Add ViTs #3
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Add ViTs #3
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TODO: update numbers
also remove todo
moboehle
reviewed
May 30, 2023
moboehle
reviewed
May 30, 2023
Comment on lines
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| baseline = { | ||
| f"{name}": update_default( | ||
| dict( | ||
| data=dict( | ||
| batch_size=DEFAULT_BATCH_SIZE | ||
| if "_l_" not in name and "simple_vit_b" not in name | ||
| else DEFAULT_BATCH_SIZE // 2, | ||
| train_transform=ImageNetClassificationPresetTrain( | ||
| crop_size=DEFAULT_CROP_SIZE, | ||
| auto_augment_policy="ra", | ||
| ra_magnitude=10, | ||
| is_bcos=False, | ||
| ), | ||
| test_transform=ImageNetClassificationPresetEval( | ||
| crop_size=DEFAULT_CROP_SIZE, | ||
| is_bcos=False, | ||
| ), | ||
| ), | ||
| model=dict( | ||
| is_bcos=False, | ||
| name=name, | ||
| args=dict( | ||
| # linear_layer and conv2d_layer set by model.py | ||
| norm_layer=nn.LayerNorm, | ||
| norm2d_layer=DetachableGNLayerNorm2d, | ||
| act_layer=nn.GELU, | ||
| channels=3, | ||
| ), | ||
| ), | ||
| criterion=nn.CrossEntropyLoss(), | ||
| test_criterion=nn.CrossEntropyLoss(), | ||
| optimizer=OptimizerFactory( | ||
| "AdamW", | ||
| lr=DEFAULT_LR, | ||
| weight_decay=0.0001, | ||
| ), | ||
| use_agc=False, | ||
| lr_scheduler=DEFAULT_LR_SCHEDULE | ||
| if "_l_" not in name and "simple_vit_b" not in name | ||
| else LONG_WARM_SCHEDULE, | ||
| trainer=dict( | ||
| gradient_clip_val=1.0, | ||
| ), | ||
| ) | ||
| ) | ||
| for name in SIMPLE_VIT_ARCHS | ||
| } | ||
|
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| bcos = { | ||
| f"bcos_{name}": update_default( | ||
| dict( | ||
| data=dict( | ||
| batch_size=DEFAULT_BATCH_SIZE | ||
| if "_l_" not in name and "simple_vit_b" not in name | ||
| else DEFAULT_BATCH_SIZE // 2, | ||
| train_transform=ImageNetClassificationPresetTrain( | ||
| crop_size=DEFAULT_CROP_SIZE, | ||
| auto_augment_policy="ra", | ||
| ra_magnitude=10, | ||
| is_bcos=True, | ||
| ), | ||
| test_transform=ImageNetClassificationPresetEval( | ||
| crop_size=DEFAULT_CROP_SIZE, | ||
| is_bcos=True, | ||
| ), | ||
| num_workers=10, | ||
| ), | ||
| model=dict( | ||
| is_bcos=True, | ||
| name=name, | ||
| args=dict( | ||
| # linear_layer and conv2d_layer set by model.py | ||
| norm_layer=norms.NoBias(norms.DetachableLayerNorm), | ||
| act_layer=nn.Identity, | ||
| channels=6, | ||
| norm2d_layer=norms.NoBias(DetachableGNLayerNorm2d), | ||
| ), | ||
| bcos_args=dict( | ||
| b=2, | ||
| max_out=1, | ||
| ), | ||
| logit_bias=math.log(1 / (NUM_CLASSES - 1)), | ||
| ), | ||
| criterion=UniformOffLabelsBCEWithLogitsLoss(), | ||
| lr_scheduler=DEFAULT_LR_SCHEDULE | ||
| if "_l_" not in name and "simple_vit_b" not in name | ||
| else LONG_WARM_SCHEDULE, | ||
| test_criterion=BinaryCrossEntropyLoss(), | ||
| optimizer=OptimizerFactory( | ||
| "Adam", | ||
| lr=DEFAULT_LR, | ||
| ), | ||
| ) | ||
| ) | ||
| for name in SIMPLE_VIT_ARCHS | ||
| } | ||
|
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What do you think about using something like the following in the configs, might be a bit cleaner:
is_big_model = lambda model_name: "_l_" in model_name or "simple_vit_b" in model_name
There was a problem hiding this comment.
Good idea, much more readable. I'll push a change. Thanks
moboehle
reviewed
May 30, 2023
moboehle
reviewed
May 30, 2023
Co-authored-by: moboehle <moritzboehle@me.com>
Co-authored-by: moboehle <moritzboehle@me.com>
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Great suggestions and nice catch! I'll merge and then finish the release. Thanks for taking a look! 😃 |
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@moboehle take a look please! :)
(You can ignore the
pretrained.pyandREADME.mdfiles.)I'll merge it if everything looks good and then finish it up as the
v0.1.0release