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import timm
import functools
import torch.utils.model_zoo as model_zoo
from .resnet import resnet_encoders
from .dpn import dpn_encoders
from .vgg import vgg_encoders
from .senet import senet_encoders
from .densenet import densenet_encoders
from .inceptionresnetv2 import inceptionresnetv2_encoders
from .inceptionv4 import inceptionv4_encoders
from .efficientnet import efficient_net_encoders
from .mobilenet import mobilenet_encoders
from .xception import xception_encoders
from .timm_efficientnet import timm_efficientnet_encoders
from .timm_resnest import timm_resnest_encoders
from .timm_res2net import timm_res2net_encoders
from .timm_regnet import timm_regnet_encoders
from .timm_sknet import timm_sknet_encoders
from .timm_mobilenetv3 import timm_mobilenetv3_encoders
from .timm_gernet import timm_gernet_encoders
from .mix_transformer import mix_transformer_encoders
from .mobileone import mobileone_encoders
from .timm_universal import TimmUniversalEncoder
from ._preprocessing import preprocess_input
encoders = {}
encoders.update(resnet_encoders)
encoders.update(dpn_encoders)
encoders.update(vgg_encoders)
encoders.update(senet_encoders)
encoders.update(densenet_encoders)
encoders.update(inceptionresnetv2_encoders)
encoders.update(inceptionv4_encoders)
encoders.update(efficient_net_encoders)
encoders.update(mobilenet_encoders)
encoders.update(xception_encoders)
encoders.update(timm_efficientnet_encoders)
encoders.update(timm_resnest_encoders)
encoders.update(timm_res2net_encoders)
encoders.update(timm_regnet_encoders)
encoders.update(timm_sknet_encoders)
encoders.update(timm_mobilenetv3_encoders)
encoders.update(timm_gernet_encoders)
encoders.update(mix_transformer_encoders)
encoders.update(mobileone_encoders)
def get_encoder(name, in_channels=3, depth=5, weights=None, output_stride=32, **kwargs):
if name.startswith("tu-"):
name = name[3:]
encoder = TimmUniversalEncoder(
name=name,
in_channels=in_channels,
depth=depth,
output_stride=output_stride,
pretrained=weights is not None,
**kwargs,
)
return encoder
try:
Encoder = encoders[name]["encoder"]
except KeyError:
raise KeyError(
"Wrong encoder name `{}`, supported encoders: {}".format(
name, list(encoders.keys())
)
)
params = encoders[name]["params"]
params.update(depth=depth)
encoder = Encoder(**params)
if weights is not None:
try:
settings = encoders[name]["pretrained_settings"][weights]
except KeyError:
raise KeyError(
"Wrong pretrained weights `{}` for encoder `{}`. Available options are: {}".format(
weights, name, list(encoders[name]["pretrained_settings"].keys())
)
)
encoder.load_state_dict(model_zoo.load_url(settings["url"]))
encoder.set_in_channels(in_channels, pretrained=weights is not None)
if output_stride != 32:
encoder.make_dilated(output_stride)
return encoder
def get_encoder_names():
return list(encoders.keys())
def get_preprocessing_params(encoder_name, pretrained="imagenet"):
if encoder_name.startswith("tu-"):
encoder_name = encoder_name[3:]
if not timm.models.is_model_pretrained(encoder_name):
raise ValueError(
f"{encoder_name} does not have pretrained weights and preprocessing parameters"
)
settings = timm.models.get_pretrained_cfg(encoder_name).__dict__
else:
all_settings = encoders[encoder_name]["pretrained_settings"]
if pretrained not in all_settings.keys():
raise ValueError(
"Available pretrained options {}".format(all_settings.keys())
)
settings = all_settings[pretrained]
formatted_settings = {}
formatted_settings["input_space"] = settings.get("input_space", "RGB")
formatted_settings["input_range"] = list(settings.get("input_range", [0, 1]))
formatted_settings["mean"] = list(settings["mean"])
formatted_settings["std"] = list(settings["std"])
return formatted_settings
def get_preprocessing_fn(encoder_name, pretrained="imagenet"):
params = get_preprocessing_params(encoder_name, pretrained=pretrained)
return functools.partial(preprocess_input, **params)
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