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Scope TF imports in DetectMultiBackend() #5792

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Merged
glenn-jocher merged 4 commits into from
Nov 25, 2021
Merged

Scope TF imports in DetectMultiBackend() #5792

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d16c89b
tensorflow or tflite exclusively as interpreter
phodgers Nov 25, 2021
5d85639
Scope imports
glenn-jocher Nov 25, 2021
276f64b
Nested definition line fix
glenn-jocher Nov 25, 2021
994c22a
Update common.py
glenn-jocher Nov 25, 2021
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7 changes: 5 additions & 2 deletions models/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -337,19 +337,21 @@ def __init__(self, weights='yolov5s.pt', device=None, dnn=True):
context = model.create_execution_context()
batch_size = bindings['images'].shape[0]
else: # TensorFlow model (TFLite, pb, saved_model)
import tensorflow as tf
if pb: # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
LOGGER.info(f'Loading {w} for TensorFlow *.pb inference...')
import tensorflow as tf

def wrap_frozen_graph(gd, inputs, outputs):
x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped
return x.prune(tf.nest.map_structure(x.graph.as_graph_element, inputs),
tf.nest.map_structure(x.graph.as_graph_element, outputs))

LOGGER.info(f'Loading {w} for TensorFlow *.pb inference...')
graph_def = tf.Graph().as_graph_def()
graph_def.ParseFromString(open(w, 'rb').read())
frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0")
elif saved_model:
LOGGER.info(f'Loading {w} for TensorFlow saved_model inference...')
import tensorflow as tf
model = tf.keras.models.load_model(w)
elif tflite: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
if 'edgetpu' in w.lower():
Expand All @@ -361,6 +363,7 @@ def wrap_frozen_graph(gd, inputs, outputs):
interpreter = tfli.Interpreter(model_path=w, experimental_delegates=[tfli.load_delegate(delegate)])
else:
LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
import tensorflow as tf
interpreter = tf.lite.Interpreter(model_path=w) # load TFLite model
interpreter.allocate_tensors() # allocate
input_details = interpreter.get_input_details() # inputs
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