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init - 初始化项目

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Lee Nony
2022-05-06 01:58:53 +08:00
commit 90a5cc7cb6
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60
samples/dnn/dnn_model_runner/dnn_conversion/common/test/cls_model_test_pipeline.py Normal file
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from .configs.test_config import TestClsConfig, TestClsModuleConfig
from .model_test_pipeline import ModelTestPipeline
from ..evaluation.classification.cls_accuracy_evaluator import ClsAccEvaluation
from ..utils import get_test_module
class ClsModelTestPipeline(ModelTestPipeline):
def __init__(
self,
network_model,
model_processor,
dnn_model_processor,
data_fetcher,
img_processor=None,
cls_args_parser=None,
default_input_blob_preproc=None
):
super(ClsModelTestPipeline, self).__init__(
network_model,
model_processor,
dnn_model_processor
)
if cls_args_parser:
self._parser = cls_args_parser
self.test_config = TestClsConfig()
parser_args = self._parser.parse_args()
if parser_args.test:
self._test_module_config = TestClsModuleConfig()
self._test_module = get_test_module(
self._test_module_config.test_module_name,
self._test_module_config.test_module_path
)
if parser_args.default_img_preprocess:
self._default_input_blob_preproc = default_input_blob_preproc
if parser_args.evaluate:
self._data_fetcher = data_fetcher(self.test_config, img_processor)
def _configure_test_module_params(self):
self._test_module_param_list.extend((
'--crop', self._test_module_config.crop,
'--std', *self._test_module_config.std
))
if self._test_module_config.rsz_height and self._test_module_config.rsz_width:
self._test_module_param_list.extend((
'--initial_height', self._test_module_config.rsz_height,
'--initial_width', self._test_module_config.rsz_width,
))
def _configure_acc_eval(self, log_path):
self._accuracy_evaluator = ClsAccEvaluation(
log_path,
self.test_config.img_cls_file,
self.test_config.batch_size
)

37
samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/default_preprocess_config.py Normal file
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BASE_IMG_SCALE_FACTOR = 1 / 255.0
PYTORCH_RSZ_HEIGHT = 256
PYTORCH_RSZ_WIDTH = 256
pytorch_resize_input_blob = {
"mean": ["123.675", "116.28", "103.53"],
"scale": str(BASE_IMG_SCALE_FACTOR),
"std": ["0.229", "0.224", "0.225"],
"crop": "True",
"rgb": True,
"rsz_height": str(PYTORCH_RSZ_HEIGHT),
"rsz_width": str(PYTORCH_RSZ_WIDTH)
}
pytorch_input_blob = {
"mean": ["123.675", "116.28", "103.53"],
"scale": str(BASE_IMG_SCALE_FACTOR),
"std": ["0.229", "0.224", "0.225"],
"crop": "True",
"rgb": True
}
tf_input_blob = {
"scale": str(1 / 127.5),
"mean": ["127.5", "127.5", "127.5"],
"std": [],
"crop": "True",
"rgb": True
}
tf_model_blob_caffe_mode = {
"mean": ["103.939", "116.779", "123.68"],
"scale": "1.0",
"std": [],
"crop": "True",
"rgb": False
}

40
samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py Normal file
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import os
from dataclasses import dataclass, field
from typing import List
@dataclass
class CommonConfig:
output_data_root_dir: str = "dnn_model_runner/dnn_conversion"
logs_dir: str = os.path.join(output_data_root_dir, "logs")
log_file_path: str = os.path.join(logs_dir, "{}_log.txt")
@dataclass
class TestClsConfig:
batch_size: int = 1
frame_size: int = 224
img_root_dir: str = "./ILSVRC2012_img_val"
# location of image-class matching
img_cls_file: str = "./val.txt"
bgr_to_rgb: bool = True
@dataclass
class TestClsModuleConfig:
cls_test_data_dir: str = "../data"
test_module_name: str = "classification"
test_module_path: str = "classification.py"
input_img: str = os.path.join(cls_test_data_dir, "squirrel_cls.jpg")
model: str = ""
frame_height: str = str(TestClsConfig.frame_size)
frame_width: str = str(TestClsConfig.frame_size)
scale: str = "1.0"
mean: List[str] = field(default_factory=lambda: ["0.0", "0.0", "0.0"])
std: List[str] = field(default_factory=list)
crop: str = "False"
rgb: str = "True"
rsz_height: str = ""
rsz_width: str = ""
classes: str = os.path.join(cls_test_data_dir, "dnn", "classification_classes_ILSVRC2012.txt")

126
samples/dnn/dnn_model_runner/dnn_conversion/common/test/model_test_pipeline.py Normal file
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import os
import numpy as np
from .configs.test_config import CommonConfig
from ..utils import create_parser, plot_acc
class ModelTestPipeline:
def __init__(
self,
network_model,
model_processor,
dnn_model_processor
):
self._net_model = network_model
self._model_processor = model_processor
self._dnn_model_processor = dnn_model_processor
self._parser = create_parser()
self._test_module = None
self._test_module_config = None
self._test_module_param_list = None
self.test_config = None
self._data_fetcher = None
self._default_input_blob_preproc = None
self._accuracy_evaluator = None
def init_test_pipeline(self):
cmd_args = self._parser.parse_args()
model_dict = self._net_model.get_prepared_models()
model_names = list(model_dict.keys())
print(
"The model {} was successfully obtained and converted to OpenCV {}".format(model_names[0], model_names[1])
)
if cmd_args.test:
if not self._test_module_config.model:
self._test_module_config.model = self._net_model.model_path["full_path"]
if cmd_args.default_img_preprocess:
self._test_module_config.scale = self._default_input_blob_preproc["scale"]
self._test_module_config.mean = self._default_input_blob_preproc["mean"]
self._test_module_config.std = self._default_input_blob_preproc["std"]
self._test_module_config.crop = self._default_input_blob_preproc["crop"]
if "rsz_height" in self._default_input_blob_preproc and "rsz_width" in self._default_input_blob_preproc:
self._test_module_config.rsz_height = self._default_input_blob_preproc["rsz_height"]
self._test_module_config.rsz_width = self._default_input_blob_preproc["rsz_width"]
self._test_module_param_list = [
'--model', self._test_module_config.model,
'--input', self._test_module_config.input_img,
'--width', self._test_module_config.frame_width,
'--height', self._test_module_config.frame_height,
'--scale', self._test_module_config.scale,
'--mean', *self._test_module_config.mean,
'--std', *self._test_module_config.std,
'--classes', self._test_module_config.classes,
]
if self._default_input_blob_preproc["rgb"]:
self._test_module_param_list.append('--rgb')
self._configure_test_module_params()
self._test_module.main(
self._test_module_param_list
)
if cmd_args.evaluate:
original_model_name = model_names[0]
dnn_model_name = model_names[1]
self.run_test_pipeline(
[
self._model_processor(model_dict[original_model_name], original_model_name),
self._dnn_model_processor(model_dict[dnn_model_name], dnn_model_name)
],
original_model_name.replace(" ", "_")
)
def run_test_pipeline(
self,
models_list,
formatted_exp_name,
is_plot_acc=True
):
log_path, logs_dir = self._configure_eval_log(formatted_exp_name)
print(
"===== Running evaluation of the model with the following params:\n"
"\t* val data location: {}\n"
"\t* log file location: {}\n".format(
self.test_config.img_root_dir,
log_path
)
)
os.makedirs(logs_dir, exist_ok=True)
self._configure_acc_eval(log_path)
self._accuracy_evaluator.process(models_list, self._data_fetcher)
if is_plot_acc:
plot_acc(
np.array(self._accuracy_evaluator.general_inference_time),
formatted_exp_name
)
print("===== End of the evaluation pipeline =====")
def _configure_acc_eval(self, log_path):
pass
def _configure_test_module_params(self):
pass
@staticmethod
def _configure_eval_log(formatted_exp_name):
common_test_config = CommonConfig()
return common_test_config.log_file_path.format(formatted_exp_name), common_test_config.logs_dir