"""model.py - Model and module class for EfficientNet. They are built to mirror those in the official TensorFlow implementation. """ # Author: lukemelas (github username) # Github repo: https://github.com/lukemelas/EfficientNet-PyTorch # With adjustments and added comments by workingcoder (github username). import torch from torch import nn from torch.nn import functional as F from .utils import ( round_filters, round_repeats, drop_connect, get_same_padding_conv2d, get_model_params, efficientnet_params, load_pretrained_weights, Swish, MemoryEfficientSwish, calculate_output_image_size ) VALID_MODELS = ( 'efficientnet-b0', 'efficientnet-b1', 'efficientnet-b2', 'efficientnet-b3', 'efficientnet-b4', 'efficientnet-b5', 'efficientnet-b6', 'efficientnet-b7', 'efficientnet-b8', # Support the construction of 'efficientnet-l2' without pretrained weights 'efficientnet-l2' ) class MBConvBlock(nn.Module): """Mobile Inverted Residual Bottleneck Block. Args: block_args (namedtuple): BlockArgs, defined in utils.py. global_params (namedtuple): GlobalParam, defined in utils.py. image_size (tuple or list): [image_height, image_width]. References: [1] https://arxiv.org/abs/1704.04861 (MobileNet v1) [2] https://arxiv.org/abs/1801.04381 (MobileNet v2) [3] https://arxiv.org/abs/1905.02244 (MobileNet v3) """ 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 # pytorch's difference from tensorflow 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 # whether to use skip connection and drop connect # Expansion phase (Inverted Bottleneck) 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 wouldn't modify image_size # 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) # Pointwise convolution 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): """MBConvBlock's forward function. Args: inputs (tensor): Input tensor. drop_connect_rate (bool): Drop connect rate (float, between 0 and 1). Returns: Output of this block after processing. """ # Expansion and Depthwise Convolution x = inputs if self._block_args.expand_ratio != 1: x = self._expand_conv(inputs) x = self._bn0(x) x = self._swish(x) x = self._depthwise_conv(x) x = self._bn1(x) x = self._swish(x) # Squeeze and Excitation if self.has_se: x_squeezed = F.adaptive_avg_pool2d(x, 1) x_squeezed = self._se_reduce(x_squeezed) x_squeezed = self._swish(x_squeezed) x_squeezed = self._se_expand(x_squeezed) x = torch.sigmoid(x_squeezed) * x # Pointwise Convolution x = self._project_conv(x) x = self._bn2(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: # The combination of skip connection and drop connect brings about stochastic depth. 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). Args: memory_efficient (bool): Whether to use memory-efficient version of swish. """ self._swish = MemoryEfficientSwish() if memory_efficient else Swish() class EfficientNet(nn.Module): """EfficientNet model. Most easily loaded with the .from_name or .from_pretrained methods. Args: blocks_args (list[namedtuple]): A list of BlockArgs to construct blocks. global_params (namedtuple): A set of GlobalParams shared between blocks. References: [1] https://arxiv.org/abs/1905.11946 (EfficientNet) Example: >>> import torch >>> from efficientnet.model import EfficientNet >>> inputs = torch.rand(1, 3, 224, 224) >>> model = EfficientNet.from_pretrained('efficientnet-b0') >>> model.eval() >>> outputs = model(inputs) """ 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=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: # modify block_args to keep same output size 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) # stride = 1 # 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) if self._global_params.include_top: self._dropout = nn.Dropout(self._global_params.dropout_rate) self._fc = nn.Linear(out_channels, self._global_params.num_classes) # set activation to memory efficient swish by default self._swish = MemoryEfficientSwish() def set_swish(self, memory_efficient=True): """Sets swish function as memory efficient (for training) or standard (for export). Args: memory_efficient (bool): Whether to use memory-efficient version of swish. """ self._swish = MemoryEfficientSwish() if memory_efficient else Swish() for block in self._blocks: block.set_swish(memory_efficient) def extract_endpoints(self, inputs): """Use convolution layer to extract features from reduction levels i in [1, 2, 3, 4, 5]. Args: inputs (tensor): Input tensor. Returns: Dictionary of last intermediate features with reduction levels i in [1, 2, 3, 4, 5]. Example: >>> import torch >>> from efficientnet.model import EfficientNet >>> inputs = torch.rand(1, 3, 224, 224) >>> model = EfficientNet.from_pretrained('efficientnet-b0') >>> endpoints = model.extract_endpoints(inputs) >>> print(endpoints['reduction_1'].shape) # torch.Size([1, 16, 112, 112]) >>> print(endpoints['reduction_2'].shape) # torch.Size([1, 24, 56, 56]) >>> print(endpoints['reduction_3'].shape) # torch.Size([1, 40, 28, 28]) >>> print(endpoints['reduction_4'].shape) # torch.Size([1, 112, 14, 14]) >>> print(endpoints['reduction_5'].shape) # torch.Size([1, 320, 7, 7]) >>> print(endpoints['reduction_6'].shape) # torch.Size([1, 1280, 7, 7]) """ endpoints = dict() # Stem x = self._swish(self._bn0(self._conv_stem(inputs))) prev_x = x # 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) # scale drop connect_rate x = block(x, drop_connect_rate=drop_connect_rate) if prev_x.size(2) > x.size(2): endpoints['reduction_{}'.format(len(endpoints) + 1)] = prev_x elif idx == len(self._blocks) - 1: endpoints['reduction_{}'.format(len(endpoints) + 1)] = x prev_x = x # Head x = self._swish(self._bn1(self._conv_head(x))) endpoints['reduction_{}'.format(len(endpoints) + 1)] = x return endpoints def extract_features(self, inputs): """use convolution layer to extract feature . Args: inputs (tensor): Input tensor. Returns: Output of the final convolution layer in the efficientnet model. """ # 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) # scale drop connect_rate 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): """EfficientNet's forward function. Calls extract_features to extract features, applies final linear layer, and returns logits. Args: inputs (tensor): Input tensor. Returns: Output of this model after processing. """ # Convolution layers x = self.extract_features(inputs) # Pooling and final linear layer x = self._avg_pooling(x) if self._global_params.include_top: x = x.flatten(start_dim=1) x = self._dropout(x) x = self._fc(x) return x @classmethod def from_name(cls, model_name, in_channels=3, **override_params): """Create an efficientnet model according to name. Args: model_name (str): Name for efficientnet. in_channels (int): Input data's channel number. override_params (other key word params): Params to override model's global_params. Optional key: 'width_coefficient', 'depth_coefficient', 'image_size', 'dropout_rate', 'num_classes', 'batch_norm_momentum', 'batch_norm_epsilon', 'drop_connect_rate', 'depth_divisor', 'min_depth' Returns: An efficientnet model. """ cls._check_model_name_is_valid(model_name) blocks_args, global_params = get_model_params(model_name, override_params) model = cls(blocks_args, global_params) model._change_in_channels(in_channels) return model @classmethod def from_pretrained(cls, model_name, weights_path=None, advprop=False, in_channels=3, num_classes=1000, **override_params): """Create an efficientnet model according to name. Args: model_name (str): Name for efficientnet. weights_path (None or str): str: path to pretrained weights file on the local disk. None: use pretrained weights downloaded from the Internet. advprop (bool): Whether to load pretrained weights trained with advprop (valid when weights_path is None). in_channels (int): Input data's channel number. num_classes (int): Number of categories for classification. It controls the output size for final linear layer. override_params (other key word params): Params to override model's global_params. Optional key: 'width_coefficient', 'depth_coefficient', 'image_size', 'dropout_rate', 'batch_norm_momentum', 'batch_norm_epsilon', 'drop_connect_rate', 'depth_divisor', 'min_depth' Returns: A pretrained efficientnet model. """ model = cls.from_name(model_name, num_classes=num_classes, **override_params) load_pretrained_weights(model, model_name, weights_path=weights_path, load_fc=(num_classes == 1000), advprop=advprop) model._change_in_channels(in_channels) return model @classmethod def get_image_size(cls, model_name): """Get the input image size for a given efficientnet model. Args: model_name (str): Name for efficientnet. Returns: Input image size (resolution). """ 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. Args: model_name (str): Name for efficientnet. Returns: bool: Is a valid name or not. """ if model_name not in VALID_MODELS: raise ValueError('model_name should be one of: ' + ', '.join(VALID_MODELS)) def _change_in_channels(self, in_channels): """Adjust model's first convolution layer to in_channels, if in_channels not equals 3. Args: in_channels (int): Input data's channel number. """ if in_channels != 3: Conv2d = get_same_padding_conv2d(image_size=self._global_params.image_size) out_channels = round_filters(32, self._global_params) self._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False)