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1327f34 | 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 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | # Copyright 2025 The Scenic Authors.
#
# 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.
"""Unit tests for multilabel_classification_model.py."""
from absl.testing import absltest
from flax import jax_utils
import jax
import jax.numpy as jnp
import ml_collections
import numpy as np
from scenic.model_lib.base_models import multilabel_classification_model
NUM_CLASSES = 1000
BATCH_SIZE = 4
class FakeMultiLabelClassificationModel(
multilabel_classification_model.MultiLabelClassificationModel):
"""A dummy multi-label classification model for testing purposes."""
def __init__(self):
dataset_meta_data = {'num_classes': NUM_CLASSES, 'target_is_onehot': True}
super().__init__(
ml_collections.ConfigDict(), # An empty config dict.
dataset_meta_data)
def build_flax_model(self):
pass
def default_flax_model_config(self):
pass
def get_fake_batch_output(array_size=(BATCH_SIZE, NUM_CLASSES)):
"""Generates a fake `batch`.
Args:
array_size: size of the label and output array.
Returns:
`batch`: Dictionary of None inputs and fake ground truth targets.
outputs_noaux.pop('aux_outputs')
`output`: Dictionary of a fake output logits.
"""
batch = {
'inputs': None,
'label': jnp.array(np.random.randint(2, size=array_size)),
}
output = jnp.array(np.random.random(size=array_size))
return batch, output
class TestMultiLabelClassificationModel(absltest.TestCase):
"""Tests for the MultiLabelClassificationModel."""
def is_valid(self, t, value_name):
"""Helper function to assert that tensor `t` does not have `nan`, `inf`."""
self.assertFalse(
jnp.isnan(t).any(), msg=f'Found nan\'s in {t} for {value_name}')
self.assertFalse(
jnp.isinf(t).any(), msg=f'Found inf\'s in {t} for {value_name}')
def test_loss_function(self):
"""Tests loss_function by checking its output's validity."""
model = FakeMultiLabelClassificationModel()
batch, output = get_fake_batch_output()
batch_replicated, outputs_replicated = (jax_utils.replicate(batch),
jax_utils.replicate(output))
# Test loss function in the pmapped setup:
loss_function_pmapped = jax.pmap(model.loss_function, axis_name='batch')
total_loss = loss_function_pmapped(outputs_replicated, batch_replicated)
# Check that loss is returning valid values:
self.is_valid(jax_utils.unreplicate(total_loss), value_name='loss')
def test_loss_function_masked(self):
"""Tests a masked loss_function by comparing different canonical masks."""
array_size = (BATCH_SIZE, 50, NUM_CLASSES)
model = FakeMultiLabelClassificationModel()
batch, output = get_fake_batch_output(
array_size=array_size)
# Unmasked loss
loss_value_unmasked = model.loss_function(output, batch)
# Mask with only ones (so effectively no mask).
batch['batch_mask'] = jnp.ones((BATCH_SIZE, 50))
loss_value_masked = model.loss_function(output, batch)
self.assertAlmostEqual(
float(loss_value_unmasked),
float(loss_value_masked))
# Extend the batch with random outputs and labels, but mask them with 0's.
batch_extended = {
'label': jnp.concatenate(
(batch['label'], np.random.randint(2, size=array_size)), axis=1),
'batch_mask': jnp.concatenate(
(batch['batch_mask'], np.zeros((BATCH_SIZE, 50))), axis=1),
}
output_extended = jnp.concatenate(
(output, np.random.random(size=array_size)), axis=1)
loss_value_extended = model.loss_function(output_extended, batch_extended)
# Test with `places=3` due to JAX issue: github.com/jax-ml/jax/issues/6553
# TODO(robromijnders): follow up with JAX issue and remove `places=3`.
self.assertAlmostEqual(
float(loss_value_masked),
float(loss_value_extended),
places=3)
def test_metric_function(self):
"""Tests metric_function by checking its output's format and validity."""
model = FakeMultiLabelClassificationModel()
batch, output = get_fake_batch_output()
batch_replicated, outputs_replicated = (jax_utils.replicate(batch),
jax_utils.replicate(output))
# Test metric function in the pmapped setup
metrics_fn_pmapped = jax.pmap(model.get_metrics_fn(), axis_name='batch')
all_metrics = metrics_fn_pmapped(outputs_replicated, batch_replicated)
# Check expected metrics exist in the output:
expected_metrics_keys = ['prec@1', 'loss']
self.assertSameElements(expected_metrics_keys, all_metrics.keys())
# For each metric, check that it is a valid value.
all_metrics = jax_utils.unreplicate(all_metrics)
for k, v in all_metrics.items():
self.is_valid(v[0], value_name=f'numerator of {k}')
self.is_valid(v[1], value_name=f'denominator of {k}')
if __name__ == '__main__':
absltest.main()
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