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Commit 97e333ca by Balint Cristian Committed by Lianmin Zheng
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Add Winograd matrices computation. (#3553)

parent ef909df1
Showing with 184 additions and 187 deletions
topi/python/topi/arm_cpu/conv2d.py
......@@ -20,20 +20,19 @@ from __future__ import absolute_import as _abs
import warnings
import numpy as np
import tvm
from tvm import autotvm
import tvm.contrib.nnpack
from ..generic import schedule_conv2d_nchw, schedule_conv2d_winograd_without_weight_transform, \
schedule_conv2d_winograd_nnpack_without_weight_transform
from ..util import traverse_inline, get_const_tuple, const_matrix
from ..util import traverse_inline, get_const_tuple
from ..nn import dilate, pad, conv2d, conv2d_alter_layout, \
conv2d_winograd_without_weight_transform, \
conv2d_winograd_nnpack_without_weight_transform, \
depthwise_conv2d_nchw
from ..nn.util import get_const_int, get_pad_tuple
from ..nn.winograd_util import winograd_transform_matrices
@autotvm.register_topi_compute(conv2d, 'arm_cpu', ['direct'])
def conv2d_arm_cpu(cfg, data, kernel, strides, padding, dilation, layout, out_dtype):
......@@ -330,57 +329,14 @@ def _decl_winograd(cfg, data, kernel, strides, padding, dilation, layout, out_dt
HPAD, WPAD, _, _ = get_pad_tuple(padding, kernel)
assert layout == 'NCHW'
assert KH == 3 and KW == 3 and HPAD == 1 and WPAD == 1 and HSTR == 1 and WSTR == 1
assert KH == 3 and KW == 3 and HSTR == 1 and WSTR == 1
data_pad = pad(data, (0, 0, HPAD, WPAD), name="data_pad")
if tile_size == 4:
G_data = np.array([
[1 / 4.0, 0, 0],
[-1 / 6.0, -1 / 6.0, -1 / 6.0],
[-1 / 6.0, 1 / 6.0, -1 / 6.0],
[1 / 24.0, 1 / 12.0, 1 / 6.0],
[1 / 24.0, -1 / 12.0, 1 / 6.0],
[0, 0, 1]], dtype=np.float32)
B_data = np.array([
[4, 0, 0, 0, 0, 0],
[0, -4, 4, -2, 2, 4],
[-5, -4, -4, -1, -1, 0],
[0, 1, -1, 2, -2, -5],
[1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 1]], out_dtype)
A_data = np.array([
[1, 0, 0, 0],
[1, 1, 1, 1],
[1, -1, 1, -1],
[1, 2, 4, 8],
[1, -2, 4, -8],
[0, 0, 0, 1]], out_dtype)
elif tile_size == 2:
G_data = np.array([
[1, 0, 0],
[1.0/2, 1.0/2, 1.0/2],
[1.0/2, -1.0/2, 1.0/2],
[0, 0, 1]], np.float32)
B_data = np.array([
[1, 0, 0, 0],
[0, 1, -1, 1],
[-1, 1, 1, 0],
[0, 0, 0, -1]], out_dtype)
A_data = np.array([
[1, 0],
[1, 1],
[1, -1],
[0, -1]], out_dtype)
else:
raise ValueError("Unsupported tile size for winograd: " + str(tile_size))
m = A_data.shape[1]
r = 3
r = KW
m = tile_size
alpha = m + r - 1
A, B, G = winograd_transform_matrices(m, r, out_dtype)
K = CO
C = CI
......@@ -405,7 +361,6 @@ def _decl_winograd(cfg, data, kernel, strides, padding, dilation, layout, out_dt
if pre_computed:
U = kernel
else:
G = const_matrix(G_data, 'G')
r_kh = tvm.reduce_axis((0, KH), 'r_kh')
r_kw = tvm.reduce_axis((0, KW), 'r_kw')
U = tvm.compute((alpha, alpha, K // VK, C, VK), lambda eps, nu, k, c, kk:
......@@ -413,7 +368,6 @@ def _decl_winograd(cfg, data, kernel, strides, padding, dilation, layout, out_dt
G[eps][r_kh] * G[nu][r_kw], axis=[r_kh, r_kw]), name='U')
# transform image
B = const_matrix(B_data, 'B')
r_eps = tvm.reduce_axis((0, alpha), 'r_eps')
r_nu = tvm.reduce_axis((0, alpha), 'r_nu')
V = tvm.compute((alpha, alpha, P // VP, C, VP), lambda eps, nu, b, c, bb:
......@@ -427,7 +381,6 @@ def _decl_winograd(cfg, data, kernel, strides, padding, dilation, layout, out_dt
V[eps][nu][b // VP][c][b % VP], axis=c), name='M')
# inverse transform
A = const_matrix(A_data, 'A')
r_eps = tvm.reduce_axis((0, alpha), 'r_eps')
r_nu = tvm.reduce_axis((0, alpha), 'r_nu')
Y = tvm.compute((K, P, m, m), lambda k, b, vh, vw:
......
topi/python/topi/cuda/conv2d_winograd.py
......@@ -17,15 +17,14 @@
# pylint: disable=invalid-name,unused-variable,unused-argument
"""Winograd template for cuda backend"""
import numpy as np
import tvm
from tvm import autotvm
from .. import nn
from ..nn import conv2d, group_conv2d_nchw, conv2d_winograd_without_weight_transform
from ..util import get_const_int, get_const_tuple, const_matrix, traverse_inline
from ..util import get_const_int, get_const_tuple, traverse_inline
from ..generic import schedule_conv2d_winograd_without_weight_transform
from ..nn.winograd_util import winograd_transform_matrices
def _infer_tile_size(data, kernel):
......@@ -54,7 +53,7 @@ def winograd_cuda(cfg, data, kernel, strides, padding, dilation, layout, out_dty
CO, CI, KH, KW = get_const_tuple(kernel.shape)
HPAD, WPAD, _, _ = nn.get_pad_tuple(padding, kernel)
HSTR, WSTR = (strides, strides) if isinstance(strides, int) else strides
assert HSTR == 1 and WSTR == 1 and HPAD == 1 and WPAD == 1 and KH == 3 and KW == 3
assert HSTR == 1 and WSTR == 1 and KH == KW
else: # kernel tensor is pre-transfomred. this op is created by
# alter op layout, do not check
# dilation is not supported
......@@ -65,54 +64,11 @@ def winograd_cuda(cfg, data, kernel, strides, padding, dilation, layout, out_dty
data_pad = nn.pad(data, (0, 0, HPAD, WPAD), (0, 0, HPAD, WPAD), name="data_pad")
if tile_size == 4:
G_data = np.array([
[1 / 4.0, 0, 0],
[-1 / 6.0, -1 / 6.0, -1 / 6.0],
[-1 / 6.0, 1 / 6.0, -1 / 6.0],
[1 / 24.0, 1 / 12.0, 1 / 6.0],
[1 / 24.0, -1 / 12.0, 1 / 6.0],
[0, 0, 1]], dtype=np.float32)
B_data = np.array([
[4, 0, 0, 0, 0, 0],
[0, -4, 4, -2, 2, 4],
[-5, -4, -4, -1, -1, 0],
[0, 1, -1, 2, -2, -5],
[1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 1]], out_dtype)
A_data = np.array([
[1, 0, 0, 0],
[1, 1, 1, 1],
[1, -1, 1, -1],
[1, 2, 4, 8],
[1, -2, 4, -8],
[0, 0, 0, 1]], out_dtype)
elif tile_size == 2:
G_data = np.array([
[1, 0, 0],
[1.0/2, 1.0/2, 1.0/2],
[1.0/2, -1.0/2, 1.0/2],
[0, 0, 1]], np.float32)
B_data = np.array([
[1, 0, 0, 0],
[0, 1, -1, 1],
[-1, 1, 1, 0],
[0, 0, 0, -1]], out_dtype)
A_data = np.array([
[1, 0],
[1, 1],
[1, -1],
[0, -1]], out_dtype)
else:
raise ValueError("Unsupported tile size for winograd: " + str(tile_size))
m = A_data.shape[1]
r = 3
r = KW
m = tile_size
alpha = m + r - 1
A, B, G = winograd_transform_matrices(m, r, out_dtype)
H = (H + 2 * HPAD - KH) // HSTR + 1
W = (W + 2 * WPAD - KW) // WSTR + 1
nH, nW = (H + m-1) // m, (W + m-1) // m
......@@ -120,7 +76,6 @@ def winograd_cuda(cfg, data, kernel, strides, padding, dilation, layout, out_dty
# transform kernel
if not pre_computed:
G = const_matrix(G_data, 'G')
r_kh = tvm.reduce_axis((0, KH), name='r_kh')
r_kw = tvm.reduce_axis((0, KW), name='r_kw')
kernel_pack = tvm.compute((alpha, alpha, CI, CO), lambda eps, nu, ci, co:
......@@ -136,7 +91,6 @@ def winograd_cuda(cfg, data, kernel, strides, padding, dilation, layout, out_dty
[p % nW * m + nu], name='d')
# transform data
B = const_matrix(B_data)
r_a = tvm.reduce_axis((0, alpha), 'r_a')
r_b = tvm.reduce_axis((0, alpha), 'r_a')
data_pack = tvm.compute((alpha, alpha, CI, P), lambda eps, nu, ci, p:
......@@ -151,7 +105,6 @@ def winograd_cuda(cfg, data, kernel, strides, padding, dilation, layout, out_dty
axis=[ci]), name='bgemm')
# inverse transform
A = const_matrix(A_data)
r_a = tvm.reduce_axis((0, alpha), 'r_a')
r_b = tvm.reduce_axis((0, alpha), 'r_a')
inverse = tvm.compute((CO, P, m, m), lambda co, p, vh, vw:
......
topi/python/topi/mali/conv2d.py
......@@ -16,16 +16,15 @@
# under the License.
# pylint: disable=invalid-name,unused-variable,unused-argument,no-else-return
"""conv2d schedule on ARM Mali GPU"""
import numpy as np
import tvm
from tvm import autotvm
from tvm.autotvm.task.space import get_factors
from ..generic import schedule_conv2d_nchw, schedule_conv2d_winograd_without_weight_transform
from ..util import traverse_inline, get_const_int, get_const_tuple, const_matrix
from ..util import traverse_inline, get_const_int, get_const_tuple
from ..nn import conv2d, conv2d_winograd_without_weight_transform, \
get_pad_tuple, pad, conv2d_alter_layout
from ..nn.winograd_util import winograd_transform_matrices
# reuse some compute declarations from ARM CPU
from ..arm_cpu.conv2d import _decl_spatial_pack, _alter_conv2d_layout_arm
......@@ -226,57 +225,13 @@ def _decl_winograd(cfg, data, kernel, strides, padding, dilation, layout, out_dt
HPAD, WPAD, _, _ = get_pad_tuple(padding, kernel)
assert layout == 'NCHW'
assert KH == 3 and KW == 3 and HPAD == 1 and WPAD == 1 and HSTR == 1 and WSTR == 1
assert KH == 3 and KW == 3 and HSTR == 1 and WSTR == 1
data_pad = pad(data, (0, 0, HPAD, WPAD), name="data_pad")
if tile_size == 4:
G_data = np.array([
[1 / 4.0, 0, 0],
[-1 / 6.0, -1 / 6.0, -1 / 6.0],
[-1 / 6.0, 1 / 6.0, -1 / 6.0],
[1 / 24.0, 1 / 12.0, 1 / 6.0],
[1 / 24.0, -1 / 12.0, 1 / 6.0],
[0, 0, 1]], out_dtype)
B_data = np.array([
[4, 0, 0, 0, 0, 0],
[0, -4, 4, -2, 2, 4],
[-5, -4, -4, -1, -1, 0],
[0, 1, -1, 2, -2, -5],
[1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 1]], out_dtype)
A_data = np.array([
[1, 0, 0, 0],
[1, 1, 1, 1],
[1, -1, 1, -1],
[1, 2, 4, 8],
[1, -2, 4, -8],
[0, 0, 0, 1]], out_dtype)
elif tile_size == 2:
G_data = np.array([
[1, 0, 0],
[1.0/2, 1.0/2, 1.0/2],
[1.0/2, -1.0/2, 1.0/2],
[0, 0, 1]], out_dtype)
B_data = np.array([
[1, 0, 0, 0],
[0, 1, -1, 1],
[-1, 1, 1, 0],
[0, 0, 0, -1]], out_dtype)
A_data = np.array([
[1, 0],
[1, 1],
[1, -1],
[0, -1]], out_dtype)
else:
raise ValueError("Unsupported tile size for winograd: " + str(tile_size))
m = A_data.shape[1]
r = 3
r = KW
m = tile_size
alpha = m + r - 1
A, B, G = winograd_transform_matrices(m, r, out_dtype)
H = (IH + 2 * HPAD - 3) // HSTR + 1
W = (IW + 2 * WPAD - 3) // WSTR + 1
......@@ -321,7 +276,6 @@ def _decl_winograd(cfg, data, kernel, strides, padding, dilation, layout, out_dt
if pre_computed:
U = kernel
else:
G = const_matrix(G_data, 'G')
r_kh = tvm.reduce_axis((0, KH), 'r_kh')
r_kw = tvm.reduce_axis((0, KW), 'r_kw')
U = tvm.compute((alpha, alpha, CO // bna, CI, bna), lambda eps, nu, co, ci, vco:
......@@ -329,7 +283,6 @@ def _decl_winograd(cfg, data, kernel, strides, padding, dilation, layout, out_dt
axis=[r_kh, r_kw]), name='U')
# transform image
B = const_matrix(B_data, 'B')
r_a = tvm.reduce_axis((0, alpha), 'r_a')
r_b = tvm.reduce_axis((0, alpha), 'r_b')
V = tvm.compute((alpha, alpha, P_round // bnb, CI, bnb), lambda eps, nu, p, ci, vp:
......@@ -342,7 +295,6 @@ def _decl_winograd(cfg, data, kernel, strides, padding, dilation, layout, out_dt
tvm.sum(U[eps][nu][co // bna][ci][co % bna] *
V[eps][nu][p // bnb][ci][p % bnb], axis=ci), name='M')
A = const_matrix(A_data, 'A')
r_a = tvm.reduce_axis((0, alpha), 'r_a')
r_b = tvm.reduce_axis((0, alpha), 'r_b')
Y = tvm.compute((CO, P, m, m), lambda co, p, vh, vw:
......
topi/python/topi/nn/conv2d.py
......@@ -19,12 +19,12 @@
"""Conv2D operators"""
from __future__ import absolute_import as _abs
from collections import namedtuple
import numpy as np
import tvm
from .pad import pad
from .util import get_pad_tuple
from ..util import simplify, const_matrix, get_const_tuple
from ..util import simplify, get_const_tuple
from .winograd_util import winograd_transform_matrices
# workload description of conv2d
Workload = namedtuple('Workload',
......@@ -425,7 +425,7 @@ def conv2d_winograd_weight_transform(kernel, tile_size):
Parameters
----------
kernel: Tensor
The raw kernel tensor with layout "NCHW". Only 3x3 kernel is supported for now
The raw kernel tensor with layout "NCHW".
tile_size: int
Tile size of winograd transform. e.g. 2 for F(2x2, 3x3) and 4 for F(4x4, 3x3)
......@@ -434,34 +434,15 @@ def conv2d_winograd_weight_transform(kernel, tile_size):
output : tvm.Tensor
4-D with shape [alpha, alpha, CO, CI]
"""
K = 3
shape = get_const_tuple(kernel.shape)
assert shape[2:] == (K, K), "Only support 3x3 kernel"
assert shape[2] == shape[3], "Only support NxN kernel"
K = shape[3]
r = tile_size + K - 1
shape = (r, r) + shape[:2]
if tile_size == 2:
G_data = np.array([
[1, 0, 0],
[1.0/2, 1.0/2, 1.0/2],
[1.0/2, -1.0/2, 1.0/2],
[0, 0, 1],
], dtype=kernel.dtype)
elif tile_size == 4:
G_data = np.array([
[1 / 4.0, 0, 0],
[-1 / 6.0, -1 / 6.0, -1 / 6.0],
[-1 / 6.0, 1 / 6.0, -1 / 6.0],
[1 / 24.0, 1 / 12.0, 1 / 6.0],
[1 / 24.0, -1 / 12.0, 1 / 6.0],
[0, 0, 1]
], dtype=kernel.dtype)
else:
raise ValueError("Unsupoorted tile size:" + tile_size)
_, _, G = winograd_transform_matrices(tile_size, K, kernel.dtype)
G = const_matrix(G_data, 'G')
r_kh = tvm.reduce_axis((0, K), name='r_kh')
r_kw = tvm.reduce_axis((0, K), name='r_kw')
return tvm.compute(shape, lambda eps, nu, co, ci:
......
topi/python/topi/nn/winograd_util.py 0 → 100644
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
""" Utility functions for implementing Winograd convolutions
[*] Fast Algorithms for Convolutional Neural Networks
Andrew Lavin, Scott Gray
https://arxiv.org/abs/1509.09308
https://github.com/andravin/wincnn
"""
from operator import mul
from functools import reduce
import numpy as np
from ..util import const_matrix
# pylint: disable=invalid-name
def _cook_toom_convolution(a, n, r):
"""Compute Cook-Toom convolution A,B,G matrices"""
def _F_m(a, n):
f = lambda j, i: reduce(mul, ((a[i]-a[k] if k != i else 1) for k in range(0, n-1)), 1)
F = np.fromfunction(np.vectorize(f), (1, n-1), dtype=int)
F = np.diagflat(F)
F = np.append(F, np.zeros((n-1, 1), dtype=int), axis=1)
f = lambda i, j: (1 if j == (n-1) else 0)
z = np.fromfunction(np.vectorize(f), (1, n), dtype=int)
return np.append(F, z, axis=0)
def _A_m(a, m, n):
f = lambda i, j: a[i]**j
A = np.fromfunction(np.vectorize(f), (m-1, n), dtype=int)
f = lambda i, j: (1 if j == (n-1) else 0)
z = np.fromfunction(np.vectorize(f), (1, n), dtype=int)
return np.append(A, z, axis=0)
def _B_m(a, n):
f = lambda j, i: reduce(mul, ((a[i]-a[k] if k != i else 1) for k in range(0, n-1)), 1)
Ff = np.fromfunction(np.vectorize(f), (1, n-1), dtype=int)
f = lambda i, nth: (reduce(mul, [(np.poly1d([1, -a[k]]) if k != i else 1) \
for k in range(0, n-1)], 1)).coef[n-1-nth-1]/Ff[0, i]
F = np.fromfunction(np.vectorize(f), (n-1, n-1), dtype=int)
f = lambda i, j: -a[i]**(n-1)
t = np.fromfunction(np.vectorize(f), (n-1, 1), dtype=int)
T = np.append(np.eye(n-1), t, axis=1)
return np.append(F.T.dot(T), np.array([np.eye(n)[n-1]]), axis=0)
alpha = n + r - 1
f = _F_m(a, alpha)
if f[0, 0] < 0:
f[0, :] *= -1
A = _A_m(a, alpha, n)
G = _A_m(a, alpha, r).T
G = G.dot(np.linalg.inv(f)).T
B = _B_m(a, alpha)
B = B.dot(f.T)
return (A, B, G)
def _interpolation_points(degree):
"""Propose filter points"""
assert 2 < degree < 18
# Default interpolation lookup table
#
# [1] Error Analysis and Improving the Accuracy of Winograd Convolution for Deep Neural Networks
# Barbara Barabasz, Andrew Anderson, Kirk M. Soodhalter, David Gregg
# https://arxiv.org/abs/1803.10986
#
# pylint: disable=bad-whitespace,line-too-long
in_pts = [
# {invalid}
[],
#01 {E=4.63E-08 on conv2d [1]}
[],
#02 {E=7.65E-08 on F( 2,3) [1]}
[0, -1, 1],
#03 {E=2.35E-07 on F( 3,3) [1]}
[0, -1, 1, 1/2],
#04 {E=3.29E-07 on F( 4,3) [1]}
[0, -1, 1, 1/2, -2],
#05 {E=6.81E-07 on F( 5,3) [1]}
[0, -1, 1, 1/2, -2, -1/2],
#06 {E=8.79E-07 on F( 6,3) [1]}
[0, -1, 1, 1/2, -1/2, 2, -2],
#07 {E=3.71E-06 on F( 7,3) [1]}
[0, -1, 1, 1/2, -1/2, 2, -2, -1/4],
#08 {E=7.35E-06 on F( 8,3) [1]}
[0, -1, 1, 1/2, -1/2, 2, -2, -1/4, 4],
#09 {E=2.20E-05 on F( 9,3) [1]}
[0, -1, 1, 1/2, -1/2, 2, -2, -1/4, 3/4, -4/3],
#10 {E=3.22E-05 on F(10,3) [1]}
[0, -1, 1, 1/2, -1/2, 2, -2, -1/4, 4, 3/4, -4/3],
#11 {E=1.09E-04 on F(11,3) [1]}
[0, -1, 1, 1/2, -1/2, 2, -2, -1/4, 4, 3/4, -4/3, 1/4],
#12 {E=1.99E-04 on F(12,3) [1]}
[0, -1, 1, 1/2, -1/2, 2, -2, -1/4, 4, 1/4, -3/4, 4/3, -4],
#13 {E=5.54E-04 on F(13,3) [1]}
[0, -1, 1, 1/2, -1/2, 2, -2, -1/4, 4, 1/4, -3/4, 4/3, 3/4, -4/3],
#14 {E=8.80E-04 on F(14,3) [1]}
[0, -1, 1, 1/2, -1/2, 2, -2, -1/4, 4, 1/4, -3/4, 4/3, -4, 3/4, -4/3],
#15 {E=1.07E-02 on F(15,3) [1]}
[0, -1, 1, 1/2, -1/2, 2, -2, -1/4, 4, 1/4, -3/4, 4/3, -4, 2/3, -3/2, 3/2],
#16 {E=1.93E-02 on F(16,3) [1]}
[0, -1, 1, 1/2, -1/2, 2, -2, -1/4, 4, 1/4, -3/4, 4/3, -4, 2/3, -3/2, -2/3, 3/2]
] # pylint: enable=bad-whitespace,line-too-long
return np.array(in_pts[degree-1], dtype=np.float64)
def winograd_transform_matrices(tile_size, kernel_size, out_dtype):
"""Compute the A, B, and G transform matrices for `tile_size` as a `tvm.Expr`.
"""
if not 1 < tile_size < 9:
raise ValueError("Unsupported tile size for Winograd: {}".format(tile_size))
if not 2 < kernel_size < 8:
raise ValueError("Unsupported kernel size for Winograd: {}".format(kernel_size))
degree = tile_size + kernel_size - 2
intp_pts = _interpolation_points(degree)
A_data, B_data, G_data = _cook_toom_convolution(intp_pts, tile_size, kernel_size)
return (
const_matrix(A_data.astype(out_dtype), "A"),
const_matrix(B_data.astype(out_dtype), "B"),
const_matrix(G_data.astype(out_dtype), "G"),
)
topi/tests/python/test_topi_conv2d_winograd.py
......@@ -103,11 +103,18 @@ def test_conv2d_nchw():
autotvm.DispatchContext.current.silent = True
with WinogradFallback():
# inception v3 workloads
verify_conv2d_nchw(1, 128, 17, 192, 7, 1, 3, devices=['cuda'])
verify_conv2d_nchw(1, 128, 17, 128, 7, 1, 3, devices=['cuda'])
verify_conv2d_nchw(1, 160, 17, 160, 7, 1, 3, devices=['cuda'])
# resnet 18 workloads
verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1)
verify_conv2d_nchw(1, 128, 28, 128, 3, 1, 1)
verify_conv2d_nchw(1, 256, 14, 256, 3, 1, 1)
verify_conv2d_nchw(1, 512, 7, 512, 3, 1, 1)
verify_conv2d_nchw(1, 48, 35, 64, 5, 1, 2, devices=['cuda'])
# batch size = 2
verify_conv2d_nchw(2, 64, 56, 64, 3, 1, 1)
......
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