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- # LICENSE HEADER MANAGED BY add-license-header
- #
- # Copyright 2018 Kornia Team
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- #
- import torch.nn.functional as F
- from torch import nn
- from kornia.core import Module, Tensor
- class DeDoDeDetector(nn.Module):
- def __init__(self, encoder: Module, decoder: Module, *args, **kwargs) -> None: # type: ignore[no-untyped-def]
- super().__init__(*args, **kwargs)
- self.encoder = encoder
- self.decoder = decoder
- def forward(
- self,
- images: Tensor,
- ) -> Tensor:
- dtype = images.dtype
- features, sizes = self.encoder(images)
- context = None
- logits = None
- scales = ["8", "4", "2", "1"]
- for idx, (feature_map, scale) in enumerate(zip(reversed(features), scales)):
- delta_logits, context = self.decoder(feature_map, context=context, scale=scale)
- if logits is None:
- logits = delta_logits
- else:
- logits = logits + delta_logits.float() # ensure float (need bf16 doesn't have f.interpolate)
- if idx < len(scales) - 1:
- size = sizes[-(idx + 2)]
- logits = F.interpolate(logits, size=size, mode="bicubic", align_corners=False)
- context = F.interpolate(context.float(), size=size, mode="bilinear", align_corners=False)
- return logits.to(dtype) # type: ignore
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