-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathconfig.yaml
150 lines (132 loc) · 3 KB
/
config.yaml
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
# Meta
configfile: config.yaml
tags: [single_state]
central_logging_run_name: central_logging_run
# For fmri
n_conditions: 99
limit_prediction_dimensions: -1
# Dataset
dataset: imagenet-1k
train_split_name: train
val_split_name: validation
cache_dir: /gpfs/data/oermannlab/hf_datasets/
# Training
project_name: transformer_topdown
train_batchsize: 6
eval_batchsize: 18
accumulate_steps: 16
n_gpus: 8
n_nodes: 1
workers: 6
epochs: 25
learning_rate: 0.0001
fp16: True
n_subset_train: -1
n_subset_eval: -1
# LoRA
cross_attention_heads: 8
lora_r: 64
lora_alpha: 16
lora_targets: ["query", "key"]
# Boilerplates
model_seeds: [0, 1, 2, 3]
model_prefix: facebook
source_model:
- dinov2-base-imagenet1k-1-layer
backbone_channels:
dinov2-base-imagenet1k-1-layer: 768
dinov2-small-imagenet1k-1-layer: 384
use_cpu: False
n_condition_tokens: 10
# Temporal training arguments
temporal_repeats: 3
batch_mixing: 3
# Visualizing attention maps
attn_map_sequences: [dog, diff_anchor, diff_fish]
# Feature type for model features
feature_types: [latent_vis, vis, cls, logits]
accumulate_feature_types: [accumulate_vis]
static_feature_types: [static_vis]
layer_feature_types:
[
layer_00,
layer_01,
layer_02,
layer_03,
layer_04,
layer_05,
layer_06,
layer_07,
layer_08,
layer_09,
layer_10,
layer_11,
]
feature_num_layers: 12
n_native_trials: 1000
synthetic_trials: 200
# Evaluate on behavior sequences
n_behavior_subjects: 12
fmri_subjects:
[4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25]
full_fmri_subjects:
[4, 6, 8, 9, 10, 11, 13, 14, 15, 16, 17, 21, 22, 23, 25]
model_rdm_method: correlation
fmri_rdm_method: correlation
similarity_method: corr
roi_threshold: 0.7
# Merge some rois together
roi_merging_dict:
# Coarser
EVC: [V1_L, V1_R, V2_L, V2_R, V3_L, V3_R, V4_L, V4_R]
IT: [FG_L, FG_R, LOC_L, LOC_R]
FPN: [FPN/FPN-FL, FPN/FPN-FR, FPN/FPN-PL, FPN/FPN-PR]
DMN: [DMN/DMN-MPFC, DMN/DMN-PCC, DMN/DMN-PL, DMN/DMN-PR]
# More granular
V1: [V1_L, V1_R]
V2: [V2_L, V2_R]
V3: [V3_L, V3_R]
V4: [V4_L, V4_R]
FG: [FG_L, FG_R]
LOC: [LOC_L, LOC_R]
FPNF: [FPN/FPN-FL, FPN/FPN-FR]
FPNP: [FPN/FPN-PL, FPN/FPN-PR]
DMNMPFC: [DMN/DMN-MPFC]
DMNPCC: [DMN/DMN-PCC]
DMNP: [DMN/DMN-PL, DMN/DMN-PR]
# List of ROIs to evaluate on
full_roi_list:
[
V1,
V2,
V3,
V4,
LOC,
FG,
FPNF,
FPNP,
DMNP,
IT,
EVC,
FPN,
DMN,
]
phase_order: ["pre", "post", "gray", "all", "pre-post", "pre-gray", "post-gray"]
standard_rois: [
"DMN_MPFC", "DMN_PCC", "DMN_PL", "DMN_PR",
"FG_L", "FG_R", "FPN_FL", "FPN_FR", "FPN_PL", "FPN_PR",
"LO1", "LO2", "v1", "v2", "v3", "hv4",
"IPS0", "IPS1", "IPS2", "IPS3", "IPS4", "IPS5", "SPL",
"parietal_operculum", "superior_parietal_lobule", "postcentral_gyrus",
]
# Searchlight setup
searchlight_ball_radius: 2
searchlight_ball_threshold: 0.1
searchlight_workers: 16
sl_permutations: 10000
tfce: True
p_threshold: 0.05
neg_logp_threshold: 1.3
# Create gray matter mask
gm_opening: 0
gm_threshold: 0.6