feat(modify code in pytroch): update code

.gitignore

@@ -46,4 +46,6 @@ res.txt
 imagenet_jpg_class_256_crop_224.txt
 *.csv
 __pycache__
-*.txt
+*.txt
+*.bmp
+*.raw

Tools/qnn_generate_raw.py

@@ -0,0 +1,139 @@
+#
+# Copyright (c) 2016,2018-2020 Qualcomm Technologies, Inc.
+# All Rights Reserved.
+# Confidential and Proprietary - Qualcomm Technologies, Inc.
+#
+import argparse
+import numpy as np
+import os
+
+from PIL import Image
+
+RESIZE_METHOD_ANTIALIAS = "antialias"
+RESIZE_METHOD_BILINEAR = "bilinear"
+
+def __get_img_raw(img_filepath):
+ img_filepath = os.path.abspath(img_filepath)
+ img = Image.open(img_filepath)
+ img_ndarray = np.array(img) # read it
+ if len(img_ndarray.shape) != 3:
+ raise RuntimeError('Image shape' + str(img_ndarray.shape))
+ if (img_ndarray.shape[2] != 3):
+ raise RuntimeError('Require image with rgb but channel is %d' % img_ndarray.shape[2])
+ # reverse last dimension: rgb -> bgr
+ return img_ndarray
+
+def __create_mean_raw(img_raw, mean_rgb):
+ if img_raw.shape[2] != 3:
+ raise RuntimeError('Require image with rgb but channel is %d' % img_raw.shape[2])
+ img_dim = (img_raw.shape[0], img_raw.shape[1])
+ mean_raw_r = np.empty(img_dim)
+ mean_raw_r.fill(mean_rgb[0])
+ mean_raw_g = np.empty(img_dim)
+ mean_raw_g.fill(mean_rgb[1])
+ mean_raw_b = np.empty(img_dim)
+ mean_raw_b.fill(mean_rgb[2])
+ # create with c, h, w shape first
+ tmp_transpose_dim = (img_raw.shape[2], img_raw.shape[0], img_raw.shape[1])
+ mean_raw = np.empty(tmp_transpose_dim)
+ mean_raw[0] = mean_raw_r
+ mean_raw[1] = mean_raw_g
+ mean_raw[2] = mean_raw_b
+ # back to h, w, c
+ mean_raw = np.transpose(mean_raw, (1, 2, 0))
+ return mean_raw.astype(np.float32)
+
+def __create_raw_incv3(img_filepath, mean_rgb, div, req_bgr_raw, save_uint8):
+ img_raw = __get_img_raw(img_filepath)
+ mean_raw = __create_mean_raw(img_raw, mean_rgb)
+
+ snpe_raw = img_raw - mean_raw
+ snpe_raw = snpe_raw.astype(np.float32)
+ # scalar data divide
+ snpe_raw /= div
+
+ if req_bgr_raw:
+ snpe_raw = snpe_raw[..., ::-1]
+
+ if save_uint8:
+ snpe_raw = snpe_raw.astype(np.uint8)
+ else:
+ snpe_raw = snpe_raw.astype(np.float32)
+
+ img_filepath = os.path.abspath(img_filepath)
+ filename, ext = os.path.splitext(img_filepath)
+ snpe_raw_filename = filename
+ snpe_raw_filename += '.raw'
+ snpe_raw.tofile(snpe_raw_filename)
+
+ return 0
+
+def __resize_square_to_jpg(src, dst, size,resize_type):
+ src_img = Image.open(src)
+ # If black and white image, convert to rgb (all 3 channels the same)
+ if len(np.shape(src_img)) == 2: src_img = src_img.convert(mode = 'RGB')
+ # center crop to square
+ width, height = src_img.size
+ short_dim = min(height, width)
+ crop_coord = (
+ (width - short_dim) / 2,
+ (height - short_dim) / 2,
+ (width + short_dim) / 2,
+ (height + short_dim) / 2
+ )
+ img = src_img.crop(crop_coord)
+ # resize to inceptionv3 size
+ if resize_type == RESIZE_METHOD_BILINEAR :
+ dst_img = img.resize((size, size), Image.BILINEAR)
+ else :
+ dst_img = img.resize((size, size), Image.ANTIALIAS)
+ # save output - save determined from file extension
+ dst_img.save(dst)
+ return 0
+
+def convert_img(src,dest,size,resize_type):
+ print("Converting images for inception v3 network.")
+
+ print("Scaling to square: " + src)
+ for root, dirs,files in os.walk(src):
+ for jpgs in files:
+ src_image=os.path.join(root, jpgs)
+ if('.jpg' or ".bmp" in src_image):
+ print(src_image)
+ dest_image = os.path.join(dest, jpgs)
+ __resize_square_to_jpg(src_image,dest_image,size,resize_type)
+
+ print("Image mean: " + dest)
+ for root,dirs,files in os.walk(dest):
+ for jpgs in files:
+ src_image=os.path.join(root, jpgs)
+ if('.jpg' or ".bmp" in src_image):
+ print(src_image)
+ mean_rgb=(128,128,128)
+ __create_raw_incv3(src_image,mean_rgb,128,False,False)
+
+
+def main():
+ parser = argparse.ArgumentParser(description="Batch convert jpgs",formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+ parser.add_argument('-d', '--dest',type=str, default="/Users/bruce/PycharmProjects/Pytorch_learning/Tools/qnn_output")
+ parser.add_argument('-s','--size',type=int, default=224)
+ parser.add_argument('-i','--img_folder',type=str, default="/Users/bruce/Downloads/Datasets/calibration_classification_data_bmp_bin_debug")
+ parser.add_argument('-r','--resize_type',type=str, default=RESIZE_METHOD_BILINEAR,
+ help='Select image resize type antialias or bilinear. Image resize type should match '
+ 'resize type used on images with which model was trained, otherwise there may be impact '
+ 'on model accuracy measurement.')
+
+ args = parser.parse_args()
+
+ size = args.size
+ src = os.path.abspath(args.img_folder)
+ dest = os.path.abspath(args.dest)
+ resize_type = args.resize_type
+
+ assert resize_type == RESIZE_METHOD_BILINEAR or resize_type == RESIZE_METHOD_ANTIALIAS, \
+ "Image resize method should be antialias or bilinear"
+
+ convert_img(src,dest,size,resize_type)
+
+if __name__ == '__main__':
+ exit(main())