未验证 提交 8ff2de56 编写于 作者: W whs 提交者: GitHub

Add reader for QAT demo (#1733)

上级 3944be62
import os
import math
import random
import functools
import numpy as np
import paddle
from PIL import Image, ImageEnhance
from paddle.io import Dataset
random.seed(0)
np.random.seed(0)
DATA_DIM = 224
RESIZE_DIM = 256
THREAD = 16
BUF_SIZE = 10240
DATA_DIR = 'data/ILSVRC2012/'
DATA_DIR = os.path.join(os.path.split(os.path.realpath(__file__))[0], DATA_DIR)
img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
def resize_short(img, target_size):
percent = float(target_size) / min(img.size[0], img.size[1])
resized_width = int(round(img.size[0] * percent))
resized_height = int(round(img.size[1] * percent))
img = img.resize((resized_width, resized_height), Image.LANCZOS)
return img
def crop_image(img, target_size, center):
width, height = img.size
size = target_size
if center == True:
w_start = (width - size) // 2
h_start = (height - size) // 2
else:
w_start = np.random.randint(0, width - size + 1)
h_start = np.random.randint(0, height - size + 1)
w_end = w_start + size
h_end = h_start + size
img = img.crop((w_start, h_start, w_end, h_end))
return img
def random_crop(img, size, scale=[0.08, 1.0], ratio=[3. / 4., 4. / 3.]):
aspect_ratio = math.sqrt(np.random.uniform(*ratio))
w = 1. * aspect_ratio
h = 1. / aspect_ratio
bound = min((float(img.size[0]) / img.size[1]) / (w**2),
(float(img.size[1]) / img.size[0]) / (h**2))
scale_max = min(scale[1], bound)
scale_min = min(scale[0], bound)
target_area = img.size[0] * img.size[1] * np.random.uniform(
scale_min, scale_max)
target_size = math.sqrt(target_area)
w = int(target_size * w)
h = int(target_size * h)
i = np.random.randint(0, img.size[0] - w + 1)
j = np.random.randint(0, img.size[1] - h + 1)
img = img.crop((i, j, i + w, j + h))
img = img.resize((size, size), Image.LANCZOS)
return img
def rotate_image(img):
angle = np.random.randint(-10, 11)
img = img.rotate(angle)
return img
def distort_color(img):
def random_brightness(img, lower=0.5, upper=1.5):
e = np.random.uniform(lower, upper)
return ImageEnhance.Brightness(img).enhance(e)
def random_contrast(img, lower=0.5, upper=1.5):
e = np.random.uniform(lower, upper)
return ImageEnhance.Contrast(img).enhance(e)
def random_color(img, lower=0.5, upper=1.5):
e = np.random.uniform(lower, upper)
return ImageEnhance.Color(img).enhance(e)
ops = [random_brightness, random_contrast, random_color]
np.random.shuffle(ops)
img = ops[0](img)
img = ops[1](img)
img = ops[2](img)
return img
def process_image(sample,
mode,
color_jitter,
rotate,
crop_size=DATA_DIM,
resize_size=RESIZE_DIM):
img_path = sample[0]
try:
img = Image.open(img_path)
except:
print(img_path, "not exists!")
return None
if mode == 'train':
if rotate: img = rotate_image(img)
img = random_crop(img, crop_size)
else:
img = resize_short(img, target_size=resize_size)
img = crop_image(img, target_size=crop_size, center=True)
if mode == 'train':
if color_jitter:
img = distort_color(img)
if np.random.randint(0, 2) == 1:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
if img.mode != 'RGB':
img = img.convert('RGB')
img = np.array(img).astype('float32').transpose((2, 0, 1)) / 255
img -= img_mean
img /= img_std
if mode == 'train' or mode == 'val':
return img, sample[1]
elif mode == 'test':
return [img]
def _reader_creator(file_list,
mode,
shuffle=False,
color_jitter=False,
rotate=False,
data_dir=DATA_DIR,
crop_size=DATA_DIM,
resize_size=RESIZE_DIM,
batch_size=1):
def reader():
try:
with open(file_list) as flist:
full_lines = [line.strip() for line in flist]
if shuffle:
np.random.shuffle(full_lines)
lines = full_lines
for line in lines:
if mode == 'train' or mode == 'val':
img_path, label = line.split()
img_path = os.path.join(data_dir, img_path)
yield img_path, int(label)
elif mode == 'test':
img_path = os.path.join(data_dir, line)
yield [img_path]
except Exception as e:
print("Reader failed!\n{}".format(str(e)))
os._exit(1)
mapper = functools.partial(
process_image,
mode=mode,
color_jitter=color_jitter,
rotate=rotate,
crop_size=crop_size,
resize_size=resize_size)
return paddle.reader.xmap_readers(mapper, reader, THREAD, BUF_SIZE)
def train(data_dir=DATA_DIR):
file_list = os.path.join(data_dir, 'train_list.txt')
return _reader_creator(
file_list,
'train',
shuffle=True,
color_jitter=False,
rotate=False,
data_dir=data_dir)
def val(data_dir=DATA_DIR):
file_list = os.path.join(data_dir, 'val_list.txt')
return _reader_creator(file_list, 'val', shuffle=False, data_dir=data_dir)
def test(data_dir=DATA_DIR):
file_list = os.path.join(data_dir, 'test_list.txt')
return _reader_creator(file_list, 'test', shuffle=False, data_dir=data_dir)
class ImageNetDataset(Dataset):
def __init__(self,
data_dir=DATA_DIR,
mode='train',
crop_size=DATA_DIM,
resize_size=RESIZE_DIM):
super(ImageNetDataset, self).__init__()
self.data_dir = data_dir
self.crop_size = crop_size
self.resize_size = resize_size
train_file_list = os.path.join(data_dir, 'train_list.txt')
val_file_list = os.path.join(data_dir, 'val_list.txt')
test_file_list = os.path.join(data_dir, 'test_list.txt')
self.mode = mode
if mode == 'train':
with open(train_file_list) as flist:
full_lines = [line.strip() for line in flist]
np.random.shuffle(full_lines)
lines = full_lines
self.data = [line.split() for line in lines]
else:
with open(val_file_list) as flist:
lines = [line.strip() for line in flist]
self.data = [line.split() for line in lines]
def __getitem__(self, index):
sample = self.data[index]
data_path = os.path.join(self.data_dir, sample[0])
if self.mode == 'train':
data, label = process_image(
[data_path, sample[1]],
mode='train',
color_jitter=False,
rotate=False,
crop_size=self.crop_size,
resize_size=self.resize_size)
return data, np.array([label]).astype('int64')
elif self.mode == 'val':
data, label = process_image(
[data_path, sample[1]],
mode='val',
color_jitter=False,
rotate=False,
crop_size=self.crop_size,
resize_size=self.resize_size)
return data, np.array([label]).astype('int64')
elif self.mode == 'test':
data = process_image(
[data_path, sample[1]],
mode='test',
color_jitter=False,
rotate=False,
crop_size=self.crop_size,
resize_size=self.resize_size)
return data
def __len__(self):
return len(self.data)
...@@ -156,6 +156,7 @@ def compress(args): ...@@ -156,6 +156,7 @@ def compress(args):
test(-1, model) test(-1, model)
model.train()
############################################################################################################ ############################################################################################################
# 1. quantization # 1. quantization
############################################################################################################ ############################################################################################################
......
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