Edge TPU inference fix (#6686)

* refactor: use edgetpu flag

* fix: remove bitwise and assignation to tflite

* Cleanup and fix tflite

* Cleanup

Co-authored-by: Glenn Jocher <[email protected]>

models/common.py

@@ -279,17 +279,17 @@ class DetectMultiBackend(nn.Module):
     # YOLOv5 MultiBackend class for python inference on various backends
     def __init__(self, weights='yolov5s.pt', device=None, dnn=False, data=None):
         # Usage:
-        #   PyTorch:      weights = *.pt
-        #   TorchScript:            *.torchscript
-        #   CoreML:                 *.mlmodel
-        #   OpenVINO:               *.xml
-        #   TensorFlow:             *_saved_model
-        #   TensorFlow:             *.pb
-        #   TensorFlow Lite:        *.tflite
-        #   TensorFlow Edge TPU:    *_edgetpu.tflite
-        #   ONNX Runtime:           *.onnx
-        #   OpenCV DNN:             *.onnx with dnn=True
-        #   TensorRT:               *.engine
+        #   PyTorch:              weights = *.pt
+        #   TorchScript:                    *.torchscript
+        #   ONNX Runtime:                   *.onnx
+        #   ONNX OpenCV DNN:                *.onnx with --dnn
+        #   OpenVINO:                       *.xml
+        #   CoreML:                         *.mlmodel
+        #   TensorRT:                       *.engine
+        #   TensorFlow SavedModel:          *_saved_model
+        #   TensorFlow GraphDef:            *.pb
+        #   TensorFlow Lite:                *.tflite
+        #   TensorFlow Edge TPU:            *_edgetpu.tflite
         from models.experimental import attempt_download, attempt_load  # scoped to avoid circular import
 
         super().__init__()
@@ -367,19 +367,19 @@ class DetectMultiBackend(nn.Module):
 
                 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))
+                    ge = x.graph.as_graph_element
+                    return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
 
-                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 tflite:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
+                gd = tf.Graph().as_graph_def()  # graph_def
+                gd.ParseFromString(open(w, 'rb').read())
+                frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs="Identity:0")
+            elif tflite or edgetpu:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
                 try:  # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
                     from tflite_runtime.interpreter import Interpreter, load_delegate
                 except ImportError:
                     import tensorflow as tf
                     Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,
-                if 'edgetpu' in w.lower():  # Edge TPU https://coral.ai/software/#edgetpu-runtime
+                if edgetpu:  # Edge TPU https://coral.ai/software/#edgetpu-runtime
                     LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
                     delegate = {'Linux': 'libedgetpu.so.1',
                                 'Darwin': 'libedgetpu.1.dylib',
@@ -391,6 +391,8 @@ class DetectMultiBackend(nn.Module):
                 interpreter.allocate_tensors()  # allocate
                 input_details = interpreter.get_input_details()  # inputs
                 output_details = interpreter.get_output_details()  # outputs
+            elif tfjs:
+                raise Exception('ERROR: YOLOv5 TF.js inference is not supported')
         self.__dict__.update(locals())  # assign all variables to self
 
     def forward(self, im, augment=False, visualize=False, val=False):
@@ -436,7 +438,7 @@ class DetectMultiBackend(nn.Module):
                 y = (self.model(im, training=False) if self.keras else self.model(im)[0]).numpy()
             elif self.pb:  # GraphDef
                 y = self.frozen_func(x=self.tf.constant(im)).numpy()
-            elif self.tflite:  # Lite
+            else:  # Lite or Edge TPU
                 input, output = self.input_details[0], self.output_details[0]
                 int8 = input['dtype'] == np.uint8  # is TFLite quantized uint8 model
                 if int8: