init - 初始化项目

samples/python/tutorial_code/dnn/dnn_conversion/common/test/voc_segm_test.py

@@ -0,0 +1,41 @@
+import numpy as np
+
+from ..accuracy_eval import SemSegmEvaluation
+from ..utils import plot_acc
+
+
+def test_segm_models(models_list, data_fetcher, eval_params, experiment_name, is_print_eval_params=True,
+                     is_plot_acc=True):
+    if is_print_eval_params:
+        print(
+            "===== Running evaluation of the classification models with the following params:\n"
+            "\t0. val data location: {}\n"
+            "\t1. val data labels: {}\n"
+            "\t2. frame size: {}\n"
+            "\t3. batch size: {}\n"
+            "\t4. transform to RGB: {}\n"
+            "\t5. log file location: {}\n".format(
+                eval_params.imgs_segm_dir,
+                eval_params.img_cls_file,
+                eval_params.frame_size,
+                eval_params.batch_size,
+                eval_params.bgr_to_rgb,
+                eval_params.log
+            )
+        )
+
+    accuracy_evaluator = SemSegmEvaluation(eval_params.log, eval_params.img_cls_file, eval_params.batch_size)
+    accuracy_evaluator.process(models_list, data_fetcher)
+    accuracy_array = np.array(accuracy_evaluator.general_fw_accuracy)
+
+    print(
+        "===== End of processing. Accuracy results:\n"
+        "\t1. max accuracy (top-5) for the original model: {}\n"
+        "\t2. max accuracy (top-5) for the DNN model: {}\n".format(
+            max(accuracy_array[:, 0]),
+            max(accuracy_array[:, 1]),
+        )
+    )
+
+    if is_plot_acc:
+        plot_acc(accuracy_array, experiment_name)

samples/python/tutorial_code/dnn/dnn_conversion/pytorch/segmentation/py_to_py_fcn_resnet50.py

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+from torchvision import models
+
+from ..pytorch_model import (
+    PyTorchModelPreparer,
+    PyTorchModelProcessor,
+    PyTorchDnnModelProcessor
+)
+from ...common.utils import set_pytorch_env, create_parser
+
+
+class PyTorchFcnResNet50(PyTorchModelPreparer):
+    def __init__(self, model_name, original_model):
+        super(PyTorchFcnResNet50, self).__init__(model_name, original_model)
+
+
+def main():
+    parser = create_parser()
+    cmd_args = parser.parse_args()
+    set_pytorch_env()
+
+    # Test the base process of model retrieval
+    resnets = PyTorchFcnResNet50(
+        model_name="resnet50",
+        original_model=models.segmentation.fcn_resnet50(pretrained=True)
+    )
+    model_dict = resnets.get_prepared_models()
+
+    if cmd_args.is_evaluate:
+        from ...common.test_config import TestConfig
+        from ...common.accuracy_eval import PASCALDataFetch
+        from ...common.test.voc_segm_test import test_segm_models
+
+        eval_params = TestConfig()
+
+        model_names = list(model_dict.keys())
+        original_model_name = model_names[0]
+        dnn_model_name = model_names[1]
+
+        #img_dir, segm_dir, names_file, segm_cls_colors_file, preproc)
+        data_fetcher = PASCALDataFetch(
+            imgs_dir=eval_params.imgs_segm_dir,
+            frame_size=eval_params.frame_size,
+            bgr_to_rgb=eval_params.bgr_to_rgb,
+
+        )
+
+        test_segm_models(
+            [
+                PyTorchModelProcessor(model_dict[original_model_name], original_model_name),
+                PyTorchDnnModelProcessor(model_dict[dnn_model_name], dnn_model_name)
+            ],
+            data_fetcher,
+            eval_params,
+            original_model_name
+        )
+
+
+if __name__ == "__main__":
+    main()