brails.processors.nfloors_detector.lib.efficientnet.model module

class brails.processors.nfloors_detector.lib.efficientnet.model.EfficientNet(blocks_args=None, global_params=None)

Bases: Module

An EfficientNet model. Most easily loaded with the .from_name or .from_pretrained methods

Args:

blocks_args (list): A list of BlockArgs to construct blocks global_params (namedtuple): A set of GlobalParams shared between blocks

Example:

model = EfficientNet.from_pretrained(‘efficientnet-b0’)

extract_features(inputs)

Returns output of the final convolution layer

forward(inputs)

Calls extract_features to extract features, applies final linear layer, and returns logits.

classmethod from_name(model_name, override_params=None)

classmethod from_pretrained(model_name, load_weights=True, advprop=False, num_classes=1000, in_channels=3)

classmethod get_image_size(model_name)

set_swish(memory_efficient=True)

Sets swish function as memory efficient (for training) or standard (for export)

class brails.processors.nfloors_detector.lib.efficientnet.model.MBConvBlock(block_args, global_params)

Bases: Module

Mobile Inverted Residual Bottleneck Block

Args:

block_args (namedtuple): BlockArgs, see above global_params (namedtuple): GlobalParam, see above

Attributes:

has_se (bool): Whether the block contains a Squeeze and Excitation layer.

forward(inputs, drop_connect_rate=None)

Parameters:

• inputs – input tensor

• drop_connect_rate – drop connect rate (float, between 0 and 1)

Returns:

output of block

set_swish(memory_efficient=True)

Sets swish function as memory efficient (for training) or standard (for export)