tigertang 43f2500c26

Add symbolic for SwishJitAutoFn to support onnx

2020-11-18 14:36:12 +08:00

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__init__.py

Initial Vision Transformer impl w/ patch and hybrid variants. Refactor tuple helpers.

2020-10-13 13:33:44 -07:00

activations_jit.py

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activations_me.py

Add symbolic for SwishJitAutoFn to support onnx

2020-11-18 14:36:12 +08:00

activations.py

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adaptive_avgmax_pool.py

Add 'fast' global pool option, remove redundant SEModule from tresnet, normal one is now 'fast'

2020-09-02 09:11:48 -07:00

anti_aliasing.py

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blur_pool.py

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cbam.py

AdaptiveAvgPool2d -> mean((2,3)) for all SE/attn layers to avoid NaN with AMP + channels_last layout. See https://github.com/pytorch/pytorch/issues/43992

2020-09-01 16:05:32 -07:00

classifier.py

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cond_conv2d.py

Initial Vision Transformer impl w/ patch and hybrid variants. Refactor tuple helpers.

2020-10-13 13:33:44 -07:00

config.py

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conv2d_same.py

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conv_bn_act.py

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create_act.py

Support native silu activation (aka swish). An optimized ver is available in PyTorch 1.7.

2020-10-29 15:45:17 -07:00

create_attn.py

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create_conv2d.py

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create_norm_act.py

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drop.py

Initial Vision Transformer impl w/ patch and hybrid variants. Refactor tuple helpers.

2020-10-13 13:33:44 -07:00

eca.py

AdaptiveAvgPool2d -> mean((2,3)) for all SE/attn layers to avoid NaN with AMP + channels_last layout. See https://github.com/pytorch/pytorch/issues/43992

2020-09-01 16:05:32 -07:00

evo_norm.py

Fix a silly bug in Sample version of EvoNorm missing x* part of swish, update EvoNormBatch to accumulated unbiased variance.

2020-08-13 18:25:01 -07:00

helpers.py

Initial Vision Transformer impl w/ patch and hybrid variants. Refactor tuple helpers.

2020-10-13 13:33:44 -07:00

inplace_abn.py

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median_pool.py

Initial Vision Transformer impl w/ patch and hybrid variants. Refactor tuple helpers.

2020-10-13 13:33:44 -07:00

mixed_conv2d.py

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norm_act.py

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padding.py

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pool2d_same.py

Initial Vision Transformer impl w/ patch and hybrid variants. Refactor tuple helpers.

2020-10-13 13:33:44 -07:00

se.py

AdaptiveAvgPool2d -> mean((2,3)) for all SE/attn layers to avoid NaN with AMP + channels_last layout. See https://github.com/pytorch/pytorch/issues/43992

2020-09-01 16:05:32 -07:00

selective_kernel.py

AdaptiveAvgPool2d -> mean((2,3)) for all SE/attn layers to avoid NaN with AMP + channels_last layout. See https://github.com/pytorch/pytorch/issues/43992

2020-09-01 16:05:32 -07:00

separable_conv.py

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space_to_depth.py

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split_attn.py

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split_batchnorm.py

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test_time_pool.py

More models in sotabench, more control over sotabench run, dataset filename extraction consistency

2020-09-24 15:56:57 -07:00

weight_init.py

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