Vitis-AI 的量化实例

量化的两个文件

input_fn.py

# from resnet_v1_50_preprocessing import *

# def eval_input(iter, eval_image_dir, eval_image_list, class_num, eval_batch_size):
  # images = []
  # labels = []
  # line = open(eval_image_list).readlines()
  # for index in range(0, eval_batch_size):
    # curline = line[iter * eval_batch_size + index]
    # [image_name, label_id] = curline.split(' ')
    # image = cv2.imread(eval_image_dir + image_name)
    # image = central_crop(image, 224, 224)
    # image = mean_image_subtraction(image, MEANS)
    # images.append(image)
    # labels.append(int(label_id))
  # lb = preprocessing.LabelBinarizer()
  # lb.fit(range(0, class_num))
  # labels = lb.transform(labels)
  # return {"input": images, "labels": labels}

import cv2

def mean_image_subtraction(image, means):
  B, G, R = cv2.split(image)
  B = B - means[0]
  G = G - means[1]
  R = R - means[2]
  image = cv2.merge([R, G, B])
  return image

def BGR2RGB(image):
  B, G, R = cv2.split(image)
  image = cv2.merge([R, G, B])
  return image

def central_crop(image, crop_height, crop_width):
  image_height = image.shape[0]
  image_width = image.shape[1]
  offset_height = (image_height - crop_height) // 2
  offset_width = (image_width - crop_width) // 2
  return image[offset_height:offset_height + crop_height, offset_width:
               offset_width + crop_width, :]

_R_MEAN = 123.68
_G_MEAN = 116.78
_B_MEAN = 103.94

MEANS = [_B_MEAN,_G_MEAN,_R_MEAN]

calib_image_dir = "./Imagenet/calibration_images/"
calib_image_list = "./Imagenet/calibration.txt"
calib_batch_size = 50

def calib_input(iter):
  images = []
  line = open(calib_image_list).readlines()
  for index in range(0, calib_batch_size):
    curline = line[iter * calib_batch_size + index]
    calib_image_name = curline.strip()
    calib_image_name = calib_image_name.split(' ', 1)[0]
    print(calib_image_dir + calib_image_name)
    image = cv2.imread(calib_image_dir + calib_image_name)
    image = central_crop(image, 224, 224)
    image = mean_image_subtraction(image, MEANS)
    print(f"image.shape={image.shape}")
    images.append(image)
  return {"input": images}

TensorFlow 1.x Version (vai_q_tensorflow)
quantize.sh

pb=./tf_resnetv1_50_imagenet_224_224_6.97G_1.3/float/resnet_v1_50_inference.pb
#input_nodes=resnet_v1_50_conv1_Conv2D(0)
input_shapes=?,224,224,3
output_nodes=resnet_v1_50_logits_Conv2D
vai_q_tensorflow quantize \
     --input_frozen_graph ${pb} \
     --input_shapes  ?,224,224,3 \
     --input_nodes    input \
     --output_nodes   resnet_v1_50/predictions/Reshape_1 \
     --input_fn  input_fn.calib_input \
     --calib_iter 2 \
     --output_dir ./Ultra96_tf_resnetv1_build/quantize_results

Caffe Version (vai_q_caffe)
If the target hardware platform is DPUCAHX8H, the -keep_fixed_neuron option should be added to the command.

vai_q_caffe quantize -model float.prototxt -weights float.caffemodel -keep_fixed_neuron[options]

Chapter 4: Quantizing the Model UG1414

文件夹结构

john@john-virtual-machine:~/DPU-PYNQ/host$ ./docker_run.sh

(vitis-ai-tensorflow) Vitis-AI /workspace > ./quantize.sh