You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@tvm.apache.org by ha...@apache.org on 2020/01/15 00:13:32 UTC
[incubator-tvm] branch revert-4318-conv2d_transpose_fix created
(now ebfff44)
This is an automated email from the ASF dual-hosted git repository.
haichen pushed a change to branch revert-4318-conv2d_transpose_fix
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git.
at ebfff44 Revert "[Relay][TOPI]Fix meaning of conv2d_transpose output_padding parameter (#4318)"
This branch includes the following new commits:
new ebfff44 Revert "[Relay][TOPI]Fix meaning of conv2d_transpose output_padding parameter (#4318)"
The 1 revisions listed above as "new" are entirely new to this
repository and will be described in separate emails. The revisions
listed as "add" were already present in the repository and have only
been added to this reference.
[incubator-tvm] 01/01: Revert "[Relay][TOPI]Fix meaning of
conv2d_transpose output_padding parameter (#4318)"
Posted by ha...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
haichen pushed a commit to branch revert-4318-conv2d_transpose_fix
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git
commit ebfff4419fc714d173a1e365739badee1d79adfa
Author: Haichen Shen <sh...@gmail.com>
AuthorDate: Tue Jan 14 16:13:22 2020 -0800
Revert "[Relay][TOPI]Fix meaning of conv2d_transpose output_padding parameter (#4318)"
This reverts commit dcf7fbf1f962569e78c624755b2d612fffa81ada.
---
python/tvm/autotvm/tophub.py | 6 +++---
python/tvm/relay/op/nn/_nn.py | 12 +++++++----
tests/python/relay/test_op_level2.py | 25 +++++++++++-----------
topi/python/topi/arm_cpu/conv2d_transpose.py | 21 ++++++------------
topi/python/topi/cuda/conv1d_transpose_ncw.py | 7 +++---
topi/python/topi/cuda/conv2d_transpose_nchw.py | 12 +++++------
topi/python/topi/nn/conv1d_transpose.py | 6 ++----
topi/python/topi/nn/conv2d_transpose.py | 24 +++++++++------------
.../topi/testing/conv1d_transpose_ncw_python.py | 7 +++---
.../python/topi/testing/conv2d_transpose_python.py | 17 ++++++---------
topi/python/topi/x86/conv2d_transpose.py | 4 ++--
.../tests/python/test_topi_conv1d_transpose_ncw.py | 2 +-
.../python/test_topi_conv2d_transpose_nchw.py | 18 +++++++---------
vta/python/vta/top/vta_conv2d_transpose.py | 16 ++++++--------
vta/scripts/tune_conv2d_transpose.py | 19 ++++++----------
.../test_benchmark_topi_conv2d_transpose.py | 16 ++++++--------
16 files changed, 92 insertions(+), 120 deletions(-)
diff --git a/python/tvm/autotvm/tophub.py b/python/tvm/autotvm/tophub.py
index 2f07f72..d953eaa 100644
--- a/python/tvm/autotvm/tophub.py
+++ b/python/tvm/autotvm/tophub.py
@@ -46,16 +46,16 @@ AUTOTVM_TOPHUB_ROOT_PATH = os.path.join(os.path.expanduser('~'), ".tvm", "tophub
# the version of each package
PACKAGE_VERSION = {
- 'arm_cpu': "v0.05",
+ 'arm_cpu': "v0.04",
'llvm': "v0.03",
- 'cuda': "v0.07",
+ 'cuda': "v0.06",
'rocm': "v0.03",
'opencl': "v0.03",
'mali': "v0.05",
'intel_graphics': "v0.01",
- 'vta': "v0.07",
+ 'vta': "v0.06",
}
logger = logging.getLogger('autotvm')
diff --git a/python/tvm/relay/op/nn/_nn.py b/python/tvm/relay/op/nn/_nn.py
index e405fee..275e09c 100644
--- a/python/tvm/relay/op/nn/_nn.py
+++ b/python/tvm/relay/op/nn/_nn.py
@@ -339,7 +339,6 @@ def compute_conv2d_transpose(attrs, inputs, out_dtype, target):
padding = get_const_tuple(attrs.padding)
strides = get_const_tuple(attrs.strides)
dilation = get_const_tuple(attrs.dilation)
- output_padding = get_const_tuple(attrs.output_padding)
groups = attrs.groups
layout = attrs.data_layout
out_dtype = attrs.out_dtype
@@ -349,7 +348,10 @@ def compute_conv2d_transpose(attrs, inputs, out_dtype, target):
assert dilation == (1, 1), "not support dilate now"
assert groups == 1, "only support groups == 1 for now"
out = topi.nn.conv2d_transpose_nchw(
- inputs[0], inputs[1], strides, padding, out_dtype, output_padding)
+ inputs[0], inputs[1], strides, padding, out_dtype)
+ output_padding = get_const_tuple(attrs.output_padding)
+ out = topi.nn.pad(out,
+ [0, 0, 0, 0], [0, 0, output_padding[0], output_padding[1]])
return [out]
@@ -442,8 +444,10 @@ def compute_conv1d_transpose(attrs, inputs, out_dtype, target):
assert dilation == (1,), "conv1d_transpose dilation is not supported"
assert groups == 1, "conv1d_transpose groups == 1 only supported"
out = topi.nn.conv1d_transpose_ncw(
- inputs[0], inputs[1], strides, padding, out_dtype,
- get_const_tuple(attrs.output_padding))
+ inputs[0], inputs[1], strides, padding, out_dtype)
+ output_padding = get_const_tuple(attrs.output_padding)
+ out = topi.nn.pad(out,
+ [0, 0, 0], [0, 0, output_padding[0]])
return [out]
diff --git a/tests/python/relay/test_op_level2.py b/tests/python/relay/test_op_level2.py
index 68f3983..4b914ee 100644
--- a/tests/python/relay/test_op_level2.py
+++ b/tests/python/relay/test_op_level2.py
@@ -570,8 +570,11 @@ def test_conv2d_transpose_nchw_run():
dtype = "float32"
data = np.random.uniform(size=dshape).astype(dtype)
kernel = np.random.uniform(size=kshape).astype(dtype)
- ref_res = topi.testing.conv2d_transpose_nchw_python(
- data, kernel, 2, 1, (2, 2))
+ c_np = topi.testing.conv2d_transpose_nchw_python(
+ data, kernel, 2, 1)
+ d_np = np.zeros(shape=oshape)
+ d_np[:,:,0:c_np.shape[2],0:c_np.shape[3]] = c_np
+ ref_res = d_np
for target, ctx in ctx_list():
intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
@@ -596,14 +599,9 @@ def test_conv2d_transpose_nhwc_run():
data = np.random.uniform(size=dshape_nhwc).astype(dtype)
kernel = np.random.uniform(size=kshape_hwoi).astype(dtype)
# use true kshape layout here - HWOI
-
- ref_res = topi.testing.conv2d_transpose_nhwc_python(data, kernel, 'HWOI',
- 2, 1, output_padding=(2, 2))
-
- for target, ctx in ctx_list():
- intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
- op_res1 = intrp1.evaluate(func)(data, kernel)
- tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5)
+ c_np = topi.testing.conv2d_transpose_nhwc_python(data, kernel, 'HWOI', 2, 1)
+ d_np = np.zeros(shape=oshape_nhwc)
+ d_np[:,0:c_np.shape[1],0:c_np.shape[2],:] = c_np
def test_conv1d_transpose_ncw_run():
@@ -619,8 +617,11 @@ def test_conv1d_transpose_ncw_run():
dtype = "float32"
data = np.random.uniform(size=dshape).astype(dtype)
kernel = np.random.uniform(size=kshape).astype(dtype)
- ref_res = topi.testing.conv1d_transpose_ncw_python(
- data, kernel, 2, 1, output_padding=(2,))
+ c_np = topi.testing.conv1d_transpose_ncw_python(
+ data, kernel, 2, 1)
+ d_np = np.zeros(shape=oshape)
+ d_np[:,:,0:c_np.shape[2]] = c_np
+ ref_res = d_np
for target, ctx in ctx_list():
intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
diff --git a/topi/python/topi/arm_cpu/conv2d_transpose.py b/topi/python/topi/arm_cpu/conv2d_transpose.py
index 483c798..65f1024 100644
--- a/topi/python/topi/arm_cpu/conv2d_transpose.py
+++ b/topi/python/topi/arm_cpu/conv2d_transpose.py
@@ -27,8 +27,7 @@ from ..util import get_const_tuple, traverse_inline
from .conv2d_spatial_pack import schedule_conv2d_spatial_pack_nchw
@autotvm.task.register_topi_compute(conv2d_transpose_nchw, "arm_cpu", "direct")
-def conv2d_transpose_nchw_arm(cfg, Input, Filter, strides, padding, out_dtype,
- output_padding=(0, 0)):
+def conv2d_transpose_nchw_arm(cfg, Input, Filter, strides, padding, out_dtype):
"""Transposed 2D convolution nchw forward operator.
Parameters
@@ -48,33 +47,27 @@ def conv2d_transpose_nchw_arm(cfg, Input, Filter, strides, padding, out_dtype,
out_dtype: str
The output data type. This is used for mixed precision.
- output_padding : tuple of int
- Used to get the right output shape in gradients
-
Returns
-------
Output : tvm.Tensor
4-D with shape [batch, out_channel, out_height, out_width]
"""
- return _decl_spatial_pack(cfg, Input, Filter, strides, padding, "NCHW", out_dtype, 2,
- output_padding)
+ return _decl_spatial_pack(cfg, Input, Filter, strides, padding, "NCHW", out_dtype, 2)
-def _decl_spatial_pack(cfg, data, kernel, strides, padding, layout, out_dtype, num_tile,
- output_padding):
+def _decl_spatial_pack(cfg, data, kernel, strides, padding, layout, out_dtype, num_tile):
assert layout == "NCHW", "Only support NCHW"
out_dtype = out_dtype or data.dtype
N, CI, IH, IW = get_const_tuple(data.shape)
_, CO, KH, KW = get_const_tuple(kernel.shape)
- opad_h, opad_w = output_padding
pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(padding, (KH, KW))
- bpad_top, bpad_bottom = KH - 1 - pad_top, KH - 1 - pad_bottom + opad_h
- bpad_left, bpad_right = KW - 1 - pad_left, KW - 1 - pad_right + opad_w
+ bpad_top, bpad_bottom = KH - 1 - pad_top, KH - 1 - pad_bottom
+ bpad_left, bpad_right = KW - 1 - pad_left, KW - 1 - pad_right
HSTR, WSTR = strides if isinstance(strides, (tuple, list)) else (strides, strides)
- OH = (IH - 1) * HSTR - pad_top - pad_bottom + KH + opad_h
- OW = (IW - 1) * WSTR - pad_left - pad_right + KW + opad_w
+ OH = (IH - 1) * HSTR - pad_top - pad_bottom + KH
+ OW = (IW - 1) * WSTR - pad_left - pad_right + KW
dilated_input = dilate(data, [1, 1, HSTR, WSTR])
data_pad = pad(dilated_input, [0, 0, bpad_top, bpad_left], [0, 0, bpad_bottom, bpad_right])
diff --git a/topi/python/topi/cuda/conv1d_transpose_ncw.py b/topi/python/topi/cuda/conv1d_transpose_ncw.py
index 9f15264..be7824e 100644
--- a/topi/python/topi/cuda/conv1d_transpose_ncw.py
+++ b/topi/python/topi/cuda/conv1d_transpose_ncw.py
@@ -23,7 +23,7 @@ from .. import nn, generic
from ..util import get_const_tuple, traverse_inline
@autotvm.task.register_topi_compute(nn.conv1d_transpose_ncw, ['cuda', 'gpu'], "direct")
-def conv1d_transpose_ncw_cuda(cfg, data, kernel, stride, padding, out_dtype, output_padding=(0,)):
+def conv1d_transpose_ncw_cuda(cfg, data, kernel, stride, padding, out_dtype):
"""Transposed 1D convolution ncw forward operator.
Parameters
@@ -53,11 +53,10 @@ def conv1d_transpose_ncw_cuda(cfg, data, kernel, stride, padding, out_dtype, out
cfg.stride = stride
batch, inp_channels, inp_width = get_const_tuple(data.shape)
_, out_channels, kernel_size = get_const_tuple(kernel.shape)
- opad = output_padding[0]
pad_left, pad_right = nn.get_pad_tuple1d(padding, kernel_size)
- out_width = (inp_width - 1) * stride + kernel_size - pad_left - pad_right + opad
+ out_width = (inp_width - 1) * stride + kernel_size - pad_left - pad_right
pad_left = kernel_size - 1 - pad_left
- pad_right = kernel_size - 1 - pad_right + opad
+ pad_right = kernel_size - 1 - pad_right
dilated_width = stride * (inp_width - 1) + 1
data = tvm.compute(
(batch, inp_channels, pad_left + dilated_width + pad_right),
diff --git a/topi/python/topi/cuda/conv2d_transpose_nchw.py b/topi/python/topi/cuda/conv2d_transpose_nchw.py
index a630e90..274dfb0 100644
--- a/topi/python/topi/cuda/conv2d_transpose_nchw.py
+++ b/topi/python/topi/cuda/conv2d_transpose_nchw.py
@@ -25,8 +25,7 @@ from ..util import equal_const_int, get_const_tuple, traverse_inline
@autotvm.task.register_topi_compute(nn.conv2d_transpose_nchw, ['cuda', 'gpu'], "direct")
-def conv2d_transpose_nchw_cuda(cfg, Input, Filter, strides, padding, out_dtype,
- output_padding=(0, 0)):
+def conv2d_transpose_nchw_cuda(cfg, Input, Filter, strides, padding, out_dtype):
"""Transposed 2D convolution nchw forward operator.
Parameters
@@ -52,7 +51,6 @@ def conv2d_transpose_nchw_cuda(cfg, Input, Filter, strides, padding, out_dtype,
batch, in_c, in_h, in_w = get_const_tuple(Input.shape)
_, out_c, filter_h, filter_w = get_const_tuple(Filter.shape)
stride_h, stride_w = strides
- opad_h, opad_w = output_padding
# attach stride info to config, this is used in schedule space definition
cfg.stride = strides
@@ -60,9 +58,9 @@ def conv2d_transpose_nchw_cuda(cfg, Input, Filter, strides, padding, out_dtype,
# padding stage
fpad_top, fpad_left, fpad_bottom, fpad_right = nn.get_pad_tuple(padding, (filter_h, filter_w))
bpad_top = filter_h - 1 - fpad_top
- bpad_bottom = filter_h - 1 - fpad_bottom + opad_h
+ bpad_bottom = filter_h - 1 - fpad_bottom
bpad_left = filter_w - 1 - fpad_left
- bpad_right = filter_w - 1 - fpad_right + opad_w
+ bpad_right = filter_w - 1 - fpad_right
# padding stage
FirstPad = nn.pad(Input,
@@ -97,8 +95,8 @@ def conv2d_transpose_nchw_cuda(cfg, Input, Filter, strides, padding, out_dtype,
return data(*index_tuple)
# convolution stage
- out_h = (in_h - 1) * stride_h - fpad_top - fpad_bottom + filter_h + opad_h
- out_w = (in_w - 1) * stride_w - fpad_left - fpad_right + filter_w + opad_w
+ out_h = (in_h - 1) * stride_h - fpad_top - fpad_bottom + filter_h
+ out_w = (in_w - 1) * stride_w - fpad_left - fpad_right + filter_w
dc = tvm.reduce_axis((0, in_c), name='dc')
dh = tvm.reduce_axis((0, filter_h), name='dh')
dw = tvm.reduce_axis((0, filter_w), name='dw')
diff --git a/topi/python/topi/nn/conv1d_transpose.py b/topi/python/topi/nn/conv1d_transpose.py
index 23c0f57..39918e9 100644
--- a/topi/python/topi/nn/conv1d_transpose.py
+++ b/topi/python/topi/nn/conv1d_transpose.py
@@ -25,8 +25,7 @@ from .util import get_pad_tuple1d
@tvm.target.generic_func
-def conv1d_transpose_ncw(data, kernel, stride, padding, out_dtype,
- output_padding=(0,)):
+def conv1d_transpose_ncw(data, kernel, stride, padding, out_dtype):
"""Transposed 1D convolution ncw forward operator.
Parameters
@@ -57,12 +56,11 @@ def conv1d_transpose_ncw(data, kernel, stride, padding, out_dtype,
stride = stride[0]
batch, channels_in, data_width = data.shape
_, channels_out, kernel_width = kernel.shape
- opad = output_padding[0]
channels_out = simplify(channels_out)
data = dilate(data, [1, 1, stride], name='data_dilate')
pad_left, pad_right = get_pad_tuple1d(padding, (kernel_width,))
pad_left = kernel_width - 1 - pad_left
- pad_right = kernel_width - 1 - pad_right + opad
+ pad_right = kernel_width - 1 - pad_right
data = pad(data, [0, 0, pad_left], [0, 0, pad_right], name='data_pad')
# transpose kernel, switch kernel layout to IOW
diff --git a/topi/python/topi/nn/conv2d_transpose.py b/topi/python/topi/nn/conv2d_transpose.py
index 7baf18f..a240b68 100644
--- a/topi/python/topi/nn/conv2d_transpose.py
+++ b/topi/python/topi/nn/conv2d_transpose.py
@@ -26,7 +26,7 @@ from ..util import simplify
@tvm.target.generic_func
-def conv2d_transpose_nchw(Input, Filter, strides, padding, out_dtype, output_padding=(0, 0)):
+def conv2d_transpose_nchw(Input, Filter, strides, padding, out_dtype):
"""Transposed 2D convolution nchw forward operator.
Parameters
@@ -46,33 +46,28 @@ def conv2d_transpose_nchw(Input, Filter, strides, padding, out_dtype, output_pad
out_dtype : str
The output data type. This is used for mixed precision.
- output_padding : tuple of ints
- Used to get the right output shape for gradients
-
Returns
-------
Output : tvm.Tensor
4-D with shape [batch, out_channel, out_height, out_width]
"""
- return declaration_conv2d_transpose_impl(Input, Filter, strides, padding, out_dtype,
- output_padding=output_padding)
+ return declaration_conv2d_transpose_impl(Input, Filter, strides, padding, out_dtype)
-def conv2d_transpose_nchw_preprocess(data, kernel, strides, padding, out_dtype, output_padding):
+def conv2d_transpose_nchw_preprocess(data, kernel, strides, padding, out_dtype):
"""Preprocess data and kernel to make the compute pattern
of conv2d_transpose the same as conv2d"""
batch, in_c, in_h, in_w = data.shape
_, out_c, filter_h, filter_w = kernel.shape
stride_h, stride_w = strides
- opad_h, opad_w = output_padding
# dilate data
data_dilate = dilate(data, [1, 1, stride_h, stride_w], name='data_dilate')
# pad data
fpad_top, fpad_left, fpad_bottom, fpad_right = get_pad_tuple(padding, (filter_h, filter_w))
bpad_top = filter_h - 1 - fpad_top
- bpad_bottom = filter_h - 1 - fpad_bottom + opad_h
+ bpad_bottom = filter_h - 1 - fpad_bottom
bpad_left = filter_w - 1 - fpad_left
- bpad_right = filter_w - 1 - fpad_right + opad_w
+ bpad_right = filter_w - 1 - fpad_right
data_pad = pad(data_dilate, \
[0, 0, bpad_top, bpad_left], \
[0, 0, bpad_bottom, bpad_right], \
@@ -84,17 +79,18 @@ def conv2d_transpose_nchw_preprocess(data, kernel, strides, padding, out_dtype,
return data_pad, kernel_transform
-def declaration_conv2d_transpose_impl(data, kernel, strides, padding, out_dtype, output_padding):
+def declaration_conv2d_transpose_impl(data, kernel, strides, padding, out_dtype):
"""Implementation of conv2d transpose"""
data_pad, kernel_transform = \
- conv2d_transpose_nchw_preprocess(data, kernel, strides, padding, out_dtype, output_padding)
+ conv2d_transpose_nchw_preprocess(data, kernel, strides, padding, out_dtype)
batch, in_c, in_h, in_w = data_pad.shape
out_c, _, filter_h, filter_w = kernel_transform.shape
+ stride_h, stride_w = strides
# convolution stage
out_c = simplify(out_c)
- out_h = simplify(in_h - filter_h + 1 + output_padding[0])
- out_w = simplify(in_w - filter_w + 1 + output_padding[1])
+ out_h = simplify(in_h - filter_h + 1)
+ out_w = simplify(in_w - filter_w + 1)
dc = tvm.reduce_axis((0, in_c), name='dc')
dh = tvm.reduce_axis((0, filter_h), name='dh')
dw = tvm.reduce_axis((0, filter_w), name='dw')
diff --git a/topi/python/topi/testing/conv1d_transpose_ncw_python.py b/topi/python/topi/testing/conv1d_transpose_ncw_python.py
index 4461291..cb78bbf 100644
--- a/topi/python/topi/testing/conv1d_transpose_ncw_python.py
+++ b/topi/python/topi/testing/conv1d_transpose_ncw_python.py
@@ -21,7 +21,7 @@ import scipy
import topi
from topi.nn.util import get_pad_tuple1d
-def conv1d_transpose_ncw_python(a_np, w_np, stride, padding, output_padding):
+def conv1d_transpose_ncw_python(a_np, w_np, stride, padding):
"""Transposed 1D convolution operator in NCW layout.
Parameters
@@ -47,7 +47,6 @@ def conv1d_transpose_ncw_python(a_np, w_np, stride, padding, output_padding):
"""
batch, in_c, in_w = a_np.shape
_, out_c, filter_w = w_np.shape
- opad = output_padding[0]
if isinstance(stride, int):
stride_w = stride
else:
@@ -57,11 +56,11 @@ def conv1d_transpose_ncw_python(a_np, w_np, stride, padding, output_padding):
dilated_a_np = topi.testing.dilate_python(a_np, [1, 1, stride_w])
# padding stage
bpad_left = filter_w - 1 - fpad_left
- bpad_right = filter_w - 1 - fpad_right + opad
+ bpad_right = filter_w - 1 - fpad_right
padded_a_np = np.zeros((batch, in_c, dilated_a_np.shape[2]+bpad_left+bpad_right))
padded_a_np[:, :, bpad_left:dilated_a_np.shape[2]+bpad_left] = dilated_a_np
# convolution stage
- out_w = (in_w - 1) * stride_w - fpad_left - fpad_right + filter_w + opad
+ out_w = (in_w - 1) * stride_w - fpad_left - fpad_right + filter_w
b_np = np.zeros((batch, out_c, out_w))
for n in range(batch):
for f in range(out_c):
diff --git a/topi/python/topi/testing/conv2d_transpose_python.py b/topi/python/topi/testing/conv2d_transpose_python.py
index 7c56cf8..50c43eb 100644
--- a/topi/python/topi/testing/conv2d_transpose_python.py
+++ b/topi/python/topi/testing/conv2d_transpose_python.py
@@ -22,7 +22,7 @@ import topi
from topi.nn.util import get_pad_tuple
-def conv2d_transpose_nchw_python(a_np, w_np, stride, padding, output_padding=(0, 0)):
+def conv2d_transpose_nchw_python(a_np, w_np, stride, padding):
"""Transposed convolution operator in NCHW layout.
Parameters
@@ -50,22 +50,21 @@ def conv2d_transpose_nchw_python(a_np, w_np, stride, padding, output_padding=(0,
stride_h = stride_w = stride
else:
stride_h, stride_w = stride
- opad_h, opad_w = output_padding
# dilate stage
dilated_a_np = topi.testing.dilate_python(a_np, [1, 1, stride_h, stride_w])
# padding stage
fpad_top, fpad_left, fpad_bottom, fpad_right = get_pad_tuple(padding, (filter_h, filter_w))
bpad_top = filter_h - 1 - fpad_top
- bpad_bottom = filter_h - 1 - fpad_bottom + opad_h
+ bpad_bottom = filter_h - 1 - fpad_bottom
bpad_left = filter_w - 1 - fpad_left
- bpad_right = filter_w - 1 - fpad_right + opad_w
+ bpad_right = filter_w - 1 - fpad_right
padded_a_np = np.zeros((batch, in_c, dilated_a_np.shape[2]+bpad_top+bpad_bottom, \
dilated_a_np.shape[3]+bpad_left+bpad_right))
padded_a_np[:, :, bpad_top:dilated_a_np.shape[2]+bpad_top, \
bpad_left:dilated_a_np.shape[3]+bpad_left] = dilated_a_np
# convolution stage
- out_h = (in_h - 1) * stride_h - fpad_top - fpad_bottom + filter_h + opad_h
- out_w = (in_w - 1) * stride_w - fpad_left - fpad_right + filter_w + opad_w
+ out_h = (in_h - 1) * stride_h - fpad_top - fpad_bottom + filter_h
+ out_w = (in_w - 1) * stride_w - fpad_left - fpad_right + filter_w
b_np = np.zeros((batch, out_c, out_h, out_w))
for n in range(batch):
for f in range(out_c):
@@ -76,8 +75,7 @@ def conv2d_transpose_nchw_python(a_np, w_np, stride, padding, output_padding=(0,
return b_np
-def conv2d_transpose_nhwc_python(a_nhwc, weight, weight_format, stride, padding,
- output_padding=(0, 0)):
+def conv2d_transpose_nhwc_python(a_nhwc, weight, weight_format, stride, padding):
"""Transposed convolution operator in NHWC layout.
Parameters
@@ -119,7 +117,6 @@ def conv2d_transpose_nhwc_python(a_nhwc, weight, weight_format, stride, padding,
else:
raise ValueError('Valid weight_formats are HWIO, HWOI, OIHW or IOHW')
- res_nchw = conv2d_transpose_nchw_python(a_nchw, w_iohw, stride, padding,
- output_padding=output_padding)
+ res_nchw = conv2d_transpose_nchw_python(a_nchw, w_iohw, stride, padding)
res_nhwc = np.transpose(res_nchw, (0, 2, 3, 1))
return res_nhwc
diff --git a/topi/python/topi/x86/conv2d_transpose.py b/topi/python/topi/x86/conv2d_transpose.py
index 3cbc57b..27fc0af 100644
--- a/topi/python/topi/x86/conv2d_transpose.py
+++ b/topi/python/topi/x86/conv2d_transpose.py
@@ -28,9 +28,9 @@ from .conv2d import _declaration_conv_impl, \
@autotvm.register_topi_compute(conv2d_transpose_nchw, 'cpu', ['direct'])
-def _conv2d_transpose_nchw(cfg, data, kernel, strides, padding, out_dtype, output_padding=(0, 0)):
+def _conv2d_transpose_nchw(cfg, data, kernel, strides, padding, out_dtype):
data_pad, kernel_transform = \
- conv2d_transpose_nchw_preprocess(data, kernel, strides, padding, out_dtype, output_padding)
+ conv2d_transpose_nchw_preprocess(data, kernel, strides, padding, out_dtype)
# reuse conv2d implementation
_create_tuning_space_conv2d(cfg, data_pad, kernel_transform, strides=(1, 1), \
padding=(0, 0), dilation=(1, 1), layout="NCHW")
diff --git a/topi/tests/python/test_topi_conv1d_transpose_ncw.py b/topi/tests/python/test_topi_conv1d_transpose_ncw.py
index a05acd3..9d6e9db 100644
--- a/topi/tests/python/test_topi_conv1d_transpose_ncw.py
+++ b/topi/tests/python/test_topi_conv1d_transpose_ncw.py
@@ -37,7 +37,7 @@ def verify_conv1d_transpose_ncw(batch, in_channel, in_size, num_filter, kernel,
def get_ref_data():
a_np = np.random.uniform(size=a_shape).astype(dtype)
w_np = np.random.uniform(size=w_shape).astype(dtype)
- b_np = topi.testing.conv1d_transpose_ncw_python(a_np, w_np, stride, padding, (0,))
+ b_np = topi.testing.conv1d_transpose_ncw_python(a_np, w_np, stride, padding)
c_np = np.maximum(b_np, 0)
return a_np, w_np, b_np, c_np
diff --git a/topi/tests/python/test_topi_conv2d_transpose_nchw.py b/topi/tests/python/test_topi_conv2d_transpose_nchw.py
index fe07df8..0960760 100644
--- a/topi/tests/python/test_topi_conv2d_transpose_nchw.py
+++ b/topi/tests/python/test_topi_conv2d_transpose_nchw.py
@@ -24,7 +24,7 @@ from topi.util import get_const_tuple
from common import get_all_backend
-def verify_conv2d_transpose_nchw(batch, in_channel, in_size, num_filter, kernel, stride, padding, output_padding):
+def verify_conv2d_transpose_nchw(batch, in_channel, in_size, num_filter, kernel, stride, padding):
in_height = in_width = in_size
A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A')
@@ -38,7 +38,7 @@ def verify_conv2d_transpose_nchw(batch, in_channel, in_size, num_filter, kernel,
def get_ref_data():
a_np = np.random.uniform(size=a_shape).astype(dtype)
w_np = np.random.uniform(size=w_shape).astype(dtype)
- b_np = topi.testing.conv2d_transpose_nchw_python(a_np, w_np, stride, padding, output_padding)
+ b_np = topi.testing.conv2d_transpose_nchw_python(a_np, w_np, stride, padding)
c_np = np.maximum(b_np, 0)
return a_np, w_np, b_np, c_np
@@ -51,7 +51,7 @@ def verify_conv2d_transpose_nchw(batch, in_channel, in_size, num_filter, kernel,
return
print("Running on target: %s" % device)
with tvm.target.create(device):
- B = topi.nn.conv2d_transpose_nchw(A, W, [stride, stride], [padding, padding], A.dtype, output_padding)
+ B = topi.nn.conv2d_transpose_nchw(A, W, [stride, stride], [padding, padding], A.dtype)
C = topi.nn.relu(B)
s1 = topi.generic.schedule_conv2d_transpose_nchw([B])
s2 = topi.generic.schedule_conv2d_transpose_nchw([C])
@@ -72,13 +72,11 @@ def verify_conv2d_transpose_nchw(batch, in_channel, in_size, num_filter, kernel,
def test_conv2d_transpose_nchw():
- verify_conv2d_transpose_nchw(1, 3, 224, 32, 3, 1, 0, (0, 0))
- verify_conv2d_transpose_nchw(1, 3, 224, 32, 3, 2, 1, (0, 0))
- verify_conv2d_transpose_nchw(1, 3, 224, 32, 3, 2, 1, (1, 0))
- verify_conv2d_transpose_nchw(1, 3, 224, 32, 2, 2, 0, (0, 0))
- verify_conv2d_transpose_nchw(1, 3, 224, 32, 2, 2, 0, (1, 1))
- verify_conv2d_transpose_nchw(1, 32, 32, 128, 5, 1, 0, (0, 0))
- verify_conv2d_transpose_nchw(1, 32, 32, 128, 5, 2, 1, (0, 0))
+ verify_conv2d_transpose_nchw(1, 3, 224, 32, 3, 1, 0)
+ verify_conv2d_transpose_nchw(1, 3, 224, 32, 3, 2, 1)
+ verify_conv2d_transpose_nchw(1, 3, 224, 32, 2, 2, 0)
+ verify_conv2d_transpose_nchw(1, 32, 32, 128, 5, 1, 0)
+ verify_conv2d_transpose_nchw(1, 32, 32, 128, 5, 2, 1)
if __name__ == "__main__":
diff --git a/vta/python/vta/top/vta_conv2d_transpose.py b/vta/python/vta/top/vta_conv2d_transpose.py
index ff10ff0..a2750dc 100644
--- a/vta/python/vta/top/vta_conv2d_transpose.py
+++ b/vta/python/vta/top/vta_conv2d_transpose.py
@@ -27,28 +27,24 @@ from topi.nn.util import get_pad_tuple
from ..environment import get_env
@autotvm.register_topi_compute(topi.nn.conv2d_transpose_nchw, 'vta', 'direct')
-def _declaration_conv2d_transpose(cfg,
+def _declatation_conv2d_transpose(cfg,
data,
kernel,
strides,
padding,
- out_dtype,
- output_padding=(0, 0)):
+ out_dtype):
ishape = get_const_tuple(data.shape)
kshape = get_const_tuple(kernel.shape)
b, c_i, i_h, i_w, t_b, t_ci = ishape
c_o, _, k_h, k_w, t_co, t_ci = kshape
stride_h, stride_w = strides
- opad_h, opad_w = output_padding
- # FIXME(tmoreau89): currently IR pass breaks when output padding != (0,0)
- assert opad_h == 0 and opad_w == 0, "VTA does not support output padding for now"
# derive padding parameters
fpad_top, fpad_left, fpad_bottom, fpad_right = get_pad_tuple(padding, (k_h, k_w))
bpad_top = k_h - 1 - fpad_top
- bpad_bottom = k_h - 1 - fpad_bottom + opad_h
+ bpad_bottom = k_h - 1 - fpad_bottom
bpad_left = k_w - 1 - fpad_left
- bpad_right = k_w - 1 - fpad_right + opad_w
+ bpad_right = k_w - 1 - fpad_right
# padding stage
dilated_input = topi.nn.dilate(data, [1, 1, stride_h, stride_w, 1, 1])
@@ -57,8 +53,8 @@ def _declaration_conv2d_transpose(cfg,
[0, 0, bpad_bottom, bpad_right, 0, 0])
# convolution transpose stage
- out_h = (i_h - 1) * stride_h - fpad_top - fpad_bottom + k_h + opad_h
- out_w = (i_w - 1) * stride_w - fpad_left - fpad_right + k_w + opad_w
+ out_h = (i_h - 1) * stride_h - fpad_top - fpad_bottom + k_h
+ out_w = (i_w - 1) * stride_w - fpad_left - fpad_right + k_w
oshape = (b, c_o, out_h, out_w, t_b, t_co)
d_c = tvm.reduce_axis((0, c_i), name='d_c')
d_h = tvm.reduce_axis((0, k_h), name='d_h')
diff --git a/vta/scripts/tune_conv2d_transpose.py b/vta/scripts/tune_conv2d_transpose.py
index fa9900a..3e51d41 100644
--- a/vta/scripts/tune_conv2d_transpose.py
+++ b/vta/scripts/tune_conv2d_transpose.py
@@ -33,15 +33,13 @@ env = vta.get_env()
Workload = namedtuple("Conv2DTransposeWorkload",
['batch', 'height', 'width', 'in_filter', 'out_filter',
- 'hkernel', 'wkernel', 'hpad', 'wpad', 'hstride', 'wstride',
- 'o_hpad', 'o_wpad'])
+ 'hkernel', 'wkernel', 'hpad', 'wpad', 'hstride', 'wstride'])
-# DCGAN workloads
dcgan_wkls = [
# dcgan
- ('DCGAN.CT1', Workload(env.BATCH, 4, 4, 1024, 512, 4, 4, 1, 1, 2, 2, 0, 0)),
- ('DCGAN.CT2', Workload(env.BATCH, 8, 8, 512, 256, 4, 4, 1, 1, 2, 2, 0, 0)),
- ('DCGAN.CT3', Workload(env.BATCH, 16, 16, 256, 128, 4, 4, 1, 1, 2, 2, 0, 0)),
+ ('DCGAN.CT1', Workload(env.BATCH, 4, 4, 1024, 512, 4, 4, 1, 1, 2, 2)),
+ ('DCGAN.CT2', Workload(env.BATCH, 8, 8, 512, 256, 4, 4, 1, 1, 2, 2)),
+ ('DCGAN.CT3', Workload(env.BATCH, 16, 16, 256, 128, 4, 4, 1, 1, 2, 2)),
]
@tvm.tag_scope(tag=topi.tag.ELEMWISE)
@@ -53,7 +51,7 @@ def my_clip(x, a_min, a_max):
x = tvm.compute(x.shape, lambda *i: tvm.max(x(*i), const_min), name="clipB")
return x
-def conv2d_transpose(N, CI, H, W, CO, KH, KW, strides, padding, opadding):
+def conv2d_transpose(N, CI, H, W, CO, KH, KW, strides, padding):
data_shape = (N//env.BATCH, CI//env.BLOCK_IN, H, W, env.BATCH, env.BLOCK_IN)
kernel_shape = (CO//env.BLOCK_OUT, CI//env.BLOCK_IN, KH, KW, env.BLOCK_OUT, env.BLOCK_IN)
@@ -66,9 +64,7 @@ def conv2d_transpose(N, CI, H, W, CO, KH, KW, strides, padding, opadding):
Filter=kernel,
strides=strides,
padding=padding,
- out_dtype=env.acc_dtype,
- output_padding=opadding
- )
+ out_dtype=env.acc_dtype)
res = topi.right_shift(res, env.WGT_WIDTH)
res = my_clip(res, 0, (1 << env.OUT_WIDTH - 1) - 1)
res = topi.cast(res, env.out_dtype)
@@ -113,12 +109,11 @@ if __name__ == '__main__':
KW = wl.wkernel
strides = (wl.hstride, wl.wstride)
padding = (wl.hpad, wl.wpad)
- opadding = (wl.o_hpad, wl.o_wpad)
# Create task
task = autotvm.task.create(
conv2d_transpose,
- args=(N, CI, H, W, CO, KH, KW, strides, padding, opadding),
+ args=(N, CI, H, W, CO, KH, KW, strides, padding),
target=tvm.target.vta(),
target_host=env.target_host,
template_key='direct')
diff --git a/vta/tests/python/integration/test_benchmark_topi_conv2d_transpose.py b/vta/tests/python/integration/test_benchmark_topi_conv2d_transpose.py
index 235076c..e2601d1 100644
--- a/vta/tests/python/integration/test_benchmark_topi_conv2d_transpose.py
+++ b/vta/tests/python/integration/test_benchmark_topi_conv2d_transpose.py
@@ -37,8 +37,7 @@ from vta.testing import simulator
Workload = namedtuple("Conv2DTransposeWorkload",
['batch', 'height', 'width', 'in_filter', 'out_filter',
- 'hkernel', 'wkernel', 'hpad', 'wpad', 'hstride', 'wstride',
- 'o_hpad', 'o_wpad'])
+ 'hkernel', 'wkernel', 'hpad', 'wpad', 'hstride', 'wstride'])
# Get batch info from env
env = vta.get_env()
@@ -46,9 +45,9 @@ env = vta.get_env()
# DCGAN workloads
dcgan_wklds = [
# dcgan
- ('DCGAN.CT1', Workload(env.BATCH, 4, 4, 1024, 512, 4, 4, 1, 1, 2, 2, 0, 0)),
- ('DCGAN.CT2', Workload(env.BATCH, 8, 8, 512, 256, 4, 4, 1, 1, 2, 2, 0, 0)),
- ('DCGAN.CT3', Workload(env.BATCH, 16, 16, 256, 128, 4, 4, 1, 1, 2, 2, 0, 0)),
+ ('DCGAN.CT1', Workload(env.BATCH, 4, 4, 1024, 512, 4, 4, 1, 1, 2, 2)),
+ ('DCGAN.CT2', Workload(env.BATCH, 8, 8, 512, 256, 4, 4, 1, 1, 2, 2)),
+ ('DCGAN.CT3', Workload(env.BATCH, 16, 16, 256, 128, 4, 4, 1, 1, 2, 2)),
]
# FIXME: we need a custom clip operator to circumvent a pattern detection limitation
@@ -103,8 +102,7 @@ def run_conv2d_transpose(env, remote, wl, target,
# Define base computation schedule
with target:
res = topi.nn.conv2d_transpose_nchw(
- data, kernel, (wl.hstride, wl.wstride),
- (wl.hpad, wl.wpad), env.acc_dtype, (wl.o_hpad, wl.o_wpad))
+ data, kernel, (wl.hstride, wl.wstride), (wl.hpad, wl.wpad), env.acc_dtype)
res = topi.right_shift(res, env.WGT_WIDTH)
res = my_clip(res, 0, (1 << env.OUT_WIDTH - 1) - 1)
res = topi.cast(res, env.out_dtype)
@@ -114,8 +112,8 @@ def run_conv2d_transpose(env, remote, wl, target,
print(vta.lower(s, [data, kernel, res], simple_mode=True))
# Derive number of ops
- fout_height = (wl.height - 1) * wl.hstride - 2 * wl.hpad + wl.hkernel + wl.o_hpad
- fout_width = (wl.width - 1) * wl.wstride - 2 * wl.wpad + wl.wkernel + wl.o_wpad
+ fout_height = (wl.height - 1) * wl.hstride - 2 * wl.hpad + wl.hkernel
+ fout_width = (wl.width - 1) * wl.wstride - 2 * wl.wpad + wl.wkernel
num_ops = 2 * wl.batch * fout_height * fout_width * wl.hkernel * wl.wkernel * wl.out_filter * wl.in_filter
# @memoize("vta.tests.test_benchmark_topi.conv2d.verify_nhwc")