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72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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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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# Copyright 2018-2020 William Falcon
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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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from dataclasses import dataclass
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import torch
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from .pl_utils import BATCH_SIZE, NUM_BATCHES, NUM_CLASSES
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@dataclass(frozen=True)
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class LossInput:
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"""
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The input for ``nemo.collections.common.metrics.GlobalAverageLossMetric`` metric tests.
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Args:
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loss_sum_or_avg: a one dimensional float tensor which contains losses for averaging. Each element is either a
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sum or mean of several losses depending on the parameter ``take_avg_loss`` of the
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``nemo.collections.common.metrics.GlobalAverageLossMetric`` class.
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num_measurements: a one dimensional integer tensor which contains number of measurements which sums or average
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values are in ``loss_sum_or_avg``.
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"""
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loss_sum_or_avg: torch.Tensor
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num_measurements: torch.Tensor
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NO_ZERO_NUM_MEASUREMENTS = LossInput(
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loss_sum_or_avg=torch.rand(NUM_BATCHES) * 2.0 - 1.0,
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num_measurements=torch.randint(1, 100, (NUM_BATCHES,)),
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)
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SOME_NUM_MEASUREMENTS_ARE_ZERO = LossInput(
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loss_sum_or_avg=torch.rand(NUM_BATCHES) * 2.0 - 1.0,
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num_measurements=torch.cat(
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(
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torch.randint(1, 100, (NUM_BATCHES // 2,), dtype=torch.int32),
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torch.zeros(NUM_BATCHES - NUM_BATCHES // 2, dtype=torch.int32),
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)
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),
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)
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ALL_NUM_MEASUREMENTS_ARE_ZERO = LossInput(
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loss_sum_or_avg=torch.rand(NUM_BATCHES) * 2.0 - 1.0,
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num_measurements=torch.zeros(NUM_BATCHES, dtype=torch.int32),
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)
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