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Add multi-channel support to FarSeg

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Bobholamovic 3 years ago
parent 7b1c7adf4d
commit 0b87782eec
13 changed files with 265 additions and 106 deletions
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  1. 2
      paddlers/rs_models/cd/bit.py
  2. 29
      paddlers/rs_models/seg/farseg.py
  3. 33
      paddlers/tasks/change_detector.py
  4. 33
      paddlers/tasks/classifier.py
  5. 32
      paddlers/tasks/object_detector.py
  6. 27
      paddlers/tasks/restorer.py
  7. 33
      paddlers/tasks/segmenter.py
  8. 20
      paddlers/utils/checkpoint.py
  9. 3
      tests/rs_models/test_seg_models.py
  10. 1
      tutorials/train/README.md
  11. 2
      tutorials/train/semantic_segmentation/deeplabv3p.py
  12. 94
      tutorials/train/semantic_segmentation/farseg.py
  13. 2
      tutorials/train/semantic_segmentation/unet.py

2
paddlers/rs_models/cd/bit.py
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@ -56,7 +56,7 @@ class BIT(nn.Layer):
Default: 2. Default: 2.
enc_with_pos (bool, optional): Whether to add leanred positional embedding to the input feature sequence of the enc_with_pos (bool, optional): Whether to add leanred positional embedding to the input feature sequence of the
encoder. Default: True. encoder. Default: True.
enc_depth (int, optional): Number of attention blocks used in the encoder. Default: 1 enc_depth (int, optional): Number of attention blocks used in the encoder. Default: 1.
enc_head_dim (int, optional): Embedding dimension of each encoder head. Default: 64. enc_head_dim (int, optional): Embedding dimension of each encoder head. Default: 64.
dec_depth (int, optional): Number of attention blocks used in the decoder. Default: 8. dec_depth (int, optional): Number of attention blocks used in the decoder. Default: 8.
dec_head_dim (int, optional): Embedding dimension of each decoder head. Default: 8. dec_head_dim (int, optional): Embedding dimension of each decoder head. Default: 8.

29
paddlers/rs_models/seg/farseg.py
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@ -164,7 +164,7 @@ class SceneRelation(nn.Layer):
return refined_feats return refined_feats
class AssymetricDecoder(nn.Layer): class AsymmetricDecoder(nn.Layer):
def __init__(self, def __init__(self,
in_channels, in_channels,
out_channels, out_channels,
@ -172,7 +172,7 @@ class AssymetricDecoder(nn.Layer):
out_feat_output_stride=4, out_feat_output_stride=4,
norm_fn=nn.BatchNorm2D, norm_fn=nn.BatchNorm2D,
num_groups_gn=None): num_groups_gn=None):
super(AssymetricDecoder, self).__init__() super(AsymmetricDecoder, self).__init__()
if norm_fn == nn.BatchNorm2D: if norm_fn == nn.BatchNorm2D:
norm_fn_args = dict(num_features=out_channels) norm_fn_args = dict(num_features=out_channels)
elif norm_fn == nn.GroupNorm: elif norm_fn == nn.GroupNorm:
@ -215,9 +215,12 @@ class AssymetricDecoder(nn.Layer):
class ResNet50Encoder(nn.Layer): class ResNet50Encoder(nn.Layer):
def __init__(self, pretrained=True): def __init__(self, in_ch=3, pretrained=True):
super(ResNet50Encoder, self).__init__() super(ResNet50Encoder, self).__init__()
self.resnet = resnet50(pretrained=pretrained) self.resnet = resnet50(pretrained=pretrained)
if in_ch != 3:
self.resnet.conv1 = nn.Conv2D(
in_ch, 64, kernel_size=7, stride=2, padding=3, bias_attr=False)
def forward(self, inputs): def forward(self, inputs):
x = inputs x = inputs
@ -237,22 +240,32 @@ class FarSeg(nn.Layer):
The FarSeg implementation based on PaddlePaddle. The FarSeg implementation based on PaddlePaddle.
The original article refers to The original article refers to
Zheng, Zhuo, et al. "Foreground-Aware Relation Network for Geospatial Object Zheng, Zhuo, et al. "Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution
Segmentation in High Spatial Resolution Remote Sensing Imagery" Remote Sensing Imagery"
(https://openaccess.thecvf.com/content_CVPR_2020/papers/Zheng_Foreground-Aware_Relation_Network_for_Geospatial_Object_Segmentation_in_High_Spatial_CVPR_2020_paper.pdf) (https://openaccess.thecvf.com/content_CVPR_2020/papers/Zheng_Foreground-Aware_Relation_Network_for_Geospatial_Object_Segmentation_in_High_Spatial_CVPR_2020_paper.pdf)
Args:
in_channels (int, optional): Number of bands of the input images. Default: 3.
num_classes (int, optional): Number of target classes. Default: 16.
fpn_ch_list (list[int]|tuple[int], optional): Channel list of the FPN. Default: (256, 512, 1024, 2048).
mid_ch (int, optional): Output channels of the FPN. Default: 256.
out_ch (int, optional): Output channels of the decoder. Default: 128.
sr_ch_list (list[int]|tuple[int], optional): Channel list of the foreground-scene relation module. Default: (256, 256, 256, 256).
pretrained_encoder (bool, optional): Whether to use a pretrained encoder. Default: True.
""" """
def __init__(self, def __init__(self,
in_channels=3,
num_classes=16, num_classes=16,
fpn_ch_list=(256, 512, 1024, 2048), fpn_ch_list=(256, 512, 1024, 2048),
mid_ch=256, mid_ch=256,
out_ch=128, out_ch=128,
sr_ch_list=(256, 256, 256, 256), sr_ch_list=(256, 256, 256, 256),
encoder_pretrained=True): pretrained_encoder=True):
super(FarSeg, self).__init__() super(FarSeg, self).__init__()
self.en = ResNet50Encoder(encoder_pretrained) self.en = ResNet50Encoder(in_channels, pretrained_encoder)
self.fpn = FPN(in_channels_list=fpn_ch_list, out_channels=mid_ch) self.fpn = FPN(in_channels_list=fpn_ch_list, out_channels=mid_ch)
self.decoder = AssymetricDecoder( self.decoder = AsymmetricDecoder(
in_channels=mid_ch, out_channels=out_ch) in_channels=mid_ch, out_channels=out_ch)
self.cls_pred_conv = nn.Conv2D(out_ch, num_classes, 1) self.cls_pred_conv = nn.Conv2D(out_ch, num_classes, 1)
self.upsample4x_op = nn.UpsamplingBilinear2D(scale_factor=4) self.upsample4x_op = nn.UpsamplingBilinear2D(scale_factor=4)

33
paddlers/tasks/change_detector.py
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@ -31,7 +31,7 @@ import paddlers.utils.logging as logging
from paddlers.models import seg_losses from paddlers.models import seg_losses
from paddlers.transforms import Resize, decode_image from paddlers.transforms import Resize, decode_image
from paddlers.utils import get_single_card_bs from paddlers.utils import get_single_card_bs
from paddlers.utils.checkpoint import seg_pretrain_weights_dict from paddlers.utils.checkpoint import cd_pretrain_weights_dict
from .base import BaseModel from .base import BaseModel
from .utils import seg_metrics as metrics from .utils import seg_metrics as metrics
from .utils.infer_nets import InferCDNet from .utils.infer_nets import InferCDNet
@ -275,7 +275,7 @@ class BaseChangeDetector(BaseModel):
exit=True) exit=True)
if pretrain_weights is not None and resume_checkpoint is not None: if pretrain_weights is not None and resume_checkpoint is not None:
logging.error( logging.error(
"pretrain_weights and resume_checkpoint cannot be set simultaneously.", "`pretrain_weights` and `resume_checkpoint` cannot be set simultaneously.",
exit=True) exit=True)
self.labels = train_dataset.labels self.labels = train_dataset.labels
if self.losses is None: if self.losses is None:
@ -289,22 +289,29 @@ class BaseChangeDetector(BaseModel):
else: else:
self.optimizer = optimizer self.optimizer = optimizer
if pretrain_weights is not None and not osp.exists(pretrain_weights): if pretrain_weights is not None:
if pretrain_weights not in seg_pretrain_weights_dict[ if not osp.exists(pretrain_weights):
if self.model_name not in cd_pretrain_weights_dict:
logging.warning(
"Path of pretrained weights ('{}') does not exist!".
format(pretrain_weights))
pretrain_weights = None
elif pretrain_weights not in cd_pretrain_weights_dict[
self.model_name]: self.model_name]:
logging.warning( logging.warning(
"Path of pretrain_weights('{}') does not exist!".format( "Path of pretrained weights ('{}') does not exist!".
format(pretrain_weights))
pretrain_weights = cd_pretrain_weights_dict[
self.model_name][0]
logging.warning(
"`pretrain_weights` is forcibly set to '{}'. "
"If you don't want to use pretrained weights, "
"please set `pretrain_weights` to None.".format(
pretrain_weights)) pretrain_weights))
logging.warning("Pretrain_weights is forcibly set to '{}'. " else:
"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]
elif pretrain_weights is not None and osp.exists(pretrain_weights):
if osp.splitext(pretrain_weights)[-1] != '.pdparams': if osp.splitext(pretrain_weights)[-1] != '.pdparams':
logging.error( logging.error(
"Invalid pretrain weights. Please specify a '.pdparams' file.", "Invalid pretrained weights. Please specify a .pdparams file.",
exit=True) exit=True)
pretrained_dir = osp.join(save_dir, 'pretrain') pretrained_dir = osp.join(save_dir, 'pretrain')
is_backbone_weights = pretrain_weights == 'IMAGENET' is_backbone_weights = pretrain_weights == 'IMAGENET'

33
paddlers/tasks/classifier.py
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@ -246,7 +246,7 @@ class BaseClassifier(BaseModel):
exit=True) exit=True)
if pretrain_weights is not None and resume_checkpoint is not None: if pretrain_weights is not None and resume_checkpoint is not None:
logging.error( logging.error(
"pretrain_weights and resume_checkpoint cannot be set simultaneously.", "`pretrain_weights` and `resume_checkpoint` cannot be set simultaneously.",
exit=True) exit=True)
self.labels = train_dataset.labels self.labels = train_dataset.labels
if self.losses is None: if self.losses is None:
@ -262,25 +262,32 @@ class BaseClassifier(BaseModel):
else: else:
self.optimizer = optimizer self.optimizer = optimizer
if pretrain_weights is not None and not osp.exists(pretrain_weights): if pretrain_weights is not None:
if pretrain_weights not in cls_pretrain_weights_dict[ if not osp.exists(pretrain_weights):
if self.model_name not in cls_pretrain_weights_dict:
logging.warning(
"Path of `pretrain_weights` ('{}') does not exist!".
format(pretrain_weights))
pretrain_weights = None
elif pretrain_weights not in cls_pretrain_weights_dict[
self.model_name]: self.model_name]:
logging.warning( logging.warning(
"Path of pretrain_weights('{}') does not exist!".format( "Path of `pretrain_weights` ('{}') does not exist!".
format(pretrain_weights))
pretrain_weights = cls_pretrain_weights_dict[
self.model_name][0]
logging.warning(
"`pretrain_weights` is forcibly set to '{}'. "
"If you don't want to use pretrained weights, "
"set `pretrain_weights` to None.".format(
pretrain_weights)) pretrain_weights))
logging.warning("Pretrain_weights is forcibly set to '{}'. " else:
"If don't want to use pretrain weights, "
"set pretrain_weights to be None.".format(
cls_pretrain_weights_dict[self.model_name][
0]))
pretrain_weights = cls_pretrain_weights_dict[self.model_name][0]
elif pretrain_weights is not None and osp.exists(pretrain_weights):
if osp.splitext(pretrain_weights)[-1] != '.pdparams': if osp.splitext(pretrain_weights)[-1] != '.pdparams':
logging.error( logging.error(
"Invalid pretrain weights. Please specify a '.pdparams' file.", "Invalid pretrained weights. Please specify a .pdparams file.",
exit=True) exit=True)
pretrained_dir = osp.join(save_dir, 'pretrain') pretrained_dir = osp.join(save_dir, 'pretrain')
is_backbone_weights = False # pretrain_weights == 'IMAGENET' # TODO: this is backbone is_backbone_weights = False
self.net_initialize( self.net_initialize(
pretrain_weights=pretrain_weights, pretrain_weights=pretrain_weights,
save_dir=pretrained_dir, save_dir=pretrained_dir,

32
paddlers/tasks/object_detector.py
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@ -288,7 +288,7 @@ class BaseDetector(BaseModel):
exit=True) exit=True)
if pretrain_weights is not None and resume_checkpoint is not None: if pretrain_weights is not None and resume_checkpoint is not None:
logging.error( logging.error(
"pretrain_weights and resume_checkpoint cannot be set simultaneously.", "`pretrain_weights` and `resume_checkpoint` cannot be set simultaneously.",
exit=True) exit=True)
if train_dataset.__class__.__name__ == 'VOCDetDataset': if train_dataset.__class__.__name__ == 'VOCDetDataset':
train_dataset.data_fields = { train_dataset.data_fields = {
@ -337,22 +337,28 @@ class BaseDetector(BaseModel):
self.optimizer = optimizer self.optimizer = optimizer
# Initiate weights # Initiate weights
if pretrain_weights is not None and not osp.exists(pretrain_weights): if pretrain_weights is not None:
if pretrain_weights not in det_pretrain_weights_dict['_'.join( if not osp.exists(pretrain_weights):
[self.model_name, self.backbone_name])]: key = '_'.join([self.model_name, self.backbone_name])
if key not in det_pretrain_weights_dict:
logging.warning( logging.warning(
"Path of pretrain_weights('{}') does not exist!".format( "Path of pretrained weights ('{}') does not exist!".
pretrain_weights)) format(pretrain_weights))
pretrain_weights = det_pretrain_weights_dict['_'.join( pretrain_weights = None
[self.model_name, self.backbone_name])][0] elif pretrain_weights not in det_pretrain_weights_dict[key]:
logging.warning("Pretrain_weights is forcibly set to '{}'. " logging.warning(
"If you don't want to use pretrain weights, " "Path of pretrained weights ('{}') does not exist!".
"set pretrain_weights to be None.".format( format(pretrain_weights))
pretrain_weights = det_pretrain_weights_dict[key][0]
logging.warning(
"`pretrain_weights` is forcibly set to '{}'. "
"If you don't want to use pretrained weights, "
"please set `pretrain_weights` to None.".format(
pretrain_weights)) pretrain_weights))
elif pretrain_weights is not None and osp.exists(pretrain_weights): else:
if osp.splitext(pretrain_weights)[-1] != '.pdparams': if osp.splitext(pretrain_weights)[-1] != '.pdparams':
logging.error( logging.error(
"Invalid pretrain weights. Please specify a '.pdparams' file.", "Invalid pretrained weights. Please specify a .pdparams file.",
exit=True) exit=True)
pretrained_dir = osp.join(save_dir, 'pretrain') pretrained_dir = osp.join(save_dir, 'pretrain')
self.net_initialize( self.net_initialize(

27
paddlers/tasks/restorer.py
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@ -31,6 +31,7 @@ from paddlers.models import res_losses
from paddlers.transforms import Resize, decode_image from paddlers.transforms import Resize, decode_image
from paddlers.transforms.functions import calc_hr_shape from paddlers.transforms.functions import calc_hr_shape
from paddlers.utils import get_single_card_bs from paddlers.utils import get_single_card_bs
from paddlers.utils.checkpoint import res_pretrain_weights_dict
from .base import BaseModel from .base import BaseModel
from .utils.res_adapters import GANAdapter, OptimizerAdapter from .utils.res_adapters import GANAdapter, OptimizerAdapter
from .utils.infer_nets import InferResNet from .utils.infer_nets import InferResNet
@ -234,7 +235,7 @@ class BaseRestorer(BaseModel):
exit=True) exit=True)
if pretrain_weights is not None and resume_checkpoint is not None: if pretrain_weights is not None and resume_checkpoint is not None:
logging.error( logging.error(
"pretrain_weights and resume_checkpoint cannot be set simultaneously.", "`pretrain_weights` and `resume_checkpoint` cannot be set simultaneously.",
exit=True) exit=True)
if self.losses is None: if self.losses is None:
@ -256,13 +257,29 @@ class BaseRestorer(BaseModel):
else: else:
self.optimizer = optimizer self.optimizer = optimizer
if pretrain_weights is not None and not osp.exists(pretrain_weights): if pretrain_weights is not None:
logging.warning("Path of pretrain_weights('{}') does not exist!". if not osp.exists(pretrain_weights):
if self.model_name not in res_pretrain_weights_dict:
logging.warning(
"Path of pretrained weights ('{}') does not exist!".
format(pretrain_weights))
pretrain_weights = None
elif pretrain_weights not in res_pretrain_weights_dict[
self.model_name]:
logging.warning(
"Path of pretrained weights ('{}') does not exist!".
format(pretrain_weights)) format(pretrain_weights))
elif pretrain_weights is not None and osp.exists(pretrain_weights): pretrain_weights = res_pretrain_weights_dict[
self.model_name][0]
logging.warning(
"`pretrain_weights` is forcibly set to '{}'. "
"If you don't want to use pretrained weights, "
"please set `pretrain_weights` to None.".format(
pretrain_weights))
else:
if osp.splitext(pretrain_weights)[-1] != '.pdparams': if osp.splitext(pretrain_weights)[-1] != '.pdparams':
logging.error( logging.error(
"Invalid pretrain weights. Please specify a '.pdparams' file.", "Invalid pretrained weights. Please specify a .pdparams file.",
exit=True) exit=True)
pretrained_dir = osp.join(save_dir, 'pretrain') pretrained_dir = osp.join(save_dir, 'pretrain')
is_backbone_weights = pretrain_weights == 'IMAGENET' is_backbone_weights = pretrain_weights == 'IMAGENET'

33
paddlers/tasks/segmenter.py
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@ -267,7 +267,7 @@ class BaseSegmenter(BaseModel):
exit=True) exit=True)
if pretrain_weights is not None and resume_checkpoint is not None: if pretrain_weights is not None and resume_checkpoint is not None:
logging.error( logging.error(
"pretrain_weights and resume_checkpoint cannot be set simultaneously.", "`pretrain_weights` and `resume_checkpoint` cannot be set simultaneously.",
exit=True) exit=True)
self.labels = train_dataset.labels self.labels = train_dataset.labels
if self.losses is None: if self.losses is None:
@ -281,22 +281,29 @@ class BaseSegmenter(BaseModel):
else: else:
self.optimizer = optimizer self.optimizer = optimizer
if pretrain_weights is not None and not osp.exists(pretrain_weights): if pretrain_weights is not None:
if pretrain_weights not in seg_pretrain_weights_dict[ if not osp.exists(pretrain_weights):
if self.model_name not in seg_pretrain_weights_dict:
logging.warning(
"Path of pretrained weights ('{}') does not exist!".
format(pretrain_weights))
pretrain_weights = None
elif pretrain_weights not in seg_pretrain_weights_dict[
self.model_name]: self.model_name]:
logging.warning( logging.warning(
"Path of pretrain_weights('{}') does not exist!".format( "Path of pretrained weights ('{}') does not exist!".
format(pretrain_weights))
pretrain_weights = seg_pretrain_weights_dict[
self.model_name][0]
logging.warning(
"`pretrain_weights` is forcibly set to '{}'. "
"If you don't want to use pretrained weights, "
"please set `pretrain_weights` to None.".format(
pretrain_weights)) pretrain_weights))
logging.warning("Pretrain_weights is forcibly set to '{}'. " else:
"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]
elif pretrain_weights is not None and osp.exists(pretrain_weights):
if osp.splitext(pretrain_weights)[-1] != '.pdparams': if osp.splitext(pretrain_weights)[-1] != '.pdparams':
logging.error( logging.error(
"Invalid pretrain weights. Please specify a '.pdparams' file.", "Invalid pretrained weights. Please specify a .pdparams file.",
exit=True) exit=True)
pretrained_dir = osp.join(save_dir, 'pretrain') pretrained_dir = osp.join(save_dir, 'pretrain')
is_backbone_weights = pretrain_weights == 'IMAGENET' is_backbone_weights = pretrain_weights == 'IMAGENET'
@ -909,6 +916,7 @@ class BiSeNetV2(BaseSegmenter):
class FarSeg(BaseSegmenter): class FarSeg(BaseSegmenter):
def __init__(self, def __init__(self,
in_channels=3,
num_classes=2, num_classes=2,
use_mixed_loss=False, use_mixed_loss=False,
losses=None, losses=None,
@ -918,4 +926,5 @@ class FarSeg(BaseSegmenter):
num_classes=num_classes, num_classes=num_classes,
use_mixed_loss=use_mixed_loss, use_mixed_loss=use_mixed_loss,
losses=losses, losses=losses,
in_channels=in_channels,
**params) **params)

20
paddlers/utils/checkpoint.py
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@ -21,20 +21,14 @@ import paddle
from . import logging from . import logging
from .download import download_and_decompress from .download import download_and_decompress
cd_pretrain_weights_dict = {}
cls_pretrain_weights_dict = { cls_pretrain_weights_dict = {
'ResNet50_vd': ['IMAGENET'], 'ResNet50_vd': ['IMAGENET'],
'MobileNetV3_small_x1_0': ['IMAGENET'], 'MobileNetV3_small_x1_0': ['IMAGENET'],
'HRNet_W18_C': ['IMAGENET'], 'HRNet_W18_C': ['IMAGENET'],
} }
seg_pretrain_weights_dict = {
'UNet': ['CITYSCAPES'],
'DeepLabV3P': ['CITYSCAPES', 'PascalVOC', 'IMAGENET'],
'FastSCNN': ['CITYSCAPES'],
'HRNet': ['CITYSCAPES', 'PascalVOC'],
'BiSeNetV2': ['CITYSCAPES']
}
det_pretrain_weights_dict = { det_pretrain_weights_dict = {
'PicoDet_ESNet_s': ['COCO', 'IMAGENET'], 'PicoDet_ESNet_s': ['COCO', 'IMAGENET'],
'PicoDet_ESNet_m': ['COCO', 'IMAGENET'], 'PicoDet_ESNet_m': ['COCO', 'IMAGENET'],
@ -74,6 +68,16 @@ det_pretrain_weights_dict = {
'MaskRCNN_ResNet101_vd_fpn': ['COCO', 'IMAGENET'] 'MaskRCNN_ResNet101_vd_fpn': ['COCO', 'IMAGENET']
} }
res_pretrain_weights_dict = {}
seg_pretrain_weights_dict = {
'UNet': ['CITYSCAPES'],
'DeepLabV3P': ['CITYSCAPES', 'PascalVOC', 'IMAGENET'],
'FastSCNN': ['CITYSCAPES'],
'HRNet': ['CITYSCAPES', 'PascalVOC'],
'BiSeNetV2': ['CITYSCAPES']
}
cityscapes_weights = { cityscapes_weights = {
'UNet_CITYSCAPES': 'UNet_CITYSCAPES':
'https://bj.bcebos.com/paddleseg/dygraph/cityscapes/unet_cityscapes_1024x512_160k/model.pdparams', 'https://bj.bcebos.com/paddleseg/dygraph/cityscapes/unet_cityscapes_1024x512_160k/model.pdparams',

3
tests/rs_models/test_seg_models.py
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@ -50,7 +50,8 @@ class TestFarSegModel(TestSegModel):
def set_specs(self): def set_specs(self):
self.specs = [ self.specs = [
dict(), dict(num_classes=20), dict(encoder_pretrained=False) dict(), dict(num_classes=20), dict(encoder_pretrained=False),
dict(in_channels=10)
] ]
def set_targets(self): def set_targets(self):

1
tutorials/train/README.md
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@ -26,6 +26,7 @@
|object_detection/ppyolov2.py | 目标检测 | PP-YOLOv2 | |object_detection/ppyolov2.py | 目标检测 | PP-YOLOv2 |
|object_detection/yolov3.py | 目标检测 | YOLOv3 | |object_detection/yolov3.py | 目标检测 | YOLOv3 |
|semantic_segmentation/deeplabv3p.py | 图像分割 | DeepLab V3+ | |semantic_segmentation/deeplabv3p.py | 图像分割 | DeepLab V3+ |
|semantic_segmentation/farseg.py | 图像分割 | FarSeg |
|semantic_segmentation/unet.py | 图像分割 | UNet | |semantic_segmentation/unet.py | 图像分割 | UNet |
## 环境准备 ## 环境准备

2
tutorials/train/semantic_segmentation/deeplabv3p.py
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@ -71,7 +71,7 @@ eval_dataset = pdrs.datasets.SegDataset(
# 目前已支持的模型请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/intro/model_zoo.md # 目前已支持的模型请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/intro/model_zoo.md
# 模型输入参数请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/segmenter.py # 模型输入参数请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/segmenter.py
model = pdrs.tasks.seg.DeepLabV3P( model = pdrs.tasks.seg.DeepLabV3P(
input_channel=NUM_BANDS, in_channels=NUM_BANDS,
num_classes=len(train_dataset.labels), num_classes=len(train_dataset.labels),
backbone='ResNet50_vd') backbone='ResNet50_vd')

94
tutorials/train/semantic_segmentation/farseg.py
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@ -0,0 +1,94 @@
#!/usr/bin/env python
# 图像分割模型FarSeg训练示例脚本
# 执行此脚本前，请确认已正确安装PaddleRS库
import paddlers as pdrs
from paddlers import transforms as T
# 数据集存放目录
DATA_DIR = './data/rsseg/'
# 训练集`file_list`文件路径
TRAIN_FILE_LIST_PATH = './data/rsseg/train.txt'
# 验证集`file_list`文件路径
EVAL_FILE_LIST_PATH = './data/rsseg/val.txt'
# 数据集类别信息文件路径
LABEL_LIST_PATH = './data/rsseg/labels.txt'
# 实验目录，保存输出的模型权重和结果
EXP_DIR = './output/farseg/'
# 影像波段数量
NUM_BANDS = 10
# 下载和解压多光谱地块分类数据集
pdrs.utils.download_and_decompress(
'https://paddlers.bj.bcebos.com/datasets/rsseg.zip', path='./data/')
# 定义训练和验证时使用的数据变换（数据增强、预处理等）
# 使用Compose组合多种变换方式。Compose中包含的变换将按顺序串行执行
# API说明：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/apis/data.md
train_transforms = T.Compose([
# 读取影像
T.DecodeImg(),
# 将影像缩放到512x512大小
T.Resize(target_size=512),
# 以50%的概率实施随机水平翻转
T.RandomHorizontalFlip(prob=0.5),
# 将数据归一化到[-1,1]
T.Normalize(
mean=[0.5] * NUM_BANDS, std=[0.5] * NUM_BANDS),
T.ArrangeSegmenter('train')
])
eval_transforms = T.Compose([
T.DecodeImg(),
T.Resize(target_size=512),
# 验证阶段与训练阶段的数据归一化方式必须相同
T.Normalize(
mean=[0.5] * NUM_BANDS, std=[0.5] * NUM_BANDS),
T.ReloadMask(),
T.ArrangeSegmenter('eval')
])
# 分别构建训练和验证所用的数据集
train_dataset = pdrs.datasets.SegDataset(
data_dir=DATA_DIR,
file_list=TRAIN_FILE_LIST_PATH,
label_list=LABEL_LIST_PATH,
transforms=train_transforms,
num_workers=0,
shuffle=True)
eval_dataset = pdrs.datasets.SegDataset(
data_dir=DATA_DIR,
file_list=EVAL_FILE_LIST_PATH,
label_list=LABEL_LIST_PATH,
transforms=eval_transforms,
num_workers=0,
shuffle=False)
# 构建FarSeg模型
# 目前已支持的模型请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/intro/model_zoo.md
# 模型输入参数请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/segmenter.py
model = pdrs.tasks.seg.FarSeg(
in_channels=NUM_BANDS, num_classes=len(train_dataset.labels))
# 执行模型训练
model.train(
num_epochs=10,
train_dataset=train_dataset,
train_batch_size=4,
eval_dataset=eval_dataset,
save_interval_epochs=5,
# 每多少次迭代记录一次日志
log_interval_steps=4,
save_dir=EXP_DIR,
pretrain_weights=None,
# 初始学习率大小
learning_rate=0.001,
# 是否使用early stopping策略，当精度不再改善时提前终止训练
early_stop=False,
# 是否启用VisualDL日志功能
use_vdl=True,
# 指定从某个检查点继续训练
resume_checkpoint=None)

2
tutorials/train/semantic_segmentation/unet.py
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@ -71,7 +71,7 @@ eval_dataset = pdrs.datasets.SegDataset(
# 目前已支持的模型请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/intro/model_zoo.md # 目前已支持的模型请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/intro/model_zoo.md
# 模型输入参数请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/segmenter.py # 模型输入参数请参考：https://github.com/PaddlePaddle/PaddleRS/blob/develop/paddlers/tasks/segmenter.py
model = pdrs.tasks.seg.UNet( model = pdrs.tasks.seg.UNet(
input_channel=NUM_BANDS, num_classes=len(train_dataset.labels)) in_channels=NUM_BANDS, num_classes=len(train_dataset.labels))
# 执行模型训练 # 执行模型训练
model.train( model.train(

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