Trying to convert emotion detection into TensorRT

I am trying to run the emotion detection on the Jetson Nano

Trying to increase the inference speed of the model by trying to convert it into TENSORRT with the guidance of tf_to_trt_image_classification

I was able to convert fer2013_mini_XCEPTION.102-0.66.hdf5 into checkpoints using this

For Tensorflow 2.x

import tensorflow as tf
from keras.models import load_model

import os
# to use CPU not GPU
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
config = tf.compat.v1.ConfigProto()

session = tf.compat.v1.Session(config=config)
saver = tf.train.Checkpoint()
model = load_model('trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5', compile=False)
sess = tf.compat.v1.keras.backend.get_session()

save_path = saver.save("extractedCheckPoints/fer2013_mini_XCEPTION.102-0.66.ckpt")

source: how to convert hd5 file to ckpt

I have to extract the Graph from the model

I am using this script as guide tf_to_trt_image_classification/scripts/models_to_frozen_graphs.py

This requires information regarding the model that is to be converted for example inception_resnet_v2

source: tf_to_trt_image_classification/scripts/model_meta.py

'inception_resnet_v2': {
        'model': nets.inception.inception_resnet_v2,
        'arg_scope': nets.inception.inception_resnet_v2_arg_scope,
        'num_classes': 1001,
        'input_name': 'input',
        'input_width': 299,
        'input_height': 299,
        'input_channels': 3,
        'output_names': ['InceptionResnetV2/Logits/Logits/BiasAdd'],
        'checkpoint_filename': CHECKPOINT_DIR + 'inception_resnet_v2_2016_08_30.ckpt',
        'frozen_graph_filename': FROZEN_GRAPHS_DIR + 'inception_resnet_v2.pb',
        'preprocess_fn': preprocess_inception,
        'postprocess_fn': postprocess_inception,
        'trt_convert_status': "works",
        'plan_filename': PLAN_DIR + 'inception_resnet_v2.plan'
    },

This is what I was able to extract for emotion

emotion_labels = get_labels('fer2013')
emotion_model_path = 'trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
emotion_classifier = load_model(emotion_model_path, compile=False)
'emotions': {
        'model': emotion_classifier ,
        'arg_scope': emotion_arg_scope, # what should this be changed to
        'num_classes': 7,
        'input_name': 'input', # what should this be changed to
        'output_names': ['InceptionResnetV2/Logits/Logits/BiasAdd'],, # what should this be changed to
        'input_width': 64,
        'input_height': 64,
        'input_channels': 1, 
        'preprocess_fn': preprocess_emotion, #preprocessing 
        'postprocess_fn': postprocess_emotion, #postprocessing
        'checkpoint_filename': CHECKPOINT_DIR + 'emotions.ckpt', 
        'frozen_graph_filename': FROZEN_GRAPHS_DIR + 'emotions.pb',
        'trt_convert_status': "works", # what should this be changed to
        'plan_filename': PLAN_DIR + 'inception_resnet_v2.plan' # what should this be changed to
    }}

This is the content of the inception_resnet_v2_arg_scope source: models/research/slim/nets/inception_resnet_v2.py

def inception_resnet_v2_arg_scope(
    weight_decay=0.00004,
    batch_norm_decay=0.9997,
    batch_norm_epsilon=0.001,
    activation_fn=tf.nn.relu,
    batch_norm_updates_collections=tf.GraphKeys.UPDATE_OPS,
    batch_norm_scale=False):
  """Returns the scope with the default parameters for inception_resnet_v2.
  Args:
    weight_decay: the weight decay for weights variables.
    batch_norm_decay: decay for the moving average of batch_norm momentums.
    batch_norm_epsilon: small float added to variance to avoid dividing by zero.
    activation_fn: Activation function for conv2d.
    batch_norm_updates_collections: Collection for the update ops for
      batch norm.
    batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the
      activations in the batch normalization layer.
  Returns:
    a arg_scope with the parameters needed for inception_resnet_v2.
  """
  # Set weight_decay for weights in conv2d and fully_connected layers.
  with slim.arg_scope([slim.conv2d, slim.fully_connected],
                      weights_regularizer=slim.l2_regularizer(weight_decay),
                      biases_regularizer=slim.l2_regularizer(weight_decay)):

    batch_norm_params = {
        'decay': batch_norm_decay,
        'epsilon': batch_norm_epsilon,
        'updates_collections': batch_norm_updates_collections,
        'fused': None,  # Use fused batch norm if possible.
        'scale': batch_norm_scale,
    }
    # Set activation_fn and parameters for batch_norm.
    with slim.arg_scope([slim.conv2d], activation_fn=activation_fn,
                        normalizer_fn=slim.batch_norm,
                        normalizer_params=batch_norm_params) as scope:
      return scope

Few variables values I would like to know here is

arg_scope This is the content of the inception_resnet_v2_arg_scope what is its equivalent of emotion_arg_scope?
output_names ?

Please guide

oarriaga / face_classification

Trying to convert emotion detection into TensorRT #121