id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
23,989 | import math
import itertools
from typing import List, Dict, Any, NoReturn, Tuple
import numpy as np
from functools import partial
from nndct_shared.base import NNDCT_OP, NNDCT_KEYS
from nndct_shared.nndct_graph import Tensor, Node
from .xgraph import XGraph
from nndct_shared.utils import calculate_op_scale, DataXopErro... | r""" nndct reshape is a macro operator, including pack, reshape |
23,996 | import math
import itertools
from typing import List, Dict, Any, NoReturn, Tuple
import numpy as np
from functools import partial
from nndct_shared.base import NNDCT_OP, NNDCT_KEYS
from nndct_shared.nndct_graph import Tensor, Node
from .xgraph import XGraph
from nndct_shared.utils import calculate_op_scale, DataXopErro... | null |
23,999 | import math
import itertools
from typing import List, Dict, Any, NoReturn, Tuple
import numpy as np
from functools import partial
from nndct_shared.base import NNDCT_OP, NNDCT_KEYS
from nndct_shared.nndct_graph import Tensor, Node
from .xgraph import XGraph
from nndct_shared.utils import calculate_op_scale, DataXopErro... | null |
24,009 | import copy
import networkx as nx
from networkx.algorithms import is_isomorphic
from nndct_shared.base import NNDCT_OP
from nndct_shared.inspector.utils import build_xir_nndct_op_map, log_debug_info
from nndct_shared.compile.xir_helper import XIRHelper
from .graph import Graph, Node
def get_templates_from_dpu_compiler(... | null |
24,013 | from typing import Mapping
from nndct_shared.expanding.spec import BatchNormStructuredExpanding, InstanceNormStructuredExpanding, \
DataInsert, GenericStructuredExpanding, StructuredExpanding, WeightedNodeStructuredExpanding
from nndct_shared.nndct_graph.base_graph import Graph
from nndct_shared.nndct_graph.base_node... | null |
24,014 | from typing import Mapping
from nndct_shared.expanding.spec import BatchNormStructuredExpanding, InstanceNormStructuredExpanding, \
DataInsert, GenericStructuredExpanding, StructuredExpanding, WeightedNodeStructuredExpanding
from nndct_shared.nndct_graph.base_graph import Graph
from nndct_shared.nndct_graph.base_node... | null |
24,023 | import numpy as np
import math
from nndct_shared.base import NNDCT_KEYS, GLOBAL_MAP
from nndct_shared.utils import NndctOption
from nndct_shared.algorithms import breadth_first_search_handler
from .quant_ops import normal_quant_neuron
def maybe_get_quantizer(quantizer=None):
def quantize_tensors(tensors, node, tensor_... | null |
24,039 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging as _logging
import os as _os
import sys as _sys
import time as _time
import traceback as _traceback
from logging import DEBUG
from logging import ERROR
from logging import FATAL
from logging impor... | null |
24,043 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging as _logging
import os as _os
import sys as _sys
import time as _time
import traceback as _traceback
from logging import DEBUG
from logging import ERROR
from logging import FATAL
from logging impor... | null |
24,046 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging as _logging
import os as _os
import sys as _sys
import time as _time
import traceback as _traceback
from logging import DEBUG
from logging import ERROR
from logging import FATAL
from logging impor... | null |
24,056 | from typing import TypeVar, NoReturn, Optional, Iterator, List
from .option_list import NndctOption
from .option_def import Option, T
class NndctOption(object):
nndct_help = Option(name="help", dtype=bool, default=False, action="store_true",
help="list all api usage description")
nndct_quan... | null |
24,057 | from typing import TypeVar, NoReturn, Optional, Iterator, List
from .option_list import NndctOption
from .option_def import Option, T
class NndctOption(object):
nndct_help = Option(name="help", dtype=bool, default=False, action="store_true",
help="list all api usage description")
nndct_quan... | null |
24,058 | from typing import TypeVar, NoReturn, Optional, Iterator, List
from .option_list import NndctOption
from .option_def import Option, T
class NndctOption(object):
nndct_help = Option(name="help", dtype=bool, default=False, action="store_true",
help="list all api usage description")
nndct_quan... | null |
24,098 | import h5py
import json
from nndct_shared.nndct_graph.base_tensor import Tensor
class GraphHDF5Saver():
def __init__(self, nndct_graph):
self.graph = nndct_graph
def get_node_config(self, node):
node_info = dict()
node_info['idx'] = node.idx
node_info['name'] = node.name
node_info['dtype'] = str... | null |
24,146 | import math
import itertools
from typing import List, Dict, Any, NoReturn, Tuple
import numpy as np
from functools import partial
from nndct_shared.base import NNDCT_OP, NNDCT_KEYS
from nndct_shared.nndct_graph import Tensor, Node
from .xgraph import XGraph
from nndct_shared.utils import calculate_op_scale, DataXopErro... | null |
24,171 | from typing import Mapping
from nndct_shared.expanding.spec import BatchNormStructuredExpanding, InstanceNormStructuredExpanding, \
DataInsert, GenericStructuredExpanding, StructuredExpanding, WeightedNodeStructuredExpanding
from nndct_shared.nndct_graph.base_graph import Graph
from nndct_shared.nndct_graph.base_node... | null |
24,178 | import numpy as np
import math
from nndct_shared.base import NNDCT_KEYS, GLOBAL_MAP
from nndct_shared.utils import NndctOption
from nndct_shared.algorithms import breadth_first_search_handler
from .quant_ops import normal_quant_neuron
def maybe_get_quantizer(quantizer=None):
def quant_reluk_params(node, channel_max):
... | null |
24,205 | import numpy as np
from nndct_shared.base import NNDCT_OP
def get_in_out_channel_idx(ndim, optype, data_formats):
def get_tensor_out_dim(tensor, optype, data_formats):
_, out_idx = get_in_out_channel_idx(tensor.ndim, optype, data_formats)
return tensor.shape[out_idx] | null |
24,236 | import os
import shutil
import json
import sys
import numpy as np
from .log import log_or_print
from nndct_shared.base import NNDCT_KEYS, GLOBAL_MAP
def log_or_print(str, logger=None):
def basic_info(mat, name=None, logger=None, to_str=False):
if isinstance(mat, np.ndarray):
info_str = "<Array>{}[{}]: max:{}, m... | null |
24,269 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
from google.protobuf import text_format
from tensorflow.core.framework import graph_pb2
from tf_nndct.graph import ops
from tf_nndct.utils import generic_utils
from tf_nndct.uti... | null |
24,272 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
from google.protobuf import text_format
from tensorflow.core.framework import graph_pb2
from tf_nndct.graph import ops
from tf_nndct.utils import generic_utils
from tf_nndct.uti... | null |
24,274 | import collections
from tensorflow.python.util import nest
from tf_nndct.graph import OpTypes
from tf_nndct.utils import logging
from tf_nndct.utils import tf_utils
class FoldConst(GraphRefiner):
def fold_to_dense(self, const_op, dense_op):
tensor = list(const_op.params.values())[0]
assert len(tensor.shape) =... | null |
24,275 | import json
import os
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.core.protobuf import config_pb2
from tensorflow.lite.python.util import run_graph_optimizations as _run_graph_optimizations
from tf_nndct.graph import OpTypes
from tf_nndct.graph import converter
from tf_nndct.graph import... | Trace model call to get a func graph and convert that func graph to nndct graph. |
24,278 | import imp
from tensorflow import keras
from tensorflow.python.ops import array_ops
from tensorflow.python.util import nest
from nndct_shared.pruning import pruning_lib
from tf_nndct.graph import OpTypes
from tf_nndct.graph import parser
from tf_nndct.graph import utils
from tf_nndct.graph import writer as writer_lib
f... | null |
24,281 | import numpy as np
from enum import Enum
from tensorflow.core.framework import types_pb2
_TF_TO_NNDCT = {
types_pb2.DT_FLOAT: DType.FLOAT,
types_pb2.DT_HALF: DType.FLOAT16,
types_pb2.DT_DOUBLE: DType.DOUBLE,
types_pb2.DT_INT32: DType.INT32,
types_pb2.DT_INT16: DType.INT16,
types_pb2.DT_INT8: DTy... | null |
24,282 | import numpy as np
from enum import Enum
from tensorflow.core.framework import types_pb2
_NNDCT_TO_TF = {nndct: tf for tf, nndct in _TF_TO_NNDCT.items()}
def to_tf(dtype):
return _NNDCT_TO_TF[dtype] | null |
24,283 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import numpy as np
import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras import layers
from tensorflow.python.util import nest
from tf_nndct.graph import OpTypes
from... | Convert a parser's computation node to one or more TF graph's nodes. Looks up node's convertion function in the registry and calls it to generate a new ops.Node object according to the attributes of node. The node's name will be used to set the name of the converted node. A tf.keras.layers.Layer instance without type r... |
24,284 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import numpy as np
import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras import layers
from tensorflow.python.util import nest
from tf_nndct.graph import OpTypes
from... | null |
24,285 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import numpy as np
import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras import layers
from tensorflow.python.util import nest
from tf_nndct.graph import OpTypes
from... | null |
24,286 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import numpy as np
import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras import layers
from tensorflow.python.util import nest
from tf_nndct.graph import OpTypes
from... | null |
24,289 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import numpy as np
import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras import layers
from tensorflow.python.util import nest
from tf_nndct.graph import OpTypes
from... | null |
24,290 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import numpy as np
import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras import layers
from tensorflow.python.util import nest
from tf_nndct.graph import OpTypes
from... | null |
24,293 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import numpy as np
import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras import layers
from tensorflow.python.util import nest
from tf_nndct.graph import OpTypes
from... | null |
24,294 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import numpy as np
import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras import layers
from tensorflow.python.util import nest
from tf_nndct.graph import OpTypes
from... | null |
24,295 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import numpy as np
import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras import layers
from tensorflow.python.util import nest
from tf_nndct.graph import OpTypes
from... | null |
24,302 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import numpy as np
import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras import layers
from tensorflow.python.util import nest
from tf_nndct.graph import OpTypes
from... | null |
24,305 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import numpy as np
import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras import layers
from tensorflow.python.util import nest
from tf_nndct.graph import OpTypes
from... | null |
24,313 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import numpy as np
import tensorflow as tf
from tensorflow.keras import activations
from tensorflow.keras import layers
from tensorflow.python.util import nest
from tf_nndct.graph import OpTypes
from... | null |
24,315 | import os
from typing import Optional
import tensorflow as tf
from nndct_shared.base import NNDCT_KEYS, NNDCT_OP, GLOBAL_MAP
from nndct_shared.utils import option_util, NndctOption, NndctScreenLogger
from tf_nndct.graph import OpTypes
from tf_nndct.graph import builder
from tf_nndct.graph import ops
from tf_nndct.graph... | null |
24,328 | import os
import tensorflow as tf
from distutils.version import LooseVersion
from google.protobuf import text_format
from tensorflow.core.framework import tensor_pb2
from tensorflow.python.framework import dtypes as tf_dtypes
from tensorflow.python.framework import tensor_util
from tf_nndct.graph import dtypes as nndct... | Extracts the values from a const NodeDef as a numpy ndarray. Args: node_def: Const NodeDef that has the values we want to access. Returns: Numpy ndarray containing the values. Raises: ValueError: If the node isn't a Const. |
24,330 | import os
import tensorflow as tf
from distutils.version import LooseVersion
from google.protobuf import text_format
from tensorflow.core.framework import tensor_pb2
from tensorflow.python.framework import dtypes as tf_dtypes
from tensorflow.python.framework import tensor_util
from tf_nndct.graph import dtypes as nndct... | Get shape from tensorflow attr 'shape'. |
24,333 | import os
import tensorflow as tf
from distutils.version import LooseVersion
from google.protobuf import text_format
from tensorflow.core.framework import tensor_pb2
from tensorflow.python.framework import dtypes as tf_dtypes
from tensorflow.python.framework import tensor_util
from tf_nndct.graph import dtypes as nndct... | null |
24,335 | import os
import tensorflow as tf
from distutils.version import LooseVersion
from google.protobuf import text_format
from tensorflow.core.framework import tensor_pb2
from tensorflow.python.framework import dtypes as tf_dtypes
from tensorflow.python.framework import tensor_util
from tf_nndct.graph import dtypes as nndct... | null |
24,346 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging as _logging
import os as _os
import sys as _sys
import time as _time
import traceback as _traceback
from logging import DEBUG
from logging import ERROR
from logging import FATAL
from logging impor... | null |
24,347 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging as _logging
import os as _os
import sys as _sys
import time as _time
import traceback as _traceback
from logging import DEBUG
from logging import ERROR
from logging import FATAL
from logging impor... | null |
24,349 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging as _logging
import os as _os
import sys as _sys
import time as _time
import traceback as _traceback
from logging import DEBUG
from logging import ERROR
from logging import FATAL
from logging impor... | null |
24,357 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import shutil
import tempfile
from google.protobuf import text_format
def write_proto(path, message, as_text=False):
dir_name = os.path.dirname(path)
mkdir_if_not_exist(dir_name)
if dir_name:
... | null |
24,359 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import shutil
import tempfile
from google.protobuf import text_format
The provided code snippet includes necessary dependencies for implementing the `path_to_string` function. Write a Python function ... | Convert `PathLike` objects to their string representation. If given a non-string typed path object, converts it to its string representation. If the object passed to `path` is not among the above, then it is returned unchanged. This allows e.g. passthrough of file objects through this function. Args: path: `PathLike` o... |
24,360 | import collections
import tensorflow as tf
from typing import Any, Callable, Dict, List, Optional, Union
from tensorflow.keras import layers
from tensorflow.python.eager import def_function
from tensorflow.python.keras.engine import base_layer_utils
from tensorflow.python.ops import array_ops
from tensorflow.python.tra... | null |
24,361 | import collections
import tensorflow as tf
from typing import Any, Callable, Dict, List, Optional, Union
from tensorflow.keras import layers
from tensorflow.python.eager import def_function
from tensorflow.python.keras.engine import base_layer_utils
from tensorflow.python.ops import array_ops
from tensorflow.python.tra... | null |
24,362 | import collections
import tensorflow as tf
from typing import Any, Callable, Dict, List, Optional, Union
from tensorflow.keras import layers
from tensorflow.python.eager import def_function
from tensorflow.python.keras.engine import base_layer_utils
from tensorflow.python.ops import array_ops
from tensorflow.python.tra... | null |
24,364 | import collections
import tensorflow as tf
from typing import Any, Callable, Dict, List, Optional, Union
from tensorflow.keras import layers
from tensorflow.python.eager import def_function
from tensorflow.python.keras.engine import base_layer_utils
from tensorflow.python.ops import array_ops
from tensorflow.python.tra... | null |
24,366 | import collections
import tensorflow as tf
from typing import Any, Callable, Dict, List, Optional, Union
from tensorflow.keras import layers
from tensorflow.python.eager import def_function
from tensorflow.python.keras.engine import base_layer_utils
from tensorflow.python.ops import array_ops
from tensorflow.python.tra... | Gather all sub layers from given model. Args: layer: An instance of keras.Layer include_container: Whether to include layer container |
24,367 | import collections
import tensorflow as tf
from typing import Any, Callable, Dict, List, Optional, Union
from tensorflow.keras import layers
from tensorflow.python.eager import def_function
from tensorflow.python.keras.engine import base_layer_utils
from tensorflow.python.ops import array_ops
from tensorflow.python.tra... | Trace the model call to create a tf.function for exporting a Keras model. Args: model: A Keras model. input_signature: optional, a list of tf.TensorSpec objects specifying the inputs to the model. Returns: A tf.function wrapping the model's call function with input signatures set. Raises: ValueError: if input signature... |
24,371 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.core.framework import graph_pb2
from tensorflow.python.platform import gfile
def _to_graph_def(graph):
"""Convert nndct graph to tensorflow's GraphDef."""
graph_def = graph_pb2.GraphDef()
#... | Export the nndct `graph` to a serialized file specified by `filepath`. Here we use GraphDef as netron's input. See https://github.com/lutzroeder/netron |
24,373 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from nndct_shared.utils.tensor_util import convert_parameter_tensor_format
from nndct_shared.utils.tensor_util import DataFormatMap
from nndct_shared.pruning import pruning_lib
from nndct_shar... | null |
24,377 | import argparse
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tf_nndct import IterativePruningRunner
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data('mnist.npz')
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("flo... | null |
24,383 | from tf1_nndct.optimization.pruning import IterativePruningRunner
import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
def mnist_convnet():
def eval_fn(frozen_graph_def: tf.compat.v1.GraphDef) -> float:
class IterativePruningRunner(object):
def __init__(
self, model_name: str, ... | null |
24,391 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import tensorflow as tf
slim = tf.contrib.slim
The provided code snippet includes necessary dependencies for implementing the `conv2d_same` function. Write a Python function `def conv2d_same(... | Strided 2-D convolution with 'SAME' padding. When stride > 1, then we do explicit zero-padding, followed by conv2d with 'VALID' padding. Note that net = conv2d_same(inputs, num_outputs, 3, stride=stride) is equivalent to net = slim.conv2d(inputs, num_outputs, 3, stride=1, padding='SAME') net = subsample(net, factor=str... |
24,392 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import tensorflow as tf
slim = tf.contrib.slim
def subsample(inputs, factor, scope=None):
"""Subsamples the input along the spatial dimensions.
Args:
inputs: A `Tensor` of size [batch,... | Stacks ResNet `Blocks` and controls output feature density. First, this function creates scopes for the ResNet in the form of 'block_name/unit_1', 'block_name/unit_2', etc. Second, this function allows the user to explicitly control the ResNet output_stride, which is the ratio of the input to output spatial resolution.... |
24,411 | import argparse
import os
import time
import torch
import torchvision.datasets as datasets
from torchvision.models.resnet import resnet18
import torchvision.transforms as transforms
from pytorch_nndct import get_pruning_runner
class AverageMeter(object):
def __init__(self, name, fmt=':f'):
def reset(self):
... | null |
24,417 | import argparse
import os
import time
import torch
import torch.nn as nn
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from pytorch_nndct import get_pruning_runner
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt='... | null |
24,420 | import argparse
import os
import shutil
import time
import torch
import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torch.nn as nn
from pytorch_nndct.nn.modules import functional
from pytorch_nndct.quantization import bfp
The provided code snippet includes nec... | 3x3 convolution with padding |
24,421 | import argparse
import os
import shutil
import time
import torch
import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torch.nn as nn
from pytorch_nndct.nn.modules import functional
from pytorch_nndct.quantization import bfp
The provided code snippet includes nec... | 1x1 convolution |
24,422 | import argparse
import os
import shutil
import time
import torch
import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torch.nn as nn
from pytorch_nndct.nn.modules import functional
from pytorch_nndct.quantization import bfp
class Bottleneck(nn.Module):
def _... | null |
24,423 | import argparse
import os
import shutil
import time
import torch
import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torch.nn as nn
from pytorch_nndct.nn.modules import functional
from pytorch_nndct.quantization import bfp
class Bottleneck(nn.Module):
expansio... | null |
24,424 | import argparse
import os
import shutil
import time
import torch
import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torch.nn as nn
from pytorch_nndct.nn.modules import functional
from pytorch_nndct.quantization import bfp
class Bottleneck(nn.Module):
expansio... | null |
24,425 | import argparse
import os
import shutil
import time
import torch
import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torch.nn as nn
from pytorch_nndct.nn.modules import functional
from pytorch_nndct.quantization import bfp
torch.backends.cudnn.deterministic = Tr... | null |
24,426 | import argparse
import os
import shutil
import time
import torch
import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torch.nn as nn
from pytorch_nndct.nn.modules import functional
from pytorch_nndct.quantization import bfp
def validate(val_loader, model, criteri... | null |
24,427 | import argparse
import os
import shutil
import time
import torch
import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torch.nn as nn
from pytorch_nndct.nn.modules import functional
from pytorch_nndct.quantization import bfp
The provided code snippet includes nec... | Sets the learning rate to the initial LR decayed by 10 every 10 epochs |
24,428 | import os
import re
import sys
import argparse
import time
import pdb
import random
from pytorch_nndct.apis import torch_quantizer
import torch
import torchvision
import torchvision.transforms as transforms
from torchvision.models.resnet import resnet18
from tqdm import tqdm
device = torch.device("cuda" if torch.cuda.i... | null |
24,429 | import os
import re
import sys
import argparse
import time
import random
from pytorch_nndct.apis import torch_quantizer, dump_xmodel
import torch
import torchvision
import torchvision.transforms as transforms
from torchvision.models.mobilenet import mobilenet_v2
from tqdm import tqdm
device = torch.device("cuda" if tor... | null |
24,430 | import os
import re
import sys
import argparse
import time
import pdb
import random
from pytorch_nndct.apis import torch_quantizer
from pytorch_nndct.utils import register_custom_op
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from torchvision.models.resnet import Re... | null |
24,431 | import os
import re
import sys
import argparse
import time
import pdb
import random
from pytorch_nndct.apis import torch_quantizer
from pytorch_nndct.utils import register_custom_op
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
from torchvision.models.resnet import Re... | null |
24,432 | import argparse
import json
import logging
import time
from collections import OrderedDict
from contextlib import suppress
import torch
import torch.nn as nn
import torch.nn.parallel
from timm.data import create_dataset, create_loader, resolve_data_config, RealLabelsImagenet
from timm.models import create_model, load_c... | null |
24,433 | import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from pytorch_nndct import nn as nndct_nn
from pytorch_nndct.nn.modules import functional
from pytorch_nndct import QatProcessor
T... | 3x3 convolution with padding |
24,434 | import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from pytorch_nndct import nn as nndct_nn
from pytorch_nndct.nn.modules import functional
from pytorch_nndct import QatProcessor
T... | 1x1 convolution |
24,435 | import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from pytorch_nndct import nn as nndct_nn
from pytorch_nndct.nn.modules import functional
from pytorch_nndct import QatProcessor
cl... | null |
24,436 | import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from pytorch_nndct import get_pruning_runner
from pytorch_nndct import nn as nndct_nn
from pytorch_nndct.nn.modules import functio... | 3x3 convolution with padding |
24,437 | import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from pytorch_nndct import get_pruning_runner
from pytorch_nndct import nn as nndct_nn
from pytorch_nndct.nn.modules import functio... | 1x1 convolution |
24,438 | import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from pytorch_nndct import get_pruning_runner
from pytorch_nndct import nn as nndct_nn
from pytorch_nndct.nn.modules import functio... | null |
24,439 | import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from pytorch_nndct import get_pruning_runner
from pytorch_nndct import nn as nndct_nn
from pytorch_nndct.nn.modules import functio... | null |
24,440 | import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from pytorch_nndct import get_pruning_runner
from pytorch_nndct import nn as nndct_nn
from pytorch_nndct.nn.modules import functio... | null |
24,441 | import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from pytorch_nndct import get_pruning_runner
from pytorch_nndct import nn as nndct_nn
from pytorch_nndct.nn.modules import functio... | null |
24,442 | import os
import re
import sys
import argparse
import time
import pdb
import random
from pytorch_nndct.apis import torch_quantizer
import torch
import torchvision
import torchvision.transforms as transforms
from torchvision.models.resnet import resnet18
from tqdm import tqdm
import random
import os
import numpy as np
... | null |
24,443 | import os
import re
import sys
import argparse
import time
import pdb
import random
from pytorch_nndct.apis import torch_quantizer
import torch
import torchvision
import torchvision.transforms as transforms
from torchvision.models.resnet import resnet18
from tqdm import tqdm
import random
import os
import numpy as np
d... | null |
24,444 | from torch import Tensor, nn
import torch
import numpy as np
from pytorch_nndct.expanding.structured import ExpandingRunner
from torchvision.models.resnet import resnet18, resnet34, resnet50, resnet152
from torchvision.models.inception import inception_v3
import argparse
args, _ = parser.parse_known_args()
class MnistC... | null |
24,445 | from torch import Tensor, nn
import torch
import numpy as np
from pytorch_nndct.expanding.structured import ExpandingRunner
from torchvision.models.resnet import resnet18, resnet34, resnet50, resnet152
from torchvision.models.inception import inception_v3
import argparse
args, _ = parser.parse_known_args()
def do_expan... | null |
24,446 | from torch import Tensor, nn
import torch
import numpy as np
from pytorch_nndct.expanding.structured import ExpandingRunner
from torchvision.models.resnet import resnet18, resnet34, resnet50, resnet152
from torchvision.models.inception import inception_v3
import argparse
args, _ = parser.parse_known_args()
def do_expan... | null |
24,447 | from torch import Tensor, nn
import torch
import numpy as np
from pytorch_nndct.expanding.structured import ExpandingRunner
from torchvision.models.resnet import resnet18, resnet34, resnet50, resnet152
from torchvision.models.inception import inception_v3
import argparse
args, _ = parser.parse_known_args()
def do_expan... | null |
24,448 | from torch import Tensor, nn
import torch
import numpy as np
from pytorch_nndct.expanding.structured import ExpandingRunner
from torchvision.models.resnet import resnet18, resnet34, resnet50, resnet152
from torchvision.models.inception import inception_v3
import argparse
args, _ = parser.parse_known_args()
def do_expan... | null |
24,449 | from torch import Tensor, nn
import torch
import numpy as np
from pytorch_nndct.expanding.structured import ExpandingRunner
from torchvision.models.resnet import resnet18, resnet34, resnet50, resnet152
from torchvision.models.inception import inception_v3
import argparse
args, _ = parser.parse_known_args()
def do_expan... | null |
24,450 | import tensorflow as tf
from tensorflow.keras import layers
from tf_nndct.optimization.expanding import ExpandingRunner
import numpy as np
keras = tf.keras
def mnist_convnet():
num_classes = 10
input_shape = (28, 28, 1)
model = keras.Sequential([
keras.Input(shape=input_shape),
layers.Conv2D(32, ker... | null |
24,451 | import tensorflow as tf
from tensorflow.keras import layers
from tf_nndct.optimization.expanding import expand_and_export
keras = tf.keras
def mnist_convnet():
num_classes = 10
input_shape = (28, 28, 1)
model = keras.Sequential([
keras.Input(shape=input_shape),
layers.Conv2D(32, kernel_size=(3, 3), acti... | null |
24,452 | from pytorch_nndct.expanding.expanding_lib import expand_and_export, load_expanded_model
from torchvision.models.inception import inception_v3
import torch
from torch import nn
import os
import onnxruntime
import argparse
import numpy as np
model = inception_v3(init_weights=True).eval()
input_signature = torch.rand((1,... | null |
24,453 | from pytorch_nndct.expanding.expanding_lib import expand_and_export, load_expanded_model
from torchvision.models.inception import inception_v3
import torch
from torch import nn
import os
import onnxruntime
import argparse
import numpy as np
model_name = "inception_v3"
model = inception_v3(init_weights=True).eval()
inpu... | null |
24,456 | import os
import shutil
import subprocess
import sys
import setuptools.command.develop
import setuptools.command.install
import torch
from setuptools import find_packages, setup
from torch.utils.cpp_extension import BuildExtension, CppExtension
from distutils import core
from distutils.core import Distribution
from dis... | null |
24,461 | import torch
from torch.autograd import Variable
import math
from nndct_shared.utils import NndctOption, NndctScreenLogger, QError, QWarning
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from .quant_noise import eval_qnoise
import pytorch_nndct.utils a... | null |
24,462 | import torch
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
import pytorch_nndct.utils as py_utils
class deephi_Sub(torch.nn.Module):
def __init__(self):
super(deephi_Sub, self).__init__()
self.quant_mode, self.quantizer = maybe_get_quantizer()... | null |
24,467 | import torch
from torch.autograd import Variable
import torch.nn.functional as F
from nndct_shared.quantization import maybe_get_quantizer
from nndct_shared.quantization import quantize_tensors
from nndct_shared.utils import NndctOption
import pytorch_nndct.utils as py_utils
from nndct_shared.utils import NNDCT_KEYS, ... | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.