import os
import math
import random
import functools
import numpy as np
import paddle
from PIL import Image, ImageEnhance

random.seed(0)
np.random.seed(0)

DATA_DIM = 224

THREAD = 8
BUF_SIZE = 102400

DATA_DIR = 'data/ILSVRC2012'

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):
    img_path = sample[0]

    img = Image.open(img_path)
    if mode == 'train':
        if rotate: img = rotate_image(img)
        img = random_crop(img, DATA_DIM)
    else:
        img = resize_short(img, target_size=256)
        img = crop_image(img, target_size=DATA_DIM, 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):
    def reader():
        with open(file_list) as flist:
            full_lines = [line.strip() for line in flist]
            if shuffle:
                np.random.shuffle(full_lines)
            if mode == 'train' and os.getenv('PADDLE_TRAINING_ROLE'):
                # distributed mode if the env var `PADDLE_TRAINING_ROLE` exits
                trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
                trainer_count = int(os.getenv("PADDLE_TRAINERS", "1"))
                per_node_lines = len(full_lines) / trainer_count
                lines = full_lines[trainer_id * per_node_lines:(trainer_id + 1)
                                   * per_node_lines]
                print(
                    "read images from %d, length: %d, lines length: %d, total: %d"
                    % (trainer_id * per_node_lines, per_node_lines, len(lines),
                       len(full_lines)))
            else:
                lines = full_lines

            for line in lines:
                if mode == 'train' or mode == 'val':
                    img_path, label = line.split()
                    img_path = img_path.replace("JPEG", "jpeg")
                    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]

    mapper = functools.partial(
        process_image, mode=mode, color_jitter=color_jitter, rotate=rotate)

    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, 'val_list.txt')
    return _reader_creator(file_list, 'test', shuffle=False, data_dir=data_dir)

@@ -4221,16 +4221,17 @@
 lines) /
+/
  trainer

Fix image_classification bug