id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
24,753 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,758 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,761 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,762 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,763 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,764 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,768 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,770 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,771 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,772 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,773 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,774 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,775 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,776 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,777 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,778 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,779 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,781 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,786 | import contextlib
import re
import itertools
import torch.jit
from torch.jit import _unique_state_dict
from torch.nn import ModuleList
from .schema import SchemaHelper, convert_type_str
from .torch_const import TorchGraphSymbol
from nndct_shared.utils import DeprecatedAPIError, NndctScreenLogger, NndctDebugLogger, Nndc... | null |
24,787 | import torch
from torch import nn
from distutils.version import LooseVersion
from enum import unique, Enum
class CmpFlag(Enum):
"""
Enum for comparison flags
"""
EQUAL = 0
LESS = 1
LESS_EQUAL = 2
GREATER = 3
GREATER_EQUAL = 4
NOT_EQUAL = 5
def compare_torch_version(compare_type:CmpFlag, version:str):... | null |
24,788 | import torch
from torch import nn
from distutils.version import LooseVersion
from enum import unique, Enum
def strip_parallel(model):
if isinstance(
model, (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)):
return model.module
return model | null |
24,791 | import functools
import inspect
import types
from typing import List, Optional
from nndct_shared.utils import GLOBAL_MAP, NNDCT_KEYS, NndctScreenLogger, NNDCT_OP, QError, QWarning, QNote
import torch
from .nndct2torch_op_map import add_mapping_item
from .torch_const import TorchSymbol
from .torch_op_attr import gen_at... | null |
24,793 | from typing import NamedTuple
from typing import Iterator, Iterable
import math
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `is_subnormal` function. Write a Python function `def is_subnormal(x)` to solve the following problem:
Returns a boolean `numpy` array for wh... | Returns a boolean `numpy` array for whether the elements in `x` are subnormal. Args: x (numpy array): input Returns: Boolean array indicating whether elements are subnormal |
24,794 | from typing import NamedTuple
from typing import Iterator, Iterable
import math
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `is_normal` function. Write a Python function `def is_normal(x)` to solve the following problem:
Returns a boolean `numpy` array for whether ... | Returns a boolean `numpy` array for whether the elements in `x` are normal. Args: x (numpy array): input Returns: Boolean array indicating whether elements are normal |
24,799 | from torch import nn
The provided code snippet includes necessary dependencies for implementing the `load_state_dict` function. Write a Python function `def load_state_dict(model, state_dict)` to solve the following problem:
Update the model so that the shape of the parameters match the weights in state dict, and then... | Update the model so that the shape of the parameters match the weights in state dict, and then load the state dict to the updated model. |
24,811 | import re
def convert_type_str(typing_str):
if '[' not in typing_str:
return typing_str
special_type = typing_str.split('[')[0]
pattern, rlp_fn = patterns_rlp_pair[special_type]
return pattern.sub(rlp_fn, typing_str)
def _match_brackets(match):
outer_type_str = match[1]
inner_type_str =... | null |
24,814 | from glob import glob
from nndct_shared.base import NNDCT_OP
from nndct_shared.utils import NndctScreenLogger
def get_torch_op_2_nndct_op_map():
def get_nndct_op_type(torch_op_type):
if get_torch_op_2_nndct_op_map().get(torch_op_type, None) is None:
#raise Exception('please register the operator:"{}"'.format(... | null |
24,816 | import time
import torch
from torch import nn
from nndct_shared.utils import common
from pytorch_nndct.utils import logging
from pytorch_nndct.utils import torch_utils
class MetricName(object):
def _accumulate_metric_value(module, metric_name, value):
def count_linear(module, input, output):
# (N, *, Hin) x (Hin, Ho... | null |
24,820 | import time
import torch
from torch import nn
from nndct_shared.utils import common
from pytorch_nndct.utils import logging
from pytorch_nndct.utils import torch_utils
class MetricName(object):
def _accumulate_metric_value(module, metric_name, value):
def count_prelu(module, input, output):
MACs = input[0].numel()
... | null |
24,822 | import time
import torch
from torch import nn
from nndct_shared.utils import common
from pytorch_nndct.utils import logging
from pytorch_nndct.utils import torch_utils
class MetricName(object):
def _accumulate_metric_value(module, metric_name, value):
def count_sigmoid(module, input, output):
MACs = 0
Flops = 4 * ... | null |
24,825 | import time
import torch
from torch import nn
from nndct_shared.utils import common
from pytorch_nndct.utils import logging
from pytorch_nndct.utils import torch_utils
class MetricName(object):
MACs = 'MACs'
FLOPs = 'FLOPs'
TrainableParams = 'trainable'
NonTrainableParams = 'non-trainable'
def _accumulate_metri... | null |
24,826 | import time
import torch
from torch import nn
from nndct_shared.utils import common
from pytorch_nndct.utils import logging
from pytorch_nndct.utils import torch_utils
def prepare_for_inference(model, inputs):
model = torch_utils.strip_parallel(model)
if torch.cuda.is_available():
model.cuda()
if isinstance... | null |
24,829 | import tensorflow as tf
from typing import List, Mapping
from tf1_nndct.optimization.constant import OpType
from queue import Queue
import numpy as np
class NodeGroupUnion(object):
def __init__(self) -> None:
def add_node(self, node: str) -> None:
def find(self, node: str) -> int:
def union(self, no... | null |
24,830 | import tensorflow as tf
from typing import List, Mapping
from tf1_nndct.optimization.constant import OpType
from queue import Queue
import numpy as np
def find_weight_nodes(node: tf.compat.v1.NodeDef, node_def_map: Mapping[str, tf.compat.v1.NodeDef]) -> List[tf.compat.v1.NodeDef]:
class OpType(object):
def calculate_... | null |
24,833 | import tensorflow as tf
from typing import List, Mapping
from tf1_nndct.optimization.constant import OpType
from queue import Queue
import numpy as np
def get_input_node_name(input_name: str) -> str:
if input_name.startswith("^"):
input_name = input_name[1:]
return input_name.split(":")[0]
def topo_sort(graph_... | null |
24,834 | import tensorflow as tf
from tensorflow.core.framework.tensor_pb2 import TensorProto
from tf1_nndct.optimization.utils import group_conv_nodes, find_weight_nodes, is_matmul, \
is_conv, is_depthwise_conv, is_concat, is_weighted_node, calculate_flops, get_input_node_name, \
topo_sort, find_ancestor_target_nodes
from ... | null |
24,835 | import argparse
import sys
import subprocess
from xnnc.version import __version__, __git_version__
def version_string():
class BatchsizeAction(argparse.Action):
def __call__(self, parser, namespace, values, option_string=None):
class ParseKwargs(argparse.Action):
def __call__(self, parser, namespace, values, ... | null |
24,836 | import sys
from pathlib import Path
from typing import List
from xnnc.ir.enums import TargetType
from xnnc.xconverter import XConverter, __version__
def validate(model_t: bool, model_files):
t = model_t.lower()
if t == "caffe":
return len(model_files) == 2
elif t in ["tensorflow", "tensorflow2"]:
... | null |
24,837 | import time
from collections import OrderedDict
from typing import Any, Dict, List, NoReturn, Optional
import numpy as np
import torch
from torch.onnx.utils import OperatorExportTypes
import graphviz
class NodePyIO(NodePy):
def __init__(self, node_cpp, input_or_output=None):
super(NodePyIO, self).__init__(n... | null |
24,838 | from pathlib import Path
from enum import Enum, auto
import graphviz
The provided code snippet includes necessary dependencies for implementing the `copy_data` function. Write a Python function `def copy_data(src_layer, dst_layer)` to solve the following problem:
Copy items from src_layer into dst_layer, which are in ... | Copy items from src_layer into dst_layer, which are in src_layer, but not in dst_layer :param src_layer: a source dict :param dst_layer: a destination dict |
24,839 | from pathlib import Path
from enum import Enum, auto
import graphviz
The provided code snippet includes necessary dependencies for implementing the `check_filepath` function. Write a Python function `def check_filepath(file_path: Path, extension: str = None) -> (bool, str, Path)` to solve the following problem:
Check ... | Check if the specified file path is valid. If extension is specified, also check if the file contained in the file path has the same extension name. Parameters: file_path: Path, an instance of Path indicating the path of a file. extension: str, the extension name the file should have. Return: (flag, error_msg, file_pat... |
24,840 | from pathlib import Path
from enum import Enum, auto
import graphviz
The provided code snippet includes necessary dependencies for implementing the `render_xmodel` function. Write a Python function `def render_xmodel(xmodel, filename=None, directory=None, view=False, cleanup=False)` to solve the following problem:
Vis... | Visualize an XModel instance with the Graphviz engine. Parameters: - xgraph: an XModel instance - filename: Filename for saving the source. - directory: (Sub)directory for source saving and rendering. - view: Open the rendered result with the default application. - cleanup: Delete the source file after rendering. |
24,841 | from pathlib import Path
from enum import Enum, auto
import graphviz
The provided code snippet includes necessary dependencies for implementing the `render_xmodel_opt` function. Write a Python function `def render_xmodel_opt(xmodel, filename=None, directory=None, view=False, cleanup=False)` to solve the following prob... | Visualize an optimized XModel instance with the Graphviz engine. Parameters: - xgraph: an XModel instance - filename: Filename for saving the source. - directory: (Sub)directory for source saving and rendering. - view: Open the rendered result with the default application. - cleanup: Delete the source file after render... |
24,842 | from functools import wraps
from contextlib import contextmanager
import time
def timefunc(func):
@wraps(func)
def wrapper(*args, **kwargs):
start = time.clock()
r = func(*args, **kwargs)
end = time.clock()
print("{}.{}: {} seconds".format(func.__module__, func.__name__, end - s... | null |
24,843 | from functools import wraps
from contextlib import contextmanager
import time
def timeblock(label):
start = time.clock()
try:
yield
finally:
end = time.clock()
print("{} : {} seconds".format(label, end - start)) | null |
24,844 | import numpy as np
import tensorflow as tf
import onnx
import os
from tf2onnx import utils
from tf2onnx.handler import tf_op
from tf2onnx.tf_loader import tf_placeholder
tf_library_path = os.path.join(DIR_PATH, "double_and_add_one.so")
import onnxruntime as ort
def func(x):
custom_op = tf.load_op_library(tf_librar... | null |
24,845 | import os
import sys
import time
import tarfile
import subprocess
import datetime
import numpy
from tqdm import tqdm
import tensorflow_hub as hub
import onnxruntime as ort
from tf2onnx import utils
imgs = generate_random_images()
def generate_random_images(shape=(100, 100), n=10):
imgs = []
for i in range(n):
... | null |
24,846 | import os
import sys
import time
import tarfile
import subprocess
import datetime
import numpy
from tqdm import tqdm
import tensorflow_hub as hub
import onnxruntime as ort
from tf2onnx import utils
The provided code snippet includes necessary dependencies for implementing the `measure_time` function. Write a Python fu... | Runs *n* times the same function taking one parameter from *imgs*. It stops if the total time overcomes *timeout*. It also runs once the function before measuring. |
24,847 | import os
import sys
import time
import tarfile
import subprocess
import datetime
import numpy
from tqdm import tqdm
import tensorflow_hub as hub
import onnxruntime as ort
from tf2onnx import utils
fpath, tname = download_model(url, dest)
print("Created %r, %r." % (fpath, tname))
print("Convert model in %r." % dest)
pr... | Downloads a model from tfhub and unzips it. The function assumes the format is `.tar.gz`. |
24,848 | import os
import sys
import time
import tarfile
import subprocess
import datetime
import numpy
from tqdm import tqdm
import tensorflow_hub as hub
import onnxruntime as ort
from tf2onnx import utils
print("Created %r, %r." % (fpath, tname))
print("Convert model in %r." % dest)
print("Created %r." % onnx_name)
print("Gen... | Converts the downloaded model into ONNX. |
24,849 | import tensorflow as tf
import tf2onnx
from onnx import helper
_TENSORFLOW_DOMAIN = "ai.onnx.converters.tensorflow"
def print_handler(ctx, node, name, args):
# replace tf.Print() with Identity
# T output = Print(T input, data, @list(type) U, @string message, @int first_n, @int summarize)
# becomes:
#... | null |
24,850 | import tensorflow as tf
import tf2onnx
import numpy as np
import onnxruntime as ort
import os
def f(a, b):
return a + b | null |
24,851 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,852 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | Avoid name conflicts by initializing the counter used by make_name based on the provided model |
24,853 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | Save onnx model as file. Save a pbtxt file as well if as_text is True |
24,854 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | Produces an onnx float seed from two tf int seeds. Returns None if both seeds are 0. |
24,855 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,856 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | Construct Graph from nodes and outputs with specified shapes and dtypes. |
24,857 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,858 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,859 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,860 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,861 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,862 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | Returns True iff src is compatible with dest. None is compatible with all shapes, different ranks are not considered as compatible |
24,863 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | Check whether 2 shapes are equal. |
24,864 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,865 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,866 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,867 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,868 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,869 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,870 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,871 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,872 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,873 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,874 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,875 | import os
import re
import shutil
import tempfile
import types
import zipfile
import logging
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import numpy as np
from google.protobuf import text_format
from onnx import helper, onnx_pb, defs, numpy_helper, ModelProto, __versi... | null |
24,876 | import logging
import numpy as np
from onnx import onnx_pb
from tf2onnx import constants, utils
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import common
def make_min_or_max_op(ctx, op_type, inputs, outputs,
output_shapes=None, output_dtypes=None):
# support more dtype
supp... | null |
24,877 | import copy
import logging
import numpy as np
from onnx import onnx_pb
from onnx.onnx_pb import TensorProto
from tf2onnx import utils
from tf2onnx.handler import tf_op
from tf2onnx.tf_loader import find_function
from tf2onnx.graph_builder import GraphBuilder
def inline_subgraph(parent, g, scope, binding):
# make a ... | Wire subgraph graph into main. |
24,878 | import copy
import logging
import numpy as np
from onnx import onnx_pb
from onnx.onnx_pb import TensorProto
from tf2onnx import utils
from tf2onnx.handler import tf_op
from tf2onnx.tf_loader import find_function
from tf2onnx.graph_builder import GraphBuilder
def parameter_binding(g, inputs, state_vars=None):
bindin... | Wire subgraph graph into main. |
24,879 | import copy
import logging
import numpy as np
from onnx import onnx_pb
from onnx.onnx_pb import TensorProto
from tf2onnx import utils
from tf2onnx.handler import tf_op
from tf2onnx.tf_loader import find_function
from tf2onnx.graph_builder import GraphBuilder
def dump_graph(g):
print()
print("--, graph=", g.gra... | null |
24,880 | import logging
import sys
import numpy as np
from onnx import onnx_pb, helper
from onnx.onnx_pb import TensorProto
from tf2onnx import constants, utils
from tf2onnx.graph_builder import GraphBuilder
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import nn, math
from tf2onnx.constants import NCHW_TO_NHWC, NHW... | cast int32 shape into int64 shape. |
24,881 | import logging
import sys
import numpy as np
from onnx import onnx_pb, helper
from onnx.onnx_pb import TensorProto
from tf2onnx import constants, utils
from tf2onnx.graph_builder import GraphBuilder
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import nn, math
from tf2onnx.constants import NCHW_TO_NHWC, NHW... | wrap concat in casts for opset < 8 since it only supports. |
24,882 | import logging
import sys
import numpy as np
from onnx import onnx_pb, helper
from onnx.onnx_pb import TensorProto
from tf2onnx import constants, utils
from tf2onnx.graph_builder import GraphBuilder
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import nn, math
from tf2onnx.constants import NCHW_TO_NHWC, NHW... | null |
24,883 | import logging
import sys
import numpy as np
from onnx import onnx_pb, helper
from onnx.onnx_pb import TensorProto
from tf2onnx import constants, utils
from tf2onnx.graph_builder import GraphBuilder
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import nn, math
from tf2onnx.constants import NCHW_TO_NHWC, NHW... | null |
24,884 | import logging
from onnx import TensorProto
from tf2onnx import utils
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import common
def _add_cast_to_inputs(graph, node, supported_dtypes, target_dtype):
is_support = True
for inp in node.input:
if graph.get_dtype(inp) not in supported_dtypes:
... | null |
24,885 | import logging
from onnx import TensorProto
from tf2onnx import utils
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import common
def _add_cast_to_same_type_to_inputs(graph, node):
common_dtype = graph.get_dtype(node.input[0])
for inp in node.input[1:]:
if graph.get_dtype(inp) != common_dt... | null |
24,886 | import logging
import numpy as np
from onnx import onnx_pb, helper
from onnx.numpy_helper import to_array
from tf2onnx import utils
from tf2onnx.handler import tf_op
from tf2onnx.graph_builder import GraphBuilder
def make_dft_constant(length, dtype, fft_length):
utils.make_sure(fft_length > 0, "fft_length must be ... | null |
24,887 | import logging
import numpy as np
from onnx import onnx_pb, helper
from onnx.onnx_pb import TensorProto
from tf2onnx import constants, utils
from tf2onnx.graph_builder import GraphBuilder
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import common, controlflow, tensor
The provided code snippet includes nec... | Makes a (N, C, ...) shape into (N, ..., C). |
24,888 | import logging
import numpy as np
from onnx import onnx_pb, helper
from onnx.onnx_pb import TensorProto
from tf2onnx import constants, utils
from tf2onnx.graph_builder import GraphBuilder
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import common, controlflow, tensor
def spatial_map(shape, perm):
new_s... | Convert input and kernel from tensorflow to onnx. This may be required to insert transpose ops for input, kernel, and output unless they are constants and we can transpose the constant. We transpose inputs if they are in NHWC. We always transpose the kernel from HWNC to NCHW. Outputs are transposed if the format is NHW... |
24,889 | import logging
import numpy as np
from onnx import onnx_pb, helper
from onnx.onnx_pb import TensorProto
from tf2onnx import constants, utils
from tf2onnx.graph_builder import GraphBuilder
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import common, controlflow, tensor
logger = logging.getLogger(__name__)
de... | null |
24,890 | import logging
import numpy as np
from onnx import onnx_pb, helper
from onnx.onnx_pb import TensorProto
from tf2onnx import constants, utils
from tf2onnx.graph_builder import GraphBuilder
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import common, controlflow, tensor
def parse_dims_attr(node, dims, spatial... | null |
24,891 | import logging
import numpy as np
from onnx import onnx_pb, helper
from onnx.onnx_pb import TensorProto
from tf2onnx import constants, utils
from tf2onnx.graph_builder import GraphBuilder
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import common, controlflow, tensor
def conv_kernel_shape(ctx, node, input... | null |
24,892 | import logging
import numpy as np
from onnx import onnx_pb, helper
from onnx.onnx_pb import TensorProto
from tf2onnx import constants, utils
from tf2onnx.graph_builder import GraphBuilder
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import common, controlflow, tensor
class GraphBuilder(object):
"""hel... | Build the target tensor shape for the Resize op. Args: - ctx: the graph context - transposed_intput: A tensor of rank 4 of shape [n c h w] - target_hw: tensor of rank 2 containing the target size for a resize: [nh nw] Returns: A tensor of rank 2 containing [n c nh nw] |
24,893 | import logging
import numpy as np
from onnx import onnx_pb, helper
from onnx.onnx_pb import TensorProto
from tf2onnx import constants, utils
from tf2onnx.graph_builder import GraphBuilder
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import common, controlflow, tensor
def get_shape_from_const_or_concat(ctx... | null |
24,894 | import logging
import numpy as np
from onnx import onnx_pb, helper
from onnx.onnx_pb import TensorProto
from tf2onnx import constants, utils
from tf2onnx.graph_builder import GraphBuilder
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import common, controlflow, tensor
class GraphBuilder(object):
def _... | null |
24,895 | import logging
import numpy as np
from onnx import onnx_pb, helper
from onnx.onnx_pb import TensorProto
from tf2onnx import constants, utils
from tf2onnx.graph_builder import GraphBuilder
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import common, controlflow, tensor
class GraphBuilder(object):
"""hel... | null |
24,896 | import logging
import numpy as np
from onnx import onnx_pb, helper
from onnx.onnx_pb import TensorProto
from tf2onnx import constants, utils
from tf2onnx.graph_builder import GraphBuilder
from tf2onnx.handler import tf_op
from tf2onnx.onnx_opset import common, controlflow, tensor
class GraphBuilder(object):
"""hel... | null |
24,897 | import logging
import copy
from collections import defaultdict, OrderedDict
from onnx import defs, helper, TensorProto, OperatorSetIdProto, shape_inference
from . import constants
from . import utils
class OnnxOpSchema(object):
"""Wrapper for Onnx schema."""
def __init__(self, name, domain, since_version, attri... | Register all schemas with history |
24,898 | import logging
import copy
from collections import defaultdict, OrderedDict
from onnx import defs, helper, TensorProto, OperatorSetIdProto, shape_inference
from . import constants
from . import utils
The provided code snippet includes necessary dependencies for implementing the `_parse_domain_opset_versions` function.... | Get max opset version among all schemas within each domain. |
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