diff --git a/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_EfficientDet_Models.md b/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_EfficientDet_Models.md index 7c28307fa4e..7ae4386dbd9 100644 --- a/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_EfficientDet_Models.md +++ b/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_EfficientDet_Models.md @@ -8,48 +8,20 @@ There are several public versions of EfficientDet model implementation available convert models from the [repository](https://github.com/google/automl/tree/master/efficientdet) (commit 96e1fee) to the OpenVINO format. -### Getting a Frozen TensorFlow Model +Download and extract the model checkpoint [efficientdet-d4.tar.gz](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d4.tar.gz) referenced in the **Pretrained EfficientDet Checkpoints** section of the model repository: -Follow the instructions below to get frozen TensorFlow EfficientDet model. EfficientDet-D4 model is an example: - -1. Clone the repository:
-```sh -git clone https://github.com/google/automl -cd automl/efficientdet -``` -2. (Optional) Checkout to the commit that the conversion was tested on:
-```sh -git checkout 96e1fee -``` -3. Install required dependencies:
-```sh -python3 -m pip install --upgrade pip -python3 -m pip install -r requirements.txt -python3 -m pip install --upgrade tensorflow-model-optimization -``` -4. Download and extract the model checkpoint [efficientdet-d4.tar.gz](https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d4.tar.gz) -referenced in the **"Pretrained EfficientDet Checkpoints"** section of the model repository:
```sh wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/efficientdet-d4.tar.gz tar zxvf efficientdet-d4.tar.gz ``` -5. Freeze the model:
-```sh - mo --runmode=saved_model --model_name=efficientdet-d4 --ckpt_path=efficientdet-d4 --saved_model_dir=savedmodeldir -``` -As a result, the frozen model file `savedmodeldir/efficientdet-d4_frozen.pb` will be generated. - -> **NOTE**: For custom trained models, specify `--hparams` flag to `config.yaml` which was used during training. - -> **NOTE**: If you see an error *AttributeError: module 'tensorflow_core.python.keras.api._v2.keras.initializers' has no attribute 'variance_scaling'*, apply the fix from the [patch](https://github.com/google/automl/pull/846). ### Converting an EfficientDet TensorFlow Model to the IR To generate the IR of the EfficientDet TensorFlow model, run:
+ ```sh mo \ ---input_model savedmodeldir/efficientdet-d4_frozen.pb \ ---transformations_config front/tf/automl_efficientdet.json \ +--input_meta_graph efficientdet-d4/model.meta \ --input_shape [1,$IMAGE_SIZE,$IMAGE_SIZE,3] \ --reverse_input_channels ``` @@ -59,12 +31,6 @@ EfficientDet models were trained with different input image sizes. To determine dictionary in the [hparams_config.py](https://github.com/google/automl/blob/96e1fee/efficientdet/hparams_config.py#L304) file. The attribute `image_size` specifies the shape to be defined for the model conversion. -The `transformations_config` command line parameter specifies the configuration json file containing hints -for the Model Optimizer on how to convert the model and trigger transformations implemented in the -`/openvino/tools/mo/front/tf/AutomlEfficientDet.py`. The json file contains some parameters which must be changed if you -train the model yourself and modified the `hparams_config` file or the parameters are different from the ones used for EfficientDet-D4. -The attribute names are self-explanatory or match the name in the `hparams_config` file. - > **NOTE**: The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For more information about the parameter, refer to the **When to Reverse Input Channels** section of the [Converting a Model to Intermediate Representation (IR)](@ref openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model) guide. OpenVINO toolkit provides samples that can be used to infer EfficientDet model. @@ -73,21 +39,21 @@ For more information, refer to the [Open Model Zoo Demos](@ref omz_demos). ## Interpreting Results of the TensorFlow Model and the IR The TensorFlow model produces as output a list of 7-element tuples: `[image_id, y_min, x_min, y_max, x_max, confidence, class_id]`, where: -* `image_id` -- image batch index. -* `y_min` -- absolute `y` coordinate of the lower left corner of the detected object. -* `x_min` -- absolute `x` coordinate of the lower left corner of the detected object. -* `y_max` -- absolute `y` coordinate of the upper right corner of the detected object. -* `x_max` -- absolute `x` coordinate of the upper right corner of the detected object. -* `confidence` -- is the confidence of the detected object. -* `class_id` -- is the id of the detected object class counted from 1. +* `image_id` - image batch index. +* `y_min` - absolute `y` coordinate of the lower left corner of the detected object. +* `x_min` - absolute `x` coordinate of the lower left corner of the detected object. +* `y_max` - absolute `y` coordinate of the upper right corner of the detected object. +* `x_max` - absolute `x` coordinate of the upper right corner of the detected object. +* `confidence` - is the confidence of the detected object. +* `class_id` - is the id of the detected object class counted from 1. The output of the IR is a list of 7-element tuples: `[image_id, class_id, confidence, x_min, y_min, x_max, y_max]`, where: -* `image_id` -- image batch index. -* `class_id` -- is the id of the detected object class counted from 0. -* `confidence` -- is the confidence of the detected object. -* `x_min` -- normalized `x` coordinate of the lower left corner of the detected object. -* `y_min` -- normalized `y` coordinate of the lower left corner of the detected object. -* `x_max` -- normalized `x` coordinate of the upper right corner of the detected object. -* `y_max` -- normalized `y` coordinate of the upper right corner of the detected object. +* `image_id` - image batch index. +* `class_id` - is the id of the detected object class counted from 0. +* `confidence` - is the confidence of the detected object. +* `x_min` - normalized `x` coordinate of the lower left corner of the detected object. +* `y_min` - normalized `y` coordinate of the lower left corner of the detected object. +* `x_max` - normalized `x` coordinate of the upper right corner of the detected object. +* `y_max` - normalized `y` coordinate of the upper right corner of the detected object. The first element with `image_id = -1` means end of data.