import torch from torch import nn from torch.nn import functional as F from .utils import ( Swish, MemoryEfficientSwish, drop_connect, round_filters, round_repeats, get_model_params, efficientnet_params, get_same_padding_conv2d, load_pretrained_weights, calculate_output_image_size ) from ..build import BACKBONE_REGISTRY from ..backbone import Backbone class MBConvBlock(nn.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. """ def __init__(self, block_args, global_params, image_size=None): super().__init__() self._block_args = block_args self._bn_mom = 1 - global_params.batch_norm_momentum self._bn_eps = global_params.batch_norm_epsilon self.has_se = (self._block_args.se_ratio is not None) and (0 < self._block_args.se_ratio <= 1) self.id_skip = block_args.id_skip # skip connection and drop connect # Expansion phase inp = self._block_args.input_filters # number of input channels oup = ( self._block_args.input_filters * self._block_args.expand_ratio ) # number of output channels if self._block_args.expand_ratio != 1: Conv2d = get_same_padding_conv2d(image_size=image_size) self._expand_conv = Conv2d( in_channels=inp, out_channels=oup, kernel_size=1, bias=False ) self._bn0 = nn.BatchNorm2d( num_features=oup, momentum=self._bn_mom, eps=self._bn_eps ) # image_size = calculate_output_image_size(image_size, 1) <-- this would do nothing # Depthwise convolution phase k = self._block_args.kernel_size s = self._block_args.stride Conv2d = get_same_padding_conv2d(image_size=image_size) self._depthwise_conv = Conv2d( in_channels=oup, out_channels=oup, groups=oup, # groups makes it depthwise kernel_size=k, stride=s, bias=False, ) self._bn1 = nn.BatchNorm2d( num_features=oup, momentum=self._bn_mom, eps=self._bn_eps ) image_size = calculate_output_image_size(image_size, s) # Squeeze and Excitation layer, if desired if self.has_se: Conv2d = get_same_padding_conv2d(image_size=(1, 1)) num_squeezed_channels = max( 1, int( self._block_args.input_filters * self._block_args.se_ratio ) ) self._se_reduce = Conv2d( in_channels=oup, out_channels=num_squeezed_channels, kernel_size=1 ) self._se_expand = Conv2d( in_channels=num_squeezed_channels, out_channels=oup, kernel_size=1 ) # Output phase final_oup = self._block_args.output_filters Conv2d = get_same_padding_conv2d(image_size=image_size) self._project_conv = Conv2d( in_channels=oup, out_channels=final_oup, kernel_size=1, bias=False ) self._bn2 = nn.BatchNorm2d( num_features=final_oup, momentum=self._bn_mom, eps=self._bn_eps ) self._swish = MemoryEfficientSwish() def forward(self, inputs, drop_connect_rate=None): """ :param inputs: input tensor :param drop_connect_rate: drop connect rate (float, between 0 and 1) :return: output of block """ # Expansion and Depthwise Convolution x = inputs if self._block_args.expand_ratio != 1: x = self._swish(self._bn0(self._expand_conv(inputs))) x = self._swish(self._bn1(self._depthwise_conv(x))) # Squeeze and Excitation if self.has_se: x_squeezed = F.adaptive_avg_pool2d(x, 1) x_squeezed = self._se_expand( self._swish(self._se_reduce(x_squeezed)) ) x = torch.sigmoid(x_squeezed) * x x = self._bn2(self._project_conv(x)) # Skip connection and drop connect input_filters, output_filters = ( self._block_args.input_filters, self._block_args.output_filters, ) if ( self.id_skip and self._block_args.stride == 1 and input_filters == output_filters ): if drop_connect_rate: x = drop_connect( x, p=drop_connect_rate, training=self.training ) x = x + inputs # skip connection return x def set_swish(self, memory_efficient=True): """Sets swish function as memory efficient (for training) or standard (for export)""" self._swish = MemoryEfficientSwish() if memory_efficient else Swish() class EfficientNet(Backbone): """ 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') """ def __init__(self, blocks_args=None, global_params=None): super().__init__() assert isinstance(blocks_args, list), "blocks_args should be a list" assert len(blocks_args) > 0, "block args must be greater than 0" self._global_params = global_params self._blocks_args = blocks_args # Batch norm parameters bn_mom = 1 - self._global_params.batch_norm_momentum bn_eps = self._global_params.batch_norm_epsilon # Get stem static or dynamic convolution depending on image size image_size = global_params.image_size Conv2d = get_same_padding_conv2d(image_size=global_params.image_size) # Stem in_channels = 3 # rgb out_channels = round_filters( 32, self._global_params ) # number of output channels self._conv_stem = Conv2d( in_channels, out_channels, kernel_size=3, stride=2, bias=False ) self._bn0 = nn.BatchNorm2d( num_features=out_channels, momentum=bn_mom, eps=bn_eps ) image_size = calculate_output_image_size(image_size, 2) # Build blocks self._blocks = nn.ModuleList([]) for block_args in self._blocks_args: # Update block input and output filters based on depth multiplier. block_args = block_args._replace( input_filters=round_filters( block_args.input_filters, self._global_params ), output_filters=round_filters( block_args.output_filters, self._global_params ), num_repeat=round_repeats( block_args.num_repeat, self._global_params ), ) # The first block needs to take care of stride and filter size increase. self._blocks.append( MBConvBlock( block_args, self._global_params, image_size=image_size ) ) image_size = calculate_output_image_size( image_size, block_args.stride ) if block_args.num_repeat > 1: block_args = block_args._replace( input_filters=block_args.output_filters, stride=1 ) for _ in range(block_args.num_repeat - 1): self._blocks.append( MBConvBlock( block_args, self._global_params, image_size=image_size ) ) # image_size = calculate_output_image_size(image_size, block_args.stride) # ? # Head in_channels = block_args.output_filters # output of final block out_channels = round_filters(1280, self._global_params) Conv2d = get_same_padding_conv2d(image_size=image_size) self._conv_head = Conv2d( in_channels, out_channels, kernel_size=1, bias=False ) self._bn1 = nn.BatchNorm2d( num_features=out_channels, momentum=bn_mom, eps=bn_eps ) # Final linear layer self._avg_pooling = nn.AdaptiveAvgPool2d(1) self._dropout = nn.Dropout(self._global_params.dropout_rate) # self._fc = nn.Linear(out_channels, self._global_params.num_classes) self._swish = MemoryEfficientSwish() self._out_features = out_channels def set_swish(self, memory_efficient=True): """Sets swish function as memory efficient (for training) or standard (for export)""" self._swish = MemoryEfficientSwish() if memory_efficient else Swish() for block in self._blocks: block.set_swish(memory_efficient) def extract_features(self, inputs): """Returns output of the final convolution layer""" # Stem x = self._swish(self._bn0(self._conv_stem(inputs))) # Blocks for idx, block in enumerate(self._blocks): drop_connect_rate = self._global_params.drop_connect_rate if drop_connect_rate: drop_connect_rate *= float(idx) / len(self._blocks) x = block(x, drop_connect_rate=drop_connect_rate) # Head x = self._swish(self._bn1(self._conv_head(x))) return x def forward(self, inputs): """ Calls extract_features to extract features, applies final linear layer, and returns logits. """ bs = inputs.size(0) # Convolution layers x = self.extract_features(inputs) # Pooling and final linear layer x = self._avg_pooling(x) x = x.view(bs, -1) x = self._dropout(x) # x = self._fc(x) return x @classmethod def from_name(cls, model_name, override_params=None): cls._check_model_name_is_valid(model_name) blocks_args, global_params = get_model_params( model_name, override_params ) return cls(blocks_args, global_params) @classmethod def from_pretrained( cls, model_name, advprop=False, num_classes=1000, in_channels=3 ): model = cls.from_name( model_name, override_params={"num_classes": num_classes} ) load_pretrained_weights( model, model_name, load_fc=(num_classes == 1000), advprop=advprop ) model._change_in_channels(in_channels) return model @classmethod def get_image_size(cls, model_name): cls._check_model_name_is_valid(model_name) _, _, res, _ = efficientnet_params(model_name) return res @classmethod def _check_model_name_is_valid(cls, model_name): """Validates model name.""" valid_models = ["efficientnet-b" + str(i) for i in range(9)] if model_name not in valid_models: raise ValueError( "model_name should be one of: " + ", ".join(valid_models) ) def _change_in_channels(model, in_channels): if in_channels != 3: Conv2d = get_same_padding_conv2d( image_size=model._global_params.image_size ) out_channels = round_filters(32, model._global_params) model._conv_stem = Conv2d( in_channels, out_channels, kernel_size=3, stride=2, bias=False ) def build_efficientnet(name, pretrained): if pretrained: return EfficientNet.from_pretrained("efficientnet-{}".format(name)) else: return EfficientNet.from_name("efficientnet-{}".format(name)) @BACKBONE_REGISTRY.register() def efficientnet_b0(pretrained=True, **kwargs): return build_efficientnet("b0", pretrained) @BACKBONE_REGISTRY.register() def efficientnet_b1(pretrained=True, **kwargs): return build_efficientnet("b1", pretrained) @BACKBONE_REGISTRY.register() def efficientnet_b2(pretrained=True, **kwargs): return build_efficientnet("b2", pretrained) @BACKBONE_REGISTRY.register() def efficientnet_b3(pretrained=True, **kwargs): return build_efficientnet("b3", pretrained) @BACKBONE_REGISTRY.register() def efficientnet_b4(pretrained=True, **kwargs): return build_efficientnet("b4", pretrained) @BACKBONE_REGISTRY.register() def efficientnet_b5(pretrained=True, **kwargs): return build_efficientnet("b5", pretrained) @BACKBONE_REGISTRY.register() def efficientnet_b6(pretrained=True, **kwargs): return build_efficientnet("b6", pretrained) @BACKBONE_REGISTRY.register() def efficientnet_b7(pretrained=True, **kwargs): return build_efficientnet("b7", pretrained)