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add 'actual_shape' attribute. test=develop

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revert-14324-fix_vlog
dengkaipeng 7 years ago
parent fef2faa709
commit 8b47d90f5d
4 changed files with 148 additions and 28 deletions
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6
paddle/fluid/API.spec
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@ -118,10 +118,10 @@ paddle.fluid.layers.label_smooth ArgSpec(args=['label', 'prior_dist', 'epsilon',
paddle.fluid.layers.roi_pool ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0))
paddle.fluid.layers.roi_align ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None))
paddle.fluid.layers.dice_loss ArgSpec(args=['input', 'label', 'epsilon'], varargs=None, keywords=None, defaults=(1e-05,))
paddle.fluid.layers.image_resize ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR'))
paddle.fluid.layers.image_resize ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample', 'actual_shape'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None))
paddle.fluid.layers.image_resize_short ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',))
paddle.fluid.layers.resize_bilinear ArgSpec(args=['input', 'out_shape', 'scale', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.resize_nearest ArgSpec(args=['input', 'out_shape', 'scale', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.resize_bilinear ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.layers.resize_nearest ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.layers.gather ArgSpec(args=['input', 'index'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.scatter ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_scatter ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,))

6
paddle/fluid/operators/interpolate_op.cc
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@ -40,11 +40,13 @@ class InterpolateOp : public framework::OperatorWithKernel {
int out_w = ctx->Attrs().Get<int>("out_w");
PADDLE_ENFORCE_EQ(dim_x.size(), 4, "X's dimension must be 4");
if (ctx->HasInput("OutSize")) {
if (ctx->HasInput("OutSize") && ctx->IsRuntime()) {
auto out_size_dim = ctx->GetInputDim("OutSize");
PADDLE_ENFORCE_EQ(out_size_dim.size(), 1,
"OutSize's dimension size must be 1");
PADDLE_ENFORCE_EQ(out_size_dim[0], 2, "OutSize's dim[0] must be 2");
ctx->ShareLoD("X", "Out");
return;
}
std::vector<int64_t> dim_out({dim_x[0], dim_x[1], out_h, out_w});
ctx->SetOutputDim("Out", framework::make_ddim(dim_out));
@ -86,7 +88,7 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker {
interpolation.
Nearest neighbor interpolation is to perform nearest neighbor interpolation
in bot the 3rd dimention(in height direction) and the 4th dimention(in width
in both the 3rd dimention(in height direction) and the 4th dimention(in width
direction) on input tensor.
Bilinear interpolation is an extension of linear interpolation for

124
python/paddle/fluid/layers/nn.py
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@ -5575,7 +5575,8 @@ def image_resize(input,
out_shape=None,
scale=None,
name=None,
resample='BILINEAR'):
resample='BILINEAR',
actual_shape=None):
"""
**Resize a Batch of Images**
@ -5600,25 +5601,50 @@ 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 can only be 'BILINEAR' currently.
resample(str): The resample method. It supports 'BILINEAR' 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
actual_shape instead of :attr:`out_shape` if you
want to specify output shape dynamically. When
using actual_shape to specify output shape, one of
:attr:`out_shape` and :attr:`scale` should also be
set, otherwise errors would be occured in graph
constructing stage.
Default: None
Returns:
Variable: The output is a 4-D tensor of the shape
(num_batches, channls, out_h, out_w).
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'
or 'NEAREST' currently.
ValueError: One of out_shape and scale must not be None.
ValueError: out_shape length should be 2.
Examples:
.. code-block:: python
out = fluid.layers.image_resize(input, out_shape=[12, 12])
"""
resample_methods = {'BILINEAR': 'bilinear', 'NEAREST': 'nearest'}
resample_methods = {
'BILINEAR': 'bilinear',
'NEAREST': 'nearest',
}
if resample not in resample_methods:
raise ValueError(
"The 'resample' of image_resize can only be 'BILINEAR' and 'NEAREST' currently."
"The 'resample' of image_resize can only be 'BILINEAR' or 'NEAREST' currently."
)
if out_shape is None and scale is None:
raise ValueError("One of out_shape and scale must not be None")
raise ValueError("One of out_shape and scale must not be None.")
helper = LayerHelper('interpolate', **locals())
dtype = helper.input_dtype()
@ -5629,19 +5655,28 @@ def image_resize(input,
out_w = 0
inputs = {"X": input}
if out_shape is not None:
if not (_is_list_or_turple_(out_shape) and
len(out_shape) == 2) and not isinstance(out_shape, Variable):
raise ValueError('out_shape should be a list or tuple or variable')
if _is_list_or_turple_(out_shape):
out_shape = list(map(int, out_shape))
out_h = out_shape[0]
out_w = out_shape[1]
else:
if isinstance(out_shape, Variable):
warnings.warn("out_shape as Variable type is deprecated, \
it is recommended to use actual_shape instead of \
out_shape to specify output shape dynamically.")
inputs['OutSize'] = out_shape
elif not (_is_list_or_turple_(out_shape)):
raise TypeError("out_shape should be a list or tuple or Variable.")
elif len(out_shape) != 2:
raise ValueError("out_shape length should be 2.")
out_shape = list(map(int, out_shape))
out_h = out_shape[0]
out_w = out_shape[1]
else:
out_h = int(input.shape[2] * scale)
out_w = int(input.shape[3] * scale)
if isinstance(actual_shape, Variable):
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='interpolate',
@ -5656,9 +5691,24 @@ def image_resize(input,
@templatedoc(op_type="interpolate")
def resize_bilinear(input, out_shape=None, scale=None, name=None):
def resize_bilinear(input,
out_shape=None,
scale=None,
name=None,
actual_shape=None):
"""
${comment}
Resize input by performing bilinear interpolation based on given
output shape which specified by actual_shape, out_shape and scale
in priority order.
Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and
W-direction in this op) on a rectilinear 2D grid. The key idea is
to perform linear interpolation first in one direction, and then
again in the other direction.
For details of bilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bilinear_interpolation
Args:
input(${x_type}): ${x_comment}.
@ -5670,18 +5720,41 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None):
a higher priority than scale. Default: None.
name(str|None): The output variable 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
actual_shape instead of :attr:`out_shape` if you
want to specify output shape dynamically. When
using actual_shape to specify output shape, one of
:attr:`out_shape` and :attr:`scale` should also be
set, otherwise errors would be occured in graph
constructing stage.
Default: None
Returns:
${out_comment}.
"""
return image_resize(input, out_shape, scale, name, 'BILINEAR')
return image_resize(input, out_shape, scale, name, 'BILINEAR', actual_shape)
@templatedoc(op_type="interpolate")
def resize_nearest(input, out_shape=None, scale=None, name=None):
def resize_nearest(input,
out_shape=None,
scale=None,
name=None,
actual_shape=None):
"""
${comment}
Resize input by performing nearest neighbor interpolation in both the
3rd dimention(in height direction) and the 4th dimention(in width
direction) based on given output shape which specified by actual_shape,
out_shape and scale in priority order.
For details of nearest neighbor interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
Args:
input(${x_type}): ${x_comment}.
@ -5693,12 +5766,25 @@ def resize_nearest(input, out_shape=None, scale=None, name=None):
a higher priority than scale. Default: None.
name(str|None): The output variable 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
actual_shape instead of :attr:`out_shape` if you
want to specify output shape dynamically. When
using actual_shape to specify output shape, one of
:attr:`out_shape` and :attr:`scale` should also be
set, otherwise errors would be occured in graph
constructing stage.
Default: None
Returns:
${out_comment}.
"""
return image_resize(input, out_shape, scale, name, 'NEAREST')
return image_resize(input, out_shape, scale, name, 'NEAREST', actual_shape)
def image_resize_short(input, out_short_len, resample='BILINEAR'):

40
python/paddle/fluid/tests/unittests/test_interpolate_op.py
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@ -20,11 +20,18 @@ from op_test import OpTest
import paddle.fluid.core as core
def nearest_neighbor_interp_np(X, out_h, out_w, out_size=None):
def nearest_neighbor_interp_np(X,
out_h,
out_w,
out_size=None,
actual_shape=None):
"""nearest neighbor interpolation implement in shape [N, C, H, W]"""
if out_size is not None:
out_h = out_size[0]
out_w = out_size[1]
if actual_shape is not None:
out_h = actual_shape[0]
out_w = actual_shape[1]
n, c, in_h, in_w = X.shape
ratio_h = ratio_w = 0.0
@ -43,11 +50,14 @@ def nearest_neighbor_interp_np(X, out_h, out_w, out_size=None):
return out.astype(X.dtype)
def bilinear_interp_np(input, out_h, out_w, out_size):
def bilinear_interp_np(input, out_h, out_w, out_size=None, actual_shape=None):
"""bilinear interpolation implement in shape [N, C, H, W]"""
if out_size is not None:
out_h = out_size[0]
out_w = out_size[1]
if actual_shape is not None:
out_h = actual_shape[0]
out_w = actual_shape[1]
batch_size, channel, in_h, in_w = input.shape
if out_h > 1:
ratio_h = (in_h - 1.0) / (out_h - 1.0)
@ -86,15 +96,18 @@ INTERPOLATE_FUNCS = {
class TestInterpolateOp(OpTest):
def setUp(self):
self.out_size = None
self.actual_shape = None
self.init_test_case()
self.op_type = "interpolate"
input_np = np.random.random(self.input_shape).astype("float32")
output_np = INTERPOLATE_FUNCS[self.interp_method](
input_np, self.out_h, self.out_w, self.out_size)
input_np, self.out_h, self.out_w, self.out_size, self.actual_shape)
self.inputs = {'X': input_np}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
if self.actual_shape is not None:
self.inputs['OutSize'] = self.actual_shape
self.attrs = {
'out_h': self.out_h,
'out_w': self.out_w,
@ -167,6 +180,15 @@ class TestBilinearInterpCase6(TestInterpolateOp):
self.out_size = np.array([65, 129]).astype("int32")
class TestBilinearInterpActualShape(TestInterpolateOp):
def init_test_case(self):
self.interp_method = 'bilinear'
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.out_size = np.array([66, 40]).astype("int32")
class TestBilinearInterpBigScale(TestInterpolateOp):
def init_test_case(self):
self.interp_method = 'bilinear'
@ -179,12 +201,13 @@ class TestBilinearInterpBigScale(TestInterpolateOp):
class TestInterpolateOpUint8(OpTest):
def setUp(self):
self.out_size = None
self.actual_shape = None
self.init_test_case()
self.op_type = "interpolate"
input_np = np.random.randint(
low=0, high=256, size=self.input_shape).astype("uint8")
output_np = INTERPOLATE_FUNCS[self.interp_method](
input_np, self.out_h, self.out_w, self.out_size)
input_np, self.out_h, self.out_w, self.out_size, self.actual_shape)
self.inputs = {'X': input_np}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
@ -273,6 +296,15 @@ class TestNearestNeighborInterpCase6(TestInterpolateOp):
self.out_size = np.array([65, 129]).astype("int32")
class TestNearestNeighborInterpActualShape(TestInterpolateOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.out_size = np.array([66, 40]).astype("int32")
class TestNearestNeighborInterpBigScale(TestInterpolateOp):
def init_test_case(self):
self.interp_method = 'nearest'

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