Add RemoteSensing scenario (#230)

contrib/RemoteSensing/__init__.py

+#   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.
+
+from . import utils
+from . import nets
+from . import models
+from . import transforms
+from . import readers
+from .utils.utils import get_environ_info
+
+env_info = get_environ_info()
+
+log_level = 2

contrib/RemoteSensing/main.py

+import sys
+import os
+import os.path as osp
+import cv2
+import numpy as np
+from PIL import Image as Image
+
+#================================setting========================
+os.environ['CUDA_VISIBLE_DEVICES'] = "1"
+
+batch_size = 4
+channel = 10
+epochs = 1
+
+save_dir = 'saved_model/snow2019_unet_all_channel_vertical'
+data_dir = "../../../dataset/snow2019/all_channel_data/"
+#=============================================================
+
+sys.path.append(osp.join(os.getcwd(), '..'))
+import RemoteSensing.transforms.transforms as T
+from RemoteSensing.readers.reader import Reader
+from RemoteSensing.models import UNet, load_model
+
+if not os.path.exists(save_dir):
+    os.makedirs(save_dir)
+
+train_list = osp.join(data_dir, 'train.txt')
+val_list = osp.join(data_dir, 'val.txt')
+label_list = osp.join(data_dir, 'labels.txt')
+
+os.system('cp ./{} {}'.format(__file__, osp.join(save_dir, __file__)))
+
+train_transforms = T.Compose([
+    T.RandomVerticalFlip(0.5),
+    T.RandomHorizontalFlip(0.5),
+    T.ResizeStepScaling(0.5, 2.0, 0.25),
+    T.RandomPaddingCrop(769),
+    T.Normalize(mean=[0.5] * channel, std=[0.5] * channel),
+])
+
+eval_transforms = T.Compose([
+    T.Padding([1049, 1049]),
+    T.Normalize(mean=[0.5] * channel, std=[0.5] * channel),
+])
+
+test_transforms = T.Compose([
+    T.Padding([1049, 1049]),
+    T.Normalize(mean=[0.5] * channel, std=[0.5] * channel),
+])
+
+train_reader = Reader(
+    data_dir=data_dir,
+    file_list=train_list,
+    label_list=label_list,
+    transforms=train_transforms,
+    num_workers=8,
+    buffer_size=16,
+    shuffle=True,
+    parallel_method='thread')
+
+eval_reader = Reader(
+    data_dir=data_dir,
+    file_list=val_list,
+    label_list=label_list,
+    transforms=eval_transforms,
+    num_workers=8,
+    buffer_size=16,
+    shuffle=False,
+    parallel_method='thread')
+
+model = UNet(
+    num_classes=2, input_channel=channel, use_bce_loss=True, use_dice_loss=True)
+
+model.train(
+    num_epochs=epochs,
+    train_reader=train_reader,
+    train_batch_size=batch_size,
+    eval_reader=eval_reader,
+    save_interval_epochs=5,
+    log_interval_steps=10,
+    save_dir=save_dir,
+    pretrain_weights=None,
+    optimizer=None,
+    learning_rate=0.01,
+)
+
+# predict
+model = load_model(osp.join(save_dir, 'best_model'))
+pred_dir = osp.join(save_dir, 'pred')
+if not osp.exists(pred_dir):
+    os.mkdir(pred_dir)
+
+color_map = [0, 0, 0, 255, 255, 255]
+
+with open(val_list) as f:
+    lines = f.readlines()
+    for line in lines:
+        img_path = line.split(' ')[0]
+        print('Predicting {}'.format(img_path))
+        img_path_ = osp.join(data_dir, img_path)
+
+        pred = model.predict(img_path_)
+
+        pred_name = osp.basename(img_path).rstrip('npy') + 'png'
+        pred_path = osp.join(pred_dir, pred_name)
+        pred_mask = Image.fromarray(pred.astype(np.uint8), mode='P')
+        pred_mask.putpalette(color_map)
+        pred_mask.save(pred_path)

contrib/RemoteSensing/models/__init__.py

+from .load_model import *
+from .unet import *

contrib/RemoteSensing/models/base.py

+#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+#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 __future__ import absolute_import
+import paddle.fluid as fluid
+import os
+import numpy as np
+import time
+import math
+import yaml
+import copy
+import json
+import functools
+import RemoteSensing.utils.logging as logging
+import RemoteSensing
+from collections import OrderedDict
+from os import path as osp
+from paddle.fluid.framework import Program
+from ..utils.pretrain_weights import get_pretrain_weights
+
+
+def dict2str(dict_input):
+    out = ''
+    for k, v in dict_input.items():
+        try:
+            v = round(float(v), 6)
+        except:
+            pass
+        out = out + '{}={}, '.format(k, v)
+    return out.strip(', ')
+
+
+class BaseAPI:
+    def __init__(self):
+        # 现有的CV模型都有这个属性，而这个属且也需要在eval时用到
+        self.num_classes = None
+        self.labels = None
+        if RemoteSensing.env_info['place'] == 'cpu':
+            self.places = fluid.cpu_places()
+        else:
+            self.places = fluid.cuda_places()
+        self.exe = fluid.Executor(self.places[0])
+        self.train_prog = None
+        self.test_prog = None
+        self.parallel_train_prog = None
+        self.train_inputs = None
+        self.test_inputs = None
+        self.train_outputs = None
+        self.test_outputs = None
+        self.train_data_loader = None
+        self.eval_metrics = None
+        # 若模型是从inference model加载进来的，无法调用训练接口进行训练
+        self.trainable = True
+        # 是否使用多卡间同步BatchNorm均值和方差
+        self.sync_bn = False
+        # 当前模型状态
+        self.status = 'Normal'
+
+    def _get_single_card_bs(self, batch_size):
+        if batch_size % len(self.places) == 0:
+            return int(batch_size // len(self.places))
+        else:
+            raise Exception("Please support correct batch_size, \
+                            which can be divided by available cards({}) in {}".
+                            format(RemoteSensing.env_info['num'],
+                                   RemoteSensing.env_info['place']))
+
+    def build_program(self):
+        # 构建训练网络
+        self.train_inputs, self.train_outputs = self.build_net(mode='train')
+        self.train_prog = fluid.default_main_program()
+        startup_prog = fluid.default_startup_program()
+
+        # 构建预测网络
+        self.test_prog = fluid.Program()
+        with fluid.program_guard(self.test_prog, startup_prog):
+            with fluid.unique_name.guard():
+                self.test_inputs, self.test_outputs = self.build_net(
+                    mode='test')
+        self.test_prog = self.test_prog.clone(for_test=True)
+
+    def arrange_transforms(self, transforms, mode='train'):
+        # 给transforms添加arrange操作
+        if transforms.transforms[-1].__class__.__name__.startswith('Arrange'):
+            transforms.transforms[
+                -1] = RemoteSensing.transforms.transforms.ArrangeSegmenter(
+                    mode=mode)
+        else:
+            transforms.transforms.append(
+                RemoteSensing.transforms.transforms.ArrangeSegmenter(mode=mode))
+
+    def build_train_data_loader(self, reader, batch_size):
+        # 初始化data_loader
+        if self.train_data_loader is None:
+            self.train_data_loader = fluid.io.DataLoader.from_generator(
+                feed_list=list(self.train_inputs.values()),
+                capacity=64,
+                use_double_buffer=True,
+                iterable=True)
+        batch_size_each_gpu = self._get_single_card_bs(batch_size)
+        generator = reader.generator(
+            batch_size=batch_size_each_gpu, drop_last=True)
+        self.train_data_loader.set_sample_list_generator(
+            reader.generator(batch_size=batch_size_each_gpu),
+            places=self.places)
+
+    def net_initialize(self,
+                       startup_prog=None,
+                       pretrain_weights=None,
+                       fuse_bn=False,
+                       save_dir='.',
+                       sensitivities_file=None,
+                       eval_metric_loss=0.05):
+        if hasattr(self, 'backbone'):
+            backbone = self.backbone
+        else:
+            backbone = self.__class__.__name__
+        pretrain_weights = get_pretrain_weights(pretrain_weights, backbone,
+                                                save_dir)
+        if startup_prog is None:
+            startup_prog = fluid.default_startup_program()
+        self.exe.run(startup_prog)
+        if pretrain_weights is not None:
+            logging.info(
+                "Load pretrain weights from {}.".format(pretrain_weights))
+            RemoteSensing.utils.utils.load_pretrain_weights(
+                self.exe, self.train_prog, pretrain_weights, fuse_bn)
+        # 进行裁剪
+        if sensitivities_file is not None:
+            from .slim.prune_config import get_sensitivities
+            sensitivities_file = get_sensitivities(sensitivities_file, self,
+                                                   save_dir)
+            from .slim.prune import get_params_ratios, prune_program
+            prune_params_ratios = get_params_ratios(
+                sensitivities_file, eval_metric_loss=eval_metric_loss)
+            prune_program(self, prune_params_ratios)
+            self.status = 'Prune'
+
+    def get_model_info(self):
+        info = dict()
+        info['Model'] = self.__class__.__name__
+        info['_Attributes'] = {}
+        if 'self' in self.init_params:
+            del self.init_params['self']
+        if '__class__' in self.init_params:
+            del self.init_params['__class__']
+        info['_init_params'] = self.init_params
+
+        info['_Attributes']['num_classes'] = self.num_classes
+        info['_Attributes']['labels'] = self.labels
+        try:
+            primary_metric_key = list(self.eval_metrics.keys())[0]
+            primary_metric_value = float(self.eval_metrics[primary_metric_key])
+            info['_Attributes']['eval_metrics'] = {
+                primary_metric_key: primary_metric_value
+            }
+        except:
+            pass
+
+        if hasattr(self, 'test_transforms'):
+            if self.test_transforms is not None:
+                info['Transforms'] = list()
+                for op in self.test_transforms.transforms:
+                    name = op.__class__.__name__
+                    attr = op.__dict__
+                    info['Transforms'].append({name: attr})
+        return info
+
+    def save_model(self, save_dir):
+        if not osp.isdir(save_dir):
+            if osp.exists(save_dir):
+                os.remove(save_dir)
+            os.makedirs(save_dir)
+        fluid.save(self.train_prog, osp.join(save_dir, 'model'))
+        model_info = self.get_model_info()
+        model_info['status'] = self.status
+        with open(
+                osp.join(save_dir, 'model.yml'), encoding='utf-8',
+                mode='w') as f:
+            yaml.dump(model_info, f)
+        # 评估结果保存
+        if hasattr(self, 'eval_details'):
+            with open(osp.join(save_dir, 'eval_details.json'), 'w') as f:
+                json.dump(self.eval_details, f)
+
+        if self.status == 'Prune':
+            # 保存裁剪的shape
+            shapes = {}
+            for block in self.train_prog.blocks:
+                for param in block.all_parameters():
+                    pd_var = fluid.global_scope().find_var(param.name)
+                    pd_param = pd_var.get_tensor()
+                    shapes[param.name] = np.array(pd_param).shape
+            with open(
+                    osp.join(save_dir, 'prune.yml'), encoding='utf-8',
+                    mode='w') as f:
+                yaml.dump(shapes, f)
+
+        # 模型保存成功的标志
+        open(osp.join(save_dir, '.success'), 'w').close()
+        logging.info("Model saved in {}.".format(save_dir))
+
+    def export_inference_model(self, save_dir):
+        test_input_names = [var.name for var in list(self.test_inputs.values())]
+        test_outputs = list(self.test_outputs.values())
+        if self.__class__.__name__ == 'MaskRCNN':
+            from RemoteSensing.utils.save import save_mask_inference_model
+            save_mask_inference_model(
+                dirname=save_dir,
+                executor=self.exe,
+                params_filename='__params__',
+                feeded_var_names=test_input_names,
+                target_vars=test_outputs,
+                main_program=self.test_prog)
+        else:
+            fluid.io.save_inference_model(
+                dirname=save_dir,
+                executor=self.exe,
+                params_filename='__params__',
+                feeded_var_names=test_input_names,
+                target_vars=test_outputs,
+                main_program=self.test_prog)
+        model_info = self.get_model_info()
+        model_info['status'] = 'Infer'
+
+        # 保存模型输出的变量描述
+        model_info['_ModelInputsOutputs'] = dict()
+        model_info['_ModelInputsOutputs']['test_inputs'] = [
+            [k, v.name] for k, v in self.test_inputs.items()
+        ]
+        model_info['_ModelInputsOutputs']['test_outputs'] = [
+            [k, v.name] for k, v in self.test_outputs.items()
+        ]
+
+        with open(
+                osp.join(save_dir, 'model.yml'), encoding='utf-8',
+                mode='w') as f:
+            yaml.dump(model_info, f)
+        # 模型保存成功的标志
+        open(osp.join(save_dir, '.success'), 'w').close()
+        logging.info("Model for inference deploy saved in {}.".format(save_dir))
+
+    def train_loop(self,
+                   num_epochs,
+                   train_reader,
+                   train_batch_size,
+                   eval_reader=None,
+                   save_interval_epochs=1,
+                   log_interval_steps=10,
+                   save_dir='output',
+                   use_vdl=False):
+        if not osp.isdir(save_dir):
+            if osp.exists(save_dir):
+                os.remove(save_dir)
+            os.makedirs(save_dir)
+        if use_vdl:
+            from visualdl import LogWriter
+            vdl_logdir = osp.join(save_dir, 'vdl_log')
+        # 给transform添加arrange操作
+        self.arrange_transforms(
+            transforms=train_reader.transforms, mode='train')
+        # 构建train_data_loader
+        self.build_train_data_loader(
+            reader=train_reader, batch_size=train_batch_size)
+
+        if eval_reader is not None:
+            self.eval_transforms = eval_reader.transforms
+            self.test_transforms = copy.deepcopy(eval_reader.transforms)
+
+        # 获取实时变化的learning rate
+        lr = self.optimizer._learning_rate
+        if isinstance(lr, fluid.framework.Variable):
+            self.train_outputs['lr'] = lr
+
+        # 在多卡上跑训练
+        if self.parallel_train_prog is None:
+            build_strategy = fluid.compiler.BuildStrategy()
+            build_strategy.fuse_all_optimizer_ops = False
+            if RemoteSensing.env_info['place'] != 'cpu' and len(
+                    self.places) > 1:
+                build_strategy.sync_batch_norm = self.sync_bn
+            exec_strategy = fluid.ExecutionStrategy()
+            exec_strategy.num_iteration_per_drop_scope = 1
+            self.parallel_train_prog = fluid.CompiledProgram(
+                self.train_prog).with_data_parallel(
+                    loss_name=self.train_outputs['loss'].name,
+                    build_strategy=build_strategy,
+                    exec_strategy=exec_strategy)
+
+        total_num_steps = math.floor(
+            train_reader.num_samples / train_batch_size)
+        num_steps = 0
+        time_stat = list()
+
+        if use_vdl:
+            # VisualDL component
+            log_writer = LogWriter(vdl_logdir, sync_cycle=20)
+            train_step_component = OrderedDict()
+            eval_component = OrderedDict()
+
+        best_accuracy_key = ""
+        best_accuracy = -1.0
+        best_model_epoch = 1
+        for i in range(num_epochs):
+            records = list()
+            step_start_time = time.time()
+            for step, data in enumerate(self.train_data_loader()):
+                outputs = self.exe.run(
+                    self.parallel_train_prog,
+                    feed=data,
+                    fetch_list=list(self.train_outputs.values()))
+                outputs_avg = np.mean(np.array(outputs), axis=1)
+                records.append(outputs_avg)
+
+                # 训练完成剩余时间预估
+                current_time = time.time()
+                step_cost_time = current_time - step_start_time
+                step_start_time = current_time
+                if len(time_stat) < 20:
+                    time_stat.append(step_cost_time)
+                else:
+                    time_stat[num_steps % 20] = step_cost_time
+                eta = ((num_epochs - i) * total_num_steps - step -
+                       1) * np.mean(time_stat)
+                eta_h = math.floor(eta / 3600)
+                eta_m = math.floor((eta - eta_h * 3600) / 60)
+                eta_s = int(eta - eta_h * 3600 - eta_m * 60)
+                eta_str = "{}:{}:{}".format(eta_h, eta_m, eta_s)
+
+                # 每间隔log_interval_steps，输出loss信息
+                num_steps += 1
+                if num_steps % log_interval_steps == 0:
+                    step_metrics = OrderedDict(
+                        zip(list(self.train_outputs.keys()), outputs_avg))
+
+                    if use_vdl:
+                        for k, v in step_metrics.items():
+                            if k not in train_step_component.keys():
+                                with log_writer.mode('Each_Step_while_Training'
+                                                     ) as step_logger:
+                                    train_step_component[
+                                        k] = step_logger.scalar(
+                                            'Training: {}'.format(k))
+                            train_step_component[k].add_record(num_steps, v)
+
+                    logging.info(
+                        "[TRAIN] Epoch={}/{}, Step={}/{}, {}, eta={}".format(
+                            i + 1, num_epochs, step + 1, total_num_steps,
+                            dict2str(step_metrics), eta_str))
+            train_metrics = OrderedDict(
+                zip(list(self.train_outputs.keys()), np.mean(records, axis=0)))
+            logging.info('[TRAIN] Epoch {} finished, {} .'.format(
+                i + 1, dict2str(train_metrics)))
+
+            # 每间隔save_interval_epochs, 在验证集上评估和对模型进行保存
+            if (i + 1) % save_interval_epochs == 0 or i == num_epochs - 1:
+                current_save_dir = osp.join(save_dir, "epoch_{}".format(i + 1))
+                if not osp.isdir(current_save_dir):
+                    os.makedirs(current_save_dir)
+                if eval_reader is not None:
+                    # 检测目前仅支持单卡评估，训练数据batch大小与显卡数量之商为验证数据batch大小。
+                    eval_batch_size = train_batch_size
+                    self.eval_metrics, self.eval_details = self.evaluate(
+                        eval_reader=eval_reader,
+                        batch_size=eval_batch_size,
+                        verbose=True,
+                        epoch_id=i + 1,
+                        return_details=True)
+                    logging.info('[EVAL] Finished, Epoch={}, {} .'.format(
+                        i + 1, dict2str(self.eval_metrics)))
+                    # 保存最优模型
+                    best_accuracy_key = list(self.eval_metrics.keys())[0]
+                    current_accuracy = self.eval_metrics[best_accuracy_key]
+                    if current_accuracy > best_accuracy:
+                        best_accuracy = current_accuracy
+                        best_model_epoch = i + 1
+                        best_model_dir = osp.join(save_dir, "best_model")
+                        self.save_model(save_dir=best_model_dir)
+                    if use_vdl:
+                        for k, v in self.eval_metrics.items():
+                            if isinstance(v, list):
+                                continue
+                            if isinstance(v, np.ndarray):
+                                if v.size > 1:
+                                    continue
+                            if k not in eval_component:
+                                with log_writer.mode('Each_Epoch_on_Eval_Data'
+                                                     ) as eval_logger:
+                                    eval_component[k] = eval_logger.scalar(
+                                        'Evaluation: {}'.format(k))
+                            eval_component[k].add_record(i + 1, v)
+                self.save_model(save_dir=current_save_dir)
+                logging.info(
+                    'Current evaluated best model in eval_reader is epoch_{}, {}={}'
+                    .format(best_model_epoch, best_accuracy_key, best_accuracy))

contrib/RemoteSensing/models/load_model.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 yaml
+import os.path as osp
+import six
+import copy
+from collections import OrderedDict
+import paddle.fluid as fluid
+from paddle.fluid.framework import Parameter
+from ..utils import logging
+import RemoteSensing
+
+
+def load_model(model_dir):
+    if not osp.exists(osp.join(model_dir, "model.yml")):
+        raise Exception("There's not model.yml in {}".format(model_dir))
+    with open(osp.join(model_dir, "model.yml")) as f:
+        info = yaml.load(f.read(), Loader=yaml.Loader)
+    status = info['status']
+
+    if not hasattr(RemoteSensing.models, info['Model']):
+        raise Exception(
+            "There's no attribute {} in RemoteSensing.models".format(
+                info['Model']))
+
+    model = getattr(RemoteSensing.models, info['Model'])(**info['_init_params'])
+    if status == "Normal" or \
+            status == "Prune":
+        startup_prog = fluid.Program()
+        model.test_prog = fluid.Program()
+        with fluid.program_guard(model.test_prog, startup_prog):
+            with fluid.unique_name.guard():
+                model.test_inputs, model.test_outputs = model.build_net(
+                    mode='test')
+        model.test_prog = model.test_prog.clone(for_test=True)
+        model.exe.run(startup_prog)
+        if status == "Prune":
+            from .slim.prune import update_program
+            model.test_prog = update_program(model.test_prog, model_dir,
+                                             model.places[0])
+        import pickle
+        with open(osp.join(model_dir, 'model.pdparams'), 'rb') as f:
+            load_dict = pickle.load(f)
+        fluid.io.set_program_state(model.test_prog, load_dict)
+
+    elif status == "Infer" or \
+            status == "Quant":
+        [prog, input_names, outputs] = fluid.io.load_inference_model(
+            model_dir, model.exe, params_filename='__params__')
+        model.test_prog = prog
+        test_outputs_info = info['_ModelInputsOutputs']['test_outputs']
+        model.test_inputs = OrderedDict()
+        model.test_outputs = OrderedDict()
+        for name in input_names:
+            model.test_inputs[name] = model.test_prog.global_block().var(name)
+        for i, out in enumerate(outputs):
+            var_desc = test_outputs_info[i]
+            model.test_outputs[var_desc[0]] = out
+    if 'Transforms' in info:
+        model.test_transforms = build_transforms(info['Transforms'])
+        model.eval_transforms = copy.deepcopy(model.test_transforms)
+
+    if '_Attributes' in info:
+        for k, v in info['_Attributes'].items():
+            if k in model.__dict__:
+                model.__dict__[k] = v
+
+    logging.info("Model[{}] loaded.".format(info['Model']))
+    return model
+
+
+def build_transforms(transforms_info):
+    from ..transforms import transforms as T
+    transforms = list()
+    for op_info in transforms_info:
+        op_name = list(op_info.keys())[0]
+        op_attr = op_info[op_name]
+        if not hasattr(T, op_name):
+            raise Exception(
+                "There's no operator named '{}' in transforms".format(op_name))
+        transforms.append(getattr(T, op_name)(**op_attr))
+    eval_transforms = T.Compose(transforms)
+    return eval_transforms

contrib/RemoteSensing/models/unet.py

+#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+#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 __future__ import absolute_import
+import os.path as osp
+import numpy as np
+import math
+import cv2
+import paddle.fluid as fluid
+import RemoteSensing
+import RemoteSensing.utils.logging as logging
+from collections import OrderedDict
+from .base import BaseAPI
+from ..utils.metrics import ConfusionMatrix
+
+
+class UNet(BaseAPI):
+    """实现UNet网络的构建并进行训练、评估、预测和模型导出。
+
+    Args:
+        num_classes (int): 类别数。
+        upsample_mode (str): UNet decode时采用的上采样方式，取值为'bilinear'时利用双线行差值进行上菜样，
+            当输入其他选项时则利用反卷积进行上菜样，默认为'bilinear'。
+        use_bce_loss (bool): 是否使用bce loss作为网络的损失函数，只能用于两类分割。可与dice loss同时使用。默认False。
+        use_dice_loss (bool): 是否使用dice loss作为网络的损失函数，只能用于两类分割，可与bce loss同时使用。
+            当use_bce_loss和use_dice_loss都为False时，使用交叉熵损失函数。默认False。
+        class_weight (list/str): 交叉熵损失函数各类损失的权重。当class_weight为list的时候，长度应为
+            num_classes。当class_weight为str时， weight.lower()应为'dynamic'，这时会根据每一轮各类像素的比重
+            自行计算相应的权重，每一类的权重为：每类的比例 * num_classes。class_weight取默认值None是，各类的权重1，
+            即平时使用的交叉熵损失函数。
+        ignore_index (int): label上忽略的值，label为ignore_index的像素不参与损失函数的计算。默认255。
+
+    Raises:
+        ValueError: use_bce_loss或use_dice_loss为真且num_calsses > 2。
+        ValueError: class_weight为list, 但长度不等于num_class。
+            class_weight为str, 但class_weight.low()不等于dynamic。
+        TypeError: class_weight不为None时，其类型不是list或str。
+    """
+
+    def __init__(self,
+                 num_classes=2,
+                 upsample_mode='bilinear',
+                 input_channel=3,
+                 use_bce_loss=False,
+                 use_dice_loss=False,
+                 class_weight=None,
+                 ignore_index=255):
+        self.init_params = locals()
+        super(UNet, self).__init__()
+        # dice_loss或bce_loss只适用两类分割中
+        if num_classes > 2 and (use_bce_loss or use_dice_loss):
+            raise ValueError(
+                "dice loss and bce loss is only applicable to binary classfication"
+            )
+
+        if class_weight is not None:
+            if isinstance(class_weight, list):
+                if len(class_weight) != num_classes:
+                    raise ValueError(
+                        "Length of class_weight should be equal to number of classes"
+                    )
+            elif isinstance(class_weight, str):
+                if class_weight.lower() != 'dynamic':
+                    raise ValueError(
+                        "if class_weight is string, must be dynamic!")
+            else:
+                raise TypeError(
+                    'Expect class_weight is a list or string but receive {}'.
+                    format(type(class_weight)))
+        self.num_classes = num_classes
+        self.upsample_mode = upsample_mode
+        self.input_channel = input_channel
+        self.use_bce_loss = use_bce_loss
+        self.use_dice_loss = use_dice_loss
+        self.class_weight = class_weight
+        self.ignore_index = ignore_index
+        self.labels = None
+        # 若模型是从inference model加载进来的，无法调用训练接口进行训练
+        self.trainable = True
+
+    def build_net(self, mode='train'):
+        model = RemoteSensing.nets.UNet(
+            self.num_classes,
+            mode=mode,
+            upsample_mode=self.upsample_mode,
+            input_channel=self.input_channel,
+            use_bce_loss=self.use_bce_loss,
+            use_dice_loss=self.use_dice_loss,
+            class_weight=self.class_weight,
+            ignore_index=self.ignore_index)
+        inputs = model.generate_inputs()
+        model_out = model.build_net(inputs)
+        outputs = OrderedDict()
+        if mode == 'train':
+            self.optimizer.minimize(model_out)
+            outputs['loss'] = model_out
+        elif mode == 'eval':
+            outputs['loss'] = model_out[0]
+            outputs['pred'] = model_out[1]
+            outputs['label'] = model_out[2]
+            outputs['mask'] = model_out[3]
+        else:
+            outputs['pred'] = model_out[0]
+            outputs['logit'] = model_out[1]
+        return inputs, outputs
+
+    def default_optimizer(self,
+                          learning_rate,
+                          num_epochs,
+                          num_steps_each_epoch,
+                          lr_decay_power=0.9):
+        decay_step = num_epochs * num_steps_each_epoch
+        lr_decay = fluid.layers.polynomial_decay(
+            learning_rate,
+            decay_step,
+            end_learning_rate=0,
+            power=lr_decay_power)
+        optimizer = fluid.optimizer.Momentum(
+            lr_decay,
+            momentum=0.9,
+            regularization=fluid.regularizer.L2Decay(
+                regularization_coeff=4e-05))
+        return optimizer
+
+    def train(self,
+              num_epochs,
+              train_reader,
+              train_batch_size=2,
+              eval_reader=None,
+              save_interval_epochs=1,
+              log_interval_steps=2,
+              save_dir='output',
+              pretrain_weights='COCO',
+              optimizer=None,
+              learning_rate=0.01,
+              lr_decay_power=0.9,
+              use_vdl=False,
+              sensitivities_file=None,
+              eval_metric_loss=0.05):
+        """训练。
+
+        Args:
+            num_epochs (int): 训练迭代轮数。
+            train_reader (RemoteSensing.readers): 训练数据读取器。
+            train_batch_size (int): 训练数据batch大小。同时作为验证数据batch大小。默认2。
+            eval_reader (RemoteSensing.readers): 评估数据读取器。
+            save_interval_epochs (int): 模型保存间隔（单位：迭代轮数）。默认为1。
+            log_interval_steps (int): 训练日志输出间隔（单位：迭代次数）。默认为2。
+            save_dir (str): 模型保存路径。默认'output'。
+            pretrain_weights (str): 若指定为路径时，则加载路径下预训练模型；若为字符串'COCO'，
+                则自动下载在COCO图片数据上预训练的模型权重；若为None，则不使用预训练模型。默认为'COCO'。
+            optimizer (paddle.fluid.optimizer): 优化器。当改参数为None时，使用默认的优化器：使用
+                fluid.optimizer.Momentum优化方法，polynomial的学习率衰减策略。
+            learning_rate (float): 默认优化器的初始学习率。默认0.01。
+            lr_decay_power (float): 默认优化器学习率多项式衰减系数。默认0.9。
+            use_vdl (bool): 是否使用VisualDL进行可视化。默认False。
+            sensitivities_file (str): 若指定为路径时，则加载路径下敏感度信息进行裁剪；若为字符串'DEFAULT'，
+                则自动下载在ImageNet图片数据上获得的敏感度信息进行裁剪；若为None，则不进行裁剪。默认为None。
+            eval_metric_loss (float): 可容忍的精度损失。默认为0.05。
+
+        Raises:
+            ValueError: 模型从inference model进行加载。
+        """
+        if not self.trainable:
+            raise ValueError(
+                "Model is not trainable since it was loaded from a inference model."
+            )
+
+        self.labels = train_reader.labels
+
+        if optimizer is None:
+            num_steps_each_epoch = train_reader.num_samples // train_batch_size
+            optimizer = self.default_optimizer(
+                learning_rate=learning_rate,
+                num_epochs=num_epochs,
+                num_steps_each_epoch=num_steps_each_epoch,
+                lr_decay_power=lr_decay_power)
+        self.optimizer = optimizer
+        # 构建训练、验证、预测网络
+        self.build_program()
+        # 初始化网络权重
+        self.net_initialize(
+            startup_prog=fluid.default_startup_program(),
+            pretrain_weights=pretrain_weights,
+            save_dir=save_dir,
+            sensitivities_file=sensitivities_file,
+            eval_metric_loss=eval_metric_loss)
+        # 训练
+        self.train_loop(
+            num_epochs=num_epochs,
+            train_reader=train_reader,
+            train_batch_size=train_batch_size,
+            eval_reader=eval_reader,
+            save_interval_epochs=save_interval_epochs,
+            log_interval_steps=log_interval_steps,
+            save_dir=save_dir,
+            use_vdl=use_vdl)
+
+    def evaluate(self,
+                 eval_reader,
+                 batch_size=1,
+                 verbose=True,
+                 epoch_id=None,
+                 return_details=False):
+        """评估。
+
+        Args:
+            eval_reader (RemoteSensing.readers): 评估数据读取器。
+            batch_size (int): 评估时的batch大小。默认1。
+            verbose (bool): 是否打印日志。默认True。
+            epoch_id (int): 当前评估模型所在的训练轮数。
+            return_details (bool): 是否返回详细信息。默认False。
+
+        Returns:
+            dict: 当return_details为False时，返回dict。包含关键字：'miou'、'categore_iou'、'macc'、
+                'category_acc'和'kappa'，分别表示平均iou、各类别iou、平均准确率、各类别准确率和kappa系数。
+            tuple (metrics, eval_details)：当return_details为True时，增加返回dict (eval_details)，
+                包含关键字：'confusion_matrix'，表示评估的混淆矩阵。
+        """
+        self.arrange_transforms(transforms=eval_reader.transforms, mode='eval')
+        total_steps = math.ceil(eval_reader.num_samples * 1.0 / batch_size)
+        conf_mat = ConfusionMatrix(self.num_classes, streaming=True)
+        data_generator = eval_reader.generator(
+            batch_size=batch_size, drop_last=False)
+        if not hasattr(self, 'parallel_test_prog'):
+            self.parallel_test_prog = fluid.CompiledProgram(
+                self.test_prog).with_data_parallel(
+                    share_vars_from=self.parallel_train_prog)
+        batch_size_each_gpu = self._get_single_card_bs(batch_size)
+
+        for step, data in enumerate(data_generator()):
+            images = np.array([d[0] for d in data])
+            labels = np.array([d[1] for d in data])
+            num_samples = images.shape[0]
+            if num_samples < batch_size:
+                num_pad_samples = batch_size - num_samples
+                pad_images = np.tile(images[0:1], (num_pad_samples, 1, 1, 1))
+                images = np.concatenate([images, pad_images])
+            feed_data = {'image': images}
+            outputs = self.exe.run(
+                self.parallel_test_prog,
+                feed=feed_data,
+                fetch_list=list(self.test_outputs.values()),
+                return_numpy=True)
+            pred = outputs[0]
+            if num_samples < batch_size:
+                pred = pred[0:num_samples]
+
+            mask = labels != self.ignore_index
+            conf_mat.calculate(pred=pred, label=labels, ignore=mask)
+            _, iou = conf_mat.mean_iou()
+
+            if verbose:
+                logging.info("[EVAL] Epoch={}, Step={}/{}, iou={}".format(
+                    epoch_id, step + 1, total_steps, iou))
+
+        category_iou, miou = conf_mat.mean_iou()
+        category_acc, macc = conf_mat.accuracy()
+
+        metrics = OrderedDict(
+            zip(['miou', 'categore_iou', 'macc', 'category_acc', 'kappa'],
+                [miou, category_iou, macc, category_acc,
+                 conf_mat.kappa()]))
+        if return_details:
+            eval_details = {
+                'confusion_matrix': conf_mat.confusion_matrix.tolist()
+            }
+            return metrics, eval_details
+        return metrics
+
+    def predict(self, im_file, transforms=None):
+        """预测。
+        Args:
+            img_file(str): 预测图像路径。
+            transforms(RemoteSensing.transforms): 数据预处理操作。
+
+        Returns:
+            np.ndarray: 预测结果灰度图。
+        """
+        if transforms is None and not hasattr(self, 'test_transforms'):
+            raise Exception("transforms need to be defined, now is None.")
+        if transforms is not None:
+            self.arrange_transforms(transforms=transforms, mode='test')
+            im, im_info = transforms(im_file)
+        else:
+            self.arrange_transforms(
+                transforms=self.test_transforms, mode='test')
+            im, im_info = self.test_transforms(im_file)
+        im = np.expand_dims(im, axis=0)
+        result = self.exe.run(
+            self.test_prog,
+            feed={'image': im},
+            fetch_list=list(self.test_outputs.values()))
+        pred = result[0]
+        pred = np.squeeze(pred).astype(np.uint8)
+        keys = list(im_info.keys())
+        for k in keys[::-1]:
+            if k == 'shape_before_resize':
+                h, w = im_info[k][0], im_info[k][1]
+                pred = cv2.resize(pred, (w, h), cv2.INTER_NEAREST)
+            elif k == 'shape_before_padding':
+                h, w = im_info[k][0], im_info[k][1]
+                pred = pred[0:h, 0:w]
+
+        return pred

contrib/RemoteSensing/nets/__init__.py

+from .unet import UNet

contrib/RemoteSensing/nets/libs.py

+# coding: utf8
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import paddle
+import paddle.fluid as fluid
+import contextlib
+
+bn_regularizer = fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)
+name_scope = ""
+
+
+@contextlib.contextmanager
+def scope(name):
+    global name_scope
+    bk = name_scope
+    name_scope = name_scope + name + '/'
+    yield
+    name_scope = bk
+
+
+def max_pool(input, kernel, stride, padding):
+    data = fluid.layers.pool2d(
+        input,
+        pool_size=kernel,
+        pool_type='max',
+        pool_stride=stride,
+        pool_padding=padding)
+    return data
+
+
+def avg_pool(input, kernel, stride, padding=0):
+    data = fluid.layers.pool2d(
+        input,
+        pool_size=kernel,
+        pool_type='avg',
+        pool_stride=stride,
+        pool_padding=padding)
+    return data
+
+
+def group_norm(input, G, eps=1e-5, param_attr=None, bias_attr=None):
+    N, C, H, W = input.shape
+    if C % G != 0:
+        for d in range(10):
+            for t in [d, -d]:
+                if G + t <= 0: continue
+                if C % (G + t) == 0:
+                    G = G + t
+                    break
+            if C % G == 0:
+                break
+    assert C % G == 0, "group can not divide channle"
+    x = fluid.layers.group_norm(
+        input,
+        groups=G,
+        param_attr=param_attr,
+        bias_attr=bias_attr,
+        name=name_scope + 'group_norm')
+    return x
+
+
+def bn(*args,
+       norm_type='bn',
+       eps=1e-5,
+       bn_momentum=0.99,
+       group_norm=32,
+       **kargs):
+
+    if norm_type == 'bn':
+        with scope('BatchNorm'):
+            return fluid.layers.batch_norm(
+                *args,
+                epsilon=eps,
+                momentum=bn_momentum,
+                param_attr=fluid.ParamAttr(
+                    name=name_scope + 'gamma', regularizer=bn_regularizer),
+                bias_attr=fluid.ParamAttr(
+                    name=name_scope + 'beta', regularizer=bn_regularizer),
+                moving_mean_name=name_scope + 'moving_mean',
+                moving_variance_name=name_scope + 'moving_variance',
+                **kargs)
+    elif norm_type == 'gn':
+        with scope('GroupNorm'):
+            return group_norm(
+                args[0],
+                group_norm,
+                eps=eps,
+                param_attr=fluid.ParamAttr(
+                    name=name_scope + 'gamma', regularizer=bn_regularizer),
+                bias_attr=fluid.ParamAttr(
+                    name=name_scope + 'beta', regularizer=bn_regularizer))
+    else:
+        raise Exception("Unsupport norm type:" + norm_type)
+
+
+def bn_relu(data, norm_type='bn', eps=1e-5):
+    return fluid.layers.relu(bn(data, norm_type=norm_type, eps=eps))
+
+
+def relu(data):
+    return fluid.layers.relu(data)
+
+
+def conv(*args, **kargs):
+    kargs['param_attr'] = name_scope + 'weights'
+    if 'bias_attr' in kargs and kargs['bias_attr']:
+        kargs['bias_attr'] = fluid.ParamAttr(
+            name=name_scope + 'biases',
+            regularizer=None,
+            initializer=fluid.initializer.ConstantInitializer(value=0.0))
+    else:
+        kargs['bias_attr'] = False
+    return fluid.layers.conv2d(*args, **kargs)
+
+
+def deconv(*args, **kargs):
+    kargs['param_attr'] = name_scope + 'weights'
+    if 'bias_attr' in kargs and kargs['bias_attr']:
+        kargs['bias_attr'] = name_scope + 'biases'
+    else:
+        kargs['bias_attr'] = False
+    return fluid.layers.conv2d_transpose(*args, **kargs)
+
+
+def separate_conv(input,
+                  channel,
+                  stride,
+                  filter,
+                  dilation=1,
+                  act=None,
+                  eps=1e-5):
+    param_attr = fluid.ParamAttr(
+        name=name_scope + 'weights',
+        regularizer=fluid.regularizer.L2DecayRegularizer(
+            regularization_coeff=0.0),
+        initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.33))
+    with scope('depthwise'):
+        input = conv(
+            input,
+            input.shape[1],
+            filter,
+            stride,
+            groups=input.shape[1],
+            padding=(filter // 2) * dilation,
+            dilation=dilation,
+            use_cudnn=False,
+            param_attr=param_attr)
+        input = bn(input, eps=eps)
+        if act: input = act(input)
+
+    param_attr = fluid.ParamAttr(
+        name=name_scope + 'weights',
+        regularizer=None,
+        initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.06))
+    with scope('pointwise'):
+        input = conv(
+            input, channel, 1, 1, groups=1, padding=0, param_attr=param_attr)
+        input = bn(input, eps=eps)
+        if act: input = act(input)
+    return input
+
+
+def conv_bn_layer(input,
+                  filter_size,
+                  num_filters,
+                  stride,
+                  padding,
+                  channels=None,
+                  num_groups=1,
+                  if_act=True,
+                  name=None,
+                  use_cudnn=True):
+    conv = fluid.layers.conv2d(
+        input=input,
+        num_filters=num_filters,
+        filter_size=filter_size,
+        stride=stride,
+        padding=padding,
+        groups=num_groups,
+        act=None,
+        use_cudnn=use_cudnn,
+        param_attr=fluid.ParamAttr(name=name + '_weights'),
+        bias_attr=False)
+    bn_name = name + '_bn'
+    bn = fluid.layers.batch_norm(
+        input=conv,
+        param_attr=fluid.ParamAttr(name=bn_name + "_scale"),
+        bias_attr=fluid.ParamAttr(name=bn_name + "_offset"),
+        moving_mean_name=bn_name + '_mean',
+        moving_variance_name=bn_name + '_variance')
+    if if_act:
+        return fluid.layers.relu6(bn)
+    else:
+        return bn
+
+
+def sigmoid_to_softmax(input):
+    """
+    one channel to two channel
+    """
+    logit = fluid.layers.sigmoid(input)
+    logit_back = 1 - logit
+    logit = fluid.layers.concat([logit_back, logit], axis=1)
+    return logit

contrib/RemoteSensing/nets/loss.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 paddle.fluid as fluid
+import numpy as np
+
+
+def softmax_with_loss(logit,
+                      label,
+                      ignore_mask=None,
+                      num_classes=2,
+                      weight=None,
+                      ignore_index=255):
+    ignore_mask = fluid.layers.cast(ignore_mask, 'float32')
+    label = fluid.layers.elementwise_min(
+        label, fluid.layers.assign(np.array([num_classes - 1], dtype=np.int32)))
+    logit = fluid.layers.transpose(logit, [0, 2, 3, 1])
+    logit = fluid.layers.reshape(logit, [-1, num_classes])
+    label = fluid.layers.reshape(label, [-1, 1])
+    label = fluid.layers.cast(label, 'int64')
+    ignore_mask = fluid.layers.reshape(ignore_mask, [-1, 1])
+    if weight is None:
+        loss, probs = fluid.layers.softmax_with_cross_entropy(
+            logit, label, ignore_index=ignore_index, return_softmax=True)
+    else:
+        label_one_hot = fluid.layers.one_hot(input=label, depth=num_classes)
+        if isinstance(weight, list):
+            assert len(
+                weight
+            ) == num_classes, "weight length must equal num of classes"
+            weight = fluid.layers.assign(np.array([weight], dtype='float32'))
+        elif isinstance(weight, str):
+            assert weight.lower(
+            ) == 'dynamic', 'if weight is string, must be dynamic!'
+            tmp = []
+            total_num = fluid.layers.cast(
+                fluid.layers.shape(label)[0], 'float32')
+            for i in range(num_classes):
+                cls_pixel_num = fluid.layers.reduce_sum(label_one_hot[:, i])
+                ratio = total_num / (cls_pixel_num + 1)
+                tmp.append(ratio)
+            weight = fluid.layers.concat(tmp)
+            weight = weight / fluid.layers.reduce_sum(weight) * num_classes
+        elif isinstance(weight, fluid.layers.Variable):
+            pass
+        else:
+            raise ValueError(
+                'Expect weight is a list, string or Variable, but receive {}'.
+                format(type(weight)))
+        weight = fluid.layers.reshape(weight, [1, num_classes])
+        weighted_label_one_hot = fluid.layers.elementwise_mul(
+            label_one_hot, weight)
+        probs = fluid.layers.softmax(logit)
+        loss = fluid.layers.cross_entropy(
+            probs,
+            weighted_label_one_hot,
+            soft_label=True,
+            ignore_index=ignore_index)
+        weighted_label_one_hot.stop_gradient = True
+
+    loss = loss * ignore_mask
+    avg_loss = fluid.layers.mean(loss) / (
+        fluid.layers.mean(ignore_mask) + 0.00001)
+
+    label.stop_gradient = True
+    ignore_mask.stop_gradient = True
+    return avg_loss
+
+
+# to change, how to appicate ignore index and ignore mask
+def dice_loss(logit, label, ignore_mask=None, epsilon=0.00001):
+    if logit.shape[1] != 1 or label.shape[1] != 1 or ignore_mask.shape[1] != 1:
+        raise Exception(
+            "dice loss is only applicable to one channel classfication")
+    ignore_mask = fluid.layers.cast(ignore_mask, 'float32')
+    logit = fluid.layers.transpose(logit, [0, 2, 3, 1])
+    label = fluid.layers.transpose(label, [0, 2, 3, 1])
+    label = fluid.layers.cast(label, 'int64')
+    ignore_mask = fluid.layers.transpose(ignore_mask, [0, 2, 3, 1])
+    logit = fluid.layers.sigmoid(logit)
+    logit = logit * ignore_mask
+    label = label * ignore_mask
+    reduce_dim = list(range(1, len(logit.shape)))
+    inse = fluid.layers.reduce_sum(logit * label, dim=reduce_dim)
+    dice_denominator = fluid.layers.reduce_sum(
+        logit, dim=reduce_dim) + fluid.layers.reduce_sum(
+            label, dim=reduce_dim)
+    dice_score = 1 - inse * 2 / (dice_denominator + epsilon)
+    label.stop_gradient = True
+    ignore_mask.stop_gradient = True
+    return fluid.layers.reduce_mean(dice_score)
+
+
+def bce_loss(logit, label, ignore_mask=None, ignore_index=255):
+    if logit.shape[1] != 1 or label.shape[1] != 1 or ignore_mask.shape[1] != 1:
+        raise Exception("bce loss is only applicable to binary classfication")
+    label = fluid.layers.cast(label, 'float32')
+    loss = fluid.layers.sigmoid_cross_entropy_with_logits(
+        x=logit, label=label, ignore_index=ignore_index,
+        normalize=True)  # or False
+    loss = fluid.layers.reduce_sum(loss)
+    label.stop_gradient = True
+    ignore_mask.stop_gradient = True
+    return loss

contrib/RemoteSensing/nets/unet.py

+# coding: utf8
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from collections import OrderedDict
+
+import paddle.fluid as fluid
+from .libs import scope, name_scope
+from .libs import bn, bn_relu, relu
+from .libs import conv, max_pool, deconv
+from .libs import sigmoid_to_softmax
+from .loss import softmax_with_loss
+from .loss import dice_loss
+from .loss import bce_loss
+
+
+class UNet(object):
+    """实现Unet模型
+        `"U-Net: Convolutional Networks for Biomedical Image Segmentation"
+        <https://arxiv.org/abs/1505.04597>`
+
+        Args:
+            num_classes (int): 类别数
+            mode (str): 网络运行模式，根据mode构建网络的输入和返回。
+                当mode为'train'时，输入为image(-1, 3, -1, -1)和label (-1, 1, -1, -1) 返回loss。
+                当mode为'train'时，输入为image (-1, 3, -1, -1)和label  (-1, 1, -1, -1)，返回loss，
+                pred (与网络输入label 相同大小的预测结果，值代表相应的类别），label，mask（非忽略值的mask，
+                与label相同大小，bool类型）。
+                当mode为'test'时，输入为image(-1, 3, -1, -1)返回pred (-1, 1, -1, -1)和
+                logit (-1, num_classes, -1, -1) 通道维上代表每一类的概率值。
+            upsample_mode (str): UNet decode时采用的上采样方式，取值为'bilinear'时利用双线行差值进行上菜样，
+                当输入其他选项时则利用反卷积进行上菜样，默认为'bilinear'。
+            use_bce_loss (bool): 是否使用bce loss作为网络的损失函数，只能用于两类分割。可与dice loss同时使用。
+            use_dice_loss (bool): 是否使用dice loss作为网络的损失函数，只能用于两类分割，可与bce loss同时使用。
+                当use_bce_loss和use_dice_loss都为False时，使用交叉熵损失函数。
+            class_weight (list/str): 交叉熵损失函数各类损失的权重。当class_weight为list的时候，长度应为
+                num_classes。当class_weight为str时， weight.lower()应为'dynamic'，这时会根据每一轮各类像素的比重
+                自行计算相应的权重，每一类的权重为：每类的比例 * num_classes。class_weight取默认值None是，各类的权重1，
+                即平时使用的交叉熵损失函数。
+            ignore_index (int): label上忽略的值，label为ignore_index的像素不参与损失函数的计算。
+
+        Raises:
+            ValueError: use_bce_loss或use_dice_loss为真且num_calsses > 2。
+            ValueError: class_weight为list, 但长度不等于num_class。
+                class_weight为str, 但class_weight.low()不等于dynamic。
+            TypeError: class_weight不为None时，其类型不是list或str。
+        """
+
+    def __init__(self,
+                 num_classes,
+                 mode='train',
+                 upsample_mode='bilinear',
+                 input_channel=3,
+                 use_bce_loss=False,
+                 use_dice_loss=False,
+                 class_weight=None,
+                 ignore_index=255):
+        # dice_loss或bce_loss只适用两类分割中
+        if num_classes > 2 and (use_bce_loss or use_dice_loss):
+            raise Exception(
+                "dice loss and bce loss is only applicable to binary classfication"
+            )
+
+        if class_weight is not None:
+            if isinstance(class_weight, list):
+                if len(class_weight) != num_classes:
+                    raise ValueError(
+                        "Length of class_weight should be equal to number of classes"
+                    )
+            elif isinstance(class_weight, str):
+                if class_weight.lower() != 'dynamic':
+                    raise ValueError(
+                        "if class_weight is string, must be dynamic!")
+            else:
+                raise TypeError(
+                    'Expect class_weight is a list or string but receive {}'.
+                    format(type(class_weight)))
+        self.num_classes = num_classes
+        self.mode = mode
+        self.upsample_mode = upsample_mode
+        self.input_channel = input_channel
+        self.use_bce_loss = use_bce_loss
+        self.use_dice_loss = use_dice_loss
+        self.class_weight = class_weight
+        self.ignore_index = ignore_index
+
+    def _double_conv(self, data, out_ch):
+        param_attr = fluid.ParamAttr(
+            name='weights',
+            regularizer=fluid.regularizer.L2DecayRegularizer(
+                regularization_coeff=0.0),
+            initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.33))
+        with scope("conv0"):
+            data = bn_relu(
+                conv(
+                    data, out_ch, 3, stride=1, padding=1,
+                    param_attr=param_attr))
+        with scope("conv1"):
+            data = bn_relu(
+                conv(
+                    data, out_ch, 3, stride=1, padding=1,
+                    param_attr=param_attr))
+        return data
+
+    def _down(self, data, out_ch):
+        # 下采样：max_pool + 2个卷积
+        with scope("down"):
+            data = max_pool(data, 2, 2, 0)
+            data = self._double_conv(data, out_ch)
+        return data
+
+    def _up(self, data, short_cut, out_ch):
+        # 上采样：data上采样(resize或deconv), 并与short_cut concat
+        param_attr = fluid.ParamAttr(
+            name='weights',
+            regularizer=fluid.regularizer.L2DecayRegularizer(
+                regularization_coeff=0.0),
+            initializer=fluid.initializer.XavierInitializer(),
+        )
+        with scope("up"):
+            if self.upsample_mode == 'bilinear':
+                short_cut_shape = fluid.layers.shape(short_cut)
+                data = fluid.layers.resize_bilinear(data, short_cut_shape[2:])
+            else:
+                data = deconv(
+                    data,
+                    out_ch // 2,
+                    filter_size=2,
+                    stride=2,
+                    padding=0,
+                    param_attr=param_attr)
+            data = fluid.layers.concat([data, short_cut], axis=1)
+            data = self._double_conv(data, out_ch)
+        return data
+
+    def _encode(self, data):
+        # 编码器设置
+        short_cuts = []
+        with scope("encode"):
+            with scope("block1"):
+                data = self._double_conv(data, 64)
+                short_cuts.append(data)
+            with scope("block2"):
+                data = self._down(data, 128)
+                short_cuts.append(data)
+            with scope("block3"):
+                data = self._down(data, 256)
+                short_cuts.append(data)
+            with scope("block4"):
+                data = self._down(data, 512)
+                short_cuts.append(data)
+            with scope("block5"):
+                data = self._down(data, 512)
+        return data, short_cuts
+
+    def _decode(self, data, short_cuts):
+        # 解码器设置，与编码器对称
+        with scope("decode"):
+            with scope("decode1"):
+                data = self._up(data, short_cuts[3], 256)
+            with scope("decode2"):
+                data = self._up(data, short_cuts[2], 128)
+            with scope("decode3"):
+                data = self._up(data, short_cuts[1], 64)
+            with scope("decode4"):
+                data = self._up(data, short_cuts[0], 64)
+        return data
+
+    def _get_logit(self, data, num_classes):
+        # 根据类别数设置最后一个卷积层输出
+        param_attr = fluid.ParamAttr(
+            name='weights',
+            regularizer=fluid.regularizer.L2DecayRegularizer(
+                regularization_coeff=0.0),
+            initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.01))
+        with scope("logit"):
+            data = conv(
+                data,
+                num_classes,
+                3,
+                stride=1,
+                padding=1,
+                param_attr=param_attr)
+        return data
+
+    def _get_loss(self, logit, label, mask):
+        avg_loss = 0
+        if not (self.use_dice_loss or self.use_bce_loss):
+            avg_loss += softmax_with_loss(
+                logit,
+                label,
+                mask,
+                num_classes=self.num_classes,
+                weight=self.class_weight,
+                ignore_index=self.ignore_index)
+        else:
+            if self.use_dice_loss:
+                avg_loss += dice_loss(logit, label, mask)
+            if self.use_bce_loss:
+                avg_loss += bce_loss(
+                    logit, label, mask, ignore_index=self.ignore_index)
+
+        return avg_loss
+
+    def generate_inputs(self):
+        inputs = OrderedDict()
+        inputs['image'] = fluid.data(
+            dtype='float32',
+            shape=[None, self.input_channel, None, None],
+            name='image')
+        if self.mode == 'train':
+            inputs['label'] = fluid.data(
+                dtype='int32', shape=[None, 1, None, None], name='label')
+        elif self.mode == 'eval':
+            inputs['label'] = fluid.data(
+                dtype='int32', shape=[None, 1, None, None], name='label')
+        return inputs
+
+    def build_net(self, inputs):
+        # 在两类分割情况下，当loss函数选择dice_loss或bce_loss的时候，最后logit输出通道数设置为1
+        if self.use_dice_loss or self.use_bce_loss:
+            self.num_classes = 1
+
+        image = inputs['image']
+        encode_data, short_cuts = self._encode(image)
+        decode_data = self._decode(encode_data, short_cuts)
+        logit = self._get_logit(decode_data, self.num_classes)
+
+        if self.num_classes == 1:
+            out = sigmoid_to_softmax(logit)
+            out = fluid.layers.transpose(out, [0, 2, 3, 1])
+        else:
+            out = fluid.layers.transpose(logit, [0, 2, 3, 1])
+
+        pred = fluid.layers.argmax(out, axis=3)
+        pred = fluid.layers.unsqueeze(pred, axes=[3])
+
+        if self.mode == 'train':
+            label = inputs['label']
+            mask = label != self.ignore_index
+            return self._get_loss(logit, label, mask)
+
+        elif self.mode == 'eval':
+            label = inputs['label']
+            mask = label != self.ignore_index
+            loss = self._get_loss(logit, label, mask)
+            return loss, pred, label, mask
+        else:
+            if self.num_classes == 1:
+                logit = sigmoid_to_softmax(logit)
+            else:
+                logit = fluid.layers.softmax(logit, axis=1)
+            return pred, logit

contrib/RemoteSensing/readers/__init__.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 .reader import Reader

contrib/RemoteSensing/readers/base.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 threading import Thread
+import multiprocessing
+import collections
+import numpy as np
+import six
+import sys
+import copy
+import random
+import platform
+import chardet
+from ..utils import logging
+
+
+class EndSignal():
+    pass
+
+
+def is_pic(img_name):
+    valid_suffix = ['JPEG', 'jpeg', 'JPG', 'jpg', 'BMP', 'bmp', 'PNG', 'png']
+    suffix = img_name.split('.')[-1]
+    if suffix not in valid_suffix:
+        return False
+    return True
+
+
+def is_valid(sample):
+    if sample is None:
+        return False
+    if isinstance(sample, tuple):
+        for s in sample:
+            if s is None:
+                return False
+            elif isinstance(s, np.ndarray) and s.size == 0:
+                return False
+            elif isinstance(s, collections.Sequence) and len(s) == 0:
+                return False
+    return True
+
+
+def get_encoding(path):
+    f = open(path, 'rb')
+    data = f.read()
+    file_encoding = chardet.detect(data).get('encoding')
+    return file_encoding
+
+
+def multithread_reader(mapper,
+                       reader,
+                       num_workers=4,
+                       buffer_size=1024,
+                       batch_size=8,
+                       drop_last=True):
+    from queue import Queue
+    end = EndSignal()
+
+    # define a worker to read samples from reader to in_queue
+    def read_worker(reader, in_queue):
+        for i in reader():
+            in_queue.put(i)
+        in_queue.put(end)
+
+    # define a worker to handle samples from in_queue by mapper
+    # and put mapped samples into out_queue
+    def handle_worker(in_queue, out_queue, mapper):
+        sample = in_queue.get()
+        while not isinstance(sample, EndSignal):
+            if len(sample) == 2:
+                r = mapper(sample[0], sample[1])
+            elif len(sample) == 3:
+                r = mapper(sample[0], sample[1], sample[2])
+            else:
+                raise Exception('The sample\'s length must be 2 or 3.')
+            if is_valid(r):
+                out_queue.put(r)
+            sample = in_queue.get()
+        in_queue.put(end)
+        out_queue.put(end)
+
+    def xreader():
+        in_queue = Queue(buffer_size)
+        out_queue = Queue(buffer_size)
+        # start a read worker in a thread
+        target = read_worker
+        t = Thread(target=target, args=(reader, in_queue))
+        t.daemon = True
+        t.start()
+        # start several handle_workers
+        target = handle_worker
+        args = (in_queue, out_queue, mapper)
+        workers = []
+        for i in range(num_workers):
+            worker = Thread(target=target, args=args)
+            worker.daemon = True
+            workers.append(worker)
+        for w in workers:
+            w.start()
+
+        batch_data = []
+        sample = out_queue.get()
+        while not isinstance(sample, EndSignal):
+            batch_data.append(sample)
+            if len(batch_data) == batch_size:
+                batch_data = GenerateMiniBatch(batch_data)
+                yield batch_data
+                batch_data = []
+            sample = out_queue.get()
+        finish = 1
+        while finish < num_workers:
+            sample = out_queue.get()
+            if isinstance(sample, EndSignal):
+                finish += 1
+            else:
+                batch_data.append(sample)
+                if len(batch_data) == batch_size:
+                    batch_data = GenerateMiniBatch(batch_data)
+                    yield batch_data
+                    batch_data = []
+        if not drop_last and len(batch_data) != 0:
+            batch_data = GenerateMiniBatch(batch_data)
+            yield batch_data
+            batch_data = []
+
+    return xreader
+
+
+def multiprocess_reader(mapper,
+                        reader,
+                        num_workers=4,
+                        buffer_size=1024,
+                        batch_size=8,
+                        drop_last=True):
+    from .shared_queue import SharedQueue as Queue
+
+    def _read_into_queue(samples, mapper, queue):
+        end = EndSignal()
+        try:
+            for sample in samples:
+                if sample is None:
+                    raise ValueError("sample has None")
+                if len(sample) == 2:
+                    result = mapper(sample[0], sample[1])
+                elif len(sample) == 3:
+                    result = mapper(sample[0], sample[1], sample[2])
+                else:
+                    raise Exception('The sample\'s length must be 2 or 3.')
+                if is_valid(result):
+                    queue.put(result)
+            queue.put(end)
+        except:
+            queue.put("")
+            six.reraise(*sys.exc_info())
+
+    def queue_reader():
+        queue = Queue(buffer_size, memsize=3 * 1024**3)
+        total_samples = [[] for i in range(num_workers)]
+        for i, sample in enumerate(reader()):
+            index = i % num_workers
+            total_samples[index].append(sample)
+        for i in range(num_workers):
+            p = multiprocessing.Process(
+                target=_read_into_queue, args=(total_samples[i], mapper, queue))
+            p.start()
+
+        finish_num = 0
+        batch_data = list()
+        while finish_num < num_workers:
+            sample = queue.get()
+            if isinstance(sample, EndSignal):
+                finish_num += 1
+            elif sample == "":
+                raise ValueError("multiprocess reader raises an exception")
+            else:
+                batch_data.append(sample)
+                if len(batch_data) == batch_size:
+                    batch_data = GenerateMiniBatch(batch_data)
+                    yield batch_data
+                    batch_data = []
+        if len(batch_data) != 0 and not drop_last:
+            batch_data = GenerateMiniBatch(batch_data)
+            yield batch_data
+            batch_data = []
+
+    return queue_reader
+
+
+def GenerateMiniBatch(batch_data):
+    if len(batch_data) == 1:
+        return batch_data
+    width = [data[0].shape[2] for data in batch_data]
+    height = [data[0].shape[1] for data in batch_data]
+    if len(set(width)) == 1 and len(set(height)) == 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
+
+
+class BaseReader:
+    def __init__(self,
+                 transforms=None,
+                 num_workers=4,
+                 buffer_size=100,
+                 parallel_method='thread',
+                 shuffle=False):
+        if transforms is None:
+            raise Exception("transform should be defined.")
+        self.transforms = transforms
+        self.num_workers = num_workers
+        self.buffer_size = buffer_size
+        self.parallel_method = parallel_method
+        self.shuffle = shuffle
+
+    def generator(self, batch_size=1, drop_last=True):
+        self.batch_size = batch_size
+        parallel_reader = multithread_reader
+        if self.parallel_method == "process":
+            if platform.platform().startswith("Windows"):
+                logging.debug(
+                    "multiprocess_reader is not supported in Windows platform, force to use multithread_reader."
+                )
+            else:
+                parallel_reader = multiprocess_reader
+        return parallel_reader(
+            self.transforms,
+            self.iterator,
+            num_workers=self.num_workers,
+            buffer_size=self.buffer_size,
+            batch_size=batch_size,
+            drop_last=drop_last)

contrib/RemoteSensing/readers/reader.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 __future__ import absolute_import
+import os.path as osp
+import random
+from ..utils import logging
+from .base import BaseReader
+from .base import get_encoding
+from collections import OrderedDict
+from .base import is_pic
+
+
+class Reader(BaseReader):
+    """读取语分分割任务数据集，并对样本进行相应的处理。
+
+    Args:
+        data_dir (str): 数据集所在的目录路径。
+        file_list (str): 描述数据集图片文件和对应标注文件的文件路径（文本内每行路径为相对data_dir的相对路）。
+        label_list (str): 描述数据集包含的类别信息文件路径。
+        transforms (list): 数据集中每个样本的预处理/增强算子。
+        num_workers (int): 数据集中样本在预处理过程中的线程或进程数。默认为4。
+        buffer_size (int): 数据集中样本在预处理过程中队列的缓存长度，以样本数为单位。默认为100。
+        parallel_method (str): 数据集中样本在预处理过程中并行处理的方式，支持'thread'
+            线程和'process'进程两种方式。默认为'thread'。
+        shuffle (bool): 是否需要对数据集中样本打乱顺序。默认为False。
+    """
+
+    def __init__(self,
+                 data_dir,
+                 file_list,
+                 label_list,
+                 transforms=None,
+                 num_workers=4,
+                 buffer_size=100,
+                 parallel_method='thread',
+                 shuffle=False):
+        super(Reader, self).__init__(
+            transforms=transforms,
+            num_workers=num_workers,
+            buffer_size=buffer_size,
+            parallel_method=parallel_method,
+            shuffle=shuffle)
+        self.file_list = OrderedDict()
+        self.labels = list()
+        self._epoch = 0
+
+        with open(label_list, encoding=get_encoding(label_list)) as f:
+            for line in f:
+                item = line.strip()
+                self.labels.append(item)
+
+        with open(file_list, encoding=get_encoding(file_list)) as f:
+            for line in f:
+                items = line.strip().split()
+                full_path_im = osp.join(data_dir, items[0])
+                full_path_label = osp.join(data_dir, items[1])
+                if not osp.exists(full_path_im):
+                    raise IOError(
+                        'The image file {} is not exist!'.format(full_path_im))
+                if not osp.exists(full_path_label):
+                    raise IOError('The image file {} is not exist!'.format(
+                        full_path_label))
+                self.file_list[full_path_im] = full_path_label
+        self.num_samples = len(self.file_list)
+        logging.info("{} samples in file {}".format(
+            len(self.file_list), file_list))
+
+    def iterator(self):
+        self._epoch += 1
+        self._pos = 0
+        files = list(self.file_list.keys())
+        if self.shuffle:
+            random.shuffle(files)
+        files = files[:self.num_samples]
+        self.num_samples = len(files)
+        for f in files:
+            label_path = self.file_list[f]
+            sample = [f, None, label_path]
+            yield sample

contrib/RemoteSensing/transforms/__init__.py

+#   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.
+
+from . import transforms
+from . import ops

contrib/RemoteSensing/transforms/ops.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 cv2
+import math
+import numpy as np
+from PIL import Image, ImageEnhance
+
+
+def normalize(im, mean, std):
+    im = im.astype(np.float32, copy=False) / 255.0
+    im -= mean
+    im /= std
+    return im
+
+
+def permute(im, to_bgr=False):
+    im = np.swapaxes(im, 1, 2)
+    im = np.swapaxes(im, 1, 0)
+    if to_bgr:
+        im = im[[2, 1, 0], :, :]
+    return im
+
+
+def _resize(im, shape):
+    return cv2.resize(im, shape)
+
+
+def resize_short(im, short_size=224):
+    percent = float(short_size) / min(im.shape[0], im.shape[1])
+    resized_width = int(round(im.shape[1] * percent))
+    resized_height = int(round(im.shape[0] * percent))
+    im = _resize(im, shape=(resized_width, resized_height))
+    return im
+
+
+def resize_long(im, long_size=224, interpolation=cv2.INTER_LINEAR):
+    value = max(im.shape[0], im.shape[1])
+    scale = float(long_size) / float(value)
+    im = cv2.resize(im, (0, 0), fx=scale, fy=scale, interpolation=interpolation)
+    return im
+
+
+def random_crop(im,
+                crop_size=224,
+                lower_scale=0.08,
+                lower_ratio=3. / 4,
+                upper_ratio=4. / 3):
+    scale = [lower_scale, 1.0]
+    ratio = [lower_ratio, upper_ratio]
+    aspect_ratio = math.sqrt(np.random.uniform(*ratio))
+    w = 1. * aspect_ratio
+    h = 1. / aspect_ratio
+    bound = min((float(im.shape[0]) / im.shape[1]) / (h**2),
+                (float(im.shape[1]) / im.shape[0]) / (w**2))
+    scale_max = min(scale[1], bound)
+    scale_min = min(scale[0], bound)
+    target_area = im.shape[0] * im.shape[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, im.shape[0] - h + 1)
+    j = np.random.randint(0, im.shape[1] - w + 1)
+    im = im[i:i + h, j:j + w, :]
+    im = _resize(im, shape=(crop_size, crop_size))
+    return im
+
+
+def center_crop(im, crop_size=224):
+    height, width = im.shape[:2]
+    w_start = (width - crop_size) // 2
+    h_start = (height - crop_size) // 2
+    w_end = w_start + crop_size
+    h_end = h_start + crop_size
+    im = im[h_start:h_end, w_start:w_end, :]
+    return im
+
+
+def horizontal_flip(im):
+    if len(im.shape) == 3:
+        im = im[:, ::-1, :]
+    elif len(im.shape) == 2:
+        im = im[:, ::-1]
+    return im
+
+
+def vertical_flip(im):
+    if len(im.shape) == 3:
+        im = im[::-1, :, :]
+    elif len(im.shape) == 2:
+        im = im[::-1, :]
+    return im
+
+
+def bgr2rgb(im):
+    return im[:, :, ::-1]
+
+
+def brightness(im, brightness_lower, brightness_upper):
+    brightness_delta = np.random.uniform(brightness_lower, brightness_upper)
+    im = ImageEnhance.Brightness(im).enhance(brightness_delta)
+    return im
+
+
+def contrast(im, contrast_lower, contrast_upper):
+    contrast_delta = np.random.uniform(contrast_lower, contrast_upper)
+    im = ImageEnhance.Contrast(im).enhance(contrast_delta)
+    return im
+
+
+def saturation(im, saturation_lower, saturation_upper):
+    saturation_delta = np.random.uniform(saturation_lower, saturation_upper)
+    im = ImageEnhance.Color(im).enhance(saturation_delta)
+    return im
+
+
+def hue(im, hue_lower, hue_upper):
+    hue_delta = np.random.uniform(hue_lower, hue_upper)
+    im = np.array(im.convert('HSV'))
+    im[:, :, 0] = im[:, :, 0] + hue_delta
+    im = Image.fromarray(im, mode='HSV').convert('RGB')
+    return im
+
+
+def rotate(im, rotate_lower, rotate_upper):
+    rotate_delta = np.random.uniform(rotate_lower, rotate_upper)
+    im = im.rotate(int(rotate_delta))
+    return im
+
+
+def resize_padding(im, max_side_len=2400):
+    '''
+    resize image to a size multiple of 32 which is required by the network
+    :param im: the resized image
+    :param max_side_len: limit of max image size to avoid out of memory in gpu
+    :return: the resized image and the resize ratio
+    '''
+    h, w, _ = im.shape
+
+    resize_w = w
+    resize_h = h
+
+    # limit the max side
+    if max(resize_h, resize_w) > max_side_len:
+        ratio = float(
+            max_side_len) / resize_h if resize_h > resize_w else float(
+                max_side_len) / resize_w
+    else:
+        ratio = 1.
+    resize_h = int(resize_h * ratio)
+    resize_w = int(resize_w * ratio)
+
+    resize_h = resize_h if resize_h % 32 == 0 else (resize_h // 32 - 1) * 32
+    resize_w = resize_w if resize_w % 32 == 0 else (resize_w // 32 - 1) * 32
+    resize_h = max(32, resize_h)
+    resize_w = max(32, resize_w)
+    im = cv2.resize(im, (int(resize_w), int(resize_h)))
+    #im = cv2.resize(im, (512, 512))
+    ratio_h = resize_h / float(h)
+    ratio_w = resize_w / float(w)
+    _ratio = np.array([ratio_h, ratio_w]).reshape(-1, 2)
+    return im, _ratio

contrib/RemoteSensing/transforms/transforms.py

+# coding: utf8
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 .ops import *
+import random
+import os.path as osp
+import numpy as np
+from PIL import Image
+import cv2
+from collections import OrderedDict
+
+
+class Compose:
+    """根据数据预处理/增强算子对输入数据进行操作。
+       所有操作的输入图像流形状均是[H, W, C]，其中H为图像高，W为图像宽，C为图像通道数。
+
+    Args:
+        transforms (list): 数据预处理/增强算子。
+
+    Raises:
+        TypeError: transforms不是list对象
+        ValueError: transforms元素个数小于1。
+
+    """
+
+    def __init__(self, transforms):
+        if not isinstance(transforms, list):
+            raise TypeError('The transforms must be a list!')
+        if len(transforms) < 1:
+            raise ValueError('The length of transforms ' + \
+                            'must be equal or larger than 1!')
+        self.transforms = transforms
+
+    def __call__(self, im, im_info=None, label=None):
+        """
+        Args:
+            im (str/np.ndarray): 图像路径/图像np.ndarray数据。
+            im_info (dict): 存储与图像相关的信息，dict中的字段如下：
+                - shape_before_resize (tuple): 图像resize之前的大小（h, w）。
+                - shape_before_padding (tuple): 图像padding之前的大小（h, w）。
+            label (str/np.ndarray): 标注图像路径/标注图像np.ndarray数据。
+
+        Returns:
+            tuple: 根据网络所需字段所组成的tuple；字段由transforms中的最后一个数据预处理操作决定。
+        """
+
+        if im_info is None:
+            im_info = dict()
+        im = np.load(im)
+        if im is None:
+            raise ValueError('Can\'t read The image file {}!'.format(im))
+        if label is not None:
+            label = np.asarray(Image.open(label))
+
+        for op in self.transforms:
+            outputs = op(im, im_info, label)
+            im = outputs[0]
+            if len(outputs) >= 2:
+                im_info = outputs[1]
+            if len(outputs) == 3:
+                label = outputs[2]
+        return outputs
+
+
+class RandomHorizontalFlip:
+    """以一定的概率对图像进行水平翻转。当存在标注图像时，则同步进行翻转。
+
+    Args:
+        prob (float): 随机水平翻转的概率。默认值为0.5。
+
+    """
+
+    def __init__(self, prob=0.5):
+        self.prob = prob
+
+    def __call__(self, im, im_info=None, label=None):
+        """
+        Args:
+            im (np.ndarray): 图像np.ndarray数据。
+            im_info (dict): 存储与图像相关的信息。
+            label (np.ndarray): 标注图像np.ndarray数据。
+
+        Returns:
+            tuple: 当label为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
+                当label不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
+                存储与图像相关信息的字典和标注图像np.ndarray数据。
+        """
+        if random.random() < self.prob:
+            im = horizontal_flip(im)
+            if label is not None:
+                label = horizontal_flip(label)
+        if label is None:
+            return (im, im_info)
+        else:
+            return (im, im_info, label)
+
+
+class RandomVerticalFlip:
+    """以一定的概率对图像进行垂直翻转。当存在标注图像时，则同步进行翻转。
+
+    Args:
+        prob (float): 随机垂直翻转的概率。默认值为0.1。
+    """
+
+    def __init__(self, prob=0.1):
+        self.prob = prob
+
+    def __call__(self, im, im_info=None, label=None):
+        """
+        Args:
+            im (np.ndarray): 图像np.ndarray数据。
+            im_info (dict): 存储与图像相关的信息。
+            label (np.ndarray): 标注图像np.ndarray数据。
+
+        Returns:
+            tuple: 当label为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
+                当label不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
+                存储与图像相关信息的字典和标注图像np.ndarray数据。
+        """
+        if random.random() < self.prob:
+            im = vertical_flip(im)
+            if label is not None:
+                label = vertical_flip(label)
+        if label is None:
+            return (im, im_info)
+        else:
+            return (im, im_info, label)
+
+
+class Resize:
+    """调整图像大小（resize），当存在标注图像时，则同步进行处理。
+
+    - 当目标大小（target_size）类型为int时，根据插值方式，
+      将图像resize为[target_size, target_size]。
+    - 当目标大小（target_size）类型为list或tuple时，根据插值方式，
+      将图像resize为target_size, target_size的输入应为[w, h]或（w, h）。
+
+    Args:
+        target_size (int/list/tuple): 目标大小
+        interp (str): resize的插值方式，与opencv的插值方式对应，
+            可选的值为['NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4']，默认为"LINEAR"。
+
+    Raises:
+        TypeError: target_size不是int/list/tuple。
+        ValueError:  target_size为list/tuple时元素个数不等于2。
+        AssertionError: interp的取值不在['NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4']之内
+    """
+
+    # The interpolation mode
+    interp_dict = {
+        'NEAREST': cv2.INTER_NEAREST,
+        'LINEAR': cv2.INTER_LINEAR,
+        'CUBIC': cv2.INTER_CUBIC,
+        'AREA': cv2.INTER_AREA,
+        'LANCZOS4': cv2.INTER_LANCZOS4
+    }
+
+    def __init__(self, target_size, interp='LINEAR'):
+        self.interp = interp
+        assert interp in self.interp_dict, "interp should be one of {}".format(
+            interp_dict.keys())
+        if isinstance(target_size, list) or isinstance(target_size, tuple):
+            if len(target_size) != 2:
+                raise ValueError(
+                    'when target is list or tuple, it should include 2 elements, but it is {}'
+                    .format(target_size))
+        elif not isinstance(target_size, int):
+            raise TypeError(
+                "Type of target_size is invalid. Must be Integer or List or tuple, now is {}"
+                .format(type(target_size)))
+
+        self.target_size = target_size
+
+    def __call__(self, im, im_info=None, label=None):
+        """
+        Args:
+            im (np.ndarray): 图像np.ndarray数据。
+            im_info (dict): 存储与图像相关的信息。
+            label (np.ndarray): 标注图像np.ndarray数据。
+
+        Returns:
+            tuple: 当label为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
+                当label不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
+                存储与图像相关信息的字典和标注图像np.ndarray数据。
+                其中，im_info跟新字段为：
+                    -shape_before_resize (tuple): 保存resize之前图像的形状(h, w）。
+
+        Raises:
+            ZeroDivisionError: im的短边为0。
+            TypeError: im不是np.ndarray数据。
+            ValueError: im不是3维nd.ndarray。
+        """
+        if im_info is None:
+            im_info = OrderedDict()
+        im_info['shape_before_resize'] = im.shape[:2]
+
+        if not isinstance(im, np.ndarray):
+            raise TypeError("ResizeImage: image type is not np.ndarray.")
+        if len(im.shape) != 3:
+            raise ValueError('ResizeImage: image is not 3-dimensional.')
+        im_shape = im.shape
+        im_size_min = np.min(im_shape[0:2])
+        im_size_max = np.max(im_shape[0:2])
+        if float(im_size_min) == 0:
+            raise ZeroDivisionError('ResizeImage: min size of image is 0')
+
+        if isinstance(self.target_size, int):
+            resize_w = self.target_size
+            resize_h = self.target_size
+        else:
+            resize_w = self.target_size[0]
+            resize_h = self.target_size[1]
+        im_scale_x = float(resize_w) / float(im_shape[1])
+        im_scale_y = float(resize_h) / float(im_shape[0])
+
+        im = cv2.resize(
+            im,
+            None,
+            None,
+            fx=im_scale_x,
+            fy=im_scale_y,
+            interpolation=self.interp_dict[self.interp])
+        if label is not None:
+            label = cv2.resize(
+                label,
+                None,
+                None,
+                fx=im_scale_x,
+                fy=im_scale_y,
+                interpolation=self.interp_dict['NEAREST'])
+        if label is None:
+            return (im, im_info)
+        else:
+            return (im, im_info, label)
+
+
+class ResizeByLong:
+    """对图像长边resize到固定值，短边按比例进行缩放。当存在标注图像时，则同步进行处理。
+
+    Args:
+        long_size (int): resize后图像的长边大小。
+    """
+
+    def __init__(self, long_size):
+        self.long_size = long_size
+
+    def __call__(self, im, im_info=None, label=None):
+        """
+        Args:
+            im (np.ndarray): 图像np.ndarray数据。
+            im_info (dict): 存储与图像相关的信息。
+            label (np.ndarray): 标注图像np.ndarray数据。
+
+        Returns:
+            tuple: 当label为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
+                当label不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
+                存储与图像相关信息的字典和标注图像np.ndarray数据。
+                其中，im_info新增字段为：
+                    -shape_before_resize (tuple): 保存resize之前图像的形状(h, w）。
+        """
+        if im_info is None:
+            im = np.pad(
+                im,
+                pad_width=((0, pad_height), (0, pad_width), (0, 0)),
+                mode='constant',
+                constant_values=(self.im_padding_value, self.im_padding_value))
+            label = np.pad(
+                label,
+                pad_width=((0, pad_height), (0, pad_width)),
+                mode='constant',
+                constant_values=(self.label_padding_value,
+                                 self.label_padding_value))
+            im_info = OrderedDict()
+
+        im_info['shape_before_resize'] = im.shape[:2]
+        im = resize_long(im, self.long_size)
+        if label is not None:
+            label = resize_long(label, self.long_size, cv2.INTER_NEAREST)
+
+        if label is None:
+            return (im, im_info)
+        else:
+            return (im, im_info, label)
+
+
+class ResizeRangeScaling:
+    """对图像长边随机resize到指定范围内，短边按比例进行缩放。当存在标注图像时，则同步进行处理。
+
+    Args:
+        min_value (int): 图像长边resize后的最小值。默认值400。
+        max_value (int): 图像长边resize后的最大值。默认值600。
+
+    Raises:
+        ValueError: min_value大于max_value
+    """
+
+    def __init__(self, min_value=400, max_value=600):
+        if min_value > max_value:
+            raise ValueError('min_value must be less than max_value, '
+                             'but they are {} and {}.'.format(
+                                 min_value, max_value))
+        self.min_value = min_value
+        self.max_value = max_value
+
+    def __call__(self, im, im_info=None, label=None):
+        """
+        Args:
+            im (np.ndarray): 图像np.ndarray数据。
+            im_info (dict): 存储与图像相关的信息。
+            label (np.ndarray): 标注图像np.ndarray数据。
+
+        Returns:
+            tuple: 当label为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
+                当label不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
+                存储与图像相关信息的字典和标注图像np.ndarray数据。
+        """
+        if self.min_value == self.max_value:
+            random_size = self.max_value
+        else:
+            random_size = int(
+                np.random.uniform(self.min_value, self.max_value) + 0.5)
+        value = max(im.shape[0], im.shape[1])
+        scale = float(random_size) / float(value)
+        im = cv2.resize(
+            im, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)
+        if label is not None:
+            label = cv2.resize(
+                label, (0, 0),
+                fx=scale,
+                fy=scale,
+                interpolation=cv2.INTER_NEAREST)
+
+        if label is None:
+            return (im, im_info)
+        else:
+            return (im, im_info, label)
+
+
+class ResizeStepScaling:
+    """对图像按照某一个比例resize，这个比例以scale_step_size为步长
+    在[min_scale_factor, max_scale_factor]随机变动。当存在标注图像时，则同步进行处理。
+
+    Args:
+        min_scale_factor（float), resize最小尺度。默认值0.75。
+        max_scale_factor (float), resize最大尺度。默认值1.25。
+        scale_step_size (float), resize尺度范围间隔。默认值0.25。
+
+    Raises:
+        ValueError: min_scale_factor大于max_scale_factor
+    """
+
+    def __init__(self,
+                 min_scale_factor=0.75,
+                 max_scale_factor=1.25,
+                 scale_step_size=0.25):
+        if min_scale_factor > max_scale_factor:
+            raise ValueError(
+                'min_scale_factor must be less than max_scale_factor, '
+                'but they are {} and {}.'.format(min_scale_factor,
+                                                 max_scale_factor))
+        self.min_scale_factor = min_scale_factor
+        self.max_scale_factor = max_scale_factor
+        self.scale_step_size = scale_step_size
+
+    def __call__(self, im, im_info=None, label=None):
+        """
+        Args:
+            im (np.ndarray): 图像np.ndarray数据。
+            im_info (dict): 存储与图像相关的信息。
+            label (np.ndarray): 标注图像np.ndarray数据。
+
+        Returns:
+            tuple: 当label为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
+                当label不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
+                存储与图像相关信息的字典和标注图像np.ndarray数据。
+        """
+        if self.min_scale_factor == self.max_scale_factor:
+            scale_factor = self.min_scale_factor
+
+        elif self.scale_step_size == 0:
+            scale_factor = np.random.uniform(self.min_scale_factor,
+                                             self.max_scale_factor)
+
+        else:
+            num_steps = int((self.max_scale_factor - self.min_scale_factor) /
+                            self.scale_step_size + 1)
+            scale_factors = np.linspace(self.min_scale_factor,
+                                        self.max_scale_factor,
+                                        num_steps).tolist()
+            np.random.shuffle(scale_factors)
+            scale_factor = scale_factors[0]
+
+        im = cv2.resize(
+            im, (0, 0),
+            fx=scale_factor,
+            fy=scale_factor,
+            interpolation=cv2.INTER_LINEAR)
+        if label is not None:
+            label = cv2.resize(
+                label, (0, 0),
+                fx=scale_factor,
+                fy=scale_factor,
+                interpolation=cv2.INTER_NEAREST)
+
+        if label is None:
+            return (im, im_info)
+        else:
+            return (im, im_info, label)
+
+
+class Normalize:
+    """对图像进行标准化。
+    1.尺度缩放到 [0,1]。
+    2.对图像进行减均值除以标准差操作。
+
+    Args:
+        mean (list): 图像数据集的均值。默认值[0.5, 0.5, 0.5]。
+        std (list): 图像数据集的标准差。默认值[0.5, 0.5, 0.5]。
+
+    Raises:
+        ValueError: mean或std不是list对象。std包含0。
+    """
+
+    def __init__(self, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]):
+        self.mean = mean
+        self.std = std
+        if not (isinstance(self.mean, list) and isinstance(self.std, list)):
+            raise ValueError("{}: input type is invalid.".format(self))
+        from functools import reduce
+        if reduce(lambda x, y: x * y, self.std) == 0:
+            raise ValueError('{}: std is invalid!'.format(self))
+
+    def __call__(self, im, im_info=None, label=None):
+        """
+        Args:
+            im (np.ndarray): 图像np.ndarray数据。
+            im_info (dict): 存储与图像相关的信息。
+            label (np.ndarray): 标注图像np.ndarray数据。
+
+         Returns:
+            tuple: 当label为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
+                当label不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
+                存储与图像相关信息的字典和标注图像np.ndarray数据。
+        """
+
+        mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
+        std = np.array(self.std)[np.newaxis, np.newaxis, :]
+        im = normalize(im, mean, std)
+
+        if label is None:
+            return (im, im_info)
+        else:
+            return (im, im_info, label)
+
+
+class Padding:
+    """对图像或标注图像进行padding，padding方向为右和下。
+    根据提供的值对图像或标注图像进行padding操作。
+
+    Args:
+        target_size (int/list/tuple): padding后图像的大小。
+        im_padding_value (list): 图像padding的值。默认为[127.5, 127.5, 127.5]。
+        label_padding_value (int): 标注图像padding的值。默认值为255。
+
+    Raises:
+        TypeError: target_size不是int/list/tuple。
+        ValueError:  target_size为list/tuple时元素个数不等于2。
+    """
+
+    def __init__(self,
+                 target_size,
+                 im_padding_value=127.5,
+                 label_padding_value=255):
+        if isinstance(target_size, list) or isinstance(target_size, tuple):
+            if len(target_size) != 2:
+                raise ValueError(
+                    'when target is list or tuple, it should include 2 elements, but it is {}'
+                    .format(target_size))
+        elif not isinstance(target_size, int):
+            raise TypeError(
+                "Type of target_size is invalid. Must be Integer or List or tuple, now is {}"
+                .format(type(target_size)))
+        self.target_size = target_size
+        self.im_padding_value = im_padding_value
+        self.label_padding_value = label_padding_value
+
+    def __call__(self, im, im_info=None, label=None):
+        """
+        Args:
+            im (np.ndarray): 图像np.ndarray数据。
+            im_info (dict): 存储与图像相关的信息。
+            label (np.ndarray): 标注图像np.ndarray数据。
+
+        Returns:
+            tuple: 当label为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
+                当label不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
+                存储与图像相关信息的字典和标注图像np.ndarray数据。
+                其中，im_info新增字段为：
+                    -shape_before_padding (tuple): 保存padding之前图像的形状(h, w）。
+
+        Raises:
+            ValueError: 输入图像im或label的形状大于目标值
+        """
+        if im_info is None:
+            im_info = OrderedDict()
+        im_info['shape_before_padding'] = im.shape[:2]
+
+        im_height, im_width = im.shape[0], im.shape[1]
+        if isinstance(self.target_size, int):
+            target_height = self.target_size
+            target_width = self.target_size
+        else:
+            target_height = self.target_size[1]
+            target_width = self.target_size[0]
+        pad_height = target_height - im_height
+        pad_width = target_width - im_width
+        if pad_height < 0 or pad_width < 0:
+            raise ValueError(
+                'the size of image should be less than target_size, but the size of image ({}, {}), is larger than target_size ({}, {})'
+                .format(im_width, im_height, target_width, target_height))
+        else:
+            im = np.pad(
+                im,
+                pad_width=((0, pad_height), (0, pad_width), (0, 0)),
+                mode='constant',
+                constant_values=(self.im_padding_value, self.im_padding_value))
+            if label is not None:
+                label = np.pad(
+                    label,
+                    pad_width=((0, pad_height), (0, pad_width)),
+                    mode='constant',
+                    constant_values=(self.label_padding_value,
+                                     self.label_padding_value))
+        if label is None:
+            return (im, im_info)
+        else:
+            return (im, im_info, label)
+
+
+class RandomPaddingCrop:
+    """对图像和标注图进行随机裁剪，当所需要的裁剪尺寸大于原图时，则进行padding操作。
+
+    Args:
+        crop_size（int or list or tuple): 裁剪图像大小。默认为512。
+        im_padding_value (list): 图像padding的值。默认为[127.5, 127.5, 127.5]。
+        label_padding_value (int): 标注图像padding的值。默认值为255。
+
+    Raises:
+        TypeError: crop_size不是int/list/tuple。
+        ValueError:  target_size为list/tuple时元素个数不等于2。
+    """
+
+    def __init__(self,
+                 crop_size=512,
+                 im_padding_value=127.5,
+                 label_padding_value=255):
+        if isinstance(crop_size, list) or isinstance(crop_size, tuple):
+            if len(crop_size) != 2:
+                raise ValueError(
+                    'when crop_size is list or tuple, it should include 2 elements, but it is {}'
+                    .format(crop_size))
+        elif not isinstance(crop_size, int):
+            raise TypeError(
+                "Type of crop_size is invalid. Must be Integer or List or tuple, now is {}"
+                .format(type(crop_size)))
+        self.crop_size = crop_size
+        self.im_padding_value = im_padding_value
+        self.label_padding_value = label_padding_value
+
+    def __call__(self, im, im_info=None, label=None):
+        """
+        Args:
+            im (np.ndarray): 图像np.ndarray数据。
+            im_info (dict): 存储与图像相关的信息。
+            label (np.ndarray): 标注图像np.ndarray数据。
+
+         Returns:
+            tuple: 当label为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
+                当label不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
+                存储与图像相关信息的字典和标注图像np.ndarray数据。
+        """
+        if isinstance(self.crop_size, int):
+            crop_width = self.crop_size
+            crop_height = self.crop_size
+        else:
+            crop_width = self.crop_size[0]
+            crop_height = self.crop_size[1]
+
+        img_height = im.shape[0]
+        img_width = im.shape[1]
+
+        if img_height == crop_height and img_width == crop_width:
+            if label is None:
+                return (im, im_info)
+            else:
+                return (im, im_info, label)
+        else:
+            pad_height = max(crop_height - img_height, 0)
+            pad_width = max(crop_width - img_width, 0)
+            if (pad_height > 0 or pad_width > 0):
+                im = np.pad(
+                    im,
+                    pad_width=((0, pad_height), (0, pad_width), (0, 0)),
+                    mode='constant',
+                    constant_values=(self.im_padding_value,
+                                     self.im_padding_value))
+                if label is not None:
+                    label = np.pad(
+                        label,
+                        pad_width=((0, pad_height), (0, pad_width)),
+                        mode='constant',
+                        constant_values=(self.label_padding_value,
+                                         self.label_padding_value))
+                img_height = im.shape[0]
+                img_width = im.shape[1]
+
+            if crop_height > 0 and crop_width > 0:
+                h_off = np.random.randint(img_height - crop_height + 1)
+                w_off = np.random.randint(img_width - crop_width + 1)
+
+                im = im[h_off:(crop_height + h_off), w_off:(
+                    w_off + crop_width), :]
+                if label is not None:
+                    label = label[h_off:(crop_height + h_off), w_off:(
+                        w_off + crop_width)]
+        if label is None:
+            return (im, im_info)
+        else:
+            return (im, im_info, label)
+
+
+class RandomBlur:
+    """以一定的概率对图像进行高斯模糊。
+
+    Args：
+        prob (float): 图像模糊概率。默认为0.1。
+    """
+
+    def __init__(self, prob=0.1):
+        self.prob = prob
+
+    def __call__(self, im, im_info=None, label=None):
+        """
+        Args:
+            im (np.ndarray): 图像np.ndarray数据。
+            im_info (dict): 存储与图像相关的信息。
+            label (np.ndarray): 标注图像np.ndarray数据。
+
+        Returns:
+            tuple: 当label为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
+                当label不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
+                存储与图像相关信息的字典和标注图像np.ndarray数据。
+        """
+        if self.prob <= 0:
+            n = 0
+        elif self.prob >= 1:
+            n = 1
+        else:
+            n = int(1.0 / self.prob)
+        if n > 0:
+            if np.random.randint(0, n) == 0:
+                radius = np.random.randint(3, 10)
+                if radius % 2 != 1:
+                    radius = radius + 1
+                if radius > 9:
+                    radius = 9
+                im = cv2.GaussianBlur(im, (radius, radius), 0, 0)
+
+        if label is None:
+            return (im, im_info)
+        else:
+            return (im, im_info, label)
+
+
+class RandomRotation:
+    """对图像进行随机旋转。
+    在不超过最大旋转角度的情况下，图像进行随机旋转，当存在标注图像时，同步进行，
+    并对旋转后的图像和标注图像进行相应的padding。
+
+    Args:
+        max_rotation (float): 最大旋转角度。默认为15度。
+        im_padding_value (list): 图像padding的值。默认为[127.5, 127.5, 127.5]。
+        label_padding_value (int): 标注图像padding的值。默认为255。
+
+    """
+
+    def __init__(self,
+                 max_rotation=15,
+                 im_padding_value=[127.5, 127.5, 127.5],
+                 label_padding_value=255):
+        self.max_rotation = max_rotation
+        self.im_padding_value = im_padding_value
+        self.label_padding_value = label_padding_value
+
+    def __call__(self, im, im_info=None, label=None):
+        """
+        Args:
+            im (np.ndarray): 图像np.ndarray数据。
+            im_info (dict): 存储与图像相关的信息。
+            label (np.ndarray): 标注图像np.ndarray数据。
+
+        Returns:
+            tuple: 当label为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
+                当label不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
+                存储与图像相关信息的字典和标注图像np.ndarray数据。
+        """
+        if self.max_rotation > 0:
+            (h, w) = im.shape[:2]
+            do_rotation = np.random.uniform(-self.max_rotation,
+                                            self.max_rotation)
+            pc = (w // 2, h // 2)
+            r = cv2.getRotationMatrix2D(pc, do_rotation, 1.0)
+            cos = np.abs(r[0, 0])
+            sin = np.abs(r[0, 1])
+
+            nw = int((h * sin) + (w * cos))
+            nh = int((h * cos) + (w * sin))
+
+            (cx, cy) = pc
+            r[0, 2] += (nw / 2) - cx
+            r[1, 2] += (nh / 2) - cy
+            dsize = (nw, nh)
+            im = cv2.warpAffine(
+                im,
+                r,
+                dsize=dsize,
+                flags=cv2.INTER_LINEAR,
+                borderMode=cv2.BORDER_CONSTANT,
+                borderValue=self.im_padding_value)
+            label = cv2.warpAffine(
+                label,
+                r,
+                dsize=dsize,
+                flags=cv2.INTER_NEAREST,
+                borderMode=cv2.BORDER_CONSTANT,
+                borderValue=self.label_padding_value)
+
+        if label is None:
+            return (im, im_info)
+        else:
+            return (im, im_info, label)
+
+
+class RandomScaleAspect:
+    """裁剪并resize回原始尺寸的图像和标注图像。
+    按照一定的面积比和宽高比对图像进行裁剪，并reszie回原始图像的图像，当存在标注图时，同步进行。
+
+    Args：
+        min_scale (float)：裁取图像占原始图像的面积比，0-1，默认0返回原图。默认为0.5。
+        aspect_ratio (float): 裁取图像的宽高比范围，非负，默认0返回原图。默认为0.33。
+    """
+
+    def __init__(self, min_scale=0.5, aspect_ratio=0.33):
+        self.min_scale = min_scale
+        self.aspect_ratio = aspect_ratio
+
+    def __call__(self, im, im_info=None, label=None):
+        """
+        Args:
+            im (np.ndarray): 图像np.ndarray数据。
+            im_info (dict): 存储与图像相关的信息。
+            label (np.ndarray): 标注图像np.ndarray数据。
+
+        Returns:
+            tuple: 当label为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
+                当label不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
+                存储与图像相关信息的字典和标注图像np.ndarray数据。
+        """
+        if self.min_scale != 0 and self.aspect_ratio != 0:
+            img_height = im.shape[0]
+            img_width = im.shape[1]
+            for i in range(0, 10):
+                area = img_height * img_width
+                target_area = area * np.random.uniform(self.min_scale, 1.0)
+                aspectRatio = np.random.uniform(self.aspect_ratio,
+                                                1.0 / self.aspect_ratio)
+
+                dw = int(np.sqrt(target_area * 1.0 * aspectRatio))
+                dh = int(np.sqrt(target_area * 1.0 / aspectRatio))
+                if (np.random.randint(10) < 5):
+                    tmp = dw
+                    dw = dh
+                    dh = tmp
+
+                if (dh < img_height and dw < img_width):
+                    h1 = np.random.randint(0, img_height - dh)
+                    w1 = np.random.randint(0, img_width - dw)
+
+                    im = im[h1:(h1 + dh), w1:(w1 + dw), :]
+                    label = label[h1:(h1 + dh), w1:(w1 + dw)]
+                    im = cv2.resize(
+                        im, (img_width, img_height),
+                        interpolation=cv2.INTER_LINEAR)
+                    label = cv2.resize(
+                        label, (img_width, img_height),
+                        interpolation=cv2.INTER_NEAREST)
+                    break
+        if label is None:
+            return (im, im_info)
+        else:
+            return (im, im_info, label)
+
+
+class RandomDistort:
+    """对图像进行随机失真。
+
+    1. 确定随机失真操作[变换明亮度、变换对比度、变换饱和度、变换色彩]的执行顺序。
+    2. 以一定的概率执行每个随机扰动操作。
+
+    Args:
+        brightness_range (float): 明亮度因子的范围。默认为0.5。
+        brightness_prob (float): 随机调整明亮度的概率。默认为0.5。
+        contrast_range (float): 对比度因子的范围。默认为0.5。
+        contrast_prob (float): 随机调整对比度的概率。默认为0.5。
+        saturation_range (float): 饱和度因子的范围。默认为0.5。
+        saturation_prob (float): 随机调整饱和度的概率。默认为0.5。
+        hue_range (int): 色调因子的范围。默认为18。
+        hue_prob (float): 随机调整色调的概率。默认为0.5。
+        is_order (bool): 是否按照固定顺序
+                        [变换明亮度、变换对比度、变换饱和度、变换色彩]
+                        执行像素内容变换操作。默认为False。
+    """
+
+    def __init__(self,
+                 brightness_range=0.5,
+                 brightness_prob=0.5,
+                 contrast_range=0.5,
+                 contrast_prob=0.5,
+                 saturation_range=0.5,
+                 saturation_prob=0.5,
+                 hue_range=18,
+                 hue_prob=0.5,
+                 is_order=False):
+        self.brightness_range = brightness_range
+        self.brightness_prob = brightness_prob
+        self.contrast_range = contrast_range
+        self.contrast_prob = contrast_prob
+        self.saturation_range = saturation_range
+        self.saturation_prob = saturation_prob
+        self.hue_range = hue_range
+        self.hue_prob = hue_prob
+        self.is_order = is_order
+
+    def __call__(self, im, im_info=None, label_info=None):
+        """
+        Args:
+            im (np.ndarray): 图像np.ndarray数据。
+            im_info (dict): 存储与图像相关的信息。
+            label (np.ndarray): 标注图像np.ndarray数据。
+
+        Returns:
+            tuple: 当label为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
+                当label不为空时，返回的tuple为(im, im_info, label)，分别对应图像np.ndarray数据、
+                存储与图像相关信息的字典和标注图像np.ndarray数据。
+        """
+        brightness_lower = 1 - self.brightness_range
+        brightness_upper = 1 + self.brightness_range
+        contrast_lower = 1 - self.contrast_range
+        contrast_upper = 1 + self.contrast_range
+        saturation_lower = 1 - self.saturation_range
+        saturation_upper = 1 + self.saturation_range
+        hue_lower = -self.hue_range
+        hue_upper = self.hue_range
+        ops = [brightness, contrast, saturation, hue]
+        if self.is_order:
+            prob = np.random.uniform(0, 1)
+            if prob < 0.5:
+                ops = [
+                    brightness,
+                    saturation,
+                    hue,
+                    contrast,
+                ]
+        else:
+            random.shuffle(ops)
+        params_dict = {
+            'brightness': {
+                'brightness_lower': brightness_lower,
+                'brightness_upper': brightness_upper
+            },
+            'contrast': {
+                'contrast_lower': contrast_lower,
+                'contrast_upper': contrast_upper
+            },
+            'saturation': {
+                'saturation_lower': saturation_lower,
+                'saturation_upper': saturation_upper
+            },
+            'hue': {
+                'hue_lower': hue_lower,
+                'hue_upper': hue_upper
+            }
+        }
+        prob_dict = {
+            'brightness': self.brightness_prob,
+            'contrast': self.contrast_prob,
+            'saturation': self.saturation_prob,
+            'hue': self.hue_prob
+        }
+        im = Image.fromarray(im)
+        for id in range(4):
+            params = params_dict[ops[id].__name__]
+            prob = prob_dict[ops[id].__name__]
+            params['im'] = im
+            if np.random.uniform(0, 1) < prob:
+                im = ops[id](**params)
+        im = np.asarray(im)
+        if label is None:
+            return (im, im_info)
+        else:
+            return (im, im_info, label)
+
+
+class ArrangeSegmenter:
+    """获取训练/验证/预测所需的信息。
+
+    Args:
+        mode (str): 指定数据用于何种用途，取值范围为['train', 'eval', 'test', 'quant']。
+
+    Raises:
+        ValueError: mode的取值不在['train', 'eval', 'test', 'quant']之内
+    """
+
+    def __init__(self, mode):
+        if mode not in ['train', 'eval', 'test', 'quant']:
+            raise ValueError(
+                "mode should be defined as one of ['train', 'eval', 'test', 'quant']!"
+            )
+        self.mode = mode
+
+    def __call__(self, im, im_info, label=None):
+        """
+        Args:
+            im (np.ndarray): 图像np.ndarray数据。
+            im_info (dict): 存储与图像相关的信息。
+            label (np.ndarray): 标注图像np.ndarray数据。
+
+        Returns:
+            tuple: 当mode为'train'或'eval'时，返回的tuple为(im, label)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
+                当mode为'test'时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；当mode为
+                'quant'时，返回的tuple为(im,)，为图像np.ndarray数据。
+        """
+        im = permute(im, False)
+        if self.mode == 'train' or self.mode == 'eval':
+            label = label[np.newaxis, :, :]
+            return (im, label)
+        elif self.mode == 'test':
+            return (im, im_info)
+        else:
+            return (im, )

contrib/RemoteSensing/utils/__init__.py

+#   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.
+
+from . import logging
+from . import utils
+from .metrics import ConfusionMatrix
+from .utils import *

contrib/RemoteSensing/utils/logging.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 time
+import os
+import sys
+import RemoteSensing
+
+levels = {0: 'ERROR', 1: 'WARNING', 2: 'INFO', 3: 'DEBUG'}
+
+
+def log(level=2, message=""):
+    current_time = time.time()
+    time_array = time.localtime(current_time)
+    current_time = time.strftime("%Y-%m-%d %H:%M:%S", time_array)
+    if RemoteSensing.log_level >= level:
+        print("{} [{}]\t{}".format(current_time, levels[level],
+                                   message).encode("utf-8").decode("latin1"))
+        sys.stdout.flush()
+
+
+def debug(message=""):
+    log(level=3, message=message)
+
+
+def info(message=""):
+    log(level=2, message=message)
+
+
+def warning(message=""):
+    log(level=1, message=message)
+
+
+def error(message=""):
+    log(level=0, message=message)

contrib/RemoteSensing/utils/metrics.py

+# coding: utf8
+# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 sys
+import numpy as np
+from scipy.sparse import csr_matrix
+
+
+class ConfusionMatrix(object):
+    """
+        Confusion Matrix for segmentation evaluation
+    """
+
+    def __init__(self, num_classes=2, streaming=False):
+        self.confusion_matrix = np.zeros([num_classes, num_classes],
+                                         dtype='int64')
+        self.num_classes = num_classes
+        self.streaming = streaming
+
+    def calculate(self, pred, label, ignore=None):
+        # If not in streaming mode, clear matrix everytime when call `calculate`
+        if not self.streaming:
+            self.zero_matrix()
+
+        label = np.transpose(label, (0, 2, 3, 1))
+        ignore = np.transpose(ignore, (0, 2, 3, 1))
+        mask = np.array(ignore) == 1
+
+        label = np.asarray(label)[mask]
+        pred = np.asarray(pred)[mask]
+        one = np.ones_like(pred)
+        # Accumuate ([row=label, col=pred], 1) into sparse matrix
+        spm = csr_matrix((one, (label, pred)),
+                         shape=(self.num_classes, self.num_classes))
+        spm = spm.todense()
+        self.confusion_matrix += spm
+
+    def zero_matrix(self):
+        """ Clear confusion matrix """
+        self.confusion_matrix = np.zeros([self.num_classes, self.num_classes],
+                                         dtype='int64')
+
+    def mean_iou(self):
+        iou_list = []
+        avg_iou = 0
+        # TODO: use numpy sum axis api to simpliy
+        vji = np.zeros(self.num_classes, dtype=int)
+        vij = np.zeros(self.num_classes, dtype=int)
+        for j in range(self.num_classes):
+            v_j = 0
+            for i in range(self.num_classes):
+                v_j += self.confusion_matrix[j][i]
+            vji[j] = v_j
+
+        for i in range(self.num_classes):
+            v_i = 0
+            for j in range(self.num_classes):
+                v_i += self.confusion_matrix[j][i]
+            vij[i] = v_i
+
+        for c in range(self.num_classes):
+            total = vji[c] + vij[c] - self.confusion_matrix[c][c]
+            if total == 0:
+                iou = 0
+            else:
+                iou = float(self.confusion_matrix[c][c]) / total
+            avg_iou += iou
+            iou_list.append(iou)
+        avg_iou = float(avg_iou) / float(self.num_classes)
+        return np.array(iou_list), avg_iou
+
+    def accuracy(self):
+        total = self.confusion_matrix.sum()
+        total_right = 0
+        for c in range(self.num_classes):
+            total_right += self.confusion_matrix[c][c]
+        if total == 0:
+            avg_acc = 0
+        else:
+            avg_acc = float(total_right) / total
+
+        vij = np.zeros(self.num_classes, dtype=int)
+        for i in range(self.num_classes):
+            v_i = 0
+            for j in range(self.num_classes):
+                v_i += self.confusion_matrix[j][i]
+            vij[i] = v_i
+
+        acc_list = []
+        for c in range(self.num_classes):
+            if vij[c] == 0:
+                acc = 0
+            else:
+                acc = self.confusion_matrix[c][c] / float(vij[c])
+            acc_list.append(acc)
+        return np.array(acc_list), avg_acc
+
+    def kappa(self):
+        vji = np.zeros(self.num_classes)
+        vij = np.zeros(self.num_classes)
+        for j in range(self.num_classes):
+            v_j = 0
+            for i in range(self.num_classes):
+                v_j += self.confusion_matrix[j][i]
+            vji[j] = v_j
+
+        for i in range(self.num_classes):
+            v_i = 0
+            for j in range(self.num_classes):
+                v_i += self.confusion_matrix[j][i]
+            vij[i] = v_i
+
+        total = self.confusion_matrix.sum()
+
+        # avoid spillovers
+        # TODO: is it reasonable to hard code 10000.0?
+        total = float(total) / 10000.0
+        vji = vji / 10000.0
+        vij = vij / 10000.0
+
+        tp = 0
+        tc = 0
+        for c in range(self.num_classes):
+            tp += vji[c] * vij[c]
+            tc += self.confusion_matrix[c][c]
+
+        tc = tc / 10000.0
+        pe = tp / (total * total)
+        po = tc / total
+
+        kappa = (po - pe) / (1 - pe)
+        return kappa

contrib/RemoteSensing/utils/pretrain_weights.py

+import RemoteSensing
+import os
+import os.path as osp
+
+
+def get_pretrain_weights(flag, backbone, save_dir):
+    if flag is None:
+        return None
+    elif osp.isdir(flag):
+        return flag
+    else:
+        raise Exception(
+            "pretrain_weights need to be defined as directory path.")

contrib/RemoteSensing/utils/utils.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 sys
+import time
+import os
+import os.path as osp
+import numpy as np
+import six
+import yaml
+import math
+from . import logging
+
+
+def seconds_to_hms(seconds):
+    h = math.floor(seconds / 3600)
+    m = math.floor((seconds - h * 3600) / 60)
+    s = int(seconds - h * 3600 - m * 60)
+    hms_str = "{}:{}:{}".format(h, m, s)
+    return hms_str
+
+
+def setting_environ_flags():
+    if 'FLAGS_eager_delete_tensor_gb' not in os.environ:
+        os.environ['FLAGS_eager_delete_tensor_gb'] = '0.0'
+    if 'FLAGS_allocator_strategy' not in os.environ:
+        os.environ['FLAGS_allocator_strategy'] = 'auto_growth'
+    if "CUDA_VISIBLE_DEVICES" in os.environ:
+        if os.environ["CUDA_VISIBLE_DEVICES"].count("-1") > 0:
+            os.environ["CUDA_VISIBLE_DEVICES"] = ""
+
+
+def get_environ_info():
+    setting_environ_flags()
+    import paddle.fluid as fluid
+    info = dict()
+    info['place'] = 'cpu'
+    info['num'] = int(os.environ.get('CPU_NUM', 1))
+    if os.environ.get('CUDA_VISIBLE_DEVICES', None) != "":
+        if hasattr(fluid.core, 'get_cuda_device_count'):
+            gpu_num = 0
+            try:
+                gpu_num = fluid.core.get_cuda_device_count()
+            except:
+                os.environ['CUDA_VISIBLE_DEVICES'] = ''
+                pass
+            if gpu_num > 0:
+                info['place'] = 'cuda'
+                info['num'] = fluid.core.get_cuda_device_count()
+    return info
+
+
+def parse_param_file(param_file, return_shape=True):
+    from paddle.fluid.proto.framework_pb2 import VarType
+    f = open(param_file, 'rb')
+    version = np.fromstring(f.read(4), dtype='int32')
+    lod_level = np.fromstring(f.read(8), dtype='int64')
+    for i in range(int(lod_level)):
+        _size = np.fromstring(f.read(8), dtype='int64')
+        _ = f.read(_size)
+    version = np.fromstring(f.read(4), dtype='int32')
+    tensor_desc = VarType.TensorDesc()
+    tensor_desc_size = np.fromstring(f.read(4), dtype='int32')
+    tensor_desc.ParseFromString(f.read(int(tensor_desc_size)))
+    tensor_shape = tuple(tensor_desc.dims)
+    if return_shape:
+        f.close()
+        return tuple(tensor_desc.dims)
+    if tensor_desc.data_type != 5:
+        raise Exception(
+            "Unexpected data type while parse {}".format(param_file))
+    data_size = 4
+    for i in range(len(tensor_shape)):
+        data_size *= tensor_shape[i]
+    weight = np.fromstring(f.read(data_size), dtype='float32')
+    f.close()
+    return np.reshape(weight, tensor_shape)
+
+
+def fuse_bn_weights(exe, main_prog, weights_dir):
+    import paddle.fluid as fluid
+    logging.info("Try to fuse weights of batch_norm...")
+    bn_vars = list()
+    for block in main_prog.blocks:
+        ops = list(block.ops)
+        for op in ops:
+            if op.type == 'affine_channel':
+                scale_name = op.input('Scale')[0]
+                bias_name = op.input('Bias')[0]
+                prefix = scale_name[:-5]
+                mean_name = prefix + 'mean'
+                variance_name = prefix + 'variance'
+                if not osp.exists(osp.join(
+                        weights_dir, mean_name)) or not osp.exists(
+                            osp.join(weights_dir, variance_name)):
+                    logging.info(
+                        "There's no batch_norm weight found to fuse, skip fuse_bn."
+                    )
+                    return
+
+                bias = block.var(bias_name)
+                pretrained_shape = parse_param_file(
+                    osp.join(weights_dir, bias_name))
+                actual_shape = tuple(bias.shape)
+                if pretrained_shape != actual_shape:
+                    continue
+                bn_vars.append(
+                    [scale_name, bias_name, mean_name, variance_name])
+    eps = 1e-5
+    for names in bn_vars:
+        scale_name, bias_name, mean_name, variance_name = names
+        scale = parse_param_file(
+            osp.join(weights_dir, scale_name), return_shape=False)
+        bias = parse_param_file(
+            osp.join(weights_dir, bias_name), return_shape=False)
+        mean = parse_param_file(
+            osp.join(weights_dir, mean_name), return_shape=False)
+        variance = parse_param_file(
+            osp.join(weights_dir, variance_name), return_shape=False)
+        bn_std = np.sqrt(np.add(variance, eps))
+        new_scale = np.float32(np.divide(scale, bn_std))
+        new_bias = bias - mean * new_scale
+        scale_tensor = fluid.global_scope().find_var(scale_name).get_tensor()
+        bias_tensor = fluid.global_scope().find_var(bias_name).get_tensor()
+        scale_tensor.set(new_scale, exe.place)
+        bias_tensor.set(new_bias, exe.place)
+    if len(bn_vars) == 0:
+        logging.info(
+            "There's no batch_norm weight found to fuse, skip fuse_bn.")
+    else:
+        logging.info("There's {} batch_norm ops been fused.".format(
+            len(bn_vars)))
+
+
+def load_pdparams(exe, main_prog, model_dir):
+    import paddle.fluid as fluid
+    from paddle.fluid.proto.framework_pb2 import VarType
+    from paddle.fluid.framework import Program
+
+    vars_to_load = list()
+    import pickle
+    with open(osp.join(model_dir, 'model.pdparams'), 'rb') as f:
+        params_dict = pickle.load(f) if six.PY2 else pickle.load(
+            f, encoding='latin1')
+    unused_vars = list()
+    for var in main_prog.list_vars():
+        if not isinstance(var, fluid.framework.Parameter):
+            continue
+        if var.name not in params_dict:
+            raise Exception("{} is not in saved model".format(var.name))
+        if var.shape != params_dict[var.name].shape:
+            unused_vars.append(var.name)
+            logging.warning(
+                "[SKIP] Shape of pretrained weight {} doesn't match.(Pretrained: {}, Actual: {})"
+                .format(var.name, params_dict[var.name].shape, var.shape))
+            continue
+        vars_to_load.append(var)
+        logging.debug("Weight {} will be load".format(var.name))
+    for var_name in unused_vars:
+        del params_dict[var_name]
+    fluid.io.set_program_state(main_prog, params_dict)
+
+    if len(vars_to_load) == 0:
+        logging.warning(
+            "There is no pretrain weights loaded, maybe you should check you pretrain model!"
+        )
+    else:
+        logging.info("There are {} varaibles in {} are loaded.".format(
+            len(vars_to_load), model_dir))
+
+
+def load_pretrain_weights(exe, main_prog, weights_dir, fuse_bn=False):
+    if not osp.exists(weights_dir):
+        raise Exception("Path {} not exists.".format(weights_dir))
+    if osp.exists(osp.join(weights_dir, "model.pdparams")):
+        return load_pdparams(exe, main_prog, weights_dir)
+    import paddle.fluid as fluid
+    vars_to_load = list()
+    for var in main_prog.list_vars():
+        if not isinstance(var, fluid.framework.Parameter):
+            continue
+        if not osp.exists(osp.join(weights_dir, var.name)):
+            logging.debug("[SKIP] Pretrained weight {}/{} doesn't exist".format(
+                weights_dir, var.name))
+            continue
+        pretrained_shape = parse_param_file(osp.join(weights_dir, var.name))
+        actual_shape = tuple(var.shape)
+        if pretrained_shape != actual_shape:
+            logging.warning(
+                "[SKIP] Shape of pretrained weight {}/{} doesn't match.(Pretrained: {}, Actual: {})"
+                .format(weights_dir, var.name, pretrained_shape, actual_shape))
+            continue
+        vars_to_load.append(var)
+        logging.debug("Weight {} will be load".format(var.name))
+
+    fluid.io.load_vars(
+        executor=exe,
+        dirname=weights_dir,
+        main_program=main_prog,
+        vars=vars_to_load)
+    if len(vars_to_load) == 0:
+        logging.warning(
+            "There is no pretrain weights loaded, maybe you should check you pretrain model!"
+        )
+    else:
+        logging.info("There are {} varaibles in {} are loaded.".format(
+            len(vars_to_load), weights_dir))
+    if fuse_bn:
+        fuse_bn_weights(exe, main_prog, weights_dir)