Quantization tool example bug fix (microsoft#133)

In ResNet50DataReader, it uses an onnx session to obtain the model input shape. However it passes a madeup model name to the onnx session, resulting in file not found error.

This change provide the original float model path to the data reader

Author: chenfucn

quantization/image_classification/cpu/resnet50_data_reader.py

@@ -0,0 +1,61 @@
+import numpy
+import onnxruntime
+import os
+from onnxruntime.quantization import CalibrationDataReader
+from PIL import Image
+
+
+def _preprocess_images(images_folder: str, height: int, width: int, size_limit=0):
+    """
+    Loads a batch of images and preprocess them
+    parameter images_folder: path to folder storing images
+    parameter height: image height in pixels
+    parameter width: image width in pixels
+    parameter size_limit: number of images to load. Default is 0 which means all images are picked.
+    return: list of matrices characterizing multiple images
+    """
+    image_names = os.listdir(images_folder)
+    if size_limit > 0 and len(image_names) >= size_limit:
+        batch_filenames = [image_names[i] for i in range(size_limit)]
+    else:
+        batch_filenames = image_names
+    unconcatenated_batch_data = []
+
+    for image_name in batch_filenames:
+        image_filepath = images_folder + "/" + image_name
+        pillow_img = Image.new("RGB", (width, height))
+        pillow_img.paste(Image.open(image_filepath).resize((width, height)))
+        input_data = numpy.float32(pillow_img) - numpy.array(
+            [123.68, 116.78, 103.94], dtype=numpy.float32
+        )
+        nhwc_data = numpy.expand_dims(input_data, axis=0)
+        nchw_data = nhwc_data.transpose(0, 3, 1, 2)  # ONNX Runtime standard
+        unconcatenated_batch_data.append(nchw_data)
+    batch_data = numpy.concatenate(
+        numpy.expand_dims(unconcatenated_batch_data, axis=0), axis=0
+    )
+    return batch_data
+
+
+class ResNet50DataReader(CalibrationDataReader):
+    def __init__(self, calibration_image_folder: str, model_path: str):
+        self.image_folder = calibration_image_folder
+        self.model_path = model_path
+        self.preprocess_flag = True
+        self.enum_data_dicts = []
+        self.datasize = 0
+
+    def get_next(self):
+        if self.preprocess_flag:
+            self.preprocess_flag = False
+            session = onnxruntime.InferenceSession(self.model_path, None)
+            (_, _, height, width) = session.get_inputs()[0].shape
+            nhwc_data_list = _preprocess_images(
+                self.image_folder, height, width, size_limit=0
+            )
+            input_name = session.get_inputs()[0].name
+            self.datasize = len(nhwc_data_list)
+            self.enum_data_dicts = iter(
+                [{input_name: nhwc_data} for nhwc_data in nhwc_data_list]
+            )
+        return next(self.enum_data_dicts, None)

quantization/image_classification/cpu/run.py

@@ -1,67 +1,10 @@
-import os
-import sys
-import numpy as np
-import re
-import abc
-import subprocess
-import json
 import argparse
-import time
-from PIL import Image
-
-import onnx
+import numpy as np
 import onnxruntime
-from onnx import helper, TensorProto, numpy_helper
-from onnxruntime.quantization import quantize_static, CalibrationDataReader, QuantFormat, QuantType
-
-
-class ResNet50DataReader(CalibrationDataReader):
-    def __init__(self, calibration_image_folder, augmented_model_path='augmented_model.onnx'):
-        self.image_folder = calibration_image_folder
-        self.augmented_model_path = augmented_model_path
-        self.preprocess_flag = True
-        self.enum_data_dicts = []
-        self.datasize = 0
-
-    def get_next(self):
-        if self.preprocess_flag:
-            self.preprocess_flag = False
-            session = onnxruntime.InferenceSession(self.augmented_model_path, None)
-            (_, _, height, width) = session.get_inputs()[0].shape
-            nhwc_data_list = preprocess_func(self.image_folder, height, width, size_limit=0)
-            input_name = session.get_inputs()[0].name
-            self.datasize = len(nhwc_data_list)
-            self.enum_data_dicts = iter([{input_name: nhwc_data} for nhwc_data in nhwc_data_list])
-        return next(self.enum_data_dicts, None)
-
-
-def preprocess_func(images_folder, height, width, size_limit=0):
-    '''
-    Loads a batch of images and preprocess them
-    parameter images_folder: path to folder storing images
-    parameter height: image height in pixels
-    parameter width: image width in pixels
-    parameter size_limit: number of images to load. Default is 0 which means all images are picked.
-    return: list of matrices characterizing multiple images
-    '''
-    image_names = os.listdir(images_folder)
-    if size_limit > 0 and len(image_names) >= size_limit:
-        batch_filenames = [image_names[i] for i in range(size_limit)]
-    else:
-        batch_filenames = image_names
-    unconcatenated_batch_data = []
+import time
+from onnxruntime.quantization import QuantFormat, QuantType, quantize_static
 
-    for image_name in batch_filenames:
-        image_filepath = images_folder + '/' + image_name
-        pillow_img = Image.new("RGB", (width, height))
-        pillow_img.paste(Image.open(image_filepath).resize((width, height)))
-        input_data = np.float32(pillow_img) - \
-        np.array([123.68, 116.78, 103.94], dtype=np.float32)
-        nhwc_data = np.expand_dims(input_data, axis=0)
-        nchw_data = nhwc_data.transpose(0, 3, 1, 2)  # ONNX Runtime standard
-        unconcatenated_batch_data.append(nchw_data)
-    batch_data = np.concatenate(np.expand_dims(unconcatenated_batch_data, axis=0), axis=0)
-    return batch_data
+import resnet50_data_reader
 
 
 def benchmark(model_path):
@@ -87,11 +30,15 @@ def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument("--input_model", required=True, help="input model")
     parser.add_argument("--output_model", required=True, help="output model")
-    parser.add_argument("--calibrate_dataset", default="./test_images", help="calibration data set")
-    parser.add_argument("--quant_format",
-                        default=QuantFormat.QDQ,
-                        type=QuantFormat.from_string,
-                        choices=list(QuantFormat))
+    parser.add_argument(
+        "--calibrate_dataset", default="./test_images", help="calibration data set"
+    )
+    parser.add_argument(
+        "--quant_format",
+        default=QuantFormat.QDQ,
+        type=QuantFormat.from_string,
+        choices=list(QuantFormat),
+    )
     parser.add_argument("--per_channel", default=False, type=bool)
     args = parser.parse_args()
     return args
@@ -102,21 +49,25 @@ def main():
     input_model_path = args.input_model
     output_model_path = args.output_model
     calibration_dataset_path = args.calibrate_dataset
-    dr = ResNet50DataReader(calibration_dataset_path)
-    quantize_static(input_model_path,
-                    output_model_path,
-                    dr,
-                    quant_format=args.quant_format,
-                    per_channel=args.per_channel,
-                    weight_type=QuantType.QInt8)
-    print('Calibrated and quantized model saved.')
-
-    print('benchmarking fp32 model...')
+    dr = resnet50_data_reader.ResNet50DataReader(
+        calibration_dataset_path, input_model_path
+    )
+    quantize_static(
+        input_model_path,
+        output_model_path,
+        dr,
+        quant_format=args.quant_format,
+        per_channel=args.per_channel,
+        weight_type=QuantType.QInt8,
+    )
+    print("Calibrated and quantized model saved.")
+
+    print("benchmarking fp32 model...")
     benchmark(input_model_path)
 
-    print('benchmarking int8 model...')
+    print("benchmarking int8 model...")
     benchmark(output_model_path)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()