Python torchvision.models.vgg13_bn() Examples
The following are 7
code examples of torchvision.models.vgg13_bn().
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torchvision.models
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Example #1
| Source File: test-ww.py From AutoDL-Projects with MIT License | 6 votes |
|
def main():
# model = models.vgg19_bn(pretrained=True)
# _, summary = weight_watcher.analyze(model, alphas=False)
# for key, value in summary.items():
# print('{:10s} : {:}'.format(key, value))
_, summary = weight_watcher.analyze(models.vgg13(pretrained=True), alphas=False)
print('vgg-13 : {:}'.format(summary['lognorm']))
_, summary = weight_watcher.analyze(models.vgg13_bn(pretrained=True), alphas=False)
print('vgg-13-BN : {:}'.format(summary['lognorm']))
_, summary = weight_watcher.analyze(models.vgg16(pretrained=True), alphas=False)
print('vgg-16 : {:}'.format(summary['lognorm']))
_, summary = weight_watcher.analyze(models.vgg16_bn(pretrained=True), alphas=False)
print('vgg-16-BN : {:}'.format(summary['lognorm']))
_, summary = weight_watcher.analyze(models.vgg19(pretrained=True), alphas=False)
print('vgg-19 : {:}'.format(summary['lognorm']))
_, summary = weight_watcher.analyze(models.vgg19_bn(pretrained=True), alphas=False)
print('vgg-19-BN : {:}'.format(summary['lognorm']))
Example #2
| Source File: featureModels.py From multi-modal-regression with MIT License | 6 votes |
|
def __init__(self, model_type='vgg13', layer_type='fc6'):
super().__init__()
# get model
if model_type == 'vgg13':
self.original_model = models.vgg13_bn(pretrained=True)
elif model_type == 'vgg16':
self.original_model = models.vgg16_bn(pretrained=True)
else:
raise NameError('Unknown model_type passed')
self.features = self.original_model.features
if layer_type == 'fc6':
self.classifier = nn.Sequential(*list(self.original_model.classifier.children())[:2])
elif layer_type == 'fc7':
self.classifier = nn.Sequential(*list(self.original_model.classifier.children())[:-2])
else:
raise NameError('Uknown layer_type passed')
Example #3
| Source File: torchvision_models.py From pretorched-x with MIT License | 5 votes |
|
def vgg13_bn(num_classes=1000, pretrained='imagenet'):
"""VGG 13-layer model (configuration "B") with batch normalization
"""
model = models.vgg13_bn(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['vgg13_bn'][pretrained]
model = load_pretrained(model, num_classes, settings)
model = modify_vggs(model)
return model
Example #4
| Source File: tools.py From perceptron-benchmark with Apache License 2.0 | 5 votes |
|
def _load_pytorch_model(model_name, summary):
import torchvision.models as models
switcher = {
'alexnet': lambda: models.alexnet(pretrained=True).eval(),
"vgg11": lambda: models.vgg11(pretrained=True).eval(),
"vgg11_bn": lambda: models.vgg11_bn(pretrained=True).eval(),
"vgg13": lambda: models.vgg13(pretrained=True).eval(),
"vgg13_bn": lambda: models.vgg13_bn(pretrained=True).eval(),
"vgg16": lambda: models.vgg16(pretrained=True).eval(),
"vgg16_bn": lambda: models.vgg16_bn(pretrained=True).eval(),
"vgg19": lambda: models.vgg19(pretrained=True).eval(),
"vgg19_bn": lambda: models.vgg19_bn(pretrained=True).eval(),
"resnet18": lambda: models.resnet18(pretrained=True).eval(),
"resnet34": lambda: models.resnet34(pretrained=True).eval(),
"resnet50": lambda: models.resnet50(pretrained=True).eval(),
"resnet101": lambda: models.resnet101(pretrained=True).eval(),
"resnet152": lambda: models.resnet152(pretrained=True).eval(),
"squeezenet1_0": lambda: models.squeezenet1_0(pretrained=True).eval(),
"squeezenet1_1": lambda: models.squeezenet1_1(pretrained=True).eval(),
"densenet121": lambda: models.densenet121(pretrained=True).eval(),
"densenet161": lambda: models.densenet161(pretrained=True).eval(),
"densenet201": lambda: models.densenet201(pretrained=True).eval(),
"inception_v3": lambda: models.inception_v3(pretrained=True).eval(),
}
_load_model = switcher.get(model_name, None)
_model = _load_model()
import torch
if torch.cuda.is_available():
_model = _model.cuda()
from perceptron.models.classification.pytorch import PyTorchModel as ClsPyTorchModel
import numpy as np
mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
pmodel = ClsPyTorchModel(
_model, bounds=(
0, 1), num_classes=1000, preprocessing=(
mean, std))
return pmodel
Example #5
| Source File: tools.py From perceptron-benchmark with Apache License 2.0 | 5 votes |
|
def load_pytorch_model(model_name):
import torchvision.models as models
switcher = {
'alexnet': lambda: models.alexnet(pretrained=True).eval(),
"vgg11": lambda: models.vgg11(pretrained=True).eval(),
"vgg11_bn": lambda: models.vgg11_bn(pretrained=True).eval(),
"vgg13": lambda: models.vgg13(pretrained=True).eval(),
"vgg13_bn": lambda: models.vgg13_bn(pretrained=True).eval(),
"vgg16": lambda: models.vgg16(pretrained=True).eval(),
"vgg16_bn": lambda: models.vgg16_bn(pretrained=True).eval(),
"vgg19": lambda: models.vgg19(pretrained=True).eval(),
"vgg19_bn": lambda: models.vgg19_bn(pretrained=True).eval(),
"resnet18": lambda: models.resnet18(pretrained=True).eval(),
"resnet34": lambda: models.resnet34(pretrained=True).eval(),
"resnet50": lambda: models.resnet50(pretrained=True).eval(),
"resnet101": lambda: models.resnet101(pretrained=True).eval(),
"resnet152": lambda: models.resnet152(pretrained=True).eval(),
"squeezenet1_0": lambda: models.squeezenet1_0(pretrained=True).eval(),
"squeezenet1_1": lambda: models.squeezenet1_1(pretrained=True).eval(),
"densenet121": lambda: models.densenet121(pretrained=True).eval(),
"densenet161": lambda: models.densenet161(pretrained=True).eval(),
"densenet201": lambda: models.densenet201(pretrained=True).eval(),
"inception_v3": lambda: models.inception_v3(pretrained=True).eval(),
}
_load_model = switcher.get(model_name, None)
_model = _load_model()
return _model
Example #6
| Source File: torchvision_models.py From pretrained-models.pytorch with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def vgg13_bn(num_classes=1000, pretrained='imagenet'):
"""VGG 13-layer model (configuration "B") with batch normalization
"""
model = models.vgg13_bn(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['vgg13_bn'][pretrained]
model = load_pretrained(model, num_classes, settings)
model = modify_vggs(model)
return model
Example #7
| Source File: torchvision_models.py From models-comparison.pytorch with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def vgg13_bn(num_classes=1000, pretrained='imagenet'):
"""VGG 13-layer model (configuration "B") with batch normalization
"""
model = models.vgg13_bn(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['vgg13_bn'][pretrained]
model = load_pretrained(model, num_classes, settings)
return model
