# 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 sys import struct import importlib import paddle.fluid as fluid import numpy as np from paddle.fluid.proto.framework_pb2 import VarType import solver from utils.config import cfg from loss import multi_softmax_with_loss from loss import multi_dice_loss from loss import multi_bce_loss class ModelPhase(object): """ Standard name for model phase in PaddleSeg The following standard keys are defined: * `TRAIN`: training mode. * `EVAL`: testing/evaluation mode. * `PREDICT`: prediction/inference mode. * `VISUAL` : visualization mode """ TRAIN = 'train' EVAL = 'eval' PREDICT = 'predict' VISUAL = 'visual' @staticmethod def is_train(phase): return phase == ModelPhase.TRAIN @staticmethod def is_predict(phase): return phase == ModelPhase.PREDICT @staticmethod def is_eval(phase): return phase == ModelPhase.EVAL @staticmethod def is_visual(phase): return phase == ModelPhase.VISUAL @staticmethod def is_valid_phase(phase): """ Check valid phase """ if ModelPhase.is_train(phase) or ModelPhase.is_predict(phase) \ or ModelPhase.is_eval(phase) or ModelPhase.is_visual(phase): return True return False def map_model_name(model_name): name_dict = { "unet": "unet.unet", "deeplabv3p": "deeplab.deeplabv3p", "icnet": "icnet.icnet", "pspnet": "pspnet.pspnet", "hrnet": "hrnet.hrnet" } if model_name in name_dict.keys(): return name_dict[model_name] else: raise Exception( "unknow model name, only support unet, deeplabv3p, icnet") def get_func(func_name): """Helper to return a function object by name. func_name must identify a function in this module or the path to a function relative to the base 'modeling' module. """ if func_name == '': return None try: parts = func_name.split('.') # Refers to a function in this module if len(parts) == 1: return globals()[parts[0]] # Otherwise, assume we're referencing a module under modeling module_name = 'models.' + '.'.join(parts[:-1]) module = importlib.import_module(module_name) return getattr(module, parts[-1]) except Exception: print('Failed to find function: {}'.format(func_name)) return module def softmax(logit): logit = fluid.layers.transpose(logit, [0, 2, 3, 1]) logit = fluid.layers.softmax(logit) logit = fluid.layers.transpose(logit, [0, 3, 1, 2]) return logit def sigmoid_to_softmax(logit): """ one channel to two channel """ logit = fluid.layers.transpose(logit, [0, 2, 3, 1]) logit = fluid.layers.sigmoid(logit) logit_back = 1 - logit logit = fluid.layers.concat([logit_back, logit], axis=-1) logit = fluid.layers.transpose(logit, [0, 3, 1, 2]) return logit def build_model(main_prog, start_prog, phase=ModelPhase.TRAIN): if not ModelPhase.is_valid_phase(phase): raise ValueError("ModelPhase {} is not valid!".format(phase)) if ModelPhase.is_train(phase): width = cfg.TRAIN_CROP_SIZE[0] height = cfg.TRAIN_CROP_SIZE[1] else: width = cfg.EVAL_CROP_SIZE[0] height = cfg.EVAL_CROP_SIZE[1] image_shape = [cfg.DATASET.DATA_DIM, height, width] grt_shape = [1, height, width] class_num = cfg.DATASET.NUM_CLASSES with fluid.program_guard(main_prog, start_prog): with fluid.unique_name.guard(): # 在导出模型的时候，增加图像标准化预处理,减小预测部署时图像的处理流程 # 预测部署时只须对输入图像增加batch_size维度即可 if ModelPhase.is_predict(phase): origin_image = fluid.layers.data( name='image', shape=[-1, -1, -1, cfg.DATASET.DATA_DIM], dtype='float32', append_batch_size=False) image = fluid.layers.transpose(origin_image, [0, 3, 1, 2]) origin_shape = fluid.layers.shape(image)[-2:] # 不同AUG_METHOD方法的resize if cfg.AUG.AUG_METHOD == 'unpadding': h = cfg.AUG.FIX_RESIZE_SIZE[1] w = cfg.AUG.FIX_RESIZE_SIZE[0] image = fluid.layers.resize_bilinear( image, out_shape=[h, w], align_corners=False, align_mode=0) if cfg.AUG.AUG_METHOD == 'stepscaling': pass if cfg.AUG.AUG_METHOD == 'rangescaling': size = cfg.AUG.INF_RESIZE_VALUE value = fluid.layers.reduce_max(origin_shape) scale = float(size) / value.astype('float32') image = fluid.layers.resize_bilinear( image, scale=scale, align_corners=False, align_mode=0) # 存储resize后图像shape valid_shape = fluid.layers.shape(image)[-2:] # padding 到eval_crop_size大小 pad_target = fluid.layers.assign( np.array([height, width]).astype('float32')) up = fluid.layers.assign(np.array([0]).astype('float32')) down = pad_target[0] - valid_shape[0] left = up right = pad_target[1] - valid_shape[1] paddings = fluid.layers.concat([up, down, left, right]) paddings = fluid.layers.cast(paddings, 'int32') image = fluid.layers.pad2d( image, paddings=paddings, pad_value=127.5) #normalize mean = np.array(cfg.MEAN).reshape(1, len(cfg.MEAN), 1, 1) mean = fluid.layers.assign(mean.astype('float32')) std = np.array(cfg.STD).reshape(1, len(cfg.STD), 1, 1) std = fluid.layers.assign(std.astype('float32')) image = (image / 255 - mean) / std # 很有必要，使后面的网络能通过image.shape获取特征图的shape image = fluid.layers.reshape( image, shape=[-1, cfg.DATASET.DATA_DIM, height, width]) else: image = fluid.layers.data( name='image', shape=image_shape, dtype='float32') label = fluid.layers.data( name='label', shape=grt_shape, dtype='int32') mask = fluid.layers.data( name='mask', shape=grt_shape, dtype='int32') # use PyReader when doing traning and evaluation if ModelPhase.is_train(phase) or ModelPhase.is_eval(phase): py_reader = fluid.io.PyReader( feed_list=[image, label, mask], capacity=cfg.DATALOADER.BUF_SIZE, iterable=False, use_double_buffer=True) model_name = map_model_name(cfg.MODEL.MODEL_NAME) model_func = get_func("modeling." + model_name) loss_type = cfg.SOLVER.LOSS if not isinstance(loss_type, list): loss_type = list(loss_type) # dice_loss或bce_loss只适用两类分割中 if class_num > 2 and (("dice_loss" in loss_type) or ("bce_loss" in loss_type)): raise Exception( "dice loss and bce loss is only applicable to binary classfication" ) # 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 ("dice_loss" in loss_type) or ("bce_loss" in loss_type): class_num = 1 if "softmax_loss" in loss_type: raise Exception( "softmax loss can not combine with dice loss or bce loss" ) logits = model_func(image, class_num) # 根据选择的loss函数计算相应的损失函数 if ModelPhase.is_train(phase) or ModelPhase.is_eval(phase): loss_valid = False avg_loss_list = [] valid_loss = [] if "softmax_loss" in loss_type: avg_loss_list.append( multi_softmax_with_loss(logits, label, mask, class_num)) loss_valid = True valid_loss.append("softmax_loss") if "dice_loss" in loss_type: avg_loss_list.append(multi_dice_loss(logits, label, mask)) loss_valid = True valid_loss.append("dice_loss") if "bce_loss" in loss_type: avg_loss_list.append(multi_bce_loss(logits, label, mask)) loss_valid = True valid_loss.append("bce_loss") if not loss_valid: raise Exception( "SOLVER.LOSS: {} is set wrong. it should " "include one of (softmax_loss, bce_loss, dice_loss) at least" " example: ['softmax_loss'], ['dice_loss'], ['bce_loss', 'dice_loss']" .format(cfg.SOLVER.LOSS)) invalid_loss = [x for x in loss_type if x not in valid_loss] if len(invalid_loss) > 0: print( "Warning: the loss {} you set is invalid. it will not be included in loss computed." .format(invalid_loss)) avg_loss = 0 for i in range(0, len(avg_loss_list)): avg_loss += avg_loss_list[i] #get pred result in original size if isinstance(logits, tuple): logit = logits[0] else: logit = logits if logit.shape[2:] != label.shape[2:]: logit = fluid.layers.resize_bilinear(logit, label.shape[2:]) # return image input and logit output for inference graph prune if ModelPhase.is_predict(phase): # 两类分割中，使用dice_loss或bce_loss返回的logit为单通道，进行到两通道的变换 if class_num == 1: logit = sigmoid_to_softmax(logit) else: logit = softmax(logit) # 获取有效部分 logit = fluid.layers.slice( logit, axes=[2, 3], starts=[0, 0], ends=valid_shape) logit = fluid.layers.resize_bilinear( logit, out_shape=origin_shape, align_corners=False, align_mode=0) logit = fluid.layers.argmax(logit, axis=1) return origin_image, logit if class_num == 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 ModelPhase.is_visual(phase): if class_num == 1: logit = sigmoid_to_softmax(logit) else: logit = softmax(logit) return pred, logit if ModelPhase.is_eval(phase): return py_reader, avg_loss, pred, label, mask if ModelPhase.is_train(phase): optimizer = solver.Solver(main_prog, start_prog) decayed_lr = optimizer.optimise(avg_loss) return py_reader, avg_loss, decayed_lr, pred, label, mask def to_int(string, dest="I"): return struct.unpack(dest, string)[0] def parse_shape_from_file(filename): with open(filename, "rb") as file: version = file.read(4) lod_level = to_int(file.read(8), dest="Q") for i in range(lod_level): _size = to_int(file.read(8), dest="Q") _ = file.read(_size) version = file.read(4) tensor_desc_size = to_int(file.read(4)) tensor_desc = VarType.TensorDesc() tensor_desc.ParseFromString(file.read(tensor_desc_size)) return tuple(tensor_desc.dims)