Merge branch 'tinynet' of https://github.com/rsomani95/pytorch-image-models into rsomani95-tinynet

timm/models/efficientnet.py

@@ -23,6 +23,10 @@ An implementation of EfficienNet that covers variety of related models with effi
 * Single-Path NAS Pixel1
   - Single-Path NAS: Designing Hardware-Efficient ConvNets - https://arxiv.org/abs/1904.02877
 
+* TinyNet
+    - Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets - https://arxiv.org/abs/2010.14819
+    - Definitions & weights borrowed from https://github.com/huawei-noah/CV-Backbones/tree/master/tinynet_pytorch
+
 * And likely more...
 
 The majority of the above models (EfficientNet*, MixNet, MnasNet) and original weights were made available
@@ -407,6 +411,22 @@ default_cfgs = {
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth'),
     'tf_mixnet_l': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth'),
+
+    "tinynet_a": _cfg(
+        input_size=(3, 192, 192),  # int(224 * 0.86)
+        url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_a.pth'),
+    "tinynet_b": _cfg(
+        input_size=(3, 188, 188),  # int(224 * 0.84)
+        url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_b.pth'),
+    "tinynet_c": _cfg(
+        input_size=(3, 184, 184),  # int(224 * 0.825)
+        url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_c.pth'),
+    "tinynet_d": _cfg(
+        input_size=(3, 152, 152),  # int(224 * 0.68)
+        url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_d.pth'),
+    "tinynet_e": _cfg(
+        input_size=(3, 106, 106),  # int(224 * 0.475)
+        url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_e.pth'),
 }
 
 
@@ -1140,6 +1160,50 @@ def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrai
     return model
 
 
+def _gen_tinynet(
+    variant, model_width=1.0, depth_multiplier=1.0, pretrained=False, **kwargs
+):
+    """Creates a TinyNet 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'], ['ir_r4_k5_s2_e6_c192_se0.25'],
+        ['ir_r1_k3_s1_e6_c320_se0.25'],
+    ]
+    model_kwargs = dict(
+        block_args = decode_arch_def(arch_def, depth_multiplier, depth_trunc='round'),
+        num_features = max(1280, round_channels(1280, model_width, 8, None)),
+        stem_size = 32,
+        fix_stem = True,
+        round_chs_fn=partial(round_channels, multiplier=model_width),
+        act_layer = resolve_act_layer(kwargs, 'swish'),
+        norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
+        **kwargs,
+    )
+
+    features_only = False
+    model_cls = EfficientNet
+    kwargs_filter = None
+
+    if kwargs.pop('features_only', False):
+        features_only = True
+        # kwargs_filter = ('num_classes', 'num_features', 'head_conv', 'global_pool')
+        kwargs_filter = ('num_classes', 'num_features', 'conv_head', 'global_pool')
+        model_cls = EfficientNetFeatures
+
+    model = build_model_with_cfg(
+        model_cls, variant, pretrained,
+        default_cfg=default_cfgs[variant],
+        pretrained_strict=not features_only,
+        kwargs_filter=kwargs_filter,
+        **model_kwargs)
+    if features_only:
+        model.default_cfg = default_cfg_for_features(model.default_cfg)
+
+    return model
+
+
 @register_model
 def mnasnet_050(pretrained=False, **kwargs):
     """ MNASNet B1, depth multiplier of 0.5. """
@@ -2209,3 +2273,33 @@ def tf_mixnet_l(pretrained=False, **kwargs):
     model = _gen_mixnet_m(
         'tf_mixnet_l', channel_multiplier=1.3, pretrained=pretrained, **kwargs)
     return model
+
+
+@register_model
+def tinynet_a(pretrained=False, **kwargs):
+    model = _gen_tinynet('tinynet_a', 1.0, 1.2, **kwargs)
+    return model
+
+
+@register_model
+def tinynet_b(pretrained=False, **kwargs):
+    model = _gen_tinynet('tinynet_b', 0.75, 1.1, pretrained, **kwargs)
+    return model
+
+
+@register_model
+def tinynet_c(pretrained=False, **kwargs):
+    model = _gen_tinynet('tinynet_c', 0.54, 0.85, pretrained, **kwargs)
+    return model
+
+
+@register_model
+def tinynet_d(pretrained=False, **kwargs):
+    model = _gen_tinynet('tinynet_d', 0.54, 0.695, pretrained, **kwargs)
+    return model
+
+
+@register_model
+def tinynet_e(pretrained=False, **kwargs):
+    model = _gen_tinynet('tinynet_e', 0.51, 0.6, pretrained, **kwargs)
+    return model