本文整理匯總了Python中resnet.ResNet101方法的典型用法代碼示例。如果您正苦於以下問題：Python resnet.ResNet101方法的具體用法？Python resnet.ResNet101怎麽用？Python resnet.ResNet101使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類resnet的用法示例。
在下文中一共展示了resnet.ResNet101方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: JPU_DeepLab
# 需要導入模塊: import resnet [as 別名]
# 或者: from resnet import ResNet101 [as 別名]
def JPU_DeepLab(img_height=1024, img_width=1024, nclasses=19):
base_model = ResNet101(include_top=False,
input_shape=[img_height, img_width, 3],
weights=None)#'imagenet'
endpoint_names = ['conv2_block3_out', 'conv3_block4_out',
'conv4_block23_out', 'conv5_block3_out']
endpoints = [base_model.get_layer(x).output for x in endpoint_names]
_, image_features = JPU(endpoints)
x_a = ASPP(image_features)
h_t, w_t = x_a.shape.as_list()[1:3]
scale = (img_height / 4) // h_t, (img_width / 4) // w_t
x_a = tf.keras.layers.UpSampling2D(
size=scale, interpolation='bilinear')(x_a)
x_b = base_model.get_layer('conv2_block3_out').output
x_b = conv_block(x_b, num_filters=48, kernel_size=1)
x = tf.keras.layers.Concatenate(axis=-1)([x_a, x_b])
x = conv_block(x, num_filters=256, kernel_size=3)
x = conv_block(x, num_filters=256, kernel_size=3)
h_t, w_t = x.shape.as_list()[1:3]
scale = img_height // h_t, img_width // w_t
x = tf.keras.layers.UpSampling2D(size=scale, interpolation='bilinear')(x)
x = tf.keras.layers.Conv2D(nclasses, (1, 1), name='output_layer')(x)
model = tf.keras.Model(inputs=base_model.input, outputs=x, name='JPU')
return model 示例2: model_factory
# 需要導入模塊: import resnet [as 別名]
# 或者: from resnet import ResNet101 [as 別名]
def model_factory(model_name, **params):
model_dict = {
'densenet121': DenseNet121,
'densenet169': DenseNet169,
'densenet201': DenseNet201,
'densenet161': DenseNet161,
'densenet-cifar': densenet_cifar,
'dual-path-net-26': DPN26,
'dual-path-net-92': DPN92,
'googlenet': GoogLeNet,
'lenet': LeNet,
'mobilenet': MobileNet,
'mobilenetv2': MobileNetV2,
'pnasneta': PNASNetA,
'pnasnetb': PNASNetB,
'preact-resnet18': PreActResNet18,
'preact-resnet34': PreActResNet34,
'preact-resnet50': PreActResNet50,
'preact-resnet101': PreActResNet101,
'preact-resnet152': PreActResNet152,
'resnet18': ResNet18,
'resnet34': ResNet34,
'resnet50': ResNet50,
'resnet101': ResNet101,
'resnet152': ResNet152,
'resnext29_2x64d': ResNeXt29_2x64d,
'resnext29_4x64d': ResNeXt29_4x64d,
'resnext29_8x64d': ResNeXt29_8x64d,
'resnext29_32x64d': ResNeXt29_32x4d,
'senet18': SENet18,
'shufflenetg2': ShuffleNetG2,
'shufflenetg3': ShuffleNetG3,
'shufflenetv2_0.5': ShuffleNetV2,
'shufflenetv2_1.0': ShuffleNetV2,
'shufflenetv2_1.5': ShuffleNetV2,
'shufflenetv2_2.0': ShuffleNetV2,
'vgg11': VGG,
'vgg13': VGG,
'vgg16': VGG,
'vgg19': VGG,
}
if 'vgg' in model_name:
return model_dict[model_name](model_name)
elif 'shufflenetv2' in model_name:
return model_dict[model_name](float(model_name[-3:]))
elif model_name in model_dict.keys():
return model_dict[model_name]()
else:
raise AttributeError('Model doesn\'t exist')