SuzukiDaichi-git
commented
3 years ago 取り敢えずこんな感じかな… コメントアウト文は, 公式ドキュメントに載っているコードの内, 消した部分 resizeとnormalizeだけしている.
import tensorflow as tf
import os
_R_MEAN = 0.4029 * 256
_G_MEAN = 0.3841 * 256
_B_MEAN = 0.3744 * 256
_R_VAR = 0.2596
_G_VAR = 0.2752
_B_VAR = 0.2778
class Data_loader(object):
def __init__(self, out_height, out_width, smallest_side=256):
self._sess = tf.Session()
self._out_height = out_height
self._out_width = out_width
self._smallest_side = smallest_side
self._decode_jpeg_data = tf.placeholder(dtype=tf.string)
self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=3)
self._image_pl = tf.placeholder(tf.float32, shape=(None, None, 3))
# self._resized_image = self._aspect_preserving_resize(self._image_pl, self._smallest_side)
self._resized_image = self._my_aspect_preserving_resize(self._image_pl, self._smallest_side)
# def _center_crop(self, image):
# image_height, image_width = image.shape[:2]
# offset_height = (image_height - self._out_height) // 2
# offset_width = (image_width - self._out_width) // 2
# image = image[offset_height:offset_height + self._out_height,
# offset_width:offset_width + self._out_width, :]
# return image
# def _smallest_size_at_least(self, height, width, smallest_side):
# """Computes new shape with the smallest side equal to smallest_side`.
# Computes new shape with the smallest side equal to `smallest_side` while
# preserving the original aspect ratio.
# Args:
# height: an int32 scalar tensor indicating the current height.
# width: an int32 scalar tensor indicating the current width.
# smallest_side: A python integer or scalar `Tensor` indicating the
# size of
# the smallest side after resize.
# Returns:
# new_height: an int32 scalar tensor indicating the new height.
# new_width: and int32 scalar tensor indicating the new width.
# """
# smallest_side = tf.convert_to_tensor(smallest_side, dtype=tf.int32)
# height = tf.to_float(height)
# width = tf.to_float(width)
# smallest_side = tf.to_float(smallest_side)
# scale = tf.cond(tf.greater(height, width), lambda: smallest_side / width, lambda: smallest_side / height)
# new_height = tf.to_int32(tf.rint(height * scale))
# new_width = tf.to_int32(tf.rint(width * scale))
# return new_height, new_width
# def _aspect_preserving_resize(self, image, smallest_side):
# """Resize images preserving the original aspect ratio.
# Args:
# image: A 3-D image `Tensor`.
# smallest_side: A python integer or scalar `Tensor` indicating the
# size of
# the smallest side after resize.
# Returns:
# resized_image: A 3-D tensor containing the resized image.
# """
# smallest_side = tf.convert_to_tensor(smallest_side, dtype=tf.int32)
# shape = tf.shape(image)
# height = shape[0]
# width = shape[1]
# new_height, new_width = self._smallest_size_at_least(height, width, smallest_side)
# image = tf.expand_dims(image, 0)
# resized_image = tf.image.resize_bilinear(image, [new_height, new_width], align_corners=False)
# resized_image = tf.squeeze(resized_image)
# #resized_image.set_shape([None, None, 3])
# return resized_image
def _my_aspect_preserving_resize(self, image, smallest_side):
"""Resize images preserving the original aspect ratio.
Args:
image: A 3-D image `Tensor`.
smallest_side: A python integer or scalar `Tensor` indicating the
size of
the smallest side after resize.
Returns:
resized_image: A 3-D tensor containing the resized image.
"""
assert smallest_side % 256 == 0, "smallest_side should be a multiple of 256"
smallest_side = tf.convert_to_tensor(smallest_side, dtype=tf.int32)
shape = tf.shape(image)
width = shape[1]
new_height = height
new_width = width * 5 // 8
image = tf.expand_dims(image, 0)
resized_image = tf.image.resize_bilinear(image, [new_height, new_width], align_corners=False)
resized_image = tf.squeeze(resized_image)
return resized_image
def preprocess(self, image):
assert image is not None, "image cannot be None"
resized_image = self._sess.run(self._resized_image, feed_dict={self._image_pl: image})
# image_crop = self._center_crop(resized_image)
# image = image_crop - [_R_MEAN, _G_MEAN, _B_MEAN]
image = (resized_image - [_R_MEAN, _G_MEAN, _B_MEAN]) / tf.convert_to_tensor([_R_VAR, _G_VAR, _B_VAR])
return image
def load_image(self, img_path):
assert os.path.exists(img_path), img_path + ' doesnot exists!'
image_data = tf.gfile.GFile(img_path, 'rb').read()
image = self._sess.run(self._decode_jpeg, feed_dict={self._decode_jpeg_data: image_data})
assert len(image.shape) == 3
assert image.shape[-1] == 3
return image
calib_image_dir = "./imagenet_images/"
calib_image_list = "./imagenet_calib.txt"
calib_batch_size = 50
input_height = 224
input_width = 224
def calib_input(iter):
images = []
data_loader = Data_loader(input_height, input_width)
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()
filename = os.path.join(calib_image_dir, calib_image_name)
image = data_loader.load_image(filename)
image = data_loader.preprocess(image)
images.append(image.tolist())
return {"input": images}
などとあるので, Quantizationは学習を目的としているわけではなさそう（…？）よって, validationデータを用意して, 評価時用の前処理を行えば良い（…？）