Spaces:
Runtime error
Runtime error
prathmeshrmadhu
commited on
Commit
·
41f275b
1
Parent(s):
47976ea
fixing a few odor related files
Browse files
mmdet_configs/default_runtime.py
ADDED
@@ -0,0 +1,27 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
checkpoint_config = dict(interval=1)
|
2 |
+
# yapf:disable
|
3 |
+
log_config = dict(
|
4 |
+
interval=50,
|
5 |
+
hooks=[
|
6 |
+
dict(type='TextLoggerHook'),
|
7 |
+
# dict(type='TensorboardLoggerHook')
|
8 |
+
])
|
9 |
+
# yapf:enable
|
10 |
+
custom_hooks = [dict(type='NumClassCheckHook')]
|
11 |
+
|
12 |
+
dist_params = dict(backend='nccl')
|
13 |
+
log_level = 'INFO'
|
14 |
+
load_from = None
|
15 |
+
resume_from = None
|
16 |
+
workflow = [('train', 1)]
|
17 |
+
|
18 |
+
# disable opencv multithreading to avoid system being overloaded
|
19 |
+
opencv_num_threads = 0
|
20 |
+
# set multi-process start method as `fork` to speed up the training
|
21 |
+
mp_start_method = 'fork'
|
22 |
+
|
23 |
+
# Default setting for scaling LR automatically
|
24 |
+
# - `enable` means enable scaling LR automatically
|
25 |
+
# or not by default.
|
26 |
+
# - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
|
27 |
+
auto_scale_lr = dict(enable=False, base_batch_size=16)
|
mmdet_configs/odor-fasterrcnn.py
CHANGED
@@ -1,6 +1,6 @@
|
|
1 |
_base_ = [
|
2 |
'./fasterrcnn_r50.py',
|
3 |
'./odor3-instance.py',
|
4 |
-
'
|
5 |
-
'
|
6 |
]
|
|
|
1 |
_base_ = [
|
2 |
'./fasterrcnn_r50.py',
|
3 |
'./odor3-instance.py',
|
4 |
+
'./schedule_1x.py',
|
5 |
+
'./default_runtime.py'
|
6 |
]
|
mmdet_configs/schedule_1x.py
ADDED
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# optimizer
|
2 |
+
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
|
3 |
+
optimizer_config = dict(grad_clip=None)
|
4 |
+
# learning policy
|
5 |
+
lr_config = dict(
|
6 |
+
policy='step',
|
7 |
+
warmup='linear',
|
8 |
+
warmup_iters=500,
|
9 |
+
warmup_ratio=0.001,
|
10 |
+
step=[8, 11])
|
11 |
+
runner = dict(type='EpochBasedRunner', max_epochs=12)
|