433 lines
17 KiB
Python
433 lines
17 KiB
Python
"""model.py - Model and module class for EfficientNet.
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They are built to mirror those in the official TensorFlow implementation.
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"""
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# Author: lukemelas (github username)
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# Github repo: https://github.com/lukemelas/EfficientNet-PyTorch
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# With adjustments and added comments by workingcoder (github username).
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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 .effi_utils import (
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round_filters,
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round_repeats,
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drop_connect,
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get_same_padding_conv2d,
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get_model_params,
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efficientnet_params,
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load_pretrained_weights,
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Swish,
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MemoryEfficientSwish,
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calculate_output_image_size
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)
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VALID_MODELS = (
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'efficientnet-b0', 'efficientnet-b1', 'efficientnet-b2', 'efficientnet-b3',
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'efficientnet-b4', 'efficientnet-b5', 'efficientnet-b6', 'efficientnet-b7',
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'efficientnet-b8',
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# Support the construction of 'efficientnet-l2' without pretrained weights
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'efficientnet-l2'
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)
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class MBConvBlock(nn.Module):
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"""Mobile Inverted Residual Bottleneck Block.
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Args:
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block_args (namedtuple): BlockArgs, defined in utils.py.
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global_params (namedtuple): GlobalParam, defined in utils.py.
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image_size (tuple or list): [image_height, image_width].
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References:
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[1] https://arxiv.org/abs/1704.04861 (MobileNet v1)
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[2] https://arxiv.org/abs/1801.04381 (MobileNet v2)
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[3] https://arxiv.org/abs/1905.02244 (MobileNet v3)
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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 # pytorch's difference from tensorflow
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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 # whether to use skip connection and drop connect
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# Expansion phase (Inverted Bottleneck)
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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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Conv2d = get_same_padding_conv2d(image_size=image_size)
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self._expand_conv = Conv2d(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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# image_size = calculate_output_image_size(image_size, 1) <-- this wouldn't modify image_size
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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, 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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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(1, int(self._block_args.input_filters * self._block_args.se_ratio))
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self._se_reduce = Conv2d(in_channels=oup, out_channels=num_squeezed_channels, kernel_size=1)
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self._se_expand = Conv2d(in_channels=num_squeezed_channels, out_channels=oup, kernel_size=1)
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# Pointwise convolution 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(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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self._swish = MemoryEfficientSwish()
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def forward(self, inputs, drop_connect_rate=None):
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"""MBConvBlock's forward function.
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Args:
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inputs (tensor): Input tensor.
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drop_connect_rate (bool): Drop connect rate (float, between 0 and 1).
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Returns:
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Output of this block after processing.
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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._expand_conv(inputs)
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x = self._bn0(x)
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x = self._swish(x)
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x = self._depthwise_conv(x)
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x = self._bn1(x)
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x = self._swish(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_reduce(x_squeezed)
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x_squeezed = self._swish(x_squeezed)
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x_squeezed = self._se_expand(x_squeezed)
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x = torch.sigmoid(x_squeezed) * x
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# Pointwise Convolution
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x = self._project_conv(x)
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x = self._bn2(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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# The combination of skip connection and drop connect brings about stochastic depth.
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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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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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Args:
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memory_efficient (bool): Whether to use memory-efficient version of swish.
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"""
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self._swish = MemoryEfficientSwish() if memory_efficient else Swish()
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class EfficientNet(nn.Module):
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"""EfficientNet model.
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Most easily loaded with the .from_name or .from_pretrained methods.
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Args:
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blocks_args (list[namedtuple]): 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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References:
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[1] https://arxiv.org/abs/1905.11946 (EfficientNet)
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Example:
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>>> import torch
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>>> from efficientnet.model import EfficientNet
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>>> inputs = torch.rand(1, 3, 224, 224)
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>>> model = EfficientNet.from_pretrained('efficientnet-b0')
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>>> model.eval()
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>>> outputs = model(inputs)
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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=image_size)
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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 = Conv2d(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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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(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, image_size=image_size))
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image_size = calculate_output_image_size(image_size, block_args.stride)
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if block_args.num_repeat > 1: # modify block_args to keep same output size
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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, image_size=image_size))
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# image_size = calculate_output_image_size(image_size, block_args.stride) # stride = 1
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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(in_channels, out_channels, kernel_size=1, bias=False)
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self._bn1 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps)
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# Final linear layer
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self._avg_pooling = nn.AdaptiveAvgPool2d(1)
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if self._global_params.include_top:
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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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# set activation to memory efficient swish by default
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self._swish = MemoryEfficientSwish()
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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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Args:
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memory_efficient (bool): Whether to use memory-efficient version of swish.
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"""
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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_endpoints(self, inputs):
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"""Use convolution layer to extract features
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from reduction levels i in [1, 2, 3, 4, 5].
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Args:
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inputs (tensor): Input tensor.
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Returns:
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Dictionary of last intermediate features
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with reduction levels i in [1, 2, 3, 4, 5].
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Example:
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>>> import torch
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>>> from efficientnet.model import EfficientNet
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>>> inputs = torch.rand(1, 3, 224, 224)
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>>> model = EfficientNet.from_pretrained('efficientnet-b0')
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>>> endpoints = model.extract_endpoints(inputs)
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>>> print(endpoints['reduction_1'].shape) # torch.Size([1, 16, 112, 112])
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>>> print(endpoints['reduction_2'].shape) # torch.Size([1, 24, 56, 56])
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>>> print(endpoints['reduction_3'].shape) # torch.Size([1, 40, 28, 28])
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>>> print(endpoints['reduction_4'].shape) # torch.Size([1, 112, 14, 14])
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>>> print(endpoints['reduction_5'].shape) # torch.Size([1, 320, 7, 7])
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>>> print(endpoints['reduction_6'].shape) # torch.Size([1, 1280, 7, 7])
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"""
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endpoints = dict()
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# Stem
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x = self._swish(self._bn0(self._conv_stem(inputs)))
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prev_x = x
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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) # scale drop connect_rate
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x = block(x, drop_connect_rate=drop_connect_rate)
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if prev_x.size(2) > x.size(2):
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endpoints['reduction_{}'.format(len(endpoints) + 1)] = prev_x
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elif idx == len(self._blocks) - 1:
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endpoints['reduction_{}'.format(len(endpoints) + 1)] = x
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prev_x = x
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# Head
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x = self._swish(self._bn1(self._conv_head(x)))
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endpoints['reduction_{}'.format(len(endpoints) + 1)] = x
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return endpoints
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def extract_features(self, inputs):
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"""use convolution layer to extract feature .
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Args:
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inputs (tensor): Input tensor.
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Returns:
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Output of the final convolution
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layer in the efficientnet model.
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"""
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out = []
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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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# B2: [4, 7, 15, 22],[24, 48, 120, 352],352
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# B7: [10, 17, 37, 54],[48, 80, 224, 640],640
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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) # scale drop connect_rate
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x = block(x, drop_connect_rate=drop_connect_rate)
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if idx == 10:
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out.append(x)
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if idx == 17:
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out.append(x)
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if idx == 37:
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out.append(x)
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if idx == 54:
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out.append(x)
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# Head
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#x = self._swish(self._bn1(self._conv_head(x)))
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return out
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def forward(self, inputs):
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"""EfficientNet's forward function.
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Calls extract_features to extract features, applies final linear layer, and returns logits.
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Args:
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inputs (tensor): Input tensor.
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Returns:
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Output of this model after processing.
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"""
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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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'''
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x = self._avg_pooling(x)
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if self._global_params.include_top:
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x = x.flatten(start_dim=1)
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x = self._dropout(x)
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x = self._fc(x)
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'''
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return x
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@classmethod
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def from_name(cls, model_name, in_channels=3, **override_params):
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"""Create an efficientnet model according to name.
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Args:
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model_name (str): Name for efficientnet.
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in_channels (int): Input data's channel number.
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override_params (other key word params):
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Params to override model's global_params.
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Optional key:
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'width_coefficient', 'depth_coefficient',
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'image_size', 'dropout_rate',
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'num_classes', 'batch_norm_momentum',
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'batch_norm_epsilon', 'drop_connect_rate',
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'depth_divisor', 'min_depth'
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Returns:
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An efficientnet model.
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"""
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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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model = cls(blocks_args, global_params)
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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 from_pretrained(cls, model_name, weights_path=None, advprop=False,
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in_channels=3, num_classes=1000, **override_params):
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"""Create an efficientnet model according to name.
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Args:
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model_name (str): Name for efficientnet.
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weights_path (None or str):
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str: path to pretrained weights file on the local disk.
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None: use pretrained weights downloaded from the Internet.
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advprop (bool):
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Whether to load pretrained weights
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trained with advprop (valid when weights_path is None).
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in_channels (int): Input data's channel number.
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num_classes (int):
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Number of categories for classification.
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It controls the output size for final linear layer.
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override_params (other key word params):
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Params to override model's global_params.
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Optional key:
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'width_coefficient', 'depth_coefficient',
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'image_size', 'dropout_rate',
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'batch_norm_momentum',
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'batch_norm_epsilon', 'drop_connect_rate',
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'depth_divisor', 'min_depth'
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Returns:
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A pretrained efficientnet model.
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"""
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model = cls.from_name(model_name, num_classes=num_classes, **override_params)
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load_pretrained_weights(model, model_name, weights_path=weights_path,
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load_fc=(num_classes == 1000), advprop=advprop)
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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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"""Get the input image size for a given efficientnet model.
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Args:
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model_name (str): Name for efficientnet.
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Returns:
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Input image size (resolution).
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"""
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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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Args:
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model_name (str): Name for efficientnet.
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Returns:
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bool: Is a valid name or not.
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"""
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if model_name not in VALID_MODELS:
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raise ValueError('model_name should be one of: ' + ', '.join(VALID_MODELS))
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def _change_in_channels(self, in_channels):
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"""Adjust model's first convolution layer to in_channels, if in_channels not equals 3.
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Args:
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in_channels (int): Input data's channel number.
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"""
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if in_channels != 3:
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Conv2d = get_same_padding_conv2d(image_size=self._global_params.image_size)
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out_channels = round_filters(32, self._global_params)
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self._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False) |