blob: 9f4791f9e57a5e00941e3c525ea0c6c53cc50018 (
plain)
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
|
# @package _global_
defaults:
- override /mapping: null
- override /criterion: null
- override /datamodule: null
- override /network: null
- override /model: null
- override /lr_schedulers: null
# - override /optimizers: null
criterion:
_target_: text_recognizer.criterions.label_smoothing.LabelSmoothingLoss
smoothing: 0.1
ignore_index: 3
mapping:
_target_: text_recognizer.data.emnist_mapping.EmnistMapping
# extra_symbols: [ "\n" ]
lr_schedulers:
network:
_target_: torch.optim.lr_scheduler.CosineAnnealingLR
T_max: 512
eta_min: 4.5e-6
last_epoch: -1
interval: epoch
monitor: val/loss
datamodule:
_target_: text_recognizer.data.iam_lines.IAMLines
batch_size: 4
num_workers: 12
train_fraction: 0.8
augment: false
pin_memory: false
# optimizers:
# - _target_: madgrad.MADGRAD
# lr: 2.0e-4
# momentum: 0.9
# weight_decay: 0
# eps: 1.0e-7
# parameters: network
network:
_target_: text_recognizer.networks.vq_transformer.VqTransformer
input_dims: [1, 56, 1024]
encoder_dim: 32
hidden_dim: 32
dropout_rate: 0.1
num_classes: 58
pad_index: 3
no_grad: true
decoder:
_target_: text_recognizer.networks.transformer.Decoder
dim: 32
depth: 4
num_heads: 8
attn_fn: text_recognizer.networks.transformer.attention.Attention
attn_kwargs:
dim_head: 32
dropout_rate: 0.2
norm_fn: torch.nn.LayerNorm
ff_fn: text_recognizer.networks.transformer.mlp.FeedForward
ff_kwargs:
dim_out: null
expansion_factor: 4
glu: true
dropout_rate: 0.2
cross_attend: true
pre_norm: true
rotary_emb: null
pretrained_encoder_path: "training/logs/runs/2021-09-26/23-27-57"
model:
_target_: text_recognizer.models.vq_transformer.VqTransformerLitModel
start_token: <s>
end_token: <e>
pad_token: <p>
max_output_len: 89 # 451
alpha: 0.0
trainer:
max_epochs: 512
# limit_train_batches: 0.1
# limit_val_batches: 0.1
# gradient_clip_val: 0.5
|
