segmentation_models_pytorch/encoders/timm_regnet.py

from ._base import EncoderMixin
from timm.models.regnet import RegNet
import torch.nn as nn


class RegNetEncoder(RegNet, EncoderMixin):
    def __init__(self, out_channels, depth=5, **kwargs):
        super().__init__(**kwargs)
        self._depth = depth
        self._out_channels = out_channels
        self._in_channels = 3

        del self.head

    def get_stages(self):
        return [
            nn.Identity(),
            self.stem,
            self.s1,
            self.s2,
            self.s3,
            self.s4,
        ]

    def forward(self, x):
        stages = self.get_stages()

        features = []
        for i in range(self._depth + 1):
            x = stages[i](x)
            features.append(x)

        return features

    def load_state_dict(self, state_dict, **kwargs):
        state_dict.pop("head.fc.weight", None)
        state_dict.pop("head.fc.bias", None)
        super().load_state_dict(state_dict, **kwargs)


regnet_weights = {
    "timm-regnetx_002": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_002-e7e85e5c.pth",  # noqa
    },
    "timm-regnetx_004": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_004-7d0e9424.pth",  # noqa
    },
    "timm-regnetx_006": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_006-85ec1baa.pth",  # noqa
    },
    "timm-regnetx_008": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_008-d8b470eb.pth",  # noqa
    },
    "timm-regnetx_016": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_016-65ca972a.pth",  # noqa
    },
    "timm-regnetx_032": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_032-ed0c7f7e.pth",  # noqa
    },
    "timm-regnetx_040": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_040-73c2a654.pth",  # noqa
    },
    "timm-regnetx_064": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_064-29278baa.pth",  # noqa
    },
    "timm-regnetx_080": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_080-7c7fcab1.pth",  # noqa
    },
    "timm-regnetx_120": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_120-65d5521e.pth",  # noqa
    },
    "timm-regnetx_160": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_160-c98c4112.pth",  # noqa
    },
    "timm-regnetx_320": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_320-8ea38b93.pth",  # noqa
    },
    "timm-regnety_002": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_002-e68ca334.pth",  # noqa
    },
    "timm-regnety_004": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_004-0db870e6.pth",  # noqa
    },
    "timm-regnety_006": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_006-c67e57ec.pth",  # noqa
    },
    "timm-regnety_008": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_008-dc900dbe.pth",  # noqa
    },
    "timm-regnety_016": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_016-54367f74.pth",  # noqa
    },
    "timm-regnety_032": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/regnety_032_ra-7f2439f9.pth",  # noqa
    },
    "timm-regnety_040": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_040-f0d569f9.pth",  # noqa
    },
    "timm-regnety_064": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_064-0a48325c.pth",  # noqa
    },
    "timm-regnety_080": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_080-e7f3eb93.pth",  # noqa
    },
    "timm-regnety_120": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_120-721ba79a.pth",  # noqa
    },
    "timm-regnety_160": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_160-d64013cd.pth",  # noqa
    },
    "timm-regnety_320": {
        "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth",  # noqa
    },
}

pretrained_settings = {}
for model_name, sources in regnet_weights.items():
    pretrained_settings[model_name] = {}
    for source_name, source_url in sources.items():
        pretrained_settings[model_name][source_name] = {
            "url": source_url,
            "input_size": [3, 224, 224],
            "input_range": [0, 1],
            "mean": [0.485, 0.456, 0.406],
            "std": [0.229, 0.224, 0.225],
            "num_classes": 1000,
        }

# at this point I am too lazy to copy configs, so I just used the same configs from timm's repo


def _mcfg(**kwargs):
    cfg = dict(se_ratio=0.0, bottle_ratio=1.0, stem_width=32)
    cfg.update(**kwargs)
    return cfg


timm_regnet_encoders = {
    "timm-regnetx_002": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnetx_002"],
        "params": {
            "out_channels": (3, 32, 24, 56, 152, 368),
            "cfg": _mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13),
        },
    },
    "timm-regnetx_004": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnetx_004"],
        "params": {
            "out_channels": (3, 32, 32, 64, 160, 384),
            "cfg": _mcfg(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22),
        },
    },
    "timm-regnetx_006": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnetx_006"],
        "params": {
            "out_channels": (3, 32, 48, 96, 240, 528),
            "cfg": _mcfg(w0=48, wa=36.97, wm=2.24, group_w=24, depth=16),
        },
    },
    "timm-regnetx_008": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnetx_008"],
        "params": {
            "out_channels": (3, 32, 64, 128, 288, 672),
            "cfg": _mcfg(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16),
        },
    },
    "timm-regnetx_016": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnetx_016"],
        "params": {
            "out_channels": (3, 32, 72, 168, 408, 912),
            "cfg": _mcfg(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18),
        },
    },
    "timm-regnetx_032": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnetx_032"],
        "params": {
            "out_channels": (3, 32, 96, 192, 432, 1008),
            "cfg": _mcfg(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25),
        },
    },
    "timm-regnetx_040": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnetx_040"],
        "params": {
            "out_channels": (3, 32, 80, 240, 560, 1360),
            "cfg": _mcfg(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23),
        },
    },
    "timm-regnetx_064": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnetx_064"],
        "params": {
            "out_channels": (3, 32, 168, 392, 784, 1624),
            "cfg": _mcfg(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17),
        },
    },
    "timm-regnetx_080": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnetx_080"],
        "params": {
            "out_channels": (3, 32, 80, 240, 720, 1920),
            "cfg": _mcfg(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23),
        },
    },
    "timm-regnetx_120": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnetx_120"],
        "params": {
            "out_channels": (3, 32, 224, 448, 896, 2240),
            "cfg": _mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19),
        },
    },
    "timm-regnetx_160": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnetx_160"],
        "params": {
            "out_channels": (3, 32, 256, 512, 896, 2048),
            "cfg": _mcfg(w0=216, wa=55.59, wm=2.1, group_w=128, depth=22),
        },
    },
    "timm-regnetx_320": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnetx_320"],
        "params": {
            "out_channels": (3, 32, 336, 672, 1344, 2520),
            "cfg": _mcfg(w0=320, wa=69.86, wm=2.0, group_w=168, depth=23),
        },
    },
    # regnety
    "timm-regnety_002": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnety_002"],
        "params": {
            "out_channels": (3, 32, 24, 56, 152, 368),
            "cfg": _mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13, se_ratio=0.25),
        },
    },
    "timm-regnety_004": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnety_004"],
        "params": {
            "out_channels": (3, 32, 48, 104, 208, 440),
            "cfg": _mcfg(w0=48, wa=27.89, wm=2.09, group_w=8, depth=16, se_ratio=0.25),
        },
    },
    "timm-regnety_006": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnety_006"],
        "params": {
            "out_channels": (3, 32, 48, 112, 256, 608),
            "cfg": _mcfg(w0=48, wa=32.54, wm=2.32, group_w=16, depth=15, se_ratio=0.25),
        },
    },
    "timm-regnety_008": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnety_008"],
        "params": {
            "out_channels": (3, 32, 64, 128, 320, 768),
            "cfg": _mcfg(w0=56, wa=38.84, wm=2.4, group_w=16, depth=14, se_ratio=0.25),
        },
    },
    "timm-regnety_016": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnety_016"],
        "params": {
            "out_channels": (3, 32, 48, 120, 336, 888),
            "cfg": _mcfg(w0=48, wa=20.71, wm=2.65, group_w=24, depth=27, se_ratio=0.25),
        },
    },
    "timm-regnety_032": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnety_032"],
        "params": {
            "out_channels": (3, 32, 72, 216, 576, 1512),
            "cfg": _mcfg(w0=80, wa=42.63, wm=2.66, group_w=24, depth=21, se_ratio=0.25),
        },
    },
    "timm-regnety_040": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnety_040"],
        "params": {
            "out_channels": (3, 32, 128, 192, 512, 1088),
            "cfg": _mcfg(w0=96, wa=31.41, wm=2.24, group_w=64, depth=22, se_ratio=0.25),
        },
    },
    "timm-regnety_064": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnety_064"],
        "params": {
            "out_channels": (3, 32, 144, 288, 576, 1296),
            "cfg": _mcfg(
                w0=112, wa=33.22, wm=2.27, group_w=72, depth=25, se_ratio=0.25
            ),
        },
    },
    "timm-regnety_080": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnety_080"],
        "params": {
            "out_channels": (3, 32, 168, 448, 896, 2016),
            "cfg": _mcfg(
                w0=192, wa=76.82, wm=2.19, group_w=56, depth=17, se_ratio=0.25
            ),
        },
    },
    "timm-regnety_120": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnety_120"],
        "params": {
            "out_channels": (3, 32, 224, 448, 896, 2240),
            "cfg": _mcfg(
                w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, se_ratio=0.25
            ),
        },
    },
    "timm-regnety_160": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnety_160"],
        "params": {
            "out_channels": (3, 32, 224, 448, 1232, 3024),
            "cfg": _mcfg(
                w0=200, wa=106.23, wm=2.48, group_w=112, depth=18, se_ratio=0.25
            ),
        },
    },
    "timm-regnety_320": {
        "encoder": RegNetEncoder,
        "pretrained_settings": pretrained_settings["timm-regnety_320"],
        "params": {
            "out_channels": (3, 32, 232, 696, 1392, 3712),
            "cfg": _mcfg(
                w0=232, wa=115.89, wm=2.53, group_w=232, depth=20, se_ratio=0.25
            ),
        },
    },
}