l-bat
/ save_rpn.py
Last active
August 1, 2020 07:25
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| import numpy as np | |
| import os | |
| import onnx | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import cv2 | |
| # Class for RPN | |
| class RPN(nn.Module): |
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| def xcorr_depthwise(x, kernel): | |
| """ | |
| Deptwise convolution for input and weights with the same shapes | |
| Elementwise multiplication -> GlobalAveragePooling -> scalar mul on (kernel_h * kernel_w) | |
| """ | |
| # batch = kernel.size(0) # in our model batch = 1 | |
| # channel = kernel.size(1) | |
| # x = x.view(1, batch*channel, x.size(2), x.size(3)) # batch already = 1 | |
| # kernel = kernel.view(batch*channel, 1, kernel.size(2), kernel.size(3)) |
l-bat
/ onnx_gather.cpp
Created
July 14, 2020 05:53
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| else if (layer_type == "Gather") | |
| { | |
| CV_Assert(node_proto.input_size() == 2); | |
| Mat indexMat = getBlob(node_proto, constBlobs, 1); | |
| CV_Assert_N(indexMat.type() == CV_32S, indexMat.total() == 1); | |
| int index = indexMat.at<int>(0); | |
| if ((constBlobs.find(node_proto.input(0)) != constBlobs.end())) | |
| { | |
| Mat input = getBlob(node_proto, constBlobs, 0); |
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| #ifdef HAVE_DNN_NGRAPH | |
| virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs, | |
| const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE | |
| { | |
| auto& input = nodes[0].dynamicCast<InfEngineNgraphNode>()->node; | |
| auto parent_shape = input->get_shape(); | |
| std::cout << "parent_shape " << parent_shape << std::endl; |
l-bat
/ custom_split.cpp
Created
April 3, 2020 07:52
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| #include <iostream> | |
| #include <vector> | |
| #include <random> | |
| #include <inference_engine.hpp> | |
| #include <ngraph/ngraph.hpp> | |
| #include <ngraph/opsets/opset.hpp> | |
| using namespace InferenceEngine; | |
l-bat
/ opencl_matmul.cpp
Last active
March 19, 2020 08:33
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| if (inputs.size() == 2) | |
| { | |
| int dims = outputs[0].dims; | |
| int m = inputs[0].size[dims - 2]; | |
| int n = inputs[0].size[dims - 1]; | |
| int k = inputs[1].size[dims - 1]; | |
| int rows = inputs[0].total() / (m * n); | |
| MatShape sh_A = shape(rows, m * n); | |
| MatShape sh_B = shape(rows, n * k); |
l-bat
/ flownet2_save_data.py
Last active
February 28, 2020 08:47
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| import os | |
| import argparse | |
| import caffe | |
| import numpy as np | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--proto_dir', '-i', default='proto', help='Path to directory with prototxt.') | |
| parser.add_argument('--output_dir', '-o', default='output', help='Path to save directory.') | |
| args, _ = parser.parse_known_args() |
l-bat
/ custom_ngraph_layer.cpp
Created
January 16, 2020 13:40
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| #include <iostream> | |
| #include <vector> | |
| #include <random> | |
| #include <inference_engine.hpp> | |
| #include <ngraph/ngraph.hpp> | |
| #include <ngraph/opsets/opset.hpp> | |
| using namespace InferenceEngine; | |
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| class FeatureL2Norm(torch.nn.Module): | |
| def __init__(self): | |
| super(FeatureL2Norm, self).__init__() | |
| def forward(self, feature): | |
| norm = torch.norm(feature, p=2, dim=1, keepdim=True) | |
| return torch.div(feature, norm) |
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| date,price,txCount,txVolume,activeAddresses,symbol,name,open,high,low,close,volume,market | |
| 2013-04-28,135.3,41702.0,68798683.4463,117984.0,BTC,Bitcoin,135.3,135.98,132.1,134.21,0.0,1500520000.0 | |
| 2013-04-28,4.3,9174.0,44319520.0666,17216.0,LTC,Litecoin,4.3,4.4,4.18,4.35,0.0,73773400.0 | |
| 2013-04-29,134.44,51602.0,113812845.38,86925.0,BTC,Bitcoin,134.44,147.49,134.0,144.54,0.0,1491160000.0 | |
| 2013-04-29,4.37,9275.0,36478096.6026,18395.0,LTC,Litecoin,4.37,4.57,4.23,4.38,0.0,74952700.0 | |
| 2013-04-30,144.0,47450.0,84266323.8547,76871.0,BTC,Bitcoin,144.0,146.93,134.05,139.0,0.0,1597780000.0 | |
| 2013-04-30,4.4,9099.0,40391660.1041,17810.0,LTC,Litecoin,4.4,4.57,4.17,4.3,0.0,75726800.0 | |
| 2013-05-01,139.0,55176.0,120682532.517,83564.0,BTC,Bitcoin,139.0,139.89,107.72,116.99,0.0,1542820000.0 | |
| 2013-05-01,4.29,8990.0,76374202.7997,16991.0,LTC,Litecoin,4.29,4.36,3.52,3.8,0.0,73901200.0 | |
| 2013-05-02,116.38,55295.0,93375328.6292,81920.0,BTC,Bitcoin,116.38,125.6,92.28,105.21,0.0,1292190000.0 |
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