Google Git
Sign in
apache / tvm / d20646c7908a92891d0efc1be61ed4fab67a2acf / . / topi / tests / python / test_topi_conv2d_NCHWc.py
blob: a3af43c8d810bd5f7bde16577bdb58075e8bcf43 [file] [log] [blame]
"""Test for NCHW[x]c convolution"""
import numpy as np
import tvm
from tvm import autotvm
import topi
import topi.testing
from tvm.contrib.pickle_memoize import memoize
from topi.util import get_const_tuple
from common import get_all_backend
def _transform_data(data, bn):
# NCHW -> NCHW[x]c
batch_size, channel, height, width = data.shape
data = np.reshape(data, (batch_size, channel//bn, bn, height, width))
data = np.transpose(data, (0, 1, 3, 4, 2))
return data
def _transform_kernel(kernel, ic_bn, oc_bn):
# OIHW -> OIHW[x]i[x]o
out_channel, in_channel, kh, kw = kernel.shape
kernel = np.reshape(kernel, (out_channel//oc_bn, oc_bn, in_channel//ic_bn, ic_bn, kh, kw))
kernel = np.transpose(kernel, (0, 2, 4, 5, 3, 1))
return kernel
def _transform_bias(bias, bn):
# [num_filter, 1, 1] -> [num_filter//bn, 1, 1, bn]
num_filter, h, w = bias.shape
bias = np.reshape(bias, (num_filter//bn, bn, h, w))
bias = np.transpose(bias, (0, 2, 3, 1))
return bias
def verify_conv2d_NCHWc(batch, in_channel, in_size, num_filter, kernel, stride,
padding, dilation=1, add_bias=False, add_relu=False, dtype="float32"):
assert dilation == 1, "conv2d_NCHWc does not support dilation for now."
print("Workload: (%d, %d, %d, %d, %d, %d, %d)" %
(batch, in_channel, in_size, num_filter, kernel, stride, padding))
in_height = in_width = in_size
# for testing functionality,
# we choose arbitrary block size that can divide the channel,
# regardless of the performance.
oc_block = 1
for bn in range(16, 0, -1):
if num_filter % bn == 0:
oc_block = bn
break
ic_block = 1
for bn in range(oc_block, 0, -1):
if in_channel % bn == 0:
ic_block = bn
break
A = tvm.placeholder((batch, in_channel//ic_block, in_height, in_width, ic_block), name='A')
W = tvm.placeholder((num_filter//oc_block, in_channel//ic_block, kernel, kernel, ic_block, oc_block), name='W')
bias = tvm.placeholder((num_filter//oc_block, 1, 1, oc_block), name='bias')
@memoize("topi.tests.test_topi_conv2d_NCHWc.verify_conv2d_NCHWc")
def get_ref_data():
a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype(dtype)
w_np = np.random.uniform(size=(num_filter, in_channel, kernel, kernel)).astype(dtype)
b_np = np.random.uniform(size=(num_filter, 1, 1)).astype(dtype)
c_np = topi.testing.conv2d_nchw_python(a_np, w_np, stride, padding)
if add_bias:
c_np += b_np
if add_relu:
c_np = np.maximum(c_np, 0)
return _transform_data(a_np, ic_block), _transform_kernel(w_np, ic_block, oc_block), \
_transform_bias(b_np, oc_block), _transform_data(c_np, oc_block)
a_np, w_np, b_np, c_np = get_ref_data()
def check_device(device):
ctx = tvm.context(device, 0)
if not ctx.exist:
print("Skip because %s is not enabled" % device)
return
print("Running on target: %s" % device)
with tvm.target.create(device):
C = topi.nn.conv2d_NCHWc(A, W, (stride, stride), (padding, padding),
(dilation, dilation),
layout='NCHW%dc'%ic_block,
out_layout="NCHW%dc"%oc_block,
out_dtype=dtype)
if add_bias:
C = topi.add(C, bias)
if add_relu:
C = topi.nn.relu(C)
s = topi.generic.schedule_conv2d_NCHWc([C])
a = tvm.nd.array(a_np, ctx)
w = tvm.nd.array(w_np, ctx)
b = tvm.nd.array(b_np, ctx)
c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), ctx)
if add_bias:
func = tvm.build(s, [A, W, bias, C], device,
name="relu_%d_%d_%d_%d_%d_%d_%d_%d" %
(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation))
func(a, w, b, c)
else:
func = tvm.build(s, [A, W, C], device,
name="relu_%d_%d_%d_%d_%d_%d_%d_%d" %
(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation))
func(a, w, c)
tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
# test llvm only for now since conv2d_NCHWc implement is missing in other backend.
for device in ["llvm"]:
with autotvm.tophub.context(device): # load tophub pre-tuned parameters
check_device(device)
def test_conv2d_NCHWc():
# ResNet18 workloads
verify_conv2d_NCHWc(1, 3, 224, 64, 7, 2, 3)
verify_conv2d_NCHWc(1, 64, 56, 64, 3, 1, 1)
verify_conv2d_NCHWc(1, 64, 56, 64, 1, 1, 0)
verify_conv2d_NCHWc(1, 64, 56, 128, 3, 2, 1)
verify_conv2d_NCHWc(1, 64, 56, 128, 1, 2, 0)
verify_conv2d_NCHWc(1, 128, 28, 128, 3, 1, 1)
verify_conv2d_NCHWc(1, 128, 28, 256, 3, 2, 1)
verify_conv2d_NCHWc(1, 128, 28, 256, 1, 2, 0)
verify_conv2d_NCHWc(1, 256, 14, 256, 3, 1, 1)
verify_conv2d_NCHWc(1, 256, 14, 512, 3, 2, 1)
verify_conv2d_NCHWc(1, 256, 14, 512, 1, 2, 0)
verify_conv2d_NCHWc(1, 512, 7, 512, 3, 1, 1)
# bias, relu
verify_conv2d_NCHWc(1, 64, 56, 64, 3, 1, 1, add_relu=True)
verify_conv2d_NCHWc(1, 64, 56, 64, 3, 1, 1, add_bias=True)
verify_conv2d_NCHWc(1, 64, 56, 64, 3, 1, 1, add_bias=True, add_relu=True)
# disable dilation test since it is not supported by NCHW[x]c conv for now.
# verify_conv2d_NCHWc(1, 64, 56, 64, 3, 1, 1, dilation=2)
# batch size
verify_conv2d_NCHWc(4, 64, 56, 64, 3, 1, 1)
verify_conv2d_NCHWc(9, 64, 56, 64, 3, 1, 1)
# weird workloads
verify_conv2d_NCHWc(2, 2, 2, 2, 2, 2, 2)
verify_conv2d_NCHWc(3, 3, 3, 3, 3, 3, 3)
verify_conv2d_NCHWc(4, 4, 4, 4, 4, 4, 4)
verify_conv2d_NCHWc(5, 5, 5, 5, 5, 5, 5)
verify_conv2d_NCHWc(6, 6, 6, 6, 6, 6, 6)
# disable these tests due to some bugs of llvm with nvptx
# verify_conv2d_NCHWc(1, 1, 1, 1, 1, 1, 1, dilation=1)
# verify_conv2d_NCHWc(1, 1, 1, 1, 1, 1, 1, dilation=2)
# verify_conv2d_NCHWc(2, 13, 71, 59, 3, 1, 1)
# inception v3 workloads
verify_conv2d_NCHWc(1, 3, 299, 32, 3, 2, 0)
verify_conv2d_NCHWc(1, 32, 149, 32, 3, 1, 0)
verify_conv2d_NCHWc(1, 32, 147, 64, 3, 1, 1)
verify_conv2d_NCHWc(1, 64, 73, 80, 1, 1, 0)
verify_conv2d_NCHWc(1, 80, 73, 192, 3, 1, 0)
verify_conv2d_NCHWc(1, 192, 35, 64, 1, 1, 0)
verify_conv2d_NCHWc(1, 192, 35, 48, 1, 1, 0)
verify_conv2d_NCHWc(1, 48, 35, 64, 5, 1, 2)
verify_conv2d_NCHWc(1, 64, 35, 96, 3, 1, 1)
verify_conv2d_NCHWc(1, 96, 35, 96, 3, 1, 1)
verify_conv2d_NCHWc(1, 192, 35, 32, 1, 1, 0)
verify_conv2d_NCHWc(1, 256, 35, 64, 1, 1, 0)
verify_conv2d_NCHWc(1, 256, 35, 48, 1, 1, 0)
verify_conv2d_NCHWc(1, 288, 35, 64, 1, 1, 0)
verify_conv2d_NCHWc(1, 288, 35, 48, 1, 1, 0)
verify_conv2d_NCHWc(1, 288, 35, 384, 3, 2, 0)
verify_conv2d_NCHWc(1, 96, 35, 96, 3, 2, 0)
verify_conv2d_NCHWc(1, 768, 17, 192, 1, 1, 0)
verify_conv2d_NCHWc(1, 768, 17, 128, 1, 1, 0)
verify_conv2d_NCHWc(1, 128, 17, 128, 1, 1, 0)
verify_conv2d_NCHWc(1, 128, 17, 192, 7, 1, 3)
verify_conv2d_NCHWc(1, 128, 17, 128, 7, 1, 3)
verify_conv2d_NCHWc(1, 128, 17, 192, 1, 1, 0)
verify_conv2d_NCHWc(1, 768, 17, 160, 1, 1, 0)
verify_conv2d_NCHWc(1, 160, 17, 160, 1, 1, 0)
verify_conv2d_NCHWc(1, 160, 17, 192, 7, 1, 3)
verify_conv2d_NCHWc(1, 160, 17, 160, 7, 1, 3)
verify_conv2d_NCHWc(1, 160, 17, 192, 1, 1, 0)
verify_conv2d_NCHWc(1, 192, 17, 192, 1, 1, 0)
verify_conv2d_NCHWc(1, 192, 17, 192, 7, 1, 3)
verify_conv2d_NCHWc(1, 192, 17, 320, 3, 2, 0)
verify_conv2d_NCHWc(1, 192, 17, 192, 3, 2, 0)
verify_conv2d_NCHWc(1, 1280, 8, 320, 1, 1, 0)
verify_conv2d_NCHWc(1, 1280, 8, 384, 1, 1, 0)
verify_conv2d_NCHWc(1, 384, 8, 384, 1, 1, 0)
verify_conv2d_NCHWc(1, 384, 8, 384, 3, 1, 1)
verify_conv2d_NCHWc(1, 1280, 8, 448, 1, 1, 0)
verify_conv2d_NCHWc(1, 448, 8, 384, 3, 1, 1)
verify_conv2d_NCHWc(1, 1280, 8, 192, 1, 1, 0)
verify_conv2d_NCHWc(1, 2048, 8, 320, 1, 1, 0)
verify_conv2d_NCHWc(1, 2048, 8, 384, 1, 1, 0)
verify_conv2d_NCHWc(1, 2048, 8, 448, 1, 1, 0)
verify_conv2d_NCHWc(1, 2048, 8, 192, 1, 1, 0)
verify_conv2d_NCHWc(1, 1024, 19, 84, 3, 1, 1)
verify_conv2d_NCHWc(1, 2048, 10, 126, 3, 1, 1)
verify_conv2d_NCHWc(1, 512, 5, 126, 3, 1, 1)
verify_conv2d_NCHWc(1, 256, 3, 126, 3, 1, 1)
if __name__ == "__main__":
test_conv2d_NCHWc()