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@ -1,18 +1,9 @@ |
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# coding=utf-8 |
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# ------------------------------------------------------------------------ |
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# Copyright 2022 The IDEA Authors. All rights reserved. |
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# Grounding DINO |
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# |
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# url: https://github.com/IDEA-Research/GroundingDINO |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# Copyright (c) 2023 IDEA. All Rights Reserved. |
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# you may not use this file except in compliance with the License. |
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# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
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# You may obtain a copy of the License at |
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# ------------------------------------------------------------------------ |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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# ------------------------------------------------------------------------------------------------ |
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# Deformable DETR |
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# Deformable DETR |
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# Copyright (c) 2020 SenseTime. All Rights Reserved. |
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# Copyright (c) 2020 SenseTime. All Rights Reserved. |
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# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
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# Licensed under the Apache License, Version 2.0 [see LICENSE for details] |
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@ -26,12 +17,14 @@ |
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import math |
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import math |
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import warnings |
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import warnings |
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from typing import Optional |
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from typing import Optional |
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import torch |
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import torch |
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import torch.nn as nn |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import torch.nn.functional as F |
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from torch.autograd import Function |
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from torch.autograd import Function |
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from torch.autograd.function import once_differentiable |
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from torch.autograd.function import once_differentiable |
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from torch.nn.init import constant_, xavier_uniform_ |
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from torch.nn.init import constant_, xavier_uniform_ |
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from groundingdino import _C |
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from groundingdino import _C |
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@ -290,7 +283,6 @@ class MultiScaleDeformableAttention(nn.Module): |
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assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value |
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assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value |
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value = self.value_proj(value) |
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value = self.value_proj(value) |
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if key_padding_mask is not None: |
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if key_padding_mask is not None: |
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value = value.masked_fill(key_padding_mask[..., None], float(0)) |
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value = value.masked_fill(key_padding_mask[..., None], float(0)) |
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@ -339,7 +331,6 @@ class MultiScaleDeformableAttention(nn.Module): |
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sampling_locations = sampling_locations.float() |
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sampling_locations = sampling_locations.float() |
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attention_weights = attention_weights.float() |
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attention_weights = attention_weights.float() |
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output = MultiScaleDeformableAttnFunction.apply( |
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output = MultiScaleDeformableAttnFunction.apply( |
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value, |
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value, |
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spatial_shapes, |
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spatial_shapes, |
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@ -416,4 +407,3 @@ def create_dummy_func(func, dependency, message=""): |
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raise ImportError(err) |
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raise ImportError(err) |
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return _dummy |
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return _dummy |
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SlongLiu
2 years ago