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353 lines
16 KiB
Python
353 lines
16 KiB
Python
# Copyright (c) 2023, 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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import itertools
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import pytest
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import torch
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from nemo.collections.asr.parts.utils.asr_multispeaker_utils import (
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find_best_permutation,
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find_first_nonzero,
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get_ats_targets,
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get_hidden_length_from_sample_length,
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get_pil_targets,
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reconstruct_labels,
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)
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def reconstruct_labels_forloop(labels: torch.Tensor, batch_perm_inds: torch.Tensor) -> torch.Tensor:
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"""
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This is a for-loop implementation of reconstruct_labels built for testing purposes.
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"""
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# Expanding batch_perm_inds to align with labels dimensions
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batch_size, num_frames, num_speakers = labels.shape
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batch_perm_inds_exp = batch_perm_inds.unsqueeze(1).expand(-1, num_frames, -1)
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# Reconstructing the labels using advanced indexing
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reconstructed_labels = torch.gather(labels, 2, batch_perm_inds_exp)
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return reconstructed_labels
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class TestSortingUtils:
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@pytest.mark.unit
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@pytest.mark.parametrize(
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"mat, max_cap_val, thres, expected",
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[
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# Test 1: Basic case with clear first nonzero values
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(torch.tensor([[0.1, 0.6, 0.0], [0.0, 0.0, 0.9]]), -1, 0.5, torch.tensor([1, 2])),
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# Test 2: All elements are below threshold
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(torch.tensor([[0.1, 0.2], [0.3, 0.4]]), -1, 0.5, torch.tensor([-1, -1])),
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# Test 3: No nonzero elements, should return max_cap_val (-1)
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(torch.tensor([[0.0, 0.0], [0.0, 0.0]]), -1, 0.5, torch.tensor([-1, -1])),
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# Test 4: Large matrix with mixed values, some rows with all values below threshold
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(torch.tensor([[0.1, 0.7, 0.3], [0.0, 0.0, 0.9], [0.5, 0.6, 0.7]]), -1, 0.5, torch.tensor([1, 2, 0])),
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# Test 5: Single row matrix
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(torch.tensor([[0.0, 0.0, 0.6]]), -1, 0.5, torch.tensor([2])),
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# Test 6: Single column matrix
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(torch.tensor([[0.1], [0.6], [0.0]]), -1, 0.5, torch.tensor([-1, 0, -1])),
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# Test 7: One element matrix
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(torch.tensor([[0.501]]), -1, 0.5, torch.tensor([0], dtype=torch.long)),
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# Test 8: All values are zero, should return max_cap_val
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(torch.tensor([[0.0, 0.0], [0.0, 0.0]]), -1, 0.5, torch.tensor([-1, -1])),
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# Test 9: All values are above threshold
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(torch.tensor([[0.6, 0.7], [0.8, 0.9]]), -1, 0.5, torch.tensor([0, 0])),
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# Test 10: Custom max_cap_val different from default
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(torch.tensor([[0.0, 0.0], [0.0, 0.0]]), 99, 0.5, torch.tensor([99, 99])),
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# Test 11: Matrix with 101 columns, first nonzero value is towards the end
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(torch.cat([torch.zeros(1, 100), torch.ones(1, 1)], dim=1), -1, 0.5, torch.tensor([100])),
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# Test 12: Matrix with 1000 columns, all below threshold except one near the middle
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(
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torch.cat([torch.zeros(1, 499), torch.tensor([[0.6]]), torch.zeros(1, 500)], dim=1),
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-1,
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0.5,
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torch.tensor([499]),
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),
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],
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)
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def test_find_first_nonzero(self, mat, max_cap_val, thres, expected):
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result = find_first_nonzero(mat, max_cap_val, thres)
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assert torch.equal(result, expected), f"Expected {expected} but got {result}"
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@pytest.mark.unit
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@pytest.mark.parametrize(
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"match_score, speaker_permutations, expected",
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[
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# Test 1: Simple case with batch size 1, clear best match
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(
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torch.tensor([[0.1, 0.9, 0.2]]), # match_score (batch_size=1, num_permutations=3)
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torch.tensor([[0, 1], [1, 0], [0, 1]]), # speaker_permutations (num_permutations=3, num_speakers=2)
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torch.tensor([[1, 0]]), # expected best permutation for the batch
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),
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# Test 2: Batch size 2, different best matches for each batch
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(
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torch.tensor([[0.5, 0.3, 0.7], [0.2, 0.6, 0.4]]), # match_score (batch_size=2, num_permutations=3)
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torch.tensor([[0, 1], [1, 0], [0, 1]]), # speaker_permutations
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torch.tensor([[0, 1], [1, 0]]), # expected best permutations
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),
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# Test 3: Larger number of speakers and permutations
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(
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torch.tensor(
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[[0.1, 0.4, 0.9, 0.5], [0.6, 0.3, 0.7, 0.2]]
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), # match_score (batch_size=2, num_permutations=4)
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torch.tensor(
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[[0, 1, 2], [1, 0, 2], [2, 1, 0], [1, 2, 0]]
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), # speaker_permutations (num_permutations=4, num_speakers=3)
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torch.tensor([[2, 1, 0], [2, 1, 0]]), # expected best permutations
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),
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# Test 4: All match scores are the same, should pick the first permutation (argmax behavior)
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(
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torch.tensor([[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]]), # equal match_score across permutations
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torch.tensor([[0, 1], [1, 0], [0, 1]]), # speaker_permutations
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torch.tensor([[0, 1], [0, 1]]), # first permutation is chosen as tie-breaker
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),
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# Test 5: Single speaker case (num_speakers = 1)
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(
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torch.tensor([[0.8, 0.2]]), # match_score (batch_size=1, num_permutations=2)
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torch.tensor([[0], [0]]), # speaker_permutations (num_permutations=2, num_speakers=1)
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torch.tensor([[0]]), # expected best permutation
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),
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# Test 6: Batch size 3, varying permutations
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(
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torch.tensor([[0.3, 0.6], [0.4, 0.1], [0.2, 0.7]]), # match_score (batch_size=3, num_permutations=2)
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torch.tensor([[0, 1], [1, 0]]), # speaker_permutations
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torch.tensor([[1, 0], [0, 1], [1, 0]]), # expected best permutations for each batch
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),
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],
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)
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def test_find_best_permutation(self, match_score, speaker_permutations, expected):
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result = find_best_permutation(match_score, speaker_permutations)
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assert torch.equal(result, expected), f"Expected {expected} but got {result}"
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@pytest.mark.parametrize(
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"batch_size, num_frames, num_speakers",
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[
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(2, 4, 3), # Original test case
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(3, 5, 2), # More frames and speakers
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(1, 6, 4), # Single batch with more frames and speakers
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(5, 3, 5), # More batch size with equal frames and speakers
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],
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)
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def test_reconstruct_labels_with_forloop_ver(self, batch_size, num_frames, num_speakers):
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# Generate random labels and batch_perm_inds tensor for testing
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labels = torch.rand(batch_size, num_frames, num_speakers)
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batch_perm_inds = torch.stack([torch.randperm(num_speakers) for _ in range(batch_size)])
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# Call both functions
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result_matrix = reconstruct_labels(labels, batch_perm_inds)
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result_forloop = reconstruct_labels_forloop(labels, batch_perm_inds)
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# Assert that both methods return the same result
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assert torch.allclose(result_matrix, result_forloop), "The results are not equal!"
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@pytest.mark.parametrize(
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"labels, batch_perm_inds, expected_output",
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[
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# Example 1: Small batch size with a few frames and speakers
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(
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torch.tensor(
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[
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[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]], # First batch
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[[0.9, 0.8, 0.7], [0.6, 0.5, 0.4], [0.3, 0.2, 0.1]], # Second batch
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]
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),
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torch.tensor([[2, 0, 1], [1, 2, 0]]),
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torch.tensor(
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[
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[[0.3, 0.1, 0.2], [0.6, 0.4, 0.5], [0.9, 0.7, 0.8]], # First batch reconstructed
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[[0.8, 0.7, 0.9], [0.5, 0.4, 0.6], [0.2, 0.1, 0.3]], # Second batch reconstructed
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]
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),
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),
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# Example 2: batch_size = 1 with more frames and speakers
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(
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torch.tensor(
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[[[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8], [0.9, 1.0, 1.1, 1.2], [1.3, 1.4, 1.5, 1.6]]]
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),
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torch.tensor([[3, 0, 1, 2]]),
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torch.tensor(
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[[[0.4, 0.1, 0.2, 0.3], [0.8, 0.5, 0.6, 0.7], [1.2, 0.9, 1.0, 1.1], [1.6, 1.3, 1.4, 1.5]]]
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),
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),
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# Example 3: Larger batch size with fewer frames and speakers
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(
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torch.tensor(
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[
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[[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]], # First batch
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[[0.7, 0.8], [0.9, 1.0], [1.1, 1.2]], # Second batch
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[[1.3, 1.4], [1.5, 1.6], [1.7, 1.8]], # Third batch
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[[1.9, 2.0], [2.1, 2.2], [2.3, 2.4]], # Fourth batch
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]
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),
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torch.tensor([[1, 0], [0, 1], [1, 0], [0, 1]]),
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torch.tensor(
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[
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[[0.2, 0.1], [0.4, 0.3], [0.6, 0.5]], # First batch reconstructed
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[[0.7, 0.8], [0.9, 1.0], [1.1, 1.2]], # Second batch unchanged
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[[1.4, 1.3], [1.6, 1.5], [1.8, 1.7]], # Third batch reconstructed
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[[1.9, 2.0], [2.1, 2.2], [2.3, 2.4]], # Fourth batch unchanged
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]
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),
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),
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],
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)
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def test_reconstruct_labels(self, labels, batch_perm_inds, expected_output):
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# Call the reconstruct_labels function
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result = reconstruct_labels(labels, batch_perm_inds)
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# Assert that the result matches the expected output
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assert torch.allclose(result, expected_output), f"Expected {expected_output}, but got {result}"
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class TestTargetGenerators:
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@pytest.mark.parametrize(
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"labels, preds, num_speakers, expected_output",
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[
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# Test 1: Basic case with simple permutations
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(
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torch.tensor(
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[
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[[0.9, 0.1, 0.0], [0.1, 0.8, 0.0], [0.0, 0.1, 0.9]], # Batch 1
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[[0.0, 0.0, 0.9], [0.0, 0.9, 0.1], [0.9, 0.1, 0.0]], # Batch 2
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]
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),
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torch.tensor(
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[
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[[0.8, 0.2, 0.0], [0.2, 0.7, 0.0], [0.0, 0.1, 0.9]], # Batch 1
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[[0.0, 0.0, 0.8], [0.0, 0.8, 0.2], [0.9, 0.1, 0.0]], # Batch 2
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]
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),
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3, # Number of speakers
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torch.tensor(
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[
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[[0.9, 0.1, 0.0], [0.1, 0.8, 0.0], [0.0, 0.1, 0.9]], # Expected labels for Batch 1
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[[0.9, 0.0, 0.0], [0.1, 0.9, 0.0], [0.0, 0.1, 0.9]], # Expected labels for Batch 2
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]
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),
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),
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# Test 2: Ambiguous case
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(
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torch.tensor([[[0.9, 0.8, 0.7], [0.2, 0.8, 0.7], [0.2, 0.3, 0.9]]]), # Labels
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torch.tensor([[[0.6, 0.7, 0.2], [0.9, 0.4, 0.0], [0.1, 0.7, 0.1]]]), # Preds
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3, # Number of speakers
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torch.tensor([[[0.8, 0.7, 0.9], [0.8, 0.7, 0.2], [0.3, 0.9, 0.2]]]), # Expected output
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),
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# Test 3: Ambiguous case
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(
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torch.tensor([[[0, 0, 1, 1], [0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]]), # Labels
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torch.tensor(
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[[[0.6, 0.6, 0.1, 0.9], [0.7, 0.7, 0.2, 0.8], [0.4, 0.6, 0.2, 0.7], [0.1, 0.1, 0.1, 0.7]]]
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), # Preds
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4, # Number of speakers
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torch.tensor([[[1, 1, 0, 0], [1, 1, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0]]]), # Expected output
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),
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],
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)
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def test_get_ats_targets(self, labels, preds, num_speakers, expected_output):
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# Generate all permutations for the given number of speakers
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speaker_inds = list(range(num_speakers))
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speaker_permutations = torch.tensor(list(itertools.permutations(speaker_inds)))
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# Call the function under test
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result = get_ats_targets(labels, preds, speaker_permutations)
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# Assert that the result matches the expected output
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assert torch.allclose(result, expected_output), f"Expected {expected_output}, but got {result}"
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@pytest.mark.unit
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@pytest.mark.parametrize(
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"labels, preds, num_speakers, expected_output",
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[
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# Test 1: Basic case with simple permutations
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(
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torch.tensor(
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[[[1, 0], [0, 1]], [[1, 0], [0, 1]]]
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), # Labels (batch_size=2, num_speakers=2, num_classes=2)
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torch.tensor(
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[[[1, 0], [0, 1]], [[0, 1], [1, 0]]]
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), # Preds (batch_size=2, num_speakers=2, num_classes=2)
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2, # Number of speakers
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torch.tensor([[[1, 0], [0, 1]], [[0, 1], [1, 0]]]), # expected max_score_permed_labels
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),
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# Test 2: Batch size 1 with more complex permutations
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(
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torch.tensor([[[0.8, 0.2], [0.3, 0.7]]]), # Labels
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torch.tensor([[[0.9, 0.1], [0.2, 0.8]]]), # Preds
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2, # Number of speakers
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torch.tensor(
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[[[0.8, 0.2], [0.3, 0.7]]]
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), # expected output (labels remain the same as preds are close)
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),
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# Test 3: Ambiguous case
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(
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torch.tensor([[[0, 0, 1, 1], [0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]]), # Labels
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torch.tensor(
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[[[0.61, 0.6, 0.1, 0.9], [0.7, 0.7, 0.2, 0.8], [0.4, 0.6, 0.2, 0.7], [0.1, 0.1, 0.1, 0.7]]]
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), # Preds
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4, # Number of speakers
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torch.tensor([[[1, 0, 0, 1], [1, 0, 0, 1], [0, 0, 0, 1], [0, 0, 0, 0]]]), # Expected output
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),
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],
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)
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def test_get_pil_targets(self, labels, preds, num_speakers, expected_output):
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# Generate all permutations for the given number of speakers
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speaker_inds = list(range(num_speakers))
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speaker_permutations = torch.tensor(list(itertools.permutations(speaker_inds)))
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result = get_pil_targets(labels, preds, speaker_permutations)
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assert torch.equal(result, expected_output), f"Expected {expected_output} but got {result}"
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class TestGetHiddenLengthFromSampleLength:
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@pytest.mark.parametrize(
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"num_samples, num_sample_per_mel_frame, num_mel_frame_per_asr_frame, expected_hidden_length",
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[
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(160, 160, 8, 1),
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(1280, 160, 8, 1),
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(0, 160, 8, 0),
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(159, 160, 8, 1),
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(129, 100, 5, 1),
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(300, 150, 3, 1),
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],
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)
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def test_various_cases(
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self, num_samples, num_sample_per_mel_frame, num_mel_frame_per_asr_frame, expected_hidden_length
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):
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result = get_hidden_length_from_sample_length(
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num_samples, num_sample_per_mel_frame, num_mel_frame_per_asr_frame
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)
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assert result == expected_hidden_length
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def test_default_parameters(self):
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assert get_hidden_length_from_sample_length(160) == 1
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assert get_hidden_length_from_sample_length(1280) == 1
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assert get_hidden_length_from_sample_length(0) == 0
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assert get_hidden_length_from_sample_length(159) == 1
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def test_edge_cases(self):
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assert get_hidden_length_from_sample_length(159, 160, 8) == 1
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assert get_hidden_length_from_sample_length(160, 160, 8) == 1
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assert get_hidden_length_from_sample_length(161, 160, 8) == 1
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assert get_hidden_length_from_sample_length(1279, 160, 8) == 1
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def test_real_life_examples(self):
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# The samples tried when this function was designed.
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assert get_hidden_length_from_sample_length(160000) == 125
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assert get_hidden_length_from_sample_length(159999) == 125
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assert get_hidden_length_from_sample_length(158720) == 124
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assert get_hidden_length_from_sample_length(158719) == 124
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assert get_hidden_length_from_sample_length(158880) == 125
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assert get_hidden_length_from_sample_length(158879) == 125
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assert get_hidden_length_from_sample_length(1600) == 2
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assert get_hidden_length_from_sample_length(1599) == 2
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