merged changes

deoldify/generators.py

@@ -1,53 +1,11 @@
-<<<<<<< HEAD
-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(
-<<<<<<< HEAD
-    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(
@@ -56,7 +14,6 @@ def get_inference(learn, root_folder, weights_name) -> Learner:
     learn.path = root_folder
     learn.load(weights_name)
     learn.model.eval()
->>>>>>> 878ecf212e9f3f2f6e923e3bfff6ec899dc40143
     return learn
 
 
@@ -120,12 +77,6 @@ def unet_learner_wide(
 
 # ----------------------------------------------------------------------
 
-<<<<<<< HEAD
-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:
@@ -142,7 +93,6 @@ def gen_inference_deep(
 def gen_learner_deep(
     data: ImageDataBunch, gen_loss, arch=models.resnet34, nf_factor: float = 1.5
 ) -> Learner:
->>>>>>> 878ecf212e9f3f2f6e923e3bfff6ec899dc40143
     return unet_learner_deep(
         data,
         arch,
@@ -158,23 +108,6 @@ def gen_learner_deep(
 
 # 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,
@@ -189,7 +122,6 @@ def unet_learner_deep(
     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)