blob: bbe117825efb61b57f7a992e15caabcca50915b8 (
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
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
|
# @package _global_
defaults:
- override /mapping: null
- override /criterion: cross_entropy
- override /callbacks: htr
- override /datamodule: iam_lines
- override /network: null
- override /model: null
- override /lr_schedulers: null
- override /optimizers: null
epochs: &epochs 512
ignore_index: &ignore_index 3
num_classes: &num_classes 57
max_output_len: &max_output_len 89
summary: [[1, 1, 56, 1024], [1, 89]]
criterion:
ignore_index: *ignore_index
mapping: &mapping
mapping:
_target_: text_recognizer.data.mappings.emnist.EmnistMapping
callbacks:
stochastic_weight_averaging:
_target_: pytorch_lightning.callbacks.StochasticWeightAveraging
swa_epoch_start: 0.75
swa_lrs: 1.0e-5
annealing_epochs: 10
annealing_strategy: cos
device: null
optimizers:
madgrad:
_target_: madgrad.MADGRAD
lr: 3.0e-4
momentum: 0.9
weight_decay: 0
eps: 1.0e-6
parameters: network
lr_schedulers:
network:
_target_: torch.optim.lr_scheduler.CosineAnnealingLR
T_max: *epochs
eta_min: 1.0e-5
last_epoch: -1
interval: epoch
monitor: val/loss
datamodule:
batch_size: 16
num_workers: 12
train_fraction: 0.9
pin_memory: true
<< : *mapping
rotary_embedding: &rotary_embedding
rotary_embedding:
_target_: text_recognizer.networks.transformer.embeddings.rotary.RotaryEmbedding
dim: 64
attn: &attn
dim: &hidden_dim 512
num_heads: 4
dim_head: 64
dropout_rate: &dropout_rate 0.4
network:
_target_: text_recognizer.networks.vq_transformer.VqTransformer
input_dims: [1, 56, 1024]
hidden_dim: *hidden_dim
num_classes: *num_classes
pad_index: *ignore_index
encoder:
_target_: text_recognizer.networks.encoders.efficientnet.EfficientNet
arch: b1
stochastic_dropout_rate: 0.2
bn_momentum: 0.99
bn_eps: 1.0e-3
decoder:
depth: 6
_target_: text_recognizer.networks.transformer.layers.Decoder
self_attn:
_target_: text_recognizer.networks.transformer.attention.Attention
<< : *attn
causal: true
<< : *rotary_embedding
cross_attn:
_target_: text_recognizer.networks.transformer.attention.Attention
<< : *attn
causal: false
norm:
_target_: text_recognizer.networks.transformer.norm.ScaleNorm
normalized_shape: *hidden_dim
ff:
_target_: text_recognizer.networks.transformer.mlp.FeedForward
dim: *hidden_dim
dim_out: null
expansion_factor: 4
glu: true
dropout_rate: *dropout_rate
pre_norm: true
pixel_pos_embedding:
_target_: text_recognizer.networks.transformer.embeddings.axial.AxialPositionalEmbedding
dim: *hidden_dim
shape: [1, 32]
quantizer:
_target_: text_recognizer.networks.quantizer.quantizer.VectorQuantizer
input_dim: 512
codebook:
_target_: text_recognizer.networks.quantizer.codebook.CosineSimilarityCodebook
dim: 16
codebook_size: 4096
kmeans_init: true
kmeans_iters: 10
decay: 0.8
eps: 1.0e-5
threshold_dead: 2
commitment: 1.0
model:
_target_: text_recognizer.models.vq_transformer.VqTransformerLitModel
<< : *mapping
max_output_len: *max_output_len
start_token: <s>
end_token: <e>
pad_token: <p>
trainer:
_target_: pytorch_lightning.Trainer
stochastic_weight_avg: true
auto_scale_batch_size: binsearch
auto_lr_find: false
gradient_clip_val: 0.5
fast_dev_run: false
gpus: 1
precision: 16
max_epochs: *epochs
terminate_on_nan: true
weights_summary: null
limit_train_batches: 1.0
limit_val_batches: 1.0
limit_test_batches: 1.0
resume_from_checkpoint: null
accumulate_grad_batches: 1
overfit_batches: 0
|
