blob: 55dc8960f18a931a6c51c862a82cccf287b53474 (
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
150
151
152
153
154
155
156
157
158
|
---
# @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 200
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
# label_smoothing: 0.1
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:
radam:
_target_: torch.optim.RAdam
lr: 3.0e-4
betas: [0.9, 0.999]
weight_decay: 0
eps: 1.0e-8
parameters: network
lr_schedulers:
network:
_target_: torch.optim.lr_scheduler.ReduceLROnPlateau
mode: min
factor: 0.5
patience: 10
threshold: 1.0e-4
threshold_mode: rel
cooldown: 0
min_lr: 1.0e-5
eps: 1.0e-8
verbose: false
interval: epoch
monitor: val/loss
datamodule:
batch_size: 16
num_workers: 12
train_fraction: 0.9
pin_memory: true
<< : *mapping
encoder: &encoder
_target_: text_recognizer.networks.efficientnet.efficientnet.EfficientNet
arch: b0
stochastic_dropout_rate: 0.2
bn_momentum: 0.99
bn_eps: 1.0e-3
depth: 5
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
decoder: &decoder
_target_: text_recognizer.networks.transformer.decoder.Decoder
depth: 6
has_pos_emb: true
block:
_target_: text_recognizer.networks.transformer.decoder.DecoderBlock
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.RMSNorm
dim: *hidden_dim
ff:
_target_: text_recognizer.networks.transformer.mlp.FeedForward
dim: *hidden_dim
dim_out: null
expansion_factor: 2
glu: true
dropout_rate: *dropout_rate
pixel_pos_embedding: &pixel_pos_embedding
_target_: >
text_recognizer.networks.transformer.embeddings.axial.AxialPositionalEmbedding
dim: *hidden_dim
shape: &shape [3, 64]
network:
_target_: text_recognizer.networks.conv_transformer.ConvTransformer
input_dims: [1, 1, 56, 1024]
hidden_dim: *hidden_dim
num_classes: *num_classes
pad_index: *ignore_index
encoder:
<<: *encoder
decoder:
<<: *decoder
pixel_pos_embedding:
<<: *pixel_pos_embedding
model:
_target_: text_recognizer.models.transformer.LitTransformer
<<: *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
|
