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segmenter.py
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segmenter.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os.path as osp
import numpy as np
from collections import OrderedDict
import paddle
import paddle.nn.functional as F
from paddle.static import InputSpec
import paddlex
from paddlex.cv.nets.paddleseg import models
from paddlex.cv.transforms import arrange_transforms
from paddlex.utils import get_single_card_bs
import paddlex.utils.logging as logging
from .base import BaseModel
from .utils import seg_metrics as metrics
from paddlex.utils.checkpoint import seg_pretrain_weights_dict
from paddlex.cv.nets.paddleseg.cvlibs import manager
from paddlex.cv.transforms import Decode
__all__ = ["UNet", "DeepLabV3P", "FastSCNN", "HRNet", "BiSeNetV2"]
class BaseSegmenter(BaseModel):
def __init__(self,
model_name,
num_classes=2,
use_mixed_loss=False,
**params):
self.init_params = locals()
super(BaseSegmenter, self).__init__('segmenter')
if not hasattr(models, model_name):
raise Exception("ERROR: There's no model named {}.".format(
model_name))
self.model_name = model_name
self.num_classes = num_classes
self.use_mixed_loss = use_mixed_loss
self.losses = None
self.labels = None
self.net = self.build_net(**params)
def build_net(self, **params):
# TODO: when using paddle.utils.unique_name.guard,
# DeepLabv3p and HRNet will raise a error
net = models.__dict__[self.model_name](num_classes=self.num_classes,
**params)
return net
def get_test_inputs(self, image_shape):
input_spec = [
InputSpec(
shape=[None, 3] + image_shape, name='image', dtype='float32')
]
return input_spec
def run(self, net, inputs, mode):
net_out = net(inputs[0])
logit = net_out[0]
outputs = OrderedDict()
if mode == 'test':
origin_shape = inputs[1]
score_map = self._postprocess(
logit, origin_shape, transforms=inputs[2])
label_map = paddle.argmax(
score_map, axis=1, keepdim=True, dtype='int32')
score_map = paddle.max(score_map, axis=1, keepdim=True)
score_map = paddle.squeeze(score_map)
label_map = paddle.squeeze(label_map)
outputs = {'label_map': label_map, 'score_map': score_map}
if mode == 'eval':
pred = paddle.argmax(logit, axis=1, keepdim=True, dtype='int32')
label = inputs[1]
origin_shape = [label.shape[-2:]]
# TODO: 替换cv2后postprocess移出run
pred = self._postprocess(pred, origin_shape, transforms=inputs[2])
intersect_area, pred_area, label_area = metrics.calculate_area(
pred, label, self.num_classes)
outputs['intersect_area'] = intersect_area
outputs['pred_area'] = pred_area
outputs['label_area'] = label_area
if mode == 'train':
loss_list = metrics.loss_computation(
logits_list=net_out, labels=inputs[1], losses=self.losses)
loss = sum(loss_list)
outputs['loss'] = loss
return outputs
def default_loss(self):
if isinstance(self.use_mixed_loss, bool):
if self.use_mixed_loss:
losses = [
manager.LOSSES['CrossEntropyLoss'](),
manager.LOSSES['LovaszSoftmaxLoss']()
]
coef = [.8, .2]
loss_type = [
manager.LOSSES['MixedLoss'](losses=losses, coef=coef)
]
else:
loss_type = [manager.LOSSES['CrossEntropyLoss']()]
else:
losses, coef = list(zip(*self.use_mixed_loss))
if not set(losses).issubset(
['CrossEntropyLoss', 'DiceLoss', 'LovaszSoftmaxLoss']):
raise ValueError(
"Only 'CrossEntropyLoss', 'DiceLoss', 'LovaszSoftmaxLoss' are supported."
)
losses = [manager.LOSSES[loss]() for loss in losses]
loss_type = [
manager.LOSSES['MixedLoss'](losses=losses, coef=list(coef))
]
if self.model_name == 'FastSCNN':
loss_type *= 2
loss_coef = [1.0, 0.4]
elif self.model_name == 'BiSeNetV2':
loss_type *= 5
loss_coef = [1.0] * 5
else:
loss_coef = [1.0]
losses = {'types': loss_type, 'coef': loss_coef}
return losses
def default_optimizer(self,
parameters,
learning_rate,
num_epochs,
num_steps_each_epoch,
lr_decay_power=0.9):
decay_step = num_epochs * num_steps_each_epoch
lr_scheduler = paddle.optimizer.lr.PolynomialDecay(
learning_rate, decay_step, end_lr=0, power=lr_decay_power)
optimizer = paddle.optimizer.Momentum(
learning_rate=lr_scheduler,
parameters=parameters,
momentum=0.9,
weight_decay=4e-5)
return optimizer
def train(self,
num_epochs,
train_dataset,
train_batch_size=2,
eval_dataset=None,
optimizer=None,
save_interval_epochs=1,
log_interval_steps=2,
save_dir='output',
pretrain_weights='CITYSCAPES',
learning_rate=0.01,
lr_decay_power=0.9,
early_stop=False,
early_stop_patience=5,
use_vdl=True):
"""
Train the model.
Args:
num_epochs(int): The number of epochs.
train_dataset(paddlex.dataset): Training dataset.
train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 2.
eval_dataset(paddlex.dataset, optional):
Evaluation dataset. If None, the model will not be evaluated furing training process. Defaults to None.
optimizer(paddle.optimizer.Optimizer or None, optional):
Optimizer used in training. If None, a default optimizer is used. Defaults to None.
save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
save_dir(str, optional): Directory to save the model. Defaults to 'output'.
pretrain_weights(str or None, optional):
None or name/path of pretrained weights. If None, no pretrained weights will be loaded. Defaults to 'IMAGENET'.
learning_rate(float, optional): Learning rate for training. Defaults to .025.
lr_decay_power(float, optional): Learning decay power. Defaults to .9.
early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False.
early_stop_patience(int, optional): Early stop patience. Defaults to 5.
use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True.
"""
self.labels = train_dataset.labels
if self.losses is None:
self.losses = self.default_loss()
if optimizer is None:
num_steps_each_epoch = train_dataset.num_samples // train_batch_size
self.optimizer = self.default_optimizer(
self.net.parameters(), learning_rate, num_epochs,
num_steps_each_epoch, lr_decay_power)
else:
self.optimizer = optimizer
if pretrain_weights is not None and not osp.exists(pretrain_weights):
if pretrain_weights not in seg_pretrain_weights_dict[
self.model_name]:
logging.warning(
"Path of pretrain_weights('{}') does not exist!".format(
pretrain_weights))
logging.warning("Pretrain_weights is forcibly set to '{}'. "
"If don't want to use pretrain weights, "
"set pretrain_weights to be None.".format(
seg_pretrain_weights_dict[self.model_name][
0]))
pretrain_weights = seg_pretrain_weights_dict[self.model_name][
0]
pretrained_dir = osp.join(save_dir, 'pretrain')
self.net_initialize(
pretrain_weights=pretrain_weights, save_dir=pretrained_dir)
self.train_loop(
num_epochs=num_epochs,
train_dataset=train_dataset,
train_batch_size=train_batch_size,
eval_dataset=eval_dataset,
save_interval_epochs=save_interval_epochs,
log_interval_steps=log_interval_steps,
save_dir=save_dir,
early_stop=early_stop,
early_stop_patience=early_stop_patience,
use_vdl=use_vdl)
def evaluate(self, eval_dataset, batch_size=1, return_details=False):
"""
Evaluate the model.
Args:
eval_dataset(paddlex.dataset): Evaluation dataset.
batch_size(int, optional): Total batch size among all cards used for evaluation. Defaults to 1.
return_details(bool, optional): Whether to return evaluation details. Defaults to False.
Returns:
collections.OrderedDict with key-value pairs:
{"miou": `mean intersection over union`,
"category_iou": `category-wise mean intersection over union`,
"oacc": `overall accuracy`,
"category_acc": `category-wise accuracy`,
"kappa": ` kappa coefficient`,
"category_F1-score": `F1 score`}.
"""
arrange_transforms(
model_type=self.model_type,
transforms=eval_dataset.transforms,
mode='eval')
self.net.eval()
nranks = paddle.distributed.get_world_size()
local_rank = paddle.distributed.get_rank()
if nranks > 1:
# Initialize parallel environment if not done.
if not paddle.distributed.parallel.parallel_helper._is_parallel_ctx_initialized(
):
paddle.distributed.init_parallel_env()
batch_size_each_card = get_single_card_bs(batch_size)
if batch_size_each_card > 1:
batch_size_each_card = 1
batch_size = batch_size_each_card * paddlex.env_info['num']
logging.warning(
"Segmenter only supports batch_size=1 for each gpu/cpu card " \
"during evaluation, so batch_size " \
"is forcibly set to {}.".format(batch_size))
self.eval_data_loader = self.build_data_loader(
eval_dataset, batch_size=batch_size, mode='eval')
intersect_area_all = 0
pred_area_all = 0
label_area_all = 0
with paddle.no_grad():
for step, data in enumerate(self.eval_data_loader):
data.append(eval_dataset.transforms.transforms)
outputs = self.run(self.net, data, 'eval')
pred_area = outputs['pred_area']
label_area = outputs['label_area']
intersect_area = outputs['intersect_area']
# Gather from all ranks
if nranks > 1:
intersect_area_list = []
pred_area_list = []
label_area_list = []
paddle.distributed.all_gather(intersect_area_list,
intersect_area)
paddle.distributed.all_gather(pred_area_list, pred_area)
paddle.distributed.all_gather(label_area_list, label_area)
# Some image has been evaluated and should be eliminated in last iter
if (step + 1) * nranks > len(eval_dataset):
valid = len(eval_dataset) - step * nranks
intersect_area_list = intersect_area_list[:valid]
pred_area_list = pred_area_list[:valid]
label_area_list = label_area_list[:valid]
for i in range(len(intersect_area_list)):
intersect_area_all = intersect_area_all + intersect_area_list[
i]
pred_area_all = pred_area_all + pred_area_list[i]
label_area_all = label_area_all + label_area_list[i]
else:
intersect_area_all = intersect_area_all + intersect_area
pred_area_all = pred_area_all + pred_area
label_area_all = label_area_all + label_area
class_iou, miou = metrics.mean_iou(intersect_area_all, pred_area_all,
label_area_all)
# TODO 确认是按oacc还是macc
class_acc, oacc = metrics.accuracy(intersect_area_all, pred_area_all)
kappa = metrics.kappa(intersect_area_all, pred_area_all,
label_area_all)
category_f1score = metrics.f1_score(intersect_area_all, pred_area_all,
label_area_all)
eval_metrics = OrderedDict(
zip([
'miou', 'category_iou', 'oacc', 'category_acc', 'kappa',
'category_F1-score'
], [miou, class_iou, oacc, class_acc, kappa, category_f1score]))
return eval_metrics
def predict(self, img_file, transforms=None):
"""
Do inference.
Args:
Args:
img_file(List[np.ndarray or str], str or np.ndarray): img_file(list or str or np.array):
Image path or decoded image data in a BGR format, which also could constitute a list,
meaning all images to be predicted as a mini-batch.
transforms(paddlex.transforms.Compose or None, optional):
Transforms for inputs. If None, the transforms for evaluation process will be used. Defaults to None.
Returns:
If img_file is a string or np.array, the result is a dict with key-value pairs:
{"label map": `label map`, "score_map": `score map`}.
If img_file is a list, the result is a list composed of dicts with the corresponding fields:
label_map(np.ndarray): the predicted label map
score_map(np.ndarray): the prediction score map
"""
if transforms is None and not hasattr(self, 'test_transforms'):
raise Exception("transforms need to be defined, now is None.")
if transforms is None:
transforms = self.test_transforms
if isinstance(img_file, (str, np.ndarray)):
images = [img_file]
else:
images = img_file
batch_im, batch_origin_shape = self._preprocess(images, transforms,
self.model_type)
self.net.eval()
data = (batch_im, batch_origin_shape, transforms.transforms)
outputs = self.run(self.net, data, 'test')
label_map = outputs['label_map']
label_map = label_map.numpy().astype('uint8')
score_map = outputs['score_map']
score_map = score_map.numpy().astype('float32')
return {'label_map': label_map, 'score_map': score_map}
def _preprocess(self, images, transforms, model_type):
arrange_transforms(
model_type=model_type, transforms=transforms, mode='test')
batch_im = list()
batch_ori_shape = list()
for im in images:
sample = {'image': im}
if isinstance(sample['image'], str):
sample = Decode(to_rgb=False)(sample)
ori_shape = sample['image'].shape[:2]
im = transforms(sample)[0]
batch_im.append(im)
batch_ori_shape.append(ori_shape)
batch_im = paddle.to_tensor(batch_im)
return batch_im, batch_ori_shape
@staticmethod
def get_transforms_shape_info(batch_ori_shape, transforms):
batch_restore_list = list()
for ori_shape in batch_ori_shape:
restore_list = list()
h, w = ori_shape[0], ori_shape[1]
for op in transforms:
if op.__class__.__name__ in ['Resize', 'ResizeByShort']:
restore_list.append(('resize', (h, w)))
h, w = op.target_size
if op.__class__.__name__ in ['Padding']:
restore_list.append(('padding', (h, w)))
h, w = op.target_size
batch_restore_list.append(restore_list)
return batch_restore_list
def _postprocess(self, batch_pred, batch_origin_shape, transforms):
batch_restore_list = BaseSegmenter.get_transforms_shape_info(
batch_origin_shape, transforms)
results = list()
for pred, restore_list in zip(batch_pred, batch_restore_list):
pred = paddle.unsqueeze(pred, axis=0)
for item in restore_list[::-1]:
# TODO: 替换成cv2的interpolate(部署阶段无法使用paddle op)
h, w = item[1][0], item[1][1]
if item[0] == 'resize':
pred = F.interpolate(pred, (h, w), mode='nearest')
elif item[0] == 'padding':
pred = pred[:, :, 0:h, 0:w]
else:
pass
results.append(pred)
batch_pred = paddle.concat(results, axis=0)
return batch_pred
class UNet(BaseSegmenter):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
use_deconv=False,
align_corners=False):
params = {'use_deconv': use_deconv, 'align_corners': align_corners}
super(UNet, self).__init__(
model_name='UNet',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
**params)
class DeepLabV3P(BaseSegmenter):
def __init__(self,
num_classes=2,
backbone='ResNet50_vd',
use_mixed_loss=False,
output_stride=8,
backbone_indices=(0, 3),
aspp_ratios=(1, 12, 24, 36),
aspp_out_channels=256,
align_corners=False):
self.backbone_name = backbone
if backbone not in ['ResNet50_vd', 'ResNet101_vd']:
raise ValueError(
"backbone: {} is not supported. Please choose one of "
"('ResNet50_vd', 'ResNet101_vd')".format(backbone))
backbone = manager.BACKBONES[backbone](output_stride=output_stride)
params = {
'backbone': backbone,
'backbone_indices': backbone_indices,
'aspp_ratios': aspp_ratios,
'aspp_out_channels': aspp_out_channels,
'align_corners': align_corners
}
super(DeepLabV3P, self).__init__(
model_name='DeepLabV3P',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
**params)
class FastSCNN(BaseSegmenter):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
align_corners=False):
params = {'align_corners': align_corners}
super(FastSCNN, self).__init__(
model_name='FastSCNN',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
**params)
class HRNet(BaseSegmenter):
def __init__(self,
num_classes=2,
width=48,
use_mixed_loss=False,
align_corners=False):
if width not in (18, 48):
raise ValueError(
"width={} is not supported, please choose from [18, 48]".
format(width))
self.backbone_name = 'HRNet_W{}'.format(width)
backbone = manager.BACKBONES[self.backbone_name](
align_corners=align_corners)
params = {'backbone': backbone, 'align_corners': align_corners}
super(HRNet, self).__init__(
model_name='FCN',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
**params)
self.model_name = 'HRNet'
class BiSeNetV2(BaseSegmenter):
def __init__(self,
num_classes=2,
use_mixed_loss=False,
align_corners=False):
params = {'align_corners': align_corners}
super(BiSeNetV2, self).__init__(
model_name='BiSeNetV2',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
**params)