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Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers. ## Motivation Support CAT-Seg open-vocabulary semantic segmentation (CVPR2023). ## Modification Support CAT-Seg open-vocabulary semantic segmentation (CVPR2023). - [x] Support CAT-Seg model training. - [x] CLIP model based `backbone` (R101 & Swin-B), aggregation layers based `neck`, and `decoder` head. - [x] Provide customized coco-stuff164k_384x384 training configs. - [x] Language model supports for `open vocabulary` (OV) tasks. - [x] Support CLIP-based pretrained language model (LM) inference. - [x] Add commonly used prompts templates. - [x] Add README tutorials. - [x] Add zero-shot testing scripts. **Working on the following tasks.** - [x] Add unit test. ## BC-breaking (Optional) Does the modification introduce changes that break the backward-compatibility of the downstream repos? If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR. ## Use cases (Optional) If this PR introduces a new feature, it is better to list some use cases here, and update the documentation. ## Checklist 1. Pre-commit or other linting tools are used to fix the potential lint issues. 2. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. 3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D. 4. The documentation has been modified accordingly, like docstring or example tutorials. --------- Co-authored-by: xiexinch <xiexinch@outlook.com>
80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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from torch import nn as nn
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from torch.nn import functional as F
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class LinearAttention(nn.Module):
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"""Multi-Head linear attention proposed in "Transformers are RNNs".
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Source: https://github.com/KU-CVLAB/CAT-Seg/blob/main/cat_seg/modeling/transformer/model.py#L247 # noqa
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"""
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def __init__(self, eps=1e-6):
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super().__init__()
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self.eps = eps
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def forward(self, queries, keys, values):
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"""
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Args:
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queries: [N, L, H, D]
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keys: [N, S, H, D]
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values: [N, S, H, D]
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q_mask: [N, L]
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kv_mask: [N, S]
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Returns:
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queried_values: (N, L, H, D)
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"""
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Q = F.elu(queries) + 1
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K = F.elu(keys) + 1
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v_length = values.size(1)
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values = values / v_length # prevent fp16 overflow
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KV = torch.einsum('nshd,nshv->nhdv', K, values) # (S,D)' @ S,V
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Z = 1 / (torch.einsum('nlhd,nhd->nlh', Q, K.sum(dim=1)) + self.eps)
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queried_values = torch.einsum('nlhd,nhdv,nlh->nlhv', Q, KV,
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Z) * v_length
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return queried_values.contiguous()
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class FullAttention(nn.Module):
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"""Multi-head scaled dot-product attention, a.k.a full attention.
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Source: https://github.com/KU-CVLAB/CAT-Seg/blob/main/cat_seg/modeling/transformer/model.py#L276 # noqa
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"""
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def __init__(self, use_dropout=False, attention_dropout=0.1):
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super().__init__()
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self.use_dropout = use_dropout
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self.dropout = nn.Dropout(attention_dropout)
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def forward(self, queries, keys, values, q_mask=None, kv_mask=None):
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"""
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Args:
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queries: [N, L, H, D]
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keys: [N, S, H, D]
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values: [N, S, H, D]
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q_mask: [N, L]
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kv_mask: [N, S]
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Returns:
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queried_values: (N, L, H, D)
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"""
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# Compute the unnormalized attention and apply the masks
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QK = torch.einsum('nlhd,nshd->nlsh', queries, keys)
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if kv_mask is not None:
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QK.masked_fill_(
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~(q_mask[:, :, None, None] * kv_mask[:, None, :, None]),
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float('-inf'))
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# Compute the attention and the weighted average
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softmax_temp = 1. / queries.size(3)**.5 # sqrt(D)
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A = torch.softmax(softmax_temp * QK, dim=2)
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if self.use_dropout:
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A = self.dropout(A)
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queried_values = torch.einsum('nlsh,nshd->nlhd', A, values)
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return queried_values.contiguous()
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