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0d80452 | 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 | # Copyright 2025 The HuggingFace Inc. team. 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.
import torch
from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
from lerobot.rewards.classifier.configuration_classifier import RewardClassifierConfig
from lerobot.rewards.classifier.modeling_classifier import ClassifierOutput
from lerobot.utils.constants import OBS_IMAGE, REWARD
from tests.utils import skip_if_package_missing
def test_classifier_output():
output = ClassifierOutput(
logits=torch.tensor([1, 2, 3]),
probabilities=torch.tensor([0.1, 0.2, 0.3]),
hidden_states=None,
)
assert (
f"{output}"
== "ClassifierOutput(logits=tensor([1, 2, 3]), probabilities=tensor([0.1000, 0.2000, 0.3000]), hidden_states=None)"
)
@skip_if_package_missing("transformers")
def test_binary_classifier_with_default_params():
from lerobot.rewards.classifier.modeling_classifier import Classifier
config = RewardClassifierConfig()
config.input_features = {
OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)),
}
config.output_features = {
REWARD: PolicyFeature(type=FeatureType.REWARD, shape=(1,)),
}
config.normalization_mapping = {
"VISUAL": NormalizationMode.IDENTITY,
"REWARD": NormalizationMode.IDENTITY,
}
config.num_cameras = 1
classifier = Classifier(config)
batch_size = 10
input = {
OBS_IMAGE: torch.rand((batch_size, 3, 128, 128)),
REWARD: torch.randint(low=0, high=2, size=(batch_size,)).float(),
}
images, labels = classifier.extract_images_and_labels(input)
assert len(images) == 1
assert images[0].shape == torch.Size([batch_size, 3, 128, 128])
assert labels.shape == torch.Size([batch_size])
output = classifier.predict(images)
assert output is not None
assert output.logits.size() == torch.Size([batch_size])
assert not torch.isnan(output.logits).any(), "Tensor contains NaN values"
assert output.probabilities.shape == torch.Size([batch_size])
assert not torch.isnan(output.probabilities).any(), "Tensor contains NaN values"
assert output.hidden_states.shape == torch.Size([batch_size, 256])
assert not torch.isnan(output.hidden_states).any(), "Tensor contains NaN values"
@skip_if_package_missing("transformers")
def test_multiclass_classifier():
from lerobot.rewards.classifier.modeling_classifier import Classifier
num_classes = 5
config = RewardClassifierConfig()
config.input_features = {
OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)),
}
config.output_features = {
REWARD: PolicyFeature(type=FeatureType.REWARD, shape=(num_classes,)),
}
config.num_cameras = 1
config.num_classes = num_classes
classifier = Classifier(config)
batch_size = 10
input = {
OBS_IMAGE: torch.rand((batch_size, 3, 128, 128)),
REWARD: torch.rand((batch_size, num_classes)),
}
images, labels = classifier.extract_images_and_labels(input)
assert len(images) == 1
assert images[0].shape == torch.Size([batch_size, 3, 128, 128])
assert labels.shape == torch.Size([batch_size, num_classes])
output = classifier.predict(images)
assert output is not None
assert output.logits.shape == torch.Size([batch_size, num_classes])
assert not torch.isnan(output.logits).any(), "Tensor contains NaN values"
assert output.probabilities.shape == torch.Size([batch_size, num_classes])
assert not torch.isnan(output.probabilities).any(), "Tensor contains NaN values"
assert output.hidden_states.shape == torch.Size([batch_size, 256])
assert not torch.isnan(output.hidden_states).any(), "Tensor contains NaN values"
@skip_if_package_missing("transformers")
def test_default_device():
from lerobot.rewards.classifier.modeling_classifier import Classifier
config = RewardClassifierConfig()
assert config.device is None or config.device == "cpu"
config.input_features = {
OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)),
}
config.num_cameras = 1
classifier = Classifier(config)
for p in classifier.parameters():
assert p.device == torch.device("cpu")
@skip_if_package_missing("transformers")
def test_explicit_device_setup():
from lerobot.rewards.classifier.modeling_classifier import Classifier
config = RewardClassifierConfig(device="cpu")
assert config.device == "cpu"
config.input_features = {
OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)),
}
config.num_cameras = 1
classifier = Classifier(config)
for p in classifier.parameters():
assert p.device == torch.device("cpu")
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