cbalint13

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# 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
#

cbalint13

#!/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:

cbalint13

#!/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

cbalint13

{"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

cbalint13

#!/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

cbalint13

--- 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(),

cbalint13

Features

Loop-based Feature

For an IterVar (or an axis), it has three kinds of features

• axis attribute
• arithmetic feature
• touch feature

Axis Attribute

cbalint13

#!/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)

cbalint13

#!/usr/bin/python

import onnx
import numpy as np
import tvm
from tvm import te
import tvm.relay as relay


###

cbalint13

/*
 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/ \