id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
23,440 | 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 |
23,441 | 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 |
23,442 | 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 |
23,443 | 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 |
23,444 | 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 |
23,445 | 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 |
23,446 | 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 |
23,447 | import copy
import os
import shutil
import tensorflow as tf
from tensorflow.python.distribute import distribution_strategy_context as ds_context
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
from typing import List, Tuple
from nndct_shared.expanding.expander import Chann... | null |
23,448 | 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 |
23,449 | 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 |
23,450 | from tensorflow.python.util import tf_inspect
from tf_nndct.utils import registry
from tf_nndct.layers import quantization
_quant_module_registry = registry.Registry('quant_op')
def get_quant_module(op_type, default=None):
if op_type not in _quant_module_registry:
return default
return _quant_module_registry.l... | null |
23,451 |
def fix_neuron(input, fp_tensor, bit_width, method=4):
#return kernels.nndct_fix_neuron_v2(input, valmax, valamp, method)
return kernels.nndct_fix_neuron(input, fp_tensor, bit_width, method) | null |
23,452 |
def diffs_fix_pos(input, bit_width=8, range=5, method=4):
return kernels.nndct_diff_s(input, bit_width, range, method) | null |
23,453 |
def stat_act_pos(fp_tensor, fp_stat_tensor):
return kernels.nndct_stat_act_pos(fp_tensor, fp_stat_tensor) | null |
23,454 |
def scaleop(input, scale):
return kernels.nndct_scale_op(input, float(scale)) | null |
23,455 |
def table_lookup(input, table, fragpos, type):
return kernels.nndct_table_lookup(input, table, fragpos, type) | null |
23,456 |
def simulation(input, fragpos, type):
return kernels.nndct_simulation(input, fragpos, type) | null |
23,457 | 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... | Strips off ports and other decorations to get the underlying node name. |
23,458 | 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 |
23,459 | 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 |
23,460 | 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... | Parse data from given `tensor`. |
23,461 | 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... | Convert a list of AttributeProto to a dict, with names as keys. |
23,462 | 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 |
23,463 | 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 |
23,464 | 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 |
23,465 | 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 |
23,466 | 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 |
23,467 | 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 |
23,468 | 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 |
23,469 | 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 |
23,470 | 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 |
23,471 | 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 |
23,472 | 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 |
23,473 | 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 |
23,474 | 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 |
23,475 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.framework import graph_pb2
from tensorflow.core.framework import tensor_shape_pb2
from tensorflow.core.framework impor... | Replaces all the variables in a graph with constants of the same values. TensorFlow 2.0 function for converting all Variable ops into Const ops holding the same values. This makes it possible to describe the network fully with a single GraphDef file, and allows the removal of a lot of ops related to loading and saving ... |
23,476 | 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 |
23,477 | 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 |
23,478 | 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 |
23,482 | 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 `to_list` function. Write a Python function `def to... | Normalizes a list/tuple to a list. If a tensor is passed, we return a list of size 1 containing the tensor. Arguments: x: target object to be normalized. Returns: A list. |
23,483 | 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 get_temp_directory():
return os.environ.get("VAI_TEMP_DIRECTORY", tempfile.mkdtemp()) | null |
23,484 | 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 delete_directory(path):
if os.path.exists(path):
shutil.rmtree(path) | null |
23,485 | 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 |
23,486 | 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 is_list_or_tuple(obj):
return isinstance(obj, (list, tuple)) | null |
23,487 | 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 |
23,488 | 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 |
23,489 | 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 |
23,490 | 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... | Get inbound layers for nested Functional models. The inbound_layer of a submodel is another Functional model, this function find the output layers of that model and treats them as the actual inbound layers. |
23,491 | 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... | Stat the complexity of the given model. Currently includes macs and params. MACs: multiply–accumulate operations that performs a += b x c Params: total number of parameters of a model. Args: model: A model instance. inputs_kwargs: An optional dictionary of argument pairs specifying inputs' shape specifications to getti... |
23,492 | 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 export_to_graphviz(graph):
pass | null |
23,493 | 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 |
23,494 | 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 |
23,495 | 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... | convert the nndct tensor format -> tf format(channal last) more in nndct_shared/utils/tensor_util.py |
23,496 | import argparse
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tf_nndct import IterativePruningRunner
num_classes = 10
input_shape = (28, 28, 1)
def build_model(pretrained=None):
# Implementation from https://github.com/keras-team/keras-io/blob/master... | null |
23,497 | 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 |
23,498 | import argparse
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tf_nndct import IterativePruningRunner
input_shape = (28, 28, 1)
def evaluate(model):
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
score = model.... | null |
23,499 | import argparse
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tf_nndct import IterativePruningRunner
input_shape = (28, 28, 1)
def transform(model):
input_spec = tf.TensorSpec((1, *input_shape), tf.float32)
runner = IterativePruningRunner(model, in... | null |
23,500 | import argparse
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tf_nndct import IterativePruningRunner
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"-t",
"--train",
action="store_true",
help="If tr... | null |
23,501 | from tf1_nndct.optimization.pruning import IterativePruningRunner
import tensorflow as tf
import numpy as np
from nets.resnet_v2 import resnet_v2_50
def eval_fn(frozen_graph_def: tf.compat.v1.GraphDef) -> float:
with tf.compat.v1.Session().as_default() as sess:
return 0.5 | null |
23,502 | from tf1_nndct.optimization.pruning import IterativePruningRunner
import tensorflow as tf
import numpy as np
from nets.alexnet import alexnet_v2
def eval_fn(frozen_graph_def: tf.compat.v1.GraphDef) -> float:
with tf.compat.v1.Session().as_default() as sess:
return 0.5 | null |
23,503 | from tf1_nndct.optimization.pruning import IterativePruningRunner
import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
def mnist_convnet():
num_classes = 10
input_shape = (28, 28, 1)
model = keras.Sequential([
layers.InputLayer(input_shape=input_shape),
layers.Conv2D(16, kern... | null |
23,504 | from tf1_nndct.optimization.pruning import IterativePruningRunner
import tensorflow as tf
import numpy as np
from nets.vgg import vgg_16
def eval_fn(frozen_graph_def: tf.compat.v1.GraphDef) -> float:
with tf.compat.v1.Session().as_default() as sess:
return 0.5 | null |
23,505 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
slim = tf.contrib.slim
def alexnet_v2_arg_scope(weight_decay=0.0005):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
... | null |
23,506 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
slim = tf.contrib.slim
trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
The provided code snippet includes necessary dependencies for implementing the `alexnet_... | AlexNet version 2. Described in: http://arxiv.org/pdf/1404.5997v2.pdf Parameters from: github.com/akrizhevsky/cuda-convnet2/blob/master/layers/ layers-imagenet-1gpu.cfg Note: All the fully_connected layers have been transformed to conv2d layers. To use in classification mode, resize input to 224x224 or set global_pool=... |
23,507 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from nets import inception_utils
slim = tf.contrib.slim
trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
def inception_v3_base(inputs,
fina... | Inception model from http://arxiv.org/abs/1512.00567. "Rethinking the Inception Architecture for Computer Vision" Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna. With the default arguments this method constructs the exact model defined in the paper. However, one can experiment with ... |
23,508 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import namedtuple
import functools
import tensorflow as tf
slim = tf.contrib.slim
def mobilenet_v1_base(inputs,
final_endpoint='Conv2d_13_pointwise',
... | Mobilenet v1 model for classification. Args: inputs: a tensor of shape [batch_size, height, width, channels]. num_classes: number of predicted classes. If 0 or None, the logits layer is omitted and the input features to the logits layer (before dropout) are returned instead. dropout_keep_prob: the percentage of activat... |
23,509 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import namedtuple
import functools
import tensorflow as tf
def wrapped_partial(func, *args, **kwargs):
partial_func = functools.partial(func, *args, **kwargs)
functools.update_wrapper(parti... | null |
23,510 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import namedtuple
import functools
import tensorflow as tf
slim = tf.contrib.slim
The provided code snippet includes necessary dependencies for implementing the `mobilenet_v1_arg_scope` functio... | Defines the default MobilenetV1 arg scope. Args: is_training: Whether or not we're training the model. If this is set to None, the parameter is not added to the batch_norm arg_scope. weight_decay: The weight decay to use for regularizing the model. stddev: The standard deviation of the trunctated normal weight initiali... |
23,511 | 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 `resnet_arg_scope` function. Write a Python function `def resnet_... | Defines the default ResNet arg scope. TODO(gpapan): The batch-normalization related default values above are appropriate for use in conjunction with the reference ResNet models released at https://github.com/KaimingHe/deep-residual-networks. When training ResNets from scratch, they might need to be tuned. Args: weight_... |
23,512 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
slim = tf.contrib.slim
The provided code snippet includes necessary dependencies for implementing the `inception_arg_scope` function. Write a Python function `def inception_arg_scope(wei... | Defines the default arg scope for inception models. Args: weight_decay: The weight decay to use for regularizing the model. use_batch_norm: "If `True`, batch_norm is applied after each convolution. batch_norm_decay: Decay for batch norm moving average. batch_norm_epsilon: Small float added to variance to avoid dividing... |
23,513 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from nets import resnet_utils
def resnet_v2(inputs,
blocks,
num_classes=None,
is_training=True,
global_pool=True,
out... | ResNet-50 model of [1]. See resnet_v2() for arg and return description. |
23,514 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from nets import resnet_utils
def resnet_v2(inputs,
blocks,
num_classes=None,
is_training=True,
global_pool=True,
out... | ResNet-101 model of [1]. See resnet_v2() for arg and return description. |
23,515 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from nets import resnet_utils
def resnet_v2(inputs,
blocks,
num_classes=None,
is_training=True,
global_pool=True,
out... | ResNet-152 model of [1]. See resnet_v2() for arg and return description. |
23,516 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from nets import resnet_utils
def resnet_v2(inputs,
blocks,
num_classes=None,
is_training=True,
global_pool=True,
out... | ResNet-200 model of [2]. See resnet_v2() for arg and return description. |
23,517 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
slim = tf.contrib.slim
The provided code snippet includes necessary dependencies for implementing the `vgg_arg_scope` function. Write a Python function `def vgg_arg_scope(weight_decay=0.... | Defines the VGG arg scope. Args: weight_decay: The l2 regularization coefficient. Returns: An arg_scope. |
23,518 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
slim = tf.contrib.slim
The provided code snippet includes necessary dependencies for implementing the `vgg_a` function. Write a Python function `def vgg_a(inputs, num_classes=1... | Oxford Net VGG 11-Layers version A Example. Note: All the fully_connected layers have been transformed to conv2d layers. To use in classification mode, resize input to 224x224. Args: inputs: a tensor of size [batch_size, height, width, channels]. num_classes: number of predicted classes. If 0 or None, the logits layer ... |
23,519 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
slim = tf.contrib.slim
The provided code snippet includes necessary dependencies for implementing the `vgg_16` function. Write a Python function `def vgg_16(inputs, num_classe... | Oxford Net VGG 16-Layers version D Example. Note: All the fully_connected layers have been transformed to conv2d layers. To use in classification mode, resize input to 224x224. Args: inputs: a tensor of size [batch_size, height, width, channels]. num_classes: number of predicted classes. If 0 or None, the logits layer ... |
23,520 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
slim = tf.contrib.slim
The provided code snippet includes necessary dependencies for implementing the `vgg_19` function. Write a Python function `def vgg_19(inputs, num_classe... | Oxford Net VGG 19-Layers version E Example. Note: All the fully_connected layers have been transformed to conv2d layers. To use in classification mode, resize input to 224x224. Args: inputs: a tensor of size [batch_size, height, width, channels]. num_classes: number of predicted classes. If 0 or None, the logits layer ... |
23,521 | from tf1_nndct.optimization.pruning import IterativePruningRunner
import tensorflow as tf
import numpy as np
from nets.inception_v3 import inception_v3
def eval_fn(frozen_graph_def: tf.compat.v1.GraphDef) -> float:
with tf.compat.v1.Session().as_default() as sess:
return 0.5 | null |
23,522 | from tf1_nndct.optimization.pruning import IterativePruningRunner
import tensorflow as tf
import numpy as np
from nets.mobilenet_v1 import mobilenet_v1
def eval_fn(frozen_graph_def: tf.compat.v1.GraphDef) -> float:
with tf.compat.v1.Session().as_default() as sess:
return 0.5 | null |
23,523 | import argparse
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distri... | null |
23,524 | import argparse
import os
import torch
import torchvision.datasets as datasets
from torchvision.models.mobilenet import mobilenet_v2
import torchvision.transforms as transforms
from pytorch_nndct import OFAPruner
def get_gpus(device):
return [int(i) for i in device.split(',')] | null |
23,525 | import argparse
import os
import torch
import torchvision.datasets as datasets
from torchvision.models.mobilenet import mobilenet_v2
import torchvision.transforms as transforms
from pytorch_nndct import OFAPruner
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, ... | null |
23,526 | import argparse
import os
import torch
import torchvision.datasets as datasets
from torchvision.models.mobilenet import mobilenet_v2
import torchvision.transforms as transforms
from pytorch_nndct import OFAPruner
def calibration_fn(model, train_loader, number_forward=16):
model.eval()
for n, m in model.named_modul... | null |
23,527 | 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
def get_gpus(device):
return [int(i) for i in device.split(',')] | null |
23,528 | 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
The provided code snippet includes necessary dependencies for implementing the `adjust_learn... | Sets the learning rate to the initial LR decayed by every 2 epochs |
23,529 | 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):
"""Computes and stores the average and current value"""
def _... | null |
23,530 | 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
def train(train_loader, model, criterion, optimizer, epoch):
batch_time = AverageMeter('Tim... | null |
23,531 | 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 |
23,532 | 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
The provided code snippet includes necessary dependencies for implementing the `adjust_learning_rate` function. Write... | Sets the learning rate to the initial LR decayed by every 2 epochs |
23,533 | 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 |
23,534 | 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
def train(train_loader, model, criterion, optimizer, epoch):
batch_time = AverageMeter('Time', ':6.3f')
data_time ... | null |
23,535 | 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):
def __init__(self, name, fmt=':f'):
def reset(self):
def update(self, val, ... | null |
23,536 | 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
def load_weights(model, model_path):
checkpoint = torch.load(model_path)
model.load_state_dict(checkpoint)
retu... | null |
23,537 | 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
args, _ = parser.parse_known_args()
def train(train_loader, model, criterion, optimizer, epoch):
batch_time = Averag... | null |
23,538 | 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 |
23,539 | 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 |
23,540 | 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 |
23,541 | 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 |
23,542 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch
def get_mask(matrix, m, n):
mat = reshape_tensor(matrix, m)
topk, indices = mat.abs().topk(k=n, dim=1, sorted=False)
mask_matrix = torch.zeros_like(mat)
mask_min = torch.min(topk, dim=-1).values
mask_min = mask_min.unsqueeze(-1).r... | null |
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