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from fastai.basics import F, nn
from fastai.basic_data import DataBunch
from fastai.basic_train import Learner
from fastai.layers import NormType
from fastai.torch_core import SplitFuncOrIdxList, to_device, apply_init
from fastai.vision import *
from fastai.vision.learner import cnn_config, create_body
=======
from fastai.vision import *
from fastai.vision.learner import cnn_config
>>>>>>> 878ecf212e9f3f2f6e923e3bfff6ec899dc40143
from .unet import DynamicUnetWide, DynamicUnetDeep
from .loss import FeatureLoss
from .dataset import *
# Weights are implicitly read from ./models/ folder
def gen_inference_wide(
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root_folder: Path, weights_name: str, nf_factor: int = 2,
arch=models.resnet101
) -> Learner:
data = get_dummy_databunch()
learn = gen_learner_wide(data=data, gen_loss=F.l1_loss, nf_factor=nf_factor, arch=arch)
learn = get_inference(learn, root_folder, weights_name)
return learn
def gen_inference_deep(root_folder: Path, weights_name: str,
arch=models.resnet34, nf_factor: float = 1.5
) -> Learner:
data = get_dummy_databunch()
learn = gen_learner_deep(data=data, gen_loss=F.l1_loss, arch=arch, nf_factor=nf_factor)
learn = get_inference(learn, root_folder, weights_name)
return learn
# Weights are implicitly read from ./models/ folder
# Load loads weights from os.path.join(learner.path, learner.model_dir, weights_name)
def get_inference(learn, root_folder, weights_name) -> Learner:
learn.path = root_folder
try:
learn.load(weights_name)
print('Model loaded successfully')
except Exception as e:
print(e)
print('Error while reading the model')
learn.model.eval()
=======
root_folder: Path, weights_name: str, nf_factor: int = 2, arch=models.resnet101) -> Learner:
data = get_dummy_databunch()
learn = gen_learner_wide(
data=data, gen_loss=F.l1_loss, nf_factor=nf_factor, arch=arch
)
learn.path = root_folder
learn.load(weights_name)
learn.model.eval()
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return learn
def gen_learner_wide(
data: ImageDataBunch, gen_loss, arch=models.resnet101, nf_factor: int = 2
) -> Learner:
return unet_learner_wide(
data,
arch=arch,
wd=1e-3,
blur=True,
norm_type=NormType.Spectral,
self_attention=True,
y_range=(-3.0, 3.0),
loss_func=gen_loss,
nf_factor=nf_factor,
)
# The code below is meant to be merged into fastaiv1 ideally
def unet_learner_wide(
data: DataBunch,
arch: Callable,
pretrained: bool = True,
blur_final: bool = True,
norm_type: Optional[NormType] = NormType,
split_on: Optional[SplitFuncOrIdxList] = None,
blur: bool = False,
self_attention: bool = False,
y_range: Optional[Tuple[float, float]] = None,
last_cross: bool = True,
bottle: bool = False,
nf_factor: int = 1,
**kwargs: Any
) -> Learner:
"Build Unet learner from `data` and `arch`."
meta = cnn_config(arch)
body = create_body(arch, pretrained)
model = to_device(
DynamicUnetWide(
body,
n_classes=data.c,
blur=blur,
blur_final=blur_final,
self_attention=self_attention,
y_range=y_range,
norm_type=norm_type,
last_cross=last_cross,
bottle=bottle,
nf_factor=nf_factor,
),
data.device,
)
learn = Learner(data, model, **kwargs)
learn.split(ifnone(split_on, meta['split']))
if pretrained:
learn.freeze()
apply_init(model[2], nn.init.kaiming_normal_)
return learn
# ----------------------------------------------------------------------
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def gen_learner_deep(data: ImageDataBunch, gen_loss, arch=models.resnet34,
nf_factor: float = 1.5
) -> Learner:
=======
# Weights are implicitly read from ./models/ folder
def gen_inference_deep(
root_folder: Path, weights_name: str, arch=models.resnet34, nf_factor: float = 1.5) -> Learner:
data = get_dummy_databunch()
learn = gen_learner_deep(
data=data, gen_loss=F.l1_loss, arch=arch, nf_factor=nf_factor
)
learn.path = root_folder
learn.load(weights_name)
learn.model.eval()
return learn
def gen_learner_deep(
data: ImageDataBunch, gen_loss, arch=models.resnet34, nf_factor: float = 1.5
) -> Learner:
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return unet_learner_deep(
data,
arch,
wd=1e-3,
blur=True,
norm_type=NormType.Spectral,
self_attention=True,
y_range=(-3.0, 3.0),
loss_func=gen_loss,
nf_factor=nf_factor,
)
# The code below is meant to be merged into fastaiv1 ideally
def unet_learner_deep(
<<<<<<< HEAD
data: DataBunch,
arch: Callable,
pretrained: bool = True,
blur_final: bool = True,
norm_type: Optional[NormType] = NormType,
split_on: Optional[SplitFuncOrIdxList] = None,
blur: bool = False,
self_attention: bool = False,
y_range: Optional[Tuple[float, float]] = None,
last_cross: bool = True,
bottle: bool = False,
nf_factor: float = 1.5,
**kwargs: Any
) -> Learner:
=======
data: DataBunch,
arch: Callable,
pretrained: bool = True,
blur_final: bool = True,
norm_type: Optional[NormType] = NormType,
split_on: Optional[SplitFuncOrIdxList] = None,
blur: bool = False,
self_attention: bool = False,
y_range: Optional[Tuple[float, float]] = None,
last_cross: bool = True,
bottle: bool = False,
nf_factor: float = 1.5,
**kwargs: Any
) -> Learner:
>>>>>>> 878ecf212e9f3f2f6e923e3bfff6ec899dc40143
"Build Unet learner from `data` and `arch`."
meta = cnn_config(arch)
body = create_body(arch, pretrained)
model = to_device(
DynamicUnetDeep(
body,
n_classes=data.c,
blur=blur,
blur_final=blur_final,
self_attention=self_attention,
y_range=y_range,
norm_type=norm_type,
last_cross=last_cross,
bottle=bottle,
nf_factor=nf_factor,
),
data.device,
)
learn = Learner(data, model, **kwargs)
learn.split(ifnone(split_on, meta['split']))
if pretrained:
learn.freeze()
apply_init(model[2], nn.init.kaiming_normal_)
return learn
# -----------------------------
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