def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input',
'-i',
required=True,
help='Input image file.')
parser.add_argument('--threshold',
'-t',
type=float,
default=0.1,
help='Classification probability threshold.')
parser.add_argument('--top_k',
'-n',
type=int,
default=5,
help='Max number of returned classes.')
parser.add_argument('--sparse',
'-s',
action='store_true',
default=False,
help='Use sparse tensors.')
parser.add_argument('--model',
'-m',
choices=('squeezenet', 'mobilenet'),
default='mobilenet',
help='Model to run.')
args = parser.parse_args()
# There are two models available for image classification task:
# 1) MobileNet based (image_classification.MOBILENET), which has 59.9% top-1
# accuracy on ImageNet;
# 2) SqueezeNet based (image_classification.SQUEEZENET), which has 45.3% top-1
# accuracy on ImageNet;
model_type = {
'squeezenet': image_classification.SQUEEZENET,
'mobilenet': image_classification.MOBILENET
}[args.model]
with ImageInference(image_classification.model(model_type)) as inference:
image = Image.open(args.input)
if args.sparse:
configs = image_classification.sparse_configs(
top_k=args.top_k,
threshold=args.threshold,
model_type=model_type)
result = inference.run(image, sparse_configs=configs)
classes = image_classification.get_classes_sparse(result)
else:
result = inference.run(image)
classes = image_classification.get_classes(
result, top_k=args.top_k, threshold=args.threshold)
for i, (label, score) in enumerate(classes):
print('Result %d: %s (prob=%f)' % (i, label, score))
def test_image_classification_mobilenet_sparse(self):
sparse_configs = image_classification.sparse_configs(
threshold=CLASSIFICATION_THRESHOLD,
model_type=image_classification.MOBILENET)
avg_end_to_end, avg_bonnet = self.benchmarkModel(
image_classification.model(image_classification.MOBILENET),
image_classification.get_classes_sparse,
sparse_configs=sparse_configs)
self.assertLatency(avg_bonnet, 42.0)
self.assertLatency(avg_end_to_end, 56.0)
def test_image_classification_squeezenet_sparse(self):
sparse_configs = image_classification.sparse_configs(
threshold=CLASSIFICATION_THRESHOLD,
model_type=image_classification.SQUEEZENET)
avg_end_to_end, avg_bonnet = self.benchmarkModel(
image_classification.model(image_classification.SQUEEZENET),
partial(image_classification.get_classes_sparse),
sparse_configs=sparse_configs)
self.assertLatency(avg_bonnet, 183.0)
self.assertLatency(avg_end_to_end, 202.0)
def test_image_classification_mobilenet_sparse(self):
sparse_configs = image_classification.sparse_configs(
threshold=CLASSIFICATION_THRESHOLD,
model_type=image_classification.MOBILENET)
avg_end_to_end, avg_bonnet = self.benchmarkModel(
image_classification.model(image_classification.MOBILENET),
image_classification.get_classes_sparse,
sparse_configs=sparse_configs)
self.assertLatency(avg_bonnet, 42.0)
self.assertLatency(avg_end_to_end, 56.0)
def test_image_classification_squeezenet_sparse(self):
sparse_configs = image_classification.sparse_configs(
threshold=CLASSIFICATION_THRESHOLD,
model_type=image_classification.SQUEEZENET)
avg_end_to_end, avg_bonnet = self.benchmarkModel(
image_classification.model(image_classification.SQUEEZENET),
partial(image_classification.get_classes_sparse),
sparse_configs=sparse_configs)
self.assertLatency(avg_bonnet, 183.0)
self.assertLatency(avg_end_to_end, 202.0)
def test_classification(self):
with TestImage(self.image_file) as image:
with ImageInference(ic.model(self.model_type)) as inference:
if self.sparse:
sparse_configs = ic.sparse_configs(top_k=self.TOP_K,
threshold=self.THRESHOLD,
model_type=self.model_type)
result = inference.run(image, sparse_configs=sparse_configs)
classes = ic.get_classes_sparse(result)
else:
result = inference.run(image)
classes = ic.get_classes(result, top_k=self.TOP_K, threshold=self.THRESHOLD)
self.check(classes)
def main():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--input', '-i', required=True,
help='Input image file.')
parser.add_argument('--threshold', '-t', type=float, default=0.1,
help='Classification probability threshold.')
parser.add_argument('--top_k', '-n', type=int, default=5,
help='Max number of returned classes.')
parser.add_argument('--sparse', '-s', action='store_true', default=False,
help='Use sparse tensors.')
parser.add_argument('--model', '-m', choices=('squeezenet', 'mobilenet'), default='mobilenet',
help='Model to run.')
args = parser.parse_args()
# There are two models available for image classification task:
# 1) MobileNet based (image_classification.MOBILENET), which has 59.9% top-1
# accuracy on ImageNet;
# 2) SqueezeNet based (image_classification.SQUEEZENET), which has 45.3% top-1
# accuracy on ImageNet;
model_type = {'squeezenet': image_classification.SQUEEZENET,
'mobilenet': image_classification.MOBILENET}[args.model]
with ImageInference(image_classification.model(model_type)) as inference:
image = Image.open(args.input)
if args.sparse:
configs = image_classification.sparse_configs(top_k=args.top_k,
threshold=args.threshold,
model_type=model_type)
result = inference.run(image, sparse_configs=configs)
classes = image_classification.get_classes_sparse(result)
else:
result = inference.run(image)
classes = image_classification.get_classes(result,
top_k=args.top_k,
threshold=args.threshold)
for i, (label, score) in enumerate(classes):
print('Result %d: %s (prob=%f)' % (i, label, score))