For an IterVar (or an axis), it has three kinds of features
- axis attribute
- arithmetic feature
- touch feature
# 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 | |
# |
#!/usr/bin/python3 | |
import sys | |
import json | |
old_file = 'arm_cpu_v0.04.log' | |
new_file = 'arm_cpu_v0.05.log' | |
fn = open(new_file, 'w') | |
with open(old_file) as fp: |
#!/usr/bin/python3 | |
import nnvm | |
import nnvm.frontend.darknet | |
import tvm.relay.testing.yolo_detection | |
import tvm.relay.testing.darknet | |
import tvm.relay.transform as _transform | |
import matplotlib.pyplot as plt | |
import numpy as np | |
import tvm |
{"i": ["llvm", "topi_x86_conv2d_NCHWc", [["TENSOR", [1, 256, 26, 26], "int8"], ["TENSOR", [255, 256, 1, 1], "int8"], [1, 1], [0, 0], [1, 1], "NCHW", "int32"], {}, ["conv2d", [1, 256, 26, 26, "int8"], [255, 256, 1, 1, "int8"], [1, 1], [0, 0], [1, 1], "NCHW", "int32"], {"i": 5, "t": "direct", "c": null, "e": [["tile_ic", "sp", [8, 32]], ["tile_oc", "sp", [255, 1]], ["tile_ow", "sp", [26, 1]], ["tile_oh", "ot", 1]]}], "r": [[0.002006969857142857], 0, 1.7788748741149902, 1564778173.0481853], "v": 0.1} | |
{"i": ["llvm", "topi_x86_conv2d_NCHWc", [["TENSOR", [1, 384, 26, 26], "int8"], ["TENSOR", [256, 384, 3, 3], "int8"], [1, 1], [1, 1], [1, 1], "NCHW", "int32"], {}, ["conv2d", [1, 384, 26, 26, "int8"], [256, 384, 3, 3, "int8"], [1, 1], [1, 1], [1, 1], "NCHW", "int32"], {"i": 525, "t": "direct", "c": null, "e": [["tile_ic", "sp", [3, 128]], ["tile_oc", "sp", [8, 32]], ["tile_ow", "sp", [1, 26]], ["unroll_kw", "ot", true]]}], "r": [[0.01963637225], 0, 5.333110809326172, 1564779398.703997], "v": 0.1} | |
{"i": ["llvm", "topi |
#!/usr/bin/python3 | |
#import nnvm.frontend.darknet | |
import tvm.relay.testing.yolo_detection | |
import tvm.relay.testing.darknet | |
import tvm.relay.transform as _transform | |
import matplotlib.pyplot as plt | |
import numpy as np | |
import tvm | |
import onnx |
--- a/modules/dnn/src/cuda4dnn/csl/cudnn/convolution.hpp.orig 2020-06-08 17:01:47.788349629 +0300 | |
+++ b/modules/dnn/src/cuda4dnn/csl/cudnn/convolution.hpp 2020-06-08 16:50:51.579297388 +0300 | |
@@ -260,10 +260,10 @@ | |
const TensorDescriptor<T>& output) | |
{ | |
CUDA4DNN_CHECK_CUDNN( | |
- cudnnGetConvolutionForwardAlgorithm( | |
+ cudnnGetConvolutionForwardAlgorithm_v7( | |
handle.get(), | |
input.get(), filter.get(), conv.get(), output.get(), |
#!/usr/bin/python3 | |
import sys | |
import numpy as np | |
import matplotlib.pyplot as plt | |
def get_cmap(n, name='hsv'): | |
'''Returns a function that maps each index in 0, 1, ..., n-1 to a distinct | |
RGB color; the keyword argument name must be a standard mpl colormap name.''' | |
return plt.cm.get_cmap(name, n) |
#!/usr/bin/python | |
import onnx | |
import numpy as np | |
import tvm | |
from tvm import te | |
import tvm.relay as relay | |
### |
/* | |
COMPILE: | |
gcc testcase.c -g -O2 \ | |
-DOGS_GTP_INSIDE -DOGS_GTP_COMPILATION \ | |
-I/home/cbalint/rpmbuild/BUILD/open5gs/lib/gtp \ | |
-I/home/cbalint/rpmbuild/BUILD/open5gs/lib/core \ | |
-I/home/cbalint/rpmbuild/BUILD/open5gs/redhat-linux-build/lib \ | |
-I/home/cbalint/rpmbuild/BUILD/open5gs/lib \ | |
-I/home/cbalint/rpmbuild/BUILD/open5gs/lib/app/ \ |