Add aarch64 python 3.10 build support

Makefile

@@ -16,21 +16,28 @@ PYTHON ?= $(shell which python3)
 MAKEFILE_DIR := $(realpath $(dir $(lastword $(MAKEFILE_LIST))))
 PY3_VER ?= $(shell $(PYTHON) -c "import sys;print('%d%d' % sys.version_info[:2])")
 OS := $(shell uname -s)
+ARCH := $(shell uname -p)
 
 ifeq ($(PYTHON),)
 $(error PYTHON must be set)
 endif
 
-# Allowed CPU values: k8, armv7a, aarch64, darwin
 ifeq ($(OS),Linux)
 CPU ?= k8
 else ifeq ($(OS),Darwin)
+ifeq ($(ARCH),arm)
+CPU ?= darwin_arm64
+DARWIN_CPU := darwin_arm64
+else
 CPU ?= darwin
+DARWIN_CPU := darwin_x86_64
+endif
 else
 $(error $(OS) is not supported)
 endif
-ifeq ($(filter $(CPU),k8 armv7a aarch64 darwin),)
-$(error CPU must be k8, armv7a, aarch64, or darwin)
+
+ifeq ($(filter $(CPU),k8 armv7a aarch64 darwin darwin_arm64),)
+$(error CPU must be k8, armv7a, aarch64, darwin, or darwin_arm64)
 endif
 
 # Allowed COMPILATION_MODE values: opt, dbg, fastbuild
@@ -46,7 +53,7 @@ COMMON_BAZEL_BUILD_FLAGS := --compilation_mode=$(COMPILATION_MODE) \
 
 BAZEL_BUILD_FLAGS_Linux := --linkopt=-L$(MAKEFILE_DIR)/libedgetpu_bin/direct/$(CPU) \
                            --linkopt=-l:libedgetpu.so.1
-BAZEL_BUILD_FLAGS_Darwin := --linkopt=-L$(MAKEFILE_DIR)/libedgetpu_bin/direct/$(CPU) \
+BAZEL_BUILD_FLAGS_Darwin := --linkopt=-L$(MAKEFILE_DIR)/libedgetpu_bin/direct/$(DARWIN_CPU) \
                             --linkopt=-ledgetpu.1
 
 ifeq ($(COMPILATION_MODE), opt)
@@ -72,7 +79,7 @@ else ifeq ($(CPU),armv7a)
 BAZEL_BUILD_FLAGS_Linux += --copt=-ffp-contract=off
 PY_WRAPPER_SUFFIX := arm-linux-gnueabihf.so
 PY_DIST_PLATFORM := linux-armv7l
-else ifeq ($(CPU), darwin)
+else ifeq ($(findstring darwin,$(CPU)),darwin)
 PY_WRAPPER_SUFFIX := darwin.so
 endif
 

examples/classify_image.py

@@ -76,7 +76,7 @@ def main():
     raise ValueError('Only support uint8 input type.')
 
   size = common.input_size(interpreter)
-  image = Image.open(args.input).convert('RGB').resize(size, Image.ANTIALIAS)
+  image = Image.open(args.input).convert('RGB').resize(size, Image.LANCZOS)
 
   # Image data must go through two transforms before running inference:
   # 1. normalization: f = (input - mean) / std

examples/detect_image.py

@@ -75,7 +75,7 @@ def main():
 
   image = Image.open(args.input)
   _, scale = common.set_resized_input(
-      interpreter, image.size, lambda size: image.resize(size, Image.ANTIALIAS))
+      interpreter, image.size, lambda size: image.resize(size, Image.LANCZOS))
 
   print('----INFERENCE TIME----')
   print('Note: The first inference is slow because it includes',

examples/model_pipelining_classify_image.py

@@ -134,7 +134,7 @@ def main():
   size = common.input_size(runner.interpreters()[0])
   name = common.input_details(runner.interpreters()[0], 'name')
   image = np.array(
-      Image.open(args.input).convert('RGB').resize(size, Image.ANTIALIAS))
+      Image.open(args.input).convert('RGB').resize(size, Image.LANCZOS))
 
   def producer():
     for _ in range(args.count):

examples/movenet_pose_estimation.py

@@ -58,7 +58,7 @@ def main():
   interpreter.allocate_tensors()
 
   img = Image.open(args.input)
-  resized_img = img.resize(common.input_size(interpreter), Image.ANTIALIAS)
+  resized_img = img.resize(common.input_size(interpreter), Image.LANCZOS)
   common.set_input(interpreter, resized_img)
 
   interpreter.invoke()

examples/semantic_segmentation.py

@@ -109,9 +109,9 @@ def main():
   img = Image.open(args.input)
   if args.keep_aspect_ratio:
     resized_img, _ = common.set_resized_input(
-        interpreter, img.size, lambda size: img.resize(size, Image.ANTIALIAS))
+        interpreter, img.size, lambda size: img.resize(size, Image.LANCZOS))
   else:
-    resized_img = img.resize((width, height), Image.ANTIALIAS)
+    resized_img = img.resize((width, height), Image.LANCZOS)
     common.set_input(interpreter, resized_img)
 
   interpreter.invoke()

libedgetpu_bin/throttled/darwin_arm64/libedgetpu.1.dylib

@@ -0,0 +1 @@
+libedgetpu.1.0.dylib

libedgetpu_bin/throttled/darwin_x86_64/libedgetpu.1.dylib

@@ -0,0 +1 @@
+libedgetpu.1.0.dylib

pycoral/adapters/detect.py

@@ -234,4 +234,4 @@ def make(i):
         bbox=BBox(xmin=xmin, ymin=ymin, xmax=xmax,
                   ymax=ymax).scale(sx, sy).map(int))
 
-  return [make(i) for i in range(count) if scores[i] >= score_threshold]
+  return [make(i) for i in range(len(scores)) if scores[i] >= score_threshold]

pycoral/pipeline/pipelined_model_runner.py

@@ -80,9 +80,15 @@ def __init__(self, interpreters):
   def __del__(self):
     if self._runner:
       # Push empty request to stop the pipeline in case user forgot.
-      self.push({})
-      num_unconsumed = 0
+      try:
+        print("Push empty request to stop the pipeline...")
+        self.push({})
+      except RuntimeError:
+        print("The pipeline has already been closed successfully.")
+        return
+
       # Release any unconsumed tensors if any.
+      num_unconsumed = 0
       while self.pop():
         num_unconsumed += 1
       if num_unconsumed:

tests/detect_test.py

@@ -33,7 +33,7 @@ def get_objects(model_file, delegate, image_file, score_threshold=0.0):
   interpreter.allocate_tensors()
   image = Image.open(test_utils.test_data_path(image_file))
   _, scale = common.set_resized_input(
-      interpreter, image.size, lambda size: image.resize(size, Image.ANTIALIAS))
+      interpreter, image.size, lambda size: image.resize(size, Image.LANCZOS))
   interpreter.invoke()
   return detect.get_objects(
       interpreter, score_threshold=score_threshold, image_scale=scale)

tests/multiple_tpus_test.py

@@ -64,7 +64,7 @@ def detection_task(num_inferences):
           _, scale = common.set_resized_input(
               interpreter,
               img.size,
-              lambda size, image=img: image.resize(size, Image.ANTIALIAS))
+              lambda size, image=img: image.resize(size, Image.LANCZOS))
           interpreter.invoke()
           ret = detect.get_objects(
               interpreter, score_threshold=0.7, image_scale=scale)

tests/segment_test.py

@@ -47,7 +47,7 @@ def segment_image(model_file, delegate, image_file, mask_file):
   interpreter.allocate_tensors()
 
   image = Image.open(test_utils.test_data_path(image_file)).resize(
-      common.input_size(interpreter), Image.ANTIALIAS)
+      common.input_size(interpreter), Image.LANCZOS)
   common.set_input(interpreter, image)
   interpreter.invoke()