# Copyright (c) 2018 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. from paddle.utils.image_util import * import random from PIL import Image from PIL import ImageDraw import numpy as np import xml.etree.ElementTree import os import time import copy import six from collections import deque from roidbs import JsonDataset import data_utils from config import cfg import segm_utils def roidb_reader(roidb, mode): im, im_scales = data_utils.get_image_blob(roidb, mode) im_id = roidb['id'] im_height = np.round(roidb['height'] * im_scales) im_width = np.round(roidb['width'] * im_scales) im_info = np.array([im_height, im_width, im_scales], dtype=np.float32) if mode == 'test' or mode == 'infer': return im, im_info, im_id gt_boxes = roidb['gt_boxes'].astype('float32') gt_classes = roidb['gt_classes'].astype('int32') is_crowd = roidb['is_crowd'].astype('int32') segms = roidb['segms'] outs = (im, gt_boxes, gt_classes, is_crowd, im_info, im_id) if cfg.MASK_ON: gt_masks = [] valid = True segms = roidb['segms'] assert len(segms) == is_crowd.shape[0] for i in range(len(roidb['segms'])): segm, iscrowd = segms[i], is_crowd[i] gt_segm = [] if iscrowd: gt_segm.append([[0, 0]]) else: for poly in segm: if len(poly) == 0: valid = False break gt_segm.append(np.array(poly).reshape(-1, 2)) if (not valid) or len(gt_segm) == 0: break gt_masks.append(gt_segm) outs = outs + (gt_masks, ) return outs def coco(mode, batch_size=None, total_batch_size=None, padding_total=False, shuffle=False): if 'coco2014' in cfg.dataset: cfg.train_file_list = 'annotations/instances_train2014.json' cfg.train_data_dir = 'train2014' cfg.val_file_list = 'annotations/instances_val2014.json' cfg.val_data_dir = 'val2014' elif 'coco2017' in cfg.dataset: cfg.train_file_list = 'annotations/instances_train2017.json' cfg.train_data_dir = 'train2017' cfg.val_file_list = 'annotations/instances_val2017.json' cfg.val_data_dir = 'val2017' else: raise NotImplementedError('Dataset {} not supported'.format( cfg.dataset)) cfg.mean_value = np.array(cfg.pixel_means)[np.newaxis, np.newaxis, :].astype('float32') total_batch_size = total_batch_size if total_batch_size else batch_size if mode != 'infer': assert total_batch_size % batch_size == 0 if mode == 'train': cfg.train_file_list = os.path.join(cfg.data_dir, cfg.train_file_list) cfg.train_data_dir = os.path.join(cfg.data_dir, cfg.train_data_dir) elif mode == 'test' or mode == 'infer': cfg.val_file_list = os.path.join(cfg.data_dir, cfg.val_file_list) cfg.val_data_dir = os.path.join(cfg.data_dir, cfg.val_data_dir) json_dataset = JsonDataset(train=(mode == 'train')) roidbs = json_dataset.get_roidb() print("{} on {} with {} roidbs".format(mode, cfg.dataset, len(roidbs))) def padding_minibatch(batch_data): if len(batch_data) == 1: return batch_data max_shape = np.array([data[0].shape for data in batch_data]).max(axis=0) padding_batch = [] for data in batch_data: im_c, im_h, im_w = data[0].shape[:] padding_im = np.zeros( (im_c, max_shape[1], max_shape[2]), dtype=np.float32) padding_im[:, :im_h, :im_w] = data[0] padding_batch.append((padding_im, ) + data[1:]) return padding_batch def reader(): if mode == "train": if shuffle: roidb_perm = deque(np.random.permutation(roidbs)) else: roidb_perm = deque(roidbs) roidb_cur = 0 count = 0 batch_out = [] while True: roidb = roidb_perm[0] roidb_cur += 1 roidb_perm.rotate(-1) if roidb_cur >= len(roidbs): if shuffle: roidb_perm = deque(np.random.permutation(roidbs)) else: roidb_perm = deque(roidbs) roidb_cur = 0 # im, gt_boxes, gt_classes, is_crowd, im_info, im_id, gt_masks datas = roidb_reader(roidb, mode) if datas[1].shape[0] == 0: continue if cfg.MASK_ON: if len(datas[-1]) != datas[1].shape[0]: continue batch_out.append(datas) if not padding_total: if len(batch_out) == batch_size: yield padding_minibatch(batch_out) batch_out = [] else: if len(batch_out) == total_batch_size: batch_out = padding_minibatch(batch_out) for i in range(total_batch_size / batch_size): sub_batch_out = [] for j in range(batch_size): sub_batch_out.append(batch_out[i * batch_size + j]) yield sub_batch_out sub_batch_out = [] batch_out = [] count += 1 if count >= cfg.max_iter + 1: return elif mode == "test": batch_out = [] for roidb in roidbs: im, im_info, im_id = roidb_reader(roidb, mode) batch_out.append((im, im_info, im_id)) if len(batch_out) == batch_size: yield batch_out batch_out = [] if len(batch_out) != 0: yield batch_out else: for roidb in roidbs: if cfg.image_name not in roidb['image']: continue im, im_info, im_id = roidb_reader(roidb, mode) batch_out = [(im, im_info, im_id)] yield batch_out return reader def train(batch_size, total_batch_size=None, padding_total=False, shuffle=True): return coco( 'train', batch_size, total_batch_size, padding_total, shuffle=shuffle) def test(batch_size, total_batch_size=None, padding_total=False): return coco('test', batch_size, total_batch_size, shuffle=False) def infer(): return coco('infer')