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# Copyright The Lightning 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.
from torch import Tensor
def _check_data_shape_to_num_outputs(
preds: Tensor, target: Tensor, num_outputs: int, allow_1d_reshape: bool = False
) -> None:
"""Check that predictions and target have the correct shape, else raise error.
Args:
preds: Predicted tensor
target: Ground truth tensor
num_outputs: Number of outputs in multioutput setting
allow_1d_reshape: Allow that for num_outputs=1 that preds and target does not need to be 1d tensors. Instead
code that follows are expected to reshape the tensors to 1d.
"""
if preds.ndim > 2 or target.ndim > 2:
raise ValueError(
f"Expected both predictions and target to be either 1- or 2-dimensional tensors,"
f" but got {target.ndim} and {preds.ndim}."
)
cond1 = False
if not allow_1d_reshape:
cond1 = num_outputs == 1 and not (preds.ndim == 1 or preds.shape[1] == 1)
cond2 = num_outputs > 1 and preds.ndim > 1 and num_outputs != preds.shape[1]
if cond1 or cond2:
raise ValueError(
f"Expected argument `num_outputs` to match the second dimension of input, but got {num_outputs}"
f" and {preds.shape[1]}."
)
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