# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. 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 = 1 BUF_SIZE = 10240 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, 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) 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 = 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) 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=False, 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)