def my_bilinear_sample_nchw(bottom_data, n, c, h, w, h_size, w_size):
h_low = te.floor(h)
w_low = te.floor(w)
h_high = h_low + 1
w_high = w_low + 1
lh = h - h_low
lw = w - w_low
hh = 1 - lh
hw = 1 - lw
zero = tvm.tir.const(0.0, bottom_data.dtype)
v1 = te.if_then_else(tvm.tir.all(h_low >= 0 , w_low >= 0),\
bottom_data[n, c, h_low, w_low], zero)
return v1
v2 = te.if_then_else(tvm.tir.all(h_low >= 0, w_high <= w_size - 1), \
bottom_data[n, c, h_low, w_high], zero)
v3 = te.if_then_else(tvm.tir.all(h_high <= h_size - 1, w_low >= 0), \
bottom_data[n, c, h_high, w_low], zero)
v4 = te.if_then_else(
tvm.tir.all(h_high <= h_size - 1, w_high <= w_size - 1),
bottom_data[n, c, h_high, w_high], zero)
w1 = hh * hw
w2 = hh * lw
w3 = lh * hw
w4 = lh * lw
val = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4
return val
def _bilinear_sample(n, c, h, w):
x = grid[n, 0, h, w]
y = grid[n, 1, h, w]
y = (y + 1) * (in_height - 1) / 2
x = (x + 1) * (in_width - 1) / 2
x0 = te.floor(x).astype('int32')
y0 = te.floor(y).astype('int32')
x1 = x0 + tir.const(1, 'int32')
y1 = y0 + tir.const(1, 'int32')
return _get_pixel_value(n, c, y0, x0) * (1.0 - (y - y0)) * (1.0 - (x - x0)) \
+ _get_pixel_value(n, c, y0, x1) * (1.0 - (y - y0)) * (x - x0) \
+ _get_pixel_value(n, c, y1, x0) * (y - y0) * (1.0 - (x - x0)) \
+ _get_pixel_value(n, c, y1, x1) * (y - y0) * (x - x0)
def _bilinear_sample(n, c, h, w):
y, x = _compute_source_index(n, h, w)
y0 = te.floor(y).astype("int32")
x0 = te.floor(x).astype("int32")
y1 = y0 + tir.const(1, "int32")
x1 = x0 + tir.const(1, "int32")
return (
_get_pixel_value(n, c, y0, x0) * (1.0 - (y - y0)) * (1.0 - (x - x0))
+ _get_pixel_value(n, c, y0, x1) * (1.0 - (y - y0)) * (x - x0)
+ _get_pixel_value(n, c, y1, x0) * (y - y0) * (1.0 - (x - x0))
+ _get_pixel_value(n, c, y1, x1) * (y - y0) * (x - x0)
)
def get_closest_index(in_x, rounding_method, boxes):
"""get the closest index to a value based on a certain rounding method"""
if rounding_method == "round" or boxes is not None:
closest_x_index = te.round(in_x).astype("int32")
elif rounding_method == "round_prefer_floor":
closest_x_index = te.ceil(in_x - 0.5).astype("int32")
elif rounding_method == "round_prefer_ceil":
closest_x_index = te.floor(in_x + 0.5).astype("int32")
elif rounding_method == "floor":
# Add epsilon to floor to prevent gpu rounding errors.
epsilon = 1e-5
closest_x_index = te.floor(in_x + epsilon).astype("int32")
elif rounding_method == "ceil":
# Subract epsilon from ceil to prevent gpu rounding errors.
epsilon = 1e-5
closest_x_index = te.ceil(in_x - epsilon).astype("int32")
else:
raise ValueError("Uknown rounding method: {}".format(rounding_method))
return closest_x_index
def _trilinear_sample(n, c, d, h, w):
z, y, x = _compute_source_index(n, d, h, w)
z0 = te.floor(z).astype("int32")
y0 = te.floor(y).astype("int32")
x0 = te.floor(x).astype("int32")
z1 = z0 + tir.const(1, "int32")
y1 = y0 + tir.const(1, "int32")
x1 = x0 + tir.const(1, "int32")
return (
_get_pixel_value(n, c, z0, y0, x0) * (1 - (x - x0)) * (1 - (y - y0)) * (1 - (z - z0))
+ _get_pixel_value(n, c, z0, y0, x1) * (x - x0) * (1 - (y - y0)) * (1 - (z - z0))
+ _get_pixel_value(n, c, z1, y1, x0) * (1 - (x - x0)) * (y - y0) * (z - z0)
+ _get_pixel_value(n, c, z1, y1, x1) * (x - x0) * (y - y0) * (z - z0)
+ _get_pixel_value(n, c, z0, y1, x0) * (1 - (x - x0)) * (y - y0) * (1 - (z - z0))
+ _get_pixel_value(n, c, z1, y0, x1) * (x - x0) * (1 - (y - y0)) * (z - z0)
+ _get_pixel_value(n, c, z1, y0, x0) * (1 - (x - x0)) * (1 - (y - y0)) * (z - z0)
+ _get_pixel_value(n, c, z0, y1, x1) * (x - x0) * (y - y0) * (1 - (z - z0))
)
def _pool(i, c, ph, pw):
roi = rois[i]
batch_index = roi[0].astype("int32")
roi_start_w, roi_start_h, roi_end_w, roi_end_h = roi[1], roi[2], roi[
3], roi[4]
roi_start_h = te.round(roi_start_h * spatial_scale).astype("int32")
roi_start_w = te.round(roi_start_w * spatial_scale).astype("int32")
roi_end_h = te.round(roi_end_h * spatial_scale).astype("int32")
roi_end_w = te.round(roi_end_w * spatial_scale).astype("int32")
# force malformed ROIs to be 1x1
roi_h = tvm.te.max(roi_end_h - roi_start_h + 1,
tvm.tir.const(1, "int32"))
roi_w = tvm.te.max(roi_end_w - roi_start_w + 1,
tvm.tir.const(1, "int32"))
bin_h = roi_h.astype(dtype) / pooled_size_h
bin_w = roi_w.astype(dtype) / pooled_size_w
# use epsilon to prevent floating point precision loss in floor/ceil
epsilon = tvm.tir.const(0.00001, dtype)
hstart = te.floor(ph * bin_h + epsilon).astype("int32")
wstart = te.floor(pw * bin_w + epsilon).astype("int32")
hend = te.ceil((ph + 1) * bin_h - epsilon).astype("int32")
wend = te.ceil((pw + 1) * bin_w - epsilon).astype("int32")
hstart = tvm.te.min(tvm.te.max(hstart + roi_start_h, 0), height)
wstart = tvm.te.min(tvm.te.max(wstart + roi_start_w, 0), width)
hend = tvm.te.min(tvm.te.max(hend + roi_start_h, 0), height)
wend = tvm.te.min(tvm.te.max(wend + roi_start_w, 0), width)
non_empty = tvm.tir.all(hstart < hend, wstart < wend)
min_value = lambda dtype: tvm.tir.if_then_else(
non_empty, tvm.te.min_value(dtype), tvm.tir.const(0.0, dtype))
# pylint: disable=unnecessary-lambda
_max = te.comm_reducer(lambda x, y: tvm.te.max(x, y),
min_value,
name="max")
rh = te.reduce_axis((0, hend - hstart), "rh")
rw = te.reduce_axis((0, wend - wstart), "rw")
return _max(data[batch_index, c, hstart + rh, wstart + rw],
axis=[rh, rw])
def _nearest_neighbor(*indices):
n, c, z, y, x, cc = _get_indices(*indices)
in_z = z_ratio * z
in_y = y_ratio * y
in_x = x_ratio * x
if coordinate_transformation_mode == "align_corners":
zint = te.round(in_z).astype('int32')
yint = te.round(in_y).astype('int32')
xint = te.round(in_x).astype('int32')
elif coordinate_transformation_mode in ["asymmetric", "half_pixel"]:
# Add epsilon to floor to prevent gpu rounding errors.
epsilon = 1e-5
zint = te.floor(in_z + epsilon).astype('int32')
yint = te.floor(in_y + epsilon).astype('int32')
xint = te.floor(in_x + epsilon).astype('int32')
else:
raise ValueError("Unsupported coordinate_transformation_mode: {}".format(
coordinate_transformation_mode))
return _cast_output(_get_pixel(n, c, zint, yint, xint, cc))
def floor(x):
"""Take floor of input x.
Parameters
----------
x : tvm.te.Tensor
Input argument.
Returns
-------
y : tvm.te.Tensor
The result.
"""
return te.compute(x.shape, lambda *i: te.floor(x(*i)))
def _trilinear(*indices):
n, c, z, y, x, cc = _get_indices(*indices)
if coordinate_transformation_mode == "half_pixel":
in_z = z_ratio * (z + 0.5) - 0.5
in_y = y_ratio * (y + 0.5) - 0.5
in_x = x_ratio * (x + 0.5) - 0.5
else:
in_z = z_ratio * z
in_y = y_ratio * y
in_x = x_ratio * x
zint = te.floor(in_z).astype('int32')
zfract = in_z - te.floor(in_z)
xint = te.floor(in_x).astype('int32')
xfract = in_x - te.floor(in_x)
yint = te.floor(in_y).astype('int32')
yfract = in_y - te.floor(in_y)
p000 = _get_pixel(n, c, zint, yint, xint, cc)
p001 = _get_pixel(n, c, zint, yint, xint + 1, cc)
p010 = _get_pixel(n, c, zint, yint + 1, xint, cc)
p011 = _get_pixel(n, c, zint, yint + 1, xint + 1, cc)
p100 = _get_pixel(n, c, zint + 1, yint, xint, cc)
p101 = _get_pixel(n, c, zint + 1, yint, xint + 1, cc)
p110 = _get_pixel(n, c, zint + 1, yint + 1, xint, cc)
p111 = _get_pixel(n, c, zint + 1, yint + 1, xint + 1, cc)
dep00 = _lerp(p000, p100, zfract)
dep01 = _lerp(p001, p101, zfract)
dep10 = _lerp(p010, p110, zfract)
dep11 = _lerp(p011, p111, zfract)
col0 = _lerp(dep00, dep01, xfract)
col1 = _lerp(dep10, dep11, xfract)
value = _lerp(col0, col1, yfract)
return _cast_output(value)
def _resize_2d(
indices,
data,
roi,
image_height,
image_width,
target_height,
target_width,
boxes=None,
box_indices=None,
method=None,
extrapolation_value=0.0,
layout="NCHW",
coordinate_transformation_mode="align_corners",
rounding_method="",
alpha=-0.5,
exclude_outside=0,
out_dtype=None,
):
"""Perform resize operation on the data with selected method and options.
Parameters
----------
indices : tuple
The indices of input data
data : tvm.te.Tensor
inputs is a 4-D tensor with shape
[batch, channel, in_height, in_width]
or [batch, in_height, in_width, channel]
roi: Tuple of Float or Expr
The region of interest for cropping the input image. Expected to be of
size 4, and format [start_h, start_w, end_h, end_w].
Only used if coordinate_transformation_mode is tf_crop_and_resize.
image_height : integer
Input image height
image_width : integer
Input image width
target_height : integer
The target resized image height
target_width : integer
The target resized image width
boxes : tvm.te.Tensor, optional
A 2-D tensor of shape [num_boxes, 4]. Each row of the tensor specifies
the coordinates of a box.
method: string, optional
method of interpolation ("nearest", "linear", "bicubic")
box_indices : tvm.te.Tensor, optional
A 1-D tensor of shape [num_boxes], box_indices[i] specifies the data that
the i-th box refers to.
extrapolation_value: float, optional
Value used for extrapolation, when applicable.
layout: string, optional
"NCHW", "NHWC", or "NCHWc".
coordinate_transformation_mode : string, optional
Describes how to transform the coordinate in the resized tensor
to the coordinate in the original tensor.
[half_pixel, align_corners, asymmetric, pytorch_half_pixel,
tf_half_pixel_for_nn, and tf_crop_and_resize].
rounding_method: string, optional
indicates how to find the "nearest" pixel in nearest_neighbor method
[round, floor, ceil]
alpha: float, optional
Bicubic spline coefficient
exclude_outside: bool, optional:
Exclude values outside the image fdor bicubic interpolation
out_dtype: string, optional
Type to return. If left None will be same as input type.
Returns
-------
output : out_dtype
The computed result with type out_dtype
"""
def _cast_output(value, data_dtype="float32", out_dtype=None):
if out_dtype:
dtype = out_dtype
else:
dtype = data_dtype
return value.astype(dtype)
height_use_int_div = False
width_use_int_div = False
if method == "nearest_neighbor" and coordinate_transformation_mode == "asymmetric":
height_use_int_div = can_convert_multiply_to_intdiv(
image_height, target_height)
width_use_int_div = can_convert_multiply_to_intdiv(
image_width, target_width)
n, c, y, x, cc, inum, ic = get_2d_indices(indices, layout)
box_idx = box_indices(n) if box_indices is not None else n
if boxes is not None:
y1, x1 = boxes(n, 0), boxes(n, 1)
y2, x2 = boxes(n, 2), boxes(n, 3)
in_h = (image_height - 1) * (y2 - y1)
in_w = (image_width - 1) * (x2 - x1)
h_scale = in_h.astype("float") / (target_height - 1)
w_scale = in_w.astype("float") / (target_width - 1)
in_y = y1 * (image_height - 1) + h_scale * y
in_x = x1 * (image_width - 1) + w_scale * x
else:
in_x = get_inx(
x,
image_width,
target_width,
coordinate_transformation_mode,
roi[1],
roi[3],
width_use_int_div,
)
in_y = get_inx(
y,
image_height,
target_height,
coordinate_transformation_mode,
roi[0],
roi[2],
height_use_int_div,
)
if method == "nearest_neighbor":
if rounding_method == "":
if coordinate_transformation_mode == "align_corners":
rounding_method = "round"
else:
rounding_method = "floor"
closest_x_index = get_closest_index(in_x, rounding_method, boxes,
width_use_int_div)
closest_y_index = get_closest_index(in_y, rounding_method, boxes,
height_use_int_div)
value = get_2d_pixel(
data,
layout,
image_height,
image_width,
box_idx,
c,
closest_y_index,
closest_x_index,
cc,
inum,
ic,
)
elif method == "linear":
y_int = te.floor(in_y).astype("int32")
x_int = te.floor(in_x).astype("int32")
y_lerp = in_y - y_int
x_lerp = in_x - x_int
p = [[0 for i in range(2)] for j in range(2)]
for j in range(2):
for i in range(2):
p[j][i] = get_2d_pixel(
data,
layout,
image_height,
image_width,
box_idx,
c,
y_int + j,
x_int + i,
cc,
inum,
ic,
)
top = _lerp(*p[0], x_lerp)
bottom = _lerp(*p[1], x_lerp)
value = _lerp(top, bottom, y_lerp)
elif method == "cubic":
xint = te.floor(in_x).astype("int32")
xfract = in_x - te.floor(in_x)
yint = te.floor(in_y).astype("int32")
yfract = in_y - te.floor(in_y)
# Get the surrounding values
p = [[0 for i in range(4)] for j in range(4)]
for j in range(4):
for i in range(4):
p[j][i] = get_2d_pixel(
data,
layout,
image_height,
image_width,
box_idx,
c,
yint + j - 1,
xint + i - 1,
cc,
inum,
ic,
)
wx = _cubic_spline_weights(xfract, alpha)
wy = _cubic_spline_weights(yfract, alpha)
if exclude_outside:
for i in range(4):
wx[i] = te.if_then_else(
te.any(xint - 1 + i < 0, xint + i > image_width), 0.0,
wx[i])
wy[i] = te.if_then_else(
te.any(yint - 1 + i < 0, yint + i > image_height), 0.0,
wy[i])
sum_wx = sum(wx)
sum_wy = sum(wy)
wx = [w / sum_wx for w in wx]
wy = [w / sum_wy for w in wy]
col0 = _cubic_kernel(p[0], wx)
col1 = _cubic_kernel(p[1], wx)
col2 = _cubic_kernel(p[2], wx)
col3 = _cubic_kernel(p[3], wx)
value = _cubic_kernel([col0, col1, col2, col3], wy)
else:
raise ValueError("Unknown resize method:", method)
if coordinate_transformation_mode == "tf_crop_and_resize":
out = tvm.tir.if_then_else(
in_y < 0,
extrapolation_value,
tvm.tir.if_then_else(in_y > image_height - 1, extrapolation_value,
value),
)
# use extrapolation_value if in_x is out of boundary
value = tvm.tir.if_then_else(
in_x < 0,
extrapolation_value,
tvm.tir.if_then_else(in_x > image_width - 1, extrapolation_value,
out),
)
return _cast_output(value, data.dtype, out_dtype=out_dtype)
def _resize_1d(
indices,
data,
roi,
image_width,
target_width,
boxes=None,
box_indices=None,
method=None,
extrapolation_value=0.0,
layout="NCW",
coordinate_transformation_mode="align_corners",
rounding_method="",
alpha=-0.5,
exclude_outside=0,
out_dtype=None,
):
"""Perform resize operation on the data with selected method and options.
Parameters
----------
indices : tuple
The indices of input data
data : tvm.te.Tensor
inputs is a 3-D tensor with shape
[batch, channel, in_width]
or [batch, in_width, channel]
roi: Tuple of Float or Expr
The region of interest for cropping the input image. Expected to be of
size 2, and format [start_w, end_w].
Only used if coordinate_transformation_mode is tf_crop_and_resize.
image_width : integer
Input image width
target_width : integer
The target resized image width
boxes : tvm.te.Tensor, optional
A 2-D tensor of shape [num_boxes, 4]. Each row of the tensor specifies
the coordinates of a box.
box_indices : tvm.te.Tensor, optional
A 1-D tensor of shape [num_boxes], box_indices[i] specifies the data that
the i-th box refers to.
extrapolation_value: float, optional
Value used for extrapolation, when applicable.
layout: string, optional
"NCW", "NWC", or "NCWc".
method: string, optional
method of interpolation ("nearest", "linear", "bicubic")
coordinate_transformation_mode : string, optional
Describes how to transform the coordinate in the resized tensor
to the coordinate in the original tensor.
[half_pixel, align_corners, asymmetric, pytorch_half_pixel,
tf_half_pixel_for_nn, and tf_crop_and_resize].
rounding_method: string, optional
indicates how to find the "nearest" pixel in nearest_neighbor method
[round, floor, ceil]
alpha: float, optional
Bicubic spline coefficient
exclude_outside: bool, optional:
Exclude values outside the image fdor bicubic interpolation
out_dtype: string, optional
Type to return. If left None will be same as input type.
Returns
-------
output : out_dtype
The computed result with type out_dtype
"""
def _cast_output(value, data_dtype="float32", out_dtype=None):
if out_dtype:
dtype = out_dtype
else:
dtype = data_dtype
return value.astype(dtype)
n, c, x, cc, inum, ic = get_1d_indices(indices, layout)
box_idx = box_indices(n) if box_indices is not None else n
if boxes is not None:
# TODO(mbrookhart): Find an example of this
raise NotImplementedError(
"resize1d with image boxes not yet implemented")
in_x = get_inx(
x,
image_width,
target_width,
coordinate_transformation_mode,
roi[0],
roi[1],
)
if method == "nearest_neighbor":
if rounding_method == "":
if coordinate_transformation_mode == "align_corners":
rounding_method = "round"
else:
rounding_method = "floor"
closest_x_index = get_closest_index(in_x, rounding_method, boxes)
value = get_1d_pixel(
data,
layout,
image_width,
box_idx,
c,
closest_x_index,
cc,
inum,
ic,
)
elif method == "linear":
x_int = te.floor(in_x).astype("int32")
x_lerp = in_x - x_int
p = [0 for i in range(2)]
for i in range(2):
p[i] = get_1d_pixel(
data,
layout,
image_width,
box_idx,
c,
x_int + i,
cc,
inum,
ic,
)
value = _lerp(*p, x_lerp)
elif method == "cubic":
xint = te.floor(in_x).astype("int32")
xfract = in_x - te.floor(in_x)
# Get the surrounding values
p = [0 for i in range(4)]
for i in range(4):
p[i] = get_1d_pixel(
data,
layout,
image_width,
box_idx,
c,
xint + i - 1,
cc,
inum,
ic,
)
wx = _cubic_spline_weights(xfract, alpha)
if exclude_outside:
for i in range(4):
wx[i] = te.if_then_else(
te.any(xint - 1 + i < 0, xint + i > image_width), 0.0,
wx[i])
sum_wx = sum(wx)
wx = [w / sum_wx for w in wx]
value = _cubic_kernel(p, wx)
else:
raise ValueError("Unknown resize method:", method)
if coordinate_transformation_mode == "tf_crop_and_resize":
# use extrapolation_value if in_x is out of boundary
value = tvm.tir.if_then_else(
in_x < 0,
extrapolation_value,
tvm.tir.if_then_else(in_x > image_width - 1, extrapolation_value,
value),
)
return _cast_output(value, data.dtype, out_dtype=out_dtype)
def _resize_3d(
indices,
data,
roi,
image_depth,
image_height,
image_width,
target_depth,
target_height,
target_width,
boxes=None,
box_indices=None,
method=None,
extrapolation_value=0.0,
layout="NCHW",
coordinate_transformation_mode="align_corners",
rounding_method="",
alpha=-0.5,
exclude_outside=0,
out_dtype=None,
):
"""Perform resize operation on the data with selected method and options.
Parameters
----------
indices : tuple
The indices of input data
data : tvm.te.Tensor
inputs is a 4-D tensor with shape
[batch, channel, in_height, in_width]
or [batch, in_height, in_width, channel]
roi: Tuple of Float or Expr
The region of interest for cropping the input image. Expected to be of
size 6, and format [start_d, start_h, start_w, end_d, end_h, end_w].
Only used if coordinate_transformation_mode is tf_crop_and_resize.
image_depth : integer
Input image depth
image_height : integer
Input image height
image_width : integer
Input image width
target_depth : integer
The target resized image depth
target_height : integer
The target resized image height
target_width : integer
The target resized image width
boxes : tvm.te.Tensor, optional
A 2-D tensor of shape [num_boxes, 4]. Each row of the tensor specifies
the coordinates of a box.
box_indices : tvm.te.Tensor, optional
A 1-D tensor of shape [num_boxes], box_indices[i] specifies the data that
the i-th box refers to.
method: string, optional
method of interpolation ("nearest", "linear", "bicubic")
extrapolation_value: float, optional
Value used for extrapolation, when applicable.
layout: string, optional
"NCHW", "NHWC", or "NCHWc".
coordinate_transformation_mode : string, optional
Describes how to transform the coordinate in the resized tensor
to the coordinate in the original tensor.
[half_pixel, align_corners, asymmetric, pytorch_half_pixel,
tf_half_pixel_for_nn, and tf_crop_and_resize].
rounding_method: string, optional
indicates how to find the "nearest" pixel in nearest_neighbor method
[round, floor, ceil]
alpha: float, optional
Bicubic spline coefficient
exclude_oiutside: bool, optional:
Exclude values outside the image fdor bicubic interpolation
out_dtype: string, optional
Type to return. If left None will be same as input type.
Returns
-------
output : out_dtype
The computed result with type out_dtype
"""
def _cast_output(value, data_dtype="float32", out_dtype=None):
if out_dtype:
dtype = out_dtype
else:
dtype = data_dtype
return value.astype(dtype)
n, c, z, y, x, cc = get_3d_indices(indices, layout)
box_idx = box_indices(n) if box_indices is not None else n
if boxes is not None:
# TODO(mbrookhart): Find an example of this
raise NotImplementedError(
"resize1d with image boxes not yet implemented")
in_z = get_inx(z, image_depth, target_depth,
coordinate_transformation_mode, roi[2], roi[5])
in_y = get_inx(y, image_height, target_height,
coordinate_transformation_mode, roi[1], roi[4])
in_x = get_inx(x, image_width, target_width,
coordinate_transformation_mode, roi[0], roi[3])
if method == "nearest_neighbor":
if rounding_method == "":
if coordinate_transformation_mode == "align_corners":
rounding_method = "round"
else:
rounding_method = "floor"
closest_z_index = get_closest_index(in_z, rounding_method, boxes)
closest_y_index = get_closest_index(in_y, rounding_method, boxes)
closest_x_index = get_closest_index(in_x, rounding_method, boxes)
value = get_3d_pixel(
data,
layout,
image_depth,
image_height,
image_width,
box_idx,
c,
closest_z_index,
closest_y_index,
closest_x_index,
cc,
)
elif method == "linear":
z_int = te.floor(in_z).astype("int32")
y_int = te.floor(in_y).astype("int32")
x_int = te.floor(in_x).astype("int32")
z_lerp = in_z - z_int
y_lerp = in_y - y_int
x_lerp = in_x - x_int
p = [[[0 for i in range(2)] for j in range(2)] for k in range(2)]
for k in range(2):
for j in range(2):
for i in range(2):
p[k][j][i] = get_3d_pixel(
data,
layout,
image_depth,
image_height,
image_width,
box_idx,
c,
z_int + k,
y_int + j,
x_int + i,
cc,
)
l = [[0 for i in range(2)] for j in range(2)]
for j in range(2):
for i in range(2):
l[j][i] = _lerp(*p[j][i], x_lerp)
top = _lerp(*l[0], y_lerp)
bottom = _lerp(*l[1], y_lerp)
value = _lerp(top, bottom, z_lerp)
elif method == "cubic":
zint = te.floor(in_z).astype("int32")
zfract = in_z - te.floor(in_z)
yint = te.floor(in_y).astype("int32")
yfract = in_y - te.floor(in_y)
xint = te.floor(in_x).astype("int32")
xfract = in_x - te.floor(in_x)
# Get the surrounding values
p = [[[0 for i in range(4)] for j in range(4)] for k in range(4)]
for k in range(4):
for j in range(4):
for i in range(4):
p[k][j][i] = get_3d_pixel(
data,
layout,
image_depth,
image_height,
image_width,
box_idx,
c,
zint + k - 1,
yint + j - 1,
xint + i - 1,
cc,
)
wz = _cubic_spline_weights(zfract, alpha)
wy = _cubic_spline_weights(yfract, alpha)
wx = _cubic_spline_weights(xfract, alpha)
if exclude_outside:
for i in range(4):
wz[i] = te.if_then_else(
te.any(xint - 1 + i < 0, xint + i > image_height), 0.0,
wx[i])
wy[i] = te.if_then_else(
te.any(yint - 1 + i < 0, yint + i > image_height), 0.0,
wy[i])
wx[i] = te.if_then_else(
te.any(xint - 1 + i < 0, xint + i > image_width), 0.0,
wx[i])
sum_wz = sum(wz)
sum_wy = sum(wy)
sum_wx = sum(wx)
wz = [w / sum_wz for w in wz]
wy = [w / sum_wy for w in wy]
wx = [w / sum_wx for w in wx]
l = [[0 for i in range(4)] for j in range(4)]
for j in range(4):
for i in range(4):
l[j][i] = _cubic_kernel(p[j][i], wx)
col0 = _cubic_kernel(l[0], wy)
col1 = _cubic_kernel(l[1], wy)
col2 = _cubic_kernel(l[2], wy)
col3 = _cubic_kernel(l[3], wy)
value = _cubic_kernel([col0, col1, col2, col3], wz)
else:
raise ValueError("Unknown resize method:", method)
if coordinate_transformation_mode == "tf_crop_and_resize":
out = tvm.tir.if_then_else(
in_z < 0,
extrapolation_value,
tvm.tir.if_then_else(in_z > image_depth - 1, extrapolation_value,
value),
)
out = tvm.tir.if_then_else(
in_y < 0,
extrapolation_value,
tvm.tir.if_then_else(in_y > image_height - 1, extrapolation_value,
value),
)
# use extrapolation_value if in_x is out of boundary
value = tvm.tir.if_then_else(
in_x < 0,
extrapolation_value,
tvm.tir.if_then_else(in_x > image_width - 1, extrapolation_value,
out),
)
return _cast_output(value, data.dtype, out_dtype=out_dtype)
def test_basic_operation():
np.random.seed(0)
shape = (10, 10)
x = te.var("x", dtype='float32')
k = te.reduce_axis((0, 10), name="k")
l = te.reduce_axis((0, 10), name="l")
A0 = te.placeholder(shape, name='A0')
A1 = te.placeholder(shape, name='A1')
zeros = np.zeros(shape)
B = te.compute(shape, lambda i, j: A0[i, j], name='B')
check_grad(B, [A0])
B = te.compute(shape, lambda i, j: A0[i, j] + A1[i, j], name='B')
check_grad(B, [A0, A1])
B = te.compute(shape, lambda i, j: A0[i, j] + A0[j, i], name='B')
check_grad(B, A0)
B = te.compute(shape, lambda i, j: te.floor(A0[i, j]), name='B')
check_grad(B, A0, desired_grads=[zeros])
B = te.compute(shape, lambda i, j: te.ceil(A0[i, j]), name='B')
check_grad(B, A0, desired_grads=[zeros])
B = te.compute(shape, lambda i, j: te.trunc(A0[i, j]), name='B')
check_grad(B, A0, desired_grads=[zeros])
B = te.compute(shape, lambda i, j: te.round(A0[i, j]), name='B')
check_grad(B, A0, desired_grads=[zeros])
B = te.compute(shape, lambda i, j: A0[i, j] + te.exp(A0[j, i]), name='B')
check_grad(B, A0)
B = te.compute(
shape,
lambda i, j: te.log(0.1 + te.abs(A0[i, j] + te.exp(A0[j, i]))),
name='B')
check_grad(B, A0)
B = te.compute(shape,
lambda i, j: te.sigmoid(A0[i, j] * A0[i, j] * A0[j, i]),
name='B')
check_grad(B, A0)
B = te.compute(shape,
lambda i, j: te.tanh(A0[i, j] * A0[i, j] * A0[j, i]),
name='B')
check_grad(B, A0)
B = te.compute(shape,
lambda i, j: te.sqrt(A0[i, j] * A0[i, j] * A0[j, i]),
name='B')
check_grad(B, A0, data_range=(0.1, 10))
B = te.compute(shape,
lambda i, j: te.power(te.abs(A0[i, j]), A0[j, i]),
name='B')
check_grad(B, A0, data_range=(-4, 4))
B = te.compute(shape, lambda i, j: A0[i, j] * A0[j, i], name='B')
check_grad(B, A0)
B = te.compute((10, ),
lambda i: te.sum(A0[i, k] * A0[k, i], axis=k),
name='B')
check_grad(B, A0)
B = te.compute(shape,
lambda i, j: te.sum(A0[i, k] * A0[k, i] + 5, axis=k),
name='B')
check_grad(B, A0)
B = te.compute(shape,
lambda i, j: te.max(A0[i, k] * A0[k, j] + 5, axis=k),
name='B')
check_grad(B, A0)
B = te.compute(shape,
lambda i, j: A0[i, j] * (A1[j, i] + A0[j, i]),
name='B')
check_grad(B, [A0, A1])
B = te.compute(shape,
lambda i, j: te.sum(
A0[k, k] - A0[te.min(j + k, 9), j] * A0[i, k], axis=k),
name='B')
check_grad(B, A0)
def fcombine(x, y):
return x * y
def fidentity(t0):
return tvm.tir.const(1, t0)
prod = te.comm_reducer(fcombine, fidentity, name='prod')
B = te.compute((10, 10),
lambda i, j: prod(A0[i, k] + A0[k, i], axis=k),
name='B')
check_grad(B, A0)
X = te.placeholder((10, ), name='X')
A = te.compute((10, ), lambda i: X[i] + X[9 - i])
B = te.compute((10, ), lambda i: X[i] * X[9 - i])
Y = topi.tensordot(A, B, 1)
check_grad(Y, X)
def resize_nearest_neighbor(indices,
data,
image_height,
image_width,
target_height,
target_width,
boxes=None,
box_indices=None,
extrapolation_value=None,
layout='NCHW',
coordinate_transformation_mode="align_corners",
out_dtype=None):
"""Perform resize operation with nearest neighbor method on the data.
For details about Nearest-neighbor interpolation please refer to
https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation.
Parameters
----------
indices : tuple
The indices of input data
data : tvm.te.Tensor
inputs is a 4-D tensor with shape
[batch, channel, in_height, in_width]
or [batch, in_height, in_width, channel]
image_height : integer
Input image height
image_width : integer
Input image width
target_height : integer
The target resized image height
target_width : integer
The target resized image width
boxes : tvm.te.Tensor, optional
A 2-D tensor of shape [num_boxes, 4]. Each row of the tensor specifies
the coordinates of a box.
box_indices : tvm.te.Tensor, optional
A 1-D tensor of shape [num_boxes], box_indices[i] specifies the data that
the i-th box refers to.
extrapolation_value: float, optional
Value used for extrapolation, when applicable.
layout: string, optional
"NCHW", "NHWC", or "NCHWc".
coordinate_transformation_mode: string, optional
Describes how to transform the coordinate in the resized tensor
to the coordinate in the original tensor.
Refer to the ONNX Resize operator specification for details.
Available options are "half_pixel", "align_corners" and "asymmetric".
out_dtype: string, optional
Type to return. If left None will be same as input type.
Returns
-------
output : out_dtype
The computed result with type out_dtype
"""
def _cast_output(value, data_dtype="float32", out_dtype=None):
if out_dtype:
dtype = out_dtype
else:
dtype = data_dtype
return value.astype(dtype)
n, c, y, x, cc, inum, ic = get_2d_indices(indices, layout)
box_idx = box_indices(n) if box_indices is not None else n
if boxes is not None:
y1, x1 = boxes(n, 0), boxes(n, 1)
y2, x2 = boxes(n, 2), boxes(n, 3)
in_h = (image_height - 1) * (y2 - y1)
in_w = (image_width - 1) * (x2 - x1)
h_scale = in_h.astype('float') / (target_height - 1)
w_scale = in_w.astype('float') / (target_width - 1)
in_y = y1 * (image_height - 1) + h_scale * y
in_x = x1 * (image_width - 1) + w_scale * x
else:
if coordinate_transformation_mode == "align_corners":
h_scale = (image_height - 1).astype('float') / (target_height - 1)
w_scale = (image_width - 1).astype('float') / (target_width - 1)
elif coordinate_transformation_mode in ["asymmetric", "half_pixel"]:
h_scale = image_height.astype('float') / target_height
w_scale = image_width.astype('float') / target_width
else:
raise ValueError(
"Unsupported coordinate_transformation_mode: {}".format(
coordinate_transformation_mode))
in_y = h_scale * y
in_x = w_scale * x
if coordinate_transformation_mode == "align_corners" or boxes is not None:
closest_x_index = te.round(in_x).astype("int32")
closest_y_index = te.round(in_y).astype("int32")
else:
# Add epsilon to floor to prevent gpu rounding errors.
epsilon = 1e-5
closest_y_index = te.floor(in_y + epsilon).astype('int32')
closest_x_index = te.floor(in_x + epsilon).astype('int32')
value = get_2d_pixel(data, layout, boxes, image_height, image_width,
box_idx, c, closest_y_index, closest_x_index, cc,
inum, ic)
if extrapolation_value is not None:
out = tvm.tir.if_then_else(
in_y < 0, extrapolation_value,
tvm.tir.if_then_else(in_y > image_height - 1, extrapolation_value,
value))
# use extrapolation_value if in_x is out of boundary
value = tvm.tir.if_then_else(
in_x < 0, extrapolation_value,
tvm.tir.if_then_else(in_x > image_width - 1, extrapolation_value,
out))
return _cast_output(value, data.dtype, out_dtype=out_dtype)
def resize_bicubic(indices,
data,
image_height,
image_width,
target_height,
target_width,
boxes=None,
box_indices=None,
extrapolation_value=None,
layout='NCHW',
coordinate_transformation_mode="align_corners",
out_dtype=None):
"""Perform resize operation with bicubic method on the data.
More details about Bicubic interpolation please refer to
https://en.wikipedia.org/wiki/Bicubic_interpolation.
Parameters
----------
indices : tuple
The indices of input data
data : tvm.te.Tensor
inputs is a 4-D tensor with shape
[batch, channel, in_height, in_width]
or [batch, in_height, in_width, channel]
image_height : integer
Input image height
image_width : integer
Input image width
target_height : integer
The target resized image height
target_width : integer
The target resized image width
boxes : tvm.te.Tensor, optional
A 2-D tensor of shape [num_boxes, 4]. Each row of the tensor specifies
the coordinates of a box.
box_indices : tvm.te.Tensor, optional
A 1-D tensor of shape [num_boxes], box_indices[i] specifies the data that
the i-th box refers to.
extrapolation_value: float, optional
Value used for extrapolation, when applicable.
layout: string, optional
"NCHW", "NHWC", or "NCHWc".
coordinate_transformation_mode: string, optional
Describes how to transform the coordinate in the resized tensor
to the coordinate in the original tensor.
Refer to the ONNX Resize operator specification for details.
Available options are "half_pixel", "align_corners" and "asymmetric".
out_dtype: string, optional
Type to return. If left None will be same as input type.
Returns
-------
output : out_dtype
The computed result with type out_dtype
"""
def _cubic_kernel(A, B, C, D, t):
a = -A / 2.0 + (3.0 * B) / 2.0 - (3.0 * C) / 2.0 + D / 2.0
b = A - (5.0 * B) / 2.0 + 2.0 * C - D / 2.0
c = -A / 2.0 + C / 2.0
d = B
return a * t * t * t + b * t * t + c * t + d
def _cast_output(value, data_dtype="float32", out_dtype=None):
if out_dtype:
dtype = out_dtype
else:
dtype = data_dtype
return value.astype(dtype)
n, c, y, x, cc, inum, ic = get_2d_indices(indices, layout)
box_idx = box_indices(n) if box_indices is not None else n
if boxes is not None:
y1, x1 = boxes(n, 0), boxes(n, 1)
y2, x2 = boxes(n, 2), boxes(n, 3)
in_h = (image_height - 1) * (y2 - y1)
in_w = (image_width - 1) * (x2 - x1)
h_scale = in_h.astype('float') / (target_height - 1)
w_scale = in_w.astype('float') / (target_width - 1)
in_y = y1 * (image_height - 1) + h_scale * y
in_x = x1 * (image_width - 1) + w_scale * x
else:
if coordinate_transformation_mode == "align_corners":
h_scale = (image_height - 1).astype('float') / (target_height - 1)
w_scale = (image_width - 1).astype('float') / (target_width - 1)
elif coordinate_transformation_mode in ["asymmetric", "half_pixel"]:
h_scale = image_height.astype('float') / target_height
w_scale = image_width.astype('float') / target_width
else:
raise ValueError(
"Unsupported coordinate_transformation_mode: {}".format(
coordinate_transformation_mode))
if coordinate_transformation_mode == "half_pixel":
in_y = h_scale * (y + 0.5) - 0.5
in_x = w_scale * (x + 0.5) - 0.5
else:
in_y = h_scale * y
in_x = w_scale * x
xint = te.floor(in_x).astype('int32')
xfract = in_x - te.floor(in_x)
yint = te.floor(in_y).astype('int32')
yfract = in_y - te.floor(in_y)
# 1st row
p00 = _get_pixel(data, layout, boxes, image_height, image_width, box_idx,
c, yint - 1, xint - 1, cc, inum, ic)
p10 = _get_pixel(data, layout, boxes, image_height, image_width, box_idx,
c, yint - 1, xint + 0, cc, inum, ic)
p20 = _get_pixel(data, layout, boxes, image_height, image_width, box_idx,
c, yint - 1, xint + 1, cc, inum, ic)
p30 = _get_pixel(data, layout, boxes, image_height, image_width, box_idx,
c, yint - 1, xint + 2, cc, inum, ic)
# 2nd row
p01 = _get_pixel(data, layout, boxes, image_height, image_width, box_idx,
c, yint + 0, xint - 1, cc, inum, ic)
p11 = _get_pixel(data, layout, boxes, image_height, image_width, box_idx,
c, yint + 0, xint + 0, cc, inum, ic)
p21 = _get_pixel(data, layout, boxes, image_height, image_width, box_idx,
c, yint + 0, xint + 1, cc, inum, ic)
p31 = _get_pixel(data, layout, boxes, image_height, image_width, box_idx,
c, yint + 0, xint + 2, cc, inum, ic)
# 3rd row
p02 = _get_pixel(data, layout, boxes, image_height, image_width, box_idx,
c, yint + 1, xint - 1, cc, inum, ic)
p12 = _get_pixel(data, layout, boxes, image_height, image_width, box_idx,
c, yint + 1, xint + 0, cc, inum, ic)
p22 = _get_pixel(data, layout, boxes, image_height, image_width, box_idx,
c, yint + 1, xint + 1, cc, inum, ic)
p32 = _get_pixel(data, layout, boxes, image_height, image_width, box_idx,
c, yint + 1, xint + 2, cc, inum, ic)
# 4th row
p03 = _get_pixel(data, layout, boxes, image_height, image_width, box_idx,
c, yint + 2, xint - 1, cc, inum, ic)
p13 = _get_pixel(data, layout, boxes, image_height, image_width, box_idx,
c, yint + 2, xint + 0, cc, inum, ic)
p23 = _get_pixel(data, layout, boxes, image_height, image_width, box_idx,
c, yint + 2, xint + 1, cc, inum, ic)
p33 = _get_pixel(data, layout, boxes, image_height, image_width, box_idx,
c, yint + 2, xint + 2, cc, inum, ic)
# Interpolate bicubically
col0 = _cubic_kernel(p00, p10, p20, p30, xfract)
col1 = _cubic_kernel(p01, p11, p21, p31, xfract)
col2 = _cubic_kernel(p02, p12, p22, p32, xfract)
col3 = _cubic_kernel(p03, p13, p23, p33, xfract)
value = _cubic_kernel(col0, col1, col2, col3, yfract)
# use extrapolation_value if in_y/in_x is out of boundary
if extrapolation_value is not None:
out = tvm.tir.if_then_else(
in_y < 0, extrapolation_value,
tvm.tir.if_then_else(in_y > image_height - 1, extrapolation_value,
value))
value = tvm.tir.if_then_else(
in_x < 0, extrapolation_value,
tvm.tir.if_then_else(in_x > image_width - 1, extrapolation_value,
out))
return _cast_output(value, data.dtype, out_dtype=out_dtype)
def _bicubic_sample(n, c, h, w):
A = -0.75 # 0.75 is used in pytorch, it maybe different in other frameworks
def cubic_weight_1(fraction):
return ((A + 2) * fraction - (A + 3)) * fraction * fraction + 1
def cubic_weight_2(fraction):
return ((A * fraction - 5 * A) * fraction + 8 * A) * fraction - 4 * A
def cubic_interp_1d(pixel_0, pixel_1, pixel_2, pixel_3, fraction):
weights = [0] * 4
weights[0] = cubic_weight_2(fraction + 1)
weights[1] = cubic_weight_1(fraction)
weights[2] = cubic_weight_1(1 - fraction)
weights[3] = cubic_weight_2(2 - fraction)
return (
pixel_0 * weights[0]
+ pixel_1 * weights[1]
+ pixel_2 * weights[2]
+ pixel_3 * weights[3]
)
y = grid[n, 1, h, w]
x = grid[n, 0, h, w]
y, x = _unnormalize(y, x)
y_floor = te.floor(y).astype("int32")
x_floor = te.floor(x).astype("int32")
y_fraction = y - y_floor
x_fraction = x - x_floor
coefficients = [0] * 4
for i in range(4):
y_ = y_floor - 1 + i
x_0 = x_floor - 1
x_1 = x_floor + 0
x_2 = x_floor + 1
x_3 = x_floor + 2
if padding_mode == "border":
y_ = _clip_coordinates(y_, in_height).astype("int32")
x_0 = _clip_coordinates(x_0, in_width).astype("int32")
x_1 = _clip_coordinates(x_1, in_width).astype("int32")
x_2 = _clip_coordinates(x_2, in_width).astype("int32")
x_3 = _clip_coordinates(x_3, in_width).astype("int32")
elif padding_mode == "reflection":
y_ = _reflect_coordinates(y_, in_height)
x_0 = _reflect_coordinates(x_0, in_width)
x_1 = _reflect_coordinates(x_1, in_width)
x_2 = _reflect_coordinates(x_2, in_width)
x_3 = _reflect_coordinates(x_3, in_width)
y_ = _clip_coordinates(y_, in_height).astype("int32")
x_0 = _clip_coordinates(x_0, in_width).astype("int32")
x_1 = _clip_coordinates(x_1, in_width).astype("int32")
x_2 = _clip_coordinates(x_2, in_width).astype("int32")
x_3 = _clip_coordinates(x_3, in_width).astype("int32")
coefficients[i] = cubic_interp_1d(
_get_pixel_value(n, c, y_, x_0),
_get_pixel_value(n, c, y_, x_1),
_get_pixel_value(n, c, y_, x_2),
_get_pixel_value(n, c, y_, x_3),
x_fraction,
)
return cubic_interp_1d(
coefficients[0], coefficients[1], coefficients[2], coefficients[3], y_fraction
)
def resize_bicubic(
indices,
data,
image_height,
image_width,
target_height,
target_width,
boxes=None,
box_indices=None,
extrapolation_value=None,
layout="NCHW",
coordinate_transformation_mode="align_corners",
out_dtype=None,
alpha=-0.5,
exclude_outside=0,
):
"""Perform resize operation with bicubic method on the data.
More details about Bicubic interpolation please refer to
https://en.wikipedia.org/wiki/Bicubic_interpolation.
This algorithm is doing a bicubic spline interpolation
Parameters
----------
indices : tuple
The indices of input data
data : tvm.te.Tensor
inputs is a 4-D tensor with shape
[batch, channel, in_height, in_width]
or [:batch, in_height, in_width, channel]
image_height : integer
Input image height
image_width : integer
Input image width
target_height : integer
The target resized image height
target_width : integer
The target resized image width
boxes : tvm.te.Tensor, optional
A 2-D tensor of shape [num_boxes, 4]. Each row of the tensor specifies
the coordinates of a box.
box_indices : tvm.te.Tensor, optional
A 1-D tensor of shape [num_boxes], box_indices[i] specifies the data that
the i-th box refers to.
extrapolation_value: float, optional
Value used for extrapolation, when applicable.
layout: string, optional
"NCHW", "NHWC", or "NCHWc".
coordinate_transformation_mode: string, optional
Describes how to transform the coordinate in the resized tensor
to the coordinate in the original tensor.
Refer to the ONNX Resize operator specification for details.
Available options are "half_pixel", "align_corners" and "asymmetric".
out_dtype: string, optional
Type to return. If left None will be same as input type.
alpha: float, optional
Bicubic spline coefficient
Returns
-------
output : out_dtype
The computed result with type out_dtype
"""
def _cast_output(value, data_dtype="float32", out_dtype=None):
if out_dtype:
dtype = out_dtype
else:
dtype = data_dtype
return value.astype(dtype)
n, c, y, x, cc, inum, ic = get_2d_indices(indices, layout)
box_idx = box_indices(n) if box_indices is not None else n
if boxes is not None:
y1, x1 = boxes(n, 0), boxes(n, 1)
y2, x2 = boxes(n, 2), boxes(n, 3)
in_h = (image_height - 1) * (y2 - y1)
in_w = (image_width - 1) * (x2 - x1)
h_scale = in_h.astype("float") / (target_height - 1)
w_scale = in_w.astype("float") / (target_width - 1)
in_y = y1 * (image_height - 1) + h_scale * y
in_x = x1 * (image_width - 1) + w_scale * x
else:
in_y, in_x = get_iny_inx(
y,
x,
image_height,
image_width,
target_height,
target_width,
coordinate_transformation_mode,
)
xint = te.floor(in_x).astype("int32")
xfract = in_x - te.floor(in_x)
yint = te.floor(in_y).astype("int32")
yfract = in_y - te.floor(in_y)
# Get the surrounding values
p = [[0 for i in range(4)] for j in range(4)]
for j in range(4):
for i in range(4):
p[j][i] = get_2d_pixel(
data,
layout,
boxes,
image_height,
image_width,
box_idx,
c,
yint + j - 1,
xint + i - 1,
cc,
inum,
ic,
)
# Interpolate bicubically
def _cubic_spline_weights(t):
t2 = t * t
t3 = t * t * t
w1 = alpha * (t3 - 2 * t2 + t)
w2 = (alpha + 2) * t3 - (3 + alpha) * t2 + 1
w3 = -(alpha + 2) * t3 + (3 + 2 * alpha) * t2 - alpha * t
w4 = -alpha * t3 + alpha * t2
return [w1, w2, w3, w4]
def _cubic_kernel(inputs, w):
return sum([a_i * w_i for a_i, w_i in zip(inputs, w)])
wx = _cubic_spline_weights(xfract)
wy = _cubic_spline_weights(yfract)
if exclude_outside:
for i in range(4):
wx[i] = te.if_then_else(
te.any(xint - 1 + i < 0, xint + i > image_width), 0.0, wx[i])
wy[i] = te.if_then_else(
te.any(yint - 1 + i < 0, yint + i > image_height), 0.0, wy[i])
sum_wx = sum(wx)
sum_wy = sum(wy)
wx = [w / sum_wx for w in wx]
wy = [w / sum_wy for w in wy]
col0 = _cubic_kernel(p[0], wx)
col1 = _cubic_kernel(p[1], wx)
col2 = _cubic_kernel(p[2], wx)
col3 = _cubic_kernel(p[3], wx)
value = _cubic_kernel([col0, col1, col2, col3], wy)
# use extrapolation_value if in_y/in_x is out of boundary
if extrapolation_value is not None:
out = tvm.tir.if_then_else(
in_y < 0,
extrapolation_value,
tvm.tir.if_then_else(in_y > image_height - 1, extrapolation_value,
value),
)
value = tvm.tir.if_then_else(
in_x < 0,
extrapolation_value,
tvm.tir.if_then_else(in_x > image_width - 1, extrapolation_value,
out),
)
return _cast_output(value, data.dtype, out_dtype=out_dtype)
def resize_bilinear(
indices,
data,
image_height,
image_width,
target_height,
target_width,
boxes=None,
box_indices=None,
extrapolation_value=None,
layout="NCHW",
coordinate_transformation_mode="align_corners",
out_dtype=None,
):
"""Perform resize operation with bilinear method on the data.
For details about Bilinear interpolation please refer to
https://en.wikipedia.org/wiki/Bilinear_interpolation.
Parameters
----------
indices : tuple
The indices of input data
data : tvm.te.Tensor
inputs is a 4-D tensor with shape
[batch, channel, in_height, in_width]
or [batch, in_height, in_width, channel]
image_height : integer
Input image height
image_width : integer
Input image width
target_height : integer
The target resized image height
target_width : integer
The target resized image width
boxes : tvm.te.Tensor, optional
A 2-D tensor of shape [num_boxes, 4]. Each row of the tensor specifies
the coordinates of a box.
box_indices : tvm.te.Tensor, optional
A 1-D tensor of shape [num_boxes], box_indices[i] specifies the data that
the i-th box refers to.
extrapolation_value: float, optional
Value used for extrapolation, when applicable.
layout: string, optional
"NCHW", "NHWC", or "NCHWc".
coordinate_transformation_mode: string, optional
Describes how to transform the coordinate in the resized tensor
to the coordinate in the original tensor.
Refer to the ONNX Resize operator specification for details.
Available options are "half_pixel", "align_corners" and "asymmetric".
out_dtype: string, optional
Type to return. If left None will be same as input type.
Returns
-------
output : out_dtype
The computed result with type out_dtype
"""
def _cast_output(value, data_dtype="float32", out_dtype=None):
if out_dtype:
dtype = out_dtype
else:
dtype = data_dtype
return value.astype(dtype)
def _lerp(A, B, t):
return A * (1.0 - t) + B * t
n, c, y, x, cc, inum, ic = get_2d_indices(indices, layout=layout)
box_idx = box_indices(n) if box_indices is not None else n
if boxes is not None:
y1, x1 = boxes(n, 0), boxes(n, 1)
y2, x2 = boxes(n, 2), boxes(n, 3)
in_h = (image_height - 1) * (y2 - y1)
in_w = (image_width - 1) * (x2 - x1)
h_scale = in_h.astype("float") / (target_height - 1)
w_scale = in_w.astype("float") / (target_width - 1)
in_y = y1 * (image_height - 1) + h_scale * y
in_x = x1 * (image_width - 1) + w_scale * x
else:
in_y, in_x = get_iny_inx(
y,
x,
image_height,
image_width,
target_height,
target_width,
coordinate_transformation_mode,
)
top_y_index = te.floor(in_y).astype("int32")
bottom_y_index = te.ceil(in_y).astype("int32")
y_lerp = in_y - top_y_index
left_x_index = te.floor(in_x).astype("int32")
right_x_index = te.ceil(in_x).astype("int32")
x_lerp = in_x - left_x_index
top_left = get_2d_pixel(
data,
layout,
boxes,
image_height,
image_width,
box_idx,
c,
top_y_index,
left_x_index,
cc,
inum,
ic,
)
top_right = get_2d_pixel(
data,
layout,
boxes,
image_height,
image_width,
box_idx,
c,
top_y_index,
right_x_index,
cc,
inum,
ic,
)
bottom_left = get_2d_pixel(
data,
layout,
boxes,
image_height,
image_width,
box_idx,
c,
bottom_y_index,
left_x_index,
cc,
inum,
ic,
)
bottom_right = get_2d_pixel(
data,
layout,
boxes,
image_height,
image_width,
box_idx,
c,
bottom_y_index,
right_x_index,
cc,
inum,
ic,
)
top = _lerp(top_left, top_right, x_lerp)
bottom = _lerp(bottom_left, bottom_right, x_lerp)
value = _lerp(top, bottom, y_lerp)
# use extrapolation_value if in_y/in_x is out of boundary
if extrapolation_value is not None:
out = tvm.tir.if_then_else(
in_y < 0,
extrapolation_value,
tvm.tir.if_then_else(in_y > image_height - 1, extrapolation_value,
value),
)
value = tvm.tir.if_then_else(
in_x < 0,
extrapolation_value,
tvm.tir.if_then_else(in_x > image_width - 1, extrapolation_value,
out),
)
return _cast_output(value, data.dtype, out_dtype=out_dtype)
