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Switch from SGD optimizer to AdamW for patch classification tasks #690

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merged 9 commits into from
Oct 18, 2024
Merged

Switch from SGD optimizer to AdamW for patch classification tasks #690

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59fc5d6
change SDG to AdamW, batchsize to 256 for patch classification
nkaenzig Oct 17, 2024
d41bbca
replaced early stopping criteria
nkaenzig Oct 17, 2024
f209897
increased patience for mhist to 100
nkaenzig Oct 17, 2024
5c48217
changed early stopping criteria
nkaenzig Oct 18, 2024
382d1fc
update patience for mhist to follow protocol
nkaenzig Oct 18, 2024
a627162
updated docs
nkaenzig Oct 18, 2024
fea4b73
update leaderboard & plots
nkaenzig Oct 18, 2024
624e7ef
update sentence in protocol docs
nkaenzig Oct 18, 2024
b898973
Merge branch 'main' into sgd-to-adam
nkaenzig Oct 18, 2024
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6 changes: 2 additions & 4 deletions configs/core/tests/offline/embeddings.yaml
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Expand Up @@ -26,11 +26,9 @@ model:
out_features: &NUM_CLASSES 2
criterion: torch.nn.CrossEntropyLoss
optimizer:
class_path: torch.optim.SGD
class_path: torch.optim.AdamW
init_args:
lr: ${oc.env:LR_VALUE, 0.1}
momentum: 0.9
weight_decay: 0.0
lr: ${oc.env:LR_VALUE, 0.0003}
lr_scheduler:
class_path: torch.optim.lr_scheduler.CosineAnnealingLR
init_args:
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6 changes: 2 additions & 4 deletions configs/vision/pathology/offline/classification/bach.yaml
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Expand Up @@ -53,11 +53,9 @@ model:
out_features: &NUM_CLASSES 4
criterion: torch.nn.CrossEntropyLoss
optimizer:
class_path: torch.optim.SGD
class_path: torch.optim.AdamW
init_args:
lr: &LR_VALUE ${oc.env:LR_VALUE, 0.000625}
momentum: 0.9
weight_decay: 0.0
lr: ${oc.env:LR_VALUE, 0.0003}
lr_scheduler:
class_path: torch.optim.lr_scheduler.CosineAnnealingLR
init_args:
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2 changes: 1 addition & 1 deletion configs/vision/pathology/offline/classification/camelyon16.yaml
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Expand Up @@ -18,7 +18,7 @@ trainer:
filename: best
save_last: true
save_top_k: 1
monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryAccuracy}
monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryBalancedAccuracy}
mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max}
- class_path: lightning.pytorch.callbacks.EarlyStopping
init_args:
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2 changes: 1 addition & 1 deletion configs/vision/pathology/offline/classification/camelyon16_small.yaml
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Expand Up @@ -18,7 +18,7 @@ trainer:
filename: best
save_last: true
save_top_k: 1
monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryAccuracy}
monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryBalancedAccuracy}
mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max}
- class_path: lightning.pytorch.callbacks.EarlyStopping
init_args:
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8 changes: 3 additions & 5 deletions configs/vision/pathology/offline/classification/crc.yaml
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Expand Up @@ -53,11 +53,9 @@ model:
out_features: &NUM_CLASSES 9
criterion: torch.nn.CrossEntropyLoss
optimizer:
class_path: torch.optim.SGD
class_path: torch.optim.AdamW
init_args:
lr: &LR_VALUE ${oc.env:LR_VALUE, 0.01}
momentum: 0.9
weight_decay: 0.0
lr: ${oc.env:LR_VALUE, 0.0003}
lr_scheduler:
class_path: torch.optim.lr_scheduler.CosineAnnealingLR
init_args:
Expand Down Expand Up @@ -104,7 +102,7 @@ data:
split: val
dataloaders:
train:
batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 4096}
batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 256}
num_workers: &N_DATA_WORKERS ${oc.env:N_DATA_WORKERS, 4}
shuffle: true
val:
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10 changes: 4 additions & 6 deletions configs/vision/pathology/offline/classification/mhist.yaml
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Expand Up @@ -18,12 +18,12 @@ trainer:
filename: best
save_last: true
save_top_k: 1
monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryAccuracy}
monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryBalancedAccuracy}
mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max}
- class_path: lightning.pytorch.callbacks.EarlyStopping
init_args:
min_delta: 0
patience: 51
patience: 70
monitor: *MONITOR_METRIC
mode: *MONITOR_METRIC_MODE
- class_path: eva.callbacks.ClassificationEmbeddingsWriter
Expand Down Expand Up @@ -53,11 +53,9 @@ model:
out_features: 1
criterion: torch.nn.BCEWithLogitsLoss
optimizer:
class_path: torch.optim.SGD
class_path: torch.optim.AdamW
init_args:
lr: &LR_VALUE ${oc.env:LR_VALUE, 0.000625}
momentum: 0.9
weight_decay: 0.0
lr: ${oc.env:LR_VALUE, 0.0003}
lr_scheduler:
class_path: torch.optim.lr_scheduler.CosineAnnealingLR
init_args:
Expand Down
10 changes: 4 additions & 6 deletions configs/vision/pathology/offline/classification/patch_camelyon.yaml
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Expand Up @@ -18,7 +18,7 @@ trainer:
filename: best
save_last: true
save_top_k: 1
monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryAccuracy}
monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryBalancedAccuracy}
mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max}
- class_path: lightning.pytorch.callbacks.EarlyStopping
init_args:
Expand Down Expand Up @@ -54,11 +54,9 @@ model:
out_features: 1
criterion: torch.nn.BCEWithLogitsLoss
optimizer:
class_path: torch.optim.SGD
class_path: torch.optim.AdamW
init_args:
lr: &LR_VALUE ${oc.env:LR_VALUE, 0.01}
momentum: 0.9
weight_decay: 0.0
lr: ${oc.env:LR_VALUE, 0.0003}
lr_scheduler:
class_path: torch.optim.lr_scheduler.CosineAnnealingLR
init_args:
Expand Down Expand Up @@ -118,7 +116,7 @@ data:
split: test
dataloaders:
train:
batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 4096}
batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 256}
num_workers: &N_DATA_WORKERS ${oc.env:N_DATA_WORKERS, 4}
shuffle: true
val:
Expand Down
6 changes: 2 additions & 4 deletions configs/vision/pathology/online/classification/bach.yaml
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Expand Up @@ -45,11 +45,9 @@ model:
out_features: &NUM_CLASSES 4
criterion: torch.nn.CrossEntropyLoss
optimizer:
class_path: torch.optim.SGD
class_path: torch.optim.AdamW
init_args:
lr: &LR_VALUE ${oc.env:LR_VALUE, 0.000625}
momentum: 0.9
weight_decay: 0.0
lr: ${oc.env:LR_VALUE, 0.0003}
lr_scheduler:
class_path: torch.optim.lr_scheduler.CosineAnnealingLR
init_args:
Expand Down
8 changes: 3 additions & 5 deletions configs/vision/pathology/online/classification/crc.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -45,11 +45,9 @@ model:
out_features: &NUM_CLASSES 9
criterion: torch.nn.CrossEntropyLoss
optimizer:
class_path: torch.optim.SGD
class_path: torch.optim.AdamW
init_args:
lr: &LR_VALUE ${oc.env:LR_VALUE, 0.01}
momentum: 0.9
weight_decay: 0.0
lr: ${oc.env:LR_VALUE, 0.0003}
lr_scheduler:
class_path: torch.optim.lr_scheduler.CosineAnnealingLR
init_args:
Expand Down Expand Up @@ -86,7 +84,7 @@ data:
split: val
dataloaders:
train:
batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 4096}
batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 256}
num_workers: &N_DATA_WORKERS ${oc.env:N_DATA_WORKERS, 4}
shuffle: true
val:
Expand Down
10 changes: 4 additions & 6 deletions configs/vision/pathology/online/classification/mhist.yaml
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Expand Up @@ -17,12 +17,12 @@ trainer:
filename: best
save_last: true
save_top_k: 1
monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryAccuracy}
monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryBalancedAccuracy}
mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max}
- class_path: lightning.pytorch.callbacks.EarlyStopping
init_args:
min_delta: 0
patience: 51
patience: 70
monitor: *MONITOR_METRIC
mode: *MONITOR_METRIC_MODE
logger:
Expand All @@ -45,11 +45,9 @@ model:
out_features: 1
criterion: torch.nn.BCEWithLogitsLoss
optimizer:
class_path: torch.optim.SGD
class_path: torch.optim.AdamW
init_args:
lr: &LR_VALUE ${oc.env:LR_VALUE, 0.000625}
momentum: 0.9
weight_decay: 0.0
lr: ${oc.env:LR_VALUE, 0.0003}
lr_scheduler:
class_path: torch.optim.lr_scheduler.CosineAnnealingLR
init_args:
Expand Down
10 changes: 4 additions & 6 deletions configs/vision/pathology/online/classification/patch_camelyon.yaml
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Expand Up @@ -17,7 +17,7 @@ trainer:
filename: best
save_last: true
save_top_k: 1
monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryAccuracy}
monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/BinaryBalancedAccuracy}
mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max}
- class_path: lightning.pytorch.callbacks.EarlyStopping
init_args:
Expand Down Expand Up @@ -45,11 +45,9 @@ model:
out_features: 1
criterion: torch.nn.BCEWithLogitsLoss
optimizer:
class_path: torch.optim.SGD
class_path: torch.optim.AdamW
init_args:
lr: &LR_VALUE ${oc.env:LR_VALUE, 0.01}
momentum: 0.9
weight_decay: 0.0
lr: ${oc.env:LR_VALUE, 0.0003}
lr_scheduler:
class_path: torch.optim.lr_scheduler.CosineAnnealingLR
init_args:
Expand Down Expand Up @@ -91,7 +89,7 @@ data:
split: test
dataloaders:
train:
batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 4096}
batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 256}
num_workers: &N_DATA_WORKERS ${oc.env:N_DATA_WORKERS, 4}
shuffle: true
val:
Expand Down
6 changes: 2 additions & 4 deletions configs/vision/tests/offline/panda.yaml
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Expand Up @@ -34,11 +34,9 @@ model:
output_size: &NUM_CLASSES 6
criterion: torch.nn.CrossEntropyLoss
optimizer:
class_path: torch.optim.SGD
class_path: torch.optim.AdamW
init_args:
lr: &LR_VALUE ${oc.env:LR_VALUE, 0.00004}
momentum: 0.9
weight_decay: 0.0
lr: ${oc.env:LR_VALUE, 0.0003}
lr_scheduler:
class_path: torch.optim.lr_scheduler.CosineAnnealingLR
init_args:
Expand Down
6 changes: 2 additions & 4 deletions configs/vision/tests/offline/patch_camelyon.yaml
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Expand Up @@ -47,11 +47,9 @@ model:
out_features: 1
criterion: torch.nn.BCEWithLogitsLoss
optimizer:
class_path: torch.optim.SGD
class_path: torch.optim.AdamW
init_args:
lr: ${oc.env:LR_VALUE, 0.1}
momentum: 0.9
weight_decay: 0.0
lr: ${oc.env:LR_VALUE, 0.0003}
lr_scheduler:
class_path: torch.optim.lr_scheduler.CosineAnnealingLR
init_args:
Expand Down
6 changes: 2 additions & 4 deletions configs/vision/tests/online/patch_camelyon.yaml
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Expand Up @@ -18,11 +18,9 @@ model:
out_features: 1
criterion: torch.nn.BCEWithLogitsLoss
optimizer:
class_path: torch.optim.SGD
class_path: torch.optim.AdamW
init_args:
lr: ${oc.env:LR_VALUE, 0.1}
momentum: 0.9
weight_decay: 0.0
lr: ${oc.env:LR_VALUE, 0.0003}
lr_scheduler:
class_path: torch.optim.lr_scheduler.CosineAnnealingLR
init_args:
Expand Down
18 changes: 8 additions & 10 deletions docs/leaderboards.md
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Expand Up @@ -38,26 +38,24 @@ We selected this approach to prioritize reliable, robust and fair FM-evaluation
| **Dropout** | 0.0 | 0.0 | 0.0 |
| **Hidden activation function** | n/a | ReLU | n/a |
| **Output activation function** | none | none | none |
| **Number of steps** | 12,500 | 12,500 (2) | 2,000 |
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| **Base batch size** | 4,096 (1) | 32 | 64 |
| **Base learning rate** | 0.01 (1) | 0.001 | 0.0001 |
| **Early stopping** | 5% * [Max epochs] | 10% * [Max epochs] (3) | 10% * [Max epochs] (3) |
| **Number of steps** | 12,500 | 12,500 (1) | 2,000 |
| **Base batch size** | 256 | 32 | 64 |
| **Base learning rate** | 0.0003 | 0.001 | 0.0001 |
| **Early stopping** | 5% * [Max epochs] | 10% * [Max epochs] (2) | 10% * [Max epochs] (2) |
| **Optimizer** | SGD | AdamW | AdamW |
| **Momentum** | 0.9 | n/a | n/a |
| **Weight Decay** | 0.0 | n/a | n/a |
| **betas** | n/a | [0.9, 0.999] | [0.9, 0.999] |
| **LR Schedule** | Cosine without warmup | Cosine without warmup | PolynomialLR |
| **Loss** | Cross entropy | Cross entropy | Dice |
| **number of patches per slide**| 1 | dataset specific (4) | dataset specific (4) |
| **number of patches per slide**| 1 | dataset specific (3) | dataset specific (3) |


(1) For smaller datasets (e.g. BACH with 400 samples) we reduce the batch size to 256 and scale the learning rate accordingly.
(1) Upper cap at a maximum of 100 epochs.

(2) Upper cap at a maximum of 100 epochs.
(2) Lower cap at a minimum of 8 epochs.

(3) Lower cap at a minimum of 8 epochs.

(4) Number of patches per slide depends on task and slide size. E.g. for PANDA and Camelyon16 we use a max of 1,000 and 10,000 random patches per slide respectively.
(3) Number of patches per slide depends on task and slide size. E.g. for `PANDASmall` and `Camelyon16Small` we use a max of 200 and 1000 random patches per slide respectively.


- [1]: [Virchow: A Million-Slide Digital Pathology Foundation Model, 2024](https://arxiv.org/pdf/2309.07778.pdf)