1351 lines
55 KiB
Python
1351 lines
55 KiB
Python
""" MobileNet V3
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A PyTorch impl of MobileNet-V3, compatible with TF weights from official impl.
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Paper: Searching for MobileNetV3 - https://arxiv.org/abs/1905.02244
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Hacked together by / Copyright 2019, Ross Wightman
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"""
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from functools import partial
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from typing import Callable, List, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.utils.checkpoint import checkpoint
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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from timm.layers import SelectAdaptivePool2d, Linear, LayerType, PadType, create_conv2d, get_norm_act_layer
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from ._builder import build_model_with_cfg, pretrained_cfg_for_features
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from ._efficientnet_blocks import SqueezeExcite
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from ._efficientnet_builder import BlockArgs, EfficientNetBuilder, decode_arch_def, efficientnet_init_weights, \
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round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
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from ._features import FeatureInfo, FeatureHooks, feature_take_indices
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from ._manipulate import checkpoint_seq
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from ._registry import generate_default_cfgs, register_model, register_model_deprecations
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__all__ = ['MobileNetV3', 'MobileNetV3Features']
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class MobileNetV3(nn.Module):
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""" MobiletNet-V3
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Based on my EfficientNet implementation and building blocks, this model utilizes the MobileNet-v3 specific
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'efficient head', where global pooling is done before the head convolution without a final batch-norm
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layer before the classifier.
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Paper: `Searching for MobileNetV3` - https://arxiv.org/abs/1905.02244
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Other architectures utilizing MobileNet-V3 efficient head that are supported by this impl include:
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* HardCoRe-NAS - https://arxiv.org/abs/2102.11646 (defn in hardcorenas.py uses this class)
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* FBNet-V3 - https://arxiv.org/abs/2006.02049
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* LCNet - https://arxiv.org/abs/2109.15099
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* MobileNet-V4 - https://arxiv.org/abs/2404.10518
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"""
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def __init__(
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self,
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block_args: BlockArgs,
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num_classes: int = 1000,
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in_chans: int = 3,
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stem_size: int = 16,
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fix_stem: bool = False,
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num_features: int = 1280,
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head_bias: bool = True,
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head_norm: bool = False,
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pad_type: str = '',
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act_layer: Optional[LayerType] = None,
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norm_layer: Optional[LayerType] = None,
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aa_layer: Optional[LayerType] = None,
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se_layer: Optional[LayerType] = None,
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se_from_exp: bool = True,
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round_chs_fn: Callable = round_channels,
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drop_rate: float = 0.,
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drop_path_rate: float = 0.,
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layer_scale_init_value: Optional[float] = None,
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global_pool: str = 'avg',
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):
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"""
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Args:
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block_args: Arguments for blocks of the network.
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num_classes: Number of classes for classification head.
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in_chans: Number of input image channels.
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stem_size: Number of output channels of the initial stem convolution.
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fix_stem: If True, don't scale stem by round_chs_fn.
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num_features: Number of output channels of the conv head layer.
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head_bias: If True, add a learnable bias to the conv head layer.
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pad_type: Type of padding to use for convolution layers.
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act_layer: Type of activation layer.
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norm_layer: Type of normalization layer.
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aa_layer: Type of anti-aliasing layer.
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se_layer: Type of Squeeze-and-Excite layer.
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se_from_exp: If True, calculate SE channel reduction from expanded mid channels.
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round_chs_fn: Callable to round number of filters based on depth multiplier.
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drop_rate: Dropout rate.
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drop_path_rate: Stochastic depth rate.
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layer_scale_init_value: Enable layer scale on compatible blocks if not None.
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global_pool: Type of pooling to use for global pooling features of the FC head.
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"""
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super(MobileNetV3, self).__init__()
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act_layer = act_layer or nn.ReLU
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norm_layer = norm_layer or nn.BatchNorm2d
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norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
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se_layer = se_layer or SqueezeExcite
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self.num_classes = num_classes
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self.drop_rate = drop_rate
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self.grad_checkpointing = False
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# Stem
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if not fix_stem:
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stem_size = round_chs_fn(stem_size)
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self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
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self.bn1 = norm_act_layer(stem_size, inplace=True)
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# Middle stages (IR/ER/DS Blocks)
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builder = EfficientNetBuilder(
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output_stride=32,
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pad_type=pad_type,
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round_chs_fn=round_chs_fn,
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se_from_exp=se_from_exp,
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act_layer=act_layer,
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norm_layer=norm_layer,
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aa_layer=aa_layer,
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se_layer=se_layer,
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drop_path_rate=drop_path_rate,
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layer_scale_init_value=layer_scale_init_value,
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)
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self.blocks = nn.Sequential(*builder(stem_size, block_args))
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self.feature_info = builder.features
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self.stage_ends = [f['stage'] for f in self.feature_info]
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self.num_features = builder.in_chs # features of last stage, output of forward_features()
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self.head_hidden_size = num_features # features of conv_head, pre_logits output
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# Head + Pooling
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self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
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num_pooled_chs = self.num_features * self.global_pool.feat_mult()
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if head_norm:
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# mobilenet-v4 post-pooling PW conv is followed by a norm+act layer
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self.conv_head = create_conv2d(num_pooled_chs, self.head_hidden_size, 1, padding=pad_type) # never bias
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self.norm_head = norm_act_layer(self.head_hidden_size)
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self.act2 = nn.Identity()
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else:
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# mobilenet-v3 and others only have an activation after final PW conv
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self.conv_head = create_conv2d(num_pooled_chs, self.head_hidden_size, 1, padding=pad_type, bias=head_bias)
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self.norm_head = nn.Identity()
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self.act2 = act_layer(inplace=True)
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self.flatten = nn.Flatten(1) if global_pool else nn.Identity() # don't flatten if pooling disabled
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self.classifier = Linear(self.head_hidden_size, num_classes) if num_classes > 0 else nn.Identity()
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efficientnet_init_weights(self)
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def as_sequential(self):
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layers = [self.conv_stem, self.bn1]
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layers.extend(self.blocks)
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layers.extend([self.global_pool, self.conv_head, self.norm_head, self.act2])
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layers.extend([nn.Flatten(), nn.Dropout(self.drop_rate), self.classifier])
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return nn.Sequential(*layers)
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@torch.jit.ignore
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def group_matcher(self, coarse: bool = False):
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return dict(
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stem=r'^conv_stem|bn1',
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blocks=r'^blocks\.(\d+)' if coarse else r'^blocks\.(\d+)\.(\d+)'
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)
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@torch.jit.ignore
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def set_grad_checkpointing(self, enable: bool = True):
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self.grad_checkpointing = enable
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@torch.jit.ignore
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def get_classifier(self) -> nn.Module:
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return self.classifier
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def reset_classifier(self, num_classes: int, global_pool: str = 'avg'):
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self.num_classes = num_classes
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# NOTE: cannot meaningfully change pooling of efficient head after creation
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self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
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self.flatten = nn.Flatten(1) if global_pool else nn.Identity() # don't flatten if pooling disabled
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self.classifier = Linear(self.head_hidden_size, num_classes) if num_classes > 0 else nn.Identity()
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def forward_intermediates(
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self,
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x: torch.Tensor,
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indices: Optional[Union[int, List[int]]] = None,
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norm: bool = False,
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stop_early: bool = False,
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output_fmt: str = 'NCHW',
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intermediates_only: bool = False,
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extra_blocks: bool = False,
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) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
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""" Forward features that returns intermediates.
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Args:
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x: Input image tensor
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indices: Take last n blocks if int, all if None, select matching indices if sequence
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norm: Apply norm layer to compatible intermediates
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stop_early: Stop iterating over blocks when last desired intermediate hit
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output_fmt: Shape of intermediate feature outputs
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intermediates_only: Only return intermediate features
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extra_blocks: Include outputs of all blocks and head conv in output, does not align with feature_info
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Returns:
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"""
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assert output_fmt in ('NCHW',), 'Output shape must be NCHW.'
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if stop_early:
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assert intermediates_only, 'Must use intermediates_only for early stopping.'
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intermediates = []
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if extra_blocks:
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take_indices, max_index = feature_take_indices(len(self.blocks) + 1, indices)
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else:
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take_indices, max_index = feature_take_indices(len(self.stage_ends), indices)
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take_indices = [self.stage_ends[i] for i in take_indices]
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max_index = self.stage_ends[max_index]
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# forward pass
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feat_idx = 0 # stem is index 0
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x = self.conv_stem(x)
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x = self.bn1(x)
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if feat_idx in take_indices:
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intermediates.append(x)
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if torch.jit.is_scripting() or not stop_early: # can't slice blocks in torchscript
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blocks = self.blocks
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else:
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blocks = self.blocks[:max_index]
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for blk in blocks:
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feat_idx += 1
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x = blk(x)
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if feat_idx in take_indices:
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intermediates.append(x)
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if intermediates_only:
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return intermediates
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return x, intermediates
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def prune_intermediate_layers(
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self,
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indices: Union[int, List[int]] = 1,
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prune_norm: bool = False,
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prune_head: bool = True,
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extra_blocks: bool = False,
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):
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""" Prune layers not required for specified intermediates.
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"""
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if extra_blocks:
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take_indices, max_index = feature_take_indices(len(self.blocks) + 1, indices)
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else:
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take_indices, max_index = feature_take_indices(len(self.stage_ends), indices)
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max_index = self.stage_ends[max_index]
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self.blocks = self.blocks[:max_index] # truncate blocks w/ stem as idx 0
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if max_index < len(self.blocks):
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self.conv_head = nn.Identity()
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self.norm_head = nn.Identity()
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if prune_head:
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self.conv_head = nn.Identity()
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self.norm_head = nn.Identity()
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self.reset_classifier(0, '')
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return take_indices
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def forward_features(self, x: torch.Tensor) -> torch.Tensor:
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x = self.conv_stem(x)
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x = self.bn1(x)
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if self.grad_checkpointing and not torch.jit.is_scripting():
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x = checkpoint_seq(self.blocks, x, flatten=True)
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else:
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x = self.blocks(x)
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return x
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def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> torch.Tensor:
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x = self.global_pool(x)
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x = self.conv_head(x)
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x = self.norm_head(x)
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x = self.act2(x)
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x = self.flatten(x)
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if self.drop_rate > 0.:
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x = F.dropout(x, p=self.drop_rate, training=self.training)
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if pre_logits:
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return x
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return self.classifier(x)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.forward_features(x)
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x = self.forward_head(x)
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return x
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class MobileNetV3Features(nn.Module):
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""" MobileNetV3 Feature Extractor
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A work-in-progress feature extraction module for MobileNet-V3 to use as a backbone for segmentation
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and object detection models.
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"""
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def __init__(
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self,
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block_args: BlockArgs,
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out_indices: Tuple[int, ...] = (0, 1, 2, 3, 4),
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feature_location: str = 'bottleneck',
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in_chans: int = 3,
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stem_size: int = 16,
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fix_stem: bool = False,
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output_stride: int = 32,
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pad_type: PadType = '',
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round_chs_fn: Callable = round_channels,
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se_from_exp: bool = True,
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act_layer: Optional[LayerType] = None,
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norm_layer: Optional[LayerType] = None,
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aa_layer: Optional[LayerType] = None,
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se_layer: Optional[LayerType] = None,
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drop_rate: float = 0.,
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drop_path_rate: float = 0.,
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layer_scale_init_value: Optional[float] = None,
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):
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"""
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Args:
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block_args: Arguments for blocks of the network.
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out_indices: Output from stages at indices.
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feature_location: Location of feature before/after each block, must be in ['bottleneck', 'expansion']
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in_chans: Number of input image channels.
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stem_size: Number of output channels of the initial stem convolution.
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fix_stem: If True, don't scale stem by round_chs_fn.
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output_stride: Output stride of the network.
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pad_type: Type of padding to use for convolution layers.
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round_chs_fn: Callable to round number of filters based on depth multiplier.
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se_from_exp: If True, calculate SE channel reduction from expanded mid channels.
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act_layer: Type of activation layer.
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norm_layer: Type of normalization layer.
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se_layer: Type of Squeeze-and-Excite layer.
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drop_rate: Dropout rate.
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drop_path_rate: Stochastic depth rate.
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layer_scale_init_value: Enable layer scale on compatible blocks if not None.
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"""
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super(MobileNetV3Features, self).__init__()
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act_layer = act_layer or nn.ReLU
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norm_layer = norm_layer or nn.BatchNorm2d
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se_layer = se_layer or SqueezeExcite
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self.drop_rate = drop_rate
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self.grad_checkpointing = False
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# Stem
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if not fix_stem:
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stem_size = round_chs_fn(stem_size)
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self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
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self.bn1 = norm_layer(stem_size)
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self.act1 = act_layer(inplace=True)
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# Middle stages (IR/ER/DS Blocks)
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builder = EfficientNetBuilder(
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output_stride=output_stride,
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pad_type=pad_type,
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round_chs_fn=round_chs_fn,
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se_from_exp=se_from_exp,
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act_layer=act_layer,
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norm_layer=norm_layer,
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aa_layer=aa_layer,
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se_layer=se_layer,
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drop_path_rate=drop_path_rate,
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layer_scale_init_value=layer_scale_init_value,
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feature_location=feature_location,
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)
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self.blocks = nn.Sequential(*builder(stem_size, block_args))
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self.feature_info = FeatureInfo(builder.features, out_indices)
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self._stage_out_idx = {f['stage']: f['index'] for f in self.feature_info.get_dicts()}
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efficientnet_init_weights(self)
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# Register feature extraction hooks with FeatureHooks helper
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self.feature_hooks = None
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if feature_location != 'bottleneck':
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hooks = self.feature_info.get_dicts(keys=('module', 'hook_type'))
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self.feature_hooks = FeatureHooks(hooks, self.named_modules())
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@torch.jit.ignore
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def set_grad_checkpointing(self, enable: bool = True):
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self.grad_checkpointing = enable
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def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
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x = self.conv_stem(x)
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x = self.bn1(x)
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x = self.act1(x)
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if self.feature_hooks is None:
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features = []
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if 0 in self._stage_out_idx:
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features.append(x) # add stem out
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for i, b in enumerate(self.blocks):
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if self.grad_checkpointing and not torch.jit.is_scripting():
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x = checkpoint(b, x)
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else:
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x = b(x)
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if i + 1 in self._stage_out_idx:
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features.append(x)
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return features
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else:
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self.blocks(x)
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out = self.feature_hooks.get_output(x.device)
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return list(out.values())
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def _create_mnv3(variant: str, pretrained: bool = False, **kwargs) -> MobileNetV3:
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features_mode = ''
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model_cls = MobileNetV3
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kwargs_filter = None
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if kwargs.pop('features_only', False):
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if 'feature_cfg' in kwargs or 'feature_cls' in kwargs:
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features_mode = 'cfg'
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else:
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kwargs_filter = ('num_classes', 'num_features', 'head_conv', 'head_bias', 'head_norm', 'global_pool')
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model_cls = MobileNetV3Features
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features_mode = 'cls'
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model = build_model_with_cfg(
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model_cls,
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variant,
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pretrained,
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features_only=features_mode == 'cfg',
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pretrained_strict=features_mode != 'cls',
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kwargs_filter=kwargs_filter,
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**kwargs,
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)
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if features_mode == 'cls':
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model.default_cfg = pretrained_cfg_for_features(model.default_cfg)
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return model
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def _gen_mobilenet_v3_rw(
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variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs
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) -> MobileNetV3:
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"""Creates a MobileNet-V3 model.
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Ref impl: ?
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Paper: https://arxiv.org/abs/1905.02244
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Args:
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channel_multiplier: multiplier to number of channels per layer.
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"""
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arch_def = [
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# stage 0, 112x112 in
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['ds_r1_k3_s1_e1_c16_nre_noskip'], # relu
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# stage 1, 112x112 in
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['ir_r1_k3_s2_e4_c24_nre', 'ir_r1_k3_s1_e3_c24_nre'], # relu
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# stage 2, 56x56 in
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['ir_r3_k5_s2_e3_c40_se0.25_nre'], # relu
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# stage 3, 28x28 in
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['ir_r1_k3_s2_e6_c80', 'ir_r1_k3_s1_e2.5_c80', 'ir_r2_k3_s1_e2.3_c80'], # hard-swish
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# stage 4, 14x14in
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['ir_r2_k3_s1_e6_c112_se0.25'], # hard-swish
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# stage 5, 14x14in
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['ir_r3_k5_s2_e6_c160_se0.25'], # hard-swish
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# stage 6, 7x7 in
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['cn_r1_k1_s1_c960'], # hard-swish
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]
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model_kwargs = dict(
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block_args=decode_arch_def(arch_def),
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head_bias=False,
|
|
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
|
|
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
|
act_layer=resolve_act_layer(kwargs, 'hard_swish'),
|
|
se_layer=partial(SqueezeExcite, gate_layer='hard_sigmoid'),
|
|
**kwargs,
|
|
)
|
|
model = _create_mnv3(variant, pretrained, **model_kwargs)
|
|
return model
|
|
|
|
|
|
def _gen_mobilenet_v3(
|
|
variant: str, channel_multiplier: float = 1.0, depth_multiplier: float = 1.0,
|
|
group_size=None, pretrained: bool = False, **kwargs
|
|
) -> MobileNetV3:
|
|
"""Creates a MobileNet-V3 model.
|
|
|
|
Ref impl: ?
|
|
Paper: https://arxiv.org/abs/1905.02244
|
|
|
|
Args:
|
|
channel_multiplier: multiplier to number of channels per layer.
|
|
"""
|
|
if 'small' in variant:
|
|
num_features = 1024
|
|
if 'minimal' in variant:
|
|
act_layer = resolve_act_layer(kwargs, 'relu')
|
|
arch_def = [
|
|
# stage 0, 112x112 in
|
|
['ds_r1_k3_s2_e1_c16'],
|
|
# stage 1, 56x56 in
|
|
['ir_r1_k3_s2_e4.5_c24', 'ir_r1_k3_s1_e3.67_c24'],
|
|
# stage 2, 28x28 in
|
|
['ir_r1_k3_s2_e4_c40', 'ir_r2_k3_s1_e6_c40'],
|
|
# stage 3, 14x14 in
|
|
['ir_r2_k3_s1_e3_c48'],
|
|
# stage 4, 14x14in
|
|
['ir_r3_k3_s2_e6_c96'],
|
|
# stage 6, 7x7 in
|
|
['cn_r1_k1_s1_c576'],
|
|
]
|
|
else:
|
|
act_layer = resolve_act_layer(kwargs, 'hard_swish')
|
|
arch_def = [
|
|
# stage 0, 112x112 in
|
|
['ds_r1_k3_s2_e1_c16_se0.25_nre'], # relu
|
|
# stage 1, 56x56 in
|
|
['ir_r1_k3_s2_e4.5_c24_nre', 'ir_r1_k3_s1_e3.67_c24_nre'], # relu
|
|
# stage 2, 28x28 in
|
|
['ir_r1_k5_s2_e4_c40_se0.25', 'ir_r2_k5_s1_e6_c40_se0.25'], # hard-swish
|
|
# stage 3, 14x14 in
|
|
['ir_r2_k5_s1_e3_c48_se0.25'], # hard-swish
|
|
# stage 4, 14x14in
|
|
['ir_r3_k5_s2_e6_c96_se0.25'], # hard-swish
|
|
# stage 6, 7x7 in
|
|
['cn_r1_k1_s1_c576'], # hard-swish
|
|
]
|
|
else:
|
|
num_features = 1280
|
|
if 'minimal' in variant:
|
|
act_layer = resolve_act_layer(kwargs, 'relu')
|
|
arch_def = [
|
|
# stage 0, 112x112 in
|
|
['ds_r1_k3_s1_e1_c16'],
|
|
# stage 1, 112x112 in
|
|
['ir_r1_k3_s2_e4_c24', 'ir_r1_k3_s1_e3_c24'],
|
|
# stage 2, 56x56 in
|
|
['ir_r3_k3_s2_e3_c40'],
|
|
# stage 3, 28x28 in
|
|
['ir_r1_k3_s2_e6_c80', 'ir_r1_k3_s1_e2.5_c80', 'ir_r2_k3_s1_e2.3_c80'],
|
|
# stage 4, 14x14in
|
|
['ir_r2_k3_s1_e6_c112'],
|
|
# stage 5, 14x14in
|
|
['ir_r3_k3_s2_e6_c160'],
|
|
# stage 6, 7x7 in
|
|
['cn_r1_k1_s1_c960'],
|
|
]
|
|
else:
|
|
act_layer = resolve_act_layer(kwargs, 'hard_swish')
|
|
arch_def = [
|
|
# stage 0, 112x112 in
|
|
['ds_r1_k3_s1_e1_c16_nre'], # relu
|
|
# stage 1, 112x112 in
|
|
['ir_r1_k3_s2_e4_c24_nre', 'ir_r1_k3_s1_e3_c24_nre'], # relu
|
|
# stage 2, 56x56 in
|
|
['ir_r3_k5_s2_e3_c40_se0.25_nre'], # relu
|
|
# stage 3, 28x28 in
|
|
['ir_r1_k3_s2_e6_c80', 'ir_r1_k3_s1_e2.5_c80', 'ir_r2_k3_s1_e2.3_c80'], # hard-swish
|
|
# stage 4, 14x14in
|
|
['ir_r2_k3_s1_e6_c112_se0.25'], # hard-swish
|
|
# stage 5, 14x14in
|
|
['ir_r3_k5_s2_e6_c160_se0.25'], # hard-swish
|
|
# stage 6, 7x7 in
|
|
['cn_r1_k1_s1_c960'], # hard-swish
|
|
]
|
|
se_layer = partial(SqueezeExcite, gate_layer='hard_sigmoid', force_act_layer=nn.ReLU, rd_round_fn=round_channels)
|
|
model_kwargs = dict(
|
|
block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, group_size=group_size),
|
|
num_features=num_features,
|
|
stem_size=16,
|
|
fix_stem=channel_multiplier < 0.75,
|
|
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
|
|
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
|
act_layer=act_layer,
|
|
se_layer=se_layer,
|
|
**kwargs,
|
|
)
|
|
model = _create_mnv3(variant, pretrained, **model_kwargs)
|
|
return model
|
|
|
|
|
|
def _gen_fbnetv3(variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs):
|
|
""" FBNetV3
|
|
Paper: `FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining`
|
|
- https://arxiv.org/abs/2006.02049
|
|
FIXME untested, this is a preliminary impl of some FBNet-V3 variants.
|
|
"""
|
|
vl = variant.split('_')[-1]
|
|
if vl in ('a', 'b'):
|
|
stem_size = 16
|
|
arch_def = [
|
|
['ds_r2_k3_s1_e1_c16'],
|
|
['ir_r1_k5_s2_e4_c24', 'ir_r3_k5_s1_e2_c24'],
|
|
['ir_r1_k5_s2_e5_c40_se0.25', 'ir_r4_k5_s1_e3_c40_se0.25'],
|
|
['ir_r1_k5_s2_e5_c72', 'ir_r4_k3_s1_e3_c72'],
|
|
['ir_r1_k3_s1_e5_c120_se0.25', 'ir_r5_k5_s1_e3_c120_se0.25'],
|
|
['ir_r1_k3_s2_e6_c184_se0.25', 'ir_r5_k5_s1_e4_c184_se0.25', 'ir_r1_k5_s1_e6_c224_se0.25'],
|
|
['cn_r1_k1_s1_c1344'],
|
|
]
|
|
elif vl == 'd':
|
|
stem_size = 24
|
|
arch_def = [
|
|
['ds_r2_k3_s1_e1_c16'],
|
|
['ir_r1_k3_s2_e5_c24', 'ir_r5_k3_s1_e2_c24'],
|
|
['ir_r1_k5_s2_e4_c40_se0.25', 'ir_r4_k3_s1_e3_c40_se0.25'],
|
|
['ir_r1_k3_s2_e5_c72', 'ir_r4_k3_s1_e3_c72'],
|
|
['ir_r1_k3_s1_e5_c128_se0.25', 'ir_r6_k5_s1_e3_c128_se0.25'],
|
|
['ir_r1_k3_s2_e6_c208_se0.25', 'ir_r5_k5_s1_e5_c208_se0.25', 'ir_r1_k5_s1_e6_c240_se0.25'],
|
|
['cn_r1_k1_s1_c1440'],
|
|
]
|
|
elif vl == 'g':
|
|
stem_size = 32
|
|
arch_def = [
|
|
['ds_r3_k3_s1_e1_c24'],
|
|
['ir_r1_k5_s2_e4_c40', 'ir_r4_k5_s1_e2_c40'],
|
|
['ir_r1_k5_s2_e4_c56_se0.25', 'ir_r4_k5_s1_e3_c56_se0.25'],
|
|
['ir_r1_k5_s2_e5_c104', 'ir_r4_k3_s1_e3_c104'],
|
|
['ir_r1_k3_s1_e5_c160_se0.25', 'ir_r8_k5_s1_e3_c160_se0.25'],
|
|
['ir_r1_k3_s2_e6_c264_se0.25', 'ir_r6_k5_s1_e5_c264_se0.25', 'ir_r2_k5_s1_e6_c288_se0.25'],
|
|
['cn_r1_k1_s1_c1728'],
|
|
]
|
|
else:
|
|
raise NotImplemented
|
|
round_chs_fn = partial(round_channels, multiplier=channel_multiplier, round_limit=0.95)
|
|
se_layer = partial(SqueezeExcite, gate_layer='hard_sigmoid', rd_round_fn=round_chs_fn)
|
|
act_layer = resolve_act_layer(kwargs, 'hard_swish')
|
|
model_kwargs = dict(
|
|
block_args=decode_arch_def(arch_def),
|
|
num_features=1984,
|
|
head_bias=False,
|
|
stem_size=stem_size,
|
|
round_chs_fn=round_chs_fn,
|
|
se_from_exp=False,
|
|
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
|
act_layer=act_layer,
|
|
se_layer=se_layer,
|
|
**kwargs,
|
|
)
|
|
model = _create_mnv3(variant, pretrained, **model_kwargs)
|
|
return model
|
|
|
|
|
|
def _gen_lcnet(variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs):
|
|
""" LCNet
|
|
Essentially a MobileNet-V3 crossed with a MobileNet-V1
|
|
|
|
Paper: `PP-LCNet: A Lightweight CPU Convolutional Neural Network` - https://arxiv.org/abs/2109.15099
|
|
|
|
Args:
|
|
channel_multiplier: multiplier to number of channels per layer.
|
|
"""
|
|
arch_def = [
|
|
# stage 0, 112x112 in
|
|
['dsa_r1_k3_s1_c32'],
|
|
# stage 1, 112x112 in
|
|
['dsa_r2_k3_s2_c64'],
|
|
# stage 2, 56x56 in
|
|
['dsa_r2_k3_s2_c128'],
|
|
# stage 3, 28x28 in
|
|
['dsa_r1_k3_s2_c256', 'dsa_r1_k5_s1_c256'],
|
|
# stage 4, 14x14in
|
|
['dsa_r4_k5_s1_c256'],
|
|
# stage 5, 14x14in
|
|
['dsa_r2_k5_s2_c512_se0.25'],
|
|
# 7x7
|
|
]
|
|
model_kwargs = dict(
|
|
block_args=decode_arch_def(arch_def),
|
|
stem_size=16,
|
|
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
|
|
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
|
act_layer=resolve_act_layer(kwargs, 'hard_swish'),
|
|
se_layer=partial(SqueezeExcite, gate_layer='hard_sigmoid', force_act_layer=nn.ReLU),
|
|
num_features=1280,
|
|
**kwargs,
|
|
)
|
|
model = _create_mnv3(variant, pretrained, **model_kwargs)
|
|
return model
|
|
|
|
|
|
def _gen_mobilenet_v4(
|
|
variant: str, channel_multiplier: float = 1.0, group_size=None, pretrained: bool = False, **kwargs,
|
|
) -> MobileNetV3:
|
|
"""Creates a MobileNet-V4 model.
|
|
|
|
Ref impl: ?
|
|
Paper: https://arxiv.org/abs/1905.02244
|
|
|
|
Args:
|
|
channel_multiplier: multiplier to number of channels per layer.
|
|
"""
|
|
num_features = 1280
|
|
if 'hybrid' in variant:
|
|
layer_scale_init_value = 1e-5
|
|
if 'medium' in variant:
|
|
stem_size = 32
|
|
act_layer = resolve_act_layer(kwargs, 'relu')
|
|
arch_def = [
|
|
# stage 0, 112x112 in
|
|
[
|
|
'er_r1_k3_s2_e4_c48' # FusedIB (EdgeResidual)
|
|
],
|
|
# stage 1, 56x56 in
|
|
[
|
|
'uir_r1_a3_k5_s2_e4_c80', # ExtraDW
|
|
'uir_r1_a3_k3_s1_e2_c80', # ExtraDW
|
|
],
|
|
# stage 2, 28x28 in
|
|
[
|
|
'uir_r1_a3_k5_s2_e6_c160', # ExtraDW
|
|
'uir_r1_a0_k0_s1_e2_c160', # FFN
|
|
'uir_r1_a3_k3_s1_e4_c160', # ExtraDW
|
|
'uir_r1_a3_k5_s1_e4_c160', # ExtraDW
|
|
'mqa_r1_k3_h4_s1_v2_d64_c160', # MQA w/ KV downsample
|
|
'uir_r1_a3_k3_s1_e4_c160', # ExtraDW
|
|
'mqa_r1_k3_h4_s1_v2_d64_c160', # MQA w/ KV downsample
|
|
'uir_r1_a3_k0_s1_e4_c160', # ConvNeXt
|
|
'mqa_r1_k3_h4_s1_v2_d64_c160', # MQA w/ KV downsample
|
|
'uir_r1_a3_k3_s1_e4_c160', # ExtraDW
|
|
'mqa_r1_k3_h4_s1_v2_d64_c160', # MQA w/ KV downsample
|
|
'uir_r1_a3_k0_s1_e4_c160', # ConvNeXt
|
|
],
|
|
# stage 3, 14x14in
|
|
[
|
|
'uir_r1_a5_k5_s2_e6_c256', # ExtraDW
|
|
'uir_r1_a5_k5_s1_e4_c256', # ExtraDW
|
|
'uir_r2_a3_k5_s1_e4_c256', # ExtraDW
|
|
'uir_r1_a0_k0_s1_e2_c256', # FFN
|
|
'uir_r1_a3_k5_s1_e2_c256', # ExtraDW
|
|
'uir_r1_a0_k0_s1_e2_c256', # FFN
|
|
'uir_r1_a0_k0_s1_e4_c256', # FFN
|
|
'mqa_r1_k3_h4_s1_d64_c256', # MQA
|
|
'uir_r1_a3_k0_s1_e4_c256', # ConvNeXt
|
|
'mqa_r1_k3_h4_s1_d64_c256', # MQA
|
|
'uir_r1_a5_k5_s1_e4_c256', # ExtraDW
|
|
'mqa_r1_k3_h4_s1_d64_c256', # MQA
|
|
'uir_r1_a5_k0_s1_e4_c256', # ConvNeXt
|
|
'mqa_r1_k3_h4_s1_d64_c256', # MQA
|
|
'uir_r1_a5_k0_s1_e4_c256', # ConvNeXt
|
|
],
|
|
# stage 4, 7x7 in
|
|
[
|
|
'cn_r1_k1_s1_c960' # Conv
|
|
],
|
|
]
|
|
elif 'large' in variant:
|
|
stem_size = 24
|
|
act_layer = resolve_act_layer(kwargs, 'gelu')
|
|
arch_def = [
|
|
# stage 0, 112x112 in
|
|
[
|
|
'er_r1_k3_s2_e4_c48', # FusedIB (EdgeResidual)
|
|
],
|
|
# stage 1, 56x56 in
|
|
[
|
|
'uir_r1_a3_k5_s2_e4_c96', # ExtraDW
|
|
'uir_r1_a3_k3_s1_e4_c96', # ExtraDW
|
|
],
|
|
# stage 2, 28x28 in
|
|
[
|
|
'uir_r1_a3_k5_s2_e4_c192', # ExtraDW
|
|
'uir_r3_a3_k3_s1_e4_c192', # ExtraDW
|
|
'uir_r1_a3_k5_s1_e4_c192', # ExtraDW
|
|
'uir_r2_a5_k3_s1_e4_c192', # ExtraDW
|
|
'mqa_r1_k3_h8_s1_v2_d48_c192', # MQA w/ KV downsample
|
|
'uir_r1_a5_k3_s1_e4_c192', # ExtraDW
|
|
'mqa_r1_k3_h8_s1_v2_d48_c192', # MQA w/ KV downsample
|
|
'uir_r1_a5_k3_s1_e4_c192', # ExtraDW
|
|
'mqa_r1_k3_h8_s1_v2_d48_c192', # MQA w/ KV downsample
|
|
'uir_r1_a5_k3_s1_e4_c192', # ExtraDW
|
|
'mqa_r1_k3_h8_s1_v2_d48_c192', # MQA w/ KV downsample
|
|
'uir_r1_a3_k0_s1_e4_c192', # ConvNeXt
|
|
],
|
|
# stage 3, 14x14in
|
|
[
|
|
'uir_r4_a5_k5_s2_e4_c512', # ExtraDW
|
|
'uir_r1_a5_k0_s1_e4_c512', # ConvNeXt
|
|
'uir_r1_a5_k3_s1_e4_c512', # ExtraDW
|
|
'uir_r2_a5_k0_s1_e4_c512', # ConvNeXt
|
|
'uir_r1_a5_k3_s1_e4_c512', # ExtraDW
|
|
'uir_r1_a5_k5_s1_e4_c512', # ExtraDW
|
|
'mqa_r1_k3_h8_s1_d64_c512', # MQA
|
|
'uir_r1_a5_k0_s1_e4_c512', # ConvNeXt
|
|
'mqa_r1_k3_h8_s1_d64_c512', # MQA
|
|
'uir_r1_a5_k0_s1_e4_c512', # ConvNeXt
|
|
'mqa_r1_k3_h8_s1_d64_c512', # MQA
|
|
'uir_r1_a5_k0_s1_e4_c512', # ConvNeXt
|
|
'mqa_r1_k3_h8_s1_d64_c512', # MQA
|
|
'uir_r1_a5_k0_s1_e4_c512', # ConvNeXt
|
|
],
|
|
# stage 4, 7x7 in
|
|
[
|
|
'cn_r1_k1_s1_c960', # Conv
|
|
],
|
|
]
|
|
else:
|
|
assert False, f'Unknown variant {variant}.'
|
|
else:
|
|
layer_scale_init_value = None
|
|
if 'small' in variant:
|
|
stem_size = 32
|
|
act_layer = resolve_act_layer(kwargs, 'relu')
|
|
arch_def = [
|
|
# stage 0, 112x112 in
|
|
[
|
|
'cn_r1_k3_s2_e1_c32', # Conv
|
|
'cn_r1_k1_s1_e1_c32', # Conv
|
|
],
|
|
# stage 1, 56x56 in
|
|
[
|
|
'cn_r1_k3_s2_e1_c96', # Conv
|
|
'cn_r1_k1_s1_e1_c64', # Conv
|
|
],
|
|
# stage 2, 28x28 in
|
|
[
|
|
'uir_r1_a5_k5_s2_e3_c96', # ExtraDW
|
|
'uir_r4_a0_k3_s1_e2_c96', # IR
|
|
'uir_r1_a3_k0_s1_e4_c96', # ConvNeXt
|
|
],
|
|
# stage 3, 14x14 in
|
|
[
|
|
'uir_r1_a3_k3_s2_e6_c128', # ExtraDW
|
|
'uir_r1_a5_k5_s1_e4_c128', # ExtraDW
|
|
'uir_r1_a0_k5_s1_e4_c128', # IR
|
|
'uir_r1_a0_k5_s1_e3_c128', # IR
|
|
'uir_r2_a0_k3_s1_e4_c128', # IR
|
|
],
|
|
# stage 4, 7x7 in
|
|
[
|
|
'cn_r1_k1_s1_c960', # Conv
|
|
],
|
|
]
|
|
elif 'medium' in variant:
|
|
stem_size = 32
|
|
act_layer = resolve_act_layer(kwargs, 'relu')
|
|
arch_def = [
|
|
# stage 0, 112x112 in
|
|
[
|
|
'er_r1_k3_s2_e4_c48', # FusedIB (EdgeResidual)
|
|
],
|
|
# stage 1, 56x56 in
|
|
[
|
|
'uir_r1_a3_k5_s2_e4_c80', # ExtraDW
|
|
'uir_r1_a3_k3_s1_e2_c80', # ExtraDW
|
|
],
|
|
# stage 2, 28x28 in
|
|
[
|
|
'uir_r1_a3_k5_s2_e6_c160', # ExtraDW
|
|
'uir_r2_a3_k3_s1_e4_c160', # ExtraDW
|
|
'uir_r1_a3_k5_s1_e4_c160', # ExtraDW
|
|
'uir_r1_a3_k3_s1_e4_c160', # ExtraDW
|
|
'uir_r1_a3_k0_s1_e4_c160', # ConvNeXt
|
|
'uir_r1_a0_k0_s1_e2_c160', # ExtraDW
|
|
'uir_r1_a3_k0_s1_e4_c160', # ConvNeXt
|
|
],
|
|
# stage 3, 14x14in
|
|
[
|
|
'uir_r1_a5_k5_s2_e6_c256', # ExtraDW
|
|
'uir_r1_a5_k5_s1_e4_c256', # ExtraDW
|
|
'uir_r2_a3_k5_s1_e4_c256', # ExtraDW
|
|
'uir_r1_a0_k0_s1_e4_c256', # FFN
|
|
'uir_r1_a3_k0_s1_e4_c256', # ConvNeXt
|
|
'uir_r1_a3_k5_s1_e2_c256', # ExtraDW
|
|
'uir_r1_a5_k5_s1_e4_c256', # ExtraDW
|
|
'uir_r2_a0_k0_s1_e4_c256', # FFN
|
|
'uir_r1_a5_k0_s1_e2_c256', # ConvNeXt
|
|
],
|
|
# stage 4, 7x7 in
|
|
[
|
|
'cn_r1_k1_s1_c960', # Conv
|
|
],
|
|
]
|
|
elif 'large' in variant:
|
|
stem_size = 24
|
|
act_layer = resolve_act_layer(kwargs, 'relu')
|
|
arch_def = [
|
|
# stage 0, 112x112 in
|
|
[
|
|
'er_r1_k3_s2_e4_c48', # FusedIB (EdgeResidual)
|
|
],
|
|
# stage 1, 56x56 in
|
|
[
|
|
'uir_r1_a3_k5_s2_e4_c96', # ExtraDW
|
|
'uir_r1_a3_k3_s1_e4_c96', # ExtraDW
|
|
],
|
|
# stage 2, 28x28 in
|
|
[
|
|
'uir_r1_a3_k5_s2_e4_c192', # ExtraDW
|
|
'uir_r3_a3_k3_s1_e4_c192', # ExtraDW
|
|
'uir_r1_a3_k5_s1_e4_c192', # ExtraDW
|
|
'uir_r5_a5_k3_s1_e4_c192', # ExtraDW
|
|
'uir_r1_a3_k0_s1_e4_c192', # ConvNeXt
|
|
],
|
|
# stage 3, 14x14in
|
|
[
|
|
'uir_r4_a5_k5_s2_e4_c512', # ExtraDW
|
|
'uir_r1_a5_k0_s1_e4_c512', # ConvNeXt
|
|
'uir_r1_a5_k3_s1_e4_c512', # ExtraDW
|
|
'uir_r2_a5_k0_s1_e4_c512', # ConvNeXt
|
|
'uir_r1_a5_k3_s1_e4_c512', # ExtraDW
|
|
'uir_r1_a5_k5_s1_e4_c512', # ExtraDW
|
|
'uir_r3_a5_k0_s1_e4_c512', # ConvNeXt
|
|
|
|
],
|
|
# stage 4, 7x7 in
|
|
[
|
|
'cn_r1_k1_s1_c960', # Conv
|
|
],
|
|
]
|
|
else:
|
|
assert False, f'Unknown variant {variant}.'
|
|
|
|
model_kwargs = dict(
|
|
block_args=decode_arch_def(arch_def, group_size=group_size),
|
|
head_bias=False,
|
|
head_norm=True,
|
|
num_features=num_features,
|
|
stem_size=stem_size,
|
|
fix_stem=channel_multiplier < 1.0,
|
|
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
|
|
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
|
|
act_layer=act_layer,
|
|
layer_scale_init_value=layer_scale_init_value,
|
|
**kwargs,
|
|
)
|
|
model = _create_mnv3(variant, pretrained, **model_kwargs)
|
|
return model
|
|
|
|
|
|
def _cfg(url: str = '', **kwargs):
|
|
return {
|
|
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
|
|
'crop_pct': 0.875, 'interpolation': 'bilinear',
|
|
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
|
|
'first_conv': 'conv_stem', 'classifier': 'classifier',
|
|
**kwargs
|
|
}
|
|
|
|
|
|
default_cfgs = generate_default_cfgs({
|
|
'mobilenetv3_large_075.untrained': _cfg(url=''),
|
|
'mobilenetv3_large_100.ra_in1k': _cfg(
|
|
interpolation='bicubic',
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_large_100_ra-f55367f5.pth',
|
|
hf_hub_id='timm/'),
|
|
'mobilenetv3_large_100.ra4_e3600_r224_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
interpolation='bicubic', mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
|
|
crop_pct=0.95, test_input_size=(3, 256, 256), test_crop_pct=1.0),
|
|
'mobilenetv3_large_100.miil_in21k_ft_in1k': _cfg(
|
|
interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.),
|
|
origin_url='https://github.com/Alibaba-MIIL/ImageNet21K',
|
|
paper_ids='arXiv:2104.10972v4',
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mobilenetv3_large_100_1k_miil_78_0-66471c13.pth',
|
|
hf_hub_id='timm/'),
|
|
'mobilenetv3_large_100.miil_in21k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mobilenetv3_large_100_in21k_miil-d71cc17b.pth',
|
|
hf_hub_id='timm/',
|
|
origin_url='https://github.com/Alibaba-MIIL/ImageNet21K',
|
|
paper_ids='arXiv:2104.10972v4',
|
|
interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.), num_classes=11221),
|
|
'mobilenetv3_large_150d.ra4_e3600_r256_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
|
|
input_size=(3, 256, 256), crop_pct=0.95, pool_size=(8, 8), test_input_size=(3, 320, 320), test_crop_pct=1.0),
|
|
|
|
'mobilenetv3_small_050.lamb_in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_050_lambc-4b7bbe87.pth',
|
|
hf_hub_id='timm/',
|
|
interpolation='bicubic'),
|
|
'mobilenetv3_small_075.lamb_in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_075_lambc-384766db.pth',
|
|
hf_hub_id='timm/',
|
|
interpolation='bicubic'),
|
|
'mobilenetv3_small_100.lamb_in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_100_lamb-266a294c.pth',
|
|
hf_hub_id='timm/',
|
|
interpolation='bicubic'),
|
|
|
|
'mobilenetv3_rw.rmsp_in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth',
|
|
hf_hub_id='timm/',
|
|
interpolation='bicubic'),
|
|
|
|
'tf_mobilenetv3_large_075.in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_075-150ee8b0.pth',
|
|
hf_hub_id='timm/',
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
|
|
'tf_mobilenetv3_large_100.in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_100-427764d5.pth',
|
|
hf_hub_id='timm/',
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
|
|
'tf_mobilenetv3_large_minimal_100.in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_minimal_100-8596ae28.pth',
|
|
hf_hub_id='timm/',
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
|
|
'tf_mobilenetv3_small_075.in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_075-da427f52.pth',
|
|
hf_hub_id='timm/',
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
|
|
'tf_mobilenetv3_small_100.in1k': _cfg(
|
|
url= 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_100-37f49e2b.pth',
|
|
hf_hub_id='timm/',
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
|
|
'tf_mobilenetv3_small_minimal_100.in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_minimal_100-922a7843.pth',
|
|
hf_hub_id='timm/',
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
|
|
|
|
'fbnetv3_b.ra2_in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_b_224-ead5d2a1.pth',
|
|
hf_hub_id='timm/',
|
|
test_input_size=(3, 256, 256), crop_pct=0.95),
|
|
'fbnetv3_d.ra2_in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_d_224-c98bce42.pth',
|
|
hf_hub_id='timm/',
|
|
test_input_size=(3, 256, 256), crop_pct=0.95),
|
|
'fbnetv3_g.ra2_in1k': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_g_240-0b1df83b.pth',
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 240, 240), test_input_size=(3, 288, 288), crop_pct=0.95, pool_size=(8, 8)),
|
|
|
|
"lcnet_035.untrained": _cfg(),
|
|
"lcnet_050.ra2_in1k": _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_050-f447553b.pth',
|
|
hf_hub_id='timm/',
|
|
interpolation='bicubic',
|
|
),
|
|
"lcnet_075.ra2_in1k": _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_075-318cad2c.pth',
|
|
hf_hub_id='timm/',
|
|
interpolation='bicubic',
|
|
),
|
|
"lcnet_100.ra2_in1k": _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_100-a929038c.pth',
|
|
hf_hub_id='timm/',
|
|
interpolation='bicubic',
|
|
),
|
|
"lcnet_150.untrained": _cfg(),
|
|
|
|
'mobilenetv4_conv_small_035.untrained': _cfg(
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
|
|
test_input_size=(3, 256, 256), test_crop_pct=0.95, interpolation='bicubic'),
|
|
'mobilenetv4_conv_small_050.e3000_r224_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
|
|
test_input_size=(3, 256, 256), test_crop_pct=0.95, interpolation='bicubic'),
|
|
'mobilenetv4_conv_small.e2400_r224_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
test_input_size=(3, 256, 256), test_crop_pct=0.95, interpolation='bicubic'),
|
|
'mobilenetv4_conv_small.e1200_r224_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
test_input_size=(3, 256, 256), test_crop_pct=0.95, interpolation='bicubic'),
|
|
'mobilenetv4_conv_medium.e500_r256_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 256, 256), pool_size=(8, 8),
|
|
crop_pct=0.95, test_input_size=(3, 320, 320), test_crop_pct=1.0, interpolation='bicubic'),
|
|
'mobilenetv4_conv_medium.e500_r224_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
crop_pct=0.95, test_input_size=(3, 256, 256), test_crop_pct=1.0, interpolation='bicubic'),
|
|
'mobilenetv4_conv_large.e600_r384_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 384, 384), pool_size=(12, 12),
|
|
crop_pct=0.95, test_input_size=(3, 448, 448), test_crop_pct=1.0, interpolation='bicubic'),
|
|
'mobilenetv4_conv_large.e500_r256_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 256, 256), pool_size=(8, 8),
|
|
crop_pct=0.95, test_input_size=(3, 320, 320), test_crop_pct=1.0, interpolation='bicubic'),
|
|
|
|
'mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 256, 256), pool_size=(8, 8),
|
|
crop_pct=0.95, test_input_size=(3, 320, 320), test_crop_pct=1.0, interpolation='bicubic'),
|
|
'mobilenetv4_hybrid_medium.ix_e550_r256_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 256, 256), pool_size=(8, 8),
|
|
crop_pct=0.95, test_input_size=(3, 320, 320), test_crop_pct=1.0, interpolation='bicubic'),
|
|
'mobilenetv4_hybrid_medium.ix_e550_r384_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 384, 384), pool_size=(12, 12),
|
|
crop_pct=0.95, test_input_size=(3, 448, 448), test_crop_pct=1.0, interpolation='bicubic'),
|
|
'mobilenetv4_hybrid_medium.e500_r224_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
crop_pct=0.95, test_input_size=(3, 256, 256), test_crop_pct=1.0, interpolation='bicubic'),
|
|
'mobilenetv4_hybrid_medium.e200_r256_in12k': _cfg(
|
|
hf_hub_id='timm/',
|
|
num_classes=11821,
|
|
input_size=(3, 256, 256), pool_size=(8, 8),
|
|
crop_pct=0.95, test_input_size=(3, 320, 320), test_crop_pct=1.0, interpolation='bicubic'),
|
|
'mobilenetv4_hybrid_large.ix_e600_r384_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 384, 384), pool_size=(12, 12),
|
|
crop_pct=0.95, test_input_size=(3, 448, 448), test_crop_pct=1.0, interpolation='bicubic'),
|
|
'mobilenetv4_hybrid_large.e600_r384_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 384, 384), pool_size=(12, 12),
|
|
crop_pct=0.95, test_input_size=(3, 448, 448), test_crop_pct=1.0, interpolation='bicubic'),
|
|
|
|
# experimental
|
|
'mobilenetv4_conv_aa_medium.untrained': _cfg(
|
|
# hf_hub_id='timm/',
|
|
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.95, interpolation='bicubic'),
|
|
'mobilenetv4_conv_blur_medium.e500_r224_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
crop_pct=0.95, test_input_size=(3, 256, 256), test_crop_pct=1.0, interpolation='bicubic'),
|
|
'mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 448, 448), pool_size=(14, 14),
|
|
crop_pct=0.95, test_input_size=(3, 544, 544), test_crop_pct=1.0, interpolation='bicubic'),
|
|
'mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 384, 384), pool_size=(12, 12),
|
|
crop_pct=0.95, test_input_size=(3, 480, 480), test_crop_pct=1.0, interpolation='bicubic'),
|
|
'mobilenetv4_conv_aa_large.e600_r384_in1k': _cfg(
|
|
hf_hub_id='timm/',
|
|
input_size=(3, 384, 384), pool_size=(12, 12),
|
|
crop_pct=0.95, test_input_size=(3, 480, 480), test_crop_pct=1.0, interpolation='bicubic'),
|
|
'mobilenetv4_conv_aa_large.e230_r384_in12k': _cfg(
|
|
hf_hub_id='timm/',
|
|
num_classes=11821,
|
|
input_size=(3, 384, 384), pool_size=(12, 12),
|
|
crop_pct=0.95, test_input_size=(3, 448, 448), test_crop_pct=1.0, interpolation='bicubic'),
|
|
'mobilenetv4_hybrid_medium_075.untrained': _cfg(
|
|
# hf_hub_id='timm/',
|
|
crop_pct=0.95, interpolation='bicubic'),
|
|
'mobilenetv4_hybrid_large_075.untrained': _cfg(
|
|
# hf_hub_id='timm/',
|
|
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.95, interpolation='bicubic'),
|
|
})
|
|
|
|
|
|
@register_model
|
|
def mobilenetv3_large_075(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V3 """
|
|
model = _gen_mobilenet_v3('mobilenetv3_large_075', 0.75, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mobilenetv3_large_100(pretrained: bool = False, **kwargs) -> MobileNetV3:
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""" MobileNet V3 """
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model = _gen_mobilenet_v3('mobilenetv3_large_100', 1.0, pretrained=pretrained, **kwargs)
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return model
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@register_model
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def mobilenetv3_large_150d(pretrained: bool = False, **kwargs) -> MobileNetV3:
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""" MobileNet V3 """
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|
model = _gen_mobilenet_v3('mobilenetv3_large_150d', 1.5, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
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return model
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@register_model
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def mobilenetv3_small_050(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V3 """
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|
model = _gen_mobilenet_v3('mobilenetv3_small_050', 0.50, pretrained=pretrained, **kwargs)
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return model
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@register_model
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def mobilenetv3_small_075(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V3 """
|
|
model = _gen_mobilenet_v3('mobilenetv3_small_075', 0.75, pretrained=pretrained, **kwargs)
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return model
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|
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@register_model
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|
def mobilenetv3_small_100(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V3 """
|
|
model = _gen_mobilenet_v3('mobilenetv3_small_100', 1.0, pretrained=pretrained, **kwargs)
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|
return model
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@register_model
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|
def mobilenetv3_rw(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V3 """
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|
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
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model = _gen_mobilenet_v3_rw('mobilenetv3_rw', 1.0, pretrained=pretrained, **kwargs)
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return model
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@register_model
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|
def tf_mobilenetv3_large_075(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V3 """
|
|
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
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|
kwargs.setdefault('pad_type', 'same')
|
|
model = _gen_mobilenet_v3('tf_mobilenetv3_large_075', 0.75, pretrained=pretrained, **kwargs)
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|
return model
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|
|
|
|
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@register_model
|
|
def tf_mobilenetv3_large_100(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V3 """
|
|
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
|
|
kwargs.setdefault('pad_type', 'same')
|
|
model = _gen_mobilenet_v3('tf_mobilenetv3_large_100', 1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_mobilenetv3_large_minimal_100(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V3 """
|
|
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
|
|
kwargs.setdefault('pad_type', 'same')
|
|
model = _gen_mobilenet_v3('tf_mobilenetv3_large_minimal_100', 1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_mobilenetv3_small_075(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V3 """
|
|
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
|
|
kwargs.setdefault('pad_type', 'same')
|
|
model = _gen_mobilenet_v3('tf_mobilenetv3_small_075', 0.75, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_mobilenetv3_small_100(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V3 """
|
|
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
|
|
kwargs.setdefault('pad_type', 'same')
|
|
model = _gen_mobilenet_v3('tf_mobilenetv3_small_100', 1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def tf_mobilenetv3_small_minimal_100(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V3 """
|
|
kwargs.setdefault('bn_eps', BN_EPS_TF_DEFAULT)
|
|
kwargs.setdefault('pad_type', 'same')
|
|
model = _gen_mobilenet_v3('tf_mobilenetv3_small_minimal_100', 1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def fbnetv3_b(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" FBNetV3-B """
|
|
model = _gen_fbnetv3('fbnetv3_b', pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def fbnetv3_d(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" FBNetV3-D """
|
|
model = _gen_fbnetv3('fbnetv3_d', pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def fbnetv3_g(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" FBNetV3-G """
|
|
model = _gen_fbnetv3('fbnetv3_g', pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def lcnet_035(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" PP-LCNet 0.35"""
|
|
model = _gen_lcnet('lcnet_035', 0.35, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def lcnet_050(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" PP-LCNet 0.5"""
|
|
model = _gen_lcnet('lcnet_050', 0.5, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def lcnet_075(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" PP-LCNet 1.0"""
|
|
model = _gen_lcnet('lcnet_075', 0.75, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def lcnet_100(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" PP-LCNet 1.0"""
|
|
model = _gen_lcnet('lcnet_100', 1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def lcnet_150(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" PP-LCNet 1.5"""
|
|
model = _gen_lcnet('lcnet_150', 1.5, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mobilenetv4_conv_small_035(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V4 """
|
|
model = _gen_mobilenet_v4('mobilenetv4_conv_small_035', 0.35, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mobilenetv4_conv_small_050(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V4 """
|
|
model = _gen_mobilenet_v4('mobilenetv4_conv_small_050', 0.50, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mobilenetv4_conv_small(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V4 """
|
|
model = _gen_mobilenet_v4('mobilenetv4_conv_small', 1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mobilenetv4_conv_medium(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V4 """
|
|
model = _gen_mobilenet_v4('mobilenetv4_conv_medium', 1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mobilenetv4_conv_large(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V4 """
|
|
model = _gen_mobilenet_v4('mobilenetv4_conv_large', 1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mobilenetv4_hybrid_medium(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V4 Hybrid """
|
|
model = _gen_mobilenet_v4('mobilenetv4_hybrid_medium', 1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mobilenetv4_hybrid_large(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V4 Hybrid"""
|
|
model = _gen_mobilenet_v4('mobilenetv4_hybrid_large', 1.0, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mobilenetv4_conv_aa_medium(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V4 w/ AvgPool AA """
|
|
model = _gen_mobilenet_v4('mobilenetv4_conv_aa_medium', 1.0, pretrained=pretrained, aa_layer='avg', **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mobilenetv4_conv_blur_medium(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V4 Conv w/ Blur AA """
|
|
model = _gen_mobilenet_v4('mobilenetv4_conv_blur_medium', 1.0, pretrained=pretrained, aa_layer='blurpc', **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mobilenetv4_conv_aa_large(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V4 w/ AvgPool AA """
|
|
model = _gen_mobilenet_v4('mobilenetv4_conv_aa_large', 1.0, pretrained=pretrained, aa_layer='avg', **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mobilenetv4_hybrid_medium_075(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V4 Hybrid """
|
|
model = _gen_mobilenet_v4('mobilenetv4_hybrid_medium_075', 0.75, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def mobilenetv4_hybrid_large_075(pretrained: bool = False, **kwargs) -> MobileNetV3:
|
|
""" MobileNet V4 Hybrid"""
|
|
model = _gen_mobilenet_v4('mobilenetv4_hybrid_large_075', 0.75, pretrained=pretrained, **kwargs)
|
|
return model
|
|
|
|
|
|
register_model_deprecations(__name__, {
|
|
'mobilenetv3_large_100_miil': 'mobilenetv3_large_100.miil_in21k_ft_in1k',
|
|
'mobilenetv3_large_100_miil_in21k': 'mobilenetv3_large_100.miil_in21k',
|
|
})
|