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陈科研
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default_scope = 'opencd' | ||
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work_dir = 'work_dirs/lervicd/ttp_sam_large_levircd' | ||
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custom_imports = dict(imports=['mmseg.ttp'], allow_failed_imports=False) | ||
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env_cfg = dict( | ||
cudnn_benchmark=True, | ||
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), | ||
dist_cfg=dict(backend='nccl'), | ||
) | ||
default_hooks = dict( | ||
timer=dict(type='IterTimerHook'), | ||
logger=dict(type='LoggerHook', interval=10, log_metric_by_epoch=True), | ||
param_scheduler=dict(type='ParamSchedulerHook'), | ||
checkpoint=dict(type='CheckpointHook', by_epoch=True, interval=10, save_best='cd/iou_changed', max_keep_ckpts=5, greater_keys=['cd/iou_changed'], save_last=True), | ||
sampler_seed=dict(type='DistSamplerSeedHook'), | ||
visualization=dict(type='CDVisualizationHook', interval=1, | ||
img_shape=(1024, 1024, 3)) | ||
) | ||
vis_backends = [dict(type='CDLocalVisBackend'), | ||
] | ||
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visualizer = dict( | ||
type='CDLocalVisualizer', | ||
vis_backends=vis_backends, name='visualizer', alpha=1.0) | ||
log_processor = dict(by_epoch=True) | ||
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log_level = 'INFO' | ||
load_from = None | ||
resume = False | ||
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crop_size = (512, 512) | ||
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data_preprocessor = dict( | ||
type='DualInputSegDataPreProcessor', | ||
mean=[123.675, 116.28, 103.53] * 2, | ||
std=[58.395, 57.12, 57.375] * 2, | ||
bgr_to_rgb=True, | ||
pad_val=0, | ||
seg_pad_val=255, | ||
size_divisor=32, | ||
test_cfg=dict(size_divisor=32) | ||
) | ||
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norm_cfg = dict(type='SyncBN', requires_grad=True) | ||
fpn_norm_cfg = dict(type='LN2d', requires_grad=True) | ||
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sam_pretrain_ckpt_path = 'https://download.openmmlab.com/mmclassification/v1/vit_sam/vit-large-p16_sam-pre_3rdparty_sa1b-1024px_20230411-595feafd.pth' | ||
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model = dict( | ||
type='SiamEncoderDecoder', | ||
data_preprocessor=data_preprocessor, | ||
backbone=dict( | ||
type='MMPretrainSamVisionEncoder', | ||
encoder_cfg=dict( | ||
type='mmpretrain.ViTSAM', | ||
arch='large', | ||
img_size=crop_size[0], | ||
patch_size=16, | ||
out_channels=256, | ||
use_abs_pos=True, | ||
use_rel_pos=True, | ||
window_size=14, | ||
layer_cfgs=dict(type='TimeFusionTransformerEncoderLayer'), | ||
init_cfg=dict(type='Pretrained', checkpoint=sam_pretrain_ckpt_path, prefix='backbone.'), | ||
), | ||
peft_cfg=dict( | ||
r=16, | ||
target_modules=["qkv"], | ||
lora_dropout=0.01, | ||
bias='lora_only', | ||
), | ||
), | ||
neck=dict( | ||
type='SequentialNeck', | ||
necks=[ | ||
dict( | ||
type='FeatureFusionNeck', | ||
policy='concat', | ||
out_indices=(0,)), | ||
dict( | ||
type='SimpleFPN', | ||
backbone_channel=512, | ||
in_channels=[128, 256, 512, 512], | ||
out_channels=256, | ||
num_outs=5, | ||
norm_cfg=fpn_norm_cfg), | ||
], | ||
), | ||
decode_head=dict( | ||
type='MLPSegHead', | ||
out_size=(128, 128), | ||
in_channels=[256]*5, | ||
in_index=[0, 1, 2, 3, 4], | ||
channels=256, | ||
dropout_ratio=0, | ||
num_classes=2, | ||
norm_cfg=norm_cfg, | ||
align_corners=False, | ||
loss_decode=dict( | ||
type='mmseg.CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), | ||
train_cfg=dict(), | ||
test_cfg=dict(mode='slide', crop_size=crop_size, stride=(crop_size[0]//2, crop_size[1]//2)) | ||
) # yapf: disable | ||
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dataset_type = 'LEVIR_CD_Dataset' | ||
data_root = '/mnt/levir_datasets/levir-cd' | ||
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train_pipeline = [ | ||
dict(type='MultiImgLoadImageFromFile'), | ||
dict(type='MultiImgLoadAnnotations'), | ||
dict(type='MultiImgRandomRotate', prob=0.5, degree=180), | ||
dict(type='MultiImgRandomCrop', crop_size=crop_size, cat_max_ratio=0.75), | ||
dict(type='MultiImgRandomFlip', prob=0.5, direction='horizontal'), | ||
dict(type='MultiImgRandomFlip', prob=0.5, direction='vertical'), | ||
# dict(type='MultiImgExchangeTime', prob=0.5), | ||
dict( | ||
type='MultiImgPhotoMetricDistortion', | ||
brightness_delta=10, | ||
contrast_range=(0.8, 1.2), | ||
saturation_range=(0.8, 1.2), | ||
hue_delta=10), | ||
dict(type='MultiImgPackSegInputs') | ||
] | ||
test_pipeline = [ | ||
dict(type='MultiImgLoadImageFromFile', to_float32=True), | ||
dict(type='MultiImgResize', scale=(1024, 1024), keep_ratio=True), | ||
# add loading annotation after ``Resize`` because ground truth | ||
# does not need to do resize data transform | ||
dict(type='MultiImgLoadAnnotations'), | ||
dict(type='MultiImgPackSegInputs') | ||
] | ||
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batch_size_per_gpu = 2 | ||
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train_dataloader = dict( | ||
batch_size=batch_size_per_gpu, | ||
num_workers=8, | ||
persistent_workers=True, | ||
sampler=dict(type='DefaultSampler', shuffle=True), | ||
dataset=dict( | ||
type=dataset_type, | ||
data_root=data_root, | ||
data_prefix=dict( | ||
seg_map_path='train/label', | ||
img_path_from='train/A', | ||
img_path_to='train/B'), | ||
pipeline=train_pipeline) | ||
) | ||
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val_dataloader = dict( | ||
batch_size=1, | ||
num_workers=4, | ||
persistent_workers=True, | ||
sampler=dict(type='DefaultSampler', shuffle=False), | ||
dataset=dict( | ||
type=dataset_type, | ||
data_root=data_root, | ||
data_prefix=dict( | ||
seg_map_path='test/label', | ||
img_path_from='test/A', | ||
img_path_to='test/B'), | ||
pipeline=test_pipeline) | ||
) | ||
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test_dataloader = val_dataloader | ||
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val_evaluator = dict( | ||
type='CDMetric', | ||
) | ||
test_evaluator = val_evaluator | ||
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max_epochs = 300 | ||
base_lr = 0.0004 | ||
param_scheduler = [ | ||
dict( | ||
type='LinearLR', start_factor=1e-4, by_epoch=True, begin=0, end=5, convert_to_iter_based=True), | ||
dict( | ||
type='CosineAnnealingLR', | ||
T_max=max_epochs, | ||
begin=5, | ||
by_epoch=True, | ||
end=max_epochs, | ||
convert_to_iter_based=True | ||
), | ||
] | ||
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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=5) | ||
val_cfg = dict(type='ValLoop') | ||
test_cfg = dict(type='TestLoop') | ||
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optim_wrapper = dict( | ||
type='OptimWrapper', | ||
optimizer=dict( | ||
type='AdamW', lr=base_lr, betas=(0.9, 0.999), weight_decay=0.05), | ||
) | ||
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from argparse import ArgumentParser | ||
from opencd.apis import OpenCDInferencer | ||
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def main(): | ||
parser = ArgumentParser() | ||
parser.add_argument('img1', help='Image file') | ||
parser.add_argument('img2', help='Image file') | ||
parser.add_argument('model', help='Config file') | ||
parser.add_argument('--checkpoint', default=None, help='Checkpoint file') | ||
parser.add_argument( | ||
'--out-dir', default='', help='Path to save result file') | ||
parser.add_argument( | ||
'--show', | ||
action='store_true', | ||
default=False, | ||
help='Whether to display the drawn image.') | ||
parser.add_argument( | ||
'--dataset-name', | ||
default='cityscapes', | ||
help='Color palette used for segmentation map') | ||
parser.add_argument( | ||
'--device', default='cuda:0', help='Device used for inference') | ||
parser.add_argument( | ||
'--opacity', | ||
type=float, | ||
default=0.5, | ||
help='Opacity of painted segmentation map. In (0, 1] range.') | ||
parser.add_argument( | ||
'--with-labels', | ||
action='store_true', | ||
default=False, | ||
help='Whether to display the class labels.') | ||
args = parser.parse_args() | ||
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# build the model from a config file and a checkpoint file | ||
mmseg_inferencer = OpenCDInferencer( | ||
args.model, | ||
args.checkpoint, | ||
dataset_name=args.dataset_name, | ||
device=args.device, | ||
classes=('unchanged', 'changed'), | ||
palette=[[0, 0, 0], [255, 255, 255]] | ||
) | ||
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# test a single image | ||
mmseg_inferencer( | ||
[[args.img1, args.img2]], | ||
show=args.show, | ||
out_dir=args.out_dir, | ||
opacity=args.opacity, | ||
with_labels=args.with_labels) | ||
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if __name__ == '__main__': | ||
main() |
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Predicted Mask,flag,username,timestamp | ||
"{""path"":""gradio_cached_examples/7/Predicted Mask/b09f197703fd52794602/image.png"",""url"":null,""size"":null,""orig_name"":""image.png"",""mime_type"":null}",,,2023-12-30 11:09:51.066359 | ||
"{""path"":""gradio_cached_examples/7/Predicted Mask/ea718025cee49966aa84/image.png"",""url"":null,""size"":null,""orig_name"":""image.png"",""mime_type"":null}",,,2023-12-30 11:10:19.524873 | ||
"{""path"":""gradio_cached_examples/7/Predicted Mask/8f4a99ef4227619b272d/image.png"",""url"":null,""size"":null,""orig_name"":""image.png"",""mime_type"":null}",,,2023-12-30 11:10:49.317965 | ||
"{""path"":""gradio_cached_examples/7/Predicted Mask/47d7e3fa5c66943af8b9/image.png"",""url"":null,""size"":null,""orig_name"":""image.png"",""mime_type"":null}",,,2023-12-30 11:11:18.195255 | ||
"{""path"":""gradio_cached_examples/7/Predicted Mask/d95a7e97d2179e212eab/image.png"",""url"":null,""size"":null,""orig_name"":""image.png"",""mime_type"":null}",,,2023-12-30 11:11:48.549910 |