Python torchvision.models.resnet.resnet101() Examples
The following are 13
code examples of torchvision.models.resnet.resnet101().
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Example #1
| Source File: resnet.py From pipeline with MIT License | 5 votes |
|
def __init__(self, pretrained=True, input_channels=3):
model = resnet.resnet101(pretrained=pretrained)
super().__init__(
model=model,
input_channels=input_channels)
Example #2
| Source File: batchnorm.py From torchgpipe with Apache License 2.0 | 5 votes |
|
def convert_deferred_batch_norm(cls, module: TModule, chunks: int = 1) -> TModule:
"""Converts a :class:`nn.BatchNorm` or underlying
:class:`nn.BatchNorm`s into :class:`DeferredBatchNorm`::
from torchvision.models.resnet import resnet101
from torchgpipe.batchnorm import DeferredBatchNorm
model = resnet101()
model = DeferredBatchNorm.convert_deferred_batch_norm(model)
"""
if isinstance(module, DeferredBatchNorm) and module.chunks is chunks:
return cast(TModule, module)
module_output: nn.Module = module
if isinstance(module, _BatchNorm) and module.track_running_stats:
module_output = DeferredBatchNorm(module.num_features,
module.eps,
module.momentum,
module.affine,
chunks)
if module.affine:
module_output.register_parameter('weight', module.weight)
module_output.register_parameter('bias', module.bias)
module_output.register_buffer('running_mean', module.running_mean)
module_output.register_buffer('running_var', module.running_var)
module_output.register_buffer('num_batches_tracked', module.num_batches_tracked)
for name, child in module.named_children():
module_output.add_module(name, cls.convert_deferred_batch_norm(child, chunks))
return cast(TModule, module_output)
Example #3
| Source File: batchnorm.py From torchgpipe with Apache License 2.0 | 5 votes |
|
def convert_deferred_batch_norm(cls, module: TModule, chunks: int = 1) -> TModule:
"""Converts a :class:`nn.BatchNorm` or underlying
:class:`nn.BatchNorm`s into :class:`DeferredBatchNorm`::
from torchvision.models.resnet import resnet101
from torchgpipe.batchnorm import DeferredBatchNorm
model = resnet101()
model = DeferredBatchNorm.convert_deferred_batch_norm(model)
"""
if isinstance(module, DeferredBatchNorm) and module.chunks is chunks:
return cast(TModule, module)
module_output: nn.Module = module
if isinstance(module, _BatchNorm) and module.track_running_stats:
module_output = DeferredBatchNorm(module.num_features,
module.eps,
module.momentum,
module.affine,
chunks)
if module.affine:
module_output.register_parameter('weight', module.weight)
module_output.register_parameter('bias', module.bias)
module_output.register_buffer('running_mean', module.running_mean)
module_output.register_buffer('running_var', module.running_var)
module_output.register_buffer('num_batches_tracked', module.num_batches_tracked)
for name, child in module.named_children():
module_output.add_module(name, cls.convert_deferred_batch_norm(child, chunks))
return cast(TModule, module_output)
Example #4
| Source File: batchnorm.py From torchgpipe with Apache License 2.0 | 5 votes |
|
def convert_deferred_batch_norm(cls, module: TModule, chunks: int = 1) -> TModule:
"""Converts a :class:`nn.BatchNorm` or underlying
:class:`nn.BatchNorm`s into :class:`DeferredBatchNorm`::
from torchvision.models.resnet import resnet101
from torchgpipe.batchnorm import DeferredBatchNorm
model = resnet101()
model = DeferredBatchNorm.convert_deferred_batch_norm(model)
"""
if isinstance(module, DeferredBatchNorm) and module.chunks is chunks:
return cast(TModule, module)
module_output: nn.Module = module
if isinstance(module, _BatchNorm) and module.track_running_stats:
module_output = DeferredBatchNorm(module.num_features,
module.eps,
module.momentum,
module.affine,
chunks)
if module.affine:
module_output.register_parameter('weight', module.weight)
module_output.register_parameter('bias', module.bias)
module_output.register_buffer('running_mean', module.running_mean)
module_output.register_buffer('running_var', module.running_var)
module_output.register_buffer('num_batches_tracked', module.num_batches_tracked)
for name, child in module.named_children():
module_output.add_module(name, cls.convert_deferred_batch_norm(child, chunks))
return cast(TModule, module_output)
Example #5
| Source File: batchnorm.py From torchgpipe with Apache License 2.0 | 5 votes |
|
def convert_deferred_batch_norm(cls, module: TModule, chunks: int = 1) -> TModule:
"""Converts a :class:`nn.BatchNorm` or underlying
:class:`nn.BatchNorm`s into :class:`DeferredBatchNorm`::
from torchvision.models.resnet import resnet101
from torchgpipe.batchnorm import DeferredBatchNorm
model = resnet101()
model = DeferredBatchNorm.convert_deferred_batch_norm(model)
"""
if isinstance(module, DeferredBatchNorm) and module.chunks is chunks:
return cast(TModule, module)
module_output: nn.Module = module
if isinstance(module, _BatchNorm) and module.track_running_stats:
module_output = DeferredBatchNorm(module.num_features,
module.eps,
module.momentum,
module.affine,
chunks)
if module.affine:
module_output.register_parameter('weight', module.weight)
module_output.register_parameter('bias', module.bias)
module_output.register_buffer('running_mean', module.running_mean)
module_output.register_buffer('running_var', module.running_var)
module_output.register_buffer('num_batches_tracked', module.num_batches_tracked)
for name, child in module.named_children():
module_output.add_module(name, cls.convert_deferred_batch_norm(child, chunks))
return cast(TModule, module_output)
Example #6
| Source File: batchnorm.py From torchgpipe with Apache License 2.0 | 5 votes |
|
def convert_deferred_batch_norm(cls, module: TModule, chunks: int = 1) -> TModule:
"""Converts a :class:`nn.BatchNorm` or underlying
:class:`nn.BatchNorm`s into :class:`DeferredBatchNorm`::
from torchvision.models.resnet import resnet101
from torchgpipe.batchnorm import DeferredBatchNorm
model = resnet101()
model = DeferredBatchNorm.convert_deferred_batch_norm(model)
"""
if isinstance(module, DeferredBatchNorm) and module.chunks is chunks:
return cast(TModule, module)
module_output: nn.Module = module
if isinstance(module, _BatchNorm) and module.track_running_stats:
module_output = DeferredBatchNorm(module.num_features,
module.eps,
module.momentum,
module.affine,
chunks)
if module.affine:
module_output.register_parameter('weight', module.weight)
module_output.register_parameter('bias', module.bias)
module_output.register_buffer('running_mean', module.running_mean)
module_output.register_buffer('running_var', module.running_var)
module_output.register_buffer('num_batches_tracked', module.num_batches_tracked)
for name, child in module.named_children():
module_output.add_module(name, cls.convert_deferred_batch_norm(child, chunks))
return cast(TModule, module_output)
Example #7
| Source File: batchnorm.py From torchgpipe with Apache License 2.0 | 5 votes |
|
def convert_deferred_batch_norm(cls, module: TModule, chunks: int = 1) -> TModule:
"""Converts a :class:`nn.BatchNorm` or underlying
:class:`nn.BatchNorm`s into :class:`DeferredBatchNorm`::
from torchvision.models.resnet import resnet101
from torchgpipe.batchnorm import DeferredBatchNorm
model = resnet101()
model = DeferredBatchNorm.convert_deferred_batch_norm(model)
"""
if isinstance(module, DeferredBatchNorm) and module.chunks is chunks:
return cast(TModule, module)
module_output: nn.Module = module
if isinstance(module, _BatchNorm) and module.track_running_stats:
module_output = DeferredBatchNorm(module.num_features,
module.eps,
module.momentum,
module.affine,
chunks)
if module.affine:
module_output.register_parameter('weight', module.weight)
module_output.register_parameter('bias', module.bias)
module_output.register_buffer('running_mean', module.running_mean)
module_output.register_buffer('running_var', module.running_var)
module_output.register_buffer('num_batches_tracked', module.num_batches_tracked)
for name, child in module.named_children():
module_output.add_module(name, cls.convert_deferred_batch_norm(child, chunks))
return cast(TModule, module_output)
Example #8
| Source File: batchnorm.py From torchgpipe with Apache License 2.0 | 5 votes |
|
def convert_deferred_batch_norm(cls, module: TModule, chunks: int = 1) -> TModule:
"""Converts a :class:`nn.BatchNorm` or underlying
:class:`nn.BatchNorm`s into :class:`DeferredBatchNorm`::
from torchvision.models.resnet import resnet101
from torchgpipe.batchnorm import DeferredBatchNorm
model = resnet101()
model = DeferredBatchNorm.convert_deferred_batch_norm(model)
"""
if isinstance(module, DeferredBatchNorm) and module.chunks is chunks:
return cast(TModule, module)
module_output: nn.Module = module
if isinstance(module, _BatchNorm) and module.track_running_stats:
module_output = DeferredBatchNorm(module.num_features,
module.eps,
module.momentum,
module.affine,
chunks)
if module.affine:
module_output.register_parameter('weight', module.weight)
module_output.register_parameter('bias', module.bias)
module_output.register_buffer('running_mean', module.running_mean)
module_output.register_buffer('running_var', module.running_var)
module_output.register_buffer('num_batches_tracked', module.num_batches_tracked)
for name, child in module.named_children():
module_output.add_module(name, cls.convert_deferred_batch_norm(child, chunks))
return cast(TModule, module_output)
Example #9
| Source File: object_detector.py From neural-motifs with MIT License | 5 votes |
|
def load_resnet():
model = resnet101(pretrained=True)
del model.layer4
del model.avgpool
del model.fc
return model
Example #10
| Source File: object_detector.py From VCTree-Scene-Graph-Generation with MIT License | 5 votes |
|
def load_resnet():
model = resnet101(pretrained=True)
del model.layer4
del model.avgpool
del model.fc
return model
Example #11
| Source File: resnet101_3d.py From PyVideoResearch with GNU General Public License v3.0 | 5 votes |
|
def get(cls, args):
model = ResNet3D(Bottleneck3D, [3, 8, 36, 3]) # 101
if args.pretrained:
from torchvision.models.resnet import resnet101
model2d = resnet101(pretrained=True)
model.load_2d(model2d)
return model
Example #12
| Source File: object_detector.py From KERN with MIT License | 5 votes |
|
def load_resnet():
model = resnet101(pretrained=True)
del model.layer4
del model.avgpool
del model.fc
return model
Example #13
| Source File: model_factory.py From pytorch-dpn-pretrained with Apache License 2.0 | 4 votes |
|
def create_model(model_name, num_classes=1000, pretrained=False, **kwargs):
if 'test_time_pool' in kwargs:
test_time_pool = kwargs.pop('test_time_pool')
else:
test_time_pool = True
if model_name == 'dpn68':
model = dpn68(
pretrained=pretrained, test_time_pool=test_time_pool, num_classes=num_classes)
elif model_name == 'dpn68b':
model = dpn68b(
pretrained=pretrained, test_time_pool=test_time_pool, num_classes=num_classes)
elif model_name == 'dpn92':
model = dpn92(
pretrained=pretrained, test_time_pool=test_time_pool, num_classes=num_classes)
elif model_name == 'dpn98':
model = dpn98(
pretrained=pretrained, test_time_pool=test_time_pool, num_classes=num_classes)
elif model_name == 'dpn131':
model = dpn131(
pretrained=pretrained, test_time_pool=test_time_pool, num_classes=num_classes)
elif model_name == 'dpn107':
model = dpn107(
pretrained=pretrained, test_time_pool=test_time_pool, num_classes=num_classes)
elif model_name == 'resnet18':
model = resnet18(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'resnet34':
model = resnet34(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'resnet50':
model = resnet50(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'resnet101':
model = resnet101(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'resnet152':
model = resnet152(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'densenet121':
model = densenet121(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'densenet161':
model = densenet161(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'densenet169':
model = densenet169(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'densenet201':
model = densenet201(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'inception_v3':
model = inception_v3(
pretrained=pretrained, num_classes=num_classes, transform_input=False, **kwargs)
else:
assert False, "Unknown model architecture (%s)" % model_name
return model
