Merging

hpvm/projects/keras/README.md

@@ -25,7 +25,7 @@ conda activate keras_python36
 At the root of this project (`/projects/keras/`) install the Keras frontend pip package as:
 
 ```
-pip install -e ./
+pip3 install -e ./
 ```
 
 **NOTE:** If you are using the conda environment, activate it prior to this step.

hpvm/test/dnn_benchmarks/keras/README.md

-# Keras Benchmarks
+# Keras Frontend 
+
+Install Keras Frontend after moving to directory `/hpvm/hpvm/projects/keras`
+
+## Requirements 
+
+* python == 3.6.x
+* pip >= 18
+
+If your system uses a different Python version, we recommend using the conda environment `keras_python36.yml`. Install this using:
+
+```
+conda env create -f keras_python36.yml --name keras_python36
+```
+
+Activate the conda environment before installing the pip package (below) using:
+
+```
+conda activate keras_python36
+```
+
+**NOTE:** This step must be performed each time (for each shell process) the frontend is to be used.
 
 
 ## Installing the Keras Frontend Package
 
-Instructions for Installing the Keras Frontend are [here](https://gitlab.engr.illinois.edu/llvm/hpvm/-/blob/approx_hpvm_reorg_keras/hpvm/projects/keras/README.md)
+At the root of this project (`/projects/keras/`) install the Keras frontend pip package as:
+
+```
+pip3 install -e ./
+```
+
+**NOTE:** If you are using the conda environment, activate it prior to this step.
+
+## Suppported Operations
+
+List of supported operations and limitations detailed in https://gitlab.engr.illinois.edu/llvm/hpvm/-/blob/approx_hpvm_reorg_keras/hpvm/projects/keras/docs/Support.md
+
+
 
 
+
+# Keras Benchmarks
+
+Run the Keras benchmarks under `hpvm/hpvm/test/dnn_benchmarks/keras`
+
 ## Download CNN Model Files 
 
-The weight (model) and data files to use with the CNN benchmarks are hosted on Git LFS and need to separately downloaded. This can be done using:
+Prior to running the benchmarks, ensure you download the CNN model data (inputs and weights) if not done in automatic build script.
 
 ```
-git lfs fetch 
-git lfs checkout 
+wget https://databank.illinois.edu/datafiles/o3izd/download -O model_params.tar.gz
+tar -xf  model_params.tar.gz
 ```
 
-**NOTE:** Data donwload is necesary before running benchmarks
+Move extracted `model_params` directory to `/test/dnn_benchmarks/model_params` (Benchmarks expect data at this location)
+
 
 ## Running Benchmaks
 
@@ -23,22 +62,22 @@ List of benchmarks and the expected accuracies:
 
 | Benchmark       | Accuracy    |
 | ----------- | ----------- |
-| AlexNet-CIFAR10      | 79.28       |
-| AlexNet2-CIFAR10   | 84.98        |
-| AlexNet-ImageNet | 56.30 |
-| LeNet-MNIST | 98.70 | 
-| MobileNet-CIFAR10 | 84.42 |
-| ResNet18-CIFAR10 | 89.56 |
-| ResNet50-ImageNet | 75.10 |
-| VGG16-CIFAR10 | 89.96 |
-| VGG16-CIFAR100 | 66.50 |
-| VGG16-ImageNet | 69.46 |
+| alexnet.py      | 79.28       |
+| alexnet2.py   | 84.98        |
+| alexnet_imagenet.py | 56.30 |
+| lenet.py | 98.70 | 
+| mobilenet_cifar10.py | 84.42 |
+| resnet18_cifar10.py | 89.56 |
+| resnet50_imagenet.py | 75.10 |
+| vgg16_cifar10.py | 89.96 |
+| vgg16_cifar100.py | 66.50 |
+| vgg16_imagenet.py | 69.46 |
 
 
 ### Synopsis
 
 ```
-python ${BENCH_NAME}.py  [hpvm_reload|keras_reload]  [frontend] [compile]
+python3 ${BENCH_NAME}.py  [hpvm_reload|keras_reload]  [frontend] [compile]
 
 ```
 
@@ -111,6 +150,32 @@ These are described here:
 Trains the Keras model constructed in `buildModel` and is expected to return the 
 trained keras model - training parameters should be tuned here.
 
+### Directly using Keras Frontend API
+
+Alternate to extending the `Benchmark` class, users may directly invoke the Keras Frontend API. This can be done as:
+
+```python
+
+from keras_frontend.approxhpvm_translator import translate_to_approxhpvm
+
+# Construct and train your Keras Model (or load pre-trained weights)
+
+translate_to_approxhpvm(model, data_dir, src_dir, test_data, test_labels, tune_data, tune_labels, batch_size, num_classes)
+
+```
+
+## Running HPVM Binary 
+
+Run the `HPVM_binary` generated under the directory specified by `src_dir` (described above). Usage: 
+
+```
+./HPVM_binary -t {test|tune} -c ${config_file_path}
+```
+
+`test|tune`: Runs with either tune (autotuning data) or test set (for evaluation)
+
+`config_file_path`: Path to an HPVM tensor configuration file (includes approximation settings)
+
 
 ## Automated Tests 
 
@@ -121,9 +186,6 @@ python test_benchmarks.py
 ```
 
 
-## Suppported Operations
-
-List of supported operations and limitations detailed in https://gitlab.engr.illinois.edu/llvm/hpvm/-/blob/approx_hpvm_reorg_keras/hpvm/projects/keras/docs/Support.md