[Port Hil-SERL] Add unit tests for the reward classifier & fix imports & check script (#578)
This commit is contained in:
Eugene Mironov
GitHub
parent
7b68bfb73b
commit
70b652f791
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@ -13,7 +13,7 @@ class ClassifierConfig:
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hidden_dim: int = 256
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dropout_rate: float = 0.1
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model_name: str = "microsoft/resnet-50"
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device: str = "cuda" if torch.cuda.is_available() else "mps"
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device: str = "cpu"
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model_type: str = "cnn" # "transformer" or "cnn"
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def save_pretrained(self, save_dir):
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@ -22,6 +22,11 @@ class ClassifierOutput:
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self.probabilities = probabilities
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self.hidden_states = hidden_states
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def __repr__(self):
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return (f"ClassifierOutput(logits={self.logits}, "
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f"probabilities={self.probabilities}, "
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f"hidden_states={self.hidden_states})")
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class Classifier(
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nn.Module,
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@ -69,6 +74,8 @@ class Classifier(
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self.feature_dim = self.encoder.config.hidden_sizes[-1] # Last channel dimension
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else:
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raise ValueError("Unsupported CNN architecture")
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self.encoder = self.encoder.to(self.config.device)
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def _freeze_encoder(self) -> None:
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"""Freeze the encoder parameters."""
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@ -93,6 +100,7 @@ class Classifier(
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nn.ReLU(),
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nn.Linear(self.config.hidden_dim, 1 if self.config.num_classes == 2 else self.config.num_classes),
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)
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self.classifier_head = self.classifier_head.to(self.config.device)
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def _get_encoder_output(self, x: torch.Tensor) -> torch.Tensor:
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"""Extract the appropriate output from the encoder."""
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@ -3139,6 +3139,27 @@ dev = ["changelist (==0.5)"]
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lint = ["pre-commit (==3.7.0)"]
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test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
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[[package]]
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name = "lightning-utilities"
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version = "0.11.9"
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description = "Lightning toolbox for across the our ecosystem."
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optional = true
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python-versions = ">=3.8"
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files = [
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{file = "lightning_utilities-0.11.9-py3-none-any.whl", hash = "sha256:ac6d4e9e28faf3ff4be997876750fee10dc604753dbc429bf3848a95c5d7e0d2"},
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{file = "lightning_utilities-0.11.9.tar.gz", hash = "sha256:f5052b81344cc2684aa9afd74b7ce8819a8f49a858184ec04548a5a109dfd053"},
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]
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[package.dependencies]
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packaging = ">=17.1"
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setuptools = "*"
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typing-extensions = "*"
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[package.extras]
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cli = ["fire"]
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docs = ["requests (>=2.0.0)"]
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typing = ["mypy (>=1.0.0)", "types-setuptools"]
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[[package]]
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name = "llvmlite"
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version = "0.43.0"
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@ -6798,6 +6819,38 @@ webencodings = ">=0.4"
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doc = ["sphinx", "sphinx_rtd_theme"]
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test = ["pytest", "ruff"]
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[[package]]
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name = "tokenizers"
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version = "0.21.0"
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description = ""
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optional = true
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python-versions = ">=3.7"
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files = [
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{file = "tokenizers-0.21.0-cp39-abi3-macosx_10_12_x86_64.whl", hash = "sha256:3c4c93eae637e7d2aaae3d376f06085164e1660f89304c0ab2b1d08a406636b2"},
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{file = "tokenizers-0.21.0-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:f53ea537c925422a2e0e92a24cce96f6bc5046bbef24a1652a5edc8ba975f62e"},
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{file = "tokenizers-0.21.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6b177fb54c4702ef611de0c069d9169f0004233890e0c4c5bd5508ae05abf193"},
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{file = "tokenizers-0.21.0-cp39-abi3-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:6b43779a269f4629bebb114e19c3fca0223296ae9fea8bb9a7a6c6fb0657ff8e"},
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{file = "tokenizers-0.21.0-cp39-abi3-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9aeb255802be90acfd363626753fda0064a8df06031012fe7d52fd9a905eb00e"},
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{file = "tokenizers-0.21.0-cp39-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d8b09dbeb7a8d73ee204a70f94fc06ea0f17dcf0844f16102b9f414f0b7463ba"},
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{file = "tokenizers-0.21.0-cp39-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:400832c0904f77ce87c40f1a8a27493071282f785724ae62144324f171377273"},
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{file = "tokenizers-0.21.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e84ca973b3a96894d1707e189c14a774b701596d579ffc7e69debfc036a61a04"},
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{file = "tokenizers-0.21.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:eb7202d231b273c34ec67767378cd04c767e967fda12d4a9e36208a34e2f137e"},
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{file = "tokenizers-0.21.0-cp39-abi3-musllinux_1_2_armv7l.whl", hash = "sha256:089d56db6782a73a27fd8abf3ba21779f5b85d4a9f35e3b493c7bbcbbf0d539b"},
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{file = "tokenizers-0.21.0-cp39-abi3-musllinux_1_2_i686.whl", hash = "sha256:c87ca3dc48b9b1222d984b6b7490355a6fdb411a2d810f6f05977258400ddb74"},
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{file = "tokenizers-0.21.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:4145505a973116f91bc3ac45988a92e618a6f83eb458f49ea0790df94ee243ff"},
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{file = "tokenizers-0.21.0-cp39-abi3-win32.whl", hash = "sha256:eb1702c2f27d25d9dd5b389cc1f2f51813e99f8ca30d9e25348db6585a97e24a"},
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{file = "tokenizers-0.21.0-cp39-abi3-win_amd64.whl", hash = "sha256:87841da5a25a3a5f70c102de371db120f41873b854ba65e52bccd57df5a3780c"},
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{file = "tokenizers-0.21.0.tar.gz", hash = "sha256:ee0894bf311b75b0c03079f33859ae4b2334d675d4e93f5a4132e1eae2834fe4"},
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]
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[package.dependencies]
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huggingface-hub = ">=0.16.4,<1.0"
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[package.extras]
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dev = ["tokenizers[testing]"]
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docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
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testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
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[[package]]
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name = "tomli"
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version = "2.0.2"
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@ -6863,6 +6916,34 @@ typing-extensions = ">=4.8.0"
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opt-einsum = ["opt-einsum (>=3.3)"]
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optree = ["optree (>=0.11.0)"]
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[[package]]
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name = "torchmetrics"
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version = "1.6.0"
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description = "PyTorch native Metrics"
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optional = true
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python-versions = ">=3.9"
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files = [
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{file = "torchmetrics-1.6.0-py3-none-any.whl", hash = "sha256:a508cdd87766cedaaf55a419812bf9f493aff8fffc02cc19df5a8e2e7ccb942a"},
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{file = "torchmetrics-1.6.0.tar.gz", hash = "sha256:aebba248708fb90def20cccba6f55bddd134a58de43fb22b0c5ca0f3a89fa984"},
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]
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[package.dependencies]
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lightning-utilities = ">=0.8.0"
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numpy = ">1.20.0"
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packaging = ">17.1"
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torch = ">=2.0.0"
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[package.extras]
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all = ["SciencePlots (>=2.0.0)", "gammatone (>=1.0.0)", "ipadic (>=1.0.0)", "librosa (>=0.10.0)", "matplotlib (>=3.6.0)", "mecab-python3 (>=1.0.6)", "mypy (==1.13.0)", "nltk (>3.8.1)", "numpy (<2.0)", "onnxruntime (>=1.12.0)", "pesq (>=0.0.4)", "piq (<=0.8.0)", "pycocotools (>2.0.0)", "pystoi (>=0.4.0)", "regex (>=2021.9.24)", "requests (>=2.19.0)", "scipy (>1.0.0)", "sentencepiece (>=0.2.0)", "torch (==2.5.1)", "torch-fidelity (<=0.4.0)", "torchaudio (>=2.0.1)", "torchvision (>=0.15.1)", "tqdm (<4.68.0)", "transformers (>4.4.0)", "transformers (>=4.42.3)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"]
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audio = ["gammatone (>=1.0.0)", "librosa (>=0.10.0)", "numpy (<2.0)", "onnxruntime (>=1.12.0)", "pesq (>=0.0.4)", "pystoi (>=0.4.0)", "requests (>=2.19.0)", "torchaudio (>=2.0.1)"]
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detection = ["pycocotools (>2.0.0)", "torchvision (>=0.15.1)"]
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dev = ["PyTDC (==0.4.1)", "SciencePlots (>=2.0.0)", "bert-score (==0.3.13)", "dython (==0.7.6)", "dython (>=0.7.8,<0.8.0)", "fairlearn", "fast-bss-eval (>=0.1.0)", "faster-coco-eval (>=1.6.3)", "gammatone (>=1.0.0)", "huggingface-hub (<0.27)", "ipadic (>=1.0.0)", "jiwer (>=2.3.0)", "kornia (>=0.6.7)", "librosa (>=0.10.0)", "lpips (<=0.1.4)", "matplotlib (>=3.6.0)", "mecab-ko (>=1.0.0,<1.1.0)", "mecab-ko-dic (>=1.0.0)", "mecab-python3 (>=1.0.6)", "mir-eval (>=0.6)", "monai (==1.3.2)", "monai (==1.4.0)", "mypy (==1.13.0)", "netcal (>1.0.0)", "nltk (>3.8.1)", "numpy (<2.0)", "numpy (<2.2.0)", "onnxruntime (>=1.12.0)", "pandas (>1.4.0)", "permetrics (==2.0.0)", "pesq (>=0.0.4)", "piq (<=0.8.0)", "pycocotools (>2.0.0)", "pystoi (>=0.4.0)", "pytorch-msssim (==1.0.0)", "regex (>=2021.9.24)", "requests (>=2.19.0)", "rouge-score (>0.1.0)", "sacrebleu (>=2.3.0)", "scikit-image (>=0.19.0)", "scipy (>1.0.0)", "sentencepiece (>=0.2.0)", "sewar (>=0.4.4)", "statsmodels (>0.13.5)", "torch (==2.5.1)", "torch-complex (<0.5.0)", "torch-fidelity (<=0.4.0)", "torchaudio (>=2.0.1)", "torchvision (>=0.15.1)", "tqdm (<4.68.0)", "transformers (>4.4.0)", "transformers (>=4.42.3)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"]
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image = ["scipy (>1.0.0)", "torch-fidelity (<=0.4.0)", "torchvision (>=0.15.1)"]
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multimodal = ["piq (<=0.8.0)", "transformers (>=4.42.3)"]
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text = ["ipadic (>=1.0.0)", "mecab-python3 (>=1.0.6)", "nltk (>3.8.1)", "regex (>=2021.9.24)", "sentencepiece (>=0.2.0)", "tqdm (<4.68.0)", "transformers (>4.4.0)"]
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typing = ["mypy (==1.13.0)", "torch (==2.5.1)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"]
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visual = ["SciencePlots (>=2.0.0)", "matplotlib (>=3.6.0)"]
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[[package]]
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name = "torchvision"
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version = "0.19.1"
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@ -6956,6 +7037,75 @@ files = [
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docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
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test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
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[[package]]
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name = "transformers"
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version = "4.47.0"
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description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
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optional = true
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python-versions = ">=3.9.0"
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files = [
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{file = "transformers-4.47.0-py3-none-any.whl", hash = "sha256:a8e1bafdaae69abdda3cad638fe392e37c86d2ce0ecfcae11d60abb8f949ff4d"},
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{file = "transformers-4.47.0.tar.gz", hash = "sha256:f8ead7a5a4f6937bb507e66508e5e002dc5930f7b6122a9259c37b099d0f3b19"},
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]
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[package.dependencies]
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filelock = "*"
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huggingface-hub = ">=0.24.0,<1.0"
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numpy = ">=1.17"
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packaging = ">=20.0"
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pyyaml = ">=5.1"
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regex = "!=2019.12.17"
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requests = "*"
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safetensors = ">=0.4.1"
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tokenizers = ">=0.21,<0.22"
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tqdm = ">=4.27"
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[package.extras]
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accelerate = ["accelerate (>=0.26.0)"]
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agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
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all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch", "torchaudio", "torchvision"]
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audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
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benchmark = ["optimum-benchmark (>=0.3.0)"]
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codecarbon = ["codecarbon (==1.2.0)"]
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deepspeed = ["accelerate (>=0.26.0)", "deepspeed (>=0.9.3)"]
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deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
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dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
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dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.21,<0.22)", "urllib3 (<2.0.0)"]
|
||||
dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "libcst", "librosa", "nltk (<=3.8.1)", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
|
||||
flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
|
||||
flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
|
||||
ftfy = ["ftfy"]
|
||||
integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
|
||||
ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
|
||||
modelcreation = ["cookiecutter (==1.7.3)"]
|
||||
natten = ["natten (>=0.14.6,<0.15.0)"]
|
||||
onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
|
||||
onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
|
||||
optuna = ["optuna"]
|
||||
quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "libcst", "rich", "ruff (==0.5.1)", "urllib3 (<2.0.0)"]
|
||||
ray = ["ray[tune] (>=2.7.0)"]
|
||||
retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
|
||||
ruff = ["ruff (==0.5.1)"]
|
||||
sagemaker = ["sagemaker (>=2.31.0)"]
|
||||
sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
|
||||
serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
|
||||
sigopt = ["sigopt"]
|
||||
sklearn = ["scikit-learn"]
|
||||
speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
|
||||
testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
|
||||
tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
|
||||
tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
|
||||
tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
|
||||
tiktoken = ["blobfile", "tiktoken"]
|
||||
timm = ["timm (<=1.0.11)"]
|
||||
tokenizers = ["tokenizers (>=0.21,<0.22)"]
|
||||
torch = ["accelerate (>=0.26.0)", "torch"]
|
||||
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
|
||||
torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
|
||||
torchhub = ["filelock", "huggingface-hub (>=0.24.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch", "tqdm (>=4.27)"]
|
||||
video = ["av (==9.2.0)"]
|
||||
vision = ["Pillow (>=10.0.1,<=15.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "transforms3d"
|
||||
version = "0.4.2"
|
||||
|
|
@ -7558,6 +7708,7 @@ dev = ["debugpy", "pre-commit"]
|
|||
dora = ["gym-dora"]
|
||||
dynamixel = ["dynamixel-sdk", "pynput"]
|
||||
feetech = ["feetech-servo-sdk", "pynput"]
|
||||
hilserl = ["torchmetrics", "transformers"]
|
||||
intelrealsense = ["pyrealsense2"]
|
||||
pusht = ["gym-pusht"]
|
||||
stretch = ["hello-robot-stretch-body", "pynput", "pyrealsense2", "pyrender"]
|
||||
|
|
@ -7569,4 +7720,4 @@ xarm = ["gym-xarm"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.10,<3.13"
|
||||
content-hash = "41344f0eb2d06d9a378abcd10df8205aa3926ff0a08ac5ab1a0b1bcae7440fd8"
|
||||
content-hash = "b9d299916ced6af1d243f961a32b0a4aacbef18e0b95337a5224e8511f5d6dda"
|
||||
|
|
|
|||
|
|
@ -71,6 +71,8 @@ pyrender = {git = "https://github.com/mmatl/pyrender.git", markers = "sys_platfo
|
|||
hello-robot-stretch-body = {version = ">=0.7.27", markers = "sys_platform == 'linux'", optional = true}
|
||||
pyserial = {version = ">=3.5", optional = true}
|
||||
jsonlines = ">=4.0.0"
|
||||
transformers = {version = "^4.47.0", optional = true}
|
||||
torchmetrics = {version = "^1.6.0", optional = true}
|
||||
|
||||
|
||||
[tool.poetry.extras]
|
||||
|
|
@ -86,6 +88,7 @@ dynamixel = ["dynamixel-sdk", "pynput"]
|
|||
feetech = ["feetech-servo-sdk", "pynput"]
|
||||
intelrealsense = ["pyrealsense2"]
|
||||
stretch = ["hello-robot-stretch-body", "pyrender", "pyrealsense2", "pynput"]
|
||||
hilserl = ["transformers", "torchmetrics"]
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 110
|
||||
|
|
|
|||
|
|
@ -14,9 +14,11 @@
|
|||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import random
|
||||
import traceback
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
from serial import SerialException
|
||||
|
||||
from lerobot import available_cameras, available_motors, available_robots
|
||||
|
|
@ -124,3 +126,14 @@ def patch_builtins_input(monkeypatch):
|
|||
print(text)
|
||||
|
||||
monkeypatch.setattr("builtins.input", print_text)
|
||||
|
||||
|
||||
def pytest_addoption(parser):
|
||||
parser.addoption("--seed", action="store", default="42", help="Set random seed for reproducibility")
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def set_random_seed(request):
|
||||
seed = int(request.config.getoption("--seed"))
|
||||
random.seed(seed) # Python random
|
||||
torch.manual_seed(seed) # PyTorch
|
||||
|
|
|
|||
|
|
@ -0,0 +1,244 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
# Copyright 2024 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 logging
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss
|
||||
from torch.optim import Adam
|
||||
from torch.utils.data import DataLoader
|
||||
from torchmetrics import AUROC, Accuracy, F1Score, Precision, Recall
|
||||
from torchvision.datasets import CIFAR10
|
||||
from torchvision.transforms import ToTensor
|
||||
|
||||
from lerobot.common.policies.hilserl.classifier.modeling_classifier import Classifier, ClassifierConfig
|
||||
|
||||
BATCH_SIZE = 1000
|
||||
LR = 0.1
|
||||
EPOCH_NUM = 2
|
||||
|
||||
if torch.cuda.is_available():
|
||||
DEVICE = torch.device("cuda")
|
||||
elif torch.backends.mps.is_available():
|
||||
DEVICE = torch.device("mps")
|
||||
else:
|
||||
DEVICE = torch.device("cpu")
|
||||
|
||||
|
||||
def train_evaluate_multiclass_classifier():
|
||||
logging.info(
|
||||
f"Start multiclass classifier train eval with {DEVICE} device, batch size {BATCH_SIZE}, learning rate {LR}"
|
||||
)
|
||||
multiclass_config = ClassifierConfig(model_name="microsoft/resnet-18", device=DEVICE, num_classes=10)
|
||||
multiclass_classifier = Classifier(multiclass_config)
|
||||
|
||||
trainset = CIFAR10(root="data", train=True, download=True, transform=ToTensor())
|
||||
testset = CIFAR10(root="data", train=False, download=True, transform=ToTensor())
|
||||
|
||||
trainloader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
|
||||
testloader = DataLoader(testset, batch_size=BATCH_SIZE, shuffle=False)
|
||||
|
||||
multiclass_num_classes = 10
|
||||
epoch = 1
|
||||
|
||||
criterion = CrossEntropyLoss()
|
||||
optimizer = Adam(multiclass_classifier.parameters(), lr=LR)
|
||||
|
||||
multiclass_classifier.train()
|
||||
|
||||
logging.info("Start multiclass classifier training")
|
||||
|
||||
# Training loop
|
||||
while epoch < EPOCH_NUM: # loop over the dataset multiple times
|
||||
for i, data in enumerate(trainloader):
|
||||
inputs, labels = data
|
||||
inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
|
||||
|
||||
# Zero the parameter gradients
|
||||
optimizer.zero_grad()
|
||||
|
||||
# Forward pass
|
||||
outputs = multiclass_classifier(inputs)
|
||||
|
||||
loss = criterion(outputs.logits, labels)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
if i % 10 == 0: # print every 10 mini-batches
|
||||
logging.info(f"[Epoch {epoch}, Batch {i}] loss: {loss.item():.3f}")
|
||||
|
||||
epoch += 1
|
||||
|
||||
print("Multiclass classifier training finished")
|
||||
|
||||
multiclass_classifier.eval()
|
||||
|
||||
test_loss = 0.0
|
||||
test_labels = []
|
||||
test_pridections = []
|
||||
test_probs = []
|
||||
|
||||
with torch.no_grad():
|
||||
for data in testloader:
|
||||
images, labels = data
|
||||
images, labels = images.to(DEVICE), labels.to(DEVICE)
|
||||
outputs = multiclass_classifier(images)
|
||||
loss = criterion(outputs.logits, labels)
|
||||
test_loss += loss.item() * BATCH_SIZE
|
||||
|
||||
_, predicted = torch.max(outputs.logits, 1)
|
||||
test_labels.extend(labels.cpu())
|
||||
test_pridections.extend(predicted.cpu())
|
||||
test_probs.extend(outputs.probabilities.cpu())
|
||||
|
||||
test_loss = test_loss / len(testset)
|
||||
|
||||
logging.info(f"Multiclass classifier test loss {test_loss:.3f}")
|
||||
|
||||
test_labels = torch.stack(test_labels)
|
||||
test_predictions = torch.stack(test_pridections)
|
||||
test_probs = torch.stack(test_probs)
|
||||
|
||||
accuracy = Accuracy(task="multiclass", num_classes=multiclass_num_classes)
|
||||
precision = Precision(task="multiclass", average="weighted", num_classes=multiclass_num_classes)
|
||||
recall = Recall(task="multiclass", average="weighted", num_classes=multiclass_num_classes)
|
||||
f1 = F1Score(task="multiclass", average="weighted", num_classes=multiclass_num_classes)
|
||||
auroc = AUROC(task="multiclass", num_classes=multiclass_num_classes, average="weighted")
|
||||
|
||||
# Calculate metrics
|
||||
acc = accuracy(test_predictions, test_labels)
|
||||
prec = precision(test_predictions, test_labels)
|
||||
rec = recall(test_predictions, test_labels)
|
||||
f1_score = f1(test_predictions, test_labels)
|
||||
auroc_score = auroc(test_probs, test_labels)
|
||||
|
||||
logging.info(f"Accuracy: {acc:.2f}")
|
||||
logging.info(f"Precision: {prec:.2f}")
|
||||
logging.info(f"Recall: {rec:.2f}")
|
||||
logging.info(f"F1 Score: {f1_score:.2f}")
|
||||
logging.info(f"AUROC Score: {auroc_score:.2f}")
|
||||
|
||||
|
||||
def train_evaluate_binary_classifier():
|
||||
logging.info(
|
||||
f"Start binary classifier train eval with {DEVICE} device, batch size {BATCH_SIZE}, learning rate {LR}"
|
||||
)
|
||||
|
||||
target_binary_class = 3
|
||||
|
||||
def one_vs_rest(dataset, target_class):
|
||||
new_targets = []
|
||||
for _, label in dataset:
|
||||
new_label = float(1.0) if label == target_class else float(0.0)
|
||||
new_targets.append(new_label)
|
||||
|
||||
dataset.targets = new_targets # Replace the original labels with the binary ones
|
||||
return dataset
|
||||
|
||||
binary_train_dataset = CIFAR10(root="data", train=True, download=True, transform=ToTensor())
|
||||
binary_test_dataset = CIFAR10(root="data", train=False, download=True, transform=ToTensor())
|
||||
|
||||
# Apply one-vs-rest labeling
|
||||
binary_train_dataset = one_vs_rest(binary_train_dataset, target_binary_class)
|
||||
binary_test_dataset = one_vs_rest(binary_test_dataset, target_binary_class)
|
||||
|
||||
binary_trainloader = DataLoader(binary_train_dataset, batch_size=BATCH_SIZE, shuffle=True)
|
||||
binary_testloader = DataLoader(binary_test_dataset, batch_size=BATCH_SIZE, shuffle=False)
|
||||
|
||||
binary_epoch = 1
|
||||
|
||||
binary_config = ClassifierConfig(model_name="microsoft/resnet-50", device=DEVICE)
|
||||
binary_classifier = Classifier(binary_config)
|
||||
|
||||
class_counts = np.bincount(binary_train_dataset.targets)
|
||||
n = len(binary_train_dataset)
|
||||
w0 = n / (2.0 * class_counts[0])
|
||||
w1 = n / (2.0 * class_counts[1])
|
||||
|
||||
binary_criterion = BCEWithLogitsLoss(pos_weight=torch.tensor(w1 / w0))
|
||||
binary_optimizer = Adam(binary_classifier.parameters(), lr=LR)
|
||||
|
||||
binary_classifier.train()
|
||||
|
||||
logging.info("Start binary classifier training")
|
||||
|
||||
# Training loop
|
||||
while binary_epoch < EPOCH_NUM: # loop over the dataset multiple times
|
||||
for i, data in enumerate(binary_trainloader):
|
||||
inputs, labels = data
|
||||
inputs, labels = inputs.to(DEVICE), labels.to(torch.float32).to(DEVICE)
|
||||
|
||||
# Zero the parameter gradients
|
||||
binary_optimizer.zero_grad()
|
||||
|
||||
# Forward pass
|
||||
outputs = binary_classifier(inputs)
|
||||
loss = binary_criterion(outputs.logits, labels)
|
||||
loss.backward()
|
||||
binary_optimizer.step()
|
||||
|
||||
if i % 10 == 0: # print every 10 mini-batches
|
||||
print(f"[Epoch {binary_epoch}, Batch {i}] loss: {loss.item():.3f}")
|
||||
binary_epoch += 1
|
||||
|
||||
logging.info("Binary classifier training finished")
|
||||
logging.info("Start binary classifier evaluation")
|
||||
|
||||
binary_classifier.eval()
|
||||
|
||||
test_loss = 0.0
|
||||
test_labels = []
|
||||
test_pridections = []
|
||||
test_probs = []
|
||||
|
||||
with torch.no_grad():
|
||||
for data in binary_testloader:
|
||||
images, labels = data
|
||||
images, labels = images.to(DEVICE), labels.to(torch.float32).to(DEVICE)
|
||||
outputs = binary_classifier(images)
|
||||
loss = binary_criterion(outputs.logits, labels)
|
||||
test_loss += loss.item() * BATCH_SIZE
|
||||
|
||||
test_labels.extend(labels.cpu())
|
||||
test_pridections.extend(outputs.logits.cpu())
|
||||
test_probs.extend(outputs.probabilities.cpu())
|
||||
|
||||
test_loss = test_loss / len(binary_test_dataset)
|
||||
|
||||
logging.info(f"Binary classifier test loss {test_loss:.3f}")
|
||||
|
||||
test_labels = torch.stack(test_labels)
|
||||
test_predictions = torch.stack(test_pridections)
|
||||
test_probs = torch.stack(test_probs)
|
||||
|
||||
# Calculate metrics
|
||||
acc = Accuracy(task="binary")(test_predictions, test_labels)
|
||||
prec = Precision(task="binary", average="weighted")(test_predictions, test_labels)
|
||||
rec = Recall(task="binary", average="weighted")(test_predictions, test_labels)
|
||||
f1_score = F1Score(task="binary", average="weighted")(test_predictions, test_labels)
|
||||
auroc_score = AUROC(task="binary", average="weighted")(test_probs, test_labels)
|
||||
|
||||
logging.info(f"Accuracy: {acc:.2f}")
|
||||
logging.info(f"Precision: {prec:.2f}")
|
||||
logging.info(f"Recall: {rec:.2f}")
|
||||
logging.info(f"F1 Score: {f1_score:.2f}")
|
||||
logging.info(f"AUROC Score: {auroc_score:.2f}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
train_evaluate_multiclass_classifier()
|
||||
train_evaluate_binary_classifier()
|
||||
|
|
@ -0,0 +1,78 @@
|
|||
import torch
|
||||
|
||||
from lerobot.common.policies.hilserl.classifier.modeling_classifier import (
|
||||
Classifier,
|
||||
ClassifierConfig,
|
||||
ClassifierOutput,
|
||||
)
|
||||
from tests.utils import require_package
|
||||
|
||||
|
||||
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)"
|
||||
)
|
||||
|
||||
|
||||
@require_package("transformers")
|
||||
def test_binary_classifier_with_default_params():
|
||||
config = ClassifierConfig()
|
||||
classifier = Classifier(config)
|
||||
|
||||
batch_size = 10
|
||||
|
||||
input = torch.rand(batch_size, 3, 224, 224)
|
||||
output = classifier(input)
|
||||
|
||||
assert output is not None
|
||||
assert output.logits.shape == 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, 2048])
|
||||
assert not torch.isnan(output.hidden_states).any(), "Tensor contains NaN values"
|
||||
|
||||
|
||||
@require_package("transformers")
|
||||
def test_multiclass_classifier():
|
||||
num_classes = 5
|
||||
config = ClassifierConfig(num_classes=num_classes)
|
||||
classifier = Classifier(config)
|
||||
|
||||
batch_size = 10
|
||||
|
||||
input = torch.rand(batch_size, 3, 224, 224)
|
||||
output = classifier(input)
|
||||
|
||||
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, 2048])
|
||||
assert not torch.isnan(output.hidden_states).any(), "Tensor contains NaN values"
|
||||
|
||||
|
||||
@require_package("transformers")
|
||||
def test_default_device():
|
||||
config = ClassifierConfig()
|
||||
assert config.device == "cpu"
|
||||
|
||||
classifier = Classifier(config)
|
||||
for p in classifier.parameters():
|
||||
assert p.device == torch.device("cpu")
|
||||
|
||||
|
||||
@require_package("transformers")
|
||||
def test_explicit_device_setup():
|
||||
config = ClassifierConfig(device="meta")
|
||||
assert config.device == "meta"
|
||||
|
||||
classifier = Classifier(config)
|
||||
for p in classifier.parameters():
|
||||
assert p.device == torch.device("meta")
|
||||
Loading…
Reference in New Issue