Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Edge TPU inference fix #6686

Merged
merged 4 commits into from
Feb 19, 2022
Merged

Edge TPU inference fix #6686

Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
a2e61ac
refactor: use edgetpu flag
RaffaeleGalliera Feb 18, 2022
1aa8a5c
fix: remove bitwise and assignation to tflite
RaffaeleGalliera Feb 18, 2022
e983a28
Cleanup and fix tflite
glenn-jocher Feb 19, 2022
9ef9484
Cleanup
glenn-jocher Feb 19, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
40 changes: 21 additions & 19 deletions models/common.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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__()
Expand Down Expand Up @@ -367,19 +367,19 @@ def __init__(self, weights='yolov5s.pt', device=None, dnn=False, data=None):

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',
Expand All @@ -391,6 +391,8 @@ def wrap_frozen_graph(gd, inputs, outputs):
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):
Expand Down Expand Up @@ -436,7 +438,7 @@ def forward(self, im, augment=False, visualize=False, val=False):
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:
Expand Down