Spaces:
Runtime error
Runtime error
File size: 2,626 Bytes
0558aa4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
# 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 dataclasses import dataclass
import torch
from .pl_utils import BATCH_SIZE, NUM_BATCHES, NUM_CLASSES
@dataclass(frozen=True)
class LossInput:
"""
The input for ``nemo.collections.common.metrics.GlobalAverageLossMetric`` metric tests.
Args:
loss_sum_or_avg: a one dimensional float tensor which contains losses for averaging. Each element is either a
sum or mean of several losses depending on the parameter ``take_avg_loss`` of the
``nemo.collections.common.metrics.GlobalAverageLossMetric`` class.
num_measurements: a one dimensional integer tensor which contains number of measurements which sums or average
values are in ``loss_sum_or_avg``.
"""
loss_sum_or_avg: torch.Tensor
num_measurements: torch.Tensor
NO_ZERO_NUM_MEASUREMENTS = LossInput(
loss_sum_or_avg=torch.rand(NUM_BATCHES) * 2.0 - 1.0, num_measurements=torch.randint(1, 100, (NUM_BATCHES,)),
)
SOME_NUM_MEASUREMENTS_ARE_ZERO = LossInput(
loss_sum_or_avg=torch.rand(NUM_BATCHES) * 2.0 - 1.0,
num_measurements=torch.cat(
(
torch.randint(1, 100, (NUM_BATCHES // 2,), dtype=torch.int32),
torch.zeros(NUM_BATCHES - NUM_BATCHES // 2, dtype=torch.int32),
)
),
)
ALL_NUM_MEASUREMENTS_ARE_ZERO = LossInput(
loss_sum_or_avg=torch.rand(NUM_BATCHES) * 2.0 - 1.0, num_measurements=torch.zeros(NUM_BATCHES, dtype=torch.int32),
)
|