""" Generic Efficient Networks A generic MobileNet class with building blocks to support a variety of models: * EfficientNet (B0-B8, L2 + Tensorflow pretrained AutoAug/RandAug/AdvProp/NoisyStudent ports) - EfficientNet: Rethinking Model Scaling for CNNs - https://arxiv.org/abs/1905.11946 - CondConv: Conditionally Parameterized Convolutions for Efficient Inference - https://arxiv.org/abs/1904.04971 - Adversarial Examples Improve Image Recognition - https://arxiv.org/abs/1911.09665 - Self-training with Noisy Student improves ImageNet classification - https://arxiv.org/abs/1911.04252 * EfficientNet-Lite * MixNet (Small, Medium, and Large) - MixConv: Mixed Depthwise Convolutional Kernels - https://arxiv.org/abs/1907.09595 * MNasNet B1, A1 (SE), Small - MnasNet: Platform-Aware Neural Architecture Search for Mobile - https://arxiv.org/abs/1807.11626 * FBNet-C - FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable NAS - https://arxiv.org/abs/1812.03443 * Single-Path NAS Pixel1 - Single-Path NAS: Designing Hardware-Efficient ConvNets - https://arxiv.org/abs/1904.02877 * And likely more... Hacked together by / Copyright 2020 Ross Wightman """ import torch.nn as nn import torch.nn.functional as F from .config import layer_config_kwargs, is_scriptable from .conv2d_layers import select_conv2d from .helpers import load_pretrained from .efficientnet_builder import * __all__ = ['GenEfficientNet', 'mnasnet_050', 'mnasnet_075', 'mnasnet_100', 'mnasnet_b1', 'mnasnet_140', 'semnasnet_050', 'semnasnet_075', 'semnasnet_100', 'mnasnet_a1', 'semnasnet_140', 'mnasnet_small', 'mobilenetv2_100', 'mobilenetv2_140', 'mobilenetv2_110d', 'mobilenetv2_120d', 'fbnetc_100', 'spnasnet_100', 'efficientnet_b0', 'efficientnet_b1', 'efficientnet_b2', 'efficientnet_b3', 'efficientnet_b4', 'efficientnet_b5', 'efficientnet_b6', 'efficientnet_b7', 'efficientnet_b8', 'efficientnet_l2', 'efficientnet_es', 'efficientnet_em', 'efficientnet_el', 'efficientnet_cc_b0_4e', 'efficientnet_cc_b0_8e', 'efficientnet_cc_b1_8e', 'efficientnet_lite0', 'efficientnet_lite1', 'efficientnet_lite2', 'efficientnet_lite3', 'efficientnet_lite4', 'tf_efficientnet_b0', 'tf_efficientnet_b1', 'tf_efficientnet_b2', 'tf_efficientnet_b3', 'tf_efficientnet_b4', 'tf_efficientnet_b5', 'tf_efficientnet_b6', 'tf_efficientnet_b7', 'tf_efficientnet_b8', 'tf_efficientnet_b0_ap', 'tf_efficientnet_b1_ap', 'tf_efficientnet_b2_ap', 'tf_efficientnet_b3_ap', 'tf_efficientnet_b4_ap', 'tf_efficientnet_b5_ap', 'tf_efficientnet_b6_ap', 'tf_efficientnet_b7_ap', 'tf_efficientnet_b8_ap', 'tf_efficientnet_b0_ns', 'tf_efficientnet_b1_ns', 'tf_efficientnet_b2_ns', 'tf_efficientnet_b3_ns', 'tf_efficientnet_b4_ns', 'tf_efficientnet_b5_ns', 'tf_efficientnet_b6_ns', 'tf_efficientnet_b7_ns', 'tf_efficientnet_l2_ns', 'tf_efficientnet_l2_ns_475', 'tf_efficientnet_es', 'tf_efficientnet_em', 'tf_efficientnet_el', 'tf_efficientnet_cc_b0_4e', 'tf_efficientnet_cc_b0_8e', 'tf_efficientnet_cc_b1_8e', 'tf_efficientnet_lite0', 'tf_efficientnet_lite1', 'tf_efficientnet_lite2', 'tf_efficientnet_lite3', 'tf_efficientnet_lite4', 'mixnet_s', 'mixnet_m', 'mixnet_l', 'mixnet_xl', 'tf_mixnet_s', 'tf_mixnet_m', 'tf_mixnet_l'] model_urls = { 'mnasnet_050': None, 'mnasnet_075': None, 'mnasnet_100': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth', 'mnasnet_140': None, 'mnasnet_small': None, 'semnasnet_050': None, 'semnasnet_075': None, 'semnasnet_100': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth', 'semnasnet_140': None, 'mobilenetv2_100': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_100_ra-b33bc2c4.pth', 'mobilenetv2_110d': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_110d_ra-77090ade.pth', 'mobilenetv2_120d': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_120d_ra-5987e2ed.pth', 'mobilenetv2_140': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_140_ra-21a4e913.pth', 'fbnetc_100': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth', 'spnasnet_100': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/spnasnet_100-048bc3f4.pth', 'efficientnet_b0': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0_ra-3dd342df.pth', 'efficientnet_b1': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b1-533bc792.pth', 'efficientnet_b2': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2_ra-bcdf34b7.pth', 'efficientnet_b3': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra2-cf984f9c.pth', 'efficientnet_b4': None, 'efficientnet_b5': None, 'efficientnet_b6': None, 'efficientnet_b7': None, 'efficientnet_b8': None, 'efficientnet_l2': None, 'efficientnet_es': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_ra-f111e99c.pth', 'efficientnet_em': None, 'efficientnet_el': None, 'efficientnet_cc_b0_4e': None, 'efficientnet_cc_b0_8e': None, 'efficientnet_cc_b1_8e': None, 'efficientnet_lite0': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_lite0_ra-37913777.pth', 'efficientnet_lite1': None, 'efficientnet_lite2': None, 'efficientnet_lite3': None, 'efficientnet_lite4': None, 'tf_efficientnet_b0': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth', 'tf_efficientnet_b1': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_aa-ea7a6ee0.pth', 'tf_efficientnet_b2': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_aa-60c94f97.pth', 'tf_efficientnet_b3': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_aa-84b4657e.pth', 'tf_efficientnet_b4': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_aa-818f208c.pth', 'tf_efficientnet_b5': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ra-9a3e5369.pth', 'tf_efficientnet_b6': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_aa-80ba17e4.pth', 'tf_efficientnet_b7': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ra-6c08e654.pth', 'tf_efficientnet_b8': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ra-572d5dd9.pth', 'tf_efficientnet_b0_ap': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ap-f262efe1.pth', 'tf_efficientnet_b1_ap': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ap-44ef0a3d.pth', 'tf_efficientnet_b2_ap': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ap-2f8e7636.pth', 'tf_efficientnet_b3_ap': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ap-aad25bdd.pth', 'tf_efficientnet_b4_ap': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ap-dedb23e6.pth', 'tf_efficientnet_b5_ap': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ap-9e82fae8.pth', 'tf_efficientnet_b6_ap': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ap-4ffb161f.pth', 'tf_efficientnet_b7_ap': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ap-ddb28fec.pth', 'tf_efficientnet_b8_ap': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ap-00e169fa.pth', 'tf_efficientnet_b0_ns': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ns-c0e6a31c.pth', 'tf_efficientnet_b1_ns': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ns-99dd0c41.pth', 'tf_efficientnet_b2_ns': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ns-00306e48.pth', 'tf_efficientnet_b3_ns': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ns-9d44bf68.pth', 'tf_efficientnet_b4_ns': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ns-d6313a46.pth', 'tf_efficientnet_b5_ns': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ns-6f26d0cf.pth', 'tf_efficientnet_b6_ns': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ns-51548356.pth', 'tf_efficientnet_b7_ns': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ns-1dbc32de.pth', 'tf_efficientnet_l2_ns_475': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns_475-bebbd00a.pth', 'tf_efficientnet_l2_ns': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns-df73bb44.pth', 'tf_efficientnet_es': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth', 'tf_efficientnet_em': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_em-e78cfe58.pth', 'tf_efficientnet_el': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_el-5143854e.pth', 'tf_efficientnet_cc_b0_4e': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_4e-4362b6b2.pth', 'tf_efficientnet_cc_b0_8e': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_8e-66184a25.pth', 'tf_efficientnet_cc_b1_8e': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b1_8e-f7c79ae1.pth', 'tf_efficientnet_lite0': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite0-0aa007d2.pth', 'tf_efficientnet_lite1': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite1-bde8b488.pth', 'tf_efficientnet_lite2': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite2-dcccb7df.pth', 'tf_efficientnet_lite3': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite3-b733e338.pth', 'tf_efficientnet_lite4': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite4-741542c3.pth', 'mixnet_s': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_s-a907afbc.pth', 'mixnet_m': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_m-4647fc68.pth', 'mixnet_l': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_l-5a9a2ed8.pth', 'mixnet_xl': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_xl_ra-aac3c00c.pth', 'tf_mixnet_s': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_s-89d3354b.pth', 'tf_mixnet_m': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth', 'tf_mixnet_l': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth', } class GenEfficientNet(nn.Module): """ Generic EfficientNets An implementation of mobile optimized networks that covers: * EfficientNet (B0-B8, L2, CondConv, EdgeTPU) * MixNet (Small, Medium, and Large, XL) * MNASNet A1, B1, and small * FBNet C * Single-Path NAS Pixel1 """ def __init__(self, block_args, num_classes=1000, in_chans=3, num_features=1280, stem_size=32, fix_stem=False, channel_multiplier=1.0, channel_divisor=8, channel_min=None, pad_type='', act_layer=nn.ReLU, drop_rate=0., drop_connect_rate=0., se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None, weight_init='goog'): super(GenEfficientNet, self).__init__() self.drop_rate = drop_rate if not fix_stem: stem_size = round_channels(stem_size, channel_multiplier, channel_divisor, channel_min) self.conv_stem = select_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type) self.bn1 = norm_layer(stem_size, **norm_kwargs) self.act1 = act_layer(inplace=True) in_chs = stem_size builder = EfficientNetBuilder( channel_multiplier, channel_divisor, channel_min, pad_type, act_layer, se_kwargs, norm_layer, norm_kwargs, drop_connect_rate) self.blocks = nn.Sequential(*builder(in_chs, block_args)) in_chs = builder.in_chs self.conv_head = select_conv2d(in_chs, num_features, 1, padding=pad_type) self.bn2 = norm_layer(num_features, **norm_kwargs) self.act2 = act_layer(inplace=True) self.global_pool = nn.AdaptiveAvgPool2d(1) self.classifier = nn.Linear(num_features, num_classes) for n, m in self.named_modules(): if weight_init == 'goog': initialize_weight_goog(m, n) else: initialize_weight_default(m, n) def features(self, x): x = self.conv_stem(x) x = self.bn1(x) x = self.act1(x) x = self.blocks(x) x = self.conv_head(x) x = self.bn2(x) x = self.act2(x) return x def as_sequential(self): layers = [self.conv_stem, self.bn1, self.act1] layers.extend(self.blocks) layers.extend([ self.conv_head, self.bn2, self.act2, self.global_pool, nn.Flatten(), nn.Dropout(self.drop_rate), self.classifier]) return nn.Sequential(*layers) def forward(self, x): x = self.features(x) x = self.global_pool(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) def _create_model(model_kwargs, variant, pretrained=False): as_sequential = model_kwargs.pop('as_sequential', False) model = GenEfficientNet(**model_kwargs) if pretrained: load_pretrained(model, model_urls[variant]) if as_sequential: model = model.as_sequential() return model def _gen_mnasnet_a1(variant, channel_multiplier=1.0, pretrained=False, **kwargs): """Creates a mnasnet-a1 model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet Paper: https://arxiv.org/pdf/1807.11626.pdf. Args: channel_multiplier: multiplier to number of channels per layer. """ arch_def = [ # stage 0, 112x112 in ['ds_r1_k3_s1_e1_c16_noskip'], # stage 1, 112x112 in ['ir_r2_k3_s2_e6_c24'], # stage 2, 56x56 in ['ir_r3_k5_s2_e3_c40_se0.25'], # stage 3, 28x28 in ['ir_r4_k3_s2_e6_c80'], # stage 4, 14x14in ['ir_r2_k3_s1_e6_c112_se0.25'], # stage 5, 14x14in ['ir_r3_k5_s2_e6_c160_se0.25'], # stage 6, 7x7 in ['ir_r1_k3_s1_e6_c320'], ] with layer_config_kwargs(kwargs): model_kwargs = dict( block_args=decode_arch_def(arch_def), stem_size=32, channel_multiplier=channel_multiplier, act_layer=resolve_act_layer(kwargs, 'relu'), norm_kwargs=resolve_bn_args(kwargs), **kwargs ) model = _create_model(model_kwargs, variant, pretrained) return model def _gen_mnasnet_b1(variant, channel_multiplier=1.0, pretrained=False, **kwargs): """Creates a mnasnet-b1 model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet Paper: https://arxiv.org/pdf/1807.11626.pdf. Args: channel_multiplier: multiplier to number of channels per layer. """ arch_def = [ # stage 0, 112x112 in ['ds_r1_k3_s1_c16_noskip'], # stage 1, 112x112 in ['ir_r3_k3_s2_e3_c24'], # stage 2, 56x56 in ['ir_r3_k5_s2_e3_c40'], # stage 3, 28x28 in ['ir_r3_k5_s2_e6_c80'], # stage 4, 14x14in ['ir_r2_k3_s1_e6_c96'], # stage 5, 14x14in ['ir_r4_k5_s2_e6_c192'], # stage 6, 7x7 in ['ir_r1_k3_s1_e6_c320_noskip'] ] with layer_config_kwargs(kwargs): model_kwargs = dict( block_args=decode_arch_def(arch_def), stem_size=32, channel_multiplier=channel_multiplier, act_layer=resolve_act_layer(kwargs, 'relu'), norm_kwargs=resolve_bn_args(kwargs), **kwargs ) model = _create_model(model_kwargs, variant, pretrained) return model def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwargs): """Creates a mnasnet-b1 model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet Paper: https://arxiv.org/pdf/1807.11626.pdf. Args: channel_multiplier: multiplier to number of channels per layer. """ arch_def = [ ['ds_r1_k3_s1_c8'], ['ir_r1_k3_s2_e3_c16'], ['ir_r2_k3_s2_e6_c16'], ['ir_r4_k5_s2_e6_c32_se0.25'], ['ir_r3_k3_s1_e6_c32_se0.25'], ['ir_r3_k5_s2_e6_c88_se0.25'], ['ir_r1_k3_s1_e6_c144'] ] with layer_config_kwargs(kwargs): model_kwargs = dict( block_args=decode_arch_def(arch_def), stem_size=8, channel_multiplier=channel_multiplier, act_layer=resolve_act_layer(kwargs, 'relu'), norm_kwargs=resolve_bn_args(kwargs), **kwargs ) model = _create_model(model_kwargs, variant, pretrained) return model def _gen_mobilenet_v2( variant, channel_multiplier=1.0, depth_multiplier=1.0, fix_stem_head=False, pretrained=False, **kwargs): """ Generate MobileNet-V2 network Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py Paper: https://arxiv.org/abs/1801.04381 """ arch_def = [ ['ds_r1_k3_s1_c16'], ['ir_r2_k3_s2_e6_c24'], ['ir_r3_k3_s2_e6_c32'], ['ir_r4_k3_s2_e6_c64'], ['ir_r3_k3_s1_e6_c96'], ['ir_r3_k3_s2_e6_c160'], ['ir_r1_k3_s1_e6_c320'], ] with layer_config_kwargs(kwargs): model_kwargs = dict( block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, fix_first_last=fix_stem_head), num_features=1280 if fix_stem_head else round_channels(1280, channel_multiplier, 8, None), stem_size=32, fix_stem=fix_stem_head, channel_multiplier=channel_multiplier, norm_kwargs=resolve_bn_args(kwargs), act_layer=nn.ReLU6, **kwargs ) model = _create_model(model_kwargs, variant, pretrained) return model def _gen_fbnetc(variant, channel_multiplier=1.0, pretrained=False, **kwargs): """ FBNet-C Paper: https://arxiv.org/abs/1812.03443 Ref Impl: https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_modeldef.py NOTE: the impl above does not relate to the 'C' variant here, that was derived from paper, it was used to confirm some building block details """ arch_def = [ ['ir_r1_k3_s1_e1_c16'], ['ir_r1_k3_s2_e6_c24', 'ir_r2_k3_s1_e1_c24'], ['ir_r1_k5_s2_e6_c32', 'ir_r1_k5_s1_e3_c32', 'ir_r1_k5_s1_e6_c32', 'ir_r1_k3_s1_e6_c32'], ['ir_r1_k5_s2_e6_c64', 'ir_r1_k5_s1_e3_c64', 'ir_r2_k5_s1_e6_c64'], ['ir_r3_k5_s1_e6_c112', 'ir_r1_k5_s1_e3_c112'], ['ir_r4_k5_s2_e6_c184'], ['ir_r1_k3_s1_e6_c352'], ] with layer_config_kwargs(kwargs): model_kwargs = dict( block_args=decode_arch_def(arch_def), stem_size=16, num_features=1984, # paper suggests this, but is not 100% clear channel_multiplier=channel_multiplier, act_layer=resolve_act_layer(kwargs, 'relu'), norm_kwargs=resolve_bn_args(kwargs), **kwargs ) model = _create_model(model_kwargs, variant, pretrained) return model def _gen_spnasnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs): """Creates the Single-Path NAS model from search targeted for Pixel1 phone. Paper: https://arxiv.org/abs/1904.02877 Args: channel_multiplier: multiplier to number of channels per layer. """ arch_def = [ # stage 0, 112x112 in ['ds_r1_k3_s1_c16_noskip'], # stage 1, 112x112 in ['ir_r3_k3_s2_e3_c24'], # stage 2, 56x56 in ['ir_r1_k5_s2_e6_c40', 'ir_r3_k3_s1_e3_c40'], # stage 3, 28x28 in ['ir_r1_k5_s2_e6_c80', 'ir_r3_k3_s1_e3_c80'], # stage 4, 14x14in ['ir_r1_k5_s1_e6_c96', 'ir_r3_k5_s1_e3_c96'], # stage 5, 14x14in ['ir_r4_k5_s2_e6_c192'], # stage 6, 7x7 in ['ir_r1_k3_s1_e6_c320_noskip'] ] with layer_config_kwargs(kwargs): model_kwargs = dict( block_args=decode_arch_def(arch_def), stem_size=32, channel_multiplier=channel_multiplier, act_layer=resolve_act_layer(kwargs, 'relu'), norm_kwargs=resolve_bn_args(kwargs), **kwargs ) model = _create_model(model_kwargs, variant, pretrained) return model def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs): """Creates an EfficientNet model. Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py Paper: https://arxiv.org/abs/1905.11946 EfficientNet params name: (channel_multiplier, depth_multiplier, resolution, dropout_rate) 'efficientnet-b0': (1.0, 1.0, 224, 0.2), 'efficientnet-b1': (1.0, 1.1, 240, 0.2), 'efficientnet-b2': (1.1, 1.2, 260, 0.3), 'efficientnet-b3': (1.2, 1.4, 300, 0.3), 'efficientnet-b4': (1.4, 1.8, 380, 0.4), 'efficientnet-b5': (1.6, 2.2, 456, 0.4), 'efficientnet-b6': (1.8, 2.6, 528, 0.5), 'efficientnet-b7': (2.0, 3.1, 600, 0.5), 'efficientnet-b8': (2.2, 3.6, 672, 0.5), Args: channel_multiplier: multiplier to number of channels per layer depth_multiplier: multiplier to number of repeats per stage """ arch_def = [ ['ds_r1_k3_s1_e1_c16_se0.25'], ['ir_r2_k3_s2_e6_c24_se0.25'], ['ir_r2_k5_s2_e6_c40_se0.25'], ['ir_r3_k3_s2_e6_c80_se0.25'], ['ir_r3_k5_s1_e6_c112_se0.25'], ['ir_r4_k5_s2_e6_c192_se0.25'], ['ir_r1_k3_s1_e6_c320_se0.25'], ] with layer_config_kwargs(kwargs): model_kwargs = dict( block_args=decode_arch_def(arch_def, depth_multiplier), num_features=round_channels(1280, channel_multiplier, 8, None), stem_size=32, channel_multiplier=channel_multiplier, act_layer=resolve_act_layer(kwargs, 'swish'), norm_kwargs=resolve_bn_args(kwargs), **kwargs, ) model = _create_model(model_kwargs, variant, pretrained) return model def _gen_efficientnet_edge(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs): arch_def = [ # NOTE `fc` is present to override a mismatch between stem channels and in chs not # present in other models ['er_r1_k3_s1_e4_c24_fc24_noskip'], ['er_r2_k3_s2_e8_c32'], ['er_r4_k3_s2_e8_c48'], ['ir_r5_k5_s2_e8_c96'], ['ir_r4_k5_s1_e8_c144'], ['ir_r2_k5_s2_e8_c192'], ] with layer_config_kwargs(kwargs): model_kwargs = dict( block_args=decode_arch_def(arch_def, depth_multiplier), num_features=round_channels(1280, channel_multiplier, 8, None), stem_size=32, channel_multiplier=channel_multiplier, act_layer=resolve_act_layer(kwargs, 'relu'), norm_kwargs=resolve_bn_args(kwargs), **kwargs, ) model = _create_model(model_kwargs, variant, pretrained) return model def _gen_efficientnet_condconv( variant, channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=1, pretrained=False, **kwargs): """Creates an efficientnet-condconv model.""" arch_def = [ ['ds_r1_k3_s1_e1_c16_se0.25'], ['ir_r2_k3_s2_e6_c24_se0.25'], ['ir_r2_k5_s2_e6_c40_se0.25'], ['ir_r3_k3_s2_e6_c80_se0.25'], ['ir_r3_k5_s1_e6_c112_se0.25_cc4'], ['ir_r4_k5_s2_e6_c192_se0.25_cc4'], ['ir_r1_k3_s1_e6_c320_se0.25_cc4'], ] with layer_config_kwargs(kwargs): model_kwargs = dict( block_args=decode_arch_def(arch_def, depth_multiplier, experts_multiplier=experts_multiplier), num_features=round_channels(1280, channel_multiplier, 8, None), stem_size=32, channel_multiplier=channel_multiplier, act_layer=resolve_act_layer(kwargs, 'swish'), norm_kwargs=resolve_bn_args(kwargs), **kwargs, ) model = _create_model(model_kwargs, variant, pretrained) return model def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs): """Creates an EfficientNet-Lite model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite Paper: https://arxiv.org/abs/1905.11946 EfficientNet params name: (channel_multiplier, depth_multiplier, resolution, dropout_rate) 'efficientnet-lite0': (1.0, 1.0, 224, 0.2), 'efficientnet-lite1': (1.0, 1.1, 240, 0.2), 'efficientnet-lite2': (1.1, 1.2, 260, 0.3), 'efficientnet-lite3': (1.2, 1.4, 280, 0.3), 'efficientnet-lite4': (1.4, 1.8, 300, 0.3), Args: channel_multiplier: multiplier to number of channels per layer depth_multiplier: multiplier to number of repeats per stage """ arch_def = [ ['ds_r1_k3_s1_e1_c16'], ['ir_r2_k3_s2_e6_c24'], ['ir_r2_k5_s2_e6_c40'], ['ir_r3_k3_s2_e6_c80'], ['ir_r3_k5_s1_e6_c112'], ['ir_r4_k5_s2_e6_c192'], ['ir_r1_k3_s1_e6_c320'], ] with layer_config_kwargs(kwargs): model_kwargs = dict( block_args=decode_arch_def(arch_def, depth_multiplier, fix_first_last=True), num_features=1280, stem_size=32, fix_stem=True, channel_multiplier=channel_multiplier, act_layer=nn.ReLU6, norm_kwargs=resolve_bn_args(kwargs), **kwargs, ) model = _create_model(model_kwargs, variant, pretrained) return model def _gen_mixnet_s(variant, channel_multiplier=1.0, pretrained=False, **kwargs): """Creates a MixNet Small model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet Paper: https://arxiv.org/abs/1907.09595 """ arch_def = [ # stage 0, 112x112 in ['ds_r1_k3_s1_e1_c16'], # relu # stage 1, 112x112 in ['ir_r1_k3_a1.1_p1.1_s2_e6_c24', 'ir_r1_k3_a1.1_p1.1_s1_e3_c24'], # relu # stage 2, 56x56 in ['ir_r1_k3.5.7_s2_e6_c40_se0.5_nsw', 'ir_r3_k3.5_a1.1_p1.1_s1_e6_c40_se0.5_nsw'], # swish # stage 3, 28x28 in ['ir_r1_k3.5.7_p1.1_s2_e6_c80_se0.25_nsw', 'ir_r2_k3.5_p1.1_s1_e6_c80_se0.25_nsw'], # swish # stage 4, 14x14in ['ir_r1_k3.5.7_a1.1_p1.1_s1_e6_c120_se0.5_nsw', 'ir_r2_k3.5.7.9_a1.1_p1.1_s1_e3_c120_se0.5_nsw'], # swish # stage 5, 14x14in ['ir_r1_k3.5.7.9.11_s2_e6_c200_se0.5_nsw', 'ir_r2_k3.5.7.9_p1.1_s1_e6_c200_se0.5_nsw'], # swish # 7x7 ] with layer_config_kwargs(kwargs): model_kwargs = dict( block_args=decode_arch_def(arch_def), num_features=1536, stem_size=16, channel_multiplier=channel_multiplier, act_layer=resolve_act_layer(kwargs, 'relu'), norm_kwargs=resolve_bn_args(kwargs), **kwargs ) model = _create_model(model_kwargs, variant, pretrained) return model def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs): """Creates a MixNet Medium-Large model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet Paper: https://arxiv.org/abs/1907.09595 """ arch_def = [ # stage 0, 112x112 in ['ds_r1_k3_s1_e1_c24'], # relu # stage 1, 112x112 in ['ir_r1_k3.5.7_a1.1_p1.1_s2_e6_c32', 'ir_r1_k3_a1.1_p1.1_s1_e3_c32'], # relu # stage 2, 56x56 in ['ir_r1_k3.5.7.9_s2_e6_c40_se0.5_nsw', 'ir_r3_k3.5_a1.1_p1.1_s1_e6_c40_se0.5_nsw'], # swish # stage 3, 28x28 in ['ir_r1_k3.5.7_s2_e6_c80_se0.25_nsw', 'ir_r3_k3.5.7.9_a1.1_p1.1_s1_e6_c80_se0.25_nsw'], # swish # stage 4, 14x14in ['ir_r1_k3_s1_e6_c120_se0.5_nsw', 'ir_r3_k3.5.7.9_a1.1_p1.1_s1_e3_c120_se0.5_nsw'], # swish # stage 5, 14x14in ['ir_r1_k3.5.7.9_s2_e6_c200_se0.5_nsw', 'ir_r3_k3.5.7.9_p1.1_s1_e6_c200_se0.5_nsw'], # swish # 7x7 ] with layer_config_kwargs(kwargs): model_kwargs = dict( block_args=decode_arch_def(arch_def, depth_multiplier, depth_trunc='round'), num_features=1536, stem_size=24, channel_multiplier=channel_multiplier, act_layer=resolve_act_layer(kwargs, 'relu'), norm_kwargs=resolve_bn_args(kwargs), **kwargs ) model = _create_model(model_kwargs, variant, pretrained) return model def mnasnet_050(pretrained=False, **kwargs): """ MNASNet B1, depth multiplier of 0.5. """ model = _gen_mnasnet_b1('mnasnet_050', 0.5, pretrained=pretrained, **kwargs) return model def mnasnet_075(pretrained=False, **kwargs): """ MNASNet B1, depth multiplier of 0.75. """ model = _gen_mnasnet_b1('mnasnet_075', 0.75, pretrained=pretrained, **kwargs) return model def mnasnet_100(pretrained=False, **kwargs): """ MNASNet B1, depth multiplier of 1.0. """ model = _gen_mnasnet_b1('mnasnet_100', 1.0, pretrained=pretrained, **kwargs) return model def mnasnet_b1(pretrained=False, **kwargs): """ MNASNet B1, depth multiplier of 1.0. """ return mnasnet_100(pretrained, **kwargs) def mnasnet_140(pretrained=False, **kwargs): """ MNASNet B1, depth multiplier of 1.4 """ model = _gen_mnasnet_b1('mnasnet_140', 1.4, pretrained=pretrained, **kwargs) return model def semnasnet_050(pretrained=False, **kwargs): """ MNASNet A1 (w/ SE), depth multiplier of 0.5 """ model = _gen_mnasnet_a1('semnasnet_050', 0.5, pretrained=pretrained, **kwargs) return model def semnasnet_075(pretrained=False, **kwargs): """ MNASNet A1 (w/ SE), depth multiplier of 0.75. """ model = _gen_mnasnet_a1('semnasnet_075', 0.75, pretrained=pretrained, **kwargs) return model def semnasnet_100(pretrained=False, **kwargs): """ MNASNet A1 (w/ SE), depth multiplier of 1.0. """ model = _gen_mnasnet_a1('semnasnet_100', 1.0, pretrained=pretrained, **kwargs) return model def mnasnet_a1(pretrained=False, **kwargs): """ MNASNet A1 (w/ SE), depth multiplier of 1.0. """ return semnasnet_100(pretrained, **kwargs) def semnasnet_140(pretrained=False, **kwargs): """ MNASNet A1 (w/ SE), depth multiplier of 1.4. """ model = _gen_mnasnet_a1('semnasnet_140', 1.4, pretrained=pretrained, **kwargs) return model def mnasnet_small(pretrained=False, **kwargs): """ MNASNet Small, depth multiplier of 1.0. """ model = _gen_mnasnet_small('mnasnet_small', 1.0, pretrained=pretrained, **kwargs) return model def mobilenetv2_100(pretrained=False, **kwargs): """ MobileNet V2 w/ 1.0 channel multiplier """ model = _gen_mobilenet_v2('mobilenetv2_100', 1.0, pretrained=pretrained, **kwargs) return model def mobilenetv2_140(pretrained=False, **kwargs): """ MobileNet V2 w/ 1.4 channel multiplier """ model = _gen_mobilenet_v2('mobilenetv2_140', 1.4, pretrained=pretrained, **kwargs) return model def mobilenetv2_110d(pretrained=False, **kwargs): """ MobileNet V2 w/ 1.1 channel, 1.2 depth multipliers""" model = _gen_mobilenet_v2( 'mobilenetv2_110d', 1.1, depth_multiplier=1.2, fix_stem_head=True, pretrained=pretrained, **kwargs) return model def mobilenetv2_120d(pretrained=False, **kwargs): """ MobileNet V2 w/ 1.2 channel, 1.4 depth multipliers """ model = _gen_mobilenet_v2( 'mobilenetv2_120d', 1.2, depth_multiplier=1.4, fix_stem_head=True, pretrained=pretrained, **kwargs) return model def fbnetc_100(pretrained=False, **kwargs): """ FBNet-C """ if pretrained: # pretrained model trained with non-default BN epsilon kwargs['bn_eps'] = BN_EPS_TF_DEFAULT model = _gen_fbnetc('fbnetc_100', 1.0, pretrained=pretrained, **kwargs) return model def spnasnet_100(pretrained=False, **kwargs): """ Single-Path NAS Pixel1""" model = _gen_spnasnet('spnasnet_100', 1.0, pretrained=pretrained, **kwargs) return model def efficientnet_b0(pretrained=False, **kwargs): """ EfficientNet-B0 """ # NOTE for train set drop_rate=0.2, drop_connect_rate=0.2 model = _gen_efficientnet( 'efficientnet_b0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs) return model def efficientnet_b1(pretrained=False, **kwargs): """ EfficientNet-B1 """ # NOTE for train set drop_rate=0.2, drop_connect_rate=0.2 model = _gen_efficientnet( 'efficientnet_b1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs) return model def efficientnet_b2(pretrained=False, **kwargs): """ EfficientNet-B2 """ # NOTE for train set drop_rate=0.3, drop_connect_rate=0.2 model = _gen_efficientnet( 'efficientnet_b2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs) return model def efficientnet_b3(pretrained=False, **kwargs): """ EfficientNet-B3 """ # NOTE for train set drop_rate=0.3, drop_connect_rate=0.2 model = _gen_efficientnet( 'efficientnet_b3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs) return model def efficientnet_b4(pretrained=False, **kwargs): """ EfficientNet-B4 """ # NOTE for train set drop_rate=0.4, drop_connect_rate=0.2 model = _gen_efficientnet( 'efficientnet_b4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs) return model def efficientnet_b5(pretrained=False, **kwargs): """ EfficientNet-B5 """ # NOTE for train set drop_rate=0.4, drop_connect_rate=0.2 model = _gen_efficientnet( 'efficientnet_b5', channel_multiplier=1.6, depth_multiplier=2.2, pretrained=pretrained, **kwargs) return model def efficientnet_b6(pretrained=False, **kwargs): """ EfficientNet-B6 """ # NOTE for train set drop_rate=0.5, drop_connect_rate=0.2 model = _gen_efficientnet( 'efficientnet_b6', channel_multiplier=1.8, depth_multiplier=2.6, pretrained=pretrained, **kwargs) return model def efficientnet_b7(pretrained=False, **kwargs): """ EfficientNet-B7 """ # NOTE for train set drop_rate=0.5, drop_connect_rate=0.2 model = _gen_efficientnet( 'efficientnet_b7', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs) return model def efficientnet_b8(pretrained=False, **kwargs): """ EfficientNet-B8 """ # NOTE for train set drop_rate=0.5, drop_connect_rate=0.2 model = _gen_efficientnet( 'efficientnet_b8', channel_multiplier=2.2, depth_multiplier=3.6, pretrained=pretrained, **kwargs) return model def efficientnet_l2(pretrained=False, **kwargs): """ EfficientNet-L2. """ # NOTE for train, drop_rate should be 0.5 model = _gen_efficientnet( 'efficientnet_l2', channel_multiplier=4.3, depth_multiplier=5.3, pretrained=pretrained, **kwargs) return model def efficientnet_es(pretrained=False, **kwargs): """ EfficientNet-Edge Small. """ model = _gen_efficientnet_edge( 'efficientnet_es', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs) return model def efficientnet_em(pretrained=False, **kwargs): """ EfficientNet-Edge-Medium. """ model = _gen_efficientnet_edge( 'efficientnet_em', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs) return model def efficientnet_el(pretrained=False, **kwargs): """ EfficientNet-Edge-Large. """ model = _gen_efficientnet_edge( 'efficientnet_el', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs) return model def efficientnet_cc_b0_4e(pretrained=False, **kwargs): """ EfficientNet-CondConv-B0 w/ 8 Experts """ # NOTE for train set drop_rate=0.25, drop_connect_rate=0.2 model = _gen_efficientnet_condconv( 'efficientnet_cc_b0_4e', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs) return model def efficientnet_cc_b0_8e(pretrained=False, **kwargs): """ EfficientNet-CondConv-B0 w/ 8 Experts """ # NOTE for train set drop_rate=0.25, drop_connect_rate=0.2 model = _gen_efficientnet_condconv( 'efficientnet_cc_b0_8e', channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=2, pretrained=pretrained, **kwargs) return model def efficientnet_cc_b1_8e(pretrained=False, **kwargs): """ EfficientNet-CondConv-B1 w/ 8 Experts """ # NOTE for train set drop_rate=0.25, drop_connect_rate=0.2 model = _gen_efficientnet_condconv( 'efficientnet_cc_b1_8e', channel_multiplier=1.0, depth_multiplier=1.1, experts_multiplier=2, pretrained=pretrained, **kwargs) return model def efficientnet_lite0(pretrained=False, **kwargs): """ EfficientNet-Lite0 """ model = _gen_efficientnet_lite( 'efficientnet_lite0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs) return model def efficientnet_lite1(pretrained=False, **kwargs): """ EfficientNet-Lite1 """ model = _gen_efficientnet_lite( 'efficientnet_lite1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs) return model def efficientnet_lite2(pretrained=False, **kwargs): """ EfficientNet-Lite2 """ model = _gen_efficientnet_lite( 'efficientnet_lite2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs) return model def efficientnet_lite3(pretrained=False, **kwargs): """ EfficientNet-Lite3 """ model = _gen_efficientnet_lite( 'efficientnet_lite3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs) return model def efficientnet_lite4(pretrained=False, **kwargs): """ EfficientNet-Lite4 """ model = _gen_efficientnet_lite( 'efficientnet_lite4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b0(pretrained=False, **kwargs): """ EfficientNet-B0 AutoAug. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b1(pretrained=False, **kwargs): """ EfficientNet-B1 AutoAug. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b2(pretrained=False, **kwargs): """ EfficientNet-B2 AutoAug. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b3(pretrained=False, **kwargs): """ EfficientNet-B3 AutoAug. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b4(pretrained=False, **kwargs): """ EfficientNet-B4 AutoAug. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b5(pretrained=False, **kwargs): """ EfficientNet-B5 RandAug. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b5', channel_multiplier=1.6, depth_multiplier=2.2, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b6(pretrained=False, **kwargs): """ EfficientNet-B6 AutoAug. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b6', channel_multiplier=1.8, depth_multiplier=2.6, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b7(pretrained=False, **kwargs): """ EfficientNet-B7 RandAug. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b7', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b8(pretrained=False, **kwargs): """ EfficientNet-B8 RandAug. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b8', channel_multiplier=2.2, depth_multiplier=3.6, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b0_ap(pretrained=False, **kwargs): """ EfficientNet-B0 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b0_ap', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b1_ap(pretrained=False, **kwargs): """ EfficientNet-B1 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b1_ap', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b2_ap(pretrained=False, **kwargs): """ EfficientNet-B2 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b2_ap', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b3_ap(pretrained=False, **kwargs): """ EfficientNet-B3 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b3_ap', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b4_ap(pretrained=False, **kwargs): """ EfficientNet-B4 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b4_ap', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b5_ap(pretrained=False, **kwargs): """ EfficientNet-B5 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b5_ap', channel_multiplier=1.6, depth_multiplier=2.2, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b6_ap(pretrained=False, **kwargs): """ EfficientNet-B6 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) """ # NOTE for train, drop_rate should be 0.5 kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b6_ap', channel_multiplier=1.8, depth_multiplier=2.6, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b7_ap(pretrained=False, **kwargs): """ EfficientNet-B7 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) """ # NOTE for train, drop_rate should be 0.5 kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b7_ap', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b8_ap(pretrained=False, **kwargs): """ EfficientNet-B8 AdvProp. Tensorflow compatible variant Paper: Adversarial Examples Improve Image Recognition (https://arxiv.org/abs/1911.09665) """ # NOTE for train, drop_rate should be 0.5 kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b8_ap', channel_multiplier=2.2, depth_multiplier=3.6, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b0_ns(pretrained=False, **kwargs): """ EfficientNet-B0 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b0_ns', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b1_ns(pretrained=False, **kwargs): """ EfficientNet-B1 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b1_ns', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b2_ns(pretrained=False, **kwargs): """ EfficientNet-B2 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b2_ns', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b3_ns(pretrained=False, **kwargs): """ EfficientNet-B3 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b3_ns', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b4_ns(pretrained=False, **kwargs): """ EfficientNet-B4 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b4_ns', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b5_ns(pretrained=False, **kwargs): """ EfficientNet-B5 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b5_ns', channel_multiplier=1.6, depth_multiplier=2.2, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b6_ns(pretrained=False, **kwargs): """ EfficientNet-B6 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) """ # NOTE for train, drop_rate should be 0.5 kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b6_ns', channel_multiplier=1.8, depth_multiplier=2.6, pretrained=pretrained, **kwargs) return model def tf_efficientnet_b7_ns(pretrained=False, **kwargs): """ EfficientNet-B7 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) """ # NOTE for train, drop_rate should be 0.5 kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_b7_ns', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs) return model def tf_efficientnet_l2_ns_475(pretrained=False, **kwargs): """ EfficientNet-L2 NoisyStudent @ 475x475. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) """ # NOTE for train, drop_rate should be 0.5 kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_l2_ns_475', channel_multiplier=4.3, depth_multiplier=5.3, pretrained=pretrained, **kwargs) return model def tf_efficientnet_l2_ns(pretrained=False, **kwargs): """ EfficientNet-L2 NoisyStudent. Tensorflow compatible variant Paper: Self-training with Noisy Student improves ImageNet classification (https://arxiv.org/abs/1911.04252) """ # NOTE for train, drop_rate should be 0.5 kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( 'tf_efficientnet_l2_ns', channel_multiplier=4.3, depth_multiplier=5.3, pretrained=pretrained, **kwargs) return model def tf_efficientnet_es(pretrained=False, **kwargs): """ EfficientNet-Edge Small. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet_edge( 'tf_efficientnet_es', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs) return model def tf_efficientnet_em(pretrained=False, **kwargs): """ EfficientNet-Edge-Medium. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet_edge( 'tf_efficientnet_em', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs) return model def tf_efficientnet_el(pretrained=False, **kwargs): """ EfficientNet-Edge-Large. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet_edge( 'tf_efficientnet_el', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs) return model def tf_efficientnet_cc_b0_4e(pretrained=False, **kwargs): """ EfficientNet-CondConv-B0 w/ 4 Experts """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet_condconv( 'tf_efficientnet_cc_b0_4e', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs) return model def tf_efficientnet_cc_b0_8e(pretrained=False, **kwargs): """ EfficientNet-CondConv-B0 w/ 8 Experts """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet_condconv( 'tf_efficientnet_cc_b0_8e', channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=2, pretrained=pretrained, **kwargs) return model def tf_efficientnet_cc_b1_8e(pretrained=False, **kwargs): """ EfficientNet-CondConv-B1 w/ 8 Experts """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet_condconv( 'tf_efficientnet_cc_b1_8e', channel_multiplier=1.0, depth_multiplier=1.1, experts_multiplier=2, pretrained=pretrained, **kwargs) return model def tf_efficientnet_lite0(pretrained=False, **kwargs): """ EfficientNet-Lite0. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet_lite( 'tf_efficientnet_lite0', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs) return model def tf_efficientnet_lite1(pretrained=False, **kwargs): """ EfficientNet-Lite1. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet_lite( 'tf_efficientnet_lite1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs) return model def tf_efficientnet_lite2(pretrained=False, **kwargs): """ EfficientNet-Lite2. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet_lite( 'tf_efficientnet_lite2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs) return model def tf_efficientnet_lite3(pretrained=False, **kwargs): """ EfficientNet-Lite3. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet_lite( 'tf_efficientnet_lite3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs) return model def tf_efficientnet_lite4(pretrained=False, **kwargs): """ EfficientNet-Lite4. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet_lite( 'tf_efficientnet_lite4', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs) return model def mixnet_s(pretrained=False, **kwargs): """Creates a MixNet Small model. """ # NOTE for train set drop_rate=0.2 model = _gen_mixnet_s( 'mixnet_s', channel_multiplier=1.0, pretrained=pretrained, **kwargs) return model def mixnet_m(pretrained=False, **kwargs): """Creates a MixNet Medium model. """ # NOTE for train set drop_rate=0.25 model = _gen_mixnet_m( 'mixnet_m', channel_multiplier=1.0, pretrained=pretrained, **kwargs) return model def mixnet_l(pretrained=False, **kwargs): """Creates a MixNet Large model. """ # NOTE for train set drop_rate=0.25 model = _gen_mixnet_m( 'mixnet_l', channel_multiplier=1.3, pretrained=pretrained, **kwargs) return model def mixnet_xl(pretrained=False, **kwargs): """Creates a MixNet Extra-Large model. Not a paper spec, experimental def by RW w/ depth scaling. """ # NOTE for train set drop_rate=0.25, drop_connect_rate=0.2 model = _gen_mixnet_m( 'mixnet_xl', channel_multiplier=1.6, depth_multiplier=1.2, pretrained=pretrained, **kwargs) return model def mixnet_xxl(pretrained=False, **kwargs): """Creates a MixNet Double Extra Large model. Not a paper spec, experimental def by RW w/ depth scaling. """ # NOTE for train set drop_rate=0.3, drop_connect_rate=0.2 model = _gen_mixnet_m( 'mixnet_xxl', channel_multiplier=2.4, depth_multiplier=1.3, pretrained=pretrained, **kwargs) return model def tf_mixnet_s(pretrained=False, **kwargs): """Creates a MixNet Small model. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_mixnet_s( 'tf_mixnet_s', channel_multiplier=1.0, pretrained=pretrained, **kwargs) return model def tf_mixnet_m(pretrained=False, **kwargs): """Creates a MixNet Medium model. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_mixnet_m( 'tf_mixnet_m', channel_multiplier=1.0, pretrained=pretrained, **kwargs) return model def tf_mixnet_l(pretrained=False, **kwargs): """Creates a MixNet Large model. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_mixnet_m( 'tf_mixnet_l', channel_multiplier=1.3, pretrained=pretrained, **kwargs) return model