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“0ba63475659822bd146f1f1dcfc7eabca8b7047d”上不存在“paddle/fluid/framework/data_device_transform.h”
未验证 提交 790e7c38 编写于 作者: Y yunyaoXYY 提交者： GitHub
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[Fluid Clean] Clean image_resize, resize_bilinear, resize_trilinear and resize_nearest. (#48691) 

* clean fluild resize_trilinear

* clean fluild resize_bilinear

* clean fluild resize_nearest

* clean fluid image_resize

* fix test_trt_nearest_interp_op.py

* fix yolov3.py

* fix yolov3.py
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python/paddle/fluid/layers/nn.py
浏览文件 @ 790e7c38
......@@ -81,10 +81,6 @@ __all__ = [
'autoincreased_step_counter',
'unsqueeze',
'lod_reset',
'image_resize',
'resize_bilinear',
'resize_trilinear',
'resize_nearest',
'relu',
'elementwise_add',
'elementwise_div',
......@@ -3149,1049 +3145,6 @@ def lod_reset(x, y=None, target_lod=None):
return out
def image_resize(
input,
out_shape=None,
scale=None,
name=None,
resample='BILINEAR',
actual_shape=None,
align_corners=True,
align_mode=1,
data_format='NCHW',
):
"""
This op resizes a batch of images.
The input must be a 3-D Tensor of the shape (num_batches, channels, in_w)
or 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
(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:
'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.
Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and
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.
Trilinear interpolation is an extension of linear interpolation for
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.
Example:
.. code-block:: text
For scale:
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)
Nearest neighbor interpolation:
if:
align_corners = False
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = floor (H_{in} * scale_{factor})
W_out = floor (W_{in} * scale_{factor})
else:
align_corners = True
input : (N,C,H_in,W_in)
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,H_out,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}
W_out = W_{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}
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): 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 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`.
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', 'BICUBIC', 'BILINEAR', 'TRILINEAR'
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
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 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:`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 'LINEAR', 'BILINEAR',
'TRILINEAR', 'BICUBIC' or 'NEAREST' currently.
ValueError: 'LINEAR' only support 3-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.
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: align_corners should be a bool value
ValueError: align_mode can only be '0' or '1'
ValueError: data_format can only be 'NCW', 'NWC', 'NCHW', 'NHWC', 'NCDHW' or 'NDHWC'.
Examples:
.. code-block:: python
#declarative mode
import paddle
import paddle.fluid as fluid
import numpy as np
paddle.enable_static()
input = fluid.data(name="input", shape=[None,3,6,10])
#1
output = fluid.layers.image_resize(input=input,out_shape=[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 = fluid.layers.image_resize(input=input,out_shape=[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 = fluid.layers.image_resize(input=input,out_shape=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 = fluid.layers.image_resize(input=input,scale=scale_tensor)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
input_data = np.random.rand(2,3,6,10).astype("float32")
output_data = exe.run(fluid.default_main_program(),
feed={"input":input_data},
fetch_list=[output],
return_numpy=True)
print(output_data[0].shape)
#1
# (2, 3, 12, 12)
#2
# (2, 3, 12, 2)
#3
# (2, 3, 3, 12)
#4
# (2, 3, 3, 5)
#imperative mode
import paddle.fluid.dygraph as dg
with dg.guard(place) as g:
input = dg.to_variable(input_data)
output = fluid.layers.image_resize(input=input, out_shape=[12,12])
print(output.shape)
# [2L, 3L, 12L, 12L]
"""
resample_methods = {
'LINEAR': 'linear',
'BILINEAR': 'bilinear',
'TRILINEAR': 'trilinear',
'NEAREST': 'nearest',
'LINEAR': 'linear',
}
resample = resample.upper()
if resample not in resample_methods:
raise ValueError(
"The 'resample' of image_resize can only be 'LINEAR', 'BILINEAR', 'TRILINEAR' "
"or 'NEAREST' currently."
)
resample_type = resample_methods[resample]
if resample == 'LINEAR' and len(input.shape) != 3:
raise ValueError("'LINER only support 3-D tensor.")
elif resample in ['BILINEAR', 'NEAREST'] and len(input.shape) != 4:
raise ValueError("'BILINEAR' and 'NEAREST' only support 4-D tensor.")
elif resample == 'TRILINEAR' and len(input.shape) != 5:
raise ValueError("'TRILINEAR'only support 5-D tensor.")
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 ['NCW', 'NWC']:
raise ValueError(
"Got wrong value for param `data_format`: "
+ data_format
+ " 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
+ " received but only `NCHW` or `NHWC` supported for 4-D input."
)
elif len(input.shape) == 5 and data_format not in ['NCDHW', 'NDHWC']:
raise ValueError(
"Got wrong value for param `data_format`: "
+ data_format
+ " received but only `NCDHW` or `NDHWC` supported for 5-D input."
)
def _is_list_or_turple_(data):
return isinstance(data, list) or isinstance(data, tuple)
if data_format == 'NCHW' or data_format == 'NCDHW' or data_format == 'NCW':
data_layout = 'NCHW'
if data_format == 'NHWC' or data_format == 'NDHWC' or data_format == 'NWC':
data_layout = 'NHWC'
inputs = {"X": input}
attrs = {
"out_d": -1,
"out_h": -1,
"out_w": -1,
"interp_method": resample_type,
"align_corners": align_corners,
"align_mode": align_mode,
"data_layout": data_layout,
}
if out_shape is not None:
if isinstance(out_shape, Variable) and not _non_static_mode():
out_shape.stop_gradient = True
inputs['OutSize'] = out_shape
else:
if _non_static_mode():
if isinstance(out_shape, Variable):
out_shape = list(out_shape.numpy())
else:
out_shape = list(out_shape)
for i, dim in enumerate(out_shape):
if isinstance(dim, Variable):
out_shape[i] = dim.numpy()[0]
if not (_is_list_or_turple_(out_shape)):
raise TypeError(
"out_shape should be a list or tuple or Variable."
)
# Validate the shape
contain_var = False
for dim_idx, dim_size in enumerate(out_shape):
if isinstance(dim_size, Variable):
contain_var = True
continue
assert (
dim_size > 0
), "Each dimension size given in out_shape must be greater than 0."
if contain_var:
new_size_tensor = []
size_list = []
for dim in out_shape:
if isinstance(dim, Variable):
dim.stop_gradient = True
new_size_tensor.append(dim)
size_list.append(-1)
else:
assert isinstance(dim, int)
temp_out = helper.create_variable_for_type_inference(
'int32'
)
fill_constant(
[1], 'int32', dim, force_cpu=True, out=temp_out
)
new_size_tensor.append(temp_out)
size_list.append(dim)
inputs['SizeTensor'] = new_size_tensor
if len(input.shape) == 3:
if len(out_shape) != 1:
raise ValueError(
"out_shape length should be 1 for " "input 3-D tensor."
)
if contain_var:
attrs['out_w'] = size_list[0]
else:
out_shape = list(map(int, out_shape))
attrs['out_w'] = out_shape[0]
elif len(input.shape) == 4:
if len(out_shape) != 2:
raise ValueError(
"out_shape length should be 2 for " "input 4-D tensor."
)
if contain_var:
attrs['out_h'] = size_list[0]
attrs['out_w'] = size_list[1]
else:
out_shape = list(map(int, out_shape))
attrs['out_h'] = out_shape[0]
attrs['out_w'] = out_shape[1]
if len(input.shape) == 5:
if len(out_shape) != 3:
raise ValueError(
"out_shape length should be 3 for " "input 5-D tensor."
)
if contain_var:
attrs['out_d'] = size_list[0]
attrs['out_h'] = size_list[1]
attrs['out_w'] = size_list[2]
else:
out_shape = list(map(int, out_shape))
attrs['out_d'] = out_shape[0]
attrs['out_h'] = out_shape[1]
attrs['out_w'] = out_shape[2]
else:
if _non_static_mode() and isinstance(scale, Variable):
scale = scale.numpy()
elif isinstance(scale, Variable):
scale.stop_gradient = True
inputs["Scale"] = scale
elif isinstance(scale, float) or isinstance(scale, int):
if scale <= 0:
raise ValueError("Attr(scale) should be greater than zero.")
attrs['scale'] = float(scale)
else:
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.")
if _non_static_mode():
attr_list = []
for k, v in attrs.items():
attr_list.append(k)
attr_list.append(v)
dy_attr = tuple(attr_list)
if resample_type == "linear":
out = _legacy_C_ops.linear_interp(input, actual_shape, *dy_attr)
elif resample_type == "bilinear":
out = _legacy_C_ops.bilinear_interp(input, actual_shape, *dy_attr)
elif resample_type == "trilinear":
out = _legacy_C_ops.trilinear_interp(input, actual_shape, *dy_attr)
elif resample_type == "nearest":
out = _legacy_C_ops.nearest_interp(input, actual_shape, *dy_attr)
elif resample_type == "bicubic":
out = _legacy_C_ops.bicubic_interp(input, actual_shape, *dy_attr)
return out
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='{}_interp'.format(resample_type),
inputs=inputs,
outputs={"Out": out},
attrs=attrs,
)
return out
@templatedoc(op_type="bilinear_interp")
def resize_bilinear(
input,
out_shape=None,
scale=None,
name=None,
actual_shape=None,
align_corners=True,
align_mode=1,
data_format='NCHW',
):
"""
This op resizes the input by performing bilinear interpolation based on given
output shape which specified by actual_shape, out_shape and scale
in priority order.
**Warning:** the parameter :attr:`actual_shape` will be deprecated in
the future and only use :attr:`out_shape` instead.
Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and
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.
For details of bilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bilinear_interpolation
Align_corners and align_mode are optional parameters,the calculation
method of interpolation can be selected by them.
Example:
.. code-block:: text
For scale:
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)
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}
Parameters:
input(Variable): 4-D Tensor(NCHW), 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 bilinear
layer, the shape is (out_h, out_w).Default: None. If a list, each
element can be an integer or a Tensor Variable with shape: [1]. If a
Tensor Variable, its dimension size should be 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`.
Default: None.
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
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`
Returns:
Variable: 4-D tensor(NCHW or NHWC).
Examples:
.. code-block:: python
#declarative mode
import paddle.fluid as fluid
import numpy as np
import paddle
paddle.enable_static()
input = fluid.data(name="input", shape=[None,3,6,10])
#1
output = fluid.layers.resize_bilinear(input=input,out_shape=[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 = fluid.layers.resize_bilinear(input=input,out_shape=[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 = fluid.layers.resize_bilinear(input=input,out_shape=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 = fluid.layers.resize_bilinear(input=input,scale=scale_tensor)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
input_data = np.random.rand(2,3,6,10).astype("float32")
output_data = exe.run(fluid.default_main_program(),
feed={"input":input_data},
fetch_list=[output],
return_numpy=True)
print(output_data[0].shape)
#1
# (2, 3, 12, 12)
#2
# (2, 3, 12, 2)
#3
# (2, 3, 3, 12)
#4
# (2, 3, 3, 5)
#imperative mode
import paddle.fluid.dygraph as dg
with dg.guard(place) as g:
input = dg.to_variable(input_data)
output = fluid.layers.resize_bilinear(input=input, out_shape=[12,12])
print(output.shape)
# [2L, 3L, 12L, 12L]
"""
return image_resize(
input,
out_shape,
scale,
name,
'BILINEAR',
actual_shape,
align_corners,
align_mode,
data_format,
)
@templatedoc(op_type="trilinear_interp")
def resize_trilinear(
input,
out_shape=None,
scale=None,
name=None,
actual_shape=None,
align_corners=True,
align_mode=1,
data_format='NCDHW',
):
"""
This op resizes the input by performing trilinear interpolation based on given
output shape which specified by actual_shape, out_shape and scale
in priority order.
**Warning:** the parameter :attr:`actual_shape` will be deprecated
in the future and only use :attr:`out_shape` instead.
Trilinear interpolation is an extension of linear interpolation for
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.
For details of trilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Trilinear_interpolation
Align_corners and align_mode are optional parameters,the calculation
method of interpolation can be selected by them.
Example:
.. code-block:: text
For scale:
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)
Bilinear 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}
Parameters:
input(${x_type}): 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): The output shape of resized tensor, the shape is (out_d, out_h, out_w). Default: None. Every element should be an integer or a Tensor Variable with shape: [1] if it is a list. If it is a Tensor Variable, its dimension size should be 1.
scale(float|Variable|None): The multiplier for the input depth, 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`.
Default: None.
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`
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
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: `"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]`.
Returns:
Variable: A 5-D Tensor(NCDHW or NDHWC)
Examples:
.. code-block:: python
#declarative mode
import paddle.fluid as fluid
import paddle
import numpy as np
paddle.enable_static()
input = fluid.data(name="input", shape=[None,3,6,8,10])
#1
output = fluid.layers.resize_trilinear(input=input,out_shape=[12,12,12])
#2
#x = np.array([2]).astype("int32")
#dim1 = fluid.data(name="dim1", shape=[1], dtype="int32")
#fluid.layers.assign(input=x, output=dim1)
#output = fluid.layers.resize_trilinear(input=input,out_shape=[12,dim1,4])
#3
#x = np.array([3,12,12]).astype("int32")
#shape_tensor = fluid.data(name="shape_tensor", shape=[3], dtype="int32")
#fluid.layers.assign(input=x, output=shape_tensor)
#output = fluid.layers.resize_trilinear(input=input,out_shape=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 = fluid.layers.resize_trilinear(input=input,scale=scale_tensor)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
input_data = np.random.rand(2,3,6,8,10).astype("float32")
output_data = exe.run(fluid.default_main_program(),
feed={"input":input_data},
fetch_list=[output],
return_numpy=True)
print(output_data[0].shape)
#1
# (2, 3, 12, 12, 12)
#2
# (2, 3, 12, 2, 4)
#3
# (2, 3, 3, 12, 12)
#4
# (2, 3, 3, 4, 5)
#imperative mode
import paddle.fluid.dygraph as dg
with dg.guard(place) as g:
input = dg.to_variable(input_data)
output = fluid.layers.resize_trilinear(input=input, out_shape=[12,12,12])
print(output.shape)
# [2L, 3L, 12L, 12L, 12L]
"""
return image_resize(
input,
out_shape,
scale,
name,
'TRILINEAR',
actual_shape,
align_corners,
align_mode,
data_format,
)
@templatedoc(op_type="nearest_interp")
def resize_nearest(
input,
out_shape=None,
scale=None,
name=None,
actual_shape=None,
align_corners=True,
data_format='NCHW',
):
"""
This op resizes the input by performing nearest neighbor interpolation in both the
height direction and the width direction based on given output shape
which is specified by actual_shape, out_shape and scale in priority order.
**Warning:** the parameter :attr:`actual_shape` will be deprecated in the
future and only use :attr:`out_shape` instead.
Example:
.. code-block:: text
For scale:
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)
Nearest neighbor interpolation:
if:
align_corners = False
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = floor(H_{in} * scale_{factor})
W_out = floor(W_{in} * scale_{factor})
else:
align_corners = True
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = round(H_{in} * scale_{factor})
W_out = round(W_{in} * scale_{factor})
For details of nearest neighbor interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
Parameters:
input(${x_type}): 4-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): The output shape of resized tensor, the shape is (out_h, out_w). Default: None. Every element should be an integer or a tensor Variable with shape: [1] if it is a list. If it is a tensor Variable, its dimension size should be 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`.
Default: None.
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`
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
align_corners(bool): ${align_corners_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]`.
Returns:
Variable: 4-D tensor(NCHW or NHWC).
Examples:
.. code-block:: python
#declarative mode
import paddle.fluid as fluid
import numpy as np
import paddle
paddle.enable_static()
input = fluid.data(name="input", shape=[None,3,6,10])
#1
output = fluid.layers.resize_nearest(input=input,out_shape=[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 = fluid.layers.resize_nearest(input=input,out_shape=[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 = fluid.layers.resize_nearest(input=input,out_shape=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 = fluid.layers.resize_nearest(input=input,scale=scale_tensor)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
input_data = np.random.rand(2,3,6,10).astype("float32")
output_data = exe.run(fluid.default_main_program(),
feed={"input":input_data},
fetch_list=[output],
return_numpy=True)
print(output_data[0].shape)
#1
# (2, 3, 12, 12)
#2
# (2, 3, 12, 2)
#3
# (2, 3, 3, 12)
#4
# (2, 3, 3, 5)
#imperative mode
import paddle.fluid.dygraph as dg
with dg.guard(place) as g:
input = dg.to_variable(input_data)
output = fluid.layers.resize_nearest(input=input, out_shape=[12,12])
print(output.shape)
# [2L, 3L, 12L, 12L]
"""
return image_resize(
input,
out_shape,
scale,
name,
'NEAREST',
actual_shape,
align_corners,
align_mode=1,
data_format=data_format,
)
@deprecated(since="2.0.0", update_to="paddle.nn.functional.relu")
def relu(x, name=None):
"""
......
python/paddle/fluid/tests/unittests/dygraph_to_static/yolov3.py
浏览文件 @ 790e7c38
......@@ -211,9 +211,10 @@ class Upsample(fluid.dygraph.Layer):
out_shape.stop_gradient = True
# reisze by actual_shape
out = fluid.layers.resize_nearest(
input=inputs, scale=self.scale, actual_shape=out_shape
out = paddle.nn.functional.interpolate(
x=inputs, size=out_shape, mode='nearest'
)
return out
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_nearest_interp_op.py
浏览文件 @ 790e7c38
......@@ -17,6 +17,7 @@ import unittest
import numpy as np
from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
......@@ -81,16 +82,14 @@ class TRTNearestInterpTest(InferencePassTest):
def append_nearest_interp(self, data):
if self.scale > 0.0:
return fluid.layers.resize_nearest(
return paddle.nn.functional.interpolate(
data,
scale=self.scale,
align_corners=self.align_corners,
scale_factor=self.scale,
data_format=self.data_layout,
)
return fluid.layers.resize_nearest(
return paddle.nn.functional.interpolate(
data,
out_shape=self.resize_shape,
align_corners=self.align_corners,
size=self.resize_shape,
data_format=self.data_layout,
)
......
python/paddle/fluid/tests/unittests/mlu/test_bilinear_interp_v2_op_mlu.py
浏览文件 @ 790e7c38
......@@ -511,57 +511,6 @@ class TestBilinearInterp_attr_tensor_Case3(TestBilinearInterpOp_attr_tensor):
self.scale_by_1Dtensor = True
class TestBilinearInterpOpAPI(unittest.TestCase):
def test_case(self):
x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
dim = fluid.data(name="dim", shape=[1], dtype="int32")
shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
scale_tensor = fluid.data(
name="scale_tensor", shape=[1], dtype="float32"
)
out1 = fluid.layers.resize_bilinear(x, out_shape=[12, 12])
out2 = fluid.layers.resize_bilinear(x, out_shape=[12, dim])
out3 = fluid.layers.resize_bilinear(x, out_shape=shape_tensor)
out4 = fluid.layers.resize_bilinear(
x, out_shape=[4, 4], actual_shape=actual_size
)
out5 = fluid.layers.resize_bilinear(x, scale=scale_tensor)
x_data = np.random.random((2, 3, 6, 6)).astype("float32")
dim_data = np.array([12]).astype("int32")
shape_data = np.array([12, 12]).astype("int32")
actual_size_data = np.array([12, 12]).astype("int32")
scale_data = np.array([2.0]).astype("float32")
if core.is_compiled_with_mlu():
place = paddle.device.MLUPlace(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,
"dim": dim_data,
"shape_tensor": shape_data,
"actual_size": actual_size_data,
"scale_tensor": scale_data,
},
fetch_list=[out1, out2, out3, out4, out5],
return_numpy=True,
)
expect_res = bilinear_interp_np(
x_data, out_h=12, out_w=12, align_corners=True
)
for res in results:
np.testing.assert_allclose(res, expect_res, rtol=1e-6)
class TestBilinearInterpOpAPI_dy(unittest.TestCase):
def test_case(self):
import paddle
......
python/paddle/fluid/tests/unittests/mlu/test_nearest_interp_v2_op_mlu.py
浏览文件 @ 790e7c38
......@@ -546,101 +546,5 @@ class TestNearestInterp_attr_tensor_Case3(TestNearestInterpOp_attr_tensor):
self.scale_by_1Dtensor = True
class TestNearestAPI(unittest.TestCase):
def test_case(self):
x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
y = fluid.data(name="y", shape=[2, 6, 6, 3], dtype="float32")
dim = fluid.data(name="dim", shape=[1], dtype="int32")
shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
scale_tensor = fluid.data(
name="scale_tensor", shape=[1], dtype="float32"
)
out1 = fluid.layers.resize_nearest(
y, out_shape=[12, 12], data_format='NHWC', align_corners=False
)
out2 = fluid.layers.resize_nearest(
x, out_shape=[12, dim], align_corners=False
)
out3 = fluid.layers.resize_nearest(
x, out_shape=shape_tensor, align_corners=False
)
out4 = fluid.layers.resize_nearest(
x, out_shape=[4, 4], actual_shape=actual_size, align_corners=False
)
out5 = fluid.layers.resize_nearest(
x, scale=scale_tensor, align_corners=False
)
x_data = np.random.random((2, 3, 6, 6)).astype("float32")
dim_data = np.array([12]).astype("int32")
shape_data = np.array([12, 12]).astype("int32")
actual_size_data = np.array([12, 12]).astype("int32")
scale_data = np.array([2.0]).astype("float32")
place = paddle.MLUPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
results = exe.run(
fluid.default_main_program(),
feed={
"x": x_data,
"y": np.transpose(x_data, (0, 2, 3, 1)),
"dim": dim_data,
"shape_tensor": shape_data,
"actual_size": actual_size_data,
"scale_tensor": scale_data,
},
fetch_list=[out1, out2, out3, out4, out5],
return_numpy=True,
)
expect_res = nearest_neighbor_interp_np(
x_data, out_h=12, out_w=12, align_corners=False
)
np.testing.assert_allclose(
results[0], np.transpose(expect_res, (0, 2, 3, 1))
)
for i in range(len(results) - 1):
np.testing.assert_allclose(results[i + 1], expect_res)
class TestNearestInterpException(unittest.TestCase):
def test_exception(self):
import paddle
input = fluid.data(name="input", shape=[1, 3, 6, 6], dtype="float32")
def attr_data_format():
# for 4-D input, data_format can only be NCHW or NHWC
out = fluid.layers.resize_nearest(
input, out_shape=[4, 8], data_format='NDHWC'
)
def attr_scale_type():
out = fluid.layers.resize_nearest(input, scale='scale')
def attr_scale_value():
out = fluid.layers.resize_nearest(input, scale=-0.3)
def input_shape_error():
x = paddle.randn([1, 3])
out = paddle.nn.functional.interpolate(x, scale_factor='scale')
def mode_error():
x = paddle.randn([1, 3])
out = paddle.nn.functional.interpolate(
x, scale_factor='scale', mode="BILINEAR"
)
self.assertRaises(ValueError, attr_data_format)
self.assertRaises(TypeError, attr_scale_type)
self.assertRaises(ValueError, attr_scale_value)
self.assertRaises(ValueError, input_shape_error)
self.assertRaises(ValueError, mode_error)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/npu/test_nearest_interp_op_npu.py
浏览文件 @ 790e7c38
......@@ -404,87 +404,5 @@ class TestNearestInterp_attr_tensor_Case3(TestNearestInterpOp_attr_tensor):
self.scale_by_1Dtensor = True
class TestNearestAPI(unittest.TestCase):
def test_case(self):
x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
y = fluid.data(name="y", shape=[2, 6, 6, 3], dtype="float32")
dim = fluid.data(name="dim", shape=[1], dtype="int32")
shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
scale_tensor = fluid.data(
name="scale_tensor", shape=[1], dtype="float32"
)
out1 = fluid.layers.resize_nearest(
y, out_shape=[12, 12], data_format='NHWC', align_corners=False
)
out2 = fluid.layers.resize_nearest(
x, out_shape=[12, dim], align_corners=False
)
out3 = fluid.layers.resize_nearest(
x, out_shape=shape_tensor, align_corners=False
)
out4 = fluid.layers.resize_nearest(
x, out_shape=[4, 4], actual_shape=actual_size, align_corners=False
)
out5 = fluid.layers.resize_nearest(
x, scale=scale_tensor, align_corners=False
)
x_data = np.random.random((2, 3, 6, 6)).astype("float32")
dim_data = np.array([12]).astype("int32")
shape_data = np.array([12, 12]).astype("int32")
actual_size_data = np.array([12, 12]).astype("int32")
scale_data = np.array([2.0]).astype("float32")
place = paddle.NPUPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
results = exe.run(
fluid.default_main_program(),
feed={
"x": x_data,
"y": np.transpose(x_data, (0, 2, 3, 1)),
"dim": dim_data,
"shape_tensor": shape_data,
"actual_size": actual_size_data,
"scale_tensor": scale_data,
},
fetch_list=[out1, out2, out3, out4, out5],
return_numpy=True,
)
expect_res = nearest_neighbor_interp_np(
x_data, out_h=12, out_w=12, align_corners=False
)
np.testing.assert_allclose(
results[0], np.transpose(expect_res, (0, 2, 3, 1))
)
for i in range(len(results) - 1):
np.testing.assert_allclose(results[i + 1], expect_res)
class TestNearestInterpException(unittest.TestCase):
def test_exception(self):
input = fluid.data(name="input", shape=[1, 3, 6, 6], dtype="float32")
def attr_data_format():
# for 4-D input, data_format can only be NCHW or NHWC
out = fluid.layers.resize_nearest(
input, out_shape=[4, 8], data_format='NDHWC'
)
def attr_scale_type():
out = fluid.layers.resize_nearest(input, scale='scale')
def attr_scale_value():
out = fluid.layers.resize_nearest(input, scale=-0.3)
self.assertRaises(ValueError, attr_data_format)
self.assertRaises(TypeError, attr_scale_type)
self.assertRaises(ValueError, attr_scale_value)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_bilinear_interp_op.py
浏览文件 @ 790e7c38
......@@ -18,7 +18,6 @@ import numpy as np
from op_test import OpTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
paddle.enable_static()
......@@ -529,56 +528,5 @@ class TestBilinearInterp_attr_tensor_Case3(TestBilinearInterpOp_attr_tensor):
self.scale_by_1Dtensor = True
class TestBilinearInterpOpAPI(unittest.TestCase):
def test_case(self):
x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
dim = fluid.data(name="dim", shape=[1], dtype="int32")
shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
scale_tensor = fluid.data(
name="scale_tensor", shape=[1], dtype="float32"
)
out1 = fluid.layers.resize_bilinear(x, out_shape=[12, 12])
out2 = fluid.layers.resize_bilinear(x, out_shape=[12, dim])
out3 = fluid.layers.resize_bilinear(x, out_shape=shape_tensor)
out4 = fluid.layers.resize_bilinear(
x, out_shape=[4, 4], actual_shape=actual_size
)
out5 = fluid.layers.resize_bilinear(x, scale=scale_tensor)
x_data = np.random.random((2, 3, 6, 6)).astype("float32")
dim_data = np.array([12]).astype("int32")
shape_data = np.array([12, 12]).astype("int32")
actual_size_data = np.array([12, 12]).astype("int32")
scale_data = np.array([2.0]).astype("float32")
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,
"dim": dim_data,
"shape_tensor": shape_data,
"actual_size": actual_size_data,
"scale_tensor": scale_data,
},
fetch_list=[out1, out2, out3, out4, out5],
return_numpy=True,
)
expect_res = bilinear_interp_np(
x_data, out_h=12, out_w=12, align_corners=True
)
for res in results:
np.testing.assert_allclose(res, expect_res, rtol=1e-05)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_bilinear_interp_v2_op.py
浏览文件 @ 790e7c38
......@@ -638,57 +638,6 @@ class TestBilinearInterp_attr_tensor_Case3(TestBilinearInterpOp_attr_tensor):
self.scale_by_1Dtensor = True
class TestBilinearInterpOpAPI(unittest.TestCase):
def test_case(self):
x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
dim = fluid.data(name="dim", shape=[1], dtype="int32")
shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
scale_tensor = fluid.data(
name="scale_tensor", shape=[1], dtype="float32"
)
out1 = fluid.layers.resize_bilinear(x, out_shape=[12, 12])
out2 = fluid.layers.resize_bilinear(x, out_shape=[12, dim])
out3 = fluid.layers.resize_bilinear(x, out_shape=shape_tensor)
out4 = fluid.layers.resize_bilinear(
x, out_shape=[4, 4], actual_shape=actual_size
)
out5 = fluid.layers.resize_bilinear(x, scale=scale_tensor)
x_data = np.random.random((2, 3, 6, 6)).astype("float32")
dim_data = np.array([12]).astype("int32")
shape_data = np.array([12, 12]).astype("int32")
actual_size_data = np.array([12, 12]).astype("int32")
scale_data = np.array([2.0]).astype("float32")
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,
"dim": dim_data,
"shape_tensor": shape_data,
"actual_size": actual_size_data,
"scale_tensor": scale_data,
},
fetch_list=[out1, out2, out3, out4, out5],
return_numpy=True,
)
expect_res = bilinear_interp_np(
x_data, out_h=12, out_w=12, align_corners=True
)
for res in results:
np.testing.assert_allclose(res, expect_res, rtol=1e-05)
class TestBilinearInterpOpAPI_dy(unittest.TestCase):
def test_case(self):
import paddle
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 790e7c38
......@@ -3187,94 +3187,6 @@ class TestBook(LayerTest):
return values
return indices
def make_resize_bilinear(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
x = self._get_data(name='x', shape=[3, 9, 6], dtype="float32")
output = layers.resize_bilinear(x, out_shape=[12, 12])
return output
def make_resize_bilinear_by_scale(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
x = self._get_data(name='x', shape=[3, 9, 6], dtype="float32")
output = layers.resize_bilinear(x, scale=1.5)
return output
def make_resize_nearest(self):
try:
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
x = self._get_data(name='x1', shape=[3, 9, 6], dtype="float32")
output = layers.resize_nearest(x, out_shape=[12, 12])
except ValueError:
pass
try:
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
x = self._get_data(
name='x2', shape=[3, 9, 6, 7], dtype="float32"
)
output = layers.resize_nearest(x, out_shape=[12, 12, 12])
except ValueError:
pass
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
x = self._get_data(name='x', shape=[3, 9, 6], dtype="float32")
output = layers.resize_nearest(x, out_shape=[12, 12])
return output
def make_resize_nearest_by_scale(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
x = self._get_data(name='x1', shape=[3, 9, 6], dtype="float32")
output = layers.resize_nearest(x, scale=1.8)
return output
def make_resize_trilinear(self):
try:
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
x = self._get_data(name='x2', shape=[3, 9, 6], dtype="float32")
output = layers.resize_trilinear(x, out_shape=[12, 12, 12])
except ValueError:
pass
try:
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
x = self._get_data(
name='x', shape=[3, 9, 6, 7], dtype="float32"
)
output = layers.resize_trilinear(x, out_shape=[12, 12])
except ValueError:
pass
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
x = self._get_data(name='x', shape=[3, 9, 6, 7], dtype="float32")
output = layers.resize_trilinear(x, out_shape=[12, 12, 12])
return output
def make_resize_trilinear_by_scale(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
):
x = self._get_data(name='x', shape=[3, 9, 6, 7], dtype="float32")
output = layers.resize_trilinear(x, scale=2.1)
return output
def make_polygon_box_transform(self):
with program_guard(
fluid.default_main_program(), fluid.default_startup_program()
......
python/paddle/fluid/tests/unittests/test_nearest_interp_op.py
浏览文件 @ 790e7c38
......@@ -17,7 +17,6 @@ import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
import paddle.fluid.core as core
......@@ -459,85 +458,6 @@ class TestNearestInterp_attr_tensor_Case3(TestNearestInterpOp_attr_tensor):
self.scale_by_1Dtensor = True
class TestNearestAPI(unittest.TestCase):
def test_case(self):
x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
y = fluid.data(name="y", shape=[2, 6, 6, 3], dtype="float32")
dim = fluid.data(name="dim", shape=[1], dtype="int32")
shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
scale_tensor = fluid.data(
name="scale_tensor", shape=[1], dtype="float32"
)
out1 = fluid.layers.resize_nearest(
y, out_shape=[12, 12], data_format='NHWC'
)
out2 = fluid.layers.resize_nearest(x, out_shape=[12, dim])
out3 = fluid.layers.resize_nearest(x, out_shape=shape_tensor)
out4 = fluid.layers.resize_nearest(
x, out_shape=[4, 4], actual_shape=actual_size
)
out5 = fluid.layers.resize_nearest(x, scale=scale_tensor)
x_data = np.random.random((2, 3, 6, 6)).astype("float32")
dim_data = np.array([12]).astype("int32")
shape_data = np.array([12, 12]).astype("int32")
actual_size_data = np.array([12, 12]).astype("int32")
scale_data = np.array([2.0]).astype("float32")
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,
"y": np.transpose(x_data, (0, 2, 3, 1)),
"dim": dim_data,
"shape_tensor": shape_data,
"actual_size": actual_size_data,
"scale_tensor": scale_data,
},
fetch_list=[out1, out2, out3, out4, out5],
return_numpy=True,
)
expect_res = nearest_neighbor_interp_np(
x_data, out_h=12, out_w=12, align_corners=True
)
np.testing.assert_allclose(
results[0], np.transpose(expect_res, (0, 2, 3, 1)), rtol=1e-05
)
for i in range(len(results) - 1):
np.testing.assert_allclose(results[i + 1], expect_res, rtol=1e-05)
class TestNearestInterpException(unittest.TestCase):
def test_exception(self):
input = fluid.data(name="input", shape=[1, 3, 6, 6], dtype="float32")
def attr_data_format():
# for 4-D input, data_format can only be NCHW or NHWC
out = fluid.layers.resize_nearest(
input, out_shape=[4, 8], data_format='NDHWC'
)
def attr_scale_type():
out = fluid.layers.resize_nearest(input, scale='scale')
def attr_scale_value():
out = fluid.layers.resize_nearest(input, scale=-0.3)
self.assertRaises(ValueError, attr_data_format)
self.assertRaises(TypeError, attr_scale_type)
self.assertRaises(ValueError, attr_scale_value)
if __name__ == "__main__":
import paddle
......
python/paddle/fluid/tests/unittests/test_nearest_interp_v2_op.py
浏览文件 @ 790e7c38
......@@ -684,64 +684,6 @@ class TestNearestInterp_attr_tensor_Case3(TestNearestInterpOp_attr_tensor):
self.scale_by_1Dtensor = True
class TestNearestAPI(unittest.TestCase):
def test_case(self):
x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
y = fluid.data(name="y", shape=[2, 6, 6, 3], dtype="float32")
dim = fluid.data(name="dim", shape=[1], dtype="int32")
shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
scale_tensor = fluid.data(
name="scale_tensor", shape=[1], dtype="float32"
)
out1 = fluid.layers.resize_nearest(
y, out_shape=[12, 12], data_format='NHWC'
)
out2 = fluid.layers.resize_nearest(x, out_shape=[12, dim])
out3 = fluid.layers.resize_nearest(x, out_shape=shape_tensor)
out4 = fluid.layers.resize_nearest(
x, out_shape=[4, 4], actual_shape=actual_size
)
out5 = fluid.layers.resize_nearest(x, scale=scale_tensor)
x_data = np.random.random((2, 3, 6, 6)).astype("float32")
dim_data = np.array([12]).astype("int32")
shape_data = np.array([12, 12]).astype("int32")
actual_size_data = np.array([12, 12]).astype("int32")
scale_data = np.array([2.0]).astype("float32")
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,
"y": np.transpose(x_data, (0, 2, 3, 1)),
"dim": dim_data,
"shape_tensor": shape_data,
"actual_size": actual_size_data,
"scale_tensor": scale_data,
},
fetch_list=[out1, out2, out3, out4, out5],
return_numpy=True,
)
expect_res = nearest_neighbor_interp_np(
x_data, out_h=12, out_w=12, align_corners=True
)
np.testing.assert_allclose(
results[0], np.transpose(expect_res, (0, 2, 3, 1)), rtol=1e-05
)
for i in range(len(results) - 1):
np.testing.assert_allclose(results[i + 1], expect_res, rtol=1e-05)
class TestNearestInterpOpAPI_dy(unittest.TestCase):
def test_case(self):
import paddle
......@@ -793,41 +735,6 @@ class TestNearestInterp3DOpAPI_dy(unittest.TestCase):
np.testing.assert_allclose(out.numpy(), expect_res, rtol=1e-05)
class TestNearestInterpException(unittest.TestCase):
def test_exception(self):
import paddle
input = fluid.data(name="input", shape=[1, 3, 6, 6], dtype="float32")
def attr_data_format():
# for 4-D input, data_format can only be NCHW or NHWC
out = fluid.layers.resize_nearest(
input, out_shape=[4, 8], data_format='NDHWC'
)
def attr_scale_type():
out = fluid.layers.resize_nearest(input, scale='scale')
def attr_scale_value():
out = fluid.layers.resize_nearest(input, scale=-0.3)
def input_shape_error():
x = paddle.randn([1, 3])
out = paddle.nn.functional.interpolate(x, scale_factor='scale')
def mode_error():
x = paddle.randn([1, 3])
out = paddle.nn.functional.interpolate(
x, scale_factor='scale', mode="BILINEAR"
)
self.assertRaises(ValueError, attr_data_format)
self.assertRaises(TypeError, attr_scale_type)
self.assertRaises(ValueError, attr_scale_value)
self.assertRaises(ValueError, input_shape_error)
self.assertRaises(ValueError, mode_error)
@unittest.skipIf(
not fluid.core.is_compiled_with_cuda(), "core is not compiled with CUDA"
)
......
python/paddle/fluid/tests/unittests/test_trilinear_interp_op.py
浏览文件 @ 790e7c38
......@@ -17,9 +17,7 @@ import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.nn.functional import interpolate
def trilinear_interp_np(
......@@ -623,85 +621,5 @@ class TestTrilinearInterp_attr_tensor_Case3(TestTrilinearInterpOp_attr_tensor):
self.scale_by_1Dtensor = True
class TestTrilinearInterpAPI(unittest.TestCase):
def test_case(self):
x = fluid.data(name="x", shape=[2, 3, 6, 9, 4], dtype="float32")
y = fluid.data(name="y", shape=[2, 6, 9, 4, 3], dtype="float32")
dim = fluid.data(name="dim", shape=[1], dtype="int32")
shape_tensor = fluid.data(name="shape_tensor", shape=[3], dtype="int32")
actual_size = fluid.data(name="actual_size", shape=[3], dtype="int32")
scale_tensor = fluid.data(
name="scale_tensor", shape=[1], dtype="float32"
)
out1 = fluid.layers.resize_trilinear(
y, out_shape=[12, 18, 8], data_format='NDHWC'
)
out2 = fluid.layers.resize_trilinear(x, out_shape=[12, dim, 8])
out3 = fluid.layers.resize_trilinear(x, out_shape=shape_tensor)
out4 = fluid.layers.resize_trilinear(
x, out_shape=[4, 4, 8], actual_shape=actual_size
)
out5 = fluid.layers.resize_trilinear(x, scale=scale_tensor)
out6 = interpolate(
x, scale_factor=scale_tensor, mode='trilinear', data_format="NCDHW"
)
out7 = interpolate(
x, size=[4, 4, 8], mode='trilinear', data_format="NCDHW"
)
out8 = interpolate(
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")
shape_data = np.array([12, 18, 8]).astype("int32")
actual_size_data = np.array([12, 18, 8]).astype("int32")
scale_data = np.array([2.0]).astype("float32")
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,
"y": np.transpose(x_data, (0, 2, 3, 4, 1)),
"dim": dim_data,
"shape_tensor": shape_data,
"actual_size": actual_size_data,
"scale_tensor": scale_data,
},
fetch_list=[out1, out2, out3, out4, out5],
return_numpy=True,
)
expect_res = trilinear_interp_np(
x_data, out_d=12, out_h=18, out_w=8, align_mode=1
)
np.testing.assert_allclose(
results[0], np.transpose(expect_res, (0, 2, 3, 4, 1)), rtol=1e-05
)
for i in range(len(results) - 1):
np.testing.assert_allclose(results[i + 1], expect_res, rtol=1e-05)
class TestTrilinearInterpOpException(unittest.TestCase):
def test_exception(self):
input = fluid.data(name="input", shape=[2, 3, 6, 9, 4], dtype="float32")
def attr_data_format():
# for 5-D input, data_format only can be NCDHW or NDHWC
out = fluid.layers.resize_trilinear(
input, out_shape=[4, 8, 4], data_format='NHWC'
)
self.assertRaises(ValueError, attr_data_format)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_trilinear_interp_v2_op.py
浏览文件 @ 790e7c38
......@@ -20,7 +20,6 @@ from op_test import OpTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.framework import _test_eager_guard
from paddle.nn.functional import interpolate
np.random.seed(123)
......@@ -741,100 +740,6 @@ class TestTrilinearInterp_attr_tensor_Case3(TestTrilinearInterpOp_attr_tensor):
self.scale_by_1Dtensor = True
class TestTrilinearInterpAPI(unittest.TestCase):
def test_imperative_case(self):
with _test_eager_guard():
self.func_case()
self.func_case()
def func_case(self):
x = fluid.data(name="x", shape=[2, 3, 6, 9, 4], dtype="float32")
y = fluid.data(name="y", shape=[2, 6, 9, 4, 3], dtype="float32")
dim = fluid.data(name="dim", shape=[1], dtype="int32")
shape_tensor = fluid.data(name="shape_tensor", shape=[3], dtype="int32")
actual_size = fluid.data(name="actual_size", shape=[3], dtype="int32")
scale_tensor = fluid.data(
name="scale_tensor", shape=[1], dtype="float32"
)
out1 = fluid.layers.resize_trilinear(
y, out_shape=[12, 18, 8], data_format='NDHWC'
)
out2 = fluid.layers.resize_trilinear(x, out_shape=[12, dim, 8])
out3 = fluid.layers.resize_trilinear(x, out_shape=shape_tensor)
out4 = fluid.layers.resize_trilinear(
x, out_shape=[4, 4, 8], actual_shape=actual_size
)
out5 = fluid.layers.resize_trilinear(x, scale=scale_tensor)
out6 = interpolate(
x, scale_factor=scale_tensor, mode='trilinear', data_format="NCDHW"
)
out7 = interpolate(
x, size=[4, 4, 8], mode='trilinear', data_format="NCDHW"
)
out8 = interpolate(
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")
shape_data = np.array([12, 18, 8]).astype("int32")
actual_size_data = np.array([12, 18, 8]).astype("int32")
scale_data = np.array([2.0]).astype("float32")
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,
"y": np.transpose(x_data, (0, 2, 3, 4, 1)),
"dim": dim_data,
"shape_tensor": shape_data,
"actual_size": actual_size_data,
"scale_tensor": scale_data,
},
fetch_list=[out1, out2, out3, out4, out5],
return_numpy=True,
)
expect_res = trilinear_interp_np(
x_data, out_d=12, out_h=18, out_w=8, align_mode=1
)
np.testing.assert_allclose(
results[0], np.transpose(expect_res, (0, 2, 3, 4, 1)), rtol=1e-05
)
for i in range(len(results) - 1):
np.testing.assert_allclose(results[i + 1], expect_res, rtol=1e-05)
# Follow the calculation of preceding out6, out7, out8.
# To pass CI-coverage, calculate out9 without verifying accuracy.
# Preceding PR link: https://github.com/PaddlePaddle/Paddle/pull/26520/files#diff-ee0c2b73d08659e90a8f3ac48451a6588d35e1613742f864f9aad4394e12c290
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(x_data)
out9 = interpolate(
x, size=[12, 18, 8], mode='trilinear', data_format="NCDHW"
)
class TestTrilinearInterpOpException(unittest.TestCase):
def test_exception(self):
input = fluid.data(name="input", shape=[2, 3, 6, 9, 4], dtype="float32")
def attr_data_format():
# for 5-D input, data_format only can be NCDHW or NDHWC
out = fluid.layers.resize_trilinear(
input, out_shape=[4, 8, 4], data_format='NHWC'
)
self.assertRaises(ValueError, attr_data_format)
@unittest.skipIf(
not fluid.core.is_compiled_with_cuda(), "core is not compiled with CUDA"
)
......
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