def test_register_metric_postprocess(self):
import PIL.Image
image = np.array(PIL.Image.open(self.image_path))
resize_image = np.resize(image, (224, 224, 3))
mean = [123.68, 116.78, 103.94]
resize_image = resize_image - mean
images = np.expand_dims(resize_image, axis=0)
labels = [768]
from lpot import Benchmark, Quantization
from lpot.experimental.data.transforms.imagenet_transform import LabelShift
from lpot.experimental.metric.metric import TensorflowTopK
evaluator = Benchmark('fake_yaml.yaml')
evaluator.postprocess('label_benchmark', LabelShift, label_shift=1)
evaluator.metric('topk_benchmark', TensorflowTopK)
dataloader = evaluator.dataloader(dataset=list(zip(images, labels)))
result = evaluator(self.pb_path, b_dataloader=dataloader)
acc, batch_size, result_list = result['accuracy']
self.assertEqual(acc, 0.0)
quantizer = Quantization('fake_yaml.yaml')
quantizer.postprocess('label_quantize', LabelShift, label_shift=1)
quantizer.metric('topk_quantize', TensorflowTopK)
evaluator = Benchmark('fake_yaml.yaml')
evaluator.metric('topk_second', TensorflowTopK)
dataloader = evaluator.dataloader(dataset=list(zip(images, labels)))
result = evaluator(self.pb_path, b_dataloader=dataloader)
acc, batch_size, result_list = result['accuracy']
self.assertEqual(acc, 0.0)
def test_register_metric_postprocess(self):
import PIL.Image
image = np.array(PIL.Image.open(self.image_path))
resize_image = np.resize(image, (224, 224, 3))
mean = [123.68, 116.78, 103.94]
resize_image = resize_image - mean
images = np.expand_dims(resize_image, axis=0)
labels = [768]
from lpot import Benchmark, Quantization
from lpot.experimental.data.transforms.imagenet_transform import LabelShift
from lpot.experimental.metric.metric import TensorflowTopK
evaluator = Benchmark('fake_yaml.yaml')
evaluator.postprocess('label_benchmark', LabelShift, label_shift=1)
evaluator.metric('topk_benchmark', TensorflowTopK)
# as we supported multi instance, the result will print out instead of return
dataloader = evaluator.dataloader(dataset=list(zip(images, labels)))
evaluator(self.pb_path, b_dataloader=dataloader)
quantizer = Quantization('fake_yaml.yaml')
quantizer.postprocess('label_quantize', LabelShift, label_shift=1)
quantizer.metric('topk_quantize', TensorflowTopK)
evaluator = Benchmark('fake_yaml.yaml')
evaluator.metric('topk_second', TensorflowTopK)
dataloader = evaluator.dataloader(dataset=list(zip(images, labels)))
result = evaluator(self.pb_path, b_dataloader=dataloader)
