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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# 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.
# Copyright 2018-2020 William Falcon
#
# 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 collections import namedtuple
import torch
from .pl_utils import BATCH_SIZE, NUM_BATCHES, NUM_CLASSES
Input = namedtuple('Input', ["probs", "logits"])
ONLY_PROBS = Input(probs=torch.rand(NUM_BATCHES, BATCH_SIZE, NUM_CLASSES), logits=None)
ONLY_LOGITS1 = Input(probs=None, logits=torch.rand(NUM_BATCHES, BATCH_SIZE, NUM_CLASSES))
ONLY_LOGITS100 = Input(probs=None, logits=torch.rand(NUM_BATCHES, BATCH_SIZE, NUM_CLASSES) * 200 - 100)
PROBS_AND_LOGITS = Input(
probs=torch.rand(NUM_BATCHES, BATCH_SIZE, NUM_CLASSES),
logits=torch.rand(NUM_BATCHES, BATCH_SIZE, NUM_CLASSES) * 200 - 100,
)
NO_PROBS_NO_LOGITS = Input(probs=None, logits=None)
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