File size: 6,538 Bytes
91b09fa
 
 
c599a73
 
 
 
 
91b09fa
c599a73
91b09fa
 
 
 
c599a73
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
91b09fa
c599a73
 
 
 
 
 
91b09fa
 
 
 
c599a73
 
 
 
 
91b09fa
 
 
 
 
 
 
 
 
 
 
c599a73
f2c0d71
c599a73
91b09fa
c599a73
 
91b09fa
 
 
 
 
 
c599a73
 
 
 
 
 
 
f2c0d71
91b09fa
c599a73
91b09fa
 
 
c599a73
 
 
91b09fa
c599a73
 
 
 
 
 
 
 
 
 
 
 
 
 
91b09fa
 
 
c599a73
91b09fa
 
c599a73
91b09fa
 
 
c599a73
 
91b09fa
 
 
c599a73
 
91b09fa
 
 
c599a73
 
91b09fa
 
 
c599a73
 
91b09fa
 
 
c599a73
 
91b09fa
 
 
c599a73
91b09fa
c599a73
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
91b09fa
c599a73
 
91b09fa
c599a73
 
 
 
91b09fa
 
 
 
 
 
c599a73
 
 
 
 
d8c0ec4
c599a73
 
91b09fa
 
 
 
 
 
c599a73
91b09fa
c599a73
 
 
 
91b09fa
 
c599a73
 
 
 
91b09fa
 
c599a73
 
 
 
 
 
 
 
91b09fa
c599a73
91b09fa
 
c599a73
 
 
 
 
 
 
 
91b09fa
c599a73
fed16ce
c599a73
91b09fa
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
import os 
custom_imports = dict(imports=['geospatial_fm'])

dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
cudnn_benchmark = True

dataset_type = 'GeospatialDataset'

# TO BE DEFINED BY USER: data directory
data_root = '<path to data root>'

num_frames = 1
img_size = 224
num_workers = 4
samples_per_gpu = 4
img_norm_cfg = dict(
    means=[
        0.033349706741586264, 0.05701185520536176, 0.05889748132001316,
        0.2323245113436119, 0.1972854853760658, 0.11944914225186566
    ],
    stds=[
        0.02269135568823774, 0.026807560223070237, 0.04004109844362779,
        0.07791732423672691, 0.08708738838140137, 0.07241979477437814
    ])
bands = [0, 1, 2, 3, 4, 5]
tile_size = 224
orig_nsize = 512
crop_size = (tile_size, tile_size)
img_suffix = '_merged.tif'
seg_map_suffix = '.mask.tif'
ignore_index = -1
image_nodata = -9999
image_nodata_replace = 0
image_to_float32 = True

# model
# TO BE DEFINED BY USER: model path
pretrained_weights_path = '<path to pretrained weights>'
num_layers = 12
patch_size = 16
embed_dim = 768
num_heads = 12
tubelet_size = 1
output_embed_dim = num_frames*embed_dim
max_intervals=10000
evaluation_interval=1000

# TO BE DEFINED BY USER: model path
experiment = '<experiment name>'
project_dir = '<project directory name>'
work_dir = os.path.join(project_dir, experiment)
save_path = work_dir

save_path = work_dir
train_pipeline = [
    dict(type='LoadGeospatialImageFromFile', to_float32=image_to_float32),
    dict(type='LoadGeospatialAnnotations', reduce_zero_label=False),
    dict(type='BandsExtract', bands=bands),
    dict(type='RandomFlip', prob=0.5),
    dict(type='ToTensor', keys=['img', 'gt_semantic_seg']),
    # to channels first
    dict(type="TorchPermute", keys=["img"], order=(2, 0, 1)),
    dict(type='TorchNormalize', **img_norm_cfg),
    dict(type='TorchRandomCrop', crop_size=(tile_size, tile_size)),
    dict(type='Reshape', keys=['img'], new_shape=(len(bands), num_frames, tile_size, tile_size)),
    dict(type='Reshape', keys=['gt_semantic_seg'], new_shape=(1, tile_size, tile_size)),
    dict(
        type='CastTensor',
        keys=['gt_semantic_seg'],
        new_type='torch.LongTensor'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]
test_pipeline = [
    dict(type='LoadGeospatialImageFromFile', to_float32=image_to_float32),
    dict(type='BandsExtract', bands=bands),
    dict(type='ToTensor', keys=['img']),
    # to channels first
    dict(type="TorchPermute", keys=["img"], order=(2, 0, 1)),
    dict(type='TorchNormalize', **img_norm_cfg),
    dict(
        type='Reshape',
        keys=['img'],
        new_shape=(len(bands), num_frames, -1, -1),
        look_up=dict({
            '2': 1,
            '3': 2
        })),
    dict(type='CastTensor', keys=['img'], new_type='torch.FloatTensor'),
    dict(
        type='CollectTestList',
        keys=['img'],
        meta_keys=[
            'img_info', 'seg_fields', 'img_prefix', 'seg_prefix', 'filename',
            'ori_filename', 'img', 'img_shape', 'ori_shape', 'pad_shape',
            'scale_factor', 'img_norm_cfg'
        ])
]

CLASSES = ('Unburnt land', 'Burn scar')

data = dict(
    samples_per_gpu=samples_per_gpu,
    workers_per_gpu=num_workers,
    train=dict(
        type=dataset_type,
        CLASSES=CLASSES,
        data_root=data_root,
        img_dir='training',
        ann_dir='training',
        img_suffix=img_suffix,
        seg_map_suffix=seg_map_suffix,
        pipeline=train_pipeline,
        ignore_index=-1),
    val=dict(
        type=dataset_type,
        CLASSES=CLASSES,
        data_root=data_root,
        img_dir='validation',
        ann_dir='validation',
        img_suffix=img_suffix,
        seg_map_suffix=seg_map_suffix,
        pipeline=test_pipeline,
        ignore_index=-1),
    test=dict(
        type=dataset_type,
        CLASSES=CLASSES,
        data_root=data_root,
        img_dir='validation',
        ann_dir='validation',
        img_suffix=img_suffix,
        seg_map_suffix=seg_map_suffix,
        pipeline=test_pipeline,
        ignore_index=-1))

optimizer = dict(type='Adam', lr=1.3e-05, betas=(0.9, 0.999))
optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy='poly',
    warmup='linear',
    warmup_iters=1500,
    warmup_ratio=1e-06,
    power=1.0,
    min_lr=0.0,
    by_epoch=False)
log_config = dict(
    interval=20,
    hooks=[
        dict(type='TextLoggerHook', by_epoch=False),
        dict(type='TensorboardLoggerHook', by_epoch=False)
    ])
checkpoint_config = dict(
    by_epoch=True,
    interval=10,
    out_dir=save_path 
)
evaluation = dict(
    interval=evaluation_interval,
    metric='mIoU',
    pre_eval=True,
    save_best='mIoU',
    by_epoch=False)

loss_func=dict(
            type='DiceLoss', use_sigmoid=False, loss_weight=1,
            ignore_index=-1)

runner = dict(type='IterBasedRunner', max_iters=max_intervals)
workflow = [('train', 1)]
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
    type='TemporalEncoderDecoder',
    frozen_backbone=False,
    pretrained=pretrained_weights_path,
    backbone=dict(
        type='TemporalViTEncoder',
        img_size=img_size,
        patch_size=patch_size,
        num_frames=num_frames,
        tubelet_size=tubelet_size,
        in_chans=len(bands),
        embed_dim=embed_dim,
        depth=12,
        num_heads=num_heads,
        mlp_ratio=4.0,
        norm_pix_loss=False),
    neck=dict(
        type='ConvTransformerTokensToEmbeddingNeck',
        embed_dim=embed_dim*num_frames,
        output_embed_dim=output_embed_dim,
        drop_cls_token=True,
        Hp=14,
        Wp=14),
    decode_head=dict(
        num_classes=len(CLASSES),
        in_channels=output_embed_dim,
        type='FCNHead',
        in_index=-1,
        channels=256,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        norm_cfg=dict(type='BN', requires_grad=True),
        align_corners=False,
        loss_decode=loss_func),
    auxiliary_head=dict(
        num_classes=len(CLASSES),
        in_channels=output_embed_dim,
        type='FCNHead',
        in_index=-1,
        channels=256,
        num_convs=2,
        concat_input=False,
        dropout_ratio=0.1,
        norm_cfg=dict(type='BN', requires_grad=True),
        align_corners=False,
        loss_decode=loss_func),
    train_cfg=dict(),
    test_cfg=dict(mode='slide', stride=(int(tile_size/2), int(tile_size/2)), crop_size=(tile_size, tile_size)))
gpu_ids = range(0, 1)
auto_resume = False