232 lines
8.8 KiB
Python
Executable File
232 lines
8.8 KiB
Python
Executable File
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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relu_fn,
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round_filters,
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round_repeats,
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drop_connect,
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Conv2dSamePadding,
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get_model_params,
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efficientnet_params,
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load_pretrained_weights,
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)
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INDICES = {
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'efficientnet-b3': [7, 17, 25]
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}
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EXTRAS = {
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'efficientnet-b3': [
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# in, out, k, s, p
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[(384, 128, 1, 1, 0), (128, 256, 3, 2, 1)], # 5 x 5
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[(256, 128, 1, 1, 0), (128, 256, 3, 1, 0)], # 3 x 3
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[(256, 128, 1, 1, 0), (128, 256, 3, 1, 0)], # 1 x 1
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]
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}
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def add_extras(cfgs):
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extras = nn.ModuleList()
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for cfg in cfgs:
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extra = []
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for prams in cfg:
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in_channels, out_channels, kernel_size, stride, padding = prams
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extra.append(nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding))
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extra.append(nn.ReLU())
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extras.append(nn.Sequential(*extra))
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return extras
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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):
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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 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 = self._block_args.input_filters * self._block_args.expand_ratio # number of output channels
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if self._block_args.expand_ratio != 1:
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self._expand_conv = Conv2dSamePadding(in_channels=inp, out_channels=oup, kernel_size=1, bias=False)
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self._bn0 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps)
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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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self._depthwise_conv = Conv2dSamePadding(
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in_channels=oup, out_channels=oup, groups=oup, # groups makes it depthwise
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kernel_size=k, stride=s, bias=False)
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self._bn1 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps)
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# Squeeze and Excitation layer, if desired
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if self.has_se:
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num_squeezed_channels = max(1, int(self._block_args.input_filters * self._block_args.se_ratio))
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self._se_reduce = Conv2dSamePadding(in_channels=oup, out_channels=num_squeezed_channels, kernel_size=1)
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self._se_expand = Conv2dSamePadding(in_channels=num_squeezed_channels, out_channels=oup, kernel_size=1)
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# Output phase
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final_oup = self._block_args.output_filters
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self._project_conv = Conv2dSamePadding(in_channels=oup, out_channels=final_oup, kernel_size=1, bias=False)
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self._bn2 = nn.BatchNorm2d(num_features=final_oup, momentum=self._bn_mom, eps=self._bn_eps)
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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 = relu_fn(self._bn0(self._expand_conv(inputs)))
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x = relu_fn(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(relu_fn(self._se_reduce(x_squeezed)))
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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 = self._block_args.input_filters, self._block_args.output_filters
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if self.id_skip and self._block_args.stride == 1 and input_filters == output_filters:
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if drop_connect_rate:
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x = drop_connect(x, p=drop_connect_rate, training=self.training)
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x = x + inputs # skip connection
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return x
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class EfficientNet(nn.Module):
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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, model_name, blocks_args=None, global_params=None):
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super().__init__()
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self.indices = INDICES[model_name]
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self.extras = add_extras(EXTRAS[model_name])
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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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# Stem
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in_channels = 3 # rgb
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out_channels = round_filters(32, self._global_params) # number of output channels
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self._conv_stem = Conv2dSamePadding(in_channels, out_channels, kernel_size=3, stride=2, bias=False)
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self._bn0 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps)
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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(block_args.input_filters, self._global_params),
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output_filters=round_filters(block_args.output_filters, self._global_params),
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num_repeat=round_repeats(block_args.num_repeat, self._global_params)
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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(MBConvBlock(block_args, self._global_params))
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if block_args.num_repeat > 1:
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block_args = block_args._replace(input_filters=block_args.output_filters, stride=1)
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for _ in range(block_args.num_repeat - 1):
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self._blocks.append(MBConvBlock(block_args, self._global_params))
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self.reset_parameters()
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def reset_parameters(self):
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for m in self.extras.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.xavier_uniform_(m.weight)
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nn.init.zeros_(m.bias)
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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 = relu_fn(self._bn0(self._conv_stem(inputs)))
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features = []
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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)
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if idx in self.indices:
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features.append(x)
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return x, features
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def forward(self, inputs):
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""" Calls extract_features to extract features, applies final linear layer, and returns logits. """
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# Convolution layers
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x, features = self.extract_features(inputs)
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for layer in self.extras:
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x = layer(x)
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features.append(x)
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return tuple(features)
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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(model_name, override_params)
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return EfficientNet(model_name, blocks_args, global_params)
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@classmethod
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def from_pretrained(cls, model_name):
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model = EfficientNet.from_name(model_name)
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load_pretrained_weights(model, model_name)
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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, also_need_pretrained_weights=False):
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""" Validates model name. None that pretrained weights are only available for
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the first four models (efficientnet-b{i} for i in 0,1,2,3) at the moment. """
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num_models = 4 if also_need_pretrained_weights else 8
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valid_models = ['efficientnet_b' + str(i) for i in range(num_models)]
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if model_name.replace('-', '_') not in valid_models:
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raise ValueError('model_name should be one of: ' + ', '.join(valid_models))
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