""" MobileNet V3 A PyTorch impl of MobileNet-V3, compatible with TF weights from official impl. Paper: Searching for MobileNetV3 - https://arxiv.org/abs/1905.02244 Hacked together by Ross Wightman """ from .efficientnet_builder import * from .registry import register_model from .helpers import load_pretrained from .layers import SelectAdaptivePool2d, create_conv2d from .layers.activations import HardSwish, hard_sigmoid from .feature_hooks import FeatureHooks from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD __all__ = ['MobileNetV3'] def _cfg(url='', **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 = { 'mobilenetv3_large_075': _cfg(url='', interoplation='bicubic'), 'mobilenetv3_large_100': _cfg(url='', interoplation='bicubic'), 'mobilenetv3_small_075': _cfg(url='', interoplation='bicubic'), 'mobilenetv3_small_100': _cfg(url='', interoplation='bicubic'), 'mobilenetv3_eca_large': _cfg(url='', interoplation='bicubic'), 'xmobilenetv3_large_100': _cfg(url='', interoplation='bicubic'), 'mobilenetv3_rw': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth', interpolation='bicubic'), 'tf_mobilenetv3_large_075': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_075-150ee8b0.pth', mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), 'tf_mobilenetv3_large_100': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_100-427764d5.pth', mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), 'tf_mobilenetv3_large_minimal_100': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_minimal_100-8596ae28.pth', mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), 'tf_mobilenetv3_small_075': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_075-da427f52.pth', mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), 'tf_mobilenetv3_small_100': _cfg( url= 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_100-37f49e2b.pth', mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), 'tf_mobilenetv3_small_minimal_100': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_minimal_100-922a7843.pth', mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), } _DEBUG = True class MobileNetV3(nn.Module): """ MobiletNet-V3 Based on my EfficientNet implementation and building blocks, this model utilizes the MobileNet-v3 specific 'efficient head', where global pooling is done before the head convolution without a final batch-norm layer before the classifier. Paper: https://arxiv.org/abs/1905.02244 """ def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=16, num_features=1280, head_bias=True, channel_multiplier=1.0, pad_type='', act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0., attn_layer=None, attn_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None, global_pool='avg'): super(MobileNetV3, self).__init__() self.num_classes = num_classes self.num_features = num_features self.drop_rate = drop_rate self._in_chs = in_chans # Stem stem_size = round_channels(stem_size, channel_multiplier) self.conv_stem = create_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type) self.bn1 = norm_layer(stem_size, **norm_kwargs) self.act1 = act_layer(inplace=True) self._in_chs = stem_size # Middle stages (IR/ER/DS Blocks) builder = EfficientNetBuilder( channel_multiplier, 8, None, 32, pad_type, act_layer, attn_layer, attn_kwargs, norm_layer, norm_kwargs, drop_path_rate, verbose=_DEBUG) self.blocks = nn.Sequential(*builder(self._in_chs, block_args)) self.feature_info = builder.features self._in_chs = builder.in_chs # Head + Pooling self.global_pool = SelectAdaptivePool2d(pool_type=global_pool) self.conv_head = create_conv2d(self._in_chs, self.num_features, 1, padding=pad_type, bias=head_bias) self.act2 = act_layer(inplace=True) # Classifier self.classifier = nn.Linear(self.num_features * self.global_pool.feat_mult(), self.num_classes) efficientnet_init_weights(self) def as_sequential(self): layers = [self.conv_stem, self.bn1, self.act1] layers.extend(self.blocks) layers.extend([self.global_pool, self.conv_head, self.act2]) layers.extend([nn.Flatten(), nn.Dropout(self.drop_rate), self.classifier]) return nn.Sequential(*layers) def get_classifier(self): return self.classifier def reset_classifier(self, num_classes, global_pool='avg'): self.global_pool = SelectAdaptivePool2d(pool_type=global_pool) self.num_classes = num_classes self.classifier = nn.Linear( self.num_features * self.global_pool.feat_mult(), num_classes) if self.num_classes else None def forward_features(self, x): x = self.conv_stem(x) x = self.bn1(x) x = self.act1(x) x = self.blocks(x) x = self.global_pool(x) x = self.conv_head(x) x = self.act2(x) return x def forward(self, x): x = self.forward_features(x) x = x.flatten(1) if self.drop_rate > 0.: x = F.dropout(x, p=self.drop_rate, training=self.training) return self.classifier(x) class MobileNetV3Features(nn.Module): """ MobileNetV3 Feature Extractor A work-in-progress feature extraction module for MobileNet-V3 to use as a backbone for segmentation and object detection models. """ def __init__(self, block_args, out_indices=(0, 1, 2, 3, 4), feature_location='pre_pwl', in_chans=3, stem_size=16, channel_multiplier=1.0, output_stride=32, pad_type='', act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0., attn_layer=None, attn_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None): super(MobileNetV3Features, self).__init__() norm_kwargs = norm_kwargs or {} # TODO only create stages needed, currently all stages are created regardless of out_indices num_stages = max(out_indices) + 1 self.out_indices = out_indices self.drop_rate = drop_rate self._in_chs = in_chans # Stem stem_size = round_channels(stem_size, channel_multiplier) self.conv_stem = create_conv2d(self._in_chs, stem_size, 3, stride=2, padding=pad_type) self.bn1 = norm_layer(stem_size, **norm_kwargs) self.act1 = act_layer(inplace=True) self._in_chs = stem_size # Middle stages (IR/ER/DS Blocks) builder = EfficientNetBuilder( channel_multiplier, 8, None, output_stride, pad_type, act_layer, attn_layer, attn_kwargs, norm_layer, norm_kwargs, drop_path_rate, feature_location=feature_location, verbose=_DEBUG) self.blocks = nn.Sequential(*builder(self._in_chs, block_args)) self.feature_info = builder.features # builder provides info about feature channels for each block self._in_chs = builder.in_chs efficientnet_init_weights(self) if _DEBUG: for k, v in self.feature_info.items(): print('Feature idx: {}: Name: {}, Channels: {}'.format(k, v['name'], v['num_chs'])) # Register feature extraction hooks with FeatureHooks helper hook_type = 'forward_pre' if feature_location == 'pre_pwl' else 'forward' hooks = [dict(name=self.feature_info[idx]['name'], type=hook_type) for idx in out_indices] self.feature_hooks = FeatureHooks(hooks, self.named_modules()) def feature_channels(self, idx=None): """ Feature Channel Shortcut Returns feature channel count for each output index if idx == None. If idx is an integer, will return feature channel count for that feature block index (independent of out_indices setting). """ if isinstance(idx, int): return self.feature_info[idx]['num_chs'] return [self.feature_info[i]['num_chs'] for i in self.out_indices] def forward(self, x): x = self.conv_stem(x) x = self.bn1(x) x = self.act1(x) self.blocks(x) return self.feature_hooks.get_output(x.device) def _create_model(model_kwargs, default_cfg, pretrained=False): if model_kwargs.pop('features_only', False): load_strict = False model_kwargs.pop('num_classes', 0) model_kwargs.pop('num_features', 0) model_kwargs.pop('head_conv', None) model_class = MobileNetV3Features else: load_strict = True model_class = MobileNetV3 model = model_class(**model_kwargs) model.default_cfg = default_cfg if pretrained: load_pretrained( model, default_cfg, num_classes=model_kwargs.get('num_classes', 0), in_chans=model_kwargs.get('in_chans', 3), strict=load_strict) return model def _gen_mobilenet_v3_rw(variant, channel_multiplier=1.0, pretrained=False, **kwargs): """Creates a MobileNet-V3 model. Ref impl: ? Paper: https://arxiv.org/abs/1905.02244 Args: channel_multiplier: multiplier to number of channels per layer. """ arch_def = [ # stage 0, 112x112 in ['ds_r1_k3_s1_e1_c16_nre_noskip'], # 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 ] model_kwargs = dict( block_args=decode_arch_def(arch_def), head_bias=False, channel_multiplier=channel_multiplier, norm_kwargs=resolve_bn_args(kwargs), act_layer=HardSwish, attn_kwargs=dict(gate_fn=hard_sigmoid, reduce_mid=True, divisor=1), **kwargs, ) model = _create_model(model_kwargs, default_cfgs[variant], pretrained) return model def _gen_mobilenet_v3(variant, channel_multiplier=1.0, pretrained=False, **kwargs): """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 = nn.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 = HardSwish 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 = nn.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 = HardSwish 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 ] model_kwargs = dict( block_args=decode_arch_def(arch_def), num_features=num_features, stem_size=16, channel_multiplier=channel_multiplier, norm_kwargs=resolve_bn_args(kwargs), act_layer=act_layer, attn_kwargs=dict(act_layer=nn.ReLU, gate_fn=hard_sigmoid, reduce_mid=True, divisible_by=8), **kwargs, ) model = _create_model(model_kwargs, default_cfgs[variant], pretrained) return model def _gen_xmobilenet_v3(variant, channel_multiplier=1.0, pretrained=False, **kwargs): """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 = nn.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 = HardSwish 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 = nn.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 = HardSwish 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', 'xir_r2_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 ['xir_r2_k5_s1_e6_c112_se0.25'], # hard-swish # stage 5, 14x14in ['ir_r1_k5_s2_e6_c160_se0.25', 'xir_r2_k5_s2_e6_c160_se0.25'], # hard-swish # stage 6, 7x7 in ['cn_r1_k1_s1_c960'], # hard-swish ] model_kwargs = dict( block_args=decode_arch_def(arch_def), num_features=num_features, stem_size=16, channel_multiplier=channel_multiplier, norm_kwargs=resolve_bn_args(kwargs), act_layer=act_layer, attn_kwargs=dict(act_layer=nn.ReLU, gate_fn=hard_sigmoid, reduce_mid=True, divisible_by=8), **kwargs, ) model = _create_model(model_kwargs, default_cfgs[variant], pretrained) return model def _gen_mobilenet_v3_eca(variant, channel_multiplier=1.0, pretrained=False, **kwargs): """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 act_layer = HardSwish arch_def = [ # stage 0, 112x112 in ['ds_r1_k3_s2_e1_c16_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', 'ir_r2_k5_s1_e6_c40'], # hard-swish # stage 3, 14x14 in ['ir_r2_k5_s1_e3_c48'], # hard-swish # stage 4, 14x14in ['ir_r3_k5_s2_e6_c96'], # hard-swish # stage 6, 7x7 in ['cn_r1_k1_s1_c576'], # hard-swish ] else: num_features = 1280 act_layer = HardSwish # 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_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'], # hard-swish # # stage 5, 14x14in # ['ir_r3_k5_s2_e6_c160'], # hard-swish # # stage 6, 7x7 in # ['cn_r1_k1_s1_c960'], # 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_eca3_nre'], # relu # stage 3, 28x28 in ['ir_r1_k3_s2_e6_c80_eca3', 'ir_r1_k3_s1_e2.5_c80_eca3', 'ir_r2_k3_s1_e2.3_c80_eca3'], # hard-swish # stage 4, 14x14in ['ir_r2_k3_s1_e6_c112_eca5'], # hard-swish # stage 5, 14x14in ['ir_r3_k5_s2_e6_c160_eca5'], # hard-swish # stage 6, 7x7 in ['cn_r1_k1_s1_c960'], # hard-swish ] model_kwargs = dict( block_args=decode_arch_def(arch_def), num_features=num_features, stem_size=16, channel_multiplier=channel_multiplier, norm_kwargs=resolve_bn_args(kwargs), act_layer=act_layer, #attn_layer='ceca', attn_kwargs=dict(gate_fn=hard_sigmoid), **kwargs, ) model = _create_model(model_kwargs, default_cfgs[variant], pretrained) return model @register_model def mobilenetv3_large_075(pretrained=False, **kwargs): """ MobileNet V3 """ model = _gen_mobilenet_v3('mobilenetv3_large_075', 0.75, pretrained=pretrained, **kwargs) return model @register_model def mobilenetv3_large_100(pretrained=False, **kwargs): """ MobileNet V3 """ model = _gen_mobilenet_v3('mobilenetv3_large_100', 1.0, pretrained=pretrained, **kwargs) return model @register_model def mobilenetv3_small_075(pretrained=False, **kwargs): """ MobileNet V3 """ model = _gen_mobilenet_v3('mobilenetv3_small_075', 0.75, pretrained=pretrained, **kwargs) return model @register_model def mobilenetv3_small_100(pretrained=False, **kwargs): """ MobileNet V3 """ model = _gen_mobilenet_v3('mobilenetv3_small_100', 1.0, pretrained=pretrained, **kwargs) return model @register_model def mobilenetv3_eca_large(pretrained=False, **kwargs): """ MobileNet V3 """ model = _gen_mobilenet_v3_eca('mobilenetv3_eca_large', 1.0, pretrained=pretrained, **kwargs) return model @register_model def xmobilenetv3_large_100(pretrained=False, **kwargs): """ MobileNet V3 """ model = _gen_xmobilenet_v3('xmobilenetv3_large_100', 1.0, pretrained=pretrained, **kwargs) return model @register_model def mobilenetv3_rw(pretrained=False, **kwargs): """ MobileNet V3 """ if pretrained: # pretrained model trained with non-default BN epsilon kwargs['bn_eps'] = BN_EPS_TF_DEFAULT model = _gen_mobilenet_v3_rw('mobilenetv3_rw', 1.0, pretrained=pretrained, **kwargs) return model @register_model def tf_mobilenetv3_large_075(pretrained=False, **kwargs): """ MobileNet V3 """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_mobilenet_v3('tf_mobilenetv3_large_075', 0.75, pretrained=pretrained, **kwargs) return model @register_model def tf_mobilenetv3_large_100(pretrained=False, **kwargs): """ MobileNet V3 """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['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=False, **kwargs): """ MobileNet V3 """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['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=False, **kwargs): """ MobileNet V3 """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['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=False, **kwargs): """ MobileNet V3 """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['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=False, **kwargs): """ MobileNet V3 """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_mobilenet_v3('tf_mobilenetv3_small_minimal_100', 1.0, pretrained=pretrained, **kwargs) return model