update script

Author: chilo-ms

quantization/image_classification/trt/resnet50/e2e_tensorrt_resnet_example.py

@@ -9,6 +9,7 @@
 import onnxruntime
 from onnxruntime.quantization import CalibrationDataReader, create_calibrator, write_calibration_table
 
+# onnxruntime.set_default_logger_severity(0)
 
 class ImageNetDataReader(CalibrationDataReader):
     def __init__(self,
@@ -126,12 +127,13 @@ def preprocess_imagenet(self, images_folder, height, width, start_index=0, size_
         return: list of matrices characterizing multiple images
         '''
         def preprocess_images(input, channels=3, height=224, width=224):
-            image = input.resize((width, height), Image.ANTIALIAS)
+            image = input.resize((width, height), Image.Resampling.LANCZOS) # Image.ANTIALIAS was removed in Pillow 10.0.0
             input_data = np.asarray(image).astype(np.float32)
             if len(input_data.shape) != 2:
                 input_data = input_data.transpose([2, 0, 1])
             else:
                 input_data = np.stack([input_data] * 3)
+            # image normalization
             mean = np.array([0.079, 0.05, 0]) + 0.406
             std = np.array([0.005, 0, 0.001]) + 0.224
             for channel in range(input_data.shape[0]):
@@ -153,7 +155,8 @@ def preprocess_images(input, channels=3, height=224, width=224):
 
         for image_name in batch_filenames:
             image_filepath = images_folder + '/' + image_name
-            img = Image.open(image_filepath)
+            # Note: There is one image ILSVRC2012_val_00019877.JPEG which has 4 channels, so here we convert it to RGB with 3 channels for all images
+            img = Image.open(image_filepath).convert("RGB")
             image_data = preprocess_images(img)
             image_data = np.expand_dims(image_data, 0)
             unconcatenated_batch_data.append(image_data)
@@ -163,7 +166,7 @@ def preprocess_images(input, channels=3, height=224, width=224):
         return batch_data, batch_filenames, image_size_list
 
     def get_synset_id(self, image_folder, offset, dataset_size):
-        ilsvrc2012_meta = scipy.io.loadmat(image_folder + "/devkit/data/meta.mat")
+        ilsvrc2012_meta = scipy.io.loadmat(image_folder + "/ILSVRC2012_devkit_t12/data/meta.mat")
         id_to_synset = {}
         for i in range(1000):
             id = int(ilsvrc2012_meta["synsets"][i, 0][0][0][0])
@@ -178,7 +181,7 @@ def get_synset_id(self, image_folder, offset, dataset_size):
             index = index + 1
         file.close()
 
-        file = open(image_folder + "/devkit/data/ILSVRC2012_validation_ground_truth.txt", "r")
+        file = open(image_folder + "/ILSVRC2012_devkit_t12/data/ILSVRC2012_validation_ground_truth.txt", "r")
         id = file.read().strip().split("\n")
         id = list(map(int, id))
         file.close()
@@ -318,11 +321,14 @@ def get_dataset_size(dataset_path, calibration_dataset_size):
     calibration_table_generation_enable = True  # Enable/Disable INT8 calibration
 
     # TensorRT EP INT8 settings
-    os.environ["ORT_TENSORRT_FP16_ENABLE"] = "1"  # Enable FP16 precision
-    os.environ["ORT_TENSORRT_INT8_ENABLE"] = "1"  # Enable INT8 precision
-    os.environ["ORT_TENSORRT_INT8_CALIBRATION_TABLE_NAME"] = "calibration.flatbuffers"  # Calibration table name
-    os.environ["ORT_TENSORRT_ENGINE_CACHE_ENABLE"] = "1"  # Enable engine caching
-    execution_provider = ["TensorrtExecutionProvider"]
+    execution_provider = [
+        ('TensorrtExecutionProvider', {
+            'trt_int8_enable': True,
+            'trt_fp16_enable': True,
+            'trt_engine_cache_enable': True,
+            'trt_int8_calibration_table_name': 'calibration.flatbuffers', # The implicit quantization is deprecated in TRT 10
+        })
+    ]
 
     # Convert static batch to dynamic batch
     [new_model_path, input_name] = convert_model_batch_to_dynamic(model_path)
@@ -343,7 +349,7 @@ def get_dataset_size(dataset_path, calibration_dataset_size):
                                          model_path=augmented_model_path,
                                          input_name=input_name)
         calibrator.collect_data(data_reader)
-        write_calibration_table(calibrator.compute_range())
+        write_calibration_table(calibrator.compute_data())
 
     # Run prediction in Tensorrt EP
     data_reader = ImageNetDataReader(ilsvrc2012_dataset_path,