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__init__.py
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Update attention / self-attn based models from a series of experiments:
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2021-08-20 16:13:11 -07:00 |
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activations.py
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activations_jit.py
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activations_me.py
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adaptive_avgmax_pool.py
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Refactoring, cleanup, improved test coverage.
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2021-06-12 16:40:02 -07:00 |
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attention_pool2d.py
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Add initial AttentionPool2d that's being trialed. Fix comment and still trying to improve reliability of sgd test.
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2021-09-05 12:41:14 -07:00 |
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blur_pool.py
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bottleneck_attn.py
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Add option to include relative pos embedding in the attention scaling as per references. See discussion #912
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2021-10-12 15:37:01 -07:00 |
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cbam.py
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Add Gather-Excite and Global Context attn modules. Refactor existing SE-like attn for consistency and refactor byob/byoanet for less redundancy.
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2021-05-27 18:03:29 -07:00 |
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classifier.py
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Refactoring, cleanup, improved test coverage.
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2021-06-12 16:40:02 -07:00 |
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cond_conv2d.py
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config.py
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conv2d_same.py
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conv_bn_act.py
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create_act.py
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Fix #661, move hardswish out of default args for LeViT. Enable native torch support for hardswish, hardsigmoid, mish if present.
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2021-05-26 15:28:42 -07:00 |
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create_attn.py
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Update attention / self-attn based models from a series of experiments:
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2021-08-20 16:13:11 -07:00 |
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create_conv2d.py
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create_norm_act.py
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drop.py
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eca.py
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Add non-local and BAT attention. Merge attn and self-attn factories into one. Add attention references to README. Add mlp 'mode' to ECA.
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2021-05-31 13:18:11 -07:00 |
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evo_norm.py
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gather_excite.py
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Add Gather-Excite and Global Context attn modules. Refactor existing SE-like attn for consistency and refactor byob/byoanet for less redundancy.
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2021-05-27 18:03:29 -07:00 |
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global_context.py
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Add gc_efficientnetv2_rw_t weights (global context instead of SE attn). Add TF XL weights even though the fine-tuned ones don't validate that well. Change default arg for GlobalContext to use scal (mul) mode.
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2021-08-07 16:45:29 -07:00 |
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halo_attn.py
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Add option to include relative pos embedding in the attention scaling as per references. See discussion #912
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2021-10-12 15:37:01 -07:00 |
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helpers.py
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Throw in some FBNetV3 code I had lying around, some refactoring of SE reduction channel calcs for all EffNet archs.
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2021-05-28 20:47:24 -07:00 |
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inplace_abn.py
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lambda_layer.py
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Halo, bottleneck attn, lambda layer additions and cleanup along w/ experimental model defs
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2021-10-06 16:32:48 -07:00 |
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linear.py
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median_pool.py
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mixed_conv2d.py
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mlp.py
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Refactoring, cleanup, improved test coverage.
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2021-06-12 16:40:02 -07:00 |
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non_local_attn.py
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Fix torchscript issue in bat
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2021-06-08 23:19:51 -07:00 |
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norm.py
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Realized LayerNorm2d won't work in all cases as is, fixed.
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2021-07-05 18:21:34 -07:00 |
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norm_act.py
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padding.py
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patch_embed.py
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pool2d_same.py
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Support npz custom load for vision transformer hybrid models. Add posembed rescale for npz load.
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2021-06-14 12:31:44 -07:00 |
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selective_kernel.py
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Remove min channels for SelectiveKernel, divisor should cover cases well enough.
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2021-05-31 15:38:56 -07:00 |
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separable_conv.py
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space_to_depth.py
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split_attn.py
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Add non-local and BAT attention. Merge attn and self-attn factories into one. Add attention references to README. Add mlp 'mode' to ECA.
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2021-05-31 13:18:11 -07:00 |
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split_batchnorm.py
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squeeze_excite.py
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Add non-local and BAT attention. Merge attn and self-attn factories into one. Add attention references to README. Add mlp 'mode' to ECA.
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2021-05-31 13:18:11 -07:00 |
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std_conv.py
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Use reshape instead of view in std_conv, causing issues in recent PyTorch in channels_last
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2021-09-23 15:43:48 -07:00 |
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test_time_pool.py
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weight_init.py
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