def max_pool(data, kernel, stride, padding):
"""Perform max pooling on the data
Parameters
----------
data : tvm.Tensor
4-D with shape [batch, channel, in_height, in_width]
kernel : list/tuple of two ints
Kernel size, or [kernel_height, kernel_width]
stride : list/tuple of two ints
Stride size, or [stride_height, stride_width]
paddding : list/tuple of two ints
Pad size, or [pad_height, pad_width]
Returns
-------
output : tvm.Tensor
4-D with shape [batch, channel, out_height, out_width]
"""
assert len(data.shape) == 4, "only support 4-dim pooling"
assert len(stride) == 2, "only support 2-dim stride"
kernel_height, kernel_width = kernel
stride_height, stride_width = stride
batch, channel, height, width = data.shape
pad_top, pad_left, pad_down, pad_right = get_pad_tuple(
padding, (kernel_height, kernel_width))
pad_before = [0, 0, pad_top, pad_left]
pad_after = [0, 0, pad_down, pad_right]
temp = pad(data,
pad_before,
pad_after,
name="pad_temp",
pad_value=tvm.min_value("float32"))
out_height = util.simplify((height - kernel_height + pad_top + pad_down) //
stride_height + 1)
out_width = util.simplify((width - kernel_width + pad_left + pad_right) //
stride_width + 1)
dheight = tvm.reduce_axis((0, kernel_height))
dwidth = tvm.reduce_axis((0, kernel_width))
return tvm.compute(
(batch, channel, out_height, out_width),
lambda i, c, h, w: tvm.max(temp[i, c, h * stride_height + dheight, w *
stride_width + dwidth],
axis=[dheight, dwidth]),
tag="max_pool")
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 = tvm.round(roi_start_h * spatial_scale).astype('int32')
roi_start_w = tvm.round(roi_start_w * spatial_scale).astype('int32')
roi_end_h = tvm.round(roi_end_h * spatial_scale).astype('int32')
roi_end_w = tvm.round(roi_end_w * spatial_scale).astype('int32')
# force malformed ROIs to be 1x1
roi_h = tvm.max(roi_end_h - roi_start_h + 1, tvm.const(1, 'int32'))
roi_w = tvm.max(roi_end_w - roi_start_w + 1, tvm.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.const(0.00001, dtype)
hstart = tvm.floor(ph * bin_h + epsilon).astype('int32')
wstart = tvm.floor(pw * bin_w + epsilon).astype('int32')
hend = tvm.ceil((ph + 1) * bin_h - epsilon).astype('int32')
wend = tvm.ceil((pw + 1) * bin_w - epsilon).astype('int32')
hstart = tvm.min(tvm.max(hstart + roi_start_h, 0), height)
wstart = tvm.min(tvm.max(wstart + roi_start_w, 0), width)
hend = tvm.min(tvm.max(hend + roi_start_h, 0), height)
wend = tvm.min(tvm.max(wend + roi_start_w, 0), width)
non_empty = tvm.all(hstart < hend, wstart < wend)
min_value = lambda dtype: tvm.if_then_else(
non_empty, tvm.min_value(dtype), tvm.const(0.0, dtype))
# pylint: disable=unnecessary-lambda
_max = tvm.comm_reducer(lambda x, y: tvm.make._OpMax(x, y),
min_value,
name='max')
rh = tvm.reduce_axis((0, hend - hstart), 'rh')
rw = tvm.reduce_axis((0, wend - wstart), 'rw')
return _max(data[batch_index, c, hstart + rh, wstart + rw],
axis=[rh, rw])
def argmax_init(idx_typ, val_typ):
return tvm.const(-1, idx_typ), tvm.min_value(val_typ)
def fidentity(t0, t1):
return tvm.const(-1, t0), tvm.min_value(t1)
def pool(data, kernel, stride, padding, pool_type, ceil_mode=False):
"""Perform pooling on the data
Parameters
----------
data : tvm.Tensor
4-D with shape [batch, channel, in_height, in_width]
kernel : list/tuple of two ints
Kernel size, [kernel_height, kernel_width]
stride : list/tuple of two ints
Stride size, [stride_height, stride_width]
paddding : list/tuple of two ints
Pad size, [pad_height, pad_width]
pool_type : str
Pool type, 'max' or 'avg'
ceil_mode : bool
Whether to use ceil when caculate output size.
Returns
-------
output : tvm.Tensor
4-D with shape [batch, channel, out_height, out_width]
"""
assert len(data.shape) == 4, "only support 4-dim pooling"
assert len(stride) == 2, "only support 2-dim stride"
kernel_height, kernel_width = kernel
stride_height, stride_width = stride
batch, channel, height, width = data.shape
pad_top, pad_left, pad_down, pad_right = get_pad_tuple(
padding, (kernel_height, kernel_width))
if ceil_mode:
# Additional padding to ensure we do ceil instead of floor when divide stride.
pad_down += stride_height - 1
pad_right += stride_width - 1
pad_before = [0, 0, pad_top, pad_left]
pad_after = [0, 0, pad_down, pad_right]
out_height = util.simplify((height - kernel_height + pad_top + pad_down) //
stride_height + 1)
out_width = util.simplify((width - kernel_width + pad_left + pad_right) //
stride_width + 1)
dheight = tvm.reduce_axis((0, kernel_height))
dwidth = tvm.reduce_axis((0, kernel_width))
if pool_type == 'max':
temp = pad(data, pad_before, pad_after, name="pad_temp", \
pad_value=tvm.min_value(data.dtype))
return tvm.compute((batch, channel, out_height, out_width), \
lambda n, c, h, w: \
tvm.max(temp[n, c, h*stride_height+dheight, w*stride_width+dwidth], \
axis=[dheight, dwidth]), \
tag="pool_max")
elif pool_type == 'avg':
temp = pad(data, pad_before, pad_after, name="pad_temp", \
pad_value=tvm.const(0.).astype(data.dtype))
tsum = tvm.compute((batch, channel, out_height, out_width), \
lambda n, c, h, w: \
tvm.sum(temp[n, c, h*stride_height+dheight, w*stride_width+dwidth], \
axis=[dheight, dwidth]), \
tag="pool_avg")
return tvm.compute((batch, channel, out_height, out_width), \
lambda n, c, h, w: \
tsum[n, c, h, w] / (kernel_height*kernel_width), \
tag=tag.ELEMWISE)
else:
raise ValueError("Pool type should be 'avg' or 'max'.")
def argmax_init(idx_typ, val_typ):
return tvm.const(-1, idx_typ), tvm.min_value(val_typ)
def _argmax_init(idx_typ, val_typ):
"""Initial ind and val of argmax"""
return tvm.const(-1, idx_typ), tvm.min_value(val_typ)
def fidentity(t0, t1):
return tvm.const(-1, t0), tvm.min_value(t1)
def pool_nchw(data, kernel, stride, padding, pool_type, ceil_mode=False):
"""Perform pooling on the data in NCHW layout
Parameters
----------
data : tvm.Tensor
4-D with shape [batch, channel, in_height, in_width]
kernel : list/tuple of two ints
Kernel size, [kernel_height, kernel_width]
stride : list/tuple of two ints
Stride size, [stride_height, stride_width]
paddding : list/tuple of two ints
Pad size, [pad_height, pad_width]
pool_type : str
Pool type, 'max' or 'avg'
ceil_mode : bool
Whether to use ceil when caculate output size.
Returns
-------
output : tvm.Tensor
4-D with shape [batch, channel, out_height, out_width]
"""
assert len(data.shape) == 4, "only support 4-dim pooling"
assert len(stride) == 2, "only support 2-dim stride"
kernel_height, kernel_width = kernel
stride_height, stride_width = stride
batch, channel, height, width = data.shape
pad_top, pad_left, pad_down, pad_right = get_pad_tuple(
padding, (kernel_height, kernel_width))
if ceil_mode:
# Additional padding to ensure we do ceil instead of floor when divide stride.
pad_down += stride_height -1
pad_right += stride_width - 1
pad_before = [0, 0, pad_top, pad_left]
pad_after = [0, 0, pad_down, pad_right]
out_height = util.simplify((height - kernel_height + pad_top + pad_down) // stride_height + 1)
out_width = util.simplify((width - kernel_width + pad_left + pad_right) // stride_width + 1)
dheight = tvm.reduce_axis((0, kernel_height))
dwidth = tvm.reduce_axis((0, kernel_width))
if pool_type == 'max':
temp = pad(data, pad_before, pad_after, name="pad_temp", \
pad_value=tvm.min_value(data.dtype))
return tvm.compute((batch, channel, out_height, out_width), \
lambda n, c, h, w: \
tvm.max(temp[n, c, h*stride_height+dheight, w*stride_width+dwidth], \
axis=[dheight, dwidth]), \
tag="pool_max")
elif pool_type == 'avg':
temp = pad(data, pad_before, pad_after, name="pad_temp", \
pad_value=tvm.const(0.).astype(data.dtype))
tsum = tvm.compute((batch, channel, out_height, out_width), \
lambda n, c, h, w: \
tvm.sum(temp[n, c, h*stride_height+dheight, w*stride_width+dwidth], \
axis=[dheight, dwidth]), \
tag="pool_avg")
return tvm.compute((batch, channel, out_height, out_width), \
lambda n, c, h, w: \
tsum[n, c, h, w] / (kernel_height*kernel_width), \
tag=tag.ELEMWISE)
else:
raise ValueError("Pool type should be 'avg' or 'max'.")
def pool3d_ncdhw_python(np_data,
kernel,
strides,
padding,
out_shape,
pool_type,
count_include_pad=True,
ceil_mode=False,
dtype="float32"):
"""baseline for max_pool3d and avg_pool3d, default layout is "NCDHW"""
in_n, in_c, in_d, in_h, in_w = in_shape = np_data.shape
k_d, k_h, k_w = kernel
s_d, s_h, s_w = strides
pf, pt, pl, pk, pb, pr = padding
if ceil_mode:
assert out_shape[2] == int(
math.ceil(float(in_shape[2] - k_d + pf + pk) / s_d) + 1)
assert out_shape[3] == int(
math.ceil(float(in_shape[3] - k_h + pt + pb) / s_h) + 1)
assert out_shape[4] == int(
math.ceil(float(in_shape[4] - k_w + pl + pr) / s_w) + 1)
else:
assert out_shape[2] == int(
math.floor(float(in_shape[2] - k_d + pf + pk) / s_d) + 1)
assert out_shape[3] == int(
math.floor(float(in_shape[3] - k_h + pt + pb) / s_h) + 1)
assert out_shape[4] == int(
math.floor(float(in_shape[4] - k_w + pl + pr) / s_w) + 1)
fill_value = tvm.const(0.0, dtype).value
if not (count_include_pad) and pool_type == 'max':
fill_value = tvm.min_value(dtype).value
pad_np = np.full(shape=(in_n, in_c, in_d + pf + pk, in_h + pt + pb,
in_w + pl + pr),
fill_value=fill_value,
dtype=dtype)
no_zero = (range(in_n), range(in_c), (range(pf, in_d + pf)),
(range(pt, in_h + pt)), (range(pl, in_w + pl)))
pad_np[np.ix_(*no_zero)] = np_data
ret_np = np.zeros(shape=out_shape).astype(dtype)
if pool_type == 'avg':
for k in range(out_shape[2]):
for i in range(out_shape[3]):
for j in range(out_shape[4]):
if count_include_pad:
ret_np[:, :, k, i, j] = \
np.mean(pad_np[:, :, k * s_d: k * s_d + k_d,
i * s_h: i * s_h + k_h,
j * s_w: j * s_w + k_w], axis=(2, 3, 4))
else:
pad_count = np.sum(pad_np[:, :, k * s_d:k * s_d + k_d,
i * s_h:i * s_h + k_h,
j * s_w:j * s_w + k_w] > 0,
axis=(2, 3, 4))
ret_np[:, :, k, i, j] = np.sum(
pad_np[:, :, k * s_d:k * s_d + k_d, i *
s_h:i * s_h + k_h, j * s_w:j * s_w + k_w],
axis=(2, 3, 4)) / np.maximum(pad_count, 1)
elif pool_type == 'max':
for k in range(out_shape[2]):
for i in range(out_shape[3]):
for j in range(out_shape[4]):
ret_np[:, :, k, i,
j] = np.max(pad_np[:, :, k * s_d:k * s_d + k_d,
i * s_h:i * s_h + k_h,
j * s_w:j * s_w + k_w],
axis=(2, 3, 4))
else:
raise ValueError("pool type {} is not supported".format(pool_type))
ret_np = np.maximum(ret_np, fill_value)
return ret_np
def _argmax_init(idx_typ, val_typ):
"""Initial ind and val of argmax"""
return tvm.const(-1, idx_typ), tvm.min_value(val_typ)
def min_value(dtype):
return tvm.expr.Select(non_empty, tvm.min_value(dtype),
tvm.const(0.0, dtype))
