README.md

EfficientNet Model Inference

EfficientNet Model Inference using Intel® Extension for TensorFlow.

Model Information

Use Case	Framework	Model Repo	Branch/Commit/Tag	Optional Patch
Inference	TensorFlow	-	-	-

Note: Refer to CONTAINER.md for EfficientNet instructions using docker containers.

Pre-Requisite

• Host has Intel® Data Center GPU Flex Series
• Host has installed latest Intel® Data Center GPU Flex Series Driver https://dgpu-docs.intel.com/driver/installation.html
• Install Intel® Extension for TensorFlow

Run the model

1. git clone https://github.com/IntelAI/models.git
2. cd models/models_v2/tensorflow/efficientnet/inference/gpu
3. Create virtual environment venv and activate it:

   python3 -m venv venv
   . ./venv/bin/activate
   

4. Run setup.sh

   ./setup.sh
   

5. Install tensorflow and ITEX
6. Setup required environment paramaters (The precision is fp16 for all the efficientnet models).

Parameter	export command
MODEL_NAME	export MODEL_NAME=EfficientNetB0 (EfficientNetB0, EfficientNetB3 or EfficientNetB4)
BATCH_SIZE (optional)	export BATCH_SIZE=128
OUTPUT_DIR	export OUTPUT_DIR=<path to output log files>

7. Run run_model.sh

Output

Output will typically look like:

load data ......
input shape (128, 224, 224, 3)
Creating model finished.
Batchsize is 128
Avg time: 0.0484589417775472 s.
Throughput: 2641.4113743463354 img/s.

Final results of the training run can be found in results.yaml file.

results:
 - key: throughput
   value: 2641.4113743463354
   unit: img/s