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Update README.md

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Micaela Verucchi
2020-03-29 20:07:27 +02:00
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@@ -38,7 +38,7 @@ If TEST_DATA is not set to False, weights needed to run some tests will be autom
## Workflow
Steps needed to do inference on tkDNN with a custom neural network.
* Build and train a NN model with your favourite framework.
* Build and train a NN model with your favorite framework.
* Export weights and bias for each layer and save them in a binary file (one for layer).
* Export outputs for each layer and save them in a binary file (one for layer).
* Create a new test and define the network, layer by layer using the weights extracted and the output to check the results.
@@ -53,10 +53,10 @@ Weights are essential for any network to run inference. For each test a folder o
|---- layers/ (folder containing a binary file for each layer with the corresponding wieghts and bias)
|---- debug/ (folder containing a binary file for each layer with the corresponding outputs)
```
Therefore, once the weights have been exported, the folders layers ans debug should be placed in the corresponding test.
Therefore, once the weights have been exported, the folders layers and debug should be placed in the corresponding test.
### 1)Export weights from darknet
To export weights for NN that are defined in darknet framework, use [this](https://github.com/ceccocats/darknet) fork of darknet and follow these step to obtain a correct debug and layers folder, ready for tkDNN.
To export weights for NNs that are defined in darknet framework, use [this](https://github.com/ceccocats/darknet) fork of darknet and follow these steps to obtain a correct debug and layers folder, ready for tkDNN.
```
git clone https://github.com/ceccocats/darknet
@@ -89,7 +89,7 @@ python demo.py --input_res 512 --arch dla_34 ctdet --demo /path/to/image/or/fold
```
### 4)Export weights for MobileNetSSD
To get the weights needed to run Mobilenet tests use [this](https://github.com/mive93/pytorch-ssd) fork of the a Pytorch implementation of SSD network.
To get the weights needed to run Mobilenet tests use [this](https://github.com/mive93/pytorch-ssd) fork of a Pytorch implementation of SSD network.
```
git clone https://github.com/mive93/pytorch-ssd
@@ -130,9 +130,9 @@ To compute the map, the following parameters are needed:
./map_demo <network rt> <network type [y|c|m]> <labels file path> <config file path>
```
where
* ```<network rt>```: rt file of a choosen network on wich compute the mAP.
* ```<network rt>```: rt file of a chosen network on which compute the mAP.
* ```<network type [y|c|m]>```: type of network. Right now only y(yolo), c(centernet) and m(mobilenet) are allowed
* ```<labels file path>```: path to a text file containing all the paths of the groundtruth labels. It is important that all the labels of the groundtruth are in a folder called 'labels'. In the folder containing the folder 'labels' there should be also a folder 'images', containing all the groundtruth images having the same same as the labels. To better understand, if there is a label path/to/labels/000001.txt there should be a corresponding image path/to/images/000001.jpg.
* ```<labels file path>```: path to a text file containing all the paths of the ground-truth labels. It is important that all the labels of the ground-truth are in a folder called 'labels'. In the folder containing the folder 'labels' there should be also a folder 'images', containing all the ground-truth images having the same same as the labels. To better understand, if there is a label path/to/labels/000001.txt there should be a corresponding image path/to/images/000001.jpg.
* ```<config file path>```: path to a yaml file with the parameters needed for the mAP computation, similar to demo/config.yaml
Example: