None.")
if not isinstance(align_corners, bool):
raise TypeError("Attr align_corners should be a bool value")
if align_mode != 0 and align_mode != 1:
raise ValueError("align_mode can only be 0 or 1")
if out_shape is None and scale is None:
raise ValueError("One of out_shape and scale must not be None.")
helper = LayerHelper('{}_interp'.format(resample_type), **locals())
dtype = helper.input_dtype()
if len(input.shape) == 3 and data_format not in ['NCHW', 'NHWC']:
if len(input.shape) == 3 and data_format not in ['NCW', 'NWC']:
raise ValueError(
"Got wrong value for param `data_format`: " + data_format +
" received but only `NCHW` or `NHWC` supported for 3-D input.")
" received but only `NCW` or `NWC` supported for 3-D input.")
elif len(input.shape) == 4 and data_format not in ['NCHW', 'NHWC']:
raise ValueError(
"Got wrong value for param `data_format`: " + data_format +
......@@ -337,9 +339,9 @@ def interpolate(input,
def _is_list_or_turple_(data):
return (isinstance(data, list) or isinstance(data, tuple))
if data_format == 'NCHW' or data_format == 'NCDHW':
if data_format == 'NCHW' or data_format == 'NCDHW' or data_format == 'NCW':
data_layout = 'NCHW'
if data_format == 'NHWC' or data_format == 'NDHWC':
if data_format == 'NHWC' or data_format == 'NDHWC' or data_format == 'NWC':
data_layout = 'NHWC'
inputs = {"X": input}
......@@ -353,6 +355,8 @@ def interpolate(input,
"data_layout": data_layout
}
out_shape = size
scale = scale_factor
if out_shape is not None:
if isinstance(out_shape, Variable):
out_shape.stop_gradient = True
......@@ -435,16 +439,6 @@ def interpolate(input,
raise TypeError(
"Attr(scale)'s type should be float, int or Variable.")
if isinstance(actual_shape, Variable):
warnings.warn(
"actual_shape will be deprecated, it is recommended to use "
"out_shape instead of actual_shape to specify output shape dynamically."
)
actual_shape.stop_gradient = True
inputs["OutSize"] = actual_shape
elif actual_shape is not None:
raise TypeError("actual_shape should either be Variable or None.")
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='{}_interp'.format(resample_type),
......
python/paddle/nn/layer/common.py
浏览文件 @ 350d20fe
......@@ -33,12 +33,12 @@ class UpSample(layers.Layer):
**Warning:** the parameter :attr:`actual_shape` will be deprecated in the
future and only use :attr:`out_shape` instead.
Supporting resample methods:
'LINEAR' : linear interpolation
'BILINEAR' : Bilinear interpolation
'TRILINEAR' : Trilinear interpolation
'NEAREST' : Nearest neighbor interpolation
'BICUBIC' : Bicubic interpolation
'linear' : Linear interpolation
'bilinear' : Bilinear interpolation
'trilinear' : Trilinear interpolation
'nearest' : Nearest neighbor interpolation
'bicubic' : Bicubic interpolation
Linear interpolation is the method of using a line connecting two known quantities
to determine the value of an unknown quantity between the two known quantities.
......@@ -51,6 +51,11 @@ class UpSample(layers.Layer):
W-direction in this op) on a rectilinear 2D grid. The key idea is
to perform linear interpolation first in one direction, and then
again in the other direction.
Bicubic interpolation is an extension of cubic interpolation for interpolating
data points on a two-dimensional regular grid. The interpolated surface is
smoother than corresponding surfaces obtained by bilinear interpolation or
nearest-neighbor interpolation.
Trilinear interpolation is an extension of linear interpolation for
interpolating functions of three variables (e.g. D-direction,
......@@ -59,24 +64,26 @@ class UpSample(layers.Layer):
Align_corners and align_mode are optional parameters,the calculation method
of interpolation can be selected by them.
Bicubic interpolation is an extension of cubic interpolation for interpolating
data points on a two-dimensional regular grid. The interpolated surface is
smoother than corresponding surfaces obtained by bilinear interpolation or
nearest-neighbor interpolation.
Example:
.. code-block:: text
For scale:
For scale_factor:
if align_corners = True && out_size > 1 :
scale_factor = (in_size-1.0)/(out_size-1.0)
else:
scale_factor = float(in_size/out_size)
Linear interpolation:
if:
align_corners = False , align_mode = 0
input : (N,C,W_in)
output: (N,C,W_out) where:
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,W_in)
output: (N,C,W_out) where:
W_out = W_{in} * scale_{factor}
Nearest neighbor interpolation:
if:
......@@ -91,26 +98,17 @@ class UpSample(layers.Layer):
output: (N,C,H_out,W_out) where:
H_out = round(H_{in} * scale_{factor})
W_out = round(W_{in} * scale_{factor})
Linear interpolation:
if:
align_corners = False , align_mode = 0
input : (N,C,W_in)
output: (N,C,W_out) where:
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,W_in)
output: (N,C,W_out) where:
W_out = W_{in} * scale_{factor}
Bilinear interpolation:
if:
align_corners = False , align_mode = 0
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = H_{in} * scale_{factor}
......@@ -123,6 +121,7 @@ class UpSample(layers.Layer):
output: (N,C,H_out,W_out) where:
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
else:
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
......@@ -144,73 +143,76 @@ class UpSample(layers.Layer):
H_out = H_{in} * scale_{factor}
W_out = W_{in} * scale_{factor}
https://en.wikipedia.org/wiki/Linear_interpolation.
For details of linear interpolation, please refer to Wikipedia:
For details of nearest neighbor interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation.
For details of bilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Linear_interpolation.
For details of bilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bilinear_interpolation.
For details of bicubic interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bicubic_interpolation
For details of trilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Trilinear_interpolation.
For details of bicubic interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bicubic_interpolation
Parameters:
input (Variable): 3-D, 4-D or 5-D Tensor, its data type is float32, float64, or uint8,
its data format is specified by :attr:`data_format`.
out_shape(list|tuple|Variable|None): Output shape of image resize
layer, the shape is (out_w, ) when input is 3-D Tensor ,
the shape is (out_h, out_w) when input is a 4-D Tensor and is
(out_d, out_h, out_w) when input is a 5-D Tensor. Default: None. If
a list, each element can be an integer or a Tensor Variable of shape: [1].
size (list|tuple|Variable|None): Output shape of image resize
layer, the shape is (out_w, ) when input is a 3-D Tensor, the shape is (out_h, out_w)
when input is a 4-D Tensor and is (out_d, out_h, out_w) when input is a 5-D Tensor.
Default: None. If a list, each element can be an integer or a Tensor Variable of shape: [1].
If a Tensor Variable, its dimensions size should be a 1.
scale(float|Variable|None): The multiplier for the input height or width. At
least one of :attr:`out_shape` or :attr:`scale` must be set.
And :attr:`out_shape` has a higher priority than :attr:`scale`.
scale_factor (float|Variable|None): The multiplier for the input height or width. At
least one of :attr:`out_shape` or :attr:`scale_factor` must be set.
And :attr:`out_shape` has a higher priority than :attr:`scale_factor`.
Default: None.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
resample(str): The resample method. It supports 'LINEAR', 'BILINEAR', 'TRILINEAR' ,
'BICUBIC' and 'NEAREST' currently. Default: 'BILINEAR'
mode (str): The resample method. It supports 'linear', 'nearest', 'bilinear',
'bicubic' and 'trilinear' currently. Default: 'nearest'
align_corners(bool) : An optional bool, If True, the centers of the 4 corner pixels of the
input and output tensors are aligned, preserving the values at the
corner pixels.
Default: True
align_mode(int) : An optional for bilinear interpolation. can be \'0\'
for src_idx = scale*(dst_indx+0.5)-0.5 , can be \'1\' for
src_idx = scale*dst_index.
Default: False
align_mode(int) : An optional for linear/bilinear/trilinear interpolation. Refer to the formula in the example above,
it can be \'0\' for src_idx = scale_factor*(dst_indx+0.5)-0.5 , can be \'1\' for
src_idx = scale_factor*dst_index.
data_format (str, optional): Specify the data format of the input, and the data format of the output
will be consistent with that of the input. An optional string from:'NCW', `"NCHW"`, `"NHWC"`, `"NCDHW"`,
will be consistent with that of the input. An optional string from:`NCW`, `NWC`, `"NCHW"`, `"NHWC"`, `"NCDHW"`,
`"NDHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. When it is `"NCHW"`, the data is stored
in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`.
name(str, optional): The default value is None.
Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`
Returns:
A 3-D Tensor of the shape (num_batches, channels, out_w) or (num_batches, out_w, channels),
A 4-D Tensor of the shape (num_batches, channels, out_h, out_w) or (num_batches, out_h, out_w, channels),
or 5-D Tensor of the shape (num_batches, channels, out_d, out_h, out_w) or (num_batches, out_d, out_h, out_w, channels).
or 5-D Tensor of the shape (num_batches, channels, out_d, out_h, out_w) or (num_batches, out_d, out_h, out_w, channels).
Raises:
TypeError: out_shape should be a list or tuple or Variable.
TypeError: actual_shape should either be Variable or None.
ValueError: The 'resample' of image_resize can only be 'BILINEAR',
'TRILINEAR', 'BICUBIC', or 'NEAREST' currently.
ValueError: 'BILINEAR', 'BICUBIC' and 'NEAREST' only support 4-D tensor.
ValueError: 'TRILINEAR' only support 5-D tensor.
ValueError: One of out_shape and scale must not be None.
ValueError: out_shape length should be 2 for input 4-D tensor.
ValueError: out_shape length should be 3 for input 5-D tensor.
ValueError: scale should be greater than zero.
TypeError: size should be a list or tuple or Variable.
ValueError: The 'mode' of image_resize can only be 'linear', 'bilinear',
'trilinear', 'bicubic', or 'nearest' currently.
ValueError: 'linear' only support 3-D tensor.
ValueError: 'bilinear', 'bicubic' and 'nearest' only support 4-D tensor.
ValueError: 'trilinear' only support 5-D tensor.
ValueError: One of size and scale_factor must not be None.
ValueError: size length should be 1 for input 3-D tensor.
ValueError: size length should be 2 for input 4-D tensor.
ValueError: size length should be 3 for input 5-D tensor.
ValueError: scale_factor should be greater than zero.
TypeError: align_corners should be a bool value
ValueError: align_mode can only be '0' or '1'
ValueError: data_format can only be 'NCW', 'NCHW', 'NHWC', 'NCDHW' or 'NDHWC'.
ValueError: data_format can only be 'NCW', 'NWC', 'NCHW', 'NHWC', 'NCDHW' or 'NDHWC'.
Examples:
.. code-block:: python
import paddle
import numpy as np
import paddle.fluid.dygraph as dg
upsample_op = paddle.nn.UpSample(out_shape=[12,12])
upsample_op = paddle.nn.UpSample(size=[12,12])
input_data = np.random.rand(2,3,6,10).astype("float32")
place = paddle.fluid.CPUPlace()
with dg.guard(place) as g:
......@@ -221,16 +223,16 @@ class UpSample(layers.Layer):
"""
def __init__(self,
out_shape=None,
scale=None,
resample='BILINEAR',
align_corners=True,
size=None,
scale_factor=None,
mode='nearest',
align_corners=False,
align_mode=1,
data_format='NCHW'):
super(UpSample, self).__init__()
self.out_shape = out_shape
self.scale = scale
self.resample = resample
self.size = size
self.scale_factor = scale_factor
self.mode = mode.lower()
self.align_corners = align_corners
self.align_mode = align_mode
self.data_format = data_format
......@@ -238,9 +240,9 @@ class UpSample(layers.Layer):
def forward(self, input):
out = F.interpolate(
input,
out_shape=self.out_shape,
scale=self.scale,
resample=self.resample,
size=self.size,
scale_factor=self.scale_factor,
mode=self.mode,
align_corners=self.align_corners,
align_mode=self.align_mode,
data_format=self.data_format)
......
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