| | from functools import partial |
| |
|
| | import torch.nn as nn |
| |
|
| | from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD |
| | from ._builder import build_model_with_cfg |
| | from ._builder import pretrained_cfg_for_features |
| | from ._efficientnet_blocks import SqueezeExcite |
| | from ._efficientnet_builder import decode_arch_def, resolve_act_layer, resolve_bn_args, round_channels |
| | from ._registry import register_model, generate_default_cfgs |
| | from .mobilenetv3 import MobileNetV3, MobileNetV3Features |
| |
|
| | __all__ = [] |
| |
|
| |
|
| | def _gen_hardcorenas(pretrained, variant, arch_def, **kwargs): |
| | """Creates a hardcorenas model |
| | |
| | Ref impl: https://github.com/Alibaba-MIIL/HardCoReNAS |
| | Paper: https://arxiv.org/abs/2102.11646 |
| | |
| | """ |
| | num_features = 1280 |
| | 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), |
| | num_features=num_features, |
| | stem_size=32, |
| | norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)), |
| | act_layer=resolve_act_layer(kwargs, 'hard_swish'), |
| | se_layer=se_layer, |
| | **kwargs, |
| | ) |
| |
|
| | features_only = False |
| | model_cls = MobileNetV3 |
| | kwargs_filter = None |
| | if model_kwargs.pop('features_only', False): |
| | features_only = True |
| | kwargs_filter = ('num_classes', 'num_features', 'global_pool', 'head_conv', 'head_bias', 'global_pool') |
| | model_cls = MobileNetV3Features |
| | model = build_model_with_cfg( |
| | model_cls, |
| | variant, |
| | pretrained, |
| | pretrained_strict=not features_only, |
| | kwargs_filter=kwargs_filter, |
| | **model_kwargs, |
| | ) |
| | if features_only: |
| | model.default_cfg = pretrained_cfg_for_features(model.default_cfg) |
| | return model |
| |
|
| |
|
| | 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 = generate_default_cfgs({ |
| | 'hardcorenas_a.miil_green_in1k': _cfg(hf_hub_id='timm/'), |
| | 'hardcorenas_b.miil_green_in1k': _cfg(hf_hub_id='timm/'), |
| | 'hardcorenas_c.miil_green_in1k': _cfg(hf_hub_id='timm/'), |
| | 'hardcorenas_d.miil_green_in1k': _cfg(hf_hub_id='timm/'), |
| | 'hardcorenas_e.miil_green_in1k': _cfg(hf_hub_id='timm/'), |
| | 'hardcorenas_f.miil_green_in1k': _cfg(hf_hub_id='timm/'), |
| | }) |
| |
|
| |
|
| | @register_model |
| | def hardcorenas_a(pretrained=False, **kwargs) -> MobileNetV3: |
| | """ hardcorenas_A """ |
| | arch_def = [['ds_r1_k3_s1_e1_c16_nre'], ['ir_r1_k5_s2_e3_c24_nre', 'ir_r1_k5_s1_e3_c24_nre_se0.25'], |
| | ['ir_r1_k5_s2_e3_c40_nre', 'ir_r1_k5_s1_e6_c40_nre_se0.25'], |
| | ['ir_r1_k5_s2_e6_c80_se0.25', 'ir_r1_k5_s1_e6_c80_se0.25'], |
| | ['ir_r1_k5_s1_e6_c112_se0.25', 'ir_r1_k5_s1_e6_c112_se0.25'], |
| | ['ir_r1_k5_s2_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25'], ['cn_r1_k1_s1_c960']] |
| | model = _gen_hardcorenas(pretrained=pretrained, variant='hardcorenas_a', arch_def=arch_def, **kwargs) |
| | return model |
| |
|
| |
|
| | @register_model |
| | def hardcorenas_b(pretrained=False, **kwargs) -> MobileNetV3: |
| | """ hardcorenas_B """ |
| | arch_def = [['ds_r1_k3_s1_e1_c16_nre'], |
| | ['ir_r1_k5_s2_e3_c24_nre', 'ir_r1_k5_s1_e3_c24_nre_se0.25', 'ir_r1_k3_s1_e3_c24_nre'], |
| | ['ir_r1_k5_s2_e3_c40_nre', 'ir_r1_k5_s1_e3_c40_nre', 'ir_r1_k5_s1_e3_c40_nre'], |
| | ['ir_r1_k5_s2_e3_c80', 'ir_r1_k5_s1_e3_c80', 'ir_r1_k3_s1_e3_c80', 'ir_r1_k3_s1_e3_c80'], |
| | ['ir_r1_k5_s1_e3_c112', 'ir_r1_k3_s1_e3_c112', 'ir_r1_k3_s1_e3_c112', 'ir_r1_k3_s1_e3_c112'], |
| | ['ir_r1_k5_s2_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25', 'ir_r1_k3_s1_e3_c192_se0.25'], |
| | ['cn_r1_k1_s1_c960']] |
| | model = _gen_hardcorenas(pretrained=pretrained, variant='hardcorenas_b', arch_def=arch_def, **kwargs) |
| | return model |
| |
|
| |
|
| | @register_model |
| | def hardcorenas_c(pretrained=False, **kwargs) -> MobileNetV3: |
| | """ hardcorenas_C """ |
| | arch_def = [['ds_r1_k3_s1_e1_c16_nre'], ['ir_r1_k5_s2_e3_c24_nre', 'ir_r1_k5_s1_e3_c24_nre_se0.25'], |
| | ['ir_r1_k5_s2_e3_c40_nre', 'ir_r1_k5_s1_e3_c40_nre', 'ir_r1_k5_s1_e3_c40_nre', |
| | 'ir_r1_k5_s1_e3_c40_nre'], |
| | ['ir_r1_k5_s2_e4_c80', 'ir_r1_k5_s1_e6_c80_se0.25', 'ir_r1_k3_s1_e3_c80', 'ir_r1_k3_s1_e3_c80'], |
| | ['ir_r1_k5_s1_e6_c112_se0.25', 'ir_r1_k3_s1_e3_c112', 'ir_r1_k3_s1_e3_c112', 'ir_r1_k3_s1_e3_c112'], |
| | ['ir_r1_k5_s2_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25', 'ir_r1_k3_s1_e3_c192_se0.25'], |
| | ['cn_r1_k1_s1_c960']] |
| | model = _gen_hardcorenas(pretrained=pretrained, variant='hardcorenas_c', arch_def=arch_def, **kwargs) |
| | return model |
| |
|
| |
|
| | @register_model |
| | def hardcorenas_d(pretrained=False, **kwargs) -> MobileNetV3: |
| | """ hardcorenas_D """ |
| | arch_def = [['ds_r1_k3_s1_e1_c16_nre'], ['ir_r1_k5_s2_e3_c24_nre_se0.25', 'ir_r1_k5_s1_e3_c24_nre_se0.25'], |
| | ['ir_r1_k5_s2_e3_c40_nre_se0.25', 'ir_r1_k5_s1_e4_c40_nre_se0.25', 'ir_r1_k3_s1_e3_c40_nre_se0.25'], |
| | ['ir_r1_k5_s2_e4_c80_se0.25', 'ir_r1_k3_s1_e3_c80_se0.25', 'ir_r1_k3_s1_e3_c80_se0.25', |
| | 'ir_r1_k3_s1_e3_c80_se0.25'], |
| | ['ir_r1_k3_s1_e4_c112_se0.25', 'ir_r1_k5_s1_e4_c112_se0.25', 'ir_r1_k3_s1_e3_c112_se0.25', |
| | 'ir_r1_k5_s1_e3_c112_se0.25'], |
| | ['ir_r1_k5_s2_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25', |
| | 'ir_r1_k3_s1_e6_c192_se0.25'], ['cn_r1_k1_s1_c960']] |
| | model = _gen_hardcorenas(pretrained=pretrained, variant='hardcorenas_d', arch_def=arch_def, **kwargs) |
| | return model |
| |
|
| |
|
| | @register_model |
| | def hardcorenas_e(pretrained=False, **kwargs) -> MobileNetV3: |
| | """ hardcorenas_E """ |
| | arch_def = [['ds_r1_k3_s1_e1_c16_nre'], ['ir_r1_k5_s2_e3_c24_nre_se0.25', 'ir_r1_k5_s1_e3_c24_nre_se0.25'], |
| | ['ir_r1_k5_s2_e6_c40_nre_se0.25', 'ir_r1_k5_s1_e4_c40_nre_se0.25', 'ir_r1_k5_s1_e4_c40_nre_se0.25', |
| | 'ir_r1_k3_s1_e3_c40_nre_se0.25'], ['ir_r1_k5_s2_e4_c80_se0.25', 'ir_r1_k3_s1_e6_c80_se0.25'], |
| | ['ir_r1_k5_s1_e6_c112_se0.25', 'ir_r1_k5_s1_e6_c112_se0.25', 'ir_r1_k5_s1_e6_c112_se0.25', |
| | 'ir_r1_k5_s1_e3_c112_se0.25'], |
| | ['ir_r1_k5_s2_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25', |
| | 'ir_r1_k3_s1_e6_c192_se0.25'], ['cn_r1_k1_s1_c960']] |
| | model = _gen_hardcorenas(pretrained=pretrained, variant='hardcorenas_e', arch_def=arch_def, **kwargs) |
| | return model |
| |
|
| |
|
| | @register_model |
| | def hardcorenas_f(pretrained=False, **kwargs) -> MobileNetV3: |
| | """ hardcorenas_F """ |
| | arch_def = [['ds_r1_k3_s1_e1_c16_nre'], ['ir_r1_k5_s2_e3_c24_nre_se0.25', 'ir_r1_k5_s1_e3_c24_nre_se0.25'], |
| | ['ir_r1_k5_s2_e6_c40_nre_se0.25', 'ir_r1_k5_s1_e6_c40_nre_se0.25'], |
| | ['ir_r1_k5_s2_e6_c80_se0.25', 'ir_r1_k5_s1_e6_c80_se0.25', 'ir_r1_k3_s1_e3_c80_se0.25', |
| | 'ir_r1_k3_s1_e3_c80_se0.25'], |
| | ['ir_r1_k3_s1_e6_c112_se0.25', 'ir_r1_k5_s1_e6_c112_se0.25', 'ir_r1_k5_s1_e6_c112_se0.25', |
| | 'ir_r1_k3_s1_e3_c112_se0.25'], |
| | ['ir_r1_k5_s2_e6_c192_se0.25', 'ir_r1_k5_s1_e6_c192_se0.25', 'ir_r1_k3_s1_e6_c192_se0.25', |
| | 'ir_r1_k3_s1_e6_c192_se0.25'], ['cn_r1_k1_s1_c960']] |
| | model = _gen_hardcorenas(pretrained=pretrained, variant='hardcorenas_f', arch_def=arch_def, **kwargs) |
| | return model |
| |
|
