index int64 | repo_name string | branch_name string | path string | content string | import_graph string |
|---|---|---|---|---|---|
59,122 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/splittosequence.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class SplitToSequence(Base):
@staticmethod
def export_with_split_1() -> None:
data = np.arange(18).reshape((3, 6)).astype(np.float32)
split = np.array(2, dtype=np.int64)
node = onnx.helper.make_node(
"SplitToSequence", ["data", "split"], ["seq"], axis=1
)
expected_outputs = [
[
np.array([[0.0, 1.0], [6.0, 7.0], [12.0, 13.0]], dtype=np.float32),
np.array([[2.0, 3.0], [8.0, 9.0], [14.0, 15.0]], dtype=np.float32),
np.array([[4.0, 5.0], [10.0, 11.0], [16.0, 17.0]], dtype=np.float32),
]
]
expect(
node,
inputs=[data, split],
outputs=expected_outputs,
name="test_split_to_sequence_1",
)
@staticmethod
def export_with_split_2() -> None:
data = np.arange(18).reshape((3, 6)).astype(np.float32)
split = np.array([1, 2], dtype=np.int64)
node = onnx.helper.make_node(
"SplitToSequence", ["data", "split"], ["seq"], axis=0
)
expected_outputs = [
[
data[:1],
data[1:],
]
]
expect(
node,
inputs=[data, split],
outputs=expected_outputs,
name="test_split_to_sequence_2",
)
@staticmethod
def export_nokeepdims() -> None:
data = np.arange(18).reshape((3, 6)).astype(np.float32)
node = onnx.helper.make_node(
"SplitToSequence",
["data"],
["seq"],
axis=1,
keepdims=0,
)
expected_outputs = [[data[:, i] for i in range(data.shape[1])]]
expect(
node,
inputs=[data],
outputs=expected_outputs,
name="test_split_to_sequence_nokeepdims",
)
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"/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/identity.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/sin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gemm.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_layer_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_bernoulli.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/backend/test/case/node/deformconv.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_upsample.py": ["/onnx/reference/op_run.py"], "/onnx/test/test_with_ort.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_thresholded_relu.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/concat.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], 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"/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": 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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,123 | onnx/onnx | refs/heads/main | /onnx/serialization.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
__all__ = [
"registry",
]
import typing
from typing import Any, Collection, Optional, Protocol, TypeVar
import google.protobuf.message
import google.protobuf.text_format
import onnx
_Proto = TypeVar("_Proto", bound=google.protobuf.message.Message)
# Encoding used for serializing and deserializing text files
_ENCODING = "utf-8"
class ProtoSerializer(Protocol):
"""A serializer-deserializer to and from in-memory Protocol Buffers representations."""
# Format supported by the serializer. E.g. "protobuf"
supported_format: str
# File extensions supported by the serializer. E.g. frozenset({".onnx", ".pb"})
# Be careful to include the dot in the file extension.
file_extensions: Collection[str]
# NOTE: The methods defined are serialize_proto and deserialize_proto and not the
# more generic serialize and deserialize to leave space for future protocols
# that are defined to serialize/deserialize the ONNX in memory IR.
# This way a class can implement both protocols.
def serialize_proto(self, proto: _Proto) -> Any:
"""Serialize a in-memory proto to a serialized data type."""
def deserialize_proto(self, serialized: Any, proto: _Proto) -> _Proto:
"""Parse a serialized data type into a in-memory proto."""
class _Registry:
def __init__(self) -> None:
self._serializers: dict[str, ProtoSerializer] = {}
# A mapping from file extension to format
self._extension_to_format: dict[str, str] = {}
def register(self, serializer: ProtoSerializer) -> None:
self._serializers[serializer.supported_format] = serializer
self._extension_to_format.update(
{ext: serializer.supported_format for ext in serializer.file_extensions}
)
def get(self, fmt: str) -> ProtoSerializer:
"""Get a serializer for a format.
Args:
fmt: The format to get a serializer for.
Returns:
ProtoSerializer: The serializer for the format.
Raises:
ValueError: If the format is not supported.
"""
try:
return self._serializers[fmt]
except KeyError:
raise ValueError(
f"Unsupported format: '{fmt}'. Supported formats are: {self._serializers.keys()}"
) from None
def get_format_from_file_extension(self, file_extension: str) -> str | None:
"""Get the corresponding format from a file extension.
Args:
file_extension: The file extension to get a format for.
Returns:
The format for the file extension, or None if not found.
"""
return self._extension_to_format.get(file_extension)
class _ProtobufSerializer(ProtoSerializer):
"""Serialize and deserialize protobuf message."""
supported_format = "protobuf"
file_extensions = frozenset({".onnx", ".pb"})
def serialize_proto(self, proto: _Proto) -> bytes:
if hasattr(proto, "SerializeToString") and callable(proto.SerializeToString):
try:
result = proto.SerializeToString()
except ValueError as e:
if proto.ByteSize() >= onnx.checker.MAXIMUM_PROTOBUF:
raise ValueError(
"The proto size is larger than the 2 GB limit. "
"Please use save_as_external_data to save tensors separately from the model file."
) from e
raise
return result # type: ignore
raise TypeError(
f"No SerializeToString method is detected.\ntype is {type(proto)}"
)
def deserialize_proto(self, serialized: bytes, proto: _Proto) -> _Proto:
if not isinstance(serialized, bytes):
raise TypeError(
f"Parameter 'serialized' must be bytes, but got type: {type(serialized)}"
)
decoded = typing.cast(Optional[int], proto.ParseFromString(serialized))
if decoded is not None and decoded != len(serialized):
raise google.protobuf.message.DecodeError(
f"Protobuf decoding consumed too few bytes: {decoded} out of {len(serialized)}"
)
return proto
class _TextProtoSerializer(ProtoSerializer):
"""Serialize and deserialize text proto."""
supported_format = "textproto"
file_extensions = frozenset({".textproto", ".prototxt", ".pbtxt"})
def serialize_proto(self, proto: _Proto) -> bytes:
textproto = google.protobuf.text_format.MessageToString(proto)
return textproto.encode(_ENCODING)
def deserialize_proto(self, serialized: bytes | str, proto: _Proto) -> _Proto:
if not isinstance(serialized, (bytes, str)):
raise TypeError(
f"Parameter 'serialized' must be bytes or str, but got type: {type(serialized)}"
)
if isinstance(serialized, bytes):
serialized = serialized.decode(_ENCODING)
assert isinstance(serialized, str)
return google.protobuf.text_format.Parse(serialized, proto)
# Register default serializers
registry = _Registry()
registry.register(_ProtobufSerializer())
registry.register(_TextProtoSerializer())
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59,124 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_svm_classifier.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0911,R0912,R0913,R0914,W0221
import numpy as np
from onnx.reference.ops.aionnxml._common_classifier import (
compute_logistic,
compute_probit,
compute_softmax_zero,
logistic,
softmax,
softmax_zero,
)
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
from onnx.reference.ops.aionnxml.op_svm_helper import SVMCommon
def multiclass_probability(k, R):
max_iter = max(100, k)
Q = np.empty((k, k), dtype=R.dtype)
Qp = np.empty((k,), dtype=R.dtype)
P = np.empty((k,), dtype=R.dtype)
eps = 0.005 / k
for t in range(0, k):
P[t] = 1.0 / k
Q[t, t] = (R[:t, t] ** 2).sum()
Q[t, :t] = Q[:t, t]
Q[t, t] += (R[t + 1 :, t] ** 2).sum()
Q[t, t + 1 :] = -R[t + 1 :, t] @ R[t, t + 1 :]
for _ in range(max_iter):
# stopping condition, recalculate QP,pQP for numerical accuracy
Qp[:] = Q @ P
pQp = (P * Qp).sum()
max_error = 0
for t in range(0, k):
error = np.abs(Qp[t] - pQp)
if error > max_error:
max_error = error
if max_error < eps:
break
for t in range(k):
diff = (-Qp[t] + pQp) / Q[t, t]
P[t] += diff
pQp = (pQp + diff * (diff * Q[t, t] + 2 * Qp[t])) / (1 + diff) ** 2
P /= 1 + diff
Qp[:] = (Qp + diff * Q[t, :]) / (1 + diff)
return P
def sigmoid_probability(score, proba, probb):
# ref: https://github.com/arnaudsj/libsvm/blob/eaaefac5ebd32d0e07902e1ae740e038eaaf0826/svm.cpp#L1818
val = score * proba + probb
return 1 - compute_logistic(val)
def write_scores(n_classes, scores, post_transform, add_second_class):
if n_classes >= 2:
if post_transform == "PROBIT":
res = [compute_probit(score) for score in scores]
return np.array(res, dtype=scores.dtype)
if post_transform == "LOGISTIC":
return logistic(scores)
if post_transform == "SOFTMAX":
return softmax(scores)
if post_transform == "SOFTMAX_ZERO":
return compute_softmax_zero(scores)
return scores
if n_classes == 1:
if post_transform == "PROBIT":
return np.array([compute_probit(scores[0])], dtype=scores.dtype)
if add_second_class in (0, 1):
return np.array([1 - scores[0], scores[0]], dtype=scores.dtype)
if add_second_class in (2, 3):
if post_transform == "LOGISTIC":
return np.array(
[logistic(-scores[0]), logistic(scores[0])], dtype=scores.dtype
)
if post_transform == "SOFTMAX":
return softmax(np.array([-scores[0], scores[0]], dtype=scores.dtype))
if post_transform == "SOFTMAX_ZERO":
return softmax_zero(
np.array([-scores[0], scores[0]], dtype=scores.dtype)
)
if post_transform == "PROBIT":
raise RuntimeError(
f"post_transform={post_transform!r} not applicable here."
)
return np.array([-scores[0], scores[0]], dtype=scores.dtype)
return np.array([scores[0]], dtype=scores.dtype)
raise NotImplementedError(f"n_classes={n_classes} not supported.")
def set_score_svm(
max_weight,
maxclass,
has_proba,
weights_are_all_positive_,
classlabels,
posclass,
negclass,
):
write_additional_scores = -1
if len(classlabels) == 2:
write_additional_scores = 2
if not has_proba:
if weights_are_all_positive_ and max_weight >= 0.5:
return classlabels[1], write_additional_scores
if max_weight > 0 and not weights_are_all_positive_:
return classlabels[maxclass], write_additional_scores
return classlabels[maxclass], write_additional_scores
if max_weight > 0:
return posclass, write_additional_scores
return negclass, write_additional_scores
class SVMClassifier(OpRunAiOnnxMl):
def _run_linear(self, X, coefs, class_count_, kernel_type_):
scores = []
for j in range(class_count_):
d = self._svm.kernel_dot(X, coefs[j], kernel_type_)
score = self._svm.atts.rho[0] + d # type: ignore
scores.append(score)
return np.array(scores, dtype=X.dtype)
def _run_svm(
self, X, sv, vector_count_, kernel_type_, class_count_, starting_vector_, coefs
):
evals = 0
kernels_list = []
for j in range(vector_count_):
kernels_list.append(self._svm.kernel_dot(X, sv[j], kernel_type_))
kernels = np.array(kernels_list)
votes = np.zeros((class_count_,), dtype=X.dtype)
scores = []
for i in range(class_count_):
si_i = starting_vector_[i]
class_i_sc = self._svm.atts.vectors_per_class[i] # type: ignore
for j in range(i + 1, class_count_):
si_j = starting_vector_[j]
class_j_sc = self._svm.atts.vectors_per_class[j] # type: ignore
s1 = np.dot(
coefs[j - 1, si_i : si_i + class_i_sc],
kernels[si_i : si_i + class_i_sc],
)
s2 = np.dot(
coefs[i, si_j : si_j + class_j_sc],
kernels[si_j : si_j + class_j_sc],
)
s = self._svm.atts.rho[evals] + s1 + s2 # type: ignore
scores.append(s)
if s > 0:
votes[i] += 1
else:
votes[j] += 1
evals += 1
return votes, np.array(scores, dtype=X.dtype)
def _probabilities(self, scores, class_count_):
probsp2 = np.zeros((class_count_, class_count_), dtype=scores.dtype)
index = 0
for i in range(class_count_):
for j in range(i + 1, class_count_):
val1 = sigmoid_probability(
scores[index],
self._svm.atts.prob_a[index], # type: ignore
self._svm.atts.prob_b[index], # type: ignore
)
val2 = max(val1, 1.0e-7)
val2 = min(val2, (1 - 1.0e-7))
probsp2[i, j] = val2
probsp2[j, i] = 1 - val2
index += 1
return multiclass_probability(class_count_, probsp2)
def _compute_final_scores(
self, votes, scores, weights_are_all_positive_, has_proba, classlabels_ints
):
max_weight = 0
if votes is not None and len(votes) > 0:
max_class = np.argmax(votes)
max_weight = votes[max_class]
else:
max_class = np.argmax(scores)
max_weight = scores[max_class]
write_additional_scores = -1
if self._svm.atts.rho.size == 1: # type: ignore
label, write_additional_scores = set_score_svm(
max_weight,
max_class,
has_proba,
weights_are_all_positive_,
classlabels_ints,
1,
0,
)
elif classlabels_ints is not None and len(classlabels_ints) > 0:
label = classlabels_ints[max_class]
else:
label = max_class
new_scores = write_scores(
scores.size, scores, self._svm.atts.post_transform, write_additional_scores # type: ignore
)
return label, new_scores
def _run( # type: ignore
self,
X,
classlabels_ints=None,
classlabels_strings=None,
coefficients=None,
kernel_params=None,
kernel_type=None,
post_transform=None,
prob_a=None,
prob_b=None,
rho=None,
support_vectors=None,
vectors_per_class=None,
):
svm = SVMCommon(
coefficients=coefficients,
kernel_params=kernel_params,
kernel_type=kernel_type,
post_transform=post_transform,
prob_a=prob_a,
prob_b=prob_b,
rho=rho,
support_vectors=support_vectors,
vectors_per_class=vectors_per_class,
)
# unused unless for debugging purposes
self._svm = svm # pylint: disable=W0201
vector_count_ = 0
class_count_ = max(len(classlabels_ints or classlabels_strings or []), 1)
starting_vector_ = []
if svm.atts.vectors_per_class is not None: # type: ignore
for vc in svm.atts.vectors_per_class: # type: ignore
starting_vector_.append(vector_count_)
vector_count_ += vc
if vector_count_ > 0:
# length of each support vector
mode = "SVM_SVC"
sv = svm.atts.support_vectors.reshape((vector_count_, -1)) # type: ignore
kernel_type_ = svm.atts.kernel_type # type: ignore
coefs = svm.atts.coefficients.reshape((-1, vector_count_)) # type: ignore
else:
# liblinear mode
mode = "SVM_LINEAR"
kernel_type_ = "LINEAR"
coefs = svm.atts.coefficients.reshape((class_count_, -1)) # type: ignore
weights_are_all_positive_ = min(svm.atts.coefficients) >= 0 # type: ignore
# SVM part
if vector_count_ == 0 and mode == "SVM_LINEAR":
res = np.empty((X.shape[0], class_count_), dtype=X.dtype)
for n in range(X.shape[0]):
scores = self._run_linear(X[n], coefs, class_count_, kernel_type_)
res[n, :] = scores
votes = None
else:
res = np.empty(
(X.shape[0], class_count_ * (class_count_ - 1) // 2), dtype=X.dtype
)
votes = np.empty((X.shape[0], class_count_), dtype=X.dtype)
for n in range(X.shape[0]):
vote, scores = self._run_svm(
X[n],
sv,
vector_count_,
kernel_type_,
class_count_,
starting_vector_,
coefs,
)
res[n, :] = scores
votes[n, :] = vote
# proba
if (
svm.atts.prob_a is not None # type: ignore
and len(svm.atts.prob_a) > 0 # type: ignore
and mode == "SVM_SVC"
):
scores = np.empty((res.shape[0], class_count_), dtype=X.dtype)
for n in range(scores.shape[0]):
s = self._probabilities(res[n], class_count_)
scores[n, :] = s
has_proba = True
else:
scores = res
has_proba = False
# finalization
final_scores = None
labels = []
for n in range(scores.shape[0]):
label, new_scores = self._compute_final_scores(
None if votes is None else votes[n],
scores[n],
weights_are_all_positive_,
has_proba,
classlabels_ints,
)
if final_scores is None:
final_scores = np.empty((X.shape[0], new_scores.size), dtype=X.dtype)
final_scores[n, :] = new_scores
labels.append(label)
# labels
if classlabels_strings is not None and len(classlabels_strings) > 0:
return (np.array([classlabels_strings[i] for i in labels]), final_scores)
return (np.array(labels, dtype=np.int64), final_scores)
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"/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", 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"/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,125 | onnx/onnx | refs/heads/main | /onnx/reference/ops/_helpers.py | # Copyright (c) ONNX Project Contributors
# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from typing import Any, Dict, Union
from onnx.reference.op_run import OpRun, _split_class_name
def build_registered_operators_any_domain(
module_context: Dict[str, Any]
) -> Dict[str, Dict[Union[int, None], OpRun]]:
reg_ops: Dict[str, Dict[Union[int, None], OpRun]] = {}
for class_name, class_type in module_context.items():
if class_name.startswith("_") or class_name in {
"Any",
"Dict",
"List",
"TOptional",
"Union",
"cl",
"class_name",
"get_schema",
"module_context",
"textwrap",
}:
continue
if isinstance(class_type, type(build_registered_operators_any_domain)):
continue
try:
issub = issubclass(class_type, OpRun)
except TypeError as e:
raise TypeError(
f"Unexpected variable type {class_type!r} and class_name={class_name!r}."
) from e
if issub:
op_type, op_version = _split_class_name(class_name)
if op_type not in reg_ops:
reg_ops[op_type] = {}
reg_ops[op_type][op_version] = class_type
if not reg_ops:
raise RuntimeError(
"No registered operator. This error happens when no implementation "
"of type 'OpRun' was detected. It may be due to an error during installation. Please try reinstalling onnx."
)
# Set default implementation to the latest one.
for impl in reg_ops.values():
if None in impl:
# default already exists
continue
max_version = max(impl) # type: ignore[type-var]
impl[None] = impl[max_version]
return reg_ops
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"/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,126 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/tfidfvectorizer.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from typing import Any
import numpy as np
import onnx
from onnx import NodeProto
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class TfIdfVectorizerHelper:
def __init__(self, **params: Any) -> None:
# Attr names
mode = "mode"
min_gram_length = "min_gram_length"
max_gram_length = "max_gram_length"
max_skip_count = "max_skip_count"
ngram_counts = "ngram_counts"
ngram_indexes = "ngram_indexes"
pool_int64s = "pool_int64s"
required_attr = [
mode,
min_gram_length,
max_gram_length,
max_skip_count,
ngram_counts,
ngram_indexes,
pool_int64s,
]
for i in required_attr:
assert i in params, f"Missing attribute: {i}"
self.mode = params[mode]
self.min_gram_length = params[min_gram_length]
self.max_gram_length = params[max_gram_length]
self.max_skip_count = params[max_skip_count]
self.ngram_counts = params[ngram_counts]
self.ngram_indexes = params[ngram_indexes]
self.pool_int64s = params[pool_int64s]
def make_node_noweights(self) -> NodeProto:
return onnx.helper.make_node(
"TfIdfVectorizer",
inputs=["X"],
outputs=["Y"],
mode=self.mode,
min_gram_length=self.min_gram_length,
max_gram_length=self.max_gram_length,
max_skip_count=self.max_skip_count,
ngram_counts=self.ngram_counts,
ngram_indexes=self.ngram_indexes,
pool_int64s=self.pool_int64s,
)
class TfIdfVectorizer(Base):
@staticmethod
def export_tf_only_bigrams_skip0() -> None:
input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
output = np.array([0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]).astype(np.float32)
ngram_counts = np.array([0, 4]).astype(np.int64)
ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams
np.int64
) # bigrams
helper = TfIdfVectorizerHelper(
mode="TF",
min_gram_length=2,
max_gram_length=2,
max_skip_count=0,
ngram_counts=ngram_counts,
ngram_indexes=ngram_indexes,
pool_int64s=pool_int64s,
)
node = helper.make_node_noweights()
expect(
node,
inputs=[input],
outputs=[output],
name="test_tfidfvectorizer_tf_only_bigrams_skip0",
)
@staticmethod
def export_tf_batch_onlybigrams_skip0() -> None:
input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
output = np.array(
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0]]
).astype(np.float32)
ngram_counts = np.array([0, 4]).astype(np.int64)
ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams
np.int64
) # bigrams
helper = TfIdfVectorizerHelper(
mode="TF",
min_gram_length=2,
max_gram_length=2,
max_skip_count=0,
ngram_counts=ngram_counts,
ngram_indexes=ngram_indexes,
pool_int64s=pool_int64s,
)
node = helper.make_node_noweights()
expect(
node,
inputs=[input],
outputs=[output],
name="test_tfidfvectorizer_tf_batch_onlybigrams_skip0",
)
@staticmethod
def export_tf_onlybigrams_levelempty() -> None:
input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
output = np.array([1.0, 1.0, 1.0]).astype(np.float32)
ngram_counts = np.array([0, 0]).astype(np.int64)
ngram_indexes = np.array([0, 1, 2]).astype(np.int64)
pool_int64s = np.array([5, 6, 7, 8, 6, 7]).astype( # unigrams none
np.int64
) # bigrams
helper = TfIdfVectorizerHelper(
mode="TF",
min_gram_length=2,
max_gram_length=2,
max_skip_count=0,
ngram_counts=ngram_counts,
ngram_indexes=ngram_indexes,
pool_int64s=pool_int64s,
)
node = helper.make_node_noweights()
expect(
node,
inputs=[input],
outputs=[output],
name="test_tfidfvectorizer_tf_onlybigrams_levelempty",
)
@staticmethod
def export_tf_onlybigrams_skip5() -> None:
input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
output = np.array([0.0, 0.0, 0.0, 0.0, 1.0, 3.0, 1.0]).astype(np.float32)
ngram_counts = np.array([0, 4]).astype(np.int64)
ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams
np.int64
) # bigrams
helper = TfIdfVectorizerHelper(
mode="TF",
min_gram_length=2,
max_gram_length=2,
max_skip_count=5,
ngram_counts=ngram_counts,
ngram_indexes=ngram_indexes,
pool_int64s=pool_int64s,
)
node = helper.make_node_noweights()
expect(
node,
inputs=[input],
outputs=[output],
name="test_tfidfvectorizer_tf_onlybigrams_skip5",
)
@staticmethod
def export_tf_batch_onlybigrams_skip5() -> None:
input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
output = np.array(
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]]
).astype(np.float32)
ngram_counts = np.array([0, 4]).astype(np.int64)
ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams
np.int64
) # bigrams
helper = TfIdfVectorizerHelper(
mode="TF",
min_gram_length=2,
max_gram_length=2,
max_skip_count=5,
ngram_counts=ngram_counts,
ngram_indexes=ngram_indexes,
pool_int64s=pool_int64s,
)
node = helper.make_node_noweights()
expect(
node,
inputs=[input],
outputs=[output],
name="test_tfidfvectorizer_tf_batch_onlybigrams_skip5",
)
@staticmethod
def export_tf_uniandbigrams_skip5() -> None:
input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(np.int32)
output = np.array([0.0, 3.0, 1.0, 0.0, 1.0, 3.0, 1.0]).astype(np.float32)
ngram_counts = np.array([0, 4]).astype(np.int64)
ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams
np.int64
) # bigrams
helper = TfIdfVectorizerHelper(
mode="TF",
min_gram_length=1,
max_gram_length=2,
max_skip_count=5,
ngram_counts=ngram_counts,
ngram_indexes=ngram_indexes,
pool_int64s=pool_int64s,
)
node = helper.make_node_noweights()
expect(
node,
inputs=[input],
outputs=[output],
name="test_tfidfvectorizer_tf_uniandbigrams_skip5",
)
@staticmethod
def export_tf_batch_uniandbigrams_skip5() -> None:
input = np.array([[1, 1, 3, 3, 3, 7], [8, 6, 7, 5, 6, 8]]).astype(np.int32)
output = np.array(
[[0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0]]
).astype(np.float32)
ngram_counts = np.array([0, 4]).astype(np.int64)
ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
pool_int64s = np.array([2, 3, 5, 4, 5, 6, 7, 8, 6, 7]).astype( # unigrams
np.int64
) # bigrams
helper = TfIdfVectorizerHelper(
mode="TF",
min_gram_length=1,
max_gram_length=2,
max_skip_count=5,
ngram_counts=ngram_counts,
ngram_indexes=ngram_indexes,
pool_int64s=pool_int64s,
)
node = helper.make_node_noweights()
expect(
node,
inputs=[input],
outputs=[output],
name="test_tfidfvectorizer_tf_batch_uniandbigrams_skip5",
)
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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,127 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_average_pool.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221,R0913,R0914
from onnx.reference.ops.op_pool_common import CommonPool
class AveragePool_1(CommonPool):
def _run( # type: ignore
self,
x,
auto_pad=None,
ceil_mode=None,
kernel_shape=None,
pads=None,
strides=None,
count_include_pad=None,
):
return CommonPool._run(
self,
"AVG",
count_include_pad,
x,
auto_pad=auto_pad,
ceil_mode=ceil_mode,
dilations=None,
kernel_shape=kernel_shape,
pads=pads,
strides=strides,
)
class AveragePool_7(CommonPool):
def _run( # type: ignore
self,
x,
auto_pad=None,
ceil_mode=None,
kernel_shape=None,
pads=None,
strides=None,
count_include_pad=None,
):
return CommonPool._run(
self,
"AVG",
count_include_pad,
x,
auto_pad=auto_pad,
ceil_mode=ceil_mode,
dilations=None,
kernel_shape=kernel_shape,
pads=pads,
strides=strides,
)
class AveragePool_11(CommonPool):
def _run( # type: ignore
self,
x,
auto_pad=None,
ceil_mode=None,
kernel_shape=None,
pads=None,
strides=None,
count_include_pad=None,
):
return CommonPool._run(
self,
"AVG",
count_include_pad,
x,
auto_pad=auto_pad,
ceil_mode=ceil_mode,
dilations=None,
kernel_shape=kernel_shape,
pads=pads,
strides=strides,
)
class AveragePool_19(CommonPool):
def _run( # type: ignore
self,
x,
auto_pad=None,
ceil_mode=None,
dilations=None,
kernel_shape=None,
pads=None,
strides=None,
count_include_pad=None,
):
return CommonPool._run(
self,
"AVG",
count_include_pad,
x,
auto_pad=auto_pad,
ceil_mode=ceil_mode,
dilations=dilations,
kernel_shape=kernel_shape,
pads=pads,
strides=strides,
)
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dropout.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/model_inference_test.py": ["/onnx/__init__.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/test/inliner_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/argmin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/op_run.py": ["/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/reference_evaluator.py"], "/onnx/reference/ops/op_max_unpool.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/reversesequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/celu.py": 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59,128 | onnx/onnx | refs/heads/main | /onnx/backend/test/runner/item.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import dataclasses
from typing import Any, Callable, List, Optional, Union
from onnx import ModelProto, NodeProto
# A container that hosts the test function and the associated
# test item (ModelProto)
@dataclasses.dataclass
class TestItem:
func: Callable[..., Any]
proto: List[Optional[Union[ModelProto, NodeProto]]]
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59,129 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/gatherelements.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
# The below GatherElements' numpy implementation is from https://stackoverflow.com/a/46204790/11767360
def gather_elements(data, indices, axis=0): # type: ignore
data_swaped = np.swapaxes(data, 0, axis)
index_swaped = np.swapaxes(indices, 0, axis)
gathered = np.choose(index_swaped, data_swaped, mode="wrap")
y = np.swapaxes(gathered, 0, axis)
return y
class GatherElements(Base):
@staticmethod
def export_gather_elements_0() -> None:
axis = 1
node = onnx.helper.make_node(
"GatherElements",
inputs=["data", "indices"],
outputs=["y"],
axis=axis,
)
data = np.array([[1, 2], [3, 4]], dtype=np.float32)
indices = np.array([[0, 0], [1, 0]], dtype=np.int32)
y = gather_elements(data, indices, axis)
# print(y) produces
# [[1, 1],
# [4, 3]]
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_elements_0",
)
@staticmethod
def export_gather_elements_1() -> None:
axis = 0
node = onnx.helper.make_node(
"GatherElements",
inputs=["data", "indices"],
outputs=["y"],
axis=axis,
)
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
indices = np.array([[1, 2, 0], [2, 0, 0]], dtype=np.int32)
y = gather_elements(data, indices, axis)
# print(y) produces
# [[4, 8, 3],
# [7, 2, 3]]
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_elements_1",
)
@staticmethod
def export_gather_elements_negative_indices() -> None:
axis = 0
node = onnx.helper.make_node(
"GatherElements",
inputs=["data", "indices"],
outputs=["y"],
axis=axis,
)
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
indices = np.array([[-1, -2, 0], [-2, 0, 0]], dtype=np.int32)
y = gather_elements(data, indices, axis)
# print(y) produces
# [[7, 5, 3],
# [4, 2, 3]]
expect(
node,
inputs=[data, indices.astype(np.int64)],
outputs=[y],
name="test_gather_elements_negative_indices",
)
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59,130 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/slice.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Slice(Base):
@staticmethod
def export_slice() -> None:
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes", "steps"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
y = x[0:3, 0:10]
starts = np.array([0, 0], dtype=np.int64)
ends = np.array([3, 10], dtype=np.int64)
axes = np.array([0, 1], dtype=np.int64)
steps = np.array([1, 1], dtype=np.int64)
expect(
node, inputs=[x, starts, ends, axes, steps], outputs=[y], name="test_slice"
)
@staticmethod
def export_slice_neg() -> None:
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes", "steps"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([0], dtype=np.int64)
ends = np.array([-1], dtype=np.int64)
axes = np.array([1], dtype=np.int64)
steps = np.array([1], dtype=np.int64)
y = x[:, 0:-1]
expect(
node,
inputs=[x, starts, ends, axes, steps],
outputs=[y],
name="test_slice_neg",
)
@staticmethod
def export_slice_start_out_of_bounds() -> None:
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes", "steps"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([1000], dtype=np.int64)
ends = np.array([1000], dtype=np.int64)
axes = np.array([1], dtype=np.int64)
steps = np.array([1], dtype=np.int64)
y = x[:, 1000:1000]
expect(
node,
inputs=[x, starts, ends, axes, steps],
outputs=[y],
name="test_slice_start_out_of_bounds",
)
@staticmethod
def export_slice_end_out_of_bounds() -> None:
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes", "steps"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([1], dtype=np.int64)
ends = np.array([1000], dtype=np.int64)
axes = np.array([1], dtype=np.int64)
steps = np.array([1], dtype=np.int64)
y = x[:, 1:1000]
expect(
node,
inputs=[x, starts, ends, axes, steps],
outputs=[y],
name="test_slice_end_out_of_bounds",
)
@staticmethod
def export_slice_default_axes() -> None:
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([0, 0, 3], dtype=np.int64)
ends = np.array([20, 10, 4], dtype=np.int64)
y = x[:, :, 3:4]
expect(
node, inputs=[x, starts, ends], outputs=[y], name="test_slice_default_axes"
)
@staticmethod
def export_slice_default_steps() -> None:
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([0, 0, 3], dtype=np.int64)
ends = np.array([20, 10, 4], dtype=np.int64)
axes = np.array([0, 1, 2], dtype=np.int64)
y = x[:, :, 3:4]
expect(
node,
inputs=[x, starts, ends, axes],
outputs=[y],
name="test_slice_default_steps",
)
@staticmethod
def export_slice_neg_steps() -> None:
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes", "steps"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([20, 10, 4], dtype=np.int64)
ends = np.array([0, 0, 1], dtype=np.int64)
axes = np.array([0, 1, 2], dtype=np.int64)
steps = np.array([-1, -3, -2]).astype(np.int64)
y = x[20:0:-1, 10:0:-3, 4:1:-2]
expect(
node,
inputs=[x, starts, ends, axes, steps],
outputs=[y],
name="test_slice_neg_steps",
)
@staticmethod
def export_slice_negative_axes() -> None:
node = onnx.helper.make_node(
"Slice",
inputs=["x", "starts", "ends", "axes"],
outputs=["y"],
)
x = np.random.randn(20, 10, 5).astype(np.float32)
starts = np.array([0, 0, 3], dtype=np.int64)
ends = np.array([20, 10, 4], dtype=np.int64)
axes = np.array([0, -2, -1], dtype=np.int64)
y = x[:, :, 3:4]
expect(
node,
inputs=[x, starts, ends, axes],
outputs=[y],
name="test_slice_negative_axes",
)
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59,131 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/stft.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class STFT(Base):
@staticmethod
def export() -> None:
signal = np.arange(0, 128, dtype=np.float32).reshape(1, 128, 1)
length = np.array(16).astype(np.int64)
onesided_length = (length >> 1) + 1
step = np.array(8).astype(np.int64)
no_window = "" # optional input, not supplied
node = onnx.helper.make_node(
"STFT",
inputs=["signal", "frame_step", no_window, "frame_length"],
outputs=["output"],
)
nstfts = ((signal.shape[1] - length) // step) + 1
# [batch_size][frames][frame_length][2]
output = np.empty([1, nstfts, onesided_length, 2], dtype=np.float32)
for i in range(nstfts):
start = i * step
stop = i * step + length
complex_out = np.fft.fft(signal[0, start:stop, 0])[0:onesided_length]
output[0, i] = np.stack((complex_out.real, complex_out.imag), axis=1)
expect(node, inputs=[signal, step, length], outputs=[output], name="test_stft")
node = onnx.helper.make_node(
"STFT",
inputs=["signal", "frame_step", "window"],
outputs=["output"],
)
# Test with window
a0 = 0.5
a1 = 0.5
window = a0 + a1 * np.cos(
2 * np.pi * np.arange(0, length, 1, dtype=np.float32) / length
)
nstfts = 1 + (signal.shape[1] - window.shape[0]) // step
# [batch_size][frames][frame_length][2]
output = np.empty([1, nstfts, onesided_length, 2], dtype=np.float32)
for i in range(nstfts):
start = i * step
stop = i * step + length
complex_out = np.fft.fft(signal[0, start:stop, 0] * window)[
0:onesided_length
]
output[0, i] = np.stack((complex_out.real, complex_out.imag), axis=1)
expect(
node,
inputs=[signal, step, window],
outputs=[output],
name="test_stft_with_window",
)
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59,132 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_matmul.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunBinaryNum
def numpy_matmul(a, b): # type: ignore
"""
Implements a matmul product. See :func:`np.matmul`.
Handles sparse matrices.
"""
try:
if len(a.shape) <= 2 and len(b.shape) <= 2:
return np.dot(a, b)
return np.matmul(a, b)
except ValueError as e:
raise ValueError(f"Unable to multiply shapes {a.shape!r}, {b.shape!r}.") from e
class MatMul(OpRunBinaryNum):
def _run(self, a, b): # type: ignore
return (numpy_matmul(a, b),)
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59,133 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_mel_weight_matrix.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
import numpy as np
from onnx.helper import tensor_dtype_to_np_dtype
from onnx.reference.op_run import OpRun
class MelWeightMatrix(OpRun):
def _run( # type: ignore
self,
num_mel_bins,
dft_length,
sample_rate,
lower_edge_hertz,
upper_edge_hertz,
output_datatype=None,
):
num_spectrogram_bins = dft_length // 2 + 1
frequency_bins = np.arange(0, num_mel_bins + 2)
low_frequency_mel = 2595 * np.log10(1 + lower_edge_hertz / 700)
high_frequency_mel = 2595 * np.log10(1 + upper_edge_hertz / 700)
mel_step = (high_frequency_mel - low_frequency_mel) / frequency_bins.shape[0]
frequency_bins = frequency_bins * mel_step + low_frequency_mel
frequency_bins = 700 * (np.power(10, (frequency_bins / 2595)) - 1)
frequency_bins = ((dft_length + 1) * frequency_bins) // sample_rate
frequency_bins = frequency_bins.astype(int)
output = np.zeros((num_spectrogram_bins, num_mel_bins))
output.flags.writeable = True
for i in range(num_mel_bins):
lower_frequency_value = frequency_bins[i] # left
center_frequency_point = frequency_bins[i + 1] # center
higher_frequency_point = frequency_bins[i + 2] # right
low_to_center = center_frequency_point - lower_frequency_value
if low_to_center == 0:
output[center_frequency_point, i] = 1
else:
for j in range(lower_frequency_value, center_frequency_point + 1):
output[j, i] = float(j - lower_frequency_value) / float(
low_to_center
)
center_to_high = higher_frequency_point - center_frequency_point
if center_to_high > 0:
for j in range(center_frequency_point, higher_frequency_point):
output[j, i] = float(higher_frequency_point - j) / float(
center_to_high
)
if output_datatype is None:
output = output.astype(np.float32)
else:
dtype = tensor_dtype_to_np_dtype(output_datatype)
output = output.astype(dtype)
return (output,)
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59,134 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_cast.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,W0221
import numpy as np
from onnx.helper import (
float32_to_bfloat16,
float32_to_float8e4m3,
float32_to_float8e5m2,
tensor_dtype_to_np_dtype,
)
from onnx.numpy_helper import (
bfloat16_to_float32,
float8e4m3_to_float32,
float8e5m2_to_float32,
)
from onnx.onnx_pb import TensorProto
from onnx.reference.custom_element_types import (
bfloat16,
float8e4m3fn,
float8e4m3fnuz,
float8e5m2,
float8e5m2fnuz,
)
from onnx.reference.op_run import OpRun
def cast_to(x, to, saturate):
if x.dtype == bfloat16 and x.dtype.descr[0][0] == "bfloat16":
if to == TensorProto.BFLOAT16:
return x
xr = x.ravel()
xf = np.empty(xr.shape[0], dtype=np.float32)
for i in range(xr.shape[0]):
el = bfloat16_to_float32(xr[i])
xf[i] = el
dtype = tensor_dtype_to_np_dtype(to)
return xf.astype(dtype).reshape(x.shape)
f8 = {
(float8e4m3fn, "e4m3fn", TensorProto.FLOAT8E4M3FN): float8e4m3_to_float32,
(
float8e4m3fnuz,
"e4m3fnuz",
TensorProto.FLOAT8E4M3FNUZ,
): lambda *args: float8e4m3_to_float32(*args, uz=True),
(float8e5m2, "e5m2", TensorProto.FLOAT8E5M2): float8e5m2_to_float32,
(
float8e5m2fnuz,
"e5m2fnuz",
TensorProto.FLOAT8E5M2FNUZ,
): lambda *args: float8e5m2_to_float32(*args, fn=True, uz=True),
}
for (dt, st, proto_type), cvt in f8.items():
if x.dtype == dt and x.dtype.descr[0][0] == st:
if to == proto_type:
return x
xr = x.ravel()
xf = np.empty(xr.shape[0], dtype=np.float32)
for i in range(xr.shape[0]):
el = cvt(xr[i])
xf[i] = el
dtype = tensor_dtype_to_np_dtype(to)
return xf.astype(dtype).reshape(x.shape)
if to == TensorProto.BFLOAT16:
xf = x.astype(np.float32).ravel()
y = np.empty(xf.shape, dtype=bfloat16).ravel()
for i in range(y.shape[0]):
el = float32_to_bfloat16(xf[i], truncate=True) # type: ignore[assignment]
y[i] = el
return y.reshape(x.shape)
f8back = {
TensorProto.FLOAT8E4M3FN: (
float8e4m3fn,
lambda *args: float32_to_float8e4m3(*args, saturate=saturate),
),
TensorProto.FLOAT8E4M3FNUZ: (
float8e4m3fnuz,
lambda *args: float32_to_float8e4m3(*args, uz=True, saturate=saturate),
),
TensorProto.FLOAT8E5M2: (
float8e5m2,
lambda *args: float32_to_float8e5m2(*args, saturate=saturate),
),
TensorProto.FLOAT8E5M2FNUZ: (
float8e5m2fnuz,
lambda *args: float32_to_float8e5m2(
*args, fn=True, uz=True, saturate=saturate
),
),
}
for dt, (npdt, cvt) in f8back.items():
if to == dt:
xf = x.astype(np.float32).ravel()
y = np.empty(xf.shape, dtype=npdt).ravel()
for i in range(y.shape[0]):
el = cvt(xf[i]) # type: ignore[assignment]
y[i] = el
return y.reshape(x.shape)
if to == TensorProto.STRING:
return x.astype(np.str_)
dtype = tensor_dtype_to_np_dtype(to)
return x.astype(dtype)
class Cast_1(OpRun):
def _run(self, x, to=None): # type: ignore
return (cast_to(x, to, saturate=True),)
class Cast_19(OpRun):
def _run(self, x, to=None, saturate=None): # type: ignore
return (cast_to(x, to, saturate),)
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59,135 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/asin.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Asin(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Asin",
inputs=["x"],
outputs=["y"],
)
x = np.array([-0.5, 0, 0.5]).astype(np.float32)
y = np.arcsin(x)
expect(node, inputs=[x], outputs=[y], name="test_asin_example")
x = np.random.rand(3, 4, 5).astype(np.float32)
y = np.arcsin(x)
expect(node, inputs=[x], outputs=[y], name="test_asin")
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59,136 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_normalizer.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
import numpy as np
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
class Normalizer(OpRunAiOnnxMl):
@staticmethod
def norm_max(x): # type: ignore
"max normalization"
div = np.abs(x).max(axis=1).reshape((x.shape[0], -1))
return x / np.maximum(div, 1e-30)
@staticmethod
def norm_l1(x): # type: ignore
"L1 normalization"
div = np.abs(x).sum(axis=1).reshape((x.shape[0], -1))
return x / np.maximum(div, 1e-30)
@staticmethod
def norm_l2(x): # type: ignore
"L2 normalization"
xn = np.square(x).sum(axis=1)
np.sqrt(xn, out=xn)
norm = np.maximum(xn.reshape((x.shape[0], -1)), 1e-30)
return x / norm
def _run(self, x, norm=None): # type: ignore
if norm == "MAX":
_norm = Normalizer.norm_max
elif norm == "L1":
_norm = Normalizer.norm_l1
elif norm == "L2":
_norm = Normalizer.norm_l2
else:
raise ValueError(f"Unexpected value for norm='{norm}'.")
return (_norm(x),)
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59,137 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/unique.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def specify_int64(indices, inverse_indices, counts): # type: ignore
return (
np.array(indices, dtype=np.int64),
np.array(inverse_indices, dtype=np.int64),
np.array(counts, dtype=np.int64),
)
class Unique(Base):
@staticmethod
def export_sorted_without_axis() -> None:
node_sorted = onnx.helper.make_node(
"Unique",
inputs=["X"],
outputs=["Y", "indices", "inverse_indices", "counts"],
)
x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32)
y, indices, inverse_indices, counts = np.unique(x, True, True, True)
indices, inverse_indices, counts = specify_int64(
indices, inverse_indices, counts
)
expect(
node_sorted,
inputs=[x],
outputs=[y, indices, inverse_indices, counts],
name="test_unique_sorted_without_axis",
)
@staticmethod
def export_not_sorted_without_axis() -> None:
node_not_sorted = onnx.helper.make_node(
"Unique",
inputs=["X"],
outputs=["Y", "indices", "inverse_indices", "counts"],
sorted=0,
)
# numpy unique does not retain original order (it sorts the output unique values)
# https://github.com/numpy/numpy/issues/8621
# we need to recover unsorted output and indices
x = np.array([2.0, 1.0, 1.0, 3.0, 4.0, 3.0], dtype=np.float32)
y, indices, inverse_indices, counts = np.unique(x, True, True, True)
# prepare index mapping from sorted to unsorted
argsorted_indices = np.argsort(indices)
inverse_indices_map = dict(
zip(argsorted_indices, np.arange(len(argsorted_indices)))
)
indices = indices[argsorted_indices]
y = np.take(x, indices, axis=0)
inverse_indices = np.asarray(
[inverse_indices_map[i] for i in inverse_indices], dtype=np.int64
)
counts = counts[argsorted_indices]
indices, inverse_indices, counts = specify_int64(
indices, inverse_indices, counts
)
# print(y)
# [2.0, 1.0, 3.0, 4.0]
# print(indices)
# [0 1 3 4]
# print(inverse_indices)
# [0, 1, 1, 2, 3, 2]
# print(counts)
# [1, 2, 2, 1]
expect(
node_not_sorted,
inputs=[x],
outputs=[y, indices, inverse_indices, counts],
name="test_unique_not_sorted_without_axis",
)
@staticmethod
def export_sorted_with_axis() -> None:
node_sorted = onnx.helper.make_node(
"Unique",
inputs=["X"],
outputs=["Y", "indices", "inverse_indices", "counts"],
sorted=1,
axis=0,
)
x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]], dtype=np.float32)
y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=0)
indices, inverse_indices, counts = specify_int64(
indices, inverse_indices, counts
)
# print(y)
# [[1. 0. 0.]
# [2. 3. 4.]]
# print(indices)
# [0 2]
# print(inverse_indices)
# [0 0 1]
# print(counts)
# [2 1]
expect(
node_sorted,
inputs=[x],
outputs=[y, indices, inverse_indices, counts],
name="test_unique_sorted_with_axis",
)
@staticmethod
def export_sorted_with_axis_3d() -> None:
node_sorted = onnx.helper.make_node(
"Unique",
inputs=["X"],
outputs=["Y", "indices", "inverse_indices", "counts"],
sorted=1,
axis=1,
)
x = np.array(
[
[[1.0, 1.0], [0.0, 1.0], [2.0, 1.0], [0.0, 1.0]],
[[1.0, 1.0], [0.0, 1.0], [2.0, 1.0], [0.0, 1.0]],
],
dtype=np.float32,
)
y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=1)
indices, inverse_indices, counts = specify_int64(
indices, inverse_indices, counts
)
# print(y)
# [[[0. 1.]
# [1. 1.]
# [2. 1.]]
# [[0. 1.]
# [1. 1.]
# [2. 1.]]]
# print(indices)
# [1 0 2]
# print(inverse_indices)
# [1 0 2 0]
# print(counts)
# [2 1 1]
expect(
node_sorted,
inputs=[x],
outputs=[y, indices, inverse_indices, counts],
name="test_unique_sorted_with_axis_3d",
)
@staticmethod
def export_sorted_with_negative_axis() -> None:
node_sorted = onnx.helper.make_node(
"Unique",
inputs=["X"],
outputs=["Y", "indices", "inverse_indices", "counts"],
sorted=1,
axis=-1,
)
x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 3]], dtype=np.float32)
y, indices, inverse_indices, counts = np.unique(x, True, True, True, axis=-1)
indices, inverse_indices, counts = specify_int64(
indices, inverse_indices, counts
)
# print(y)
# [[0. 1.]
# [0. 1.]
# [3. 2.]]
# print(indices)
# [1 0]
# print(inverse_indices)
# [1 0 0]
# print(counts)
# [2 1]
expect(
node_sorted,
inputs=[x],
outputs=[y, indices, inverse_indices, counts],
name="test_unique_sorted_with_negative_axis",
)
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59,138 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_gather_elements.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.op_run import OpRun
def gather_numpy_2(self: np.ndarray, index: np.ndarray) -> np.ndarray:
res = []
for a, b in zip(self, index):
res.append(a[b[0]])
return np.array(res, dtype=self.dtype).reshape(index.shape)
def gather_numpy(self: np.ndarray, dim: int, index: np.ndarray) -> np.ndarray:
idx_xsection_shape = index.shape[:dim] + index.shape[dim + 1 :]
self_xsection_shape = self.shape[:dim] + self.shape[dim + 1 :]
if idx_xsection_shape != self_xsection_shape:
raise ValueError(
f"Except for dimension {dim!r}, all dimensions of "
f"index and self should be the same size."
)
data_swaped = np.swapaxes(self, 0, dim)
index_swaped = np.swapaxes(index, 0, dim)
try:
gathered = np.choose(index_swaped, data_swaped, mode="wrap")
except ValueError as e:
if len(index_swaped.shape) == 2 and len(data_swaped.shape) == 2:
return gather_numpy_2(self, index)
raise e # pragma: no cover
return np.swapaxes(gathered, 0, dim)
class GatherElements(OpRun):
def _run(self, data, indices, axis=None): # type: ignore
if indices.size == 0:
return (np.empty((0,), dtype=data.dtype),)
try:
return (gather_numpy(data, axis, indices),)
except TypeError:
# distribution x86 requires int32.
return (gather_numpy(data, axis, indices.astype(int)),)
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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,139 | onnx/onnx | refs/heads/main | /onnx/helper.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=C0302,R0912
import collections.abc
import numbers
import struct
from cmath import isnan
from typing import (
Any,
Callable,
Dict,
KeysView,
List,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
cast,
)
import google.protobuf.message
import numpy as np
from onnx import (
IR_VERSION,
AttributeProto,
FunctionProto,
GraphProto,
MapProto,
ModelProto,
NodeProto,
OperatorSetIdProto,
OptionalProto,
SequenceProto,
SparseTensorProto,
TensorProto,
TensorShapeProto,
TrainingInfoProto,
TypeProto,
ValueInfoProto,
defs,
mapping,
)
VersionRowType = Union[Tuple[str, int, int, int], Tuple[str, int, int, int, int]]
VersionTableType = List[VersionRowType]
AssignmentBindingType = List[Tuple[str, str]]
# This is a copy of the documented version in https://github.com/onnx/onnx/blob/main/docs/Versioning.md#released-versions
# Both must be updated whenever a new version of ONNX is released.
VERSION_TABLE: VersionTableType = [
# Release-version, IR version, ai.onnx version, ai.onnx.ml version, (optional) ai.onnx.training version
("1.0", 3, 1, 1),
("1.1", 3, 5, 1),
("1.1.2", 3, 6, 1),
("1.2", 3, 7, 1),
("1.3", 3, 8, 1),
("1.4.1", 4, 9, 1),
("1.5.0", 5, 10, 1),
("1.6.0", 6, 11, 2),
("1.7.0", 7, 12, 2, 1),
("1.8.0", 7, 13, 2, 1),
("1.8.1", 7, 13, 2, 1),
("1.9.0", 7, 14, 2, 1),
("1.10.0", 8, 15, 2, 1),
("1.10.1", 8, 15, 2, 1),
("1.10.2", 8, 15, 2, 1),
("1.11.0", 8, 16, 3, 1),
("1.12.0", 8, 17, 3, 1),
("1.13.0", 8, 18, 3, 1),
("1.13.1", 8, 18, 3, 1),
("1.14.0", 9, 19, 3, 1),
]
VersionMapType = Dict[Tuple[str, int], int]
def create_op_set_id_version_map(table: VersionTableType) -> VersionMapType:
"""create a map from (opset-domain, opset-version) to ir-version from above table"""
result: VersionMapType = {}
def process(release_version: str, ir_version: int, *args: Any) -> None:
del release_version # Unused
for pair in zip(["ai.onnx", "ai.onnx.ml", "ai.onnx.training"], args):
if pair not in result:
result[pair] = ir_version
if pair[0] == "ai.onnx.training":
result["ai.onnx.preview.training", pair[1]] = ir_version
for row in table:
process(*row)
return result
OP_SET_ID_VERSION_MAP = create_op_set_id_version_map(VERSION_TABLE)
def find_min_ir_version_for(
opsetidlist: List[OperatorSetIdProto], ignore_unknown: bool = False
) -> int:
"""Given list of opset ids, determine minimum IR version required.
Arguments:
opsetidlist (List[OperatorSetIdProto]): The list of OperatorSetIdProto
ignore_unknown (bool): If True, ignore unknown domain and return default min version for that domain.
Returns:
The minimum IR version required (integer)
"""
default_min_version = 3
def find_min(domain: Union[str, None], version: int) -> int:
key = (domain or "ai.onnx", version)
if key in OP_SET_ID_VERSION_MAP:
return OP_SET_ID_VERSION_MAP[key]
if ignore_unknown:
return default_min_version
raise ValueError("Unsupported opset-version.")
if opsetidlist:
return max(find_min(x.domain, x.version) for x in opsetidlist)
return default_min_version # if no opsets specified
def make_node(
op_type: str,
inputs: Sequence[str],
outputs: Sequence[str],
name: Optional[str] = None,
doc_string: Optional[str] = None,
domain: Optional[str] = None,
**kwargs: Any,
) -> NodeProto:
"""Construct a NodeProto.
Arguments:
op_type (string): The name of the operator to construct
inputs (list of string): list of input names
outputs (list of string): list of output names
name (string, default None): optional unique identifier for NodeProto
doc_string (string, default None): optional documentation string for NodeProto
domain (string, default None): optional domain for NodeProto.
If it's None, we will just use default domain (which is empty)
**kwargs (dict): the attributes of the node. The acceptable values
are documented in :func:`make_attribute`.
Returns:
NodeProto
"""
node = NodeProto()
node.op_type = op_type
node.input.extend(inputs)
node.output.extend(outputs)
if name:
node.name = name
if doc_string:
node.doc_string = doc_string
if domain is not None:
node.domain = domain
if kwargs:
node.attribute.extend(
make_attribute(key, value)
for key, value in sorted(kwargs.items())
if value is not None
)
return node
def make_operatorsetid(
domain: str,
version: int,
) -> OperatorSetIdProto:
"""Construct an OperatorSetIdProto.
Arguments:
domain (string): The domain of the operator set id
version (integer): Version of operator set id
Returns:
OperatorSetIdProto
"""
operatorsetid = OperatorSetIdProto()
operatorsetid.domain = domain
operatorsetid.version = version
return operatorsetid
def make_graph(
nodes: Sequence[NodeProto],
name: str,
inputs: Sequence[ValueInfoProto],
outputs: Sequence[ValueInfoProto],
initializer: Optional[Sequence[TensorProto]] = None,
doc_string: Optional[str] = None,
value_info: Optional[Sequence[ValueInfoProto]] = None,
sparse_initializer: Optional[Sequence[SparseTensorProto]] = None,
) -> GraphProto:
"""Construct a GraphProto
Arguments:
nodes: list of NodeProto
name (string): graph name
inputs: list of ValueInfoProto
outputs: list of ValueInfoProto
initializer: list of TensorProto
doc_string (string): graph documentation
value_info: list of ValueInfoProto
sparse_initializer: list of SparseTensorProto
Returns:
GraphProto
"""
if initializer is None:
initializer = []
if sparse_initializer is None:
sparse_initializer = []
if value_info is None:
value_info = []
graph = GraphProto()
graph.node.extend(nodes)
graph.name = name
graph.input.extend(inputs)
graph.output.extend(outputs)
graph.initializer.extend(initializer)
graph.sparse_initializer.extend(sparse_initializer)
graph.value_info.extend(value_info)
if doc_string:
graph.doc_string = doc_string
return graph
def make_opsetid(domain: str, version: int) -> OperatorSetIdProto:
"""Construct an OperatorSetIdProto.
Arguments:
domain (string): The domain of the operator set id
version (integer): Version of operator set id
Returns:
OperatorSetIdProto
"""
opsetid = OperatorSetIdProto()
opsetid.domain = domain
opsetid.version = version
return opsetid
def make_function(
domain: str,
fname: str,
inputs: Sequence[str],
outputs: Sequence[str],
nodes: Sequence[NodeProto],
opset_imports: Sequence[OperatorSetIdProto],
attributes: Optional[Sequence[str]] = None,
attribute_protos: Optional[Sequence[AttributeProto]] = None,
doc_string: Optional[str] = None,
) -> FunctionProto:
if attributes is None:
attributes = []
if attribute_protos is None:
attribute_protos = []
f = FunctionProto()
f.domain = domain
f.name = fname
f.input.extend(inputs)
f.output.extend(outputs)
f.node.extend(nodes)
f.opset_import.extend(opset_imports)
f.attribute.extend(attributes)
f.attribute_proto.extend(attribute_protos)
if doc_string:
f.doc_string = doc_string
return f
def make_model(graph: GraphProto, **kwargs: Any) -> ModelProto:
"""Construct a ModelProto
Arguments:
graph (GraphProto): *make_graph* returns
**kwargs: any attribute to add to the returned instance
Returns:
ModelProto
"""
model = ModelProto()
# Touch model.ir_version so it is stored as the version from which it is
# generated.
model.ir_version = IR_VERSION
model.graph.CopyFrom(graph)
opset_imports: Optional[Sequence[OperatorSetIdProto]] = None
opset_imports = kwargs.pop("opset_imports", None) # type: ignore
if opset_imports is not None:
model.opset_import.extend(opset_imports)
else:
# Default import
imp = model.opset_import.add()
imp.version = defs.onnx_opset_version()
functions: Optional[Sequence[FunctionProto]] = None
functions = kwargs.pop("functions", None) # type: ignore
if functions is not None:
model.functions.extend(functions)
for k, v in kwargs.items():
# TODO: Does this work with repeated fields?
setattr(model, k, v)
return model
# An extension of make_model that infers an IR_VERSION for the model,
# if not specified, using a best-effort-basis.
def make_model_gen_version(graph: GraphProto, **kwargs: Any) -> ModelProto:
ir_version_field = "ir_version"
if ir_version_field not in kwargs:
opset_imports_field = "opset_imports"
imports = kwargs[opset_imports_field] if opset_imports_field in kwargs else []
kwargs[ir_version_field] = find_min_ir_version_for(imports)
return make_model(graph, **kwargs)
def set_model_props(model: ModelProto, dict_value: Dict[str, str]) -> None:
del model.metadata_props[:]
for k, v in dict_value.items():
entry = model.metadata_props.add()
entry.key = k
entry.value = v
# model.metadata_properties.append(entry)
def split_complex_to_pairs(ca: Sequence[np.complex64]) -> Sequence[int]:
return [
(ca[i // 2].real if (i % 2 == 0) else ca[i // 2].imag) # type: ignore[misc]
for i in range(len(ca) * 2)
]
# convert a float32 value to a bfloat16 (as int)
# By default, this conversion rounds-to-nearest-even and supports NaN
# Setting `truncate` to True enables a simpler conversion. In this mode the
# conversion is performed by simply dropping the 2 least significant bytes of
# the significand. In this mode an error of up to 1 bit may be introduced and
# preservation of NaN values is not be guaranteed.
def float32_to_bfloat16(fval: float, truncate: bool = False) -> int:
ival = int.from_bytes(struct.pack("<f", fval), "little")
if truncate:
return ival >> 16
# NaN requires at least 1 significand bit set
if isnan(fval):
return 0x7FC0 # sign=0, exp=all-ones, sig=0b1000000
# drop bottom 16-bits
# round remaining bits using round-to-nearest-even
rounded = ((ival >> 16) & 1) + 0x7FFF
return (ival + rounded) >> 16
def float32_to_float8e4m3( # pylint: disable=too-many-statements
fval: float,
scale: float = 1.0,
fn: bool = True,
uz: bool = False,
saturate: bool = True,
) -> int:
"""
Convert a float32 value to a float8, e4m3 (as int).
:param fval: float to convert
:param scale: scale, divide *fval* by *scale* before casting it
:param fn: no infinite values
:param uz: no negative zero
:param saturate: if True, any value out of range included inf becomes the maximum value,
otherwise, it becomes NaN. The description of operator Cast fully describes the
differences.
:return: converted float
See :ref:`onnx-detail-float8` for technical details.
"""
if not fn:
raise NotImplementedError(
"float32_to_float8e4m3 not implemented with fn=False."
)
x = fval / scale
b = int.from_bytes(struct.pack("<f", np.float32(x)), "little")
ret = (b & 0x80000000) >> 24 # sign
if uz:
if (b & 0x7FC00000) == 0x7FC00000:
return 0x80
if np.isinf(x):
if saturate:
return ret | 127
return 0x80
e = (b & 0x7F800000) >> 23 # exponent
m = b & 0x007FFFFF # mantissa
if e != 0:
if e < 116:
pass
elif e < 120:
# denormalized number
ex = e - 119
if ex >= -2:
ret |= 1 << (2 + ex)
ret |= m >> (21 - ex)
elif m > 0:
ret |= 1
mask = 1 << (20 - ex)
if m & mask and ( # pylint: disable=too-many-boolean-expressions
ret & 1
or m & (mask - 1) > 0
or (m & mask and m & (mask << 1) and m & (mask - 1) == 0)
):
# rounding
ret += 1
elif e < 135:
# normalized number
ex = e - 119 # 127 - 8
if ex == 0:
ret |= 0x4
ret |= m >> 21
else:
ret |= ex << 3
ret |= m >> 20
if m & 0x80000 and ((m & 0x100000) or (m & 0x7FFFF)):
if (ret & 0x7F) < 0x7F:
# rounding
ret += 1
elif not saturate:
return 0x80
elif saturate:
ret |= 0x7F # 01111110
else:
ret = 0x80
elif m == 0:
# -0
ret = 0
return int(ret)
else:
if (b & 0x7FC00000) == 0x7FC00000:
return 0x7F | ret
if np.isinf(x):
if saturate:
return ret | 126
return 0x7F | ret
e = (b & 0x7F800000) >> 23 # exponent
m = b & 0x007FFFFF # mantissa
if e != 0:
if e < 117:
pass
elif e < 121:
# denormalized number
ex = e - 120
if ex >= -2:
ret |= 1 << (2 + ex)
ret |= m >> (21 - ex)
elif m > 0:
ret |= 1
mask = 1 << (20 - ex)
if m & mask and ( # pylint: disable=too-many-boolean-expressions
ret & 1
or m & (mask - 1) > 0
or (m & mask and m & (mask << 1) and m & (mask - 1) == 0)
):
# rounding
ret += 1
elif e < 136:
# normalized number
ex = e - 120
if ex == 0:
ret |= 0x4
ret |= m >> 21
else:
ret |= ex << 3
ret |= m >> 20
if (ret & 0x7F) == 0x7F:
ret &= 0xFE
if (m & 0x80000) and ((m & 0x100000) or (m & 0x7FFFF)):
if (ret & 0x7F) < 0x7E:
# rounding
ret += 1
elif not saturate:
ret |= 0x7F
elif saturate:
ret |= 126 # 01111110
else:
ret |= 0x7F
return int(ret)
def float32_to_float8e5m2( # pylint: disable=too-many-statements
fval: float,
scale: float = 1.0,
fn: bool = False,
uz: bool = False,
saturate: bool = True,
) -> int:
"""
Convert a float32 value to a float8, e5m2 (as int).
:param fval: float to convert
:param scale: scale, divide *fval* by *scale* before casting it
:param fn: no infinite values
:param uz: no negative zero
:param saturate: if True, any value out of range included inf becomes the maximum value,
otherwise, it becomes NaN. The description of operator Cast fully describes the
differences.
:return: converted float
"""
x = fval / scale
b = int.from_bytes(struct.pack("<f", np.float32(x)), "little")
ret = (b & 0x80000000) >> 24 # sign
if fn and uz:
if (b & 0x7FC00000) == 0x7FC00000:
return 0x80
if (b & 0x7FFFFFFF) == 0x7F800000:
# inf
if saturate:
return ret | 0x7F
return 0x80
e = (b & 0x7F800000) >> 23 # exponent
m = b & 0x007FFFFF # mantissa
if e != 0:
if e < 109:
pass
elif e < 112:
# denormalized number
ex = e - 111
if ex >= -1:
ret |= 1 << (1 + ex)
ret |= m >> (22 - ex)
elif m > 0:
ret |= 1
mask = 1 << (21 - ex)
if m & mask and ( # pylint: disable=too-many-boolean-expressions
ret & 1
or m & (mask - 1) > 0
or (m & mask and m & (mask << 1) and m & (mask - 1) == 0)
):
# rounding
ret += 1
elif e < 143:
# normalized number
ex = e - 111
ret |= ex << 2
ret |= m >> 21
if m & 0x100000 and ((m & 0xFFFFF) or (m & 0x200000)):
if (ret & 0x7F) < 0x7F:
# rounding
ret += 1
elif not saturate:
ret = 0x80
elif e == 255 and m == 0: # inf
ret = 0x80
elif saturate:
ret |= 0x7F # last possible number
else:
ret = 0x80
elif m == 0:
# -0
ret = 0
return int(ret)
elif not fn and not uz:
if (b & 0x7FC00000) == 0x7FC00000:
return 0x7F | ret
if np.isinf(x):
if saturate:
return 0x7B | ret
return 0x7C | ret
e = (b & 0x7F800000) >> 23 # exponent
m = b & 0x007FFFFF # mantissa
if e != 0:
if e < 110:
pass
elif e < 113:
# denormalized number
ex = e - 112
if ex >= -1:
ret |= 1 << (1 + ex)
ret |= m >> (22 - ex)
elif m > 0:
ret |= 1
mask = 1 << (21 - ex)
if m & mask and ( # pylint: disable=too-many-boolean-expressions
ret & 1
or m & (mask - 1) > 0
or (m & mask and m & (mask << 1) and m & (mask - 1) == 0)
):
# rounding
ret += 1
elif e < 143:
# normalized number
ex = e - 112
ret |= ex << 2
ret |= m >> 21
if m & 0x100000 and ((m & 0xFFFFF) or (m & 0x200000)):
if (ret & 0x7F) < 0x7B:
# rounding
ret += 1
elif saturate:
ret |= 0x7B
else:
ret |= 0x7C
elif saturate:
ret |= 0x7B
else:
ret |= 0x7C
return int(ret)
else:
raise NotImplementedError("fn and uz must be both False or True.")
def make_tensor(
name: str, data_type: int, dims: Sequence[int], vals: Any, raw: bool = False
) -> TensorProto:
"""
Make a TensorProto with specified arguments. If raw is False, this
function will choose the corresponding proto field to store the
values based on data_type. If raw is True, use "raw_data" proto
field to store the values, and values should be of type bytes in
this case.
Arguments:
name (string): tensor name
data_type (int): a value such as onnx.TensorProto.FLOAT
dims (List[int]): shape
vals: values
raw (bool): if True, vals contains the serialized content of the tensor,
otherwise, vals should be a list of values of the type defined by *data_type*
Returns:
TensorProto
"""
tensor = TensorProto()
tensor.data_type = data_type
tensor.name = name
if data_type == TensorProto.STRING and raw:
raise TypeError("Can not use raw_data to store string type.")
np_dtype = tensor_dtype_to_np_dtype(data_type)
# Check number of vals specified equals tensor size
expected_size = 1
if raw:
# NumPy doesn't have BFLOAT16. TENSOR_TYPE_TO_NP_TYPE maps it to float32,
# which has the wrong itemsize.
if data_type == TensorProto.BFLOAT16:
expected_size = 2
elif data_type in (
TensorProto.FLOAT8E4M3FN,
TensorProto.FLOAT8E4M3FNUZ,
TensorProto.FLOAT8E5M2,
TensorProto.FLOAT8E5M2FNUZ,
):
expected_size = 1
else:
expected_size = np_dtype.itemsize
if (
type(vals) is np.ndarray # pylint: disable=unidiomatic-typecheck
and len(vals.shape) > 1
):
vals = vals.flatten()
for d in dims:
expected_size *= d
if len(vals) != expected_size:
raise ValueError(
f"Number of values does not match tensor's size. Expected {expected_size}, but it is {len(vals)}. "
)
if raw:
tensor.raw_data = vals
else:
if data_type in (TensorProto.COMPLEX64, TensorProto.COMPLEX128):
vals = split_complex_to_pairs(vals)
elif data_type == TensorProto.FLOAT16:
vals = (
np.array(vals).astype(np_dtype).view(dtype=np.uint16).flatten().tolist()
)
elif data_type in (
TensorProto.BFLOAT16,
TensorProto.FLOAT8E4M3FN,
TensorProto.FLOAT8E4M3FNUZ,
TensorProto.FLOAT8E5M2,
TensorProto.FLOAT8E5M2FNUZ,
):
fcast = {
TensorProto.BFLOAT16: float32_to_bfloat16,
TensorProto.FLOAT8E4M3FN: float32_to_float8e4m3,
TensorProto.FLOAT8E4M3FNUZ: lambda *args: float32_to_float8e4m3( # type: ignore[misc]
*args, uz=True
),
TensorProto.FLOAT8E5M2: float32_to_float8e5m2,
TensorProto.FLOAT8E5M2FNUZ: lambda *args: float32_to_float8e5m2( # type: ignore[misc]
*args, fn=True, uz=True
),
}[
data_type # type: ignore[index]
]
vals = list(
map( # type: ignore[call-overload]
fcast,
np.array(vals).astype(np_dtype).flatten().tolist(),
)
)
elif data_type == TensorProto.BOOL:
vals = np.array(vals).astype(int)
field = tensor_dtype_to_field(data_type)
getattr(tensor, field).extend(vals)
tensor.dims.extend(dims)
return tensor
def make_sparse_tensor(
values: TensorProto, indices: TensorProto, dims: Sequence[int]
) -> SparseTensorProto:
"""Construct a SparseTensorProto
Arguments:
values (TensorProto): the values
indices (TensorProto): the indices
dims: the shape
Returns:
SparseTensorProto
"""
sparse = SparseTensorProto()
sparse.values.CopyFrom(values)
sparse.indices.CopyFrom(indices)
sparse.dims.extend(dims)
return sparse
def make_sequence(
name: str,
elem_type: SequenceProto.DataType,
values: Sequence[Any],
) -> SequenceProto:
"""
Make a Sequence with specified value arguments.
"""
sequence = SequenceProto()
sequence.name = name
sequence.elem_type = elem_type
if elem_type == SequenceProto.UNDEFINED:
return sequence
if elem_type == SequenceProto.TENSOR:
attribute = sequence.tensor_values
elif elem_type == SequenceProto.SPARSE_TENSOR:
attribute = sequence.sparse_tensor_values # type: ignore[assignment]
elif elem_type == SequenceProto.SEQUENCE:
attribute = sequence.sequence_values # type: ignore[assignment]
elif elem_type == SequenceProto.MAP:
attribute = sequence.map_values # type: ignore[assignment]
elif elem_type == OptionalProto.OPTIONAL:
attribute = sequence.optional_values # type: ignore[assignment]
else:
raise TypeError("The element type in the input sequence is not supported.")
attribute.extend(values)
return sequence
def make_map(
name: str, key_type: int, keys: List[Any], values: SequenceProto
) -> MapProto:
"""
Make a Map with specified key-value pair arguments.
Criteria for conversion:
- Keys and Values must have the same number of elements
- Every key in keys must be of the same type
- Every value in values must be of the same type
"""
map_proto = MapProto()
valid_key_int_types = [
TensorProto.INT8,
TensorProto.INT16,
TensorProto.INT32,
TensorProto.INT64,
TensorProto.UINT8,
TensorProto.UINT16,
TensorProto.UINT32,
TensorProto.UINT64,
]
map_proto.name = name
map_proto.key_type = key_type
if key_type == TensorProto.STRING:
map_proto.string_keys.extend(keys)
elif key_type in valid_key_int_types:
map_proto.keys.extend(keys)
map_proto.values.CopyFrom(values)
return map_proto
def make_optional(
name: str,
elem_type: OptionalProto.DataType,
value: Optional[Any],
) -> OptionalProto:
"""
Make an Optional with specified value arguments.
"""
optional = OptionalProto()
optional.name = name
optional.elem_type = elem_type
if elem_type == OptionalProto.UNDEFINED:
return optional
if elem_type == OptionalProto.TENSOR:
attribute = optional.tensor_value
elif elem_type == OptionalProto.SPARSE_TENSOR:
attribute = optional.sparse_tensor_value # type: ignore[assignment]
elif elem_type == OptionalProto.SEQUENCE:
attribute = optional.sequence_value # type: ignore[assignment]
elif elem_type == OptionalProto.MAP:
attribute = optional.map_value # type: ignore[assignment]
elif elem_type == OptionalProto.OPTIONAL:
attribute = optional.optional_value # type: ignore[assignment]
else:
raise TypeError("The element type in the input optional is not supported.")
attribute.CopyFrom(value) # type: ignore[arg-type]
return optional
def _to_bytes(value: Union[str, bytes]) -> bytes:
"""Coerce a string (or bytes) value into UTF-8 bytes."""
return value if isinstance(value, bytes) else value.encode("utf-8")
def make_attribute( # pylint: disable=too-many-statements
key: str,
value: Any,
doc_string: Optional[str] = None,
attr_type: Optional[int] = None,
) -> AttributeProto:
"""Makes an AttributeProto based on the value type."""
attr = AttributeProto()
attr.name = key
if doc_string:
attr.doc_string = doc_string
# Singular cases
if isinstance(value, numbers.Integral):
attr.i = int(value)
attr.type = AttributeProto.INT
elif isinstance(value, numbers.Real):
attr.f = float(value)
attr.type = AttributeProto.FLOAT
elif isinstance(value, (str, bytes)):
# Encode strings into utf-8
attr.s = _to_bytes(value)
attr.type = AttributeProto.STRING
elif isinstance(value, TensorProto):
attr.t.CopyFrom(value)
attr.type = AttributeProto.TENSOR
elif isinstance(value, SparseTensorProto):
attr.sparse_tensor.CopyFrom(value)
attr.type = AttributeProto.SPARSE_TENSOR
elif isinstance(value, GraphProto):
attr.g.CopyFrom(value)
attr.type = AttributeProto.GRAPH
elif isinstance(value, TypeProto):
attr.tp.CopyFrom(value)
attr.type = AttributeProto.TYPE_PROTO
# Iterable cases
elif isinstance(value, collections.abc.Iterable):
value = list(value)
if len(value) == 0 and attr_type is None:
raise ValueError(
f"Could not infer attribute `{key}` type from empty iterator"
)
if attr_type is None:
types = {type(v) for v in value}
for exp_t, exp_enum in (
(numbers.Integral, AttributeProto.INTS),
(numbers.Real, AttributeProto.FLOATS),
((str, bytes), AttributeProto.STRINGS),
(TensorProto, AttributeProto.TENSORS),
(SparseTensorProto, AttributeProto.SPARSE_TENSORS),
(GraphProto, AttributeProto.GRAPHS),
(TypeProto, AttributeProto.TYPE_PROTOS),
):
if all(issubclass(t, exp_t) for t in types): # type: ignore[arg-type]
attr_type = exp_enum
break
if attr_type is None:
raise ValueError(
"Could not infer the attribute type from the elements of the passed Iterable value."
)
if attr_type == AttributeProto.INTS:
attr.ints.extend(value)
attr.type = AttributeProto.INTS
elif attr_type == AttributeProto.FLOATS:
attr.floats.extend(value)
attr.type = AttributeProto.FLOATS
elif attr_type == AttributeProto.STRINGS:
attr.strings.extend(_to_bytes(v) for v in value)
attr.type = AttributeProto.STRINGS
elif attr_type == AttributeProto.TENSORS:
attr.tensors.extend(value)
attr.type = AttributeProto.TENSORS
elif attr_type == AttributeProto.SPARSE_TENSORS:
attr.sparse_tensors.extend(value)
attr.type = AttributeProto.SPARSE_TENSORS
elif attr_type == AttributeProto.GRAPHS:
attr.graphs.extend(value)
attr.type = AttributeProto.GRAPHS
elif attr_type == AttributeProto.TYPE_PROTOS:
attr.type_protos.extend(value)
attr.type = AttributeProto.TYPE_PROTOS
else:
raise AssertionError() # Should not reach since `ValueError` must be raised in attr_type checking
else:
raise TypeError(f"'{value}' is not an accepted attribute value.")
if attr_type is not None and attr.type != attr_type:
raise TypeError(
f"Inferred attribute type {attr.type} mismatched with specified type {attr_type}"
)
return attr
def make_attribute_ref(
name: str, attr_type: AttributeProto.AttributeType, doc_string: Optional[str] = None
) -> AttributeProto:
"""Make an AttributeProto holding a reference to the parent function's attribute of given name and type."""
attr = AttributeProto()
attr.name = name
attr.type = attr_type
if doc_string:
attr.doc_string = doc_string
return attr
def get_attribute_value(attr: AttributeProto) -> Any:
if attr.ref_attr_name:
raise ValueError(f"Cannot get value of reference attribute: {attr}")
if attr.type == AttributeProto.FLOAT:
return attr.f
if attr.type == AttributeProto.INT:
return attr.i
if attr.type == AttributeProto.STRING:
return attr.s
if attr.type == AttributeProto.TENSOR:
return attr.t
if attr.type == AttributeProto.SPARSE_TENSOR:
return attr.sparse_tensor
if attr.type == AttributeProto.GRAPH:
return attr.g
if attr.type == AttributeProto.TYPE_PROTO:
return attr.tp
if attr.type == AttributeProto.FLOATS:
return list(attr.floats)
if attr.type == AttributeProto.INTS:
return list(attr.ints)
if attr.type == AttributeProto.STRINGS:
return list(attr.strings)
if attr.type == AttributeProto.TENSORS:
return list(attr.tensors)
if attr.type == AttributeProto.SPARSE_TENSORS:
return list(attr.sparse_tensors)
if attr.type == AttributeProto.GRAPHS:
return list(attr.graphs)
if attr.type == AttributeProto.TYPE_PROTOS:
return list(attr.type_protos)
raise ValueError(f"Unsupported ONNX attribute: {attr}")
def get_node_attr_value(node: NodeProto, attr_name: str) -> Any:
matching = [x for x in node.attribute if x.name == attr_name]
if len(matching) > 1:
raise ValueError(f"Node has multiple attributes with name {attr_name}")
if len(matching) < 1:
raise ValueError(f"Node has no attribute with name {attr_name}")
return get_attribute_value(matching[0])
def make_empty_tensor_value_info(name: str) -> ValueInfoProto:
value_info_proto = ValueInfoProto()
value_info_proto.name = name
return value_info_proto
def make_tensor_type_proto(
elem_type: int,
shape: Optional[Sequence[Union[str, int, None]]],
shape_denotation: Optional[List[str]] = None,
) -> TypeProto:
"""Makes a Tensor TypeProto based on the data type and shape."""
type_proto = TypeProto()
tensor_type_proto = type_proto.tensor_type
tensor_type_proto.elem_type = elem_type
tensor_shape_proto = tensor_type_proto.shape
if shape is not None:
# You might think this is a no-op (extending a normal Python
# list by [] certainly is), but protobuf lists work a little
# differently; if a field is never set, it is omitted from the
# resulting protobuf; a list that is explicitly set to be
# empty will get an (empty) entry in the protobuf. This
# difference is visible to our consumers, so make sure we emit
# an empty shape!
tensor_shape_proto.dim.extend([])
if shape_denotation and len(shape_denotation) != len(shape):
raise ValueError(
"Invalid shape_denotation. Must be of the same length as shape."
)
for i, d in enumerate(shape):
dim = tensor_shape_proto.dim.add()
if d is None:
pass
elif isinstance(d, int):
dim.dim_value = d
elif isinstance(d, str):
dim.dim_param = d
else:
raise ValueError(
f"Invalid item in shape: {d}. Needs to be of int or str."
)
if shape_denotation:
dim.denotation = shape_denotation[i]
return type_proto
def make_tensor_value_info(
name: str,
elem_type: int,
shape: Optional[Sequence[Union[str, int, None]]],
doc_string: str = "",
shape_denotation: Optional[List[str]] = None,
) -> ValueInfoProto:
"""Makes a ValueInfoProto based on the data type and shape."""
value_info_proto = ValueInfoProto()
value_info_proto.name = name
if doc_string:
value_info_proto.doc_string = doc_string
tensor_type_proto = make_tensor_type_proto(elem_type, shape, shape_denotation)
value_info_proto.type.CopyFrom(tensor_type_proto)
return value_info_proto
def make_sparse_tensor_type_proto(
elem_type: int,
shape: Optional[Sequence[Union[str, int, None]]],
shape_denotation: Optional[List[str]] = None,
) -> TypeProto:
"""Makes a SparseTensor TypeProto based on the data type and shape."""
type_proto = TypeProto()
sparse_tensor_type_proto = type_proto.sparse_tensor_type
sparse_tensor_type_proto.elem_type = elem_type
sparse_tensor_shape_proto = sparse_tensor_type_proto.shape
if shape is not None:
# You might think this is a no-op (extending a normal Python
# list by [] certainly is), but protobuf lists work a little
# differently; if a field is never set, it is omitted from the
# resulting protobuf; a list that is explicitly set to be
# empty will get an (empty) entry in the protobuf. This
# difference is visible to our consumers, so make sure we emit
# an empty shape!
sparse_tensor_shape_proto.dim.extend([])
if shape_denotation and len(shape_denotation) != len(shape):
raise ValueError(
"Invalid shape_denotation. Must be of the same length as shape."
)
for i, d in enumerate(shape):
dim = sparse_tensor_shape_proto.dim.add()
if d is None:
pass
elif isinstance(d, int):
dim.dim_value = d
elif isinstance(d, str):
dim.dim_param = d
else:
raise ValueError(
f"Invalid item in shape: {d}. Needs to be of int or text."
)
if shape_denotation:
dim.denotation = shape_denotation[i]
return type_proto
def make_sparse_tensor_value_info(
name: str,
elem_type: int,
shape: Optional[Sequence[Union[str, int, None]]],
doc_string: str = "",
shape_denotation: Optional[List[str]] = None,
) -> ValueInfoProto:
"""Makes a SparseTensor ValueInfoProto based on the data type and shape."""
value_info_proto = ValueInfoProto()
value_info_proto.name = name
if doc_string:
value_info_proto.doc_string = doc_string
sparse_tensor_type_proto = make_sparse_tensor_type_proto(
elem_type, shape, shape_denotation
)
value_info_proto.type.sparse_tensor_type.CopyFrom(
sparse_tensor_type_proto.sparse_tensor_type
)
return value_info_proto
def make_sequence_type_proto(
inner_type_proto: TypeProto,
) -> TypeProto:
"""Makes a sequence TypeProto."""
type_proto = TypeProto()
type_proto.sequence_type.elem_type.CopyFrom(inner_type_proto)
return type_proto
def make_optional_type_proto(
inner_type_proto: TypeProto,
) -> TypeProto:
"""Makes an optional TypeProto."""
type_proto = TypeProto()
type_proto.optional_type.elem_type.CopyFrom(inner_type_proto)
return type_proto
def make_map_type_proto(
key_type: int,
value_type: TypeProto,
) -> TypeProto:
"""Makes a map TypeProto."""
type_proto = TypeProto()
type_proto.map_type.key_type = key_type
type_proto.map_type.value_type.CopyFrom(value_type)
return type_proto
def make_value_info(
name: str,
type_proto: TypeProto,
doc_string: str = "",
) -> ValueInfoProto:
"""Makes a ValueInfoProto with the given type_proto."""
value_info_proto = ValueInfoProto()
value_info_proto.name = name
if doc_string:
value_info_proto.doc_string = doc_string
value_info_proto.type.CopyFrom(type_proto)
return value_info_proto
def _sanitize_str(s: Union[str, bytes]) -> str:
if isinstance(s, str):
sanitized = s
elif isinstance(s, bytes):
sanitized = s.decode("utf-8", errors="ignore")
else:
sanitized = str(s)
if len(sanitized) < 64:
return sanitized
return sanitized[:64] + f"...<+len={(len(sanitized) - 64)}>"
def make_tensor_sequence_value_info(
name: str,
elem_type: int,
shape: Optional[Sequence[Union[str, int, None]]],
doc_string: str = "",
elem_shape_denotation: Optional[List[str]] = None,
) -> ValueInfoProto:
"""Makes a Sequence[Tensors] ValueInfoProto based on the data type and shape."""
value_info_proto = ValueInfoProto()
value_info_proto.name = name
if doc_string:
value_info_proto.doc_string = doc_string
tensor_type_proto = make_tensor_type_proto(elem_type, shape, elem_shape_denotation)
sequence_type_proto = make_sequence_type_proto(tensor_type_proto)
value_info_proto.type.sequence_type.CopyFrom(sequence_type_proto.sequence_type)
return value_info_proto
def printable_attribute(
attr: AttributeProto, subgraphs: bool = False
) -> Union[str, Tuple[str, List[GraphProto]]]:
content = []
content.append(attr.name)
content.append("=")
def str_float(f: float) -> str:
# NB: Different Python versions print different numbers of trailing
# decimals, specifying this explicitly keeps it consistent for all
# versions
return f"{f:.15g}"
def str_int(i: int) -> str:
return str(i)
_T = TypeVar("_T")
def str_list(str_elem: Callable[[_T], str], xs: Sequence[_T]) -> str:
return "[" + ", ".join(map(str_elem, xs)) + "]"
# for now, this logic should continue to work as long as we are running on a proto3
# implementation. If/when we switch to proto3, we will need to use attr.type
# To support printing subgraphs, if we find a graph attribute, print out
# its name here and pass the graph itself up to the caller for later
# printing.
graphs = []
if attr.HasField("f"):
content.append(str_float(attr.f))
elif attr.HasField("i"):
content.append(str_int(attr.i))
elif attr.HasField("s"):
# TODO: Bit nervous about Python 2 / Python 3 determinism implications
content.append(repr(_sanitize_str(attr.s)))
elif attr.HasField("t"):
if len(attr.t.dims) > 0:
content.append("<Tensor>")
else:
# special case to print scalars
field = tensor_dtype_to_field(attr.t.data_type)
content.append(f"<Scalar Tensor {getattr(attr.t, field)}>")
elif attr.HasField("g"):
content.append(f"<graph {attr.g.name}>")
graphs.append(attr.g)
elif attr.HasField("tp"):
content.append(f"<Type Proto {attr.tp}>")
elif attr.floats:
content.append(str_list(str_float, attr.floats))
elif attr.ints:
content.append(str_list(str_int, attr.ints))
elif attr.strings:
# TODO: Bit nervous about Python 2 / Python 3 determinism implications
content.append(str(list(map(_sanitize_str, attr.strings))))
elif attr.tensors:
content.append("[<Tensor>, ...]")
elif attr.type_protos:
content.append("[")
for i, tp in enumerate(attr.type_protos):
comma = "," if i != len(attr.type_protos) - 1 else ""
content.append(f"<Type Proto {tp}>{comma}")
content.append("]")
elif attr.graphs:
content.append("[")
for i, g in enumerate(attr.graphs):
comma = "," if i != len(attr.graphs) - 1 else ""
content.append(f"<graph {g.name}>{comma}")
content.append("]")
graphs.extend(attr.graphs)
else:
content.append("<Unknown>")
if subgraphs:
return " ".join(content), graphs
return " ".join(content)
def printable_dim(dim: TensorShapeProto.Dimension) -> str:
which = dim.WhichOneof("value")
if which is None:
raise TypeError(f"which cannot be {None}.")
return str(getattr(dim, which))
def printable_type(t: TypeProto) -> str:
if t.WhichOneof("value") == "tensor_type":
s = TensorProto.DataType.Name(t.tensor_type.elem_type)
if t.tensor_type.HasField("shape"):
if len(t.tensor_type.shape.dim):
s += str(", " + "x".join(map(printable_dim, t.tensor_type.shape.dim)))
else:
s += ", scalar"
return s
if t.WhichOneof("value") is None:
return ""
return f"Unknown type {t.WhichOneof('value')}"
def printable_value_info(v: ValueInfoProto) -> str:
s = f"%{v.name}"
if v.type:
s = f"{s}[{printable_type(v.type)}]"
return s
def printable_tensor_proto(t: TensorProto) -> str:
s = f"%{t.name}["
s += TensorProto.DataType.Name(t.data_type)
if t.dims is not None:
if len(t.dims):
s += str(", " + "x".join(map(str, t.dims)))
else:
s += ", scalar"
s += "]"
return s
def printable_node(
node: NodeProto, prefix: str = "", subgraphs: bool = False
) -> Union[str, Tuple[str, List[GraphProto]]]:
content = []
if len(node.output):
content.append(", ".join([f"%{name}" for name in node.output]))
content.append("=")
# To deal with nested graphs
graphs: List[GraphProto] = []
printed_attrs = []
for attr in node.attribute:
if subgraphs:
printed_attr_subgraphs = printable_attribute(attr, subgraphs)
if not isinstance(printed_attr_subgraphs[1], list):
raise TypeError(
f"printed_attr_subgraphs[1] must be an instance of {list}."
)
graphs.extend(printed_attr_subgraphs[1])
printed_attrs.append(printed_attr_subgraphs[0])
else:
printed = printable_attribute(attr)
if not isinstance(printed, str):
raise TypeError(f"printed must be an instance of {str}.")
printed_attrs.append(printed)
printed_attributes = ", ".join(sorted(printed_attrs))
printed_inputs = ", ".join([f"%{name}" for name in node.input])
if node.attribute:
content.append(f"{node.op_type}[{printed_attributes}]({printed_inputs})")
else:
content.append(f"{node.op_type}({printed_inputs})")
if subgraphs:
return prefix + " ".join(content), graphs
return prefix + " ".join(content)
def printable_graph(graph: GraphProto, prefix: str = "") -> str:
"""
Display a GraphProto as a string.
Arguments:
graph (GraphProto): the graph to display
prefix (string): prefix of every line
Returns:
string
"""
content = []
indent = prefix + " "
# header
header = ["graph", graph.name]
initializers = {t.name for t in graph.initializer}
if len(graph.input):
header.append("(")
in_strs = [] # required inputs
in_with_init_strs = (
[]
) # optional inputs with initializer providing default value
for inp in graph.input:
if inp.name not in initializers:
in_strs.append(printable_value_info(inp))
else:
in_with_init_strs.append(printable_value_info(inp))
if in_strs:
content.append(prefix + " ".join(header))
header = []
for line in in_strs:
content.append(prefix + " " + line)
header.append(")")
if in_with_init_strs:
header.append("optional inputs with matching initializers (")
content.append(prefix + " ".join(header))
header = []
for line in in_with_init_strs:
content.append(prefix + " " + line)
header.append(")")
# from IR 4 onwards an initializer is not required to have a matching graph input
# so output the name, type and shape of those as well
if len(in_with_init_strs) < len(initializers):
graph_inputs = {i.name for i in graph.input}
init_strs = [
printable_tensor_proto(i)
for i in graph.initializer
if i.name not in graph_inputs
]
header.append("initializers (")
content.append(prefix + " ".join(header))
header = []
for line in init_strs:
content.append(prefix + " " + line)
header.append(")")
header.append("{")
content.append(prefix + " ".join(header))
graphs: List[GraphProto] = []
# body
for node in graph.node:
contents_subgraphs = printable_node(node, indent, subgraphs=True)
if not isinstance(contents_subgraphs[1], list):
raise TypeError(f"contents_subgraphs[1] must be an instance of {list}.")
content.append(contents_subgraphs[0])
graphs.extend(contents_subgraphs[1])
# tail
tail = ["return"]
if len(graph.output):
tail.append(", ".join([f"%{out.name}" for out in graph.output]))
content.append(indent + " ".join(tail))
# closing bracket
content.append(prefix + "}")
for g in graphs:
content.append("\n" + printable_graph(g))
return "\n".join(content)
def strip_doc_string(proto: google.protobuf.message.Message) -> None:
"""
Empties `doc_string` field on any nested protobuf messages
"""
if not isinstance(proto, google.protobuf.message.Message):
raise TypeError(
f"proto must be an instance of {google.protobuf.message.Message}."
)
for descriptor in proto.DESCRIPTOR.fields:
if descriptor.name == "doc_string":
proto.ClearField(descriptor.name)
elif descriptor.type == descriptor.TYPE_MESSAGE:
if descriptor.label == descriptor.LABEL_REPEATED:
for x in getattr(proto, descriptor.name):
strip_doc_string(x)
elif proto.HasField(descriptor.name):
strip_doc_string(getattr(proto, descriptor.name))
def make_training_info(
algorithm: GraphProto,
algorithm_bindings: AssignmentBindingType,
initialization: Optional[GraphProto],
initialization_bindings: Optional[AssignmentBindingType],
) -> TrainingInfoProto:
training_info = TrainingInfoProto()
training_info.algorithm.CopyFrom(algorithm)
for k, v in algorithm_bindings:
binding = training_info.update_binding.add()
binding.key = k
binding.value = v
if initialization:
training_info.initialization.CopyFrom(initialization)
if initialization_bindings:
for k, v in initialization_bindings:
binding = training_info.initialization_binding.add()
binding.key = k
binding.value = v
return training_info
# Following functions are used for mapping
def tensor_dtype_to_np_dtype(tensor_dtype: int) -> np.dtype:
"""
Convert a TensorProto's data_type to corresponding numpy dtype. It can be used while making tensor.
:param tensor_dtype: TensorProto's data_type
:return: numpy's data_type
"""
return mapping.TENSOR_TYPE_MAP[tensor_dtype].np_dtype
def tensor_dtype_to_storage_tensor_dtype(tensor_dtype: int) -> int:
"""
Convert a TensorProto's data_type to corresponding data_type for storage.
:param tensor_dtype: TensorProto's data_type
:return: data_type for storage
"""
return mapping.TENSOR_TYPE_MAP[tensor_dtype].storage_dtype
def tensor_dtype_to_string(tensor_dtype: int) -> str:
"""
Get the name of given TensorProto's data_type.
:param tensor_dtype: TensorProto's data_type
:return: the name of data_type
"""
return mapping.TENSOR_TYPE_MAP[tensor_dtype].name
def tensor_dtype_to_field(tensor_dtype: int) -> str:
"""
Convert a TensorProto's data_type to corresponding field name for storage. It can be used while making tensors.
:param tensor_dtype: TensorProto's data_type
:return: field name
"""
return mapping._STORAGE_TENSOR_TYPE_TO_FIELD[ # pylint: disable=protected-access
mapping.TENSOR_TYPE_MAP[tensor_dtype].storage_dtype
]
def np_dtype_to_tensor_dtype(np_dtype: np.dtype) -> int:
"""
Convert a numpy's dtype to corresponding tensor type. It can be used while converting numpy arrays to tensors.
:param np_dtype: numpy's data_type
:return: TensorsProto's data_type
"""
return cast(
int,
mapping._NP_TYPE_TO_TENSOR_TYPE[np_dtype], # pylint: disable=protected-access
)
def get_all_tensor_dtypes() -> KeysView[int]:
"""
Get all tensor types from TensorProto.
:return: all tensor types from TensorProto
"""
return mapping.TENSOR_TYPE_MAP.keys()
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59,140 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/layernormalization.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
# Layer normalization's reference implementation
def _layer_normalization(X, W, B, axis=-1, epsilon=1e-5): # type: ignore
X_shape = X.shape
X_rank = len(X_shape)
if axis < 0:
# If axis = -1 and rank of X is 4,
# the axis is changed to -1 + 4 = 3,
# which means the last axis.
axis = axis + X_rank
unsqueezed_rank = X_rank - axis
reduction_shape = X_shape[0:axis] + (1,) * unsqueezed_rank
# Parameter used to convert N-D tensor layer
# normalization to equivalent 2-D matirx operations.
row_number = 1
col_number = 1
for i in range(X_rank):
if i < axis:
row_number *= X_shape[i]
else:
col_number *= X_shape[i]
# After reshaping input tensor X into a matrix,
# layer normalization is equivalent to conducting
# standardization on each column vector (s.t. each
# column has zero mean and unit variance).
x_mat = np.reshape(X, (row_number, col_number))
# This computes mean for every x_mat's column.
x_mean = np.sum(x_mat, axis=1, keepdims=True) / col_number
x_diff = x_mat - x_mean
x_squared_diff = x_diff * x_diff
# This computes variance for every x_mat's column.
variance = np.sum(x_squared_diff, axis=1, keepdims=True) / col_number
variance_eps = variance + epsilon
std_dev = np.sqrt(variance_eps)
inv_std_dev = np.reciprocal(std_dev)
# Standardization step. y_mat is zero-mean and unit-variance.
y_mat = x_diff * inv_std_dev
# Apply affine transform on normalization outcome.
# W is linear coefficient while B is bias.
Y = np.reshape(y_mat, X_shape) * W + B
# Matrix-level operations' outputs should be reshaped
# to compensate the initial tensor-to-matrix reshape.
X_mean = np.reshape(x_mean, reduction_shape)
X_inv_std_dev = np.reshape(inv_std_dev, reduction_shape)
return Y, X_mean, X_inv_std_dev
def calculate_normalized_shape(X_shape, axis): # type: ignore
X_rank = len(X_shape)
if axis < 0:
axis = axis + X_rank
return X_shape[axis:]
class LayerNormalization(Base):
@staticmethod
def export() -> None:
X = np.random.randn(2, 3, 4, 5).astype(np.float32)
def case(axis: int) -> None:
normalized_shape = calculate_normalized_shape(X.shape, axis)
W = np.random.randn(*normalized_shape).astype(np.float32)
B = np.random.randn(*normalized_shape).astype(np.float32)
Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis)
node = onnx.helper.make_node(
"LayerNormalization",
inputs=["X", "W", "B"],
outputs=["Y", "Mean", "InvStdDev"],
axis=axis,
)
if axis < 0:
name = f"test_layer_normalization_4d_axis_negative_{-axis}"
else:
name = f"test_layer_normalization_4d_axis{axis}"
expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name)
for i in range(len(X.shape)):
case(i)
case(i - len(X.shape))
@staticmethod
def export_default_axis() -> None:
X = np.random.randn(2, 3, 4, 5).astype(np.float32)
# Default axis in LayerNormalization is -1.
normalized_shape = calculate_normalized_shape(X.shape, -1)
W = np.random.randn(*normalized_shape).astype(np.float32)
B = np.random.randn(*normalized_shape).astype(np.float32)
# Axis is default to -1 in the reference implementation.
Y, mean, inv_std_dev = _layer_normalization(X, W, B)
# Not specifying axis attribute means -1.
node = onnx.helper.make_node(
"LayerNormalization",
inputs=["X", "W", "B"],
outputs=["Y", "Mean", "InvStdDev"],
)
expect(
node,
inputs=[X, W, B],
outputs=[Y, mean, inv_std_dev],
name="test_layer_normalization_default_axis",
)
@staticmethod
def export2d() -> None:
X = np.random.randn(3, 4).astype(np.float32)
def case(axis: int) -> None:
normalized_shape = calculate_normalized_shape(X.shape, axis)
W = np.random.randn(*normalized_shape).astype(np.float32)
B = np.random.randn(*normalized_shape).astype(np.float32)
Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis=axis)
node = onnx.helper.make_node(
"LayerNormalization",
inputs=["X", "W", "B"],
outputs=["Y", "Mean", "InvStdDev"],
axis=axis,
)
if axis < 0:
name = f"test_layer_normalization_2d_axis_negative_{-axis}"
else:
name = f"test_layer_normalization_2d_axis{axis}"
expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name)
for i in range(len(X.shape)):
case(i)
case(i - len(X.shape))
@staticmethod
def export3d_epsilon() -> None:
epsilon = 1e-1
X = np.random.randn(2, 3, 5).astype(np.float32)
def case(axis: int) -> None:
normalized_shape = calculate_normalized_shape(X.shape, axis)
W = np.random.randn(*normalized_shape).astype(np.float32)
B = np.random.randn(*normalized_shape).astype(np.float32)
Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis, epsilon)
node = onnx.helper.make_node(
"LayerNormalization",
inputs=["X", "W", "B"],
outputs=["Y", "Mean", "InvStdDev"],
axis=axis,
epsilon=epsilon,
)
if axis < 0:
name = f"test_layer_normalization_3d_axis_negative_{-axis}_epsilon"
else:
name = f"test_layer_normalization_3d_axis{axis}_epsilon"
expect(node, inputs=[X, W, B], outputs=[Y, mean, inv_std_dev], name=name)
for i in range(len(X.shape)):
case(i)
case(i - len(X.shape))
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59,141 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/groupnormalization.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
# Group normalization's reference implementation
def _group_normalization(x, num_groups, scale, bias, epsilon=1e-5):
# Assume channel is first dim
assert x.shape[1] % num_groups == 0
group_size = x.shape[1] // num_groups
# Reshape to [N, group_size, C/group_size, H, W, ...]
new_shape = [x.shape[0], num_groups, group_size, *list(x.shape[2:])]
x_reshaped = x.reshape(new_shape)
axes = tuple(range(2, len(new_shape)))
mean = np.mean(x_reshaped, axis=axes, keepdims=True)
var = np.var(x_reshaped, axis=axes, keepdims=True)
dim_ones = (1,) * (len(new_shape) - 2)
scale = scale.reshape(-1, *dim_ones)
bias = bias.reshape(-1, *dim_ones)
res = scale * (x_reshaped - mean) / np.sqrt(var + epsilon) + bias
return res.reshape(x.shape)
class GroupNormalization(Base):
@staticmethod
def export() -> None:
x = np.random.randn(3, 4, 2, 2).astype(np.float32)
num_groups = 2
scale = np.random.randn(num_groups).astype(np.float32)
bias = np.random.randn(num_groups).astype(np.float32)
y = _group_normalization(x, num_groups, scale, bias).astype(np.float32)
node = onnx.helper.make_node(
"GroupNormalization",
inputs=["x", "scale", "bias"],
outputs=["y"],
num_groups=num_groups,
)
expect(
node,
inputs=[x, scale, bias],
outputs=[y],
name="test_group_normalization_example",
)
x = np.random.randn(3, 4, 2, 2).astype(np.float32)
num_groups = 2
scale = np.random.randn(num_groups).astype(np.float32)
bias = np.random.randn(num_groups).astype(np.float32)
epsilon = 1e-2
y = _group_normalization(x, num_groups, scale, bias, epsilon).astype(np.float32)
node = onnx.helper.make_node(
"GroupNormalization",
inputs=["x", "scale", "bias"],
outputs=["y"],
epsilon=epsilon,
num_groups=num_groups,
)
expect(
node,
inputs=[x, scale, bias],
outputs=[y],
name="test_group_normalization_epsilon",
)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,142 | onnx/onnx | refs/heads/main | /onnx/reference/ops/__init__.py | # Copyright (c) ONNX Project Contributors
# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from onnx.reference.ops._op_list import load_op
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": 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59,143 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_random_normal.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,W0221
from onnx.reference.ops._op_common_random import _CommonRandom
class RandomNormal(_CommonRandom):
def _run(self, dtype=None, mean=None, scale=None, seed=None, shape=None): # type: ignore
state = self._get_state(seed)
numpy_type = self.numpy_type(dtype)
res = state.randn(*shape).astype(numpy_type) # type: ignore
res *= scale # type: ignore
res += mean # type: ignore
return (res.astype(numpy_type),)
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59,144 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_hann_window.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op_common_window import _CommonWindow
class HannWindow(_CommonWindow):
"""
Returns
:math:`\\omega_n = \\sin^2\\left( \\frac{\\pi n}{N-1} \\right)`
where *N* is the window length.
See `hann_window
<https://pytorch.org/docs/stable/generated/torch.hann_window.html>`_
"""
def _run(self, size, output_datatype=None, periodic=None): # type: ignore
ni, N_1 = self._begin(size, periodic, output_datatype)
res = np.sin(ni * np.pi / N_1) ** 2
return self._end(size, res, output_datatype)
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59,145 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_softmax_cross_entropy_loss.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,R0914,W0221
import numpy as np
from onnx.reference.op_run import OpRun
def softmaxcrossentropy( # type: ignore
x, target, weight=None, reduction="mean", ignore_index=None, get_log_prob=None
):
input_shape = x.shape
if len(input_shape) == 1:
raise RuntimeError(f"Unsupported shape {input_shape!r}.")
target_shape = target.shape
N = input_shape[0]
C = input_shape[1]
# compute log_softmax
max_x = np.max(x, axis=1, keepdims=True)
exp_x = np.exp(x - max_x)
p = exp_x / np.sum(exp_x, axis=1, keepdims=True)
inp = np.log(p)
log_prob = None
if get_log_prob is True:
log_prob = np.copy(inp)
# initialize the positional weights when required
gather_weight = None
if weight is not None:
gather_weight = np.take(weight, np.array(target, dtype=np.int32), mode="clip")
if ignore_index is not None:
gather_weight = np.where(target == ignore_index, 0, gather_weight).astype(
dtype=x.dtype
)
elif ignore_index is not None:
gather_weight = np.where(target == ignore_index, 0, 1).astype(dtype=x.dtype)
# if input is 4-d and above, make it 3-d
if len(input_shape) != 3:
inp = inp.reshape((N, C, -1))
target = target.reshape((N, -1))
# Get a dimension from the reshaped input.
# If the original input shape is [N, C, H, W],
# the D here should be H * W because we reshape
# [N, C, H, W] to [N, C, H * W].
D = inp.shape[2]
neg_gather_element_input = np.zeros((N, D), dtype=x.dtype)
for i in range(N):
for d in range(D):
if target[i, d] != ignore_index:
neg_gather_element_input[i, d] = -inp[i, target[i, d], d]
loss = neg_gather_element_input
# if the input was 4-d or above reshape to the right shape
if len(input_shape) != 3:
loss = loss.reshape(target_shape)
# apply the weights when required
if gather_weight is not None:
loss = gather_weight * loss
if reduction == "mean":
loss = loss.sum() / gather_weight.sum()
if get_log_prob is True:
return loss, log_prob
return (loss,)
if reduction == "mean":
loss = np.mean(loss)
elif reduction == "sum":
loss = np.sum(loss)
loss = loss.astype(x.dtype)
if get_log_prob is True:
return loss, log_prob.astype(x.dtype) # type: ignore[union-attr]
return (loss,)
class SoftmaxCrossEntropyLoss(OpRun):
def _run(self, x, target, weight=None, ignore_index=None, reduction=None): # type: ignore
n_outputs = len(self.onnx_node.output) # type: ignore
return softmaxcrossentropy(
x,
target,
weight=weight,
reduction=reduction,
ignore_index=ignore_index,
get_log_prob=n_outputs == 2,
)
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"/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,146 | onnx/onnx | refs/heads/main | /tools/protoc-gen-mypy.py | #!/usr/bin/env python
# Copyright (c) ONNX Project Contributors
# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
# Taken from https://github.com/dropbox/mypy-protobuf/blob/d984389124eae6dbbb517f766b9266bb32171510/python/protoc-gen-mypy
# (Apache 2.0 License)
# with own fixes to
# - appease flake8
# - exit without error when protobuf isn't installed
# - fix recognition of whether an identifier is defined locally
# (unfortunately, we use a python package name ONNX_NAMESPACE_FOO_BAR_FOR_CI
# on CI, which by the original protoc-gen-mypy script was recognized to be
# camel case and therefore handled as an entry in the local package)
"""Protoc Plugin to generate mypy stubs. Loosely based on @zbarsky's go implementation"""
import sys
from collections import defaultdict
from contextlib import contextmanager
from typing import Any, Callable, Dict, Generator, List, Optional, Set, cast
try:
import google.protobuf.descriptor_pb2 as d_typed
from google.protobuf.compiler import plugin_pb2 as plugin
except ImportError as e:
sys.stderr.write(f"Failed to generate mypy stubs: {e}\n")
sys.exit(0)
# Hax to get around fact that google protobuf libraries aren't in typeshed yet
d: Any = d_typed
# Split the string so phabricator doesn't think this file is generated
GENERATED = "@ge" + "nerated"
HEADER = (
f"# {GENERATED} by protoc-gen-mypy.py. Do not edit!\n"
"# mypy: disable-error-code=override\n"
)
class Descriptors:
def __init__(self, request: plugin.CodeGeneratorRequest) -> None:
files = {f.name: f for f in request.proto_file}
to_generate = {n: files[n] for n in request.file_to_generate}
self.files: Dict[str, d.FileDescriptorProto] = files
self.to_generate: Dict[str, d.FileDescriptorProto] = to_generate
self.messages: Dict[str, d.DescriptorProto] = {}
self.message_to_fd: Dict[str, d.FileDescriptorProto] = {}
def _add_enums(
enums: d.EnumDescriptorProto, prefix: str, fd: d.FileDescriptorProto
) -> None:
for enum in enums:
self.message_to_fd[prefix + enum.name] = fd
def _add_messages(
messages: d.DescriptorProto, prefix: str, fd: d.FileDescriptorProto
) -> None:
for message in messages:
self.messages[prefix + message.name] = message
self.message_to_fd[prefix + message.name] = fd
sub_prefix = prefix + message.name + "."
_add_messages(message.nested_type, sub_prefix, fd)
_add_enums(message.enum_type, sub_prefix, fd)
for fd in request.proto_file:
start_prefix = "." + fd.package + "."
_add_messages(fd.message_type, start_prefix, fd)
_add_enums(fd.enum_type, start_prefix, fd)
class PkgWriter:
"""Writes a single pyi file"""
def __init__(self, fd: d.FileDescriptorProto, descriptors: Descriptors) -> None:
self.fd = fd
self.descriptors = descriptors
self.lines: List[str] = []
self.indent = ""
# dictionary of x->y for `from {x} import {y}`
self.imports: Dict[str, Set[str]] = defaultdict(set)
self.locals: Set[str] = set()
def _import(self, path: str, name: str, import_as: Optional[str] = None) -> str:
"""Imports a stdlib path and returns a handle to it
eg. self._import("typing", "Optional") -> "Optional"
"""
imp = path.replace("/", ".")
if import_as is not None:
self.imports[imp].add(f"{name} as {import_as}")
return import_as
else:
self.imports[imp].add(name)
return name
def _import_message(self, type_name: d.FieldDescriptorProto) -> str:
"""Import a referenced message and return a handle"""
name = cast(str, type_name)
if name[0] == "." and name[1].isupper() and name[2].islower():
# Message defined in this file
return name[1:]
message_fd = self.descriptors.message_to_fd[name]
if message_fd.name == self.fd.name:
# message defined in this package
split = name.split(".")
for i, segment in enumerate(split):
if segment and segment[0].isupper() and segment[1].islower():
return ".".join(split[i:])
# Not in package. Must import
split = name.split(".")
for i, segment in enumerate(split):
if segment and segment[0].isupper() and segment[1].islower():
assert message_fd.name.endswith(".proto")
import_name = self._import(
message_fd.name[:-6].replace("-", "_") + "_pb2", segment
)
remains = ".".join(split[i + 1 :])
if not remains:
return import_name
raise AssertionError("Don't support nested imports yet")
# return new_nested_import(import_name, remains)
raise AssertionError("Could not parse local name " + name)
@contextmanager # type: ignore
def _indent(self) -> Generator[None, None, None]:
self.indent = self.indent + " "
yield
self.indent = self.indent[:-4]
def _write_line(self, line: str, *args: str) -> None:
self.lines.append(self.indent + line.format(*args))
def write_enums(self, enums: List[d.EnumDescriptorProto]) -> None:
line = self._write_line
for enum in enums:
line("class {}(int):", enum.name)
with self._indent():
line("@classmethod")
line("def Name(cls, number: int) -> str: ...")
line("@classmethod")
line("def Value(cls, name: str) -> int: ...")
line("@classmethod")
line("def keys(cls) -> {}[str]: ...", self._import("typing", "List"))
line("@classmethod")
line("def values(cls) -> {}[int]: ...", self._import("typing", "List"))
line("@classmethod")
line(
"def items(cls) -> {}[{}[str, int]]: ...",
self._import("typing", "List"),
self._import("typing", "Tuple"),
)
for val in enum.value:
line(
"{} = {}({}, {})",
val.name,
self._import("typing", "cast"),
enum.name,
val.number,
)
line("")
def write_messages(self, messages: List[d.DescriptorProto], prefix: str) -> None:
line = self._write_line
message_class = self._import("google.protobuf.message", "Message")
for desc in messages:
self.locals.add(desc.name)
qualified_name = prefix + desc.name
line("class {}({}):", desc.name, message_class)
with self._indent():
# Nested enums/messages
self.write_enums(desc.enum_type)
self.write_messages(desc.nested_type, qualified_name + ".")
# Scalar fields
for field in [f for f in desc.field if is_scalar(f)]:
if field.label == d.FieldDescriptorProto.LABEL_REPEATED:
container = self._import(
"google.protobuf.internal.containers",
"RepeatedScalarFieldContainer",
)
line(
"{} = ... # type: {}[{}]",
field.name,
container,
self.python_type(field),
)
else:
line("{} = ... # type: {}", field.name, self.python_type(field))
line("")
# Getters for non-scalar fields
for field in [f for f in desc.field if not is_scalar(f)]:
line("@property")
if field.label == d.FieldDescriptorProto.LABEL_REPEATED:
msg = self.descriptors.messages[field.type_name]
if msg.options.map_entry:
# map generates a special Entry wrapper message
container = self._import("typing", "MutableMapping")
line(
"def {}(self) -> {}[{}, {}]: ...",
field.name,
container,
self.python_type(msg.field[0]),
self.python_type(msg.field[1]),
)
else:
container = self._import(
"google.protobuf.internal.containers",
"RepeatedCompositeFieldContainer",
)
line(
"def {}(self) -> {}[{}]: ...",
field.name,
container,
self.python_type(field),
)
else:
line(
"def {}(self) -> {}: ...",
field.name,
self.python_type(field),
)
line("")
# Constructor
line("def __init__(self,")
with self._indent():
# Required args
for field in [
f
for f in desc.field
if f.label == d.FieldDescriptorProto.LABEL_REQUIRED
]:
line("{} : {},", field.name, self.python_type(field))
for field in [
f
for f in desc.field
if f.label != d.FieldDescriptorProto.LABEL_REQUIRED
]:
if field.label == d.FieldDescriptorProto.LABEL_REPEATED:
if (
field.type_name != ""
and self.descriptors.messages[
field.type_name
].options.map_entry
):
msg = self.descriptors.messages[field.type_name]
line(
"{} : {}[{}[{}, {}]] = None,",
field.name,
self._import("typing", "Optional", "OptionalType"),
self._import("typing", "Mapping"),
self.python_type(msg.field[0]),
self.python_type(msg.field[1]),
)
else:
line(
"{} : {}[{}[{}]] = None,",
field.name,
self._import("typing", "Optional", "OptionalType"),
self._import("typing", "Iterable"),
self.python_type(field),
)
else:
line(
"{} : {}[{}] = None,",
field.name,
self._import("typing", "Optional", "OptionalType"),
self.python_type(field),
)
line(") -> None: ...")
# Standard message methods
line("@classmethod")
line("def FromString(cls, s: bytes) -> {}: ...", qualified_name)
line("def MergeFrom(self, other_msg: {}) -> None: ...", message_class)
line("def CopyFrom(self, other_msg: {}) -> None: ...", message_class)
line("")
def write_services(self, services: d.ServiceDescriptorProto) -> None:
line = self._write_line
for service in services:
# The service definition interface
line(
"class {}({}, metaclass={}):",
service.name,
self._import("google.protobuf.service", "Service"),
self._import("abc", "ABCMeta"),
)
with self._indent():
for method in service.method:
line("@{}", self._import("abc", "abstractmethod"))
line("def {}(self,", method.name)
with self._indent():
line(
"rpc_controller: {},",
self._import("google.protobuf.service", "RpcController"),
)
line("request: {},", self._import_message(method.input_type))
line(
"done: {}[{}[[{}], None]],",
self._import("typing", "Optional"),
self._import("typing", "Callable"),
self._import_message(method.output_type),
)
line(
") -> {}[{}]: ...",
self._import("concurrent.futures", "Future"),
self._import_message(method.output_type),
)
# The stub client
line("class {}({}):", service.name + "_Stub", service.name)
with self._indent():
line(
"def __init__(self, rpc_channel: {}) -> None: ...",
self._import("google.protobuf.service", "RpcChannel"),
)
def python_type(self, field: d.FieldDescriptorProto) -> str:
mapping: Dict[int, Callable[[], str]] = {
d.FieldDescriptorProto.TYPE_DOUBLE: lambda: "float",
d.FieldDescriptorProto.TYPE_FLOAT: lambda: "float",
d.FieldDescriptorProto.TYPE_INT64: lambda: "int",
d.FieldDescriptorProto.TYPE_UINT64: lambda: "int",
d.FieldDescriptorProto.TYPE_FIXED64: lambda: "int",
d.FieldDescriptorProto.TYPE_SFIXED64: lambda: "int",
d.FieldDescriptorProto.TYPE_SINT64: lambda: "int",
d.FieldDescriptorProto.TYPE_INT32: lambda: "int",
d.FieldDescriptorProto.TYPE_UINT32: lambda: "int",
d.FieldDescriptorProto.TYPE_FIXED32: lambda: "int",
d.FieldDescriptorProto.TYPE_SFIXED32: lambda: "int",
d.FieldDescriptorProto.TYPE_SINT32: lambda: "int",
d.FieldDescriptorProto.TYPE_BOOL: lambda: "bool",
d.FieldDescriptorProto.TYPE_STRING: lambda: "str",
d.FieldDescriptorProto.TYPE_BYTES: lambda: "bytes",
d.FieldDescriptorProto.TYPE_ENUM: lambda: self._import_message(
field.type_name
),
d.FieldDescriptorProto.TYPE_MESSAGE: lambda: self._import_message(
field.type_name
),
d.FieldDescriptorProto.TYPE_GROUP: lambda: self._import_message(
field.type_name
),
}
assert field.type in mapping, "Unrecognized type: " + field.type
return mapping[field.type]()
def write(self) -> str:
imports = []
for pkg, items in self.imports.items():
if pkg.startswith("google."):
imports.append(f"from {pkg} import ( # type: ignore")
else:
imports.append(f"from {pkg} import (")
for item in sorted(items):
imports.append(f" {item},")
imports.append(")\n")
return "\n".join(imports + self.lines)
def is_scalar(fd: d.FileDescriptorProto) -> bool:
return not (
fd.type == d.FieldDescriptorProto.TYPE_MESSAGE
or fd.type == d.FieldDescriptorProto.TYPE_GROUP
)
def generate_mypy_stubs(
descriptors: Descriptors, response: plugin.CodeGeneratorResponse
) -> None:
for name, fd in descriptors.to_generate.items():
pkg_writer = PkgWriter(fd, descriptors)
pkg_writer.write_enums(fd.enum_type)
pkg_writer.write_messages(fd.message_type, "")
pkg_writer.write_services(fd.service)
assert name == fd.name
assert fd.name.endswith(".proto")
output = response.file.add()
output.name = fd.name[:-6].replace("-", "_") + "_pb2.pyi"
output.content = HEADER + pkg_writer.write()
print("Writing mypy to", output.name, file=sys.stderr)
def main() -> None:
# Read request message from stdin
data = sys.stdin.buffer.read()
# Parse request
request = plugin.CodeGeneratorRequest()
request.ParseFromString(data)
# Create response
response = plugin.CodeGeneratorResponse()
# Generate mypy
generate_mypy_stubs(Descriptors(request), response)
# Serialise response message
output = response.SerializeToString()
# Write to stdout
sys.stdout.buffer.write(output)
if __name__ == "__main__":
main()
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59,147 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_string_split.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,W0221
from typing import Union
import numpy as np
from onnx.reference.op_run import OpRun
_acceptable_str_dtypes = ("U", "O")
def pad_empty_string(
split_lists: Union[list, np.ndarray], padding_requirement: Union[list, int]
):
# pylint: disable=unidiomatic-typecheck`
if type(split_lists) is list:
return split_lists + ["" for _ in range(padding_requirement)]
elif type(split_lists) is np.ndarray:
return list(map(pad_empty_string, split_lists, padding_requirement))
else:
raise TypeError("Invalid array type")
def split_with_padding(x, separator=None, maxsplit=None):
split_lists = np.char.split(x.astype(np.str_), separator, maxsplit)
# Find the maximum length after splitting
num_splits = np.vectorize(len, otypes=[np.int64])(split_lists)
padding_requirement = (np.max(num_splits, initial=0) - num_splits).tolist()
# Add padding to lists that are shorter than the maximum length
split_lists_padded = np.array(
pad_empty_string(split_lists, padding_requirement), dtype=object
)
if x.size == 0:
split_lists_padded = split_lists_padded.reshape(*x.shape, 0)
return split_lists_padded, num_splits
class StringSplit(OpRun):
def _run(self, x, delimiter=None, maxsplit=None):
if delimiter == "":
delimiter = None
if x.dtype.kind not in _acceptable_str_dtypes:
raise TypeError(f"Inputs must be string tensors, received dtype {x.dtype}")
return split_with_padding(x, delimiter, maxsplit)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,148 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/max.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
from onnx.backend.test.case.utils import all_numeric_dtypes
class Max(Base):
@staticmethod
def export() -> None:
data_0 = np.array([3, 2, 1]).astype(np.float32)
data_1 = np.array([1, 4, 4]).astype(np.float32)
data_2 = np.array([2, 5, 3]).astype(np.float32)
result = np.array([3, 5, 4]).astype(np.float32)
node = onnx.helper.make_node(
"Max",
inputs=["data_0", "data_1", "data_2"],
outputs=["result"],
)
expect(
node,
inputs=[data_0, data_1, data_2],
outputs=[result],
name="test_max_example",
)
node = onnx.helper.make_node(
"Max",
inputs=["data_0"],
outputs=["result"],
)
expect(node, inputs=[data_0], outputs=[data_0], name="test_max_one_input")
result = np.maximum(data_0, data_1)
node = onnx.helper.make_node(
"Max",
inputs=["data_0", "data_1"],
outputs=["result"],
)
expect(
node, inputs=[data_0, data_1], outputs=[result], name="test_max_two_inputs"
)
@staticmethod
def export_max_all_numeric_types() -> None:
for op_dtype in all_numeric_dtypes:
data_0 = np.array([3, 2, 1]).astype(op_dtype)
data_1 = np.array([1, 4, 4]).astype(op_dtype)
result = np.array([3, 4, 4]).astype(op_dtype)
node = onnx.helper.make_node(
"Max",
inputs=["data_0", "data_1"],
outputs=["result"],
)
expect(
node,
inputs=[data_0, data_1],
outputs=[result],
name=f"test_max_{np.dtype(op_dtype).name}",
)
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59,149 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/model/expand.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from typing import Sequence
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.model import expect
class ExpandDynamicShape(Base):
@staticmethod
def export() -> None:
def make_graph(
node: onnx.helper.NodeProto,
input_shape: Sequence[int],
shape_shape: Sequence[int],
output_shape: Sequence[int],
) -> onnx.helper.GraphProto:
graph = onnx.helper.make_graph(
nodes=[node],
name="Expand",
inputs=[
onnx.helper.make_tensor_value_info(
"X", onnx.TensorProto.FLOAT, input_shape
),
onnx.helper.make_tensor_value_info(
"shape", onnx.TensorProto.INT64, shape_shape
),
],
outputs=[
onnx.helper.make_tensor_value_info(
"Y", onnx.TensorProto.FLOAT, output_shape
)
],
)
return graph
node = onnx.helper.make_node("Expand", ["X", "shape"], ["Y"], name="test")
input_shape = [1, 3, 1]
x = np.ones(input_shape, dtype=np.float32)
# 1st testcase
shape = np.array([3, 1], dtype=np.int64)
y = x * np.ones(shape, dtype=np.float32)
graph = make_graph(node, input_shape, shape.shape, y.shape)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 9)],
)
expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model1")
# 2nd testcase
shape = np.array([1, 3], dtype=np.int64)
y = x * np.ones(shape, dtype=np.float32)
graph = make_graph(node, input_shape, shape.shape, y.shape)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 9)],
)
expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model2")
# 3rd testcase
shape = np.array([3, 1, 3], dtype=np.int64)
y = x * np.ones(shape, dtype=np.float32)
graph = make_graph(node, input_shape, shape.shape, y.shape)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 9)],
)
expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model3")
# 4th testcase
shape = np.array([3, 3, 1, 3], dtype=np.int64)
y = x * np.ones(shape, dtype=np.float32)
graph = make_graph(node, input_shape, shape.shape, y.shape)
model = onnx.helper.make_model_gen_version(
graph,
producer_name="backend-test",
opset_imports=[onnx.helper.make_opsetid("", 9)],
)
expect(model, inputs=[x, shape], outputs=[y], name="test_expand_shape_model4")
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59,150 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/erf.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import math
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Erf(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Erf",
inputs=["x"],
outputs=["y"],
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
y = np.vectorize(math.erf)(x).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_erf")
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59,151 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/_common_classifier.py | # SPDX-License-Identifier: Apache-2.0
import numpy as np
def compute_logistic(val: float) -> float:
v = 1.0 / (1.0 + np.exp(-np.abs(val)))
return (1.0 - v) if val < 0 else v # type: ignore
logistic = np.vectorize(compute_logistic)
def compute_softmax_zero(values: np.ndarray) -> np.ndarray:
"""
The function modifies the input inplace.
"""
v_max = values.max()
exp_neg_v_max = np.exp(-v_max)
s = 0
for i in range(len(values)): # pylint: disable=C0200
v = values[i]
if v > 0.0000001 or v < -0.0000001:
values[i] = np.exp(v - v_max)
else:
values[i] *= exp_neg_v_max
s += values[i]
if s == 0:
values[:] = 0.5
else:
values[:] /= s
return values
def softmax_zero(values: np.ndarray) -> np.ndarray:
"Modifications in place."
if len(values.shape) == 1:
compute_softmax_zero(values)
return values
for row in values:
compute_softmax_zero(row)
return values
def softmax(values: np.ndarray) -> np.ndarray:
"Modifications in place."
if len(values.shape) == 2:
v_max = values.max(axis=1, keepdims=1) # type: ignore
values -= v_max
np.exp(values, out=values)
s = values.sum(axis=1, keepdims=1) # type: ignore
values /= s
return values
v_max = values.max()
values[:] = np.exp(values - v_max)
this_sum = values.sum()
values /= this_sum
return values
def erf_inv(x: float) -> float:
sgn = -1.0 if x < 0 else 1.0
x = (1.0 - x) * (1 + x)
if x == 0:
return 0
log = np.log(x)
v = 2.0 / (np.pi * 0.147) + 0.5 * log
v2 = 1.0 / 0.147 * log
v3 = -v + np.sqrt(v * v - v2)
x = sgn * np.sqrt(v3)
return x
def compute_probit(val: float) -> float:
return 1.41421356 * erf_inv(val * 2 - 1)
probit = np.vectorize(compute_probit)
def expit(x: np.ndarray) -> np.ndarray:
return (1.0 / (1.0 + np.exp(-x))).astype(x.dtype)
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59,152 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/reducel1.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class ReduceL1(Base):
@staticmethod
def export_do_not_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = 0
node = onnx.helper.make_node(
"ReduceL1",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[3., 7.], [11., 15.], [19., 23.]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l1_do_not_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l1_do_not_keepdims_random",
)
@staticmethod
def export_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceL1",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l1_keep_dims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l1_keep_dims_random",
)
@staticmethod
def export_default_axes_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceL1", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims
)
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
reduced = np.sum(a=np.abs(data), axis=None, keepdims=keepdims == 1)
# print(reduced)
# [[[78.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l1_default_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(a=np.abs(data), axis=None, keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l1_default_axes_keepdims_random",
)
@staticmethod
def export_negative_axes_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([-1], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceL1",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[3.], [7.]], [[11.], [15.]], [[19.], [23.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l1_negative_axes_keep_dims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sum(a=np.abs(data), axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l1_negative_axes_keep_dims_random",
)
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59,153 | onnx/onnx | refs/heads/main | /onnx/reference/ops_optimized/op_conv_optimized.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,R0914,R0915,R1702,W0221
import numpy as np
from onnx.reference.op_run import OpRun
def _make_ind(dim, shape):
m = np.empty(shape, dtype=np.int64)
ind = [slice(0, shape[i]) for i in range(len(shape))]
new_shape = [1] * len(shape)
new_shape[dim] = shape[dim]
first = np.arange(shape[dim]).reshape(new_shape)
m[tuple(ind)] = first
return m
def im2col_fast(X, kernel_shape, pads, strides):
n_dims = len(kernel_shape)
m, n_C = X.shape[:2]
kernel_size = np.prod(kernel_shape)
shape_out = []
for i, dim in enumerate(kernel_shape):
dx = X.shape[2 + i]
shape_out.append((dx + pads[i] + pads[i + n_dims] - dim) // strides[i] + 1)
indices = []
for i in range(len(shape_out)):
kind = _make_ind(i, kernel_shape)
iind = _make_ind(i, shape_out) * strides[i]
i = np.tile(kind.ravel(), n_C).reshape(-1, 1) + iind.reshape(1, -1)
indices.append(i)
d = np.repeat(np.arange(n_C), kernel_size).reshape(-1, 1)
nc = [(0, 0)] * 2
padding = [(pads[i], pads[i + n_dims]) for i in range(n_dims)]
X_padded = np.pad(X, tuple(nc) + tuple(padding), mode="constant")
getitem = (slice(0, m), d, *indices)
cols = X_padded[getitem] # type: ignore[index]
conc_cols = np.concatenate(cols, axis=-1)
return conc_cols, tuple(shape_out)
def _conv_implementation_im2col( # type: ignore
X, W, B, auto_pad, dilations, group, kernel_shape, pads, strides
):
if dilations is None:
dilations = [1 for s in X.shape[2:]]
if kernel_shape is None:
kernel_shape = W.shape[2:]
if pads is None:
pads = [0 for s in X.shape[2:]] * 2
if strides is None:
strides = [1 for s in X.shape[2:]]
kernel_shape = tuple(kernel_shape)
if X.shape[1] != W.shape[1] * group or W.shape[0] % group != 0:
raise ValueError(
f"Shape inconsistencies, X.shape={X.shape}, W.shape={W.shape}, group={group}, "
f"W should be {(W.shape[0], X.shape[1] // group, np.prod(W.shape[1:]) // X.shape[1] * group)}."
)
if group > 1:
res = []
td = 0
mg = W.shape[0] // group
dw = W.shape[1]
for b in range(X.shape[0]):
for g in range(group):
gx = X[b : b + 1, g * dw : (g + 1) * dw]
gw = W[g * mg : (g + 1) * mg]
try:
cv = _conv_implementation_im2col(
gx,
gw,
None,
auto_pad,
dilations,
1,
kernel_shape,
pads,
strides,
)
except (ValueError, RuntimeError) as e:
raise ValueError(
f"Shape inconsistencies, X.shape={X.shape}, W.shape={W.shape}, group={g}/{group}, "
f"gx.shape={gx.shape}, gw.shape={gw.shape}, auto_pad={auto_pad}, "
f"dilations={dilations}, kernel_shape={kernel_shape}, pads={pads}, "
f"strides={strides}."
) from e
if b == 0:
td += cv.shape[1]
res.append((b, cv))
new_shape = [X.shape[0], *list(res[0][1].shape[1:])]
new_shape[1] = td
final = np.zeros(tuple(new_shape), dtype=res[0][1].dtype)
p = 0
for b, cv in res:
final[b : b + 1, p : p + cv.shape[1]] = cv
p += cv.shape[1]
if p >= final.shape[1]:
p = 0
if B is not None:
new_shape = [1 for s in final.shape]
new_shape[1] = B.shape[0]
b = B.reshape(tuple(new_shape))
final += b
return final
if dilations[0] != 1 or min(dilations) != max(dilations):
# Let's compute the dilated kernel.
nd = len(dilations)
new_kernel_shape = []
new_shape = list(W.shape[:-nd])
for i, d in enumerate(dilations):
di = len(W.shape) - nd + i
new_shape.append(W.shape[di] + (W.shape[di] - 1) * (d - 1))
new_kernel_shape.append(kernel_shape[i] + (kernel_shape[i] - 1) * (d - 1))
new_w = np.zeros(tuple(new_shape), dtype=W.dtype)
indices = [slice(0, new_w.shape[0]), slice(0, new_w.shape[1])]
for i, d in enumerate(dilations):
di = len(W.shape) - nd + i
indices.append(slice(0, new_w.shape[di], d))
new_w[tuple(indices)] = W
W = new_w
kernel_shape = new_kernel_shape
if auto_pad in {"SAME_LOWER", "SAME_UPPER", "VALID"}:
head = []
tail = []
for i in range(len(X.shape) - 2):
d = X.shape[i]
target_size = (d + strides[i] - 1) // strides[i]
pad_needed = (target_size - 1) * strides[i] + kernel_shape[i] - d
if auto_pad == "SAME_LOWER":
pad_head = (pad_needed + 1) // 2
else:
pad_head = pad_needed // 2
pad_tail = pad_needed - pad_head
head.append(pad_head)
tail.append(pad_tail)
pads = head + tail
c2, out_shape = im2col_fast(X, kernel_shape, pads, strides)
w_reshaped = W.reshape((-1, c2.shape[0]))
mul = w_reshaped @ c2
mul = mul.reshape((W.shape[0], X.shape[0], *out_shape))
perm = (1, 0, *tuple(np.arange(len(X.shape) - 2) + 2))
mul = mul.transpose(perm)
if B is not None:
if B.size == 1:
return mul + B
new_shape = [1] * len(mul.shape)
new_shape[1] = -1
mul += B.reshape(tuple(new_shape))
return mul
class Conv(OpRun):
def _run( # type: ignore
self,
X,
W,
B=None,
auto_pad=None,
dilations=None,
group=None,
kernel_shape=None,
pads=None,
strides=None,
):
if len(X.shape) < 3:
raise ValueError(
f"X must have at least 3 dimensions but its shape is {X.shape}."
)
return (
# _conv_implementation(
_conv_implementation_im2col(
X, W, B, auto_pad, dilations, group, kernel_shape, pads, strides
).astype(X.dtype),
)
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59,154 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/floor.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Floor(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Floor",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1.5, 1.2, 2]).astype(np.float32)
y = np.floor(x) # expected output [-2., 1., 2.]
expect(node, inputs=[x], outputs=[y], name="test_floor_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.floor(x)
expect(node, inputs=[x], outputs=[y], name="test_floor")
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59,155 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_blackman_window.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op_common_window import _CommonWindow
class BlackmanWindow(_CommonWindow):
"""
Returns
:math:`\\omega_n = 0.42 - 0.5 \\cos \\left( \\frac{2\\pi n}{N-1} \\right) +
0.08 \\cos \\left( \\frac{4\\pi n}{N-1} \\right)`
where *N* is the window length.
See `blackman_window
<https://pytorch.org/docs/stable/generated/torch.blackman_window.html>`_
"""
def _run(self, size, output_datatype=None, periodic=None): # type: ignore
ni, N_1 = np.arange(size), size
if periodic == 0:
N_1 = N_1 - 1
alpha = 0.42
beta = 0.08
pi = np.pi
y = np.cos((ni * (pi * 2)) / N_1) * (-0.5)
y += np.cos((ni * (pi * 4)) / N_1) * beta
y += alpha
return self._end(size, y, output_datatype)
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59,156 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/bitwisexor.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np # type: ignore
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
from onnx.numpy_helper import create_random_int
class BitwiseXor(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"BitwiseXor",
inputs=["x", "y"],
outputs=["bitwisexor"],
)
# 2d
x = create_random_int((3, 4), np.int32)
y = create_random_int((3, 4), np.int32)
z = np.bitwise_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_i32_2d")
# 3d
x = create_random_int((3, 4, 5), np.int16)
y = create_random_int((3, 4, 5), np.int16)
z = np.bitwise_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_i16_3d")
@staticmethod
def export_bitwiseor_broadcast() -> None:
node = onnx.helper.make_node(
"BitwiseXor",
inputs=["x", "y"],
outputs=["bitwisexor"],
)
# 3d vs 1d
x = create_random_int((3, 4, 5), np.uint64)
y = create_random_int((5,), np.uint64)
z = np.bitwise_xor(x, y)
expect(
node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_ui64_bcast_3v1d"
)
# 4d vs 3d
x = create_random_int((3, 4, 5, 6), np.uint8)
y = create_random_int((4, 5, 6), np.uint8)
z = np.bitwise_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_bitwise_xor_ui8_bcast_4v3d")
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"/onnx/reference/ops/op_unique.py", "/onnx/reference/ops/op_unsqueeze.py", "/onnx/reference/ops/op_upsample.py", "/onnx/reference/ops/op_where.py"], "/onnx/backend/test/case/model/gradient.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py", "/onnx/defs/__init__.py"], "/onnx/compose.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_det.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_sequence_empty.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/topk.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_log_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_linear_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/center_crop_pad.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", 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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": 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59,157 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/round.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Round(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Round",
inputs=["x"],
outputs=["y"],
)
x = np.array(
[
0.1,
0.5,
0.9,
1.2,
1.5,
1.8,
2.3,
2.5,
2.7,
-1.1,
-1.5,
-1.9,
-2.2,
-2.5,
-2.8,
]
).astype(np.float32)
y = np.array(
[
0.0,
0.0,
1.0,
1.0,
2.0,
2.0,
2.0,
2.0,
3.0,
-1.0,
-2.0,
-2.0,
-2.0,
-2.0,
-3.0,
]
).astype(
np.float32
) # expected output
expect(node, inputs=[x], outputs=[y], name="test_round")
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59,158 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_random_normal_like.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,W0221
from onnx.helper import np_dtype_to_tensor_dtype
from onnx.reference.ops._op_common_random import _CommonRandom
class RandomNormalLike(_CommonRandom):
def _run(self, x, dtype=None, mean=None, scale=None, seed=None): # type: ignore
if dtype is None:
dtype = np_dtype_to_tensor_dtype(x.dtype)
dtype = self._dtype(x, dtype=dtype)
state = self._get_state(seed)
res = state.randn(*x.shape).astype(dtype)
res *= scale # type: ignore
res += mean # type: ignore
return (res.astype(dtype),)
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59,159 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_conv_integer.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,W0221
import numpy as np
from onnx.reference.op_run import OpRun
from onnx.reference.ops.op_conv import _conv_implementation
class ConvInteger(OpRun):
def _run( # type: ignore
self,
X,
W,
x_zero_point=None,
w_zero_point=None,
auto_pad=None,
dilations=None,
group=None,
kernel_shape=None,
pads=None,
strides=None,
):
if len(X.shape) < 3:
raise ValueError(
f"X must have at least 3 dimensions but its shape is {X.shape}."
)
auto_pad = auto_pad or self.auto_pad # type: ignore
dilations = dilations or self.dilations # type: ignore
group = group or self.group # type: ignore
kernel_shape = kernel_shape or self.kernel_shape # type: ignore
pads = pads or self.pads # type: ignore
strides = strides or self.strides # type: ignore
X = X.astype(np.int32)
if x_zero_point:
X -= x_zero_point
W = W.astype(np.int32)
if w_zero_point:
W -= w_zero_point
return (
_conv_implementation(
X, W, None, auto_pad, dilations, group, kernel_shape, pads, strides
).astype(np.int32),
)
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["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,160 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/cast.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import sys
import numpy as np
import onnx
from onnx import TensorProto, helper
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
from onnx.helper import (
float32_to_float8e4m3,
float32_to_float8e5m2,
make_tensor,
tensor_dtype_to_field,
)
from onnx.numpy_helper import float8e4m3_to_float32, float8e5m2_to_float32
class Cast(Base):
@staticmethod
def export() -> None:
shape = (3, 4)
test_cases = [
("FLOAT", "FLOAT16"),
("FLOAT", "DOUBLE"),
("FLOAT16", "FLOAT"),
("FLOAT16", "DOUBLE"),
("DOUBLE", "FLOAT"),
("DOUBLE", "FLOAT16"),
("FLOAT", "STRING"),
("STRING", "FLOAT"),
("FLOAT", "BFLOAT16"),
("BFLOAT16", "FLOAT"),
("FLOAT", "FLOAT8E4M3FN"),
("FLOAT16", "FLOAT8E4M3FN"),
("FLOAT", "FLOAT8E4M3FNUZ"),
("FLOAT16", "FLOAT8E4M3FNUZ"),
("FLOAT8E4M3FN", "FLOAT"),
("FLOAT8E4M3FN", "FLOAT16"),
("FLOAT8E4M3FNUZ", "FLOAT"),
("FLOAT8E4M3FNUZ", "FLOAT16"),
("FLOAT", "FLOAT8E5M2"),
("FLOAT16", "FLOAT8E5M2"),
("FLOAT", "FLOAT8E5M2FNUZ"),
("FLOAT16", "FLOAT8E5M2FNUZ"),
("FLOAT8E5M2", "FLOAT"),
("FLOAT8E5M2", "FLOAT16"),
("FLOAT8E5M2FNUZ", "FLOAT"),
("FLOAT8E5M2FNUZ", "FLOAT16"),
]
vect_float32_to_float8e4m3 = np.vectorize(float32_to_float8e4m3)
vect_float32_to_float8e5m2 = np.vectorize(float32_to_float8e5m2)
f8_types = ("FLOAT8E4M3FN", "FLOAT8E4M3FNUZ", "FLOAT8E5M2", "FLOAT8E5M2FNUZ")
for from_type, to_type in test_cases:
input_type_proto = None
output_type_proto = None
if from_type == "BFLOAT16" or to_type == "BFLOAT16":
np_fp32 = np.array(
[
"0.47892547",
"0.48033667",
"0.49968487",
"0.81910545",
"0.47031248",
"0.816468",
"0.21087195",
"0.7229038",
"NaN",
"INF",
"+INF",
"-INF",
],
dtype=np.float32,
)
little_endisan = sys.byteorder == "little"
np_uint16_view = np_fp32.view(dtype=np.uint16)
np_bfp16 = (
np_uint16_view[1::2] if little_endisan else np_uint16_view[0::2]
)
if to_type == "BFLOAT16":
assert from_type == "FLOAT"
input = np_fp32.reshape([3, 4])
output = np_bfp16.reshape([3, 4])
input_type_proto = onnx.helper.make_tensor_type_proto(
int(TensorProto.FLOAT), input.shape
)
output_type_proto = onnx.helper.make_tensor_type_proto(
int(TensorProto.BFLOAT16), output.shape
)
else:
assert to_type == "FLOAT"
input = np_bfp16.reshape([3, 4])
# convert bfloat to FLOAT
np_fp32_zeros = np.zeros((len(np_bfp16) * 2,), dtype=np.uint16)
if little_endisan:
np_fp32_zeros[1::2] = np_bfp16
else:
np_fp32_zeros[0::2] = np_bfp16
np_fp32_from_bfloat = np_fp32_zeros.view(dtype=np.float32)
output = np_fp32_from_bfloat.reshape([3, 4])
input_type_proto = onnx.helper.make_tensor_type_proto(
int(TensorProto.BFLOAT16), input.shape
)
output_type_proto = onnx.helper.make_tensor_type_proto(
int(TensorProto.FLOAT), output.shape
)
elif from_type in f8_types or to_type in f8_types:
np_fp32 = np.array(
[
"0.47892547",
"0.48033667",
"0.49968487",
"0.81910545",
"0.47031248",
"0.7229038",
"1000000",
"1e-7",
"NaN",
"INF",
"+INF",
"-INF",
],
dtype=np.float32,
)
if from_type == "FLOAT":
input_values = np_fp32
input = make_tensor(
"x", TensorProto.FLOAT, [3, 4], np_fp32.tolist()
)
elif from_type == "FLOAT16":
input_values = np_fp32.astype(np.float16).astype(np.float32)
input = make_tensor(
"x", TensorProto.FLOAT16, [3, 4], input_values.tolist()
)
elif from_type == "FLOAT8E4M3FN":
input_values = float8e4m3_to_float32(
vect_float32_to_float8e4m3(np_fp32)
)
input = make_tensor(
"x", TensorProto.FLOAT8E4M3FN, [3, 4], input_values.tolist()
)
elif from_type == "FLOAT8E4M3FNUZ":
input_values = float8e4m3_to_float32(
vect_float32_to_float8e4m3(np_fp32, uz=True), uz=True
)
input = make_tensor(
"x", TensorProto.FLOAT8E4M3FNUZ, [3, 4], input_values.tolist()
)
elif from_type == "FLOAT8E5M2":
input_values = float8e5m2_to_float32(
vect_float32_to_float8e5m2(np_fp32)
)
input = make_tensor(
"x", TensorProto.FLOAT8E5M2, [3, 4], input_values.tolist()
)
elif from_type == "FLOAT8E5M2FNUZ":
input_values = float8e5m2_to_float32(
vect_float32_to_float8e5m2(np_fp32, fn=True, uz=True),
fn=True,
uz=True,
)
input = make_tensor(
"x", TensorProto.FLOAT8E5M2FNUZ, [3, 4], input_values.tolist()
)
else:
raise ValueError(
"Conversion from {from_type} to {to_type} is not tested."
)
if to_type == "FLOAT8E4M3FN":
expected = float8e4m3_to_float32(
vect_float32_to_float8e4m3(input_values)
)
elif to_type == "FLOAT8E4M3FNUZ":
expected = float8e4m3_to_float32(
vect_float32_to_float8e4m3(input_values, uz=True), uz=True
)
elif to_type == "FLOAT8E5M2":
expected = float8e5m2_to_float32(
vect_float32_to_float8e5m2(input_values)
)
elif to_type == "FLOAT8E5M2FNUZ":
expected = float8e5m2_to_float32(
vect_float32_to_float8e5m2(input_values, fn=True, uz=True),
fn=True,
uz=True,
)
elif to_type == "FLOAT16":
expected = input_values.astype(np.float16).astype(np.float32)
elif to_type == "FLOAT":
expected = input_values
else:
raise ValueError(
"Conversion from {from_type} to {to_type} is not tested."
)
expected_tensor = make_tensor(
"x", getattr(TensorProto, to_type), [3, 4], expected.tolist()
)
output = expected_tensor
elif from_type != "STRING":
input = np.random.random_sample(shape).astype(
helper.tensor_dtype_to_np_dtype(getattr(TensorProto, from_type))
)
if to_type == "STRING":
# Converting input to str, then give it object dtype for generating script
ss = []
for i in input.flatten():
s = str(i).encode("utf-8")
su = s.decode("utf-8")
ss.append(su)
output = np.array(ss).astype(object).reshape([3, 4])
else:
output = input.astype(
helper.tensor_dtype_to_np_dtype(getattr(TensorProto, to_type))
)
else:
input = np.array(
[
"0.47892547",
"0.48033667",
"0.49968487",
"0.81910545",
"0.47031248",
"0.816468",
"0.21087195",
"0.7229038",
"NaN",
"INF",
"+INF",
"-INF",
],
dtype=np.dtype(object),
).reshape([3, 4])
output = input.astype(
helper.tensor_dtype_to_np_dtype(getattr(TensorProto, to_type))
)
node = onnx.helper.make_node(
"Cast",
inputs=["input"],
outputs=["output"],
to=getattr(TensorProto, to_type),
)
if input_type_proto and output_type_proto:
expect(
node,
inputs=[input],
outputs=[output],
name="test_cast_" + from_type + "_to_" + to_type,
input_type_protos=[input_type_proto],
output_type_protos=[output_type_proto],
)
else:
expect(
node,
inputs=[input],
outputs=[output],
name="test_cast_" + from_type + "_to_" + to_type,
)
@staticmethod
def export_saturate_false() -> None:
test_cases = [
("FLOAT", "FLOAT8E4M3FN"),
("FLOAT16", "FLOAT8E4M3FN"),
("FLOAT", "FLOAT8E4M3FNUZ"),
("FLOAT16", "FLOAT8E4M3FNUZ"),
("FLOAT", "FLOAT8E5M2"),
("FLOAT16", "FLOAT8E5M2"),
("FLOAT", "FLOAT8E5M2FNUZ"),
("FLOAT16", "FLOAT8E5M2FNUZ"),
]
vect_float32_to_float8e4m3 = np.vectorize(float32_to_float8e4m3)
vect_float32_to_float8e5m2 = np.vectorize(float32_to_float8e5m2)
for from_type, to_type in test_cases:
np_fp32 = np.array(
[
"0.47892547",
"0.48033667",
"0.49968487",
"0.81910545",
"0.47031248",
"0.7229038",
"1000000",
"1e-7",
"NaN",
"INF",
"+INF",
"-INF",
],
dtype=np.float32,
)
if from_type == "FLOAT":
input_values = np_fp32
input = make_tensor("x", TensorProto.FLOAT, [3, 4], np_fp32.tolist())
elif from_type == "FLOAT16":
input_values = np_fp32.astype(np.float16).astype(np.float32)
input = make_tensor(
"x", TensorProto.FLOAT16, [3, 4], input_values.tolist()
)
else:
raise ValueError(
"Conversion from {from_type} to {to_type} is not tested."
)
if to_type == "FLOAT8E4M3FN":
expected = vect_float32_to_float8e4m3(input_values, saturate=False)
elif to_type == "FLOAT8E4M3FNUZ":
expected = vect_float32_to_float8e4m3(
input_values, uz=True, saturate=False
)
elif to_type == "FLOAT8E5M2":
expected = vect_float32_to_float8e5m2(input_values, saturate=False)
elif to_type == "FLOAT8E5M2FNUZ":
expected = vect_float32_to_float8e5m2(
input_values, fn=True, uz=True, saturate=False
)
else:
raise ValueError(
"Conversion from {from_type} to {to_type} is not tested."
)
ivals = bytes([int(i) for i in expected])
tensor = TensorProto()
tensor.data_type = getattr(TensorProto, to_type)
tensor.name = "x"
tensor.dims.extend([3, 4])
field = tensor_dtype_to_field(tensor.data_type)
getattr(tensor, field).extend(ivals)
output = tensor
node = onnx.helper.make_node(
"Cast",
inputs=["input"],
outputs=["output"],
to=getattr(TensorProto, to_type),
saturate=0,
)
expect(
node,
inputs=[input],
outputs=[output],
name="test_cast_no_saturate_" + from_type + "_to_" + to_type,
)
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dropout.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/model_inference_test.py": ["/onnx/__init__.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/test/inliner_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/argmin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/op_run.py": ["/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/reference_evaluator.py"], "/onnx/reference/ops/op_max_unpool.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/reversesequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/celu.py": 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59,161 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/hammingwindow.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class HammingWindow(Base):
@staticmethod
def export() -> None:
# Test periodic window
node = onnx.helper.make_node(
"HammingWindow",
inputs=["x"],
outputs=["y"],
)
size = np.int32(10)
a0 = 25 / 46
a1 = 1 - a0
y = a0 - a1 * np.cos(2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / size)
expect(node, inputs=[size], outputs=[y], name="test_hammingwindow")
# Test symmetric window
node = onnx.helper.make_node(
"HammingWindow", inputs=["x"], outputs=["y"], periodic=0
)
size = np.int32(10)
a0 = 25 / 46
a1 = 1 - a0
y = a0 - a1 * np.cos(
2 * np.pi * np.arange(0, size, 1, dtype=np.float32) / (size - 1)
)
expect(node, inputs=[size], outputs=[y], name="test_hammingwindow_symmetric")
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,162 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_lp_pool.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221,R0913,R0914
import numpy as np
from onnx.reference.ops.op_pool_common import CommonPool
class LpPool(CommonPool):
def _run( # type: ignore
self,
x,
auto_pad=None,
ceil_mode=None,
dilations=None,
kernel_shape=None,
p=2,
pads=None,
strides=None,
count_include_pad=None,
):
# utilize AvgPool the same fashion Pytorch does. Note that there is a difference in computation.
# it needs another PR to address.
# https://github.com/pytorch/pytorch/blob/f58ba553b78db7f88477f9ba8c9333bd1590e30a/torch/nn/functional.py#L1015
power_average = CommonPool._run(
self,
"AVG",
count_include_pad,
np.power(np.absolute(x), p),
auto_pad=auto_pad,
ceil_mode=ceil_mode,
dilations=dilations,
kernel_shape=kernel_shape,
pads=pads,
strides=strides,
)
kernel_element_count = np.prod(kernel_shape)
return (np.power(kernel_element_count * power_average[0], 1.0 / p),)
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59,163 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/split.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Split(Base):
@staticmethod
def export_1d_opset13() -> None:
node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2", "output_3"],
axis=0,
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0]).astype(np.float32),
np.array([5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_1d_opset13",
opset_imports=[onnx.helper.make_opsetid("", 13)],
)
split = np.array([2, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Split",
inputs=["input", "split"],
outputs=["output_1", "output_2"],
axis=0,
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_1d_opset13",
opset_imports=[onnx.helper.make_opsetid("", 13)],
)
@staticmethod
def export_2d_opset13() -> None:
node_input = np.array(
[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0, 11.0, 12.0]]
).astype(np.float32)
node = onnx.helper.make_node(
"Split", inputs=["input"], outputs=["output_1", "output_2"], axis=1
)
expected_outputs = [
np.array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]]).astype(np.float32),
np.array([[4.0, 5.0, 6.0], [10.0, 11.0, 12.0]]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_2d_opset13",
opset_imports=[onnx.helper.make_opsetid("", 13)],
)
split = np.array([2, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Split",
inputs=["input", "split"],
outputs=["output_1", "output_2"],
axis=1,
)
expected_outputs = [
np.array([[1.0, 2.0], [7.0, 8.0]]).astype(np.float32),
np.array([[3.0, 4.0, 5.0, 6.0], [9.0, 10.0, 11.0, 12.0]]).astype(
np.float32
),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_2d_opset13",
opset_imports=[onnx.helper.make_opsetid("", 13)],
)
@staticmethod
def export_default_values_opset13() -> None:
node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
# If axis is not specified, split is applied on default axis 0
node = onnx.helper.make_node(
"Split", inputs=["input"], outputs=["output_1", "output_2", "output_3"]
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0]).astype(np.float32),
np.array([5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_default_axis_opset13",
opset_imports=[onnx.helper.make_opsetid("", 13)],
)
split = np.array([2, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Split", inputs=["input", "split"], outputs=["output_1", "output_2"]
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_default_axis_opset13",
opset_imports=[onnx.helper.make_opsetid("", 13)],
)
@staticmethod
def export_zero_size_splits_opset13() -> None:
# 1-dimensional tensor with dimension_size=0
node_input = np.array([]).astype(np.float32)
# Split emtpy tensor to tensors of size zero
split = np.array([0, 0, 0]).astype(np.int64)
node = onnx.helper.make_node(
"Split",
inputs=["input", "split"],
outputs=["output_1", "output_2", "output_3"],
)
expected_outputs = [
np.array([]).astype(np.float32),
np.array([]).astype(np.float32),
np.array([]).astype(np.float32),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_zero_size_splits_opset13",
opset_imports=[onnx.helper.make_opsetid("", 13)],
)
@staticmethod
def export_1d_opset18() -> None:
node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2", "output_3"],
axis=0,
num_outputs=3,
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0]).astype(np.float32),
np.array([5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_1d_opset18",
)
split = np.array([2, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Split",
inputs=["input", "split"],
outputs=["output_1", "output_2"],
axis=0,
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_1d_opset18",
)
@staticmethod
def export_2d_opset18() -> None:
node_input = np.array(
[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 9.0, 10.0, 11.0, 12.0]]
).astype(np.float32)
node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2"],
axis=1,
num_outputs=2,
)
expected_outputs = [
np.array([[1.0, 2.0, 3.0], [7.0, 8.0, 9.0]]).astype(np.float32),
np.array([[4.0, 5.0, 6.0], [10.0, 11.0, 12.0]]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_2d",
)
split = np.array([2, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Split",
inputs=["input", "split"],
outputs=["output_1", "output_2"],
axis=1,
)
expected_outputs = [
np.array([[1.0, 2.0], [7.0, 8.0]]).astype(np.float32),
np.array([[3.0, 4.0, 5.0, 6.0], [9.0, 10.0, 11.0, 12.0]]).astype(
np.float32
),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_2d_opset18",
)
@staticmethod
def export_default_values_opset18() -> None:
node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
# If axis is not specified, split is applied on default axis 0
node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2", "output_3"],
num_outputs=3,
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0]).astype(np.float32),
np.array([5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_equal_parts_default_axis_opset18",
)
split = np.array([2, 4]).astype(np.int64)
node = onnx.helper.make_node(
"Split", inputs=["input", "split"], outputs=["output_1", "output_2"]
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0, 5.0, 6.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_variable_parts_default_axis_opset18",
)
@staticmethod
def export_zero_size_splits_opset18() -> None:
# 1-dimensional tensor with dimension_size=0
node_input = np.array([]).astype(np.float32)
# Split emtpy tensor to tensors of size zero
split = np.array([0, 0, 0]).astype(np.int64)
node = onnx.helper.make_node(
"Split",
inputs=["input", "split"],
outputs=["output_1", "output_2", "output_3"],
)
expected_outputs = [
np.array([]).astype(np.float32),
np.array([]).astype(np.float32),
np.array([]).astype(np.float32),
]
expect(
node,
inputs=[node_input, split],
outputs=expected_outputs,
name="test_split_zero_size_splits_opset18",
)
@staticmethod
def export_1d_uneven_split_opset18() -> None:
node_input = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]).astype(np.float32)
# If axis is not specified, split is applied on default axis 0
node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2", "output_3", "output_4"],
num_outputs=4,
)
expected_outputs = [
np.array([1.0, 2.0]).astype(np.float32),
np.array([3.0, 4.0]).astype(np.float32),
np.array([5.0, 6.0]).astype(np.float32),
np.array([7.0]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_1d_uneven_split_opset18",
)
@staticmethod
def export_2d_uneven_split_opset18() -> None:
node_input = np.array(
[
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0],
]
).astype(np.float32)
node = onnx.helper.make_node(
"Split",
inputs=["input"],
outputs=["output_1", "output_2", "output_3"],
axis=1,
num_outputs=3,
)
expected_outputs = [
np.array([[1.0, 2.0, 3.0], [9.0, 10.0, 11.0]]).astype(np.float32),
np.array([[4.0, 5.0, 6.0], [12.0, 13.0, 14.0]]).astype(np.float32),
np.array([[7.0, 8.0], [15.0, 16.0]]).astype(np.float32),
]
expect(
node,
inputs=[node_input],
outputs=expected_outputs,
name="test_split_2d_uneven_split_opset18",
)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,164 | onnx/onnx | refs/heads/main | /onnx/reference/custom_element_types.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
bfloat16 = np.dtype((np.uint16, {"bfloat16": (np.uint16, 0)}))
float8e4m3fn = np.dtype((np.uint8, {"e4m3fn": (np.uint8, 0)}))
float8e4m3fnuz = np.dtype((np.uint8, {"e4m3fnuz": (np.uint8, 0)}))
float8e5m2 = np.dtype((np.uint8, {"e5m2": (np.uint8, 0)}))
float8e5m2fnuz = np.dtype((np.uint8, {"e5m2fnuz": (np.uint8, 0)}))
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"/onnx/reference/ops/op_unique.py", "/onnx/reference/ops/op_unsqueeze.py", "/onnx/reference/ops/op_upsample.py", "/onnx/reference/ops/op_where.py"], "/onnx/backend/test/case/model/gradient.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py", "/onnx/defs/__init__.py"], "/onnx/compose.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_det.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_sequence_empty.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/topk.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_log_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_linear_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/center_crop_pad.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", 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"/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dequantizelinear.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_isnan.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/mul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stringnormalizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducemin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/tile.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_flatten.py": ["/onnx/reference/ops/_op.py"], 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"/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": 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59,165 | onnx/onnx | refs/heads/main | /onnx/test/hub_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=protected-access
import glob
import os
import unittest
from os.path import join
import pytest
from onnx import ModelProto, hub
@pytest.mark.skipif(
"TEST_HUB" not in os.environ or not os.environ["TEST_HUB"],
reason="Conserving Git LFS quota",
)
class TestModelHub(unittest.TestCase):
def setUp(self) -> None:
self.name = "MNIST"
self.repo = "onnx/models:main"
self.opset = 7
def test_force_reload(self) -> None:
model = hub.load(self.name, self.repo, force_reload=True)
self.assertIsInstance(model, ModelProto)
cached_files = list(
glob.glob(join(hub.get_dir(), "**", "*.onnx"), recursive=True)
)
self.assertGreaterEqual(len(cached_files), 1)
def test_listing_models(self) -> None:
model_info_list_1 = hub.list_models(self.repo, model="mnist", tags=["vision"])
model_info_list_2 = hub.list_models(self.repo, tags=["vision"])
model_info_list_3 = hub.list_models(self.repo)
self.assertGreater(len(model_info_list_1), 1)
self.assertGreater(len(model_info_list_2), len(model_info_list_1))
self.assertGreater(len(model_info_list_3), len(model_info_list_2))
def test_basic_usage(self) -> None:
model = hub.load(self.name, self.repo)
self.assertIsInstance(model, ModelProto)
cached_files = list(
glob.glob(join(hub.get_dir(), "**", "*.onnx"), recursive=True)
)
self.assertGreaterEqual(len(cached_files), 1)
def test_custom_cache(self) -> None:
old_cache = hub.get_dir()
new_cache = join(old_cache, "custom")
hub.set_dir(new_cache)
model = hub.load(self.name, self.repo)
self.assertIsInstance(model, ModelProto)
cached_files = list(glob.glob(join(new_cache, "**", "*.onnx"), recursive=True))
self.assertGreaterEqual(len(cached_files), 1)
hub.set_dir(old_cache)
def test_download_with_opset(self) -> None:
model = hub.load(self.name, self.repo, opset=8)
self.assertIsInstance(model, ModelProto)
def test_opset_error(self) -> None:
self.assertRaises(
AssertionError, lambda: hub.load(self.name, self.repo, opset=-1)
)
def test_manifest_not_found(self) -> None:
self.assertRaises(
AssertionError,
lambda: hub.load(self.name, "onnx/models:unknown", silent=True),
)
def test_verify_repo_ref(self) -> None:
# Not trusted repo:
verified = hub._verify_repo_ref("mhamilton723/models")
self.assertFalse(verified)
# Not trusted repo:
verified = hub._verify_repo_ref("onnx/models:unknown")
self.assertFalse(verified)
# Trusted repo:
verified = hub._verify_repo_ref(self.repo)
self.assertTrue(verified)
def test_get_model_info(self) -> None:
hub.get_model_info("mnist", self.repo, opset=8)
hub.get_model_info("mnist", self.repo)
self.assertRaises(
AssertionError, lambda: hub.get_model_info("mnist", self.repo, opset=-1)
)
def test_download_model_with_test_data(self) -> None:
directory = hub.download_model_with_test_data("mnist")
files = os.listdir(directory)
self.assertIsInstance(directory, str)
self.assertIn(member="model.onnx", container=files, msg="Onnx model not found")
self.assertIn(
member="test_data_set_0", container=files, msg="Test data not found"
)
def test_model_with_preprocessing(self) -> None:
model = hub.load_composite_model(
"ResNet50-fp32", preprocessing_model="ResNet-preproc"
)
self.assertIsInstance(model, ModelProto)
if __name__ == "__main__":
unittest.main()
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59,166 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/shrink.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Shrink(Base):
@staticmethod
def export_hard_shrink() -> None:
node = onnx.helper.make_node(
"Shrink",
inputs=["x"],
outputs=["y"],
lambd=1.5,
)
X = np.arange(-2.0, 2.1, dtype=np.float32)
Y = np.array([-2, 0, 0, 0, 2], dtype=np.float32)
expect(node, inputs=[X], outputs=[Y], name="test_shrink_hard")
@staticmethod
def export_soft_shrink() -> None:
node = onnx.helper.make_node(
"Shrink",
inputs=["x"],
outputs=["y"],
lambd=1.5,
bias=1.5,
)
X = np.arange(-2.0, 2.1, dtype=np.float32)
Y = np.array([-0.5, 0, 0, 0, 0.5], dtype=np.float32)
expect(node, inputs=[X], outputs=[Y], name="test_shrink_soft")
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59,167 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_gru.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221,W0613
import numpy as np
from onnx.reference.op_run import OpRun
class CommonGRU(OpRun):
def __init__(self, onnx_node, run_params): # type: ignore
OpRun.__init__(self, onnx_node, run_params)
self.n_outputs = len(onnx_node.output)
self.number_of_gates = 3
def f(self, x): # type: ignore
return 1 / (1 + np.exp(-x))
def g(self, x): # type: ignore
return np.tanh(x)
def _step(self, X, R, B, W, H_0, num_directions): # type: ignore
seq_length = X.shape[0]
hidden_size = H_0.shape[-1]
batch_size = X.shape[1]
Y = np.empty([seq_length, num_directions, batch_size, hidden_size])
h_list = []
[w_z, w_r, w_h] = np.split(W, 3)
[r_z, r_r, r_h] = np.split(R, 3)
[w_bz, w_br, w_bh, r_bz, r_br, r_bh] = np.split(B, 6)
gates_w = np.transpose(np.concatenate((w_z, w_r)))
gates_r = np.transpose(np.concatenate((r_z, r_r)))
gates_b = np.add(np.concatenate((w_bz, w_br)), np.concatenate((r_bz, r_br)))
H_t = H_0
for x in np.split(X, X.shape[0], axis=0):
gates = np.dot(x, gates_w) + np.dot(H_t, gates_r) + gates_b
z, r = np.split(gates, 2, -1) # pylint: disable=W0632
z = self.f(z)
r = self.f(r)
h_default = self.g(
np.dot(x, np.transpose(w_h))
+ np.dot(r * H_t, np.transpose(r_h))
+ w_bh
+ r_bh
)
h_linear = self.g(
np.dot(x, np.transpose(w_h))
+ r * (np.dot(H_t, np.transpose(r_h)) + r_bh)
+ w_bh
)
h = h_linear if self.linear_before_reset else h_default # type: ignore
H = (1 - z) * h + z * H_t
h_list.append(H)
H_t = H
concatenated = np.concatenate(h_list)
if num_directions == 1:
Y[:, 0, :, :] = concatenated
if self.layout == 0: # type: ignore
Y_h = Y[-1]
else:
Y = np.transpose(Y, [2, 0, 1, 3])
Y_h = Y[:, :, -1, :]
return Y, Y_h
def _run( # type: ignore
self,
X,
W,
R,
B=None,
sequence_lens=None,
initial_h=None,
activation_alpha=None,
activation_beta=None,
activations=None,
clip=None,
direction=None,
hidden_size=None,
layout=None,
linear_before_reset=None,
):
# TODO: support overridden attributes.
num_directions = W.shape[0]
if num_directions == 1:
R = np.squeeze(R, axis=0)
W = np.squeeze(W, axis=0)
if B is not None:
B = np.squeeze(B, axis=0)
if sequence_lens is not None:
sequence_lens = np.squeeze(sequence_lens, axis=0)
if initial_h is not None:
initial_h = np.squeeze(initial_h, axis=0)
hidden_size = R.shape[-1]
batch_size = X.shape[1]
X = X if layout == 0 else np.swapaxes(X, 0, 1)
b = (
B
if B is not None
else np.zeros(2 * self.number_of_gates * hidden_size, dtype=X.dtype)
)
h_0 = (
initial_h
if initial_h is not None
else np.zeros((batch_size, hidden_size), dtype=X.dtype)
)
B = b
H_0 = h_0
else:
raise NotImplementedError(
f"Unsupported value {num_directions} for num_directions and operator "
f"{self.__class__.__name__!r}."
)
Y, Y_h = self._step(X, R, B, W, H_0, num_directions=num_directions)
Y = Y.astype(X.dtype)
return (Y,) if self.n_outputs == 1 else (Y, Y_h.astype(X.dtype))
class GRU(CommonGRU):
def __init__(self, onnx_node, run_params): # type: ignore
CommonGRU.__init__(self, onnx_node, run_params) # type: ignore
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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,171 | neelparekh/ExcitingNBAGames | refs/heads/master | /test_polling.py | from format_utils import *
def to_NBAAPI_format(d):
game={
'hTeam': {
'triCode': d['home_name'],
'score': d['home_score'],
},
'vTeam': {
'triCode': d['away_name'],
'score': d['away_score'],
},
'clock': d['clock'],
'period': {
'current': d['quarter'],
},
'isGameActivated': d['isGameActivated']
}
return game
def inject_no_games():
return []
def inject_no_exciting_games():
game0={
'home_name': 'PHO',
'away_name': 'SAS',
'home_score': '',
'away_score': '',
'clock': '',
'quarter': '',
'isGameActivated': False,
}
game1={
'home_name': 'MIN',
'away_name': 'MKE',
'home_score': 76,
'away_score': 45,
'clock': '8:44',
'quarter': '3',
'isGameActivated': True,
}
return [to_NBAAPI_format(game) for game in [game0, game1]]
def inject_1_newly_exciting_1_unactivated_game():
game0={
'home_name': 'LAC',
'away_name': 'DAL',
'home_score': 110,
'away_score': 112,
'clock': "04:20",
'quarter': 4,
'isGameActivated': True,
}
game1={
'home_name': 'PHO',
'away_name': 'SAS',
'home_score': '',
'away_score': '',
'clock': '',
'quarter': '',
'isGameActivated': False,
}
return [to_NBAAPI_format(game) for game in [game0, game1]]
def inject_2_unexciting_1_old_2_newly_exciting_games():
game0 = {
'home_name': 'BOS',
'away_name': 'TOR',
'home_score': 98,
'away_score': 100,
'clock': "06:22", # not in trigger range
'quarter': 4,
'isGameActivated': True,
}
game1 = {
'home_name': 'IND',
'away_name': 'MIA',
'home_score': 98,
'away_score': 100,
'clock': "04:22",
'quarter': 3, # not in trigger range
'isGameActivated': True,
}
game2={ # game already exists
'home_name': 'LAC',
'away_name': 'DAL',
'home_score': 113,
'away_score': 112,
'clock': "03:37",
'quarter': 4,
'isGameActivated': True,
}
game3={
'home_name': 'LAL',
'away_name': 'POR',
'home_score': 100,
'away_score': 92,
'clock': "2.6",
'quarter': 4,
'isGameActivated': True,
}
game4 = {
'home_name': 'DEN',
'away_name': 'UTA',
'home_score': 98,
'away_score': 93,
'score_diff': 5,
'clock': "22.7",
'quarter': 4,
'isGameActivated': True,
}
return [to_NBAAPI_format(game) for game in [game0, game1, game2, game3, game4]] | {"/test_polling.py": ["/format_utils.py"], "/clock.py": ["/app.py"], "/twilio_connectors.py": ["/settings.py"], "/process_NBA_games.py": ["/format_utils.py", "/test_polling.py"], "/app.py": ["/settings.py", "/twilio_connectors.py", "/process_NBA_games.py"]} |
59,172 | neelparekh/ExcitingNBAGames | refs/heads/master | /clock.py | from apscheduler.schedulers.background import BackgroundScheduler
from apscheduler.triggers.cron import CronTrigger
from app import update_users
import pytz
scheduler = BackgroundScheduler()
scheduler.add_job(update_users, CronTrigger.from_crontab('* 11-23 * * *'), timezone=pytz.timezone('US/Pacific'))
# scheduler.add_job(refresh_games_db, 'interval', days=1, start_date='2020-09-10 00:00:00')
scheduler.start()
| {"/test_polling.py": ["/format_utils.py"], "/clock.py": ["/app.py"], "/twilio_connectors.py": ["/settings.py"], "/process_NBA_games.py": ["/format_utils.py", "/test_polling.py"], "/app.py": ["/settings.py", "/twilio_connectors.py", "/process_NBA_games.py"]} |
59,173 | neelparekh/ExcitingNBAGames | refs/heads/master | /twilio_connectors.py | # twilio imports
from twilio.rest import Client
from twilio.request_validator import RequestValidator
# external dependencies
from typing import Dict, Tuple, List
from flask import abort, current_app, request
from functools import wraps
# custom packages
import settings
def send_SMS(data: Dict, phone_number: str):
'''
Send data to phone number
Parameters
----------
data: Dict
phone_number: str
must be E.164 format
'''
try:
# Your Account sid and Auth Token from twilio.com/console
account_sid = settings.account_sid
auth_token = settings.auth_token
twilio_number = settings.twilio_number
# Make a client and POST a message
client = Client(account_sid, auth_token)
message = client.messages \
.create(
body=data['message_body'],
from_=twilio_number,
to=phone_number
)
print(message.sid)
return f"Success! SMS sent to {phone_number}"
except Exception as e:
print(e)
return f"The SMS to {phone_number} did not send."
def validate_twilio_request(f):
"""
Validates that incoming requests genuinely originated from Twilio. Stole this from
Twilio tutorial.
Example Usage:
@app.route('/message', methods=['POST'])
@validate_twilio_request
def incoming_message():
resp = MessagingResponse()
resp.message("Message was len {len(request.values['Body'])} characters.")
return str(resp)
"""
@wraps(f)
def decorated_function(*args, **kwargs):
# Create an instance of the RequestValidator class
validator = RequestValidator(settings.auth_token)
# Validate the request using its URL, POST data,
# and X-TWILIO-SIGNATURE header
request_valid = validator.validate(
request.url,
request.form,
request.headers.get('X-TWILIO-SIGNATURE', ''))
# Continue processing the request if it's valid (or if DEBUG is True)
# and return a 403 error if it's not
if request_valid or current_app.debug:
return f(*args, **kwargs)
else:
return abort(403)
return decorated_function
| {"/test_polling.py": ["/format_utils.py"], "/clock.py": ["/app.py"], "/twilio_connectors.py": ["/settings.py"], "/process_NBA_games.py": ["/format_utils.py", "/test_polling.py"], "/app.py": ["/settings.py", "/twilio_connectors.py", "/process_NBA_games.py"]} |
59,174 | neelparekh/ExcitingNBAGames | refs/heads/master | /process_NBA_games.py | import requests
import datetime
from format_utils import *
from typing import List, Dict, Tuple
from test_polling import *
def request_NBA_scoreboard() -> List[Dict]:
'''
Get all daily NBA game data.
Returns
-------
scoreboard: List[Dict] (json)
'''
try:
NBA_BASE='https://data.nba.net/10s/'
r = requests.get(NBA_BASE + '/prod/v2/today.json')
r.raise_for_status()
todayScoreboard = r.json()['links']['todayScoreboard']
s = requests.get(NBA_BASE + todayScoreboard)
s.raise_for_status()
scoreboard = s.json()
return scoreboard
except requests.exceptions.HTTPError as err:
raise SystemExit(err)
def get_currently_exciting_games(triggers) -> List[Dict]:
'''
Get specific information for games that are currently exciting
Parameters
----------
triggers: Dict
Returns
-------
exciting_games: List[Dict]
games that are found to be exciting based on the triggers provided
'''
##### TESTS #####
scoreboard = request_NBA_scoreboard()
games = scoreboard['games']
# games = inject_no_games()
# games = inject_no_exciting_games()
# games = inject_1_newly_exciting_1_unactivated_game()
# games = inject_2_unexciting_1_old_2_newly_exciting_games()
#################
exciting_games=[]
for game in games:
# Make sure this game has started
if not game['isGameActivated']:
continue
# Parse game info
home = game['hTeam']
away = game['vTeam']
home_name = home['triCode']
away_name = away['triCode']
home_score = format_score(home['score'])
away_score = format_score(away['score'])
score_diff = abs(home_score-away_score)
clock = format_datetime(game['clock'])
quarter = game['period']['current']
# If it's an interesting game, return the game info
triggers_time = format_datetime(triggers['time'])
score_time_ratio = score_diff / (1+int(clock.strftime('%M'))) # score diff to mins remaining
if (score_diff <= triggers['score_diff']
and clock <= triggers_time
and quarter >= triggers['quarter']
and score_time_ratio <= triggers['score_time_ratio']):
exciting_games.append({
'home_name': home_name,
'away_name': away_name,
'home_score': home_score,
'away_score': away_score,
'score_diff': score_diff,
'clock': format_clock(game['clock']),
'quarter': quarter,
})
return exciting_games
def games_to_text(games: List[Dict]) -> str:
'''
convert list of game dicts to human-readable text to be sent via SMS
Parameters
----------
games: List[Dict]
Returns
-------
text: str
'''
text = ''
for game in games:
text += " \n" +\
f"{game['home_name']} {game['home_score']} - {game['away_name']} {game['away_score']} " +\
f"{format_quarter(game['quarter'])}Q {game['clock']}"
return text
| {"/test_polling.py": ["/format_utils.py"], "/clock.py": ["/app.py"], "/twilio_connectors.py": ["/settings.py"], "/process_NBA_games.py": ["/format_utils.py", "/test_polling.py"], "/app.py": ["/settings.py", "/twilio_connectors.py", "/process_NBA_games.py"]} |
59,175 | neelparekh/ExcitingNBAGames | refs/heads/master | /format_utils.py | import datetime
from typing import List, Dict, Tuple
def format_clock(t) -> str:
if t == '': # e.g. halftime or end of quarter/game
t = '00:00'
if len(t.split(':')) == 1: # e.g. 2.6 seconds remaining
t = f"00:{int(t.split('.')[0]):02}"
return t
def format_datetime(t) -> datetime.datetime:
if t == '': # e.g. halftime or end of quarter/game
t = '00:00'
if len(t.split(':')) == 1: # e.g. 2.6 seconds remaining
t = f"00:{int(t.split('.')[0]):02}"
return datetime.datetime.strptime(t, '%M:%S').time()
def format_quarter(q) -> str:
if q > 4:
return str(q%4) + 'OT'
else:
return str(q)
def format_score(s) -> str:
if s == '':
return o
else:
return int(s) | {"/test_polling.py": ["/format_utils.py"], "/clock.py": ["/app.py"], "/twilio_connectors.py": ["/settings.py"], "/process_NBA_games.py": ["/format_utils.py", "/test_polling.py"], "/app.py": ["/settings.py", "/twilio_connectors.py", "/process_NBA_games.py"]} |
59,176 | neelparekh/ExcitingNBAGames | refs/heads/master | /settings.py | from dotenv import load_dotenv
import os
load_dotenv()
account_sid = os.getenv("TWILIO_ACCOUNT_SID")
auth_token = os.getenv("TWILIO_AUTH_TOKEN")
service_sid = os.getenv("TWILIO_SERVICE_ID")
twilio_number = os.getenv("TWILIO_PHONE_NUMBER")
ENDPOINT = os.getenv("DB_ENDPOINT")
PORT = os.getenv("DB_PORT")
DBNAME = os.getenv("DB_NAME")
REGION = os.getenv("AWS_REGION")
USER = os.getenv("DB_USER")
PW = os.getenv("DB_PW")
TIMEOUT_VALUE = 15
| {"/test_polling.py": ["/format_utils.py"], "/clock.py": ["/app.py"], "/twilio_connectors.py": ["/settings.py"], "/process_NBA_games.py": ["/format_utils.py", "/test_polling.py"], "/app.py": ["/settings.py", "/twilio_connectors.py", "/process_NBA_games.py"]} |
59,177 | neelparekh/ExcitingNBAGames | refs/heads/master | /app.py | # flask imports
from flask import Flask, render_template, request, make_response, jsonify, redirect, Blueprint, url_for, flash, abort
from flask_bootstrap import Bootstrap
from flask_login import UserMixin, LoginManager, login_user, logout_user, login_required, current_user
# external dependencies
import os
import re
from random import randint
from datetime import datetime
import mysql.connector
from mysql.connector import Error
from twilio.twiml.messaging_response import MessagingResponse
from apscheduler.schedulers.background import BackgroundScheduler
from apscheduler.triggers.cron import CronTrigger
import pytz
from dotenv import load_dotenv
from typing import List, Dict, Tuple
# custom packages
import settings
from twilio_connectors import send_SMS, validate_twilio_request
from process_NBA_games import get_currently_exciting_games, games_to_text
# Basic config for Flask site
app = Flask(__name__, static_folder="templates/static")
bootstrap = Bootstrap(app)
login_manager = LoginManager(app)
login_manager.login_view = 'auth.login'
app.config['SECRET_KEY'] = b'z=\x19\xd5\xc2\xf0\x137\xc0\n\xdc\x9a*}\xd2\xd2'
TIMEOUT_VALUE = settings.TIMEOUT_VALUE
# blueprint for auth routes in our app
auth = Blueprint('auth', __name__)
# login route
@auth.route('/login')
def login():
return render_template('login.html',pageClass="login")
# login POST (entered credentials to login)
@auth.route('/login', methods=['POST'])
def login_post():
email = request.form.get('email')
print(email)
password = request.form.get('password')
return redirect(url_for('home'))
# logout route
@auth.route('/logout')
def logout():
logout_user()
return redirect(url_for('home'))
# register blueprint
app.register_blueprint(auth)
@login_manager.user_loader
def load_user(user_id):
# since the user_id is just the primary key of our user table, use it in the query for the user
return
# home page
@app.route('/')
def home():
return render_template('home.html')
# signup route
@app.route('/signup')
def signup():
return render_template('signup.html')
@app.route("/validate_phone", methods=["POST"])
def validatePhone():
inputPhone = str(request.form['phone'])
valid = re.match("^\(?([0-9]{3})\)?[-.●]?([0-9]{3})[-.●]?([0-9]{4})$", inputPhone)
inputPhone = '+1' + re.sub(r'\D', '', inputPhone) # keep digits only
code = randint(10000,99999)
if not valid:
flash('The phone number you entered was invalid. Please try again', 'error')
return redirect(url_for('home',_anchor='getstarted'))
try:
conn = mysql.connector.connect(host=settings.ENDPOINT, database=settings.DBNAME, user=settings.USER, password=settings.PW, connection_timeout=settings.TIMEOUT_VALUE)
cur = conn.cursor()
cur.execute(f"SELECT isVerified FROM {settings.DBNAME}.users WHERE phone={inputPhone}")
results = cur.fetchall()
if results and results[0][0] == 1:
cur.close()
conn.close()
raise Exception
else:
cur.execute(f"DELETE FROM {settings.DBNAME}.users WHERE phone={inputPhone}")
conn.commit()
cur.execute(f"INSERT INTO {settings.DBNAME}.users (phone, verifyCode, verifyCodeTimeStamp, isVerified, wantsNotifications) VALUES ({inputPhone}, {code}, '{datetime.now()}', {0}, {1})")
conn.commit()
cur.close()
conn.close()
except:
flash('The phone number you entered is already in our system', 'info')
return redirect(url_for('home',_anchor='getstarted'))
try:
data = {'message_body': f"Your sportsalerts.io verification code is: {str(code)}"}
send_SMS(data=data, phone_number=inputPhone)
flash('A text message containing a 5 digit code has been sent to your number', 'info')
flash('Verify', 'verify')
return redirect(url_for('home',_anchor='getstarted'))
except:
conn = mysql.connector.connect(host=ENDPOINT, database=DBNAME, user=USER, password=PW, connection_timeout=TIMEOUT_VALUE)
cur = conn.cursor()
cur.execute(f"DELETE FROM {settings.DBNAME}.users WHERE phone={inputPhone}")
conn.commit()
cur.close()
conn.close()
flash('We were unable to send a text message to the number you provided', 'error')
return redirect(url_for('home',_anchor='getstarted'))
@app.route("/verify_phone", methods=["POST"])
def verifyPhone():
verificationCode = request.form['verifyCode']
if int(verificationCode) < 10000 or int(verificationCode) > 99999:
flash('Please enter a 5 digit code', 'error')
return redirect(url_for('home',_anchor='getstarted'))
try:
conn = mysql.connector.connect(host=settings.ENDPOINT, database=settings.DBNAME, user=settings.USER, password=settings.PW, connection_timeout=TIMEOUT_VALUE)
cur = conn.cursor()
cur = conn.cursor()
cur.execute(f"SELECT * FROM {settings.DBNAME}.users WHERE verifyCode={verificationCode}")
results = cur.fetchall()
if results:
if (datetime.now()-results[0][3]).seconds < 120:
cur.execute(f"UPDATE {settings.DBNAME}.users SET wantsNotifications=1, isVerified=1 WHERE verifyCode={verificationCode}")
conn.commit()
cur.close()
conn.close()
# send a welcome text upon successful verification.
phone_number = results[0][1]
print(f"Welcome {phone_number}!")
welcome_message = "Welcome to sportsalerts.io! You will now receive messages as soon as a game becomes exciting. You may unsubscribe by replying 'STOP'. Msg&Data rates may apply."
data = {'message_body': welcome_message}
send_SMS(data, phone_number)
# flash confirmation
flash('Verification Complete! You will now receive notifications for all close games', 'success')
return redirect(url_for('home',_anchor='getstarted'))
else:
cur.execute(f"DELETE FROM {settings.DBNAME}.users WHERE verifyCode={verificationCode}")
conn.commit()
cur.close()
conn.close()
flash('You must enter the code within 2 minutes. Please refresh & try again', 'error')
return redirect(url_for('home',_anchor='getstarted'))
else:
flash('The code you entered was incorrect. Please try again', 'error')
flash('Verify', 'verify')
return redirect(url_for('home',_anchor='getstarted'))
except:
flash('We were unable to verify your number. Please refresh the page and try again', 'error')
return redirect(url_for('home',_anchor='getstarted'))
@app.route("/receive_sms", methods=["POST"])
@validate_twilio_request
def process_incoming_SMS():
message_body = request.values.get('Body', None)
phone_number = request.values.get('From', None)
message_body = re.sub(r'[\W_]+', '', message_body).lower() # remove all punctuation
phone_number = re.sub(r'\D', '', phone_number).lower() # remove all punctuation
print(f"Message from {phone_number}: {message_body}")
resp = MessagingResponse()
try:
# get the latest verification and subscription status of the requesting phone number.
conn = mysql.connector.connect(host=settings.ENDPOINT, database=settings.DBNAME, user=settings.USER, password=settings.PW, connection_timeout=settings.TIMEOUT_VALUE)
cur = conn.cursor()
cur.execute(f"SELECT isVerified, wantsNotifications from {settings.DBNAME}.users WHERE phone={phone_number} ORDER BY verifyCodeTimeStamp DESC LIMIT 1")
response = cur.fetchall()
is_verified = response[0][0]
wants_notifications = response[0][1]
# now rocess the request
if len(response) > 0: # This phone number exists in our system.
if message_body in ['unsubscribe','stop']:
if is_verified and wants_notifications: # valid unsubscription request
try:
cur.execute(f"UPDATE {settings.DBNAME}.users SET wantsNotifications=0 WHERE phone={phone_number}")
conn.commit()
cur.close()
conn.close()
resp.message("You have been successfully unsubscribed. You may resubscribe by texting 'START'.")
except:
resp.message("Please try unsubscribing again in a few minutes.")
else: # ignore this unsubscription request!
resp.message("")
elif message_body in ['start','resubscribe']:
if is_verified and not wants_notifications: # valid re-subscription request
try:
conn = mysql.connector.connect(host=settings.ENDPOINT, database=settings.DBNAME, user=settings.USER, password=settings.PW, connection_timeout=settings.TIMEOUT_VALUE)
cur = conn.cursor()
cur.execute(f"UPDATE {settings.DBNAME}.users SET wantsNotifications=1 WHERE phone={phone_number}")
conn.commit()
cur.close()
conn.close()
resp.message("Welcome back! You may not receive notifications for games that have already begun today.")
except:
resp.message("Please try re-subscribing again in a few minutes.")
else: # ignore this resubscription request!
resp.message("")
else: # ignore this request!
resp.message("")
else: # This phone number does not exist in our system. Ignore this request!!
resp.message("")
except: # probably a larger issue and we should take care of it
raise
return str(resp)
def newly_exciting_games(cur, conn, games: List[Dict]):
'''
Find users who want to be sent SMS about newly exciting games and send them an SMS with game information.
Paramters
---------
cur: sqlite3.Cursor
cursor object from the connection to our databse
conn: mysql.connector
connection to our database
exc_games: List[Dict]
list of game info dicts
'''
try:
# get all user verified consenting phone numbers
cur.execute(f"SELECT phone FROM {settings.DBNAME}.users WHERE isVerified=1 AND wantsNotifications=1")
results = cur.fetchall()
if results: # make sure we have users before trying anything!
user_numbers = [row[0] for row in results]
# convert all game data into single text message
data = {'message_body': games_to_text(games)}
# send an individual text to each phone number
for phone_number in user_numbers:
send_SMS(data, phone_number)
# update the db with newly sent games
query_game_data = ", ".join(f"(CURRENT_TIMESTAMP(), '{game['home_name']}', '{game['away_name']}', '{game['clock']}', {game['score_diff']}, 1)" for game in games)
query_str = f"INSERT INTO {settings.DBNAME}.game_data (game_date, home, away, clock_remaining, score_diff, sent_sms) VALUES " + query_game_data
cur.execute(query_str)
conn.commit()
print(f"\tSuccess! Wrote {len(games)} new games to game_data")
else:
print(f"There were {len(games)} exciting games, but no verified active users.")
except:
raise
def update_users():
'''
This polling function checks for new games and determines if they are exciting. It then updates participating users and a database table game_data with all
newly exciting games.
'''
print(f'Checked for newly exciting games: {datetime.now()}')
triggers = {
'score_diff': 8, # int
'time': '4:00', # str (e.g. '05:00' is 5 mins remaining)
'quarter': 4, # int (note that 5 is OT, 6 is 2OT)
'score_time_ratio': 2,
}
cegs = get_currently_exciting_games(triggers)
if cegs: # if there are currently exciting games
try:
conn = mysql.connector.connect(host=settings.ENDPOINT, database=settings.DBNAME, user=settings.USER, password=settings.PW, connection_timeout=settings.TIMEOUT_VALUE)
cur = conn.cursor()
# get previously processed games from today's date
cur.execute(f"SELECT * FROM {settings.DBNAME}.game_data WHERE sent_sms=1 AND date_format(game_date, '%Y-%m-%d')=CURRENT_DATE()")
sent_games = cur.fetchall()
if sent_games:
# check if we have already sent an SMS for any of our currently exciting games and exclude those.
sent_home_names = [row[2] for row in sent_games]
newly_cegs = [ceg for ceg in cegs if ceg['home_name'] not in sent_home_names]
# if any remain, try sending a text with all newly exciting games
if len(newly_cegs)>0:
newly_exciting_games(cur, conn, newly_cegs)
else:
print("\tNo newly exciting games.")
else:
# send a text for all currently exciting games
# using this might be an issue if the write to db for game_data fails even after successfully sending the message
newly_exciting_games(cur, conn, cegs)
# close our connection to db
cur.close()
conn.close()
except:
# Probably a db connection error
raise
else:
print("\tNo games are currently exciting. :(")
if __name__ == "__main__":
scheduler = BackgroundScheduler(timezone=pytz.timezone('US/Pacific'))
scheduler.add_job(update_users, CronTrigger.from_crontab('* * * * *'), timezone=pytz.timezone('US/Pacific'))
# scheduler.add_job(refresh_games_db, 'interval', days=1, start_date='2020-09-10 00:00:00')
scheduler.start()
app.run()
| {"/test_polling.py": ["/format_utils.py"], "/clock.py": ["/app.py"], "/twilio_connectors.py": ["/settings.py"], "/process_NBA_games.py": ["/format_utils.py", "/test_polling.py"], "/app.py": ["/settings.py", "/twilio_connectors.py", "/process_NBA_games.py"]} |
59,186 | enborra/turing-camera-timelapse | refs/heads/master | /app/core/__init__.py | from .core_service import CoreService
| {"/app/core/__init__.py": ["/app/core/core_service.py"]} |
59,187 | enborra/turing-camera-timelapse | refs/heads/master | /docs/giftest.py | # Requires imageio - pip install imageio
# Requires numpy - pip install numpy
import os
import imageio
# photo_dir = './frames/'
summary_year = 2018
summary_month = 6
# summary_day = 2
# summary_hour = 24
summary_day = 3
summary_hour = 2
photo_dir = '/var/lib/turing/turing-camera-timelapse/photos/%s/%s/%s/%s' % (summary_year, summary_month, summary_day, summary_hour)
movies_dir = '/var/lib/turing/turing-camera-timelapse/movies'
export_filetype = 'mp4'
images = []
for subdir, dirs, files in os.walk(photo_dir):
for file in sorted(files):
file_path = os.path.join(subdir, file)
if file_path.endswith('.jpg'):
if not file_path.endswith('.DS_Store'):
images.append(imageio.imread(file_path))
print('loaded an image: %s' % file_path)
try:
imageio.mimsave(os.path.join(movies_dir, '%s-%s-%s-%s.mp4' % (summary_year, summary_month, summary_day, summary_hour)), images, format=export_filetype)
except Exception,e:
print('Error: %s' % e)
| {"/app/core/__init__.py": ["/app/core/core_service.py"]} |
59,188 | enborra/turing-camera-timelapse | refs/heads/master | /docs/timetest.py | from datetime import datetime
import calendar
import time
while True:
now = datetime.utcnow()
unixtime = calendar.timegm(now.utctimetuple())
minstamp = unixtime - (now.second)
print('Current timestamp: %s' % unixtime)
print('Current timestamp on the minute: %s' % minstamp)
print('---')
time.sleep(1)
| {"/app/core/__init__.py": ["/app/core/core_service.py"]} |
59,189 | enborra/turing-camera-timelapse | refs/heads/master | /app/core/core_service.py | import base64
import calendar
from io import StringIO
from datetime import datetime, timedelta
import errno
import io
import json
import os
import signal
import threading
import time
import imageio
from PIL import Image
import paho.mqtt.client as mqtt
# try:
# from picamera import PiCamera
# except Exception:
# pass
class CoreService(object):
_kill_now = False
_comm_client = None
_comm_delay = 0
_thread_comms = None
_thread_lock = None
# _camera = None
_op_timer = 0
_system_channel = '/system'
_data_channel = '/camera/frames'
_dir_app_data = '/var/lib/turing/turing-camera-timelapse'
_subdir_photos = 'photos'
_subdir_movies = 'movies'
def __init__(self):
signal.signal(signal.SIGINT, self.exit_gracefully)
signal.signal(signal.SIGTERM, self.exit_gracefully)
def start(self):
start_success = True
try:
self._ensure_directory_structure()
self._comm_client = mqtt.Client(
client_id="service_camera_timelapse",
clean_session=True
)
self._comm_client.on_message = self._on_message
self._comm_client.on_connect = self._on_connect
self._comm_client.on_publish = self._on_publish
self._comm_client.on_subscribe = self._on_subscribe
self._thread_lock = threading.RLock()
self._thread_comms = threading.Thread(target=self._start_thread_comms)
self._thread_comms.setDaemon(True)
self._thread_comms.start()
except Exception as e:
start_success = False
if start_success:
while True:
if self._op_timer >= 60:
self._ensure_summary_movies()
self._op_timer = 0
else:
self._op_timer += 1
time.sleep(0.1)
if self._kill_now:
break
else:
print('[CAMERA-TIMELAPSE] Startup routine failed. Shutting down.')
pass
def _on_connect(self, client, userdata, flags, rc):
self._comm_client.subscribe(self._data_channel)
self.output('{"sender": "service_camera_timelapse", "message": "Connected to GrandCentral."}')
def _on_message(self, client, userdata, msg):
msg_struct = None
if msg.topic == self._data_channel:
self._ensure_directory_structure()
now = datetime.utcnow()
unixtime = calendar.timegm(now.utctimetuple())
ts = unixtime - (now.second)
directory_prepped = False
try:
current_year = str(now.year)
path_photos_year = os.path.join(self._dir_app_data, self._subdir_photos, current_year)
self._ensure_directory_existence(path_photos_year)
current_month = str(now.month)
path_photos_month = os.path.join(self._dir_app_data, self._subdir_photos, current_year, current_month)
self._ensure_directory_existence(path_photos_month)
current_day = str(now.day)
path_photos_day = os.path.join(self._dir_app_data, self._subdir_photos, current_year, current_month, current_day)
self._ensure_directory_existence(path_photos_day)
current_hour = str(now.hour+1)
path_photos_hour = os.path.join(self._dir_app_data, self._subdir_photos, current_year, current_month, current_day, current_hour)
self._ensure_directory_existence(path_photos_hour)
directory_prepped = True
except Exception as e:
print('[CAMERA.TIMELAPSE] Could not prepare directory to store current photo in. Cannot store photo recieved at %s' % ts )
if directory_prepped:
try:
image_base64 = msg.payload
# ts = time.time()
# filename = '%s.png' % str(ts)
filename = os.path.join(path_photos_hour, '%s.jpg'%str(ts))
if not os.path.exists(filename):
fh = open(filename, 'wb')
fh.write(base64.b64decode(image_base64))
except Exception as e:
print('[CAMERA-TIMELAPSE] problem receiving message: %s' % e)
def _on_publish(self, mosq, obj, mid):
pass
def _on_subscribe(self, mosq, obj, mid, granted_qos):
self.output('{"sender": "service_camera_timelapse", "message": "Successfully subscribed to GrandCentral /system channel."}')
def _on_log(self, mosq, obj, level, string):
pass
def _connect_to_comms(self):
print('Connecting to comms system..')
try:
self._comm_client.connect(
'localhost',
# '10.0.1.34',
1883,
60
)
except Exception as e:
print('Could not connect to local GranCentral. Retry in one second.')
time.sleep(1)
self._connect_to_comms()
def _start_thread_comms(self):
print('Comms thread started.')
self._thread_lock.acquire()
try:
self._connect_to_comms()
finally:
self._thread_lock.release()
print('Connected to comms server.')
while True:
self._thread_lock.acquire()
try:
if self._comm_delay > 2000:
self._comm_client.loop()
self._comm_delay = 0
else:
self._comm_delay += 1
finally:
self._thread_lock.release()
def _ensure_directory_structure(self):
self._ensure_directory_existence(self._dir_app_data)
path_photos = os.path.join(self._dir_app_data, self._subdir_photos)
self._ensure_directory_existence(path_photos)
path_movies = os.path.join(self._dir_app_data, self._subdir_movies)
self._ensure_directory_existence(path_movies)
def _ensure_directory_existence(self, dir_path):
if not os.path.exists(dir_path):
try:
os.makedirs(dir_path)
except OSError as e:
if e.errno != errno.EEXIST:
raise
def _ensure_summary_movies(self):
now = datetime.utcnow()
prior_hour = now - timedelta(hours=1)
summary_year = prior_hour.year
summary_month = prior_hour.month
summary_day = prior_hour.day
# Increment hour by one since our file storage is 1-indexed
summary_hour = prior_hour.hour + 1
self._compile_summary_movie(summary_year, summary_month, summary_day, summary_hour)
def _compile_summary_movie(self, summary_year, summary_month, summary_day, summary_hour):
# photo_dir = './frames/'
# summary_year = 2018
# summary_month = 6
# summary_day = 2
# summary_hour = 24
# summary_day = 3
# summary_hour = 2
photo_dir = '/var/lib/turing/turing-camera-timelapse/photos/%s/%s/%s/%s' % (summary_year, summary_month, summary_day, summary_hour)
movies_dir = '/var/lib/turing/turing-camera-timelapse/movies'
export_filetype = 'mp4'
movie_path = os.path.join(movies_dir, '%s-%s-%s-%s.mp4' % (summary_year, summary_month, summary_day, summary_hour))
if not os.path.exists(movie_path):
images = []
success_collecting_images = False
try:
for subdir, dirs, files in os.walk(photo_dir):
for file in sorted(files):
file_path = os.path.join(subdir, file)
if file_path.endswith('.jpg'):
if not file_path.endswith('.DS_Store'):
images.append(imageio.imread(file_path))
success_collecting_images = True
except Exception as e:
print('[CAMERA.TIMELAPSE] Had an issue compiling images for a summary video. ERROR: %s' % e.message)
if success_collecting_images:
if len(images) > 0:
try:
imageio.mimsave(movie_path, images, format=export_filetype)
except Exception as e:
print('[CAMERA.TIMELAPSE] Error compiling summary video: %s' % e)
# STANDARD SERVICE OPERATION METHODS
def output(self, msg, channel=_system_channel):
if self._comm_client:
self._comm_client.publish(channel, msg)
def stop(self):
pass
def exit_gracefully(self,signum, frame):
self._kill_now = True
| {"/app/core/__init__.py": ["/app/core/core_service.py"]} |
59,190 | enborra/turing-camera-timelapse | refs/heads/master | /app/boot.py | import sys
from core import CoreService
c = CoreService()
try:
print("[CAMERA-TIMELAPSE] Booting.")
c.start()
except KeyboardInterrupt:
print("[CAMERA-TIMELAPSE] Shutting down.")
c.stop()
try:
sys.stdout.close()
except:
pass
try:
sys.stderr.close()
except:
pass
except Exception as e:
print( "Error: %s" % str(e) )
c.stop()
| {"/app/core/__init__.py": ["/app/core/core_service.py"]} |
59,199 | Sandy4321/uncertain-trees-experiments | refs/heads/master | /plot_mer_ris.py | """
Used to create combined MER/RIS figures, where we plot the MER on the x axis, and the RIS
on the y axis, with each dataset/experiment being a single point.
"""
import argparse
import itertools
from pathlib import Path
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
rcParams = matplotlib.rcParams
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True)
parser.add_argument("--expected-mer", type=float, default=0.1)
return parser.parse_args()
def main():
args = parse_args()
large_text = 16
small_text = 14
fig_width = 9
fig_height = 6
params = {
'axes.labelsize': large_text,
'font.size': small_text,
'legend.fontsize': small_text,
'xtick.labelsize': small_text,
'ytick.labelsize': small_text,
'text.usetex': True,
'figure.figsize': [fig_width, fig_height]
}
rcParams.update(params)
prefix = Path(args.input)
mer_path = prefix / "mean_error_rate.means.csv"
ris_path = prefix / "relative_interval_size.means.csv"
mers = pd.read_csv(mer_path)
mer = mers.set_index("Dataset").iloc[:-3]
ris = pd.read_csv(ris_path).set_index("Dataset").iloc[:-3]
def scatter_points(x_points, y_points, marker):
xy_df = pd.DataFrame([x_points, y_points])
plt.scatter(xy_df.iloc[0], xy_df.iloc[1], marker=marker)
markers = itertools.cycle(('o', '^', 's', 'x', '*', 'D', 'J'))
# TODO: Support any method available in csv read in
for method in ["MondrianForest", "OnlineQRF", "CPApproximate", "CPExact"]:
scatter_points(mer[method], ris[method], marker=next(markers)) # Can use util or ris on the y axis
plt.legend()
plt.xlabel("MER")
plt.ylabel("RIS")
plt.ylim(ymax=1.05, ymin=-0.05)
plt.axvline(x=args.expected_mer, linestyle='dashed', color='grey')
plt.savefig(prefix / "combined-mer-ris.pdf", bbox_inches='tight')
if __name__ == '__main__':
main() | {"/moa_experiments.py": ["/parameter_sweep.py"], "/skgarden_experiments.py": ["/evaluation_functions.py"], "/interval_metrics.py": ["/generate_figures.py"]} |
59,200 | Sandy4321/uncertain-trees-experiments | refs/heads/master | /confidence_utility_calculation.py | import argparse
from collections import OrderedDict
from pathlib import Path
import pandas as pd
import numpy as np
from tabulate import tabulate
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True,
help="A dir containing output from interval_metrics.py")
parser.add_argument("--output", default="",
help="The folder to create the output in")
parser.add_argument("--overwrite", action="store_true", default=False,
help="When given, will not check if the output folder already exists ,"
"potentially overwriting its contents.")
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
inpath = Path(args.input)
outpath = Path(args.output if args.output != "" else args.input) / "adjusted_utility"
# Read in the error and ris csv's
error_rate_means = pd.read_csv(inpath / "error_rate.means.csv")\
.set_index("Dataset").iloc[:-3, :] # Drop aggregate stats rows
ris = pd.read_csv(inpath / "relative_interval_size.means.csv")\
.set_index("Dataset").iloc[:-3, :]
# Extract the percentages from the column titles and subtract the expected confidence from 1 to get significance
cols = error_rate_means.columns.tolist()
significances = [1 - float(col) for col in cols]
# For each column in the error df, use the respective significance and ris to calculate the adj utility
util_dict = OrderedDict()
# (Some) code duplication from interval_metrics.py
def dropoff(util, err, expected_mer, func):
if err > expected_mer:
return max(func(util, err, expected_mer), 0)
else:
return max(util, 0)
# Vectorize the function
tuf = np.vectorize(dropoff)
# Linear dropoff, utility is zero when MER is twice bigger than expected
def linear(util, error, expected_mer):
return max(util, 0) * max((2 - (error / expected_mer)), 0)
for column, significance in zip(error_rate_means, significances):
ris_col = ris[column]
error_col = error_rate_means[column]
util_col = 1 - ris_col
weight_col = np.maximum((2 - (error_col / significance)), 0)
util_dict[column] = tuf(util_col, error_col, significance, linear)
def add_stats(df):
df.loc['Mean'] = df.mean()
df.loc['Median'] = df.median()
df.loc['Std'] = df.std()
return df
util_df = add_stats(pd.DataFrame(util_dict, index=ris.index, columns=cols))
util_df.to_csv(outpath.with_suffix(".csv"))
def create_table_str(df):
float_format = ".2f"
return tabulate(df, headers='keys', tablefmt='latex_booktabs', floatfmt=float_format)
util_table_str = create_table_str(util_df)
outpath.with_suffix(".tex").write_text(util_table_str)
| {"/moa_experiments.py": ["/parameter_sweep.py"], "/skgarden_experiments.py": ["/evaluation_functions.py"], "/interval_metrics.py": ["/generate_figures.py"]} |
59,201 | Sandy4321/uncertain-trees-experiments | refs/heads/master | /generate_figures.py | """
Creates figures for a specific metric from a directory containing subdirs of result csv files.
Will aggregate all the results for every dataset, and plot its mean
value along with its std across experiments.
Will also create a collection of latex tables and corresponding csv output.
Used to plot the output from the skgarden_experiments and moa_experiments scripts.
"""
import argparse
from collections import defaultdict, OrderedDict
from pathlib import Path
import itertools
import logging
import json
import matplotlib.pyplot as plt
import pandas as pd
from pylab import *
from tabulate import tabulate
from natsort import natsorted
plt.style.use('seaborn-whitegrid')
rcParams = matplotlib.rcParams
METHOD_RENAMES = {"OoBConformalApproximate": "CPApproximate", "OoBConformalRegressor": "CPExact",
"MondrianForest": "MondrianForest", "PredictiveVarianceRF": "PredictiveVarianceRF",
"OnlineQRF": "OnlineQRF"}
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--input", required= True,
help="A dir containing sub-dirs of results, one per method."
"The sub-dirs contain csv files with output, one per dataset, per"
"experiment repeat."
"Sub-directory names will be used as method names in the output.")
parser.add_argument("--output", required= True,
help="The folder to create the output in")
parser.add_argument("--overwrite", action="store_true", default=False,
help="When given, will not check if the output folder already exists ,"
"potentially overwriting its contents.")
parser.add_argument("--ris-figures", action="store_true", default=False,
help="When given, will create window RIS instead of MIS figures."
"Requires that prediction files are present!")
parser.add_argument("--dont-create-tables", action="store_true", default=False,
help="When given, will not create table files")
parser.add_argument("--dont-create-figures", action="store_true", default=False,
help="When given, will not generate figures.")
parser.add_argument("--use-tex", action="store_true", default=False,
help="When given, will use the Tex engine to create text for the figures (slower)")
parser.add_argument("--expected-error", default=0.1,
help="The expected error level (significance) for the experiment. "
"Used to draw the expected error horizontal line and calculate "
"the MER deviation.")
parser.add_argument("--mark-every", default=1, type=int,
help="Place a marker on the figures every this many data points.")
parser.add_argument("--fig-height", type=int, default=6)
parser.add_argument("--fig-width", type=int, default=6)
alg_selection = parser.add_mutually_exclusive_group()
alg_selection.add_argument("--include-only", nargs='+',
help="Include only the provided input directories")
alg_selection.add_argument("--exclude", nargs='+',
help="Exclude the provided input directories")
return parser.parse_args()
def gather_method_results(res_path: Path):
""" Reads csv files into lists of pandas dfs.
:param res_path: Path
A Path object to the location of the result csv's.
The expected filenames are formatted as dataset_name_X.csv,
where X is the experiment repeat number.
:return: A dict from dataset name to a list of result dataframes
"""
res = defaultdict(list)
for res_file in res_path.glob("*.csv"):
if res_file.match("*.time.csv") or res_file.match("*.pred.csv"): # Avoid parsing time/pred files
continue
base_name = res_file.name[:-6] # _X.csv is 6 characters
df = pd.read_csv(res_file)
res[base_name].append(df)
return res
def gather_metric(results_dict, metric):
"""
Returns a dict from dataset name to a df containing the aggregated
measurements for the requested metric.
:param results_dict: {dataset_name: list_of_result_dataframes}
The list has one dataframe per experiment, with all the metrics.
It corresponds to the output of gather_method_results().
:param metric: String, name of metric (common across dataframes)
:return: dict {dataset_name: dataframe_of_metric}
The df has one line per experiment, each column is the measurement
for one window.
"""
dataset_to_metric = {}
for ds_name, df_list in results_dict.items():
# TODO: Probably faster to do the projection before the concat
metric_df = pd.concat(df_list, axis=1).loc[:, [metric]].T
dataset_to_metric[ds_name] = metric_df
return dataset_to_metric
def plot_metric(method_metric_dict, dataset_name, x_axis, metric_name, markevery, normalization=None):
"""Returns an error-bar plot of the aggregated statistics (mean, var) for
a specific dataset and metric, for each method in the provided dict.
:param method_metric_dict: {method: {ds_name: measurements_df}}
Maps each method to dataset names, each one with one aggregate
measurements df.
The df has one line per experiment, each column is the measurement
for one window.
:param dataset_name: The specific dataset name we want to plot.
:param x_axis: The x axis values tha correspond to the y measurements.
:param metric_name: The name of the metric we are plotting
:param markevery: int
Place a marker every this many points on the lines.
:param normalization: A number that we use to normalize the measurements by. This is used
to produce relative interval sizes instead of absolute.
:return: A matplotlib Axes object, containing the plotted figure (no legends)
"""
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel('Instance')
ax.set_ylabel(metric_name.title())
marker = itertools.cycle(('o', '^', 's', 'x', '*', 'D', 'J'))
# Get the method name, and its measurements for all datasets
for method, ds_metric_dict in method_metric_dict.items():
# Get the measurements for the requested data, and calc their stats
metric_df = ds_metric_dict[dataset_name]
if normalization:
metric_df = metric_df / normalization
mu = metric_df.mean()
std_dev = metric_df.std()
# Plot the line for the method and dataset
ax.plot(x_axis, mu, label=method, marker=next(marker), markevery=markevery)
ax.fill_between(x_axis, mu - std_dev, mu + std_dev, alpha=.25, linewidth=0)
return ax
def sort_nicely(l):
""" Sort the given list of Path objects in the way that humans expect.
"""
return sorted(l, key=lambda x: int(x.name) if x.name.isdigit() else x.name)
def create_tables(method_metric_dict, outpath, expected_error):
"""Creates a Latex booktabs table for a specific dataset and metric, for
each method in the provided dict as well as a csv representation,
and writes them both to disk. The metrics are aggregated over windows,
and their mean and stddev are written to disk.
:param method_metric_dict: {method: {ds_name: measurements_df}}
Maps each method to dataset names, each one with one aggregate
measurements df.
The df has one line per experiment, each column is the measurement
for one window.
:param outpath: Path
A Path object to the output tex file we will create.
:param expected_error: float
The expected error rate
"""
mean_method_to_measurements = OrderedDict()
std_method_to_measurements = OrderedDict()
median_method_to_measurements = OrderedDict()
mean_deviation_method_to_measurements = OrderedDict()
all_names = []
for method, ds_metric_dict in method_metric_dict.items():
ds_names = []
ds_means = []
ds_medians = []
ds_stds = []
ds_deviation_means = []
# Get the measurements for the requested data, and calc their stats
for dataset_name, metric_df in natsorted(ds_metric_dict.items()):
window_mean = metric_df.mean()
window_std = metric_df.std()
mean_window_deviation = abs(window_mean - expected_error).mean()
# TODO: One DF per dataset, with all methods together, will need to work around iteration order
ds_outpath = outpath / dataset_name
ds_outpath.mkdir(exist_ok=True, parents=True)
window_mean.to_csv(ds_outpath.joinpath("{}_window_means.csv".format(method)))
window_std.to_csv(ds_outpath.joinpath("{}_window_std.csv".format(method)))
overall_mean = window_mean.mean()
overall_median = window_mean.median()
overall_std = window_std.mean()
ds_names.append(dataset_name)
ds_means.append(overall_mean)
ds_medians.append(overall_median)
ds_stds.append(overall_std)
ds_deviation_means.append(mean_window_deviation)
all_names.append(ds_names)
mean_method_to_measurements[method] = ds_means
median_method_to_measurements[method] = ds_medians
std_method_to_measurements[method] = ds_stds
mean_deviation_method_to_measurements[method] = ds_deviation_means
# if outpath.name == "mean_error_rate":
# ds_outpath.joinpath("{}_mean_window_deviation.csv".format(method)).write_text(
# "method,mean_window_deviation\n{},{}\n".format(method, mean_window_deviation))
# Assert datasets are in correct order between methods
prev_names = []
for ds_names in all_names:
if len(prev_names) == 0:
prev_names = ds_names
continue
for left_name, right_name in zip(prev_names, ds_names):
assert left_name == right_name, \
"DS order mismatch: {}, {}".format(left_name, right_name)
def create_df(method_to_measurements_dict):
dictionary = OrderedDict()
dictionary["Dataset"] = ds_names
dictionary.update(method_to_measurements_dict)
df = pd.DataFrame(dictionary)
df = df.set_index("Dataset")
df.loc['Mean'] = df.mean()
df.loc['Median'] = df.median()
df.loc['Std'] = df.std()
return df
order = ["MondrianForest", "OnlineQRF", "CPApproximate", "CPExact"]
mean_aggregate_metric_df = create_df(mean_method_to_measurements)[order]
std_aggregate_metric_df = create_df(std_method_to_measurements)[order]
median_aggregate_metric_df = create_df(median_method_to_measurements)[order]
mean_aggregate_metric_df.to_csv(outpath.with_suffix(".means.csv"))
std_aggregate_metric_df.to_csv(outpath.with_suffix(".stds.csv"))
median_aggregate_metric_df.to_csv(outpath.with_suffix(".medians.csv"))
def create_table_str(df):
float_format = ".3f" if outpath.name == "mean_error_rate" else ".2f"
return tabulate(df, headers='keys', tablefmt='latex_booktabs', floatfmt=float_format)
table_str = create_table_str(mean_aggregate_metric_df)
std_table_str = create_table_str(std_aggregate_metric_df)
median_table_str = create_table_str(median_aggregate_metric_df)
outpath.with_suffix(".means.tex").write_text(table_str)
outpath.with_suffix(".stds.tex").write_text(std_table_str)
outpath.with_suffix(".medians.tex").write_text(median_table_str)
if outpath.name == "mean_error_rate":
mean_deviation_df = create_df(mean_deviation_method_to_measurements)
mean_deviation_df.to_csv(outpath.with_suffix(".deviations.csv"))
mean_deviation_table_str = create_table_str(mean_deviation_df)
outpath.with_suffix(".deviations.tex").write_text(mean_deviation_table_str)
def main():
args = parse_args()
# l=16/s=14 for airlines plots
large_text = 16
small_text = 14
params = {
'axes.labelsize': large_text,
'font.size': small_text,
'legend.fontsize': small_text,
'xtick.labelsize': small_text,
'ytick.labelsize': small_text,
'text.usetex': args.use_tex,
'figure.figsize': [args.fig_width, args.fig_height]
}
rcParams.update(params)
input_path = Path(args.input).absolute()
output_path = Path(args.output).absolute()
# TODO: Allow custom order
# fig_order =
# args.table_order if args.table_order is not None else ["MondrianForest", "OnlineQRF", "CPApproximate", "CPExact"]
assert output_path.parent != input_path, "Setting output path under input can cause issues, choose another path."
output_path.mkdir(parents=True, exist_ok=args.overwrite)
# Get all the directories under the input path
method_dirs = [subpath for subpath in input_path.iterdir() if subpath.is_dir()]
if args.exclude is not None:
method_dirs = [subpath for subpath in method_dirs if subpath.name not in args.exclude]
elif args.include_only is not None:
method_dirs = [subpath for subpath in method_dirs if subpath.name in args.include_only]
# Gather the results for each method
# Format: {method: {ds_name: measurement_df_list}}
method_to_dsname_to_result_df_list = OrderedDict()
sorted_dirs = sort_nicely(method_dirs)
for method_dir in sorted_dirs:
method_to_dsname_to_result_df_list[method_dir.name] = gather_method_results(method_dir)
json_file = output_path / "generate-figures-settings.json"
json_file.write_text(json.dumps({"plot_params": params, "args": vars(args)}))
for metric in ["mean error rate", "mean interval size"]:
# Aggregate the list of result df to a single df per dataset, per method.
# Format: {method: {ds_name: measurements_df}}
# Each line in measurements_df is one experiment
method_ds_metric = OrderedDict()
for method, ds_to_measurements in method_to_dsname_to_result_df_list.items():
# TODO: Make it possible to iterate over metrics?
method_ds_metric[method] = gather_metric(ds_to_measurements, metric)
# TODO: Check if same number of experiments exists for each method
# Will try to rearrange columns in this order:
# [MondrianForest, OnlineQRF, CPApproximate, CPExact].
# This is the order used in the paper.
# TODO: Take table order from arguments, have a sensible default with exception handling
# order = sorted(method_ds_metric.keys())
original_dict = method_ds_metric
try:
if "SGDQR" in method_ds_metric:
order = ["SGDQR", "MondrianForest", "OnlineQRF", "CPApproximate", "CPExact"]
else:
order = ["MondrianForest", "OnlineQRF", "CPApproximate", "CPExact"]
if args.exclude is not None:
for excluded_method in args.exclude:
order.remove(excluded_method)
method_ds_metric = OrderedDict((k, method_ds_metric[k]) for k in order)
except KeyError:
method_ds_metric = original_dict
# method_ds_metric = OrderedDict((k, method_ds_metric[k]) for k in order)
# Gather the names of datasets
ds_names = set()
for method, ds_name_to_measure in method_ds_metric.items():
ds_names.update(ds_name_to_measure.keys())
# All methods should have same x_axis, so just choose one
sample_method = list(method_to_dsname_to_result_df_list.keys())[0]
if not args.dont_create_tables:
table_outpath = Path(args.output) / metric.replace(' ', '_')
create_tables(method_ds_metric, table_outpath, args.expected_error)
# Create and save one figure per dataset
for dataset in ds_names:
if args.dont_create_figures:
break
# From one of the methods, for this dataset, first experiment, get the index, as ints
try:
# If this doesn't work, we don't have MOA generated experiments
x_axis = method_to_dsname_to_result_df_list[sample_method][dataset][0]["learning evaluation instances"].astype(int)
except KeyError:
# In which case we should have only Python-generated experiments, which should have an index column
x_axis = method_to_dsname_to_result_df_list[sample_method][dataset][0]["index"].astype(int)
# If we asked for RIS instead of MIS, we need to get the true values, which are in the .pred.csv files
if metric == "mean interval size" and args.ris_figures:
# We just need the true value range, so the first pred file will do, they're all the same anyway
pred_file = method_dirs[0].joinpath(dataset + "_0.pred.csv")
try:
preds = pd.read_csv(pred_file)
except FileNotFoundError:
raise FileNotFoundError("Could not find prediction file {}.\n"
"You can create these by running interval_metrics.py!".format(pred_file))
value_range = preds["true_value"].max() - preds["true_value"].min()
plot_metric(method_ds_metric, dataset, x_axis, "Relative Interval Size", args.mark_every, value_range)
else:
ax = plot_metric(method_ds_metric, dataset, x_axis, metric, args.mark_every)
if metric == "mean error rate":
ax.axhline(y=args.expected_error, linestyle='dashed', color='grey')
plt.legend()
filename = metric
if metric == "mean interval size" and args.ris_figures:
filename = "relative interval size"
outpath = Path(args.output) / (dataset + "-" + filename.replace(' ', '_') + ".pdf")
plt.savefig(str(outpath), bbox_inches='tight')
if __name__ == "__main__":
main()
| {"/moa_experiments.py": ["/parameter_sweep.py"], "/skgarden_experiments.py": ["/evaluation_functions.py"], "/interval_metrics.py": ["/generate_figures.py"]} |
59,202 | Sandy4321/uncertain-trees-experiments | refs/heads/master | /moa_experiments.py | """
Runs a number of experiments using MOA meta-learners.
The user provides an input which contains a number of arff files for regression, and a MOA
EvaluatePrequentialRegression(IntervalRegressionPerformanceEvaluator) task is run on each one.
The output is one csv file per dataset, per experiment repeat.
Usage: python moa_experiments.py --moajar /path/to/moa.jar --input /path/to/data --meta OnlineQRF
"""
import argparse
import json
from collections import defaultdict
from pathlib import Path
from subprocess import run
from joblib import Parallel, delayed
from parameter_sweep import run_print
def main():
parser = argparse.ArgumentParser(description="Runs MOA experiments and stores results into files")
parser.add_argument("--moajar", help="Path to MOA jar", required=True)
parser.add_argument("--input", help="A directory containing one or more arff data files", required=True)
parser.add_argument("--meta", help="The meta algorithm to use for training", required=True,
choices=["OnlineQRF", "CPApproximate", "PredictiveVarianceRF",
"CPExact", "SGDQR"])
parser.add_argument("--repeats", help="Number of times to repeat each experiment", type=int, default=1)
parser.add_argument("--window", help="Performance report window size", type=int, default=1000)
parser.add_argument("--njobs", type=int, default=1,
help="Number of experiment jobs to run in parallel, max one per input file")
parser.add_argument("--learner-threads", help="Number of threads to use for the learner", type=int, default=1)
parser.add_argument("--output", help="The directory to place the output files. If not given, creates "
"directory under input, using the meta learner name.")
parser.add_argument("--ensemble-size", help="The size of the ensemble", type=int, default=10)
parser.add_argument("--max-calibration-instances", type=int, default=1000,
help="The max size of the calibration set.")
parser.add_argument("--num-bins", type=int, default=100,
help="Number of bins to use for each leaf histogram.")
parser.add_argument("--confidence", help="The confidence level of the predictions", type=float, default=0.9)
parser.add_argument("--stdout", default=False, action="store_true",
help="When given, output results to stdout only instead of file")
parser.add_argument("--overwrite", default=False, action="store_true",
help="When given, it will not check if the output folder exists already.")
parser.add_argument("--dont-save-predictions", default=False, action="store_true",
help="When given, will not create file with predictions.")
parser.add_argument("--dont-measure-model-size", default=False, action="store_true",
help="When given, will not report the size of the model in the results.")
parser.add_argument("--verbose", type=int, default=0,
help="Set to 1 output per experiment, 2 to write MOA output to stdout.")
args = parser.parse_args()
# Tailor the command to each framework
task = "EvaluatePrequentialRegression -e (IntervalRegressionPerformanceEvaluator -w {})".format(args.window)
moa_jar_path = Path(args.moajar)
moa_dir = moa_jar_path.parent
command_prefix = "java -Xmx62g -cp \"{moa_jar}:{moa_dir}/dependency-jars/*\" ".format(moa_dir=moa_dir, moa_jar=moa_jar_path)
if not args.dont_measure_model_size:
command_prefix += "-javaagent:{}/dependency-jars/sizeofag-1.0.0.jar ".format(moa_dir)
# Set up input and output dirs
data_path = Path(args.input).absolute()
if args.meta == "OnlineQRF":
base_learner = "(trees.FIMTQR -e)"
else:
base_learner = "(trees.FIMTDD -e)"
# If the user did not provide an output dir, put results under the data folder
if args.output is None and not args.stdout:
output_path = (data_path / args.meta).absolute()
print("Will try to create directory {} to store the results".format(output_path))
else:
output_path = Path(args.output).absolute()
# Create the output dir if needed
output_path.mkdir(parents=True,
exist_ok=args.overwrite)
# Run experiments for each data file
commands = []
commands_per_file = defaultdict(list)
arff_files = data_path.glob("*.arff")
if len(list(arff_files)) == 0: # Note: this exhausts the iterator, need a new one
raise FileNotFoundError("No arff files found in {} !".format(data_path))
for arff_file in data_path.glob("*.arff"):
if args.meta != "SGDQR":
learner = "meta.{meta} -l {base} -s {size} -a {confidence} -j {threads}".format(
meta=args.meta, base=base_learner, size=args.ensemble_size,
confidence=args.confidence, threads=args.learner_threads)
else:
learner = "meta.{meta} -a {confidence}".format(
meta=args.meta, confidence=args.confidence)
if args.meta not in ["OnlineQRF", "SGDQR"]: # Then it's a CP method
learner += " -i {cal_size} ".format(cal_size=args.max_calibration_instances)
elif args.meta == "OnlineQRF":
learner += " -b {}".format(args.num_bins)
for i in range(args.repeats):
command = command_prefix + "moa.DoTask \" {task} -l ({learner}) " \
"-s (ArffFileStream -f {arff_file}) " \
"-f {window}".format(task=task, learner=learner,
arff_file=data_path / arff_file,
window=args.window)
if not args.stdout:
command += " -d {}".format(output_path / (arff_file.stem + "_{}.csv".format(i)))
if not args.dont_save_predictions:
command += " -o {}".format(output_path / (arff_file.stem + "_{}.pred".format(i)))
command += "\"" # Quotes necessary because of parentheses
commands_per_file[arff_file].append(command)
commands.append(command)
# print("Executing command: {}".format(command))
# Parallelize over files, ensuring that there's only one process at any time reading the
# same file. Otherwise parallel performance is crap. If not enough files, compensate with
# learner_threads > 1
# TODO: This is horrible job separation, long-running jobs are holding back the rest
# at each repeat iteration. Figure out different way.
with Parallel(n_jobs=args.njobs, verbose=args.verbose) as parallel:
for i in range(args.repeats):
print("Running repeat {}/{}".format(i+1, args.repeats))
parallel(delayed(run_print)(commands[i])
for arff_file, commands in commands_per_file.items())
# Write the settings for the experiment
json_file = output_path / "settings.json"
settings = vars(args)
json_file.write_text(json.dumps(settings))
print("\nResults files created under {}".format(output_path))
if __name__ == '__main__':
main()
| {"/moa_experiments.py": ["/parameter_sweep.py"], "/skgarden_experiments.py": ["/evaluation_functions.py"], "/interval_metrics.py": ["/generate_figures.py"]} |
59,203 | Sandy4321/uncertain-trees-experiments | refs/heads/master | /computational_measures.py | """
Used to create the computational measures for a single method. We create a csv/tex file with
the total runtime and final model size for every dataset for the method, and provide aggregate
metrics.
"""
import argparse
from pathlib import Path
import json
import pandas as pd
from tabulate import tabulate
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--input", required= True,
help="A dir containing csvs of results, one per repeat.")
parser.add_argument("--output", required=True,
help="The folder to create the output in, the last part of the input"
" (method name) will be appended.")
parser.add_argument("--overwrite", action="store_true",
help="When given, will not check if the output folder already exists ,"
"potentially overwriting its contents.")
return parser.parse_args()
def main():
args = parse_args()
input_path = Path(args.input)
# Append the method name to the output as a subdir
output_path = Path(args.output).joinpath(input_path.name)
assert len(list(input_path.glob("*.csv"))) > 0, "No experiment files found under {}".format(input_path)
assert output_path.parent != input_path, "Setting output path under input can cause issues, choose another path."
output_path.mkdir(parents=True, exist_ok=args.overwrite)
computational_metrics = ['evaluation time (cpu seconds)',
'model serialized size (bytes)']
json_file = output_path / "computational-measures-settings.json"
json_file.write_text(json.dumps(vars(args)))
# This df will have one row for each experiment
combined_df = pd.DataFrame(columns=computational_metrics)
experiment_values = {}
# TODO: Support for multiple method directories, creation of comparison tables
experiment_index = 0
# We iterate through each output file, get its last line and extract the computational metrics
for experiment_file in input_path.glob("*.csv"):
# Ignore other output files
if experiment_file.match("*.pred.csv") or experiment_file.match("*.time.csv"):
continue
# Get the experiment name, i.e. dataset and repeat number
experiment_value = experiment_file.stem
# We maintain a dict from experiment index to name, we use this as the combined_df index later
experiment_values[experiment_index] = experiment_value
df = pd.read_csv(experiment_file)
# Get the last line of the results dataframe, we want the final measurements of the requested metrics
df = df.iloc[[-1]]
metrics_df = df[computational_metrics]
# Append the measurements to the combined dataframe
combined_df = combined_df.append(metrics_df, ignore_index=True)
experiment_index += 1
combined_df = combined_df.rename(experiment_values, axis='index').sort_index(ascending=True)
# Calculate aggregate metrics
def include_aggregates(df):
df.loc['Mean'] = df.mean()
df.loc['Median'] = df.median()
df.loc['Std'] = df.std()
return df
combined_df = include_aggregates(combined_df)
def create_table_str(df):
# float_format = [".2f", ".4f", ".2f"] # if outpath.name == "mean_error_rate" else ".2f"
return tabulate(df, headers='keys', tablefmt='latex_booktabs')
# Create csv and tex output
combined_df.to_csv(output_path / "computational_metrics.csv")
table_str = create_table_str(combined_df)
table_file = output_path / "computational_metrics.tex"
table_file.write_text(table_str)
if __name__ == '__main__':
main() | {"/moa_experiments.py": ["/parameter_sweep.py"], "/skgarden_experiments.py": ["/evaluation_functions.py"], "/interval_metrics.py": ["/generate_figures.py"]} |
59,204 | Sandy4321/uncertain-trees-experiments | refs/heads/master | /evaluation_functions.py | import sys
import pickle
from collections import defaultdict
from time import perf_counter
import numpy as np
from tqdm import trange
from scipy.stats import norm
# TODO: Since these break the "y_pred is a column vector convention", is there
# any point in using them as sklearn metrics?
# Maybe it's better to break away from sklearn API
def mean_interval_size(y_true, y_interval):
"""
Calculates the mean interval size from a interval prediction array.
:param y_true: Not used, here for compatibility with scorer
:param y_interval: A n x 2 Numpy array. First column is lower interval, second is upper
:return: The average size of the intervals
"""
# y_true is not used, but we keep it to maintain function signature as scorers expect it
interval_size = y_interval[:, 1] - y_interval[:, 0] # Guaranteed to be > 0
return np.average(interval_size)
def mean_error_rate(y_true, y_interval):
"""
Calculates the mean error rate in the provided intervals
:param y_true: A numpy column array of true values
:param y_interval: A n x 2 Numpy array. First column is lower interval, second is upper
:return: The ratio of values in y_true that are within their corresponding interval in y_interval
"""
wrong_intervals = ((y_true < y_interval[:, 0]) | (y_true > y_interval[:, 1])).sum()
return wrong_intervals / y_true.shape[0]
def prequential_interval_evaluation(estimator, X, y, confidence, scoring, window_size=1000, verbose=0, additional_output=None):
"""
Prequential evaluation of an estimator by testing then training with
each example in sequence. If a window size is set the average of a tumbling window
is reported.
:param estimator: Has to support a partial_fit function
:param X: numpy array
Feature data
:param y: numpy column vector
Labels
:param confidence: float
The desired confidence level for the predictions, (0, 1.0).
:param scoring: callable or dict
If a callable, it should take y_true and y_predicted_interval (Numpy arrays) as arguments
and return a scalar metric.
dicts should have a mapping metric_name : callable (as above). can be used to report multiple
metrics.
:param window_size: int
The size of the tumbling window, we average the metric(s) every x data points
:param verbose: int
If > 0 will display experiment progress bar. If > 1 will also print statistics per window.
:param additional_output: None or Pathlib object.
When provided, will write additional information about the run to files. These include the prediction and
interval estimates, timings, and serialized model size.
:return: List or dict
If scoring is a callable, will return a list of scores, size should be ceil(n_samples / window_size)
If scoring is a dict, will return a dict {metric_name: list of scores}, list size should be
ceil(n_samples / window_size)
"""
n_samples = X.shape[0]
if isinstance(scoring, dict):
# Scores are dicts {metric_name: list of scalar scores}
window_scores = defaultdict(list)
test_scores = defaultdict(list)
else:
# Scores are lists of scalar scores
window_scores = []
test_scores = []
window_elements = 0
window_count = 0
total_windows = int(np.ceil(n_samples / window_size))
window_start = perf_counter()
def predict_interval(estimator_, data, confidence_):
"""
Returns a an interval prediction from an estimator that provides the std of a normal distribution
around the prediction.
:param estimator: An estimator that provides a .predict(X, return_std=True) function, and returns
the mean and std of a normal distribution.
:param data: A numpy array of features
:param confidence_: The desired confidence level, i.e. 1 - desired_error.
:return: A prediction interval as a numpy array of floats.
"""
assert np.isscalar(confidence_), "Confidence should be a scalar"
if hasattr(estimator_, 'vw_predict_interval'): # Duck typing for the function, defined for VW but not for MF
interval_tuple = estimator.vw_predict_interval(data)
else:
ensemble_mean, std = estimator_.predict(data, return_std=True)
std += 1e-6 # Avoid NaNs. TODO: Better solution? How are std=0 produced??
interval_tuple = norm.interval(confidence_, loc=ensemble_mean, scale=std)
return np.concatenate((interval_tuple[0][:, np.newaxis], interval_tuple[1][:, np.newaxis]), axis=1)
total_duration = 0
pred_file = None
if additional_output is not None:
pred_file = additional_output.open('w')
for i in trange(n_samples, disable=(not verbose)):
if i == 0:
# sklearn does not allow prediction on an untrained model
estimator.partial_fit(X[i, np.newaxis], y[i, np.newaxis])
y_interval = predict_interval(estimator, X[i, np.newaxis], confidence)
y_true = y[i, np.newaxis]
if pred_file is not None:
pred_file.write("{}, {}\n".format(y_interval.flatten(), y_true))
if isinstance(scoring, dict):
for score_name, score_func in scoring.items():
window_scores[score_name].append(score_func(y_true, y_interval))
else:
window_scores.append(scoring(y_true, y_interval))
if i == 0:
continue
window_elements += 1 # Easier than checking inside window_scores
# We add a final result every time we have gather window_size values,
# or we've reached the end of the data (regardless of number of points in window)
if window_elements == window_size or i == n_samples - 1:
window_count += 1
if isinstance(scoring, dict):
assert isinstance(window_scores, dict)
for score_name, score_list in window_scores.items():
window_sum = np.sum(score_list)
test_scores[score_name].append(window_sum / len(score_list))
if verbose > 1:
print("Window {}/{}: {}: {}".format(
window_count, total_windows, score_name, window_sum / len(score_list)))
else:
window_sum = np.sum(window_scores)
# Divide by current window size here, window is possibly incomplete
test_scores.append(window_sum / len(window_scores))
if verbose > 1:
print("Window {}/{}: {}".format(
window_count, total_windows, window_sum / len(test_scores)))
window_scores.clear()
window_elements = 0
window_end = perf_counter()
window_duration = window_end - window_start
total_duration += window_duration
if additional_output is not None:
test_scores["window_time"].append(window_duration)
test_scores["evaluation time (cpu seconds)"].append(total_duration)
model_size = sys.getsizeof(pickle.dumps(estimator))
test_scores["model serialized size (bytes)"].append(model_size)
if verbose > 1:
print("Time to process window: {} sec".format(window_duration))
print("Model size: {} bytes, {} MiB".format(model_size, model_size/1024**2))
window_start = perf_counter()
estimator.partial_fit(X[i, np.newaxis], y[i, np.newaxis])
return test_scores
| {"/moa_experiments.py": ["/parameter_sweep.py"], "/skgarden_experiments.py": ["/evaluation_functions.py"], "/interval_metrics.py": ["/generate_figures.py"]} |
59,205 | Sandy4321/uncertain-trees-experiments | refs/heads/master | /parameter_sweep.py | #!/usr/bin/env python
"""
Simple python script to sweep over one or two parameters for our experiments.
"""
import argparse
from subprocess import run
from pathlib import Path
import numpy as np
from joblib import Parallel, delayed
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--command", required=True,
help="The base command to run, this does not change across sweeps")
parser.add_argument("--sweep-argument", required=True,
help="The argument to sweep on, e.g. \"meta\"")
parser.add_argument("--output-prefix", required=True,
help="The prefix for the output argument")
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument("--argument-list", nargs='+',
help="A space-separated list of arguments to use, e.g. \"OnlineQRF CPExact\"")
group.add_argument("--argument-range", nargs=3,
help="A string representation of an argument range, enter"
"\"start end step\" as in np.arange(start, end, step).")
parser.add_argument("--njobs", type=int, default=1,
help="Number of experiment jobs to run in parallel, max one per input file")
parser.add_argument("--verbose", type=int, default=0,
help="Set to 1 output per experiment, 2 to write MOA output to stdout.")
parser.add_argument("--inner-sweep-argument",
help="The argument to sweep on")
group = parser.add_mutually_exclusive_group()
group.add_argument("--inner-argument-list", nargs='+',
help="A list of arguments to run")
group.add_argument("--inner-argument-range", nargs=3,
help="A string representation of an argument range, enter "
"\"start end step\" as in np.arange(start, end, step)")
return parser.parse_args()
def run_print(cur_command, order=""):
print("Running command:\n{}".format(cur_command))
if order != "":
print("Task index: {}".format(order))
run(cur_command, shell=True)
def main():
args = parse_args()
def parse_sweep(argument_list, argument_range):
if argument_list is not None:
sweep = argument_list
else:
# We use this trick to correctly parse ints or float from string, so
# that the meta-args end up with the correct type
from ast import literal_eval as le
range_args = [le(x) for x in argument_range]
start, end, step = range_args
sweep = np.arange(start, end, step)
return sweep
outer_sweep = parse_sweep(args.argument_list, args.argument_range)
inner_sweep = None
if args.inner_argument_list is not None or args.inner_argument_range is not None:
inner_sweep = parse_sweep(args.inner_argument_list, args.inner_argument_range)
command_list = []
for outer_value in outer_sweep:
outer_value = str(outer_value)
outdir = Path(args.output_prefix) / outer_value
if inner_sweep is not None:
for inner_value in inner_sweep:
inner_value = str(inner_value)
inner_ouput = outdir / inner_value
command = args.command + " --{outer_arg} {outer_val} --{inner_arg} {inner_val} --output {outdir} ".format(
outer_arg=args.sweep_argument, outer_val=outer_value, outdir=inner_ouput,
inner_arg=args.inner_sweep_argument, inner_val=inner_value)
command_list.append(command)
else:
command = args.command + " --{arg} {val} --output {outdir} ".format(
arg=args.sweep_argument, val=outer_value, outdir=outdir)
command_list.append(command)
# joblib does not support nested parallelism
if args.njobs > 1 and "njobs" in args.command:
print("WARNING: joblib does not support nested parallelism, setting sweep njobs to 1!")
args.njobs = 1
with Parallel(n_jobs=args.njobs, verbose=args.verbose) as parallel:
num_tasks = len(command_list)
parallel(delayed(run_print)(command, "{}/{}".format(i+1, num_tasks))
for i, command in enumerate(command_list))
if __name__ == "__main__":
main()
| {"/moa_experiments.py": ["/parameter_sweep.py"], "/skgarden_experiments.py": ["/evaluation_functions.py"], "/interval_metrics.py": ["/generate_figures.py"]} |
59,206 | Sandy4321/uncertain-trees-experiments | refs/heads/master | /adjusted_utility.py | import argparse
from pathlib import Path
import json
import logging
import numpy as np
import pandas as pd
from tabulate import tabulate
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True, help="Input directory, should contain utility.csv file"
"created using interval_metrics.py")
parser.add_argument("--expected-mer", type=float, default=0.1)
parser.add_argument("--table-order", nargs='+')
alg_selection = parser.add_mutually_exclusive_group()
alg_selection.add_argument("--include-only", nargs='+',
help="Include only the provided output directories")
alg_selection.add_argument("--exclude", nargs='+',
help="Exclude the provided output directories")
return parser.parse_args()
def main():
args = parse_args()
input_path = Path(args.input)
utilities = pd.read_csv(input_path / "utility.csv", index_col=0)
# Read in mean error rates, exclude final 3 lines that are aggregate statistics
error_rates = pd.read_csv(input_path / "error_rate.means.csv", index_col=0).iloc[:-3, ]
if args.exclude is not None:
try:
utilities = utilities.drop(args.exclude, axis=1)
error_rates = error_rates.drop(args.exclude, axis=1)
except ValueError as e:
logging.warning("Could not drop requested columns, error was \"{}\"".format(e))
elif args.include_only is not None:
try:
utilities = utilities[args.include_only]
error_rates = error_rates[args.include_only]
except KeyError as e:
logging.warning("Could not include requested columns, error was \"{}\"".format(e))
# We set the half life to be at an error rate of 1.5 times the requested one
# That means that the utility of a method will be half if the method has 1.5 times the requested error rate
half_life_deviation = 0.5 * args.expected_mer
decay_lambda = np.log(2) / half_life_deviation
# If the error rate is not smaller/equal to expected, multiply utility by decay rate determined by error deviation
adjusted_utilities = utilities.where(error_rates <= args.expected_mer,
np.multiply(utilities,
np.exp(-decay_lambda * (error_rates - args.expected_mer))))
try:
adjusted_utilities = adjusted_utilities[args.table_order]
except KeyError:
logging.warning("Could not set column order to {}, available columns were {}".format(
args.table_order, adjusted_utilities.columns.values))
pass
# Write json file with arguments to keep track of how output was generated
json_file = input_path/ "adjusted_utility_settings.json"
settings = vars(args)
json_file.write_text(json.dumps(settings))
def add_stats(df: pd.DataFrame) -> pd.DataFrame:
df.loc['Mean'] = df.mean()
df.loc['Median'] = df.median()
df.loc['Std'] = df.std()
return df
adjusted_utilities = add_stats(adjusted_utilities)
def create_table_str(df):
float_format = ".2f"
return tabulate(df, headers='keys', tablefmt='latex_booktabs', floatfmt=float_format)
adj_util_path = input_path / "exp_adjusted_utility"
adjusted_utilities.to_csv(adj_util_path.with_suffix(".csv"))
ajd_util_table_str = create_table_str(adjusted_utilities)
adj_util_path.with_suffix(".tex").write_text(ajd_util_table_str)
if __name__ == '__main__':
main()
| {"/moa_experiments.py": ["/parameter_sweep.py"], "/skgarden_experiments.py": ["/evaluation_functions.py"], "/interval_metrics.py": ["/generate_figures.py"]} |
59,207 | Sandy4321/uncertain-trees-experiments | refs/heads/master | /generate_violin_plots.py | """
Script to create violin plots for quantile loss and utility measurements
"""
import argparse
from pathlib import Path
import seaborn as sns
import pandas as pd
from pylab import rcParams
large_text = 16
small_text = 14
params = {
'axes.labelsize': large_text,
'font.size': small_text,
'legend.fontsize': small_text,
'xtick.labelsize': small_text,
'ytick.labelsize': small_text,
'text.usetex': True,
'figure.figsize': [9, 6]
}
rcParams.update(params)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True, help="Input directory, should contain quantile_loss.means.csv"
"or adjusted_utility.csv file.")
parser.add_argument("--metric", required=True, help="Metric to plot.", choices=['quantile_loss', "utility"])
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
# Load results CSV
filename = "quantile_loss.means.csv" if args.metric == "quantile_loss" else "exp_adjusted_utility.csv"
input_path = Path(args.input)
df = pd.read_csv(input_path / filename)[:-3].set_index("Dataset")
# Ensure method order matches rest of paper
try:
# Some old experiments used MF for MondrianForest
df.rename(index=str, columns={"MF": "MondrianForest"}, inplace=True)
except KeyError:
pass
df = df[["MondrianForest", "OnlineQRF", "CPApproximate", "CPExact"]]
# Melt data and add plots
y_label = "Quantile Loss" if args.metric == "quantile_loss" else "Utility"
melted_df = pd.melt(df, value_vars=["MondrianForest", "OnlineQRF", "CPApproximate", "CPExact"],
value_name=y_label, var_name="Method")
ax = sns.violinplot(x="Method", y=y_label, data=melted_df, cut=0, inner=None, bw=0.4,
scale="width")
ax = sns.swarmplot(x="Method", y=y_label, data=melted_df, edgecolor="grey", color="black")
ax.set_ylabel(y_label)
ax.grid(axis='y', color="0.9", linestyle='-', linewidth=1)
# Remove vertical grid lines
ax.grid(axis='x', color="1.0", linestyle='-', linewidth=1)
ax.set_axisbelow(True)
ax.get_figure().savefig(str(input_path / (args.metric + "-violin.pdf")), bbox_inches='tight')
| {"/moa_experiments.py": ["/parameter_sweep.py"], "/skgarden_experiments.py": ["/evaluation_functions.py"], "/interval_metrics.py": ["/generate_figures.py"]} |
59,208 | Sandy4321/uncertain-trees-experiments | refs/heads/master | /generate_boxplots.py | """
Script to create boxplots for quantile loss and utility measurements
"""
import argparse
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
import pandas as pd
from pylab import rcParams
plt.style.use('seaborn-whitegrid')
large_text = 16
small_text = 14
params = {
'axes.labelsize': large_text,
'font.size': small_text,
'legend.fontsize': small_text,
'xtick.labelsize': small_text,
'ytick.labelsize': small_text,
'text.usetex': True,
'figure.figsize': [9, 6]
}
rcParams.update(params)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True, help="Input directory, should contain quantile_loss.means.csv"
"or adjusted_utility.csv file.")
parser.add_argument("--metric", required=True, help="Metric to plot.", choices=['quantile_loss', "utility"])
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
# Load results CSV
filename = "quantile_loss.means.csv" if args.metric == "quantile_loss" else "exp_adjusted_utility.csv"
input_path = Path(args.input)
df = pd.read_csv(input_path / filename)[:-3].set_index("Dataset")
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(9, 6))
# Ensure method order matches rest of paper
try:
df.rename(index=str, columns={"MF": "MondrianForest"}, inplace=True)
except KeyError:
pass
df = df[["MondrianForest", "OnlineQRF", "CPApproximate", "CPExact"]]
# Get color palette
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
bp = ax.boxplot(df.transpose(), labels=df.columns)
# Color boxes, from https://github.com/jbmouret/matplotlib_for_papers#colored-boxes
for i in range(0, len(bp['boxes'])):
bp['boxes'][i].set_color(colors[i])
# we have two whiskers!
bp['whiskers'][i*2].set_color(colors[i])
bp['whiskers'][i*2 + 1].set_color(colors[i])
bp['whiskers'][i*2].set_linewidth(2)
bp['whiskers'][i*2 + 1].set_linewidth(2)
# top and bottom fliers
# (set allows us to set many parameters at once)
bp['fliers'][i].set(markerfacecolor=colors[i],
marker='o', alpha=0.75, markersize=6,
markeredgecolor='none')
bp['medians'][i].set_color('black')
bp['medians'][i].set_linewidth(2)
# and 4 caps to remove
for c in bp['caps']:
c.set_linewidth(0)
# Fil boxes with color
for i in range(0, len(bp['boxes'])):
box = bp['boxes'][i]
box.set_linewidth(0)
boxX = []
boxY = []
for j in range(5):
boxX.append(box.get_xdata()[j])
boxY.append(box.get_ydata()[j])
boxCoords = np.array([boxX, boxY]).transpose()
boxPolygon = Polygon(boxCoords, facecolor=colors[i], linewidth=0)
ax.add_patch(boxPolygon)
y_label = "Quantile Loss" if args.metric == "quantile_loss" else "Utility"
ax.set_ylabel(y_label)
ax.grid(axis='y', color="0.9", linestyle='-', linewidth=1)
# Remove vertical grid lines
ax.grid(axis='x', color="1.0", linestyle='-', linewidth=1)
ax.set_axisbelow(True)
plt.savefig(str(input_path / (args.metric + ".pdf")), bbox_inches='tight')
| {"/moa_experiments.py": ["/parameter_sweep.py"], "/skgarden_experiments.py": ["/evaluation_functions.py"], "/interval_metrics.py": ["/generate_figures.py"]} |
59,209 | Sandy4321/uncertain-trees-experiments | refs/heads/master | /skgarden_experiments.py | """
Runs a number of experiments using skgarden MondrianForest.
The user provides a datadir which contains a number of arff files for regression, and a
prequential regression task is run on each one.
The output is one csv file per dataset, per experiment repeat.
Will also output two additional files per experiment:
<name>.time.csv
<name>.pred
These contain timing measurements and each individual prediction.
Usage: python skgarden_experiments.py --input path/to/data
"""
import argparse
from pathlib import Path
import json
from collections import namedtuple
import arff
import numpy as np
from skgarden import MondrianForestRegressor
import pandas as pd
from vowpalwabbit.sklearn_vw import VWRegressor
from evaluation_functions import mean_error_rate, mean_interval_size, prequential_interval_evaluation
from joblib import Parallel, delayed
class VWIntervalRegressor(object):
def __init__(self, confidence):
half_significance = (1.0 - confidence) / 2.0
self.lower = VWRegressor(loss_function='quantile', quantile_tau=half_significance)
self.upper = VWRegressor(loss_function='quantile', quantile_tau=1.0-half_significance)
def partial_fit(self, X, y):
self.lower.fit(X, y)
self.upper.fit(X, y)
def vw_predict_interval(self, X):
lower = self.lower.predict(X)
upper = self.upper.predict(X)
return lower, upper
def get_params(self):
return {'lower': self.lower.get_params(), 'upper': self.upper.get_params()}
def __getstate__(self):
state = {}
state['lower'] = dict(
params=self.lower.get_params(), coefs=self.lower.get_coefs(), fit=self.lower.fit_)
state['upper'] = dict(
params=self.upper.get_params(), coefs=self.upper.get_coefs(), fit=self.upper.fit_)
return state
def __setstate__(self, state):
self.lower.set_params(**state['lower']['params'])
self.lower.set_coefs(state['lower']['coefs'])
self.lower.fit_(state['lower']['fit'])
self.upper.set_params(**state['upper']['params'])
self.upper.set_coefs(state['upper']['coefs'])
self.upper.fit_(state['upper']['fit'])
def load_arff_data(filepath):
with open(str(filepath), 'r') as f:
decoder = arff.ArffDecoder()
d = decoder.decode(f, encode_nominal=True)
# tvas: We are assuming the target/dependent is the last column
data = np.array(d['data'])
X = data[:, :-1]
y = data[:, -1]
return X, y
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--algorithm", required=True, choices=['vw', 'mf'])
parser.add_argument("--input", required=True,
help="Path to the folder containing the arff files.")
parser.add_argument("--output", type=str,
help="Path to output folder. If not provided will create a dir named"
"MondrianForest under the input directory.")
parser.add_argument("--n_estimators", type=int, default=10,
help="Number of trees to use.")
parser.add_argument("--confidence", type=float, default=0.9,
help="Confidence level for intervals.")
parser.add_argument("--window-size", type=int, default=1000,
help="Size of evaluation window.")
parser.add_argument("--repeats", type=int, default=1,
help="Number of times to repeat each experiment")
parser.add_argument("--overwrite", default=False, action="store_true",
help="When given, it will not check if the output folder exists already.")
parser.add_argument("--verbose", type=int, default=0,
help="Provide additional output in the console, 1 for per experiment progress, "
"2 to include per-window output")
parser.add_argument("--njobs", type=int, default=1,
help="Number of repeat experiments to run in parallel")
parser.add_argument("--no-additional-output", default=False, action="store_true",
help="When given, will not create predictions file and other computational metrics.")
return parser.parse_args()
def main():
args = parse_args()
data_path = Path(args.input).absolute()
if args.output is None:
args.output = str(data_path / "MondrianForest")
output_path = Path(args.output).absolute()
output_path.mkdir(parents=True, exist_ok=args.overwrite)
scorers = {"mean interval size": mean_interval_size,
"mean error rate": mean_error_rate}
learner_params_list = None
if len(list(data_path.glob("*.arff"))) == 0:
raise FileNotFoundError("Could not find any arff files under {}".format(data_path))
for filepath in data_path.glob("*.arff"):
X, y = load_arff_data(filepath)
print("Running experiments on {}".format(filepath.name))
# TODO: Nested parallelism, across files and repeats
try:
with Parallel(n_jobs=args.njobs, verbose=args.verbose) as parallel:
learner_params_list = parallel(
delayed(run_experiment)(i, filepath, output_path, scorers, args, X, y)
for i in range(args.repeats))
except:
"Runtime error for dataset: {}".format(filepath.name)
# Write experiment parameters to file
if learner_params_list is None:
raise Exception("No experiments performed, was the input dir correct?")
results = {"arguments": vars(args), "learner_params": learner_params_list[0]}
out_file = output_path / "settings.json"
out_file.write_text(json.dumps(results))
print("Output created under :{}".format(output_path))
def run_experiment(i, input_file, output_path, scorers, args, X, y):
np.random.seed()
window_size = args.window_size
print("Running repeat {}/{}".format(i + 1, args.repeats))
# Create and evaluate a regressor
if args.algorithm == 'mf':
regressor = MondrianForestRegressor(n_estimators=args.n_estimators)
else:
regressor = VWIntervalRegressor(args.confidence)
# If asked to save predictions, create requisite file
pred_path = output_path / (input_file.stem + "_{}.pred".format(i)) if not args.no_additional_output else None
results = prequential_interval_evaluation(
regressor, X, y, args.confidence, scorers, args.window_size, verbose=args.verbose,
additional_output=pred_path)
# Create index column
num_windows = int(np.ceil(X.shape[0] / window_size))
for score in scorers.keys():
assert num_windows == len(results[score])
window_index_list = list(range(window_size, (window_size * num_windows), window_size))
# Last element of index is the dataset size, consistent with MOA
window_index_list.append(X.shape[0])
results["index"] = window_index_list
# Save scores and index columns to csv
included_columns = ["index"]
included_columns.extend(sorted(results.keys()))
# Create a df with only the score measurements and the index
df = pd.DataFrame({k: results[k] for k in included_columns})
df.to_csv(output_path / (input_file.stem + "_{}.csv".format(i)), index=False)
return regressor.get_params()
if __name__ == "__main__":
main()
| {"/moa_experiments.py": ["/parameter_sweep.py"], "/skgarden_experiments.py": ["/evaluation_functions.py"], "/interval_metrics.py": ["/generate_figures.py"]} |
59,210 | Sandy4321/uncertain-trees-experiments | refs/heads/master | /interval_metrics.py | """
Parses the prediction files from MOA and skgarden output
and creates normalized metrics for the intervals.
"""
import argparse
import json
from pathlib import Path
from collections import OrderedDict, defaultdict
import re
import sys
import logging as log
import pandas as pd
import numpy as np
from natsort import natsorted
from tabulate import tabulate
from joblib import Parallel, delayed
from generate_figures import sort_nicely, gather_metric
MOA_METHODS = {"OnlineQRF", "OoBConformalRegressor", "OoBConformalApproximate", "PredictiveVarianceRF",
"CPExact", "CPApproximate", "SGDQR"}
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True,
help="A dir containing sub-dirs of results, one per method."
"The sub-dirs contain csv files with output, one per dataset, per"
"experiment repeat."
"Sub-directory names will be used as method names in the plots.")
parser.add_argument("--output", required=True,
help="The folder to create the output in")
parser.add_argument("--only-pred-files", action="store_true", default=False,
help="When given, will not calculate metrics, only create formatted prediction files."
" Use when you want RIS metrics for generate_figures with large datasets.")
parser.add_argument("--overwrite", action="store_true", default=False,
help="When given, will not check if the output folder already exists ,"
"potentially overwriting its contents.")
parser.add_argument("--window-size",
help="The window size to use to calculate the per window metrics.")
parser.add_argument("--expected-mer", type=float, default=0.1,
help="The expected MER for these experiments.")
parser.add_argument("--table-order", nargs='+', help="The order to put the table columns in. Should "
"match the method directory names.")
# parser.add_argument("--njobs", type=int, default=1,
# help="Number of prediction files to process in parallel.")
alg_selection = parser.add_mutually_exclusive_group()
alg_selection.add_argument("--include-only", nargs='+',
help=" Include only the provided output directories")
alg_selection.add_argument("--exclude", nargs='+',
help=" Exclude the provided output directories")
parser.add_argument("--force-moa", action="store_true", default=False,
help="Enforce parsing of the dirs using the MOA format."
"Use when directory names don't match a method name (e.g. OnlineQRF),"
"Otherwise, MondrianForest parsing is used as the default.")
parser.add_argument("--njobs", help="The number of jobs to use for prediction file parsing/creation."
"The default is to use all available cores.", type=int, default=-1)
return parser.parse_args()
def parse_moa_line(line: str) -> str:
# Expected line format: "Out 0: interval_low interval_high ,true_value"
parsed_line = line[6:].replace(',', '').strip().replace(' ', ',')
return parsed_line
def parse_skgarden_line(line: str) -> str:
# Expected line format: "[interval_low interval_high], [true_value]"
re_line = re.sub(r"[\[\],]", '', line).strip()
parsed_line = re.sub(' +', ',', re_line)
return parsed_line
def parse_file(filepath: Path, force_moa):
"""
Reads a prediction file created either by MOA or skgarden_experiments,
and creates a csv with the values, formatted as
"interval_low,interval_high,true_value"
The files are created under the same dir as the input .pred file, with
the extension .pred.csv
:param filepath: Path
A Path object to a predictions file
:param force_moa: When true will force MOA parsing to be used, regardless of containing directory name
"""
if not filepath.with_suffix(".pred.csv").exists():
# If the containing directory is one of the MOA methods, or we enforce it, use the MOA parser
if filepath.parent.name in MOA_METHODS or force_moa:
parse_line = parse_moa_line
else:
# Otherwise we assume it's a skgarden_experiments generated output file
parse_line = parse_skgarden_line
with filepath.open() as infile, filepath.with_suffix(".pred.csv").open('w') as outfile:
outfile.write("interval_low,interval_high,true_value\n")
for line in infile:
parsed_line = parse_line(line)
outfile.write(parsed_line + '\n')
# TODO: Actually it seems like skgarden parsing works for both, pandas assumes the "Out: " is an index
# TODO: Maybe include a sanity check here just to inform the user? The outcome is correct anyway
def create_pred_csvs(method_dir: Path, force_moa: bool, njobs):
with Parallel(njobs) as parallel:
parallel(delayed(parse_file)(res_file, force_moa)
for res_file in method_dir.glob("*.pred"))
def normalize(x, true_col):
normalized = (x - min(true_col)) / (max(true_col) - min(true_col))
return normalized
def gather_method_results(method_dir: Path, significance):
"""
Goes through all generated .pred.csv files in a method dir, collects the results
per dataset, and creates a list of metric dataframes per metric.1
:param method_dir: A Path to method dir, contains repeats of experiments, with the suffix _x.pred.csv
where x is the experiment repeat index.
:return: Returns a dictionary {dataset_name: metric_df_list}
"""
res = defaultdict(list)
for res_file in method_dir.glob("*.pred.csv"):
# Get rid of any suffixes in the filename, and the _X repeat indicator
base_name = res_file.name.split('.')[0][:-2]
df = pd.read_csv(res_file)
df.index = range(len(df)) # For MOA files it was using "Out\:" as index which was causing issues
df["interval_size"] = np.abs(df["interval_high"] - df["interval_low"])
true_max = df["true_value"].max()
true_min = df["true_value"].min()
df["relative_interval_size"] = df["interval_size"] / (true_max - true_min)
df['error_rate'] = np.where(
(df['true_value'] <= df['interval_high']) & (df['true_value'] >= df['interval_low']),
0, 1)
interval_deviation_upper = np.where((df['true_value'] > df['interval_high']),
df['true_value'] - df['interval_high'], 0)
interval_deviation_lower = np.where((df['true_value'] < df['interval_low']),
df['interval_low'] - df['true_value'], 0)
relative_interval_deviation = (interval_deviation_lower + interval_deviation_upper) / (true_max - true_min)
assert np.all(relative_interval_deviation >= 0), "Deviation should be >= 0"
df["quantile_loss"] = np.where(
(df['true_value'] <= df['interval_high']) & (df['true_value'] >= df['interval_low']),
df["relative_interval_size"] * significance,
significance * df["relative_interval_size"] + relative_interval_deviation)
res[base_name].append(df)
return res
def main():
args = parse_args()
input_path = Path(args.input)
output_path = Path(args.output)
assert output_path.parent != input_path, "Setting output path under input can cause issues, choose another path."
output_path.mkdir(parents=True, exist_ok=args.overwrite)
# Get all the directories under the input path
method_dirs = [subpath for subpath in input_path.iterdir() if subpath.is_dir()]
assert len(method_dirs) > 0, "There should be at least one directory under the input, found 0!".format(method_dirs)
if args.exclude is not None:
method_dirs = [subpath for subpath in method_dirs if subpath.name not in args.exclude]
elif args.include_only is not None:
method_dirs = [subpath for subpath in method_dirs if subpath.name in args.include_only]
# Check if prediction file pre-processing has been done, otherwise do it
# Gather the results for each method
# Format: {method: {ds_name: measurement_df_list}}
method_to_dsname_to_result_df_list = OrderedDict()
sorted_dirs = sort_nicely(method_dirs)
mean_tables = {}
# TODO: For experiments with large outputs (prediction files with many lines) we have computationa and memory issues
# The problem is that we maintain all results as a Pandas dataframe. For example for an experiment with 1M rows,
# 10 repeats, 3 methods, the prediction data frames hold 90M float/double values. Processing these becomes
# a challenge, we should find a way to 1) not store the complete data in memory 2) parallelize by method+metric
for method_dir in sorted_dirs:
# TODO: Have proper check that each result_X.csv file has respective result_X.pred
if len(list(method_dir.glob("*.pred"))) == 0:
raise FileNotFoundError("No prediction files found in {}!".format(method_dir))
num_pred_files = len(list(method_dir.glob("*.pred")))
num_processed_pred_files = len(list(method_dir.glob("*.pred.csv")))
if num_processed_pred_files != num_pred_files:
print(".pred.csv files missing in {}. Creating {}/{}".format(
method_dir.name, num_pred_files - num_processed_pred_files, num_pred_files))
create_pred_csvs(method_dir, args.force_moa, args.njobs)
# After .pred.csv files have been created, gather metrics
if not args.only_pred_files:
method_to_dsname_to_result_df_list[method_dir.name] = gather_method_results(method_dir, args.expected_mer)
if args.only_pred_files:
print("Finished creating prediction files, exiting...")
sys.exit()
else:
print("Prediction files created, continuing with metric calculation...")
for metric in ["error_rate", "relative_interval_size", "quantile_loss"]:
method_ds_metric = OrderedDict()
for method, ds_to_measurements in method_to_dsname_to_result_df_list.items():
# TODO: Make it possible to iterate over metrics?
method_ds_metric[method] = gather_metric(ds_to_measurements, metric)
all_names = []
method_to_mean_measurements = OrderedDict()
method_to_median_measurements = OrderedDict()
method_to_std_measurements = OrderedDict()
for method, ds_name_to_measurements in method_ds_metric.items():
ds_names = []
ds_means = []
ds_medians = []
ds_stds = []
# Get the measurements for the requested data, and calc their stats
for dataset_name, metric_df in natsorted(ds_name_to_measurements.items()):
if metric == "error_rate": # Error rate requires special treatment
error_counts = metric_df.sum(axis=1)
error_rates = error_counts / metric_df.shape[1]
overall_mean = error_rates.mean()
overall_median_mean = error_rates.median()
overall_std_mean = error_rates.std()
else: # Then it's RIS or quantile_loss
relative_interval_means = metric_df.mean() # Mean for each example over repeats
relative_interval_medians = metric_df.median() # Median for each example over repeats
relative_interval_stds = metric_df.std() # Std for each example over repeats
overall_mean = relative_interval_means.mean()
overall_median_mean = relative_interval_medians.mean()
overall_std_mean = relative_interval_stds.mean()
ds_means.append(overall_mean)
ds_medians.append(overall_median_mean)
ds_stds.append(overall_std_mean)
ds_names.append(dataset_name)
all_names.append(ds_names)
method_to_mean_measurements[method] = ds_means
method_to_median_measurements[method] = ds_medians
method_to_std_measurements[method] = ds_stds
# Assert datasets are in correct order between methods
prev_names = []
for ds_names in all_names:
if len(prev_names) == 0:
prev_names = ds_names
continue
for left_name, right_name in zip(prev_names, ds_names):
assert left_name == right_name, \
"DS order mismatch: {}, {}".format(left_name, right_name)
def add_stats(df):
df.loc['Mean'] = df.mean()
df.loc['Median'] = df.median()
df.loc['Std'] = df.std()
return df
def create_df(method_to_measurements_dict):
dictionary = OrderedDict()
dictionary["Dataset"] = ds_names
dictionary.update(method_to_measurements_dict)
df = pd.DataFrame(dictionary)
df = df.set_index("Dataset")
return add_stats(df)
mean_aggregate_metric_df = create_df(method_to_mean_measurements)
median_aggregate_metric_df = create_df(method_to_median_measurements)
std_aggregate_metric_df = create_df(method_to_std_measurements)
table_outpath = Path(args.output) / metric.replace(' ', '_')
mean_aggregate_metric_df.to_csv(table_outpath.with_suffix(".means.csv"))
median_aggregate_metric_df.to_csv(table_outpath.with_suffix(".medians.csv"))
std_aggregate_metric_df.to_csv(table_outpath.with_suffix(".std.csv"))
def create_table_str(df):
float_format = ".3f" if metric in ["error_rate", "quantile_loss"] else ".2f"
return tabulate(df, headers='keys', tablefmt='latex_booktabs', floatfmt=float_format)
# Will try to rearrange columns in the order provided by the user, otherwise just sorted.
order = args.table_order if args.table_order is not None else sorted(mean_aggregate_metric_df.keys())
try:
mean_aggregate_metric_df = mean_aggregate_metric_df[order]
median_aggregate_metric_df = median_aggregate_metric_df[order]
std_aggregate_metric_df = std_aggregate_metric_df[order]
except KeyError:
# If a column was missing just leave them as they were
log.warning("Could not rearrange colums to {}".format(order))
pass
mean_aggregate_metric_df = mean_aggregate_metric_df[order]
median_aggregate_metric_df = median_aggregate_metric_df[order]
std_aggregate_metric_df = std_aggregate_metric_df[order]
mean_tables[metric] = mean_aggregate_metric_df
# TODO: Create figures? Maybe window metric?
mean_table_str = create_table_str(mean_aggregate_metric_df)
median_table_str = create_table_str(median_aggregate_metric_df)
std_table_str = create_table_str(std_aggregate_metric_df)
table_outpath.with_suffix(".means.tex").write_text(mean_table_str)
table_outpath.with_suffix(".medians.tex").write_text(median_table_str)
table_outpath.with_suffix(".stds.tex").write_text(std_table_str)
# Write json file with arguments to keep track of how output was generated
json_file = output_path / "interval_metrics_settings.json"
settings = vars(args)
json_file.write_text(json.dumps(settings))
# Ensure that the method dirs were not numbers, i.e. not repeats of confidence experiments.
def is_number(s):
try:
float(s)
return True
except ValueError:
return False
for method_name in method_dirs:
if is_number(method_name.name):
log.warning("Seems like you're running this on top of confidence output, so we won't create utility output.")
log.warning("Method list was: {}.".format(method_dirs))
log.warning("Use confidence_utility_calculation.py instead!")
sys.exit()
# Create adjusted utility table with step time/utility function (using expected MER as deadline)
# Compute utility as one minus the RIS
mean_error_rates = mean_tables["error_rate"].iloc[:len(ds_names), ] # Drop the aggregate stats rows
utility = 1 - np.minimum(mean_tables["relative_interval_size"].iloc[:len(ds_names), ], 1)
util_path = Path(args.output) / "utility"
utility.to_csv(util_path.with_suffix(".csv"))
util_table_str = create_table_str(utility)
util_path.with_suffix(".tex").write_text(util_table_str)
# Step function
adjusted_utils = np.where(mean_error_rates > args.expected_mer, 0, utility)
# adjusted_utils = tuf(utility, mean_error_rates, step)
# Build up the adjusted util dataframe, add dataset names, aggregate stats
adj_util_df = pd.DataFrame(adjusted_utils, columns=utility.columns.tolist())
adj_util_df["Dataset"] = ds_names
adj_util_df = add_stats(adj_util_df.set_index("Dataset"))
adj_util_path = Path(args.output) / "adjusted_utility"
adj_util_df.to_csv(adj_util_path.with_suffix(".csv"))
ajd_util_table_str = create_table_str(adj_util_df)
adj_util_path.with_suffix(".tex").write_text(ajd_util_table_str)
if __name__ == '__main__':
main()
| {"/moa_experiments.py": ["/parameter_sweep.py"], "/skgarden_experiments.py": ["/evaluation_functions.py"], "/interval_metrics.py": ["/generate_figures.py"]} |
59,211 | Sandy4321/uncertain-trees-experiments | refs/heads/master | /measurement_aggregations.py | """
Creates aggregations over datasets from the per method/dataset results
created in generate_figures.py in the create_tables method.
Provided a directory created with the above method will create
additional output in the same directory with informative measures
over all datasets for which we have results.
"""
import argparse
from pathlib import Path
import pandas as pd
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True,
help="The directory containing the input files."
"Must have been created with generate_figures"
" --create-tables.")
parser.add_argument("--expected-error", default=0.1,
help="The expected error level for the experiment."
"Used to calculate the avg deviation from it.")
return parser.parse_args()
def main():
args = parse_args()
input_path = Path(args.input)
mer_file = input_path / "mean_error_rate.means.csv"
mer_df = pd.read_csv(mer_file)
# For each metric value, take its absolute difference with the requested confidence
abs_mer_diff_df = mer_df.drop("Dataset", axis=1).apply(lambda x: abs(x - args.expected_error), axis=1)
mean_abs_mer_diff = abs_mer_diff_df.mean()
std_abs_mer_diff = abs_mer_diff_df.std()
mean_abs_mer_diff.to_csv(input_path / "mean_abs_mer_diff.csv")
std_abs_mer_diff.to_csv(input_path / "std_abs_mer_diff.csv")
if __name__ == "__main__":
main() | {"/moa_experiments.py": ["/parameter_sweep.py"], "/skgarden_experiments.py": ["/evaluation_functions.py"], "/interval_metrics.py": ["/generate_figures.py"]} |
59,216 | redcartel/emojihex | refs/heads/master | /src/util.py | import logging
logger = logging.Logger("logger")
logger.addHandler(logging.FileHandler("/tmp/pgame.log"))
def log(message):
logger.info(message)
def y_x_to_row_col(y, x):
row = y // 4
col = (x + 4) // 8 if row % 2 else x // 8
return row, col
def y_x_to_offsets(y, x):
row = y // 4
yoff = y % 4
xoff = (x + 4) % 8 if row % 2 else x % 8
return yoff, xoff
# DIRECTIONS: 0 = right, 1 = down/right 2 = down/left 3 = left 4 = up/left 5 = up/right
# MOVE_DELTA[y%2][2] = delta y, delta x to move down and to the left
MOVE_DELTAS = (
((0,1), (1,0), (1,-1), (0, -1), (-1, -1), (-1, 0)),
((0, 1), (1, 1), (1, 0), (0, -1), (-1, 0), (-1, 1))
)
| {"/main.py": ["/src/util.py", "/src/state.py"], "/src/controls.py": ["/src/state.py"], "/src/display.py": ["/src/state.py", "/src/util.py", "/src/model.py", "/src/resources.py"]} |
59,217 | redcartel/emojihex | refs/heads/master | /src/resources.py | import curses
CONS = lambda x : None # used as empty object
def init(stdscr):
CONS.C_BLACK = 16
curses.init_color(CONS.C_BLACK, 0, 0, 0)
CONS.C_WHITE = 17
curses.init_color(CONS.C_WHITE, 1000, 1000, 1000) | {"/main.py": ["/src/util.py", "/src/state.py"], "/src/controls.py": ["/src/state.py"], "/src/display.py": ["/src/state.py", "/src/util.py", "/src/model.py", "/src/resources.py"]} |
59,218 | redcartel/emojihex | refs/heads/master | /src/model.py | from random import randrange
GAME = {
"maprows": 24,
"mapcols": 40,
"turn": 0
}
MAP = [[]]
ENTITIES = []
{y:{x:[] for x in range(GAME["mapcols"])} for y in range(GAME["maprows"])}
def init_entities():
global ENTITIES
cols = GAME["mapcols"]
rows = GAME["maprows"]
ENTITIES = [[[] for x in range(cols)] for y in range(rows)]
def init():
init_entities()
# init_map()
randomize_map()
place_entity(1,1,{"label": "X"})
#randomize_entities()
def randomize_map():
MAP.clear()
for row in range(GAME["maprows"]):
new_row = []
for col in range(GAME["mapcols"]):
new_row.append({"label": randrange(100)})
MAP.append(new_row)
def randomize_entities(n = 100):
place_entity(2,3, {"label": "X"})
return
for _ in range(n):
y = 10
x = 15
assert len(ENTITIES) > y, f"{len(ENTITIES)}"
place_entity(y,x, {"label": "X"})
def remove_entity(entity):
y = entity["y"]
x = entity["x"]
if entity not in ENTITIES[y][x]:
raise(ValueError(f"Entity {entity} not at {y} {x}"))
ENTITIES[y][x].remove(entity)
def place_entity(y, x, entity):
entity["y"] = y
entity["x"] = x
ENTITIES[y][x].append(entity)
def get_entities(y, x):
if 0 <= y < GAME["maprows"] and 0 <= x < GAME["mapcols"]:
return ENTITIES[y][x]
return []
def get_square(row, col):
if 0 <= row < GAME["maprows"] and 0 <= col < GAME["mapcols"]:
return MAP[row][col]
return {} | {"/main.py": ["/src/util.py", "/src/state.py"], "/src/controls.py": ["/src/state.py"], "/src/display.py": ["/src/state.py", "/src/util.py", "/src/model.py", "/src/resources.py"]} |
59,219 | redcartel/emojihex | refs/heads/master | /src/state.py | import os
STATE = {
"window_x": 0,
"window_y": 0,
"select_x": 5,
"select_y": 5,
"mode": 1
}
def move_param(pname, y, x, mode="abs"):
if mode == "abs":
STATE[pname + "_x"], STATE[pname + "_y"] = x, y
elif mode == "rel":
STATE[pname + "_x"] += x
STATE[pname + "_y"] += y
else:
raise ValueError("mode must be abs or rel")
def get_param(pname):
return STATE[pname]
def set_param(pname, value, mode="abs"):
if mode == "abs":
STATE[pname] = value
elif mode == "rel":
STATE[pname] += value
else:
raise ValueError("mode must be abs or rel")
MAP = [[]]
ENTITIES = {}
| {"/main.py": ["/src/util.py", "/src/state.py"], "/src/controls.py": ["/src/state.py"], "/src/display.py": ["/src/state.py", "/src/util.py", "/src/model.py", "/src/resources.py"]} |
59,220 | redcartel/emojihex | refs/heads/master | /main.py | import curses
from random import randrange
from src import controls
from src import display
from src import model
from src import resources
from src.util import logger
from src.state import get_param
import datetime
def main(stdscr):
logger.info(f"\nmain() at {datetime.datetime.now().isoformat()}")
controls.init(stdscr)
resources.init(stdscr)
display.init(stdscr)
model.init()
while get_param("mode") != -1:
display.display()
controls.dispatch_input()
if __name__ == "__main__":
curses.wrapper(main)
| {"/main.py": ["/src/util.py", "/src/state.py"], "/src/controls.py": ["/src/state.py"], "/src/display.py": ["/src/state.py", "/src/util.py", "/src/model.py", "/src/resources.py"]} |
59,221 | redcartel/emojihex | refs/heads/master | /src/controls.py | import curses
from src.state import move_param, get_param, set_param
STDSCR = curses.initscr()
def init(stdscr):
global STDSCR
STDSCR = stdscr
def dispatch_input():
key = STDSCR.getch()
if key == ord('q'):
set_param("mode", -1)
elif key == curses.KEY_UP:
move_param("window", -1, 0, "rel")
elif key == curses.KEY_RIGHT:
move_param("window", 0, 1, "rel")
elif key == curses.KEY_DOWN:
move_param("window", 1, 0, "rel")
elif key == curses.KEY_LEFT:
move_param("window", 0, -1, "rel")
elif key == ord('h'):
move_param("select", 0, -1, "rel")
elif key == ord('j'):
move_param("select", 1, 0, "rel")
elif key == ord('k'):
move_param("select", -1, 0, "rel")
elif key == ord('l'):
move_param("select", 0, 1, "rel") | {"/main.py": ["/src/util.py", "/src/state.py"], "/src/controls.py": ["/src/state.py"], "/src/display.py": ["/src/state.py", "/src/util.py", "/src/model.py", "/src/resources.py"]} |
59,222 | redcartel/emojihex | refs/heads/master | /src/display.py | import curses
from random import randrange
from collections import OrderedDict
from src.state import get_param
from src.util import y_x_to_offsets, y_x_to_row_col
from src.model import get_square, get_entities, GAME
from src.resources import CONS
STDSCR = curses.initscr()
TERRAIN = OrderedDict()
TERRAIN['OCEAN'] = '🌊'[0] + '\0'
TERRAIN['SHALLOW'] = '💧'[0] + '\0'
TERRAIN['PLAINS'] = '🌾'[0] + '\0'
TERRAIN['GRASSLAND'] = '🌱'[0] + '\0'
TERRAIN['FOREST'] = '🌳'[0] + '\0'
TERRAIN['HILLS'] = '🌄'[0] + '\0'
TERRAIN['DESERT'] = '🏜'[0] + '\0'
TERRAIN['TUNDRA'] = '⛄'[0] + '\0'
TERRAIN['MOUNTAIN'] = '⛰'[0] + '\0'
TERRAIN_LIST = list(TERRAIN.values())
ENTS = OrderedDict()
ENTS['NONE'] = "\u2003" + '\0'
ENTS['WORKER'] = '👨🔧'[0] + '\0'
ENTS['WARRIOR'] = '💪'[0] + '\0'
def init(stdscr):
global STDSCR
if stdscr:
STDSCR = stdscr
# curses.init_pair(0, curses.COLOR_WHITE, curses.COLOR_BLACK)
curses.init_pair(1, curses.COLOR_BLACK, curses.COLOR_WHITE)
curses.curs_set(0)
STDSCR.refresh()
HIGHLIGHTS = {}
def display():
clear_highlights()
STDSCR.clear()
highlight(get_param("select_y"), get_param("select_x"))
drawmap(get_param("window_y"), get_param("window_x"))
STDSCR.refresh()
def map_square_chars(mapsquare, ents=[], row=0, col=0):
rows = [" " * 8] * 4
if not mapsquare:
return rows
military = ENTS['NONE']
civilian = ENTS['NONE']
if ents:
if len(ents) >= 2:
military = ENTS['WARRIOR']
civilian = ENTS['WORKER']
elif (row + col) % 2:
military = ENTS['WARRIOR']
else:
civilian = ENTS['WORKER']
entstr = military + civilian
assert(len(entstr) == 4)
terrain = TERRAIN_LIST[row // 2 * col // 2 % len(TERRAIN_LIST)]
terrainstr = terrain * 2
rows[0] = "+{:=^7}".format("{}={}".format(str(row), str(col)))
rows[1] = "| " + entstr + ' '
rows[2] = "| " + terrainstr + ' '
rows[3] = "| " + ' ' * 3
return rows
def drawmap(mapy, mapx, drawy = 0, drawx = 0, height = None, width = None):
if width is None:
stoprow = mapy + curses.LINES - drawy - 1
stopcol = mapx + curses.COLS - drawx - 1
else:
stoprow = mapy + height
stopcol = mapx + width
for y in range(mapy, stoprow):
for x in range(mapx, stopcol):
screen_y = y - mapy + drawy
screen_x = x - mapx + drawx
char, pair = y_x_to_char(y, x), y_x_to_pair(y, x)
lchar = y_x_to_char(y, x-1)
if ord(lchar) >= 128512 or char == '\0':
pass
else:
STDSCR.addstr(screen_y, screen_x, char, pair)
def y_x_to_char(y, x):
row, col = y_x_to_row_col(y, x)
square = get_square(row, col)
if not square:
return '\0'
yoff, xoff = y_x_to_offsets(y, x)
ents = get_entities(y, x)
chargrid = map_square_chars(square, ents, row, col)
return chargrid[yoff][xoff]
def y_x_to_pair(y, x):
row, col = y_x_to_row_col(y, x)
yoff, xoff = y_x_to_offsets(y, x)
square = get_square(row, col)
char = y_x_to_char(y, x)
highlight = get_highlight(y, x)
if highlight:
return curses.color_pair(1)
return curses.color_pair(0)
# def y_x_to_mapchar_pair(y, x):
# row, col = y_x_to_row_col(y, x)
# yoff, xoff = y_x_to_offsets(y, x)
# square = get_square(row, col)
# ents = get_entities(row, col)
# display = square_display(square, ents, row, col)
# char = display[yoff][xoff]
# pair = y_x_to_color_pair
# pair = get_highlight(row, col)
# return char, pair
def clear_highlights():
HIGHLIGHTS.clear()
def get_highlight(row, col):
if row not in HIGHLIGHTS or col not in HIGHLIGHTS[row]:
return 0
if HIGHLIGHTS[row][col]:
return 1
def highlight(row, col, pairnumber=1):
if row not in HIGHLIGHTS:
HIGHLIGHTS[row] = {col: 1}
else:
HIGHLIGHTS[row][col] = 1 | {"/main.py": ["/src/util.py", "/src/state.py"], "/src/controls.py": ["/src/state.py"], "/src/display.py": ["/src/state.py", "/src/util.py", "/src/model.py", "/src/resources.py"]} |
59,223 | AdamJacoby/Hopf | refs/heads/master | /HopfConstructions_Functions.py | from itertools import product
import scipy.sparse as sps
import numpy as np
#Constructs the twist isomorphism from A\ot B to B\otA
#Columns correspond to input rows to outputs
def Twist_old(dimA,dimB):
dim=dimA*dimB
out = sps.csr_matrix((dim,dim),dtype=np.int8)
for i,j in product(range(0,dimA),range(0,dimB)):#i is the index of A and j is the index of B
out[i+j*dimA,dimB*i+j]=1
return out
def Twist(dimA,dimB):
dim=dimA*dimB
zeros = np.zeros(dim,dtype=np.int8)
out = zeros
out[0,0]=1
out = sps.csr_matrix(out)
for i in range(1,dimB):
temp_vector = zeros
temp_vector[dimB*i]=1
temp_vector = sps.csr_matrix(temp_vector)
out = sps.vstack([out,temp_vector])
for j in range(1,dimA):
for i in range(0,dimB)):#i is the index of A and j is the index of B
temp_vector = zeros
temp_vector[dimB*i+j]=1
temp_vector = sps.csr_matrix(temp_vector)
out = sps.vstack([out,temp_vector])
return out
#Take the kron product of more then two sparse matrixes at once
def Spare_Multi_Kron(*arg):
out = arg[0]
list = arg[1:len(arg)]
for temp in list:
out =sps.kron(out,temp)
return out
#creates a list of the standard basis vectors for a vector space of dimension dim
def CreateBasisVectors(dim):
out = []
for i in range(0,dim):
temp = np.zeros((dim),dtype=complex)
temp[i]=1
out.append(temp)
return out
#creates a list of the standard basis vectors for a vector space of dimension dim
def CreateSparseBasisVectors(dim):
out = []
for i in range(0,dim):
temp = [0]*dim
temp[i]=1
out.append(sps.csr_matrix(temp,dtype=np.int8))
return out
#given an imput of (dimA1,dimA2,...,dimAn,d) for d an interger it computes the map id_1\ot id_2\ot id_{d-1}\ot \tau\ot id_{d+2}\ot..\id_n
def Center_Twist(*arg):
length = len(arg)
d = arg[length-1]
spaces = arg[:length-1]
Ldim=1#Will be sum_{i=1}^{d-1} dim A_i
for dim in spaces[:d-1]:
Ldim = Ldim*dim
Rdim=1#Will be sum_{i=1}^{d-1} dim A_i
for dim in spaces[d+1:]:
Rdim = Rdim*dim
return Spare_Multi_Kron(sps.identity(Ldim,dtype=complex,format='csr'),Twist(spaces[d-1],spaces[d]),sps.identity(Rdim,dtype=complex,format='csr'))
#Takes two algebras as inputs and outputs the matrix for their tensor product
def Tensor_Mult(A,B):
return sps.kron(A.mult,B.mult).dot(Center_Twist(A.dim,B.dim,A.dim,B.dim,2))
#Takes two coalgebras and returns the comultiplication matrix of their tensor product
def Tensor_Comult(A,B):
return Center_Twist(A.dim,A.dim,B.dim,B.dim,2).dot(sps.kron(A.comult,B.comult))
#Given an element a in a space A with multiplication matrix mult return the matrix for the map b mapsto ab (perhaps construct it using hstack command latter)
def Left_Action_Matrix(element,mult):
dim = len(element)
element = sps.csr_matrix(element)
V = CreateSparseBasisVectors(dim)
columns=[]
for k in range(0,dim):
columns.append(sps.kron(element,V[k]).transpose())
temp = sps.hstack(columns)
return mult.dot(temp)
#Given an element a in a space A with multiplication matrix mult return the matrix for the map b mapsto ab (perhaps construct it using hstack command latter)
def Right_Action_Matrix(element,mult):
dim = len(element)
element = sps.csr_matrix(element)
V = CreateSparseBasisVectors(dim)
columns=[]
for k in range(0,dim):
columns.append(sps.kron(V[k],element).transpose())
temp = sps.hstack(columns)
return mult.dot(temp)
#Determins the strongest structure that can be put on A\ot B
def Tensor_Type(A,B):
_type='VectorSpace'
if A._type == 'HopfAlgebra':
if B._type=='HopfAlgebra' or B._type=='Algebra' or B._type=='Bialgebra' or (B._type=='ModuleAlgebra' and B.ring==A.name) or (B._type=='Module' and B.ring==A.name) or B._type=='CoAlgebra':
type=B._type
elif A._type == 'BiAlgebra':
if B._type=='HopfAlgebra':
_type = 'BiAlgebra'
elif B._type=='Algebra' or B._type=='Bialgebra' or (B._type=='ModuleAlgebra' and B.ring==A.name) or (B._type=='Module' and B.ring==A.name) or B._type=='CoAlgebra':
_type == B._type
elif A._type == 'ModuleAlgebra':
if (B._type=='ModuleAlgebra' and B.ring==A.ring) or ((B._type=='HopfAlgebra' or B._type=='BiAlgebra') and B.name==A.ring):
_type='ModuleAlgebra'
elif B._type=='Algebra' or (B._type=='Module' and B.ring==A.ring):
_type==B._type
elif A._type=='Module':
if (B._type=='Module' or B._type =='ModuleALgebra') and A.ring==B.ring:
_type=='Module'
elif (B._type =='HopfAlgebra' or B._type=='BiAlgebra') and A.ring==B.name:
_type=='Module'
elif A._type=='CoAlgebra':
if B._type=='HopfAlgebra' or B._type=='BiAlgebra' or B._type=='CoAlgebra':
_type=='CoAlgebra'
return _type
#Constructs the comultiplication matrix of H twisted by J
def Drinfeld_Twist_Comult(H,J,JI):
tensormult = Tensor_Mult(H,H)
L_J=Left_Action_Matrix(J,tensormult)
R_JI=Right_Action_Matrix(JI,tensormult)
return (L_J.dot(R_JI)).dot(H.comult)
#Constructs the antipode matrix of H twisted by J
def Drinfeld_Twist_Antipode(H,J,JI):
Id=sps.identity(H.dim,dtype=complex,format='csr')
UJ=H.mult.dot(sps.kron(Id,H.antipode).dot(J))
UJI=H.mult.dot(sps.kron(H.antipode,Id).dot(JI))
L_UJ=Left_Action_Matrix(UJ,H.mult)
R_UJI=Right_Action_Matrix(UJI,H.mult)
return R_UJI.dot(L_UJ.dot(H.antipode))
#Takes a Hopf algebra and returns the action matrix for the adjoint action
def Left_Adjoint_Action(H):
dim = H.dim
Id = sps.identity(dim,dtype=complex,format='csr')
twist = Center_Twist(dim,dim,dim,2)
return H.mult.dot(sps.kron(H.mult,Id).dot(twist.dot(sps.kron(H.comult,Id))))
#Given A a H-module algebra and H a Hoipf algebra returns the multiplication matrix of A#H
def Left_Smash_Product_Mult(A,H):
IdA=sps.identity(A.dim,dtype=complex,format='csr')
IdH=sps.identity(A.dim,dtype=complex,format='csr')
IdAH=sps.identity(A.dim*H.dim,dtype=complex,format='csr')
left_comult=sps.kron(sps.kron(IdA,H.comult),IdAH)
twist = Center_Twist(A.dim,H.dim,H.dim,A.dim,H.dim,3)
act_and_hmult=Spare_Multi_Kron(IdA,A.action,H.mult)
amult = sps.kron(A.mult,IdH)
return amult.dot(act_and_hmult.dot(twist.dot(left_comult)))
#Returns the element_names list of A$H
def Left_Smash_Element_Names(A,H):
out = []
for iB in range(0,H.dim):
for iA in range(0,A.dim):
out.append(A.element_names[iA]+'#'+H.element_names[iB])
return out
#Constructs the element names of the dual
def Dual_Element_Names(ele_names):
out = []
for ele in ele_names:
out.append('P_('+ele+')')
return out | {"/Example_CyclicGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/HopfConstructions.py": ["/HopfConstructions_Functions.py", "/HopfClass.py", "/Frobenius_Tools.py"], "/ExampleGroup_Functions.py": ["/HopfClass.py"], "/Example_Bpq.py": ["/HopfClass.py", "/HopfConstructions.py", "/Example_GeneralizedDihedralGroup.py"], "/ElementClass.py": ["/HopfClass.py"], "/Example_Symmetric_Group.py": ["/ExampleGroup_Functions.py"], "/Frobenius_Tools.py": ["/HopfConstructions_Functions.py"], "/Example_Taft.py": ["/HopfClass.py"], "/Algebra_Tools.py": ["/HopfClass.py", "/Frobenius_Tools.py", "/HopfConstructions_Functions.py"], "/Example_Matrix_Algebra.py": ["/HopfClass.py"], "/Example_DihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/Example_GeneralizedDihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"]} |
59,224 | AdamJacoby/Hopf | refs/heads/master | /Example_CyclicGroup.py | from ExampleGroup_Functions import *
from HopfClass import HopfAlgebra
import numpy as np
import scipy.sparse as sps
from itertools import product
#Constructs the multiplication matrix of the cyclic group of order dim in csr sparse format
def CyclicGroup_Mult_Matrix(dim):
mult = np.zeros((dim,dim**2),dtype=complex)
N=range(0,dim)
for i,j in product(N,N):
mult[(i+j)%dim,i*dim+j]=1
mult = sps.csr_matrix(mult.tolist(),dtype=complex)
return mult
def CyclicGroup_Antipode(dim):
antipode = np.zeros((dim,dim),dtype=complex)
for i in range(0,dim):
antipode[i,(-i)%dim]=1
antipode = sps.csr_matrix(antipode.tolist(),dtype=complex)
return antipode
def CyclicGroup_Element_Names(dim,ele_name):
out = []
for i in range(0,dim):
out.append(ele_name+'^'+str(i))
return out
def CyclicGroup(dim,element_name):
mult = CyclicGroup_Mult_Matrix(dim)
comult = Group_Comult_Matrix(dim)
counit = Group_Counit(dim)
int = Group_Integral(dim)
antipode = CyclicGroup_Antipode(dim)
name = 'C_'+str(dim)
element_names = CyclicGroup_Element_Names(dim,element_name)
out = HopfAlgebra(name,element_names,mult,comult,counit,antipode)
out.Input_Integral(int)
return out | {"/Example_CyclicGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/HopfConstructions.py": ["/HopfConstructions_Functions.py", "/HopfClass.py", "/Frobenius_Tools.py"], "/ExampleGroup_Functions.py": ["/HopfClass.py"], "/Example_Bpq.py": ["/HopfClass.py", "/HopfConstructions.py", "/Example_GeneralizedDihedralGroup.py"], "/ElementClass.py": ["/HopfClass.py"], "/Example_Symmetric_Group.py": ["/ExampleGroup_Functions.py"], "/Frobenius_Tools.py": ["/HopfConstructions_Functions.py"], "/Example_Taft.py": ["/HopfClass.py"], "/Algebra_Tools.py": ["/HopfClass.py", "/Frobenius_Tools.py", "/HopfConstructions_Functions.py"], "/Example_Matrix_Algebra.py": ["/HopfClass.py"], "/Example_DihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/Example_GeneralizedDihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"]} |
59,225 | AdamJacoby/Hopf | refs/heads/master | /HopfConstructions.py | import scipy.sparse as sps
import sympy as sym
import numpy as np
from HopfConstructions_Functions import *
from HopfClass import *
from Frobenius_Tools import HigmanTrace
#Given two spaces returns their tensor product with as much structure as possible
def Tensor_Product(A,B):
_type = Tensor_Type(A,B)
name = A.name + '(T)' + B.name
element_names=[]
for iA in range(0,A.dim):
for iB in range(0,B.dim):
element_names.append(A.element_names[iA]+'(T)'+B.element_names[iB])
if _type == 'HopfAlgebra':
mult=Tensor_Mult(A,B)
comult=Tensor_Comult(A,B)
counit=np.kron(A.counit,B.counit)
antipode = sps.kron(A.antipode,B.antipode)
out = HopfAlgebra(name,element_names,mult,comult,counit,antipode)
if A.int_flag != 'no' and B.int_flag!='no':
out.Input_Integral(np.kron(A.int,B.int))
elif _type == 'BiAlgebra':
mult=Tensor_Mult(A,B)
comult=Tensor_Comult(A,B)
counit=np.kron(A.counit,B.counit)
out = BiAlgebra(name,element_names,mult,comult,counit)
elif _type == 'Algebra':
mult=Tensor_Mult(A,B)
out = Algebra(name,element_names,mult)
elif _type == 'CoAlgebra':
comult=Tensor_Comult(A,B)
counit=np.kron(A.counit,B.counit)
out = CoAlgebra(name,element_names,comult,counit)
return out
#Take a Hopf algebra and element J and J^{-1} as inputs and returns the Drinfeld twist of the coalgebra structure J and J^{-1} are input as np.arrays in H\ot H
def Drinfeld_Twist(H,J,JI,twist_name):
comult = Drinfeld_Twist_Comult(H,J,JI)
name = H.name + '('+twist_name+')'
antipode = Drinfeld_Twist_Antipode(H,J,JI)
out = HopfAlgebra(name,H.element_names,H.mult,comult,H.counit,antipode)
out.Input_Integral(H.int)
return out
#Takes as input a Hopf algebra and outputs the adjoint module as a module algebra
def Left_Adjoint_Module(H):
action = Left_Adjoint_Action(H)
return ModuleAlgebra('ad'+H.name,H.name,action,H.element_names,H.mult)
#Given a Hopf algebra H and an H module algebra A constructs the smashed product A#H
def Left_Smash_Product(A,H):
mult = Left_Smash_Product_Mult(A,H)
ele_names = Left_Smash_Element_Names(A,H)
return Algebra(A.name+'#'+H.name,ele_names,mult)
def Dual_Hopf_Algebra(H):
mult = H.comult.transpose()
comult = H.comult.transpose()
antipode = H.antipode.transpose()
element_names = Dual_Element_Names(H.element_names)
counit = np.zeros((H.dim),dtype=complex)
counit[0]=1
return HopfAlgebra(H.name+'^*',element_names,mult,comult,counit,antipode)
| {"/Example_CyclicGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/HopfConstructions.py": ["/HopfConstructions_Functions.py", "/HopfClass.py", "/Frobenius_Tools.py"], "/ExampleGroup_Functions.py": ["/HopfClass.py"], "/Example_Bpq.py": ["/HopfClass.py", "/HopfConstructions.py", "/Example_GeneralizedDihedralGroup.py"], "/ElementClass.py": ["/HopfClass.py"], "/Example_Symmetric_Group.py": ["/ExampleGroup_Functions.py"], "/Frobenius_Tools.py": ["/HopfConstructions_Functions.py"], "/Example_Taft.py": ["/HopfClass.py"], "/Algebra_Tools.py": ["/HopfClass.py", "/Frobenius_Tools.py", "/HopfConstructions_Functions.py"], "/Example_Matrix_Algebra.py": ["/HopfClass.py"], "/Example_DihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/Example_GeneralizedDihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"]} |
59,226 | AdamJacoby/Hopf | refs/heads/master | /ExampleGroup_Functions.py | import numpy as np
from scipy.sparse import csr_matrix
from itertools import product
from HopfClass import HopfAlgebra
import sympy.combinatorics as comb
#Constructs the integral of a group algebra
def Group_Integral(dim):
out = np.zeros((dim),dtype=complex)
for i in range(0,dim):
out[i]=1
return out
#Constructs the counit of a group algebra
def Group_Counit(dim):
out = np.zeros((dim),dtype=complex)
for i in range(0,dim):
out[i]=1
return out
#Constructs the comultiplication of a group algebra
def Group_Comult_Matrix(dim):
temp=np.zeros((dim**2,dim),dtype=complex)
for i in range(0,dim):
temp[i+dim*i,i]=1
return csr_matrix(temp.tolist(),dtype=complex)
#Takes the name,element names multiplication matrix and antipode matrix and outputs the corresponding group algebr with the identity
def MakeGroupAlgebra(name,ele_names,mult,antipode):
dim = len(ele_names)
G = HopfAlgebra(name,ele_names,mult,Group_Comult_Matrix(dim),Group_Counit(dim),antipode)
G.Input_Integral(Group_Integral(dim))
return G
#Given a sympy permutation group object out puts the ocrresponding multiplication matrix
def PermutationGroupInfo(P):
Elements = list(P.elements)
dim = len(Elements)
Permutation_Size=max(list(Elements[0]))
idenity = comb.Permutation(Permutation_Size)
Elements.remove(idenity)
Elements.insert(0,idenity)
ele_names = []
for Element in Elements:
ele_names.append(str(list(Element)).replace(" ",""))
mult = np.zeros((dim,dim**2))
for i, j in product(range(0,dim),range(0,dim)):#Corresponds to g_i*g_j
k = Elements.index(Elements[i]*Elements[j])#finds the lockation of g_i*g_j in the list Elements
mult[k,i*dim+j]=1
mult = csr_matrix(mult.tolist(),dtype=np.int8)
antipode = np.zeros((dim,dim))
for i in range(0,dim):
inverse = Elements[i]**-1
j = Elements.index(inverse)
antipode[j,i]=1
antipode = csr_matrix(antipode.tolist(),dtype=np.int8)
return {'element_names':ele_names,'mult':mult,'antipode':antipode} | {"/Example_CyclicGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/HopfConstructions.py": ["/HopfConstructions_Functions.py", "/HopfClass.py", "/Frobenius_Tools.py"], "/ExampleGroup_Functions.py": ["/HopfClass.py"], "/Example_Bpq.py": ["/HopfClass.py", "/HopfConstructions.py", "/Example_GeneralizedDihedralGroup.py"], "/ElementClass.py": ["/HopfClass.py"], "/Example_Symmetric_Group.py": ["/ExampleGroup_Functions.py"], "/Frobenius_Tools.py": ["/HopfConstructions_Functions.py"], "/Example_Taft.py": ["/HopfClass.py"], "/Algebra_Tools.py": ["/HopfClass.py", "/Frobenius_Tools.py", "/HopfConstructions_Functions.py"], "/Example_Matrix_Algebra.py": ["/HopfClass.py"], "/Example_DihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/Example_GeneralizedDihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"]} |
59,227 | AdamJacoby/Hopf | refs/heads/master | /Example_Bpq.py | #This code constructs the following Hopf algebra
#as an algebra it is KG for G = {sigma,tau,a,b|simga^p=tau^p=a^q=b^q=1[sigma,b]=[a,tau]=[ba]=[sigma,tau]=1,sigma*a=a^r*sigma,tau*b=b^r*tau}
import numpy as np
import scipy.sparse as sps
from HopfClass import HopfAlgebra
from HopfConstructions import Tensor_Product, Drinfeld_Twist
from itertools import product
from Example_GeneralizedDihedralGroup import GeneralizedDihedralGroup
#Construnt a list of roots of unity the first one 1 the nextt is ptimitive
def Roots_Of_Unity(n):
out = []
for i in range(0,n):
out.append(np.exp(i*2j*np.pi/n))
return out
#two numbers p and q such that p|q-1 and roots a list of pth roots of unity out puts the twist J and J^{-1} as [J,J^{-1}]
def Bpq_Twist(p,q):
dim = (p*q)**2
omega=Roots_Of_Unity(p)
J=np.zeros((dim**2),dtype=complex)
JI=np.zeros((dim**2),dtype=complex)
for i,j in product(range(0,p),range(0,p)):#Corresponds to the element tau^i\ot sigma^j
J[dim*i+j]=(1/p)*omega[(-i*j)%p]
JI[dim*i+j]=(1/p)*omega[(i*j)%p]
return [J,JI]
def Bpq(p,q,r):
G1=GeneralizedDihedralGroup(p,q,r,'a','sigma')
G2=GeneralizedDihedralGroup(p,q,r,'b','tau')
G=Tensor_Product(G1,G2)
temp = Bpq_Twist(p,q)
J=temp[0]
JI=temp[1]
return Drinfeld_Twist(G,J,JI,'J') | {"/Example_CyclicGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/HopfConstructions.py": ["/HopfConstructions_Functions.py", "/HopfClass.py", "/Frobenius_Tools.py"], "/ExampleGroup_Functions.py": ["/HopfClass.py"], "/Example_Bpq.py": ["/HopfClass.py", "/HopfConstructions.py", "/Example_GeneralizedDihedralGroup.py"], "/ElementClass.py": ["/HopfClass.py"], "/Example_Symmetric_Group.py": ["/ExampleGroup_Functions.py"], "/Frobenius_Tools.py": ["/HopfConstructions_Functions.py"], "/Example_Taft.py": ["/HopfClass.py"], "/Algebra_Tools.py": ["/HopfClass.py", "/Frobenius_Tools.py", "/HopfConstructions_Functions.py"], "/Example_Matrix_Algebra.py": ["/HopfClass.py"], "/Example_DihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/Example_GeneralizedDihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"]} |
59,228 | AdamJacoby/Hopf | refs/heads/master | /ElementClass.py | from HopfClass import *
###########################################################################################################
#Creates a new class of objects as elements of vector spaces/algebras/modules/bialgebras/Hopf algebras
#The primarry advantage to elements is that they can have names making them much easier to work with
##############################################################################################################
#Creates the most basic element that of a vector space
#It can be created either by entering the name associated to the algebra
#or by entering in a proper size vector
#names should be entered as sums of constant*elementname
#Elements have three properties name,vector and space
#All operations are inheartyed from the space
class VectorSpaceElement(object):
#Note to save time this function does not name an unnamed vector
def __init__(self,vector,space):
if type(vector) !=str:
self.vector = vector
elif vector == '0':
self.vector = np.zeros((space.dim))
else:
self.vector = np.zeros((space.dim),dtype=complex)
temp = vector.split('+')
for item in temp:
element = item.split('*')
for i in range(0,space.dim):
if element[1] == space.element_names[i]:
self.vector[i]=complex(element[0])
break
self.space = space
self.name = 'Currently unnamed.'
#Constructs the tensor product of two elements
#Note the tensor space must be defined for this to happen
def Tensor(self,other):
if self.space.type == 'HopfAlgebra' and other.space.type == 'HopfAlgebra':
out_type = 'HopfAlgebra'
elif self.space.type == 'Algebra' or other.space.type:
out_type = 'Algebra'
elif (self.space.type == 'ModuleAlgebra' or self.space.type == 'HopfAlgebra') and (other.space.type=='HopfALgebra' or other.space.type=='ModuleALgebra'):
out_type = 'ModuleAlgebra'
else:
out_type = 'VectorSpace'
out = np.zeros((self.space.dim*other.space.dim),dtype=complex)
for i in range(0,self.space.dim):
for j in range(0,other.space.dim):
out[i*other.space.dim+j]=self.vector[i]*other.vector[j]
return eval(out_type+'Element(out,AlgebraList[\''+self.space.name+'(T)'+other.space.name+'\'])')
#Adds two elements
def __add__(self,other):
return eval(self.space.type+'Element(self.vector + other.vector,self.space)')
#Subtracts two elements
def __sub__(self,other):
return eval(self.space.type+'Element(self.vector - other.vector,self.space)')
#If the element does not have a name this function adds it
def Name(self):
if self.name == 'Currently unnamed.':
self.name = ''
for i in range(0,self.space.dim):
if self.vector[i] != 0:
self.name = self.name+'+'+str(self.vector[i])+'*'+self.space.element_names[i]
if self.name == '':
self.name = '0'
return self.name
else:
return self.name
#Adds two new functions to the vector space element class
class AlgebraElement(VectorSpaceElement):
#Defines the multiplication action multiplication comes from the space
def __mul__(self,other):
if type(other) == complex or type(other) == int or type(other) == float or type(other)==np.complex128:
return eval(self.space.type+'Element(other*self.vector,self.space)')
elif self.space==other.space:
return eval(self.space.type+'Element(self.space.Mult(self.vector,other.vector),self.space)')
elif other.space.type == 'Module':
return ModuleAlgebraElement(other.space.Action(self.vector,other.vector),other.space)
elif other.space.type == 'ModuleAlgebra':
return ModuleAlgebraElement(other.space.Action(self.vector,other.vector),other.space)
#Defines the expeniation action multiplication comes from the space
def __pow__(self,other):
out = self
if other ==0:
temp = np.zeros(self.vector.shape[0])
temp[0]=1
out = eval(self.space.type+'Element(temp,self.space)')
else:
for i in range(0,other-1):
out = out*self
return out
#Element class for a coalgebra
#Adds two functions onto a vector space element
#
class CoAlgebraElement(VectorSpaceElement):
#Defines the comultiplication all structure comes from the space
def CoMult(self):
if self.space.type == 'CoAlgebra':
return CoAlgebraElement(self.space.CoMult(self.vector),AlgebraList[self.space.name+'(T)'+self.space.name])
if self.space.type == 'HopfAlgebra':
return HopfAlgebraElement(self.space.CoMult(self.vector),AlgebraList[self.space.name+'(T)'+self.space.name])
#Defines the counit all structure comes from the space
#output is an element of the base field currently the complex numbers
def CoUnit(self):
return self.space.CoUnit(self.vector)
#Has the functions of both an algebra element and a coalgebra element
#Only new function is the antipode
class HopfAlgebraElement(AlgebraElement,CoAlgebraElement):
def Antipode(self):
return HopfAlgebraElement(self.space.Antipode(self.vector),self.space)
#Element of a module currentl uncomplete !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
class ModuleElement(VectorSpaceElement):
def Act(self,ring_element):
vector = self.space.Action(ring_element.vector,self.vector)
return eval(self.space.type+'Element(vector,self.space)')
#Element of a module algebra has all functions as that of a Module element and an algebra element
#Also contains a function for the smash product of two elements
#Note the smash product must exist for this to happen
class ModuleAlgebraElement(ModuleElement,AlgebraElement):
def Smash(self,other):
vector = ProdVectorToTensorVector(self.vector,other.vector)
return ModuleAlgebraElement(vector,AlgebraList[self.space.name+'#'+other.space.name]) | {"/Example_CyclicGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/HopfConstructions.py": ["/HopfConstructions_Functions.py", "/HopfClass.py", "/Frobenius_Tools.py"], "/ExampleGroup_Functions.py": ["/HopfClass.py"], "/Example_Bpq.py": ["/HopfClass.py", "/HopfConstructions.py", "/Example_GeneralizedDihedralGroup.py"], "/ElementClass.py": ["/HopfClass.py"], "/Example_Symmetric_Group.py": ["/ExampleGroup_Functions.py"], "/Frobenius_Tools.py": ["/HopfConstructions_Functions.py"], "/Example_Taft.py": ["/HopfClass.py"], "/Algebra_Tools.py": ["/HopfClass.py", "/Frobenius_Tools.py", "/HopfConstructions_Functions.py"], "/Example_Matrix_Algebra.py": ["/HopfClass.py"], "/Example_DihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/Example_GeneralizedDihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"]} |
59,229 | AdamJacoby/Hopf | refs/heads/master | /Example_Symmetric_Group.py | from sympy.combinatorics.named_groups import SymmetricGroup
from ExampleGroup_Functions import PermutationGroupInfo, MakeGroupAlgebra
def Symmetric_Group(n):
temp = PermutationGroupInfo(SymmetricGroup(n))
S = MakeGroupAlgebra('S_'+str(n),temp['element_names'],temp['mult'],temp['antipode'])
return S
| {"/Example_CyclicGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/HopfConstructions.py": ["/HopfConstructions_Functions.py", "/HopfClass.py", "/Frobenius_Tools.py"], "/ExampleGroup_Functions.py": ["/HopfClass.py"], "/Example_Bpq.py": ["/HopfClass.py", "/HopfConstructions.py", "/Example_GeneralizedDihedralGroup.py"], "/ElementClass.py": ["/HopfClass.py"], "/Example_Symmetric_Group.py": ["/ExampleGroup_Functions.py"], "/Frobenius_Tools.py": ["/HopfConstructions_Functions.py"], "/Example_Taft.py": ["/HopfClass.py"], "/Algebra_Tools.py": ["/HopfClass.py", "/Frobenius_Tools.py", "/HopfConstructions_Functions.py"], "/Example_Matrix_Algebra.py": ["/HopfClass.py"], "/Example_DihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/Example_GeneralizedDihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"]} |
59,230 | AdamJacoby/Hopf | refs/heads/master | /Frobenius_Tools.py | from itertools import product
import scipy.sparse as sps
import sympy as sp
import numpy as np
from HopfConstructions_Functions import Left_Action_Matrix, Right_Action_Matrix, CreateBasisVectors, Tensor_Mult
from math import floor
#Conversts s scipymatrix to sympy then converts the charicteristic poly in sympy and outputs it
def CharPoly(M):
temp = M.toarray()
temp = sp.SparseMatrix(temp)
return temp.berkowitz_charpoly()
#Given a polynomial poly with list of roots, roots, computes the multiplicity of the roots using derivatives where Der is a partial list of derivatives
def Multiplicity(poly,root,Der):
D = Der #Start list of derivatives of poly
i = 1
flag = 'go'
while flag == 'go':
if i<=(len(D)-1):
if D[i].eval(root)!=0:
multiplicity = i
flag = 'stop'
else:
temp = D[-1].diff()
D.append(temp)
if temp.eval(root) != 0:
multiplicity = i
flag = 'stop'
i = i+1
return [multiplicity,D]
def Devisors(n):#Computes all divisors of n less then sqrt(n)
out = []
for i in range(1,floor(n**.5)+1):
if n%i==0:
out.append(i)
return out
#Takes a polynomial and factors outt the zeros at 0
def Remove_Zeros_At_Zero(poly):
mon = poly.EM()
mon = mon.as_expr()
return sp.exquo(poly,mon)
#Computes the matrix corresponding to the higman trace: A\rightarrow A
def HigmanTrace(A):
dim = A.dim
if A.casimir_flag == 'no':
A.GetCasimir()
mult = A.mult
V = CreateBasisVectors(dim)
casimir = A.casimir
higman_trace = sps.csr_matrix((dim,dim))
for i,j in product(range(0,dim),range(0,dim)):#i,j corresponds to the baasis vector b^i\ot b^j
if A.casimir[dim*i+j] !=0:
higman_trace = higman_trace+Left_Action_Matrix(V[i],mult).dot(Right_Action_Matrix(V[j],mult))
return higman_trace
#Given an algerbra A computes the mattrix corresponding to the left action of
#the image of the casimir squared under the multiplication map
def Compute_M(A):
dim = A.dim
if A.casimir_flag == 'no':
A.GetCasimir()
mult = A.mult
casimir = A.casimir
tensor_mult = Tensor_Mult(A,A)
C = mult.dot(tensor_mult.dot(np.kron(A.casimir,A.casimir)))
return Left_Action_Matrix(C,A.mult) | {"/Example_CyclicGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/HopfConstructions.py": ["/HopfConstructions_Functions.py", "/HopfClass.py", "/Frobenius_Tools.py"], "/ExampleGroup_Functions.py": ["/HopfClass.py"], "/Example_Bpq.py": ["/HopfClass.py", "/HopfConstructions.py", "/Example_GeneralizedDihedralGroup.py"], "/ElementClass.py": ["/HopfClass.py"], "/Example_Symmetric_Group.py": ["/ExampleGroup_Functions.py"], "/Frobenius_Tools.py": ["/HopfConstructions_Functions.py"], "/Example_Taft.py": ["/HopfClass.py"], "/Algebra_Tools.py": ["/HopfClass.py", "/Frobenius_Tools.py", "/HopfConstructions_Functions.py"], "/Example_Matrix_Algebra.py": ["/HopfClass.py"], "/Example_DihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/Example_GeneralizedDihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"]} |
59,231 | AdamJacoby/Hopf | refs/heads/master | /Example_Taft.py | from HopfClass import *
import numpy as np
from itertools import product
import scipy.sparse as sps
#Creates the name vector for the Taft algebra
#g is the name of the group like
#x is the name of the g,1 primitive element
#basis is given by g^0x^0,g^2,...,g^1x^1,g^2x^1,...,g^{n-2}x^{n-1},g^{n-1}x^{n-1}
def Taft_Element_Names(n,g,x):
out=[]
for i in range(0,n):
for j in range(0,n):
out.append(g+'^'+str(j)+x+'^'+str(i))
return out
#Created the multiplication matrix for the taft algebra
#Uses the same ordered basis that was used for element names
def Taft_Mult(n):
poly = [0]*(n+1)
poly[0]=1
poly[n]=-1
#Omega is the lsit of roots of unity where omega[0] is the primitive one underconsideration in the definition
omega=np.roots(poly)
out = np.zeros((n**2,n**4),dtype=complex)
N=range(0,n)
for i, j, k in product(N,N,N):# the iteration i,j,l,k corresponds to the input g^ix^j\ot g^kx^l
for l in range(0,n-j):#only loops up to n-j since if j+l>=n the product should be 0
out[((i+k)%n)+n*(j+l),i+n*j+(k+n*l)*n**2]=omega[(n-1)-(-j*k)%n]
out = sps.csr_matrix(out.tolist(),dtype=complex)
return out
#Constructs the comultiplication matrix inductively perhaps latter will want to do it in a closed form!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
def Taft_Comult(n,mult):
dim = n**2
out=np.zeros((dim**2,dim),dtype=complex)
#compute list of powers of (x+g) between 0 and n-1
xplusg=np.zeros(dim)
xplusg[1]=1
xplusg[n]=1
temp = np.zeros(dim)
temp[0]=1
prods =[temp]
N=range(1,n)
for i in N:
temp2 = np.kron(temp,xplusg)
temp = mult.dot(temp2)
prods.append(temp)
N=range(0,n)
for i, j in product(N,N):#i,j corresponds to g^ix^j
for l in range(0,j+1):
out[((i+l)%n)+(j-l)*n+(i+n*l)*dim,i+j*n]=prods[j][l+(j-l)*n]
return out
def Taft_Counit(n):
out = np.zeros((n),dtype=complex)
for i in range(0,n):
out[i]=1
return out
def Taft_Antipode(n):
dim=n**2
poly = [0]*(n+1)
N=range(0,n)
poly[0]=1
out=np.zeros((dim,dim),dtype=complex)
poly[n]=-1
omega=np.roots(poly)
for i, j in product(N,N):#(i,j) corresponds to g^ix^j
out[((-i)%n)+j*n]=(-1)**j*omega[(i*j)%n]#gives coefficent of g^{n-i}x^j
out = sps.csr_matrix(out.tolist(),dtype=complex)
return out
###################################################################################################
#Code For testing only
###################################################################################################### | {"/Example_CyclicGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/HopfConstructions.py": ["/HopfConstructions_Functions.py", "/HopfClass.py", "/Frobenius_Tools.py"], "/ExampleGroup_Functions.py": ["/HopfClass.py"], "/Example_Bpq.py": ["/HopfClass.py", "/HopfConstructions.py", "/Example_GeneralizedDihedralGroup.py"], "/ElementClass.py": ["/HopfClass.py"], "/Example_Symmetric_Group.py": ["/ExampleGroup_Functions.py"], "/Frobenius_Tools.py": ["/HopfConstructions_Functions.py"], "/Example_Taft.py": ["/HopfClass.py"], "/Algebra_Tools.py": ["/HopfClass.py", "/Frobenius_Tools.py", "/HopfConstructions_Functions.py"], "/Example_Matrix_Algebra.py": ["/HopfClass.py"], "/Example_DihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/Example_GeneralizedDihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"]} |
59,232 | AdamJacoby/Hopf | refs/heads/master | /HopfClass.py | # packages
from HopfClass_Functions import *
from scipy import linalg
import scipy.sparse as sps
import numpy as np
import os
import pickle
##################################################################################################################
#Create the global variable storing algebras
###################################################################################################################
global AlgebraList
AlgebraList = {}
###############################################################################################################################
#Create a class for algebras
#The multiplication matrix should have the form of an n by n^2 matrix
#corisponding to the multiplication matrix with lexiographical ordering on the tensor product
#############################################################################################################################
#Creates a Class for vector spaces
#For the addition and scalar multiplication action see the class VectorSpaceElement
class VectorSpace(object):
def __init__(self,name,element_names):
#name of vector space
self.name = name
#List of the names of the elements in the vertor space
self.element_names = element_names
#A flag denoting the type of space
self._type = 'VectorSpace'
#The dimension of the vector space
self.dim = len(element_names)
#adds the object to the list of known objects
global AlgebraList
AlgebraList[self.name]=self
#Saves the object to a location of your choice
#if no location is given save to the current workign dirrectory
def Save(self,*location):
if len(location)==0:
location = os.getcwd()
else:
location = location[0]
temp_path = os.path.join(location, 'Save_'+self.name)
temp_file = open(temp_path,'w+')
pickle.dump(self,temp_file)
temp_file.close()
#Creates a class for an assocative algebra
class Algebra(VectorSpace):
def __init__(self,name,element_names,mult):
self.name = name
#The matrix that encodes the multiplication a dim by dim^2 matrix
self.mult = mult
self.dim = len(element_names)
self.element_names = element_names
self._type = 'Algebra'
# a flag stating wether this algebra is Frobenius
#I may replace this with a diffrent class all together?????????????????????????????????????????????????????
self.casimir_flag = 'no'
#A place to store the normalized regular character
self.nrchar_flag = 'no'
self.nrchar= None
#A variable for the casimir element if known
self.casimir = None
global AlgebraList
AlgebraList[self.name]=self
#Uses the multiplication matrix to multiply two vectors
def Mult(self,a,b):
temp = np.kron(a,b)
out = self.mult.dot(temp)
return out
#Computes the normalized regular character
def GetNRChar(self):
V = CreateBasisVectors(self.dim)
self.nrchar = np.zeros((self.dim),dtype=complex)
for i in range(0,self.dim):
temp = 0
for j in range(0,self.dim):
temp = temp + self.Mult(V[i],V[j])[j]
self.nrchar[i] = temp / self.dim
self.nrchar_flag = 'yes'
#Uses linear algebra to get casimir element if we are dealing with a hopf algebra !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
def GetCasimir(self):
V = CreateBasisVectors(self.dim)
self.casimir = np.zeros((self.dim**2),dtype=complex)
equations = np.zeros((self.dim,self.dim),dtype=complex)
if self.nrchar_flag == 'no':
self.GetNRChar()
for i in range(0,self.dim):
for j in range(0,self.dim):
equations[i,j] = self.nrchar.dot(self.Mult(V[i],V[j]))
for i in range(0,self.dim):
solutions = np.zeros((self.dim),dtype=complex)
solutions[i]=1
temp = linalg.solve(equations,solutions)
self.casimir = self.casimir + np.kron(V[i],temp)
def Input_Casimir(self,casimir):
self.casimir=casimir
self.casimir_flag = 'yes'
###########################################################################################################################
#Defines the class of coalgebras comult should be a Dim by Dim^2 matrix with top as inputs and side as outputs
#counit representes the counit map and shold be the vector of [\epsilon(T_1),\epsilon(T_2),...,\epsilon(T_{Dim})]
###########################################################################################################################
class CoAlgebra(VectorSpace):
def __init__(self,name,element_names,comult,counit):
self.name = name
#The variable that stores the comulttiplication matrix
self.comult = comult
self.dim = len(element_names)
#stores the counit
self.counit = counit
self.element_names = element_names
self._type = 'CoAlgebra'
global AlgebraList
AlgebraList[self.name]=self
#Takes an element in vector form as input and outputs it image under the comultiplication
#As always the tensor is expressed in terms of the lexiographical basis
def CoMult(self,a):
return self.comult.dot(a)
#Takes an element in vector form as the input and outputs the counit evaulated at it
#Result is an element of the base field
def CoUnit(self,a):
return self.counit.dot(a)
#####################################################################################################
#Creates a Bialgebra class simple an Algebra and a coalgebra (NOTE: As of now compatibility conditions are NOT checked)
#####################################################################################################
class BiAlgebra(CoAlgebra,Algebra):
def __init__(self,name,element_names,mult,comult,counit):
self.name = name
self.mult = mult
self.dim = len(element_names)
self.comult = comult
self.counit = counit
self.element_names = element_names
self._type = 'BiAlgebra'
self.casimir_flag = 'no'
self.nrcharflag = 'no'
self.casimir=None
self.nrchar = None
global AlgebraList
AlgebraList[self.name]=self
#############################################################################################################
#A biablgebra plus an Antipode (Note the antipode axiomes are NOT checked)
#the Antipode will be input as a Dim by Dim matrix corresponding to the antipode under the standard basis
################################################################################################################
class HopfAlgebra(BiAlgebra):
def __init__(self,name,element_names,mult,comult,counit,antipode):
self.name = name
self.element_names = element_names
self.mult = mult
self.dim = len(element_names)
self.comult = comult
self.counit = counit
self.antipode = antipode
self.int_flag = 'no'
self.casimir_flag = 'no'
self.nrchar_glag = 'no'
self.integral = None
self.casimir = None
self.nrchar = None
self._type = 'HopfAlgebra'
global AlgebraList
AlgebraList[self.name]=self
#Take an element expressed as a vector as an input and outputs the antipode evaluated at it
def Antipode(self,a):
return self.antipode.dot(a)
#This code should eventually be expanded so that it computes it for you need to be careful goal is for this to be computed at creation
#This should be input as a vector not a Hopf element need left and right integrals
def Input_Integral(self,integral):
self.integral = integral
self.int_flag = 'yes'
#Given the integral uses it to compute the Casimir element
#This should eventually be done at creation but need to get integral code running first
def GetCasimir(self):
if self.int_flag == 'yes':
temp = self.CoMult(self.integral)
temp = TensorVectorToProdVector(temp,self,self)
out = np.zeros((self.dim**2),dtype=complex)
for item in temp:
out = out + np.kron(item[0],self.Antipode(item[1]))
self.casimir = out
self.casimir_flag = 'yes'
else:
Algebra.GetCasimir(self)
##################################################################################################################################
#Creates a module the action should be in the form of the matrix representing the action map from ring tensor module to module
#interms of the standard basis
##################################################################################################################################
class Module(VectorSpace):
def __init__(self,name,element_names,ring,action):
self.dim = len(element_names)
#The ring that acts on it stored as a string
self.ring = ring
self.action = action
self.elements_names
self.name = name
self._type = 'Module'
global AlgebraList
AlgebraList[self.name]=self
#Given two vectors representing a ring element and module element respectively
#outputs the vector corresponding to the ring element acting on the vector space element
def Action(self,ring_vector,module_vector):
return self.action.dot(np.kron(ring_vector,module_vector))
#Creates a class for a module algebra which is exactly just a module class and algebra class
class ModuleAlgebra(Algebra,Module):
def __init__(self,name,element_names,ring,action,mult):
self.name = name
self.mult = mult
self.dim = len(element_names)
self.element_names = element_names
self.mult=mult
self.action = action
self.casimir_flag = 'no'
self.nrchar_flag = 'no'
self._type = 'ModuleAlgebra'
self.casimir = None
self.nrchar = None
global AlgebraList
AlgebraList[self.name]=self | {"/Example_CyclicGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/HopfConstructions.py": ["/HopfConstructions_Functions.py", "/HopfClass.py", "/Frobenius_Tools.py"], "/ExampleGroup_Functions.py": ["/HopfClass.py"], "/Example_Bpq.py": ["/HopfClass.py", "/HopfConstructions.py", "/Example_GeneralizedDihedralGroup.py"], "/ElementClass.py": ["/HopfClass.py"], "/Example_Symmetric_Group.py": ["/ExampleGroup_Functions.py"], "/Frobenius_Tools.py": ["/HopfConstructions_Functions.py"], "/Example_Taft.py": ["/HopfClass.py"], "/Algebra_Tools.py": ["/HopfClass.py", "/Frobenius_Tools.py", "/HopfConstructions_Functions.py"], "/Example_Matrix_Algebra.py": ["/HopfClass.py"], "/Example_DihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/Example_GeneralizedDihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"]} |
59,233 | AdamJacoby/Hopf | refs/heads/master | /Algebra_Tools.py | import sympy as sp
import scipy.sparse as sps
from HopfClass import *
import numpy as np
import sympy as sym
from Frobenius_Tools import HigmanTrace
from HopfConstructions_Functions import CreateBasisVectors
#Given U the change of basis matrix twiost the structure matrixes to be wirth respect to the new basis
#U(original basis element)=new basis element
def ChangeBasis(A,U,UI):
U = sps.csr_matrix(U.tolist())
UI= sps.csr_matrix(UI.tolist())
if 'Algebra' in A._type:
mult = U.dot(A.mult.dot(sps.kron(UI,UI)))
if 'Module' in A._type:
ring_dim=self.ring.dim
action = U.dot(A.action)
ring_id = sps.Identity(ring_dim,format='csr')
action = U.dot(action.dot(sps.kron(ring_id,UI)))
if A._type=='HopfAlgebra' or A._type=='BiAlgebra' or A._type=='CoAlgebra':
comult = sps.kron(U,U).dot(A.comult.dot(UI))
counit = A.counit.dot(UI)
if A._type == 'Algebra':
out = Algebra(A.name,A.element_names,mult)
if A._type == 'CoAlgebra':
out = CoAlgebra(A.name,A.element_names,comult,counit)
if A._type == 'BiAlgebra':
out = BiAlgebra(A.name,A.element_names,mult,comult,counit)
if A._type == 'HopfAlgebra':
antipode = U.dot(A.antipode.dot(UI))
out = HopfAlgebra(A.name,A.element_names,mult,comult,counit,antipode)
if A.int_flag != 'no':
A.Input_Integral(U.dot(A.Integral))
if A._type == 'Module':
out = Module(A.name,A.element_names,A.ring,action)
if A._type == 'ModuleAlgebra':
out = ModuleAlgebra(A.name,A.element_names,A.ring,action,mult)
if 'Algebra' in A._type and A.casimir_flag !='no':
out.Input_Casimir(sps.kron(U,U).dot(A.casimir))
return out
#Computes a basis for the center of A as well as a complementary basis
def Center(A):
dim = A.dim
V_temp = CreateBasisVectors(dim)
V=[]
for vector in V_temp:
V.append(vector.tolist())
higman_trace=HigmanTrace(A)
higman_trace = sym.Matrix(higman_trace.toarray())
image = higman_trace.transpose().rref()
image = image[0]
rref_matrix = image.tolist()
rref_matrix=filter(lambda a: a != [0]*dim, rref_matrix)
center_dim = len(rref_matrix)
change_of_basis = []
past_index = 0
complement = []
for vector in rref_matrix:
current_index = vector.index(1)
for i in range(past_index+1,current_index):
complement.append(i)
change_of_basis.append(vector)
past_index=current_index
for i in range(current_index+1,dim):#add the remaining basis vectors if not finished in previous step
complement.append(i)
for index in complement:
change_of_basis.append(V[index])
U=np.transpose(np.array(change_of_basis,dtype=complex))
UI=np.linalg.inv(U)
return [U,UI,center_dim] | {"/Example_CyclicGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/HopfConstructions.py": ["/HopfConstructions_Functions.py", "/HopfClass.py", "/Frobenius_Tools.py"], "/ExampleGroup_Functions.py": ["/HopfClass.py"], "/Example_Bpq.py": ["/HopfClass.py", "/HopfConstructions.py", "/Example_GeneralizedDihedralGroup.py"], "/ElementClass.py": ["/HopfClass.py"], "/Example_Symmetric_Group.py": ["/ExampleGroup_Functions.py"], "/Frobenius_Tools.py": ["/HopfConstructions_Functions.py"], "/Example_Taft.py": ["/HopfClass.py"], "/Algebra_Tools.py": ["/HopfClass.py", "/Frobenius_Tools.py", "/HopfConstructions_Functions.py"], "/Example_Matrix_Algebra.py": ["/HopfClass.py"], "/Example_DihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/Example_GeneralizedDihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"]} |
59,234 | AdamJacoby/Hopf | refs/heads/master | /Example_Matrix_Algebra.py | import numpy as np
from scipy.sparse import csr_matrix
from itertools import product
from HopfClass import Algebra
def MartixAlgebra_ElementNames(n):
ele_names=[]
for i in range(0,n):
for j in range(0,n):
ele_names.append('T_'+str(i)+','+str(j))
return ele_names
def MatrixAlgebra_Mult_Matrix(n):
dim=n**2
mult = np.zeros((dim,dim**2))
for i,j,k in product(range(0,n),range(0,n),range(0,n)): #Corresponds to the product T_i,jT_j,k
mult[i*n+k,dim*(i*n+j)+n*j+k]=1
return csr_matrix(mult.tolist())
def MatrixAlgebra_Casimir(n):
dim = n**2
casimir = np.zeros((dim**2))
for i,j in product(range(0,n),range(0,n)):#Coresponds to T_i,j\ot T_j,i
casimir[dim*(n*i+j)+n*j+i]=n
return casimir
def MatrixAlgebra(n):
M = Algebra('M_'+str(n),MartixAlgebra_ElementNames(n),MatrixAlgebra_Mult_Matrix(n))
M.Input_Casimir(MatrixAlgebra_Casimir(n))
return M | {"/Example_CyclicGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/HopfConstructions.py": ["/HopfConstructions_Functions.py", "/HopfClass.py", "/Frobenius_Tools.py"], "/ExampleGroup_Functions.py": ["/HopfClass.py"], "/Example_Bpq.py": ["/HopfClass.py", "/HopfConstructions.py", "/Example_GeneralizedDihedralGroup.py"], "/ElementClass.py": ["/HopfClass.py"], "/Example_Symmetric_Group.py": ["/ExampleGroup_Functions.py"], "/Frobenius_Tools.py": ["/HopfConstructions_Functions.py"], "/Example_Taft.py": ["/HopfClass.py"], "/Algebra_Tools.py": ["/HopfClass.py", "/Frobenius_Tools.py", "/HopfConstructions_Functions.py"], "/Example_Matrix_Algebra.py": ["/HopfClass.py"], "/Example_DihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/Example_GeneralizedDihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"]} |
59,235 | AdamJacoby/Hopf | refs/heads/master | /Example_DihedralGroup.py | from ExampleGroup_Functions import *
from HopfClass import HopfAlgebra
import numpy as np
import scipy.sparse as sps
from itertools import product
#Cronstrust the dihedrqal group {g^n=1,x^2=1,gx=xg^{-1}}
# g^ix^j corresponds to the basis vector with a 1 in position i*2+j
def DihedralGroup_Mult_Matrix(n):
dim = 2*n
mult = np.zeros((dim,dim**2),dtype=complex)
N=range(0,n)
for i,j in product(N,[0,1]):#Corresponds to the element in the product g^ix^j
for k,l in product(N,[0,1]):#Corresponds to the element in the product g^kx^l
mult[2*((i+((-1)**j)*k)%n)+(j+l)%2,dim*(i*2+j)+2*k+l]=1 #Element corresponding to g^{i+(-1)^j*k}x^{k+l} as the output
mult = sps.csr_matrix(mult.tolist(),dtype=complex)
return mult
#Constructts the antipode matrix for the dihedral group
def DihedralGroup_Antipode(n):
dim = 2*n
antipode = np.zeros((dim,dim),dtype=complex)
for i,j in product(range(0,n),[0,1]):#Corresponds to input of g^ix^j
antipode[2*(((-1)**(j+1)*i)%n)+j,i*2+j]=1#Corresponds to output g^{(-1)^{j+1}i}x^j
antipode = sps.csr_matrix(antipode.tolist(),dtype=complex)
return antipode
#Contructs the element name list were ele_name_g takes the role of g in discriptions above
# and ele_name_x takes the role of x in discriptions above
def DihedralGroup_Element_Names(n,ele_name_g,ele_name_x):
out = []
for i in range(0,n):#corresponds to i in g^ix^j
for j in [0,1]:#corresponds to j in g^ix^j
out.append(ele_name_g+'^'+str(i)+ele_name_x+'^'+str(j))#Gives the name
return out
#Constructs the dihedral group {g^n=1,x^2=1,gx=xg^{-1}} where g is names ele_name_g and x is named ele_name_x
def DihedralGroup(n,element_name_g,element_name_x):
dim=2*n
mult = DihedralGroup_Mult_Matrix(n)
comult = Group_Comult_Matrix(dim)
counit = Group_Counit(dim)
int = Group_Integral(dim)
antipode = DihedralGroup_Antipode(n)
name = 'D_'+str(n)
element_names = DihedralGroup_Element_Names(n,element_name_g,element_name_x)
out = HopfAlgebra(name,element_names,mult,comult,counit,antipode)
out.Input_Integral(int)#Sets the integral
return out | {"/Example_CyclicGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/HopfConstructions.py": ["/HopfConstructions_Functions.py", "/HopfClass.py", "/Frobenius_Tools.py"], "/ExampleGroup_Functions.py": ["/HopfClass.py"], "/Example_Bpq.py": ["/HopfClass.py", "/HopfConstructions.py", "/Example_GeneralizedDihedralGroup.py"], "/ElementClass.py": ["/HopfClass.py"], "/Example_Symmetric_Group.py": ["/ExampleGroup_Functions.py"], "/Frobenius_Tools.py": ["/HopfConstructions_Functions.py"], "/Example_Taft.py": ["/HopfClass.py"], "/Algebra_Tools.py": ["/HopfClass.py", "/Frobenius_Tools.py", "/HopfConstructions_Functions.py"], "/Example_Matrix_Algebra.py": ["/HopfClass.py"], "/Example_DihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/Example_GeneralizedDihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"]} |
59,236 | AdamJacoby/Hopf | refs/heads/master | /Example_GeneralizedDihedralGroup.py | #Constructs the group algrebra of the group G={g,x|g^q=x^p=1,xg=g^rx}
#p and q should be prime where p should divide q-1 and r should be an element of \ZZ_q of order p
from ExampleGroup_Functions import *
from HopfClass import HopfAlgebra
import numpy as np
import scipy.sparse as sps
from itertools import product
#Cronstrust the dihedrqal group {g^n=1,x^2=1,gx=xg^{-1}}
# g^ix^j corresponds to the basis vector with a 1 in position n*i+j
def GeneralizedDihedralGroup_Mult_Matrix(p,q,r):
dim = p*q
mult = np.zeros((dim,dim**2),dtype=complex)
for i,j in product(range(0,q),range(0,p)):#Corresponds to the firstt element in the product g^ix^j
for k,l in product(range(0,q),range(0,p)):#Corresponds to the firstt element in the product g^kx^l
mult[p*((i+r*k)%q)+(k+l)%p,dim*(i*p+j)+p*k+l]=1 #Element corresponding to g^{i+r*k}x^{k+l} as the output
mult = sps.csr_matrix(mult.tolist(),dtype=complex)
return mult
#Constructts the antipode matrix for the dihedral group
def GeneralizedDihedralGroup_Antipode(p,q,r):
dim = p*q
antipode = np.zeros((dim,dim),dtype=complex)
for i,j in product(range(0,q),range(0,p)):#Corresponds to input of g^ix^j
inv=(((r**j)%q)**(q-2))%q#Compute r^j inverse mod q
antipode[((-i*inv)%q)*p+((-j)%p),i*p+j]=1#Corresponds to output g^{(-1)^{j+1}i}x^(-j%p)
antipode = sps.csr_matrix(antipode.tolist(),dtype=complex)
return antipode
#Contructs the element name list were ele_name_g takes the role of g in discriptions above
# and ele_name_x takes the role of x in discriptions above
def GeneralizedDihedralGroup_Element_Names(p,q,ele_name_g,ele_name_x):
out = []
for i in range(0,q):#corresponds to i in g^ix^j
for j in range(0,p):#corresponds to j in g^ix^j
out.append(ele_name_g+'^'+str(i)+ele_name_x+'^'+str(j))#Gives the name
return out
#Constructs the dihedral group {g^n=1,x^2=1,gx=xg^{-1}} where g is names ele_name_g and x is named ele_name_x
def GeneralizedDihedralGroup(p,q,r,element_name_g,element_name_x):
dim=p*q
mult = GeneralizedDihedralGroup_Mult_Matrix(p,q,r)
comult = Group_Comult_Matrix(dim)
counit = Group_Counit(dim)
int = Group_Integral(dim)
antipode = GeneralizedDihedralGroup_Antipode(p,q,r)
name = 'B_'+str(p)+','+str(q)+','+str(r)
element_names = GeneralizedDihedralGroup_Element_Names(p,q,element_name_g,element_name_x)
out = HopfAlgebra(name,element_names,mult,comult,counit,antipode)
out.Input_Integral(int)#Sets the integral
return out | {"/Example_CyclicGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/HopfConstructions.py": ["/HopfConstructions_Functions.py", "/HopfClass.py", "/Frobenius_Tools.py"], "/ExampleGroup_Functions.py": ["/HopfClass.py"], "/Example_Bpq.py": ["/HopfClass.py", "/HopfConstructions.py", "/Example_GeneralizedDihedralGroup.py"], "/ElementClass.py": ["/HopfClass.py"], "/Example_Symmetric_Group.py": ["/ExampleGroup_Functions.py"], "/Frobenius_Tools.py": ["/HopfConstructions_Functions.py"], "/Example_Taft.py": ["/HopfClass.py"], "/Algebra_Tools.py": ["/HopfClass.py", "/Frobenius_Tools.py", "/HopfConstructions_Functions.py"], "/Example_Matrix_Algebra.py": ["/HopfClass.py"], "/Example_DihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/Example_GeneralizedDihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"]} |
59,237 | AdamJacoby/Hopf | refs/heads/master | /Frobenius_Divisibility_Algorithm.py | import sympy as sp
import numpy as np
import scipy.sparse as sps
import scipy.sparse.linalg as sps_linalg
from HopfConstructions_Functions import Tensor_Mult, Left_Action_Matrix
from math import floor
from Frobenius_Tools import *
from Algebra_Tools import Center
#Computes the matrix for the image of the casimir squared under the multiplication map acting on the center
def Comupute_Central_M(A):
M = np.array(Compute_M(A).toarray())
temp = Center(A)
U = temp[0]
center_dim = temp[2]
UI = temp[1]
M=UI.dot(M.dot(U))
M = np.array(M.tolist())
M = M[0:center_dim,0:center_dim]
print M.shape
return M
def Check_FD(A):
dim = A.dim
if A.casimir_flag == 'no':
A.GetCasimir()
mult = A.mult
casimir = A.casimir
tensor_mult = Tensor_Mult(A,A)
C = mult.dot(tensor_mult.dot(np.kron(A.casimir,A.casimir)))
C = AlgebraElement(C,A)
temp = C**2-((dim**2)/1)*C
for d in divisors[1:]:
temp = temp*C**2-((dim**2)/d)*C
zeros = np.zeros(dim)
if temp.vector==zeros:
print 'Yes FD'
else:
print 'No FD'
def Degree_Of_Irreps_Char(A):
dim = A.dim
number_of_irreps = []
sizes_of_irreps = []
M = Compute_M(A)#Compute the matrix corresponding to tthe left action by C
poly = CharPoly(M)
Der = [poly]
d = dim
bound = floor(dim**.5)
i=1
while i <= bound:
root_to_test = dim**2/i**2
if poly.eval(root_to_test) == 0:
sizes_of_irreps.append(i)
temp = Multiplicity(poly,root_to_test,Der)
number_of_irreps.append(temp[0]/i**2)
d = d - temp[0]
Der = temp[1]
bound = floor(d**.5)
i = i+1
return [sizes_of_irreps,number_of_irreps]
def Degree_Of_Irreps_Higchar(A):
mult = A.mult
dim = A.dim
number_of_irreps = []
sizes_of_irreps = []
M = Compute_M(C)#Compute the matrix corresponding to tthe left action by C
higman_trace = HigmanTrace(A)/dim
M = M.dot(higman_trace)
poly = CharPoly(M)
poly = Remove_Zeros_At_Zero(poly)
print poly
Der = [poly]
d = dim
bound = floor(dim**.5)
i=1
while i <= bound:
root_to_test = dim**2/i**2
if poly.eval(root_to_test) == 0:
sizes_of_irreps.append(i)
temp = Multiplicity(poly,root_to_test,Der)
number_of_irreps.append(temp[0])
d = d - temp[0]
Der = temp[1]
bound = floor(d**.5)
i = i+1
return [sizes_of_irreps,number_of_irreps]
#Uses a straight determinant computation
def Degree_Of_Irreps_Det(A):
dim = A.dim
sizes_of_irreps = []
M=Compute_M(A)#Compute the matrix corresponding to tthe left action by C
M=np.array(M.toarray())
d = dim
float_dim = float(dim)
bound = floor(dim**.5)
root_dim = bound
i=1
while i <= bound:
n = float(i)
value_to_test = float_dim**2/n**2
if i == root_dim:
error = ((float_dim**2*(2*n-1)/(n**2*(n-1)**2))**float_dim)/2
else:
error = ((float_dim**2*(2*n+1)/(n**2*(n+1)**2))**float_dim)/2
if np.linalg.det(M-value_to_test*np.identity(dim)) < error:
sizes_of_irreps.append(i)
d = d-i**2
bound = floor(d**.5)
i = i+1
return sizes_of_irreps
#Uses scipy Eigen value solver
def Degree_Of_Irreps_Eig(A):
dim = A.dim
M = Compute_M(A)#Compute the matrix corresponding to tthe left action by C
M=np.array(M.toarray())#COnvert to a nonsparse numpy array
temp =np.round(np.sqrt(dim**2/np.real(np.linalg.eigvals(M)))).tolist()
Eigen_Vals = sorted(list(set(temp)))
Multiplicities = []
for Val in Eigen_Vals:
Multiplicities.append(temp.count(Val)/Val**2)
print 'The diminsions of the irreps'+str(Eigen_Vals)
print 'With Corresponding Multiplicities'+str(Multiplicities)
def Degree_Of_Irreps_Center(A):
dim = A.dim
M = Comupute_Central_M(A)
temp =np.round(np.sqrt(dim**2/np.real(np.linalg.eigvals(M)))).tolist()
Eigen_Vals = sorted(list(set(temp)))
Multiplicities = []
for Val in Eigen_Vals:
Multiplicities.append(temp.count(Val))
print 'The diminsions of the irreps'+str(Eigen_Vals)
print 'With Corresponding Multiplicities'+str(Multiplicities) | {"/Example_CyclicGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/HopfConstructions.py": ["/HopfConstructions_Functions.py", "/HopfClass.py", "/Frobenius_Tools.py"], "/ExampleGroup_Functions.py": ["/HopfClass.py"], "/Example_Bpq.py": ["/HopfClass.py", "/HopfConstructions.py", "/Example_GeneralizedDihedralGroup.py"], "/ElementClass.py": ["/HopfClass.py"], "/Example_Symmetric_Group.py": ["/ExampleGroup_Functions.py"], "/Frobenius_Tools.py": ["/HopfConstructions_Functions.py"], "/Example_Taft.py": ["/HopfClass.py"], "/Algebra_Tools.py": ["/HopfClass.py", "/Frobenius_Tools.py", "/HopfConstructions_Functions.py"], "/Example_Matrix_Algebra.py": ["/HopfClass.py"], "/Example_DihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"], "/Example_GeneralizedDihedralGroup.py": ["/ExampleGroup_Functions.py", "/HopfClass.py"]} |
59,238 | sureshsaravananbabu/cpu | refs/heads/master | /Home/views.py | from django.shortcuts import render
from django.views import View
from django.http import HttpResponse
import psutil
sendmail=0
class Home(View):
template_name = 'Home.html'
def get(self, request, *args, **kwargs):
print("hello")
return render(request, self.template_name)
def emailfield(request):
global sendmail
sendmail = request.POST['email']
return HttpResponse('THANK YOU')
def return_email():
return sendmail
| {"/Home/data.py": ["/Home/views.py"]} |
59,239 | sureshsaravananbabu/cpu | refs/heads/master | /Home/data.py | import asyncio
from channels.generic.websocket import AsyncJsonWebsocketConsumer
from django.conf import settings
from .views import return_email
from django.core.mail import send_mail
import psutil
import json
class SendData(AsyncJsonWebsocketConsumer):
async def connect(self):
await self.accept()
flag=list()
while True:
await asyncio.sleep(1)
vcpu=psutil.cpu_percent()
rp=psutil.virtual_memory().percent
a=return_email()
if(rp>50):
flag.append(1)
else:
flag.clear()
if(rp>50 and len(flag)==10):
print("send")
subject = 'NOTIFICATION regarding RAM USAGE'
message = 'your RAM utilisation is more than 50%'
email_from = settings.EMAIL_HOST_USER
recipient_list = [a]
send_mail( subject, message, email_from ,recipient_list )
flag.clear()
await self.send(text_data=json.dumps({'cpu':vcpu,'ram':rp}))
async def receive(self, event):
print("hellooooo")
print("receive", event)
async def disconnect(self, event):
print("disconnected", event) | {"/Home/data.py": ["/Home/views.py"]} |
59,240 | sureshsaravananbabu/cpu | refs/heads/master | /cpu/routing.py | from channels.routing import ProtocolTypeRouter,URLRouter
from channels.auth import AuthMiddlewareStack
from Home import data
from django.urls import path,include
websocket_urlPattern=[
path('ws/cpu/',data.SendData),
]
application=ProtocolTypeRouter({
"websocket": AuthMiddlewareStack(URLRouter(websocket_urlPattern))
}) | {"/Home/data.py": ["/Home/views.py"]} |
59,311 | MareShae/BattleSnake | refs/heads/main | /Agent.py | from tAssist import *
from VEnv import VEnv
Main = 'genome.txt'
Genome = ReadGenome(Main)
class Agent:
def __init__(self, name, envShape, body):
# AGENT DETAILS
self.Name = name
self.width, self.height = envShape
self.body, self.head = body, body[0]
self.Network = NeuralNetwork(Genome) #
self.hypotenuse = Divide(Add(envShape), math.sqrt(2)) # Longest possible distance
# ACTIVITY DETAILS
self.orientation = [-1, 0]
self.stamina, self.holdStamina = 1, 1
self.angles = [0, 15, 30, 45, 60, 75,
90, 105, 120, 135, 150,
-150, -135, -120, -105,
-90, -75, -60, -45, -30, -15] # Octagon
# MEMORY
self.Snake = {self.Name: -0.20000}
self.Food, self.Boundary = -0.1, -0.3
def Update(self, body, stamina):
self.stamina = stamina
self.body, self.head = body, body[0]
def CheckBoard(self, YXAbs, Board):
Food, Snakes = Board['food'], Board['snakes']
if YXAbs in Food:
return self.Food
for name in Snakes:
if YXAbs in Snakes[name]:
return self.Snake[name]
return None
def Move(self, Board: dict) -> str:
# NETWORK:
NeuralInput = self.See(Board) + [self.stamina, self.holdStamina]
self.Network.ForwardPass(NeuralInput)
# STAMINA:
self.holdStamina = self.stamina
# MOVEMENTS:
confidence = RoundList(self.Network.ReadOutput())
if confidence == [0, 1]:
self.orientation = RoundList(RotMat(90, self.orientation)) # ClockWise: 90 deg
elif confidence == [1, 0]:
self.orientation = RoundList(RotMat(-90, self.orientation)) # ClockWise: -90 deg
if self.orientation == [1, 0]:
return 'up' # Forward/Repeat: 0 deg
elif self.orientation == [0, 1]:
return 'right' # ClockWise: 90 deg
elif self.orientation == [0, -1]:
return 'left' # ClockWise: -90 deg
elif self.orientation == [-1, 0]:
return 'down' # ClockWise: 180 deg
return response
def See(self, Board: dict) -> list:
# Searches VEnvironment for:
vFeat, vDist = [], [] # Features and Their distance
YPov, XPov = self.head
for theta in self.angles:
y, x = YPov, XPov # ...
route = RotMat(theta, self.orientation) # ...
y, x = Add([y, route[0]]), Add([x, route[1]]) # ...
YAbs, XAbs = Round(y), Round(x) # Look in 21 directions
while True:
dist = abs(YPov - YAbs) + abs(XAbs - XPov)
if not 0 <= YAbs < self.height or not 0 <= XAbs < self.width:
vDist.append(Divide(dist, self.hypotenuse))
vFeat.append(self.Boundary)
break
tile = self.CheckBoard([YAbs, XAbs], Board)
if tile:
vDist.append(Divide(dist, self.hypotenuse))
vFeat.append(tile)
break
y, x = Add([y, route[0]]), Add([x, route[1]]) # ...
YAbs, XAbs = Round(y), Round(x) # and Keep looking
return vFeat + vDist
| {"/Agent.py": ["/tAssist.py", "/VEnv.py"], "/server.py": ["/tAssist.py", "/VEnv.py", "/Agent.py"], "/VEnv.py": ["/tAssist.py"], "/tAssist.py": ["/NeuralFunctions.py", "/NeuralEvolution.py"], "/NeuralEvolution.py": ["/NeuralFunctions.py"]} |
59,312 | MareShae/BattleSnake | refs/heads/main | /server.py | # MAIN
import os
import json
import bottle
from bottle import HTTPResponse
# SUB
from tAssist import *
from VEnv import VEnv
from Agent import Agent
Name = 'Jinx'
Author = 'MareShae'
SnakeAgent = None
LogFile = 'Log.txt'
def DList2List(DList: list):
List = []
for Dict in DList:
List += [[Dict['y'], Dict['x']]]
return List
# # # # # # # # # MAIN-PROCESS # # # # # # # # # # # # # # # # # # # # #
@bottle.route("/")
def index():
return "New Game. WHO DIS!?"
@bottle.get("/")
def ping():
"""
Used by the BattleSnake Engine to make sure your snake is still working.
"""
response = {"apiversion": '1', "version": '7',
"head": 'evil', "tail": 'bwc-flake',
"author": Author, "color": '#736CCB'}
return HTTPResponse(status=200, body=json.dumps(response),
headers={"Content-Type": "application/json"})
@bottle.post("/start")
def start():
global SnakeAgent
"""
Called every time a new BattleSnake game starts and your snake is in it.
Your response will control how your snake is displayed on the board.
"""
# START
START = bottle.request.json
# Create Agent
Board = START['board']
Body = DList2List(START['you']['body'])
Shape = (Board['height'], Board['width'])
SnakeAgent = Agent(Name, Shape, Body)
# Snake Vision
for snake in Board['snakes']:
if snake['name'] != SnakeAgent.Name:
SnakeAgent.Snake[snake['name']] = Random(-0.2, -0.29)
# Print
print('START:', START['game']['id'], '\n',
'Number of Snakes: ', len(Board['snakes']))
return HTTPResponse(status=200)
@bottle.post("/move")
def move():
"""
Called when the BattleSnake Engine needs to know your next move.
The data parameter will contain information about the board.
Your response must include your move of up, down, left, or right.
"""
# MOVE
MOVE = bottle.request.json
# print("MOVE:", MOVE)
# Parameters
Snakes = {}
Board = MOVE['board']
Food = DList2List(Board['food'])
for snake in Board['snakes']:
Snakes[snake['name']] = DList2List(snake['body'])
# Update Snake:
SnakeAgent.Update(DList2List(MOVE['you']['body']),
Divide(MOVE['you']['health'], 100))
# Move
Tiles = {'food': Food, 'snakes': Snakes}
Move = SnakeAgent.Move(Tiles)
# Shouts are not displayed on the game board.
# Shouts are messages sent to all the other snakes in the game.
SHOUT = ["Sk Sk Sk!"]
RESPONSE = {"move": Move, "shout": Choice(SHOUT)}
# DUMPGlobal() # Convert to JSON
# RETURN RESPONSE
return HTTPResponse(status=200, body=json.dumps(RESPONSE),
headers={"Content-Type": "application/json"})
@bottle.post("/end")
def end():
"""
Called every time a game with your snake in it ends.
"""
END = bottle.request.json
print('END: ', END['game']['id'], '\n',
'Turn: ', END['turn'], '\n',
'Health: ', END['you']['health'], '\n',
'Snakes Left: ', len(END['board']['snakes']))
return HTTPResponse(status=200)
def main():
bottle.run(
application,
host=os.getenv("IP", "0.0.0.0"),
port=os.getenv("PORT", "8080"),
debug=os.getenv("DEBUG", True),
)
# Expose WSGI app (so gunicorn can find it)
application = bottle.default_app()
if __name__ == "__main__":
main()
| {"/Agent.py": ["/tAssist.py", "/VEnv.py"], "/server.py": ["/tAssist.py", "/VEnv.py", "/Agent.py"], "/VEnv.py": ["/tAssist.py"], "/tAssist.py": ["/NeuralFunctions.py", "/NeuralEvolution.py"], "/NeuralEvolution.py": ["/NeuralFunctions.py"]} |
59,313 | MareShae/BattleSnake | refs/heads/main | /VEnv.py | from tAssist import *
class VEnv:
def __init__(self, shape):
# BOARD INFO
self.envRecord = [] # Record the state of the environment
self.width, self.height = shape # Size of the environment
self.env = numpy.zeros(shape=[self.height, self.width]) # Constructing the environment
self.hypotenuse = Divide(self.width + self.height, math.sqrt(2)) # Longest possible distance
# BOARD POINTS
self.empty = [] # The empty tiles
self.turns, self.score = 0, 0
for y in range(self.height):
for x in range(self.width):
self.empty.append((y, x))
self.food = [] # Where food is spawned
def show(self):
print(self.env)
def record(self):
self.envRecord += [self.env]
def reset(self):
self.turns = 0
self.score = 0
self.empty = [] # The empty tiles
for y in range(self.height):
for x in range(self.width):
self.empty.append((y, x))
self.food = [] # Where food is spawned
self.envRecord = [] # Record the state of the environment
self.env = numpy.zeros(shape=[self.height, self.width]) # Constructing the environment
def spawnFood(self):
for _ in range(FoodLimit - len(self.food)):
tile = Choice(self.empty)
self.env[tile] = Food
self.food.append(tile)
self.empty.remove(tile)
def resetTile(self, tile):
if self.env[tile] == Food:
self.food.remove(tile)
self.env[tile] = Empty
self.empty.append(tile)
def setTile(self, tile, feature):
if tile in self.empty:
self.empty.remove(tile)
self.env[tile] = feature
| {"/Agent.py": ["/tAssist.py", "/VEnv.py"], "/server.py": ["/tAssist.py", "/VEnv.py", "/Agent.py"], "/VEnv.py": ["/tAssist.py"], "/tAssist.py": ["/NeuralFunctions.py", "/NeuralEvolution.py"], "/NeuralEvolution.py": ["/NeuralFunctions.py"]} |
59,314 | MareShae/BattleSnake | refs/heads/main | /tAssist.py | import cv2
import json
import math
import time
from NeuralFunctions import *
from NeuralEvolution import NeuralNetwork
# ANGLES
def Sin(val):
val = math.radians(val)
return Decimal(math.sin(val))
def Cos(val):
val = math.radians(val)
return Decimal(math.cos(val))
def RotMat(theta, mat):
cosO = Cos(theta)
sinO = Sin(theta)
return MatMul([[cosO, sinO],
[-sinO, cosO]], mat)
def Divide(y, x):
try:
return Decimal(y / x)
except ZeroDivisionError:
return 1
# READING & WRITING
def Append2File(filename, detail):
file = open(filename, 'a')
file.writelines(json.dumps(detail) + NextLine)
file.close()
def OpenFromFile(file_):
file = open(file_, 'r')
return json.loads(file.readline())
def SaveToFile(file_, data):
file = open(file_, 'w')
file.write(json.dumps(data))
file.close()
def ReadGenomeFile(name):
file = open(name, 'r')
genomePool = json.loads(file.readline())
file.close()
cont = []
for ev in genomePool:
cont += [PythonJSONDumps(ev)]
return cont
def ReadGenome(name):
file = open(name, 'r')
cont = file.readline()
file.close()
cont = json.loads(cont)
return PythonJSONDumps(cont)
| {"/Agent.py": ["/tAssist.py", "/VEnv.py"], "/server.py": ["/tAssist.py", "/VEnv.py", "/Agent.py"], "/VEnv.py": ["/tAssist.py"], "/tAssist.py": ["/NeuralFunctions.py", "/NeuralEvolution.py"], "/NeuralEvolution.py": ["/NeuralFunctions.py"]} |
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