64 lines
1.7 KiB
Python
64 lines
1.7 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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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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import torch
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from typing import Any, Callable, Optional, Union
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METRIC_EPS = 1e-6
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def dim_zero_cat(x):
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return torch.cat(x, dim=0)
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def dim_zero_sum(x):
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return torch.sum(x, dim=0)
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def dim_zero_mean(x):
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return torch.mean(x, dim=0)
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def _flatten(x):
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return [item for sublist in x for item in sublist]
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def to_onehot(
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tensor: torch.Tensor,
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num_classes: int,
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) -> torch.Tensor:
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"""
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Converts a dense label tensor to one-hot format
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Args:
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tensor: dense label tensor, with shape [N, d1, d2, ...]
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num_classes: number of classes C
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Output:
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A sparse label tensor with shape [N, C, d1, d2, ...]
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Example:
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>>> x = torch.tensor([1, 2, 3])
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>>> to_onehot(x, num_classes=4)
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tensor([[0, 1, 0, 0],
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[0, 0, 1, 0],
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[0, 0, 0, 1]])
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"""
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dtype, device, shape = tensor.dtype, tensor.device, tensor.shape
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tensor_onehot = torch.zeros(shape[0], num_classes, *shape[1:],
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dtype=dtype, device=device)
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index = tensor.long().unsqueeze(1).expand_as(tensor_onehot)
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return tensor_onehot.scatter_(1, index, 1.0)
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