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