def wnres_unet_model(in_c,
out_c,
arch,
blur=True,
blur_final=True,
self_attention=True,
last_cross=True,
bottle=True,
norm_type=NormType.Weight,
**wnres_args):
meta = cnn_config(arch)
enc_model = arch(c_in=in_c)
cut = cnn_config(arch)['cut']
body = nn.Sequential(*list(enc_model.children())[:cut])
model = DynamicUnet(body,
n_classes=out_c,
blur=blur,
blur_final=blur_final,
self_attention=self_attention,
norm_type=norm_type,
last_cross=last_cross,
bottle=bottle,
**wnres_args)
return model, meta
def unet_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,
cut: Union[int, Callable] = None,
hypercolumns=True,
**learn_kwargs: Any) -> Learner:
"Build Unet learner from `data` and `arch`."
meta = cnn_config(arch)
body = create_body(arch, pretrained, cut)
M = DynamicUnet_Hcolumns if hypercolumns else DynamicUnet
model = to_device(
M(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), data.device)
learn = Learner(data, model, **learn_kwargs)
learn.split(ifnone(split_on, meta['split']))
if pretrained: learn.freeze()
apply_init(model[2], nn.init.kaiming_normal_)
return learn
def iqa_cnn_learner(data: DataBunch,
base_arch: Callable,
cut: Union[int, Callable] = None,
pretrained: bool = True,
lin_ftrs: Optional[Collection[int]] = None,
ps: Floats = 0.5,
custom_head: Optional[nn.Module] = None,
split_on: Optional[SplitFuncOrIdxList] = None,
bn_final: bool = False,
init=nn.init.kaiming_normal_,
concat_pool: bool = True,
learner=IqaLearner,
**kwargs: Any):
"Build convnet style learner."
from fastai.vision.learner import cnn_config
meta = cnn_config(base_arch)
model = create_cnn_model(base_arch,
data.c,
cut,
pretrained,
lin_ftrs,
ps=ps,
custom_head=custom_head,
bn_final=bn_final,
concat_pool=concat_pool)
model.__name__ = base_arch.__name__
learn = learner(data, model, **kwargs)
learn.split(split_on or meta['split'])
if pretrained: learn.freeze()
if init: apply_init(model[1], init)
return learn
def __init__(self,
base_arch,
no_diseases,
dropout=0.5,
init=nn.init.kaiming_normal_):
super(CNNPretrainedModel, self).__init__()
self.body = create_body(base_arch, pretrained=True)
nf = num_features_model(nn.Sequential(*self.body.children())) * 2
self.disease_head = create_head(nf,
no_diseases,
ps=0.5,
concat_pool=True,
bn_final=False)
#self.age_head = create_head(nf, 1, ps=0.5, concat_pool=True, bn_final=False)
self.gender_head = create_head(nf,
2,
ps=0.5,
concat_pool=True,
bn_final=False)
#self.projection_head = create_head(nf, 3, ps=0.5, concat_pool=True, bn_final=False)
self.disease_model = nn.Sequential(self.body, self.disease_head)
self.meta = cnn_config(base_arch)
self.split(self.meta['split'])
self.freeze()
apply_init(self.disease_head, init)
#apply_init(self.age_head, init)
apply_init(self.gender_head, init)
def create_cnn(data, arch, pretrained=False, is_mono_input=True, **kwargs):
meta = cnn_config(arch)
body = create_body(arch, pretrained)
# sum up the weights of in_channels axis, to reduce to single input channel
# Suggestion by David Gutman
# https://forums.fast.ai/t/black-and-white-images-on-vgg16/2479/2
if is_mono_input:
first_conv_layer = body[0]
first_conv_weights = first_conv_layer.state_dict()['weight']
assert first_conv_weights.size(1) == 3 # RGB channels dim
summed_weights = torch.sum(first_conv_weights, dim=1, keepdim=True)
first_conv_layer.weight.data = summed_weights
first_conv_layer.in_channels = 1
else:
# In this case, the input is a stereo
first_conv_layer = body[0]
first_conv_weights = first_conv_layer.state_dict()['weight']
assert first_conv_weights.size(1) == 3 # RGB channels dim
summed_weights = torch.sum(first_conv_weights, dim=1, keepdim=True)
first_conv_layer.weight.data = first_conv_weights[:, :
2, :, :] # Keep only 2 channels for the weights
first_conv_layer.in_channels = 2
nf = num_features_model(body) * 2
head = create_head(nf, data.c, None, 0.5)
model = nn.Sequential(body, head)
learn = Learner(data, model, **kwargs)
learn.split(meta['split'])
if pretrained:
learn.freeze()
apply_init(model[1], nn.init.kaiming_normal_)
return learn
def Learner_create_unet(cls, data:DataBunch, arch:Callable, pretrained:bool=True,
split_on:Optional[SplitFuncOrIdxList]=None, **kwargs:Any)->None:
"Build Unet learners."
meta = cnn_config(arch)
body = create_body(arch(pretrained), meta['cut'])
model = to_device(models.unet.DynamicUnet(body, n_classes=data.c), data.device)
learn = cls(data, model, **kwargs)
learn.split(ifnone(split_on,meta['split']))
if pretrained: learn.freeze()
apply_init(model[2], nn.init.kaiming_normal_)
return learn
def __init__(self,
base_arch,
no_classes,
dropout=0.5,
init=nn.init.kaiming_normal_):
super(CNNPretrainedModel, self).__init__()
self.model = create_cnn_model(base_arch, no_classes, ps=dropout)
self.meta = cnn_config(base_arch)
self.split(self.meta['split'])
self.freeze()
apply_init(self.model[1], init)
def siamese_learner(data,
pretrained_model_class,
emsize=128,
margin=1.0,
callback_fns=None):
meta = cnn_config(pretrained_model_class)
model = create_cnn_model(pretrained_model_class, emsize)
model = SiameseNet(model)
learn = Learner(data,
model,
loss_func=ContrastiveLoss(margin),
callback_fns=callback_fns)
learn.split(meta["split"](model.embedding_net))
apply_init(model.embedding_net[1], nn.init.kaiming_normal_)
return learn
def create_cnn(data:DataBunch, arch:Callable, cut:Union[int,Callable]=None, pretrained:bool=True,
lin_ftrs:Optional[Collection[int]]=None, ps:Floats=0.5,
custom_head:Optional[nn.Module]=None, split_on:Optional[SplitFuncOrIdxList]=None,
bn_final:bool=False, **kwargs:Any)->Learner:
"Build convnet style learners."
meta = cnn_config(arch)
body = create_body(arch(pretrained), ifnone(cut,meta['cut']))
# nf = num_features_model(body) * 2
head = custom_head or create_head(2048, data.c, lin_ftrs, ps=ps, bn_final=bn_final)
model = nn.Sequential(body, head)
learn = Learner(data, model, **kwargs)
learn.split(ifnone(split_on,meta['split']))
if pretrained: learn.freeze()
apply_init(model[1], nn.init.kaiming_normal_)
return learn
def siamese_embedding_learner(data,
pretrained_model_class,
emsize=128,
margin=1.0,
callback_fns=None):
meta = cnn_config(pretrained_model_class)
model = create_cnn_model(pretrained_model_class, emsize)
learn = Learner(
data,
model,
loss_func=OnlineContrastiveLoss(margin, HardNegativePairSelector()),
callback_fns=callback_fns,
)
learn.split(meta["split"])
apply_init(model[1], nn.init.kaiming_normal_)
return learn
def create_cnn(data:DataBunch, arch:Callable, cut:Union[int,Callable]=None, pretrained:bool=True,
lin_ftrs:Optional[Collection[int]]=None, ps:Floats=0.5,
custom_head:Optional[nn.Module]=None, split_on:Optional[SplitFuncOrIdxList]=None,
classification:bool=True, **kwargs:Any)->Learner:
"Build convnet style learners."
assert classification, 'Regression CNN not implemented yet, bug us on the forums if you want this!'
meta = cnn_config(arch)
body = create_body(arch(pretrained), ifnone(cut,meta['cut']))
head = custom_head
model = nn.Sequential(body, head)
learner_cls = ifnone(data.learner_type(), ClassificationLearner)
learn = learner_cls(data, model, **kwargs)
learn.split(ifnone(split_on,meta['split']))
if pretrained: learn.freeze()
apply_init(model[1], nn.init.kaiming_normal_)
return learn
def create_body_local(arch: Callable,
pretrained: bool = True,
cut: Optional[Union[int, Callable]] = None):
"Cut off the body of a typically pretrained `model` at `cut` (int) or cut the model as specified by `cut(model)` (function)."
if pretrained:
model = arch(num_classes=1000, pretrained='imagenet')
cut = ifnone(cut, cnn_config(arch)['cut'])
if cut is None:
ll = list(enumerate(model.children()))
cut = next(i for i, o in reversed(ll) if has_pool_type(o))
if isinstance(cut, int):
return nn.Sequential(*(list(model.children())[:cut]))
elif isinstance(cut, Callable):
return cut(model)
else:
pass
def __init__(self, data, backbone=None, pretrained_path=None):
super().__init__(data, backbone)
self._code = image_classifier_prf
backbone_cut = None
backbone_split = None
_backbone = self._backbone
if hasattr(self, '_backbone_'):
_backbone = self._backbone_
if not (self._check_backbone_support(_backbone)):
raise Exception(
f"Enter only compatible backbones from {', '.join(self.supported_backbones)}"
)
if hasattr(self, '_backbone_'):
_backbone_meta = cnn_config(self._backbone_)
backbone_cut = _backbone_meta['cut']
backbone_split = _backbone_meta['split']
acc_metric = partial(accuracy,
void_code=0,
class_mapping=data.class_mapping)
self.learn = unet_learner(data,
arch=self._backbone,
metrics=acc_metric,
wd=1e-2,
bottle=True,
last_cross=True,
cut=backbone_cut,
split_on=backbone_split)
self._arcgis_init_callback() # make first conv weights learnable
self.learn.callbacks.append(LabelCallback(
self.learn)) #appending label callback
self.learn.model = self.learn.model.to(self._device)
# _set_multigpu_callback(self) # MultiGPU doesn't work for U-Net. (Fastai-Forums)
if pretrained_path is not None:
self.load(pretrained_path)
def DR_learner(data: DataBunch,
base_arch: Callable,
cut: Union[int, Callable] = None,
pretrained: bool = True,
lin_ftrs: Optional[Collection[int]] = None,
ps: Floats = 0.5,
custom_head: Optional[nn.Module] = None,
split_on: Optional[SplitFuncOrIdxList] = None,
bn_final: bool = False,
init=nn.init.kaiming_normal_,
concat_pool: bool = True,
attention=False,
**kwargs: Any) -> Learner:
"Build convnet style learner."
meta = cnn_config(base_arch)
"Create custom convnet architecture"
body = create_body(base_arch, pretrained, cut)
if custom_head is None: # quadnet head
nf = num_features_model(
nn.Sequential(*body.children())) * (2 if concat_pool else 1)
if attention:
pass
head = create_DR_head(nf,
data.c + 2,
lin_ftrs,
ps=ps,
concat_pool=concat_pool,
bn_final=bn_final)
else:
head = custom_head
model = nn.Sequential(body, head)
learn = Learner(data, model, **kwargs)
learn.split(split_on or meta['split'])
if pretrained:
learn.freeze()
if init:
apply_init(model[1], init)
return learn
def unet_learner_wide_exp(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,
nf: int = 128,
**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, #for MSGAN.was 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
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)
#preModel = torch.load('/Projects/mk_utils/Convert_Mxnet_to_Pytorch/Pytorch_NewModel.pth')
#preModel = list(preModel.children())[0]
#body = pthutils.cut_model(preModel,-3)
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)
#print(model[2])
learn = Learner(data, model, **kwargs)
learn.split(ifnone(split_on, meta['split']))
if pretrained: learn.freeze_to(-2)
#print(model[2])
#apply_init(model[2], nn.init.kaiming_normal_)
return learn
def unet4mri_learner(data: DataBunch,
arch: Callable,
pretrained: bool = True,
blur_final: bool = True,
norm_type: Optional[NormType] = None,
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,
cut: Union[int, Callable] = None,
**learn_kwargs: Any) -> Learner:
"Build Unet learner from `data` and `arch`."
meta = ln.cnn_config(arch)
body = ln.create_body(arch, pretrained, cut)
try:
size = data.train_ds[0][0].size
except:
size = next(iter(data.train_dl))[0].shape[-2:]
model = to_device(
MyDynamicUnet(body,
bs=data.batch_size,
n_classes=data.c,
img_size=size,
blur=blur,
blur_final=blur_final,
self_attention=self_attention,
y_range=y_range,
norm_type=norm_type,
last_cross=last_cross,
bottle=bottle), data.device)
learn = Learner(data, model, **learn_kwargs)
learn.split(ifnone(split_on, meta['split']))
if pretrained: learn.freeze()
apply_init(model[2], nn.init.kaiming_normal_)
return learn
def __init__(self,
data,
grids=None,
zooms=[1.],
ratios=[[1., 1.]],
backbone=None,
drop=0.3,
bias=-4.,
focal_loss=False,
pretrained_path=None,
location_loss_factor=None,
ssd_version=2,
backend='pytorch'):
super().__init__(data, backbone)
self._backend = backend
if self._backend == 'tensorflow':
self._intialize_tensorflow(data, grids, zooms, ratios, backbone,
drop, bias, pretrained_path,
location_loss_factor)
else:
# assert (location_loss_factor is not None) or ((location_loss_factor > 0) and (location_loss_factor < 1)),
if not ssd_version in [1, 2]:
raise Exception("ssd_version can be only [1,2]")
if location_loss_factor is not None:
if not ((location_loss_factor > 0) and
(location_loss_factor < 1)):
raise Exception(
'`location_loss_factor` should be greater than 0 and less than 1'
)
self.location_loss_factor = location_loss_factor
self._code = code
self.ssd_version = ssd_version
backbone_cut = None
backbone_split = None
if hasattr(self, '_orig_backbone'):
self._backbone_ms = self._backbone
self._backbone = self._orig_backbone
_backbone_meta = cnn_config(self._orig_backbone)
backbone_cut = _backbone_meta['cut']
backbone_split = _backbone_meta['split']
if backbone is None:
self._backbone = models.resnet34
backbone_name = 'res'
elif type(backbone) is str:
self._backbone = getattr(models, backbone)
backbone_name = backbone[:3]
else:
self._backbone = backbone
backbone_name = 'custom'
if not self._check_backbone_support(self._backbone):
raise Exception(
f"Enter only compatible backbones from {', '.join(self.supported_backbones)}"
)
if self._backbone == models.mobilenet_v2:
backbone_cut = -1
backbone_split = _mobilenet_split
if ssd_version == 1:
if grids == None:
grids = [4, 2, 1]
self._create_anchors(grids, zooms, ratios)
feature_sizes = model_sizes(create_body(self._backbone,
cut=backbone_cut),
size=(data.chip_size,
data.chip_size))
num_features = feature_sizes[-1][-1]
num_channels = feature_sizes[-1][1]
ssd_head = SSDHead(grids,
self._anchors_per_cell,
data.c,
num_features=num_features,
drop=drop,
bias=bias,
num_channels=num_channels)
elif ssd_version == 2:
# find bounding boxes height and width
if grids is None:
logger.info("Computing optimal grid size...")
hw = data.height_width
hw = np.array(hw)
# find most suitable centroids for dataset
centroid = kmeans(hw, 1)
avg = avg_iou(hw, centroid)
for num_anchor in range(2, 5):
new_centroid = kmeans(hw, num_anchor)
new_avg = avg_iou(hw, new_centroid)
if (new_avg - avg) < 0.05:
break
avg = new_avg
centroid = new_centroid.copy()
# find grid size
grids = list(
map(
int,
map(
round, data.chip_size /
np.sort(np.max(centroid, axis=1)))))
grids = list(set(grids))
grids.sort(reverse=True)
if grids[-1] == 0:
grids[-1] = 1
grids = list(set(grids))
self._create_anchors(grids, zooms, ratios)
feature_sizes = model_sizes(create_body(self._backbone,
cut=backbone_cut),
size=(data.chip_size,
data.chip_size))
num_features = feature_sizes[-1][-1]
num_channels = feature_sizes[-1][1]
if grids[0] > 8 and abs(num_features - grids[0]
) > 4 and backbone_name == 'res':
num_features = feature_sizes[-2][-1]
num_channels = feature_sizes[-2][1]
backbone_cut = -3
ssd_head = SSDHeadv2(grids,
self._anchors_per_cell,
data.c,
num_features=num_features,
drop=drop,
bias=bias,
num_channels=num_channels)
else:
raise Exception('SSDVersion can only be 1 or 2')
if hasattr(self, '_backbone_ms'):
self._orig_backbone = self._backbone
self._backbone = self._backbone_ms
self.learn = cnn_learner(data=data,
base_arch=self._backbone,
cut=backbone_cut,
split_on=backbone_split,
custom_head=ssd_head)
self._arcgis_init_callback() # make first conv weights learnable
self.learn.model = self.learn.model.to(self._device)
if focal_loss:
self._loss_f = FocalLoss(data.c)
else:
self._loss_f = BCE_Loss(data.c)
self.learn.loss_func = self._ssd_loss
_set_multigpu_callback(self)
if pretrained_path is not None:
self.load(pretrained_path)
every='improvement',
name=Path(MODEL_DIR.name) / f'fold{fold_num}'),
partial(CSVLogger, filename=LOG_FNAME)
]
learn = cnn_learner(data=fold_data,
base_arch=arch,
metrics=[accuracy, auc],
callbacks=learn_callbacks,
callback_fns=learn_callback_fns)
# change model to 4D input
convert_model_to_4D_input(learn.model)
# re-assign new 4d parameters to layer groups and freeze model
meta = cnn_config(arch)
learn.split(meta['split'])
learn.freeze()
# Stage-1 training
learn.lr_find()
try:
learn.recorder.plot(suggestion=True, k=5)
except:
learn.recorder.plot(suggestion=True)
max_lr = learn.recorder.min_grad_lr
print(f"Stage-1 training with lr={max_lr}")
learn.fit_one_cycle(epochs['stage1'], max_lr=max_lr)
# Stage-2 training
learn.freeze_to(1)
def unet_learner_deep_MS(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,
nc=3,
nz=8,
sz=128,
**kwargs: Any) -> Learner:
"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()
# adjust layers to allow 11D input
# could be appended with a new network at position 0 instead, but that way it would not fit an 11GB GPU
# thus, replace the first layer with 11D input
learn.model[0][0] = nn.Conv2d((nc + nz),
int(sz / 2),
kernel_size=(7, 7),
stride=(2, 2),
padding=(3, 3),
bias=False)
learn.layer_groups[0][0] = nn.Conv2d((nc + nz),
int(sz / 2),
kernel_size=(7, 7),
stride=(2, 2),
padding=(3, 3),
bias=False)
# and replace the merge layer
learn.model[10][0][0] = nn.Conv2d(311,
311,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1))
learn.layer_groups[-1][62] = nn.Conv2d(311,
311,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1))
learn.model[10][1][0] = nn.Conv2d(311,
311,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1))
learn.layer_groups[-1][64] = nn.Conv2d(311,
311,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1))
learn.model[11][0] = nn.Conv2d(311, 3, kernel_size=(1, 1), stride=(1, 1))
learn.layer_groups[-1][67] = nn.Conv2d(311,
3,
kernel_size=(1, 1),
stride=(1, 1))
# initialize the newly created layers
apply_init(model[0], nn.init.kaiming_normal_)
apply_init(model[10][0][0], nn.init.kaiming_normal_)
apply_init(model[10][1][0], nn.init.kaiming_normal_)
apply_init(model[11][0], nn.init.kaiming_normal_)
apply_init(model[2], nn.init.kaiming_normal_)
return learn
num_workers=NUM_WORKERS,
normalization=STATS)
# ============================== model learning ==============================
model = create_cnn_model(base_arch=ARCH, nc=NUM_CLASSES)
model.load_state_dict(torch.load(STATE_FILE)['model'])
model = to_device(model, DEVICE)
learn = AccGradLearner(data,
model,
metrics=[METRIC],
model_dir=LOGGING_FOLDER)
learn.split(cnn_config(model)['split'])
set_BN_momentum(learn.model, batch_size=BATCH_SIZE)
learn.clip_grad(1.)
# callbacks
csv_logger = CSVLogger(learn=learn,
filename=f'{LOGGING_FOLDER}/fit_trace',
append=True)
early_stopping = EarlyStoppingCallback(learn=learn,
monitor='accuracy',
patience=PATIENCE)
save_model = SaveModelCallback(learn=learn,
monitor='accuracy',
name='best_model')
acc_grad = AccumulateStep(learn, 64 // BATCH_SIZE)
def __init__(self,
data,
backbone=None,
pretrained_path=None,
backend='pytorch',
*args,
**kwargs):
self._backend = backend
if self._backend == 'tensorflow':
super().__init__(data, None)
self._intialize_tensorflow(data, backbone, pretrained_path, kwargs)
else:
super().__init__(data, backbone)
# import pdb; pdb.set_trace();
self._ignore_classes = kwargs.get('ignore_classes', [])
if self._ignore_classes != [] and len(data.classes) <= 3:
raise Exception(
f"`ignore_classes` parameter can only be used when the dataset has more than 2 classes."
)
data_classes = list(self._data.class_mapping.keys())
if 0 not in list(data.class_mapping.values()):
self._ignore_mapped_class = [
data_classes.index(k) + 1 for k in self._ignore_classes
if k != 0
]
else:
self._ignore_mapped_class = [
data_classes.index(k) + 1 for k in self._ignore_classes
]
if self._ignore_classes != []:
if 0 not in self._ignore_mapped_class:
self._ignore_mapped_class.insert(0, 0)
global accuracy
accuracy = partial(
accuracy, ignore_mapped_class=self._ignore_mapped_class)
self.mixup = kwargs.get('mixup', False)
self.class_balancing = kwargs.get('class_balancing', False)
self.focal_loss = kwargs.get('focal_loss', False)
self._code = image_classifier_prf
backbone_cut = None
backbone_split = None
_backbone = self._backbone
if hasattr(self, '_orig_backbone'):
_backbone = self._orig_backbone
if not (self._check_backbone_support(_backbone)):
raise Exception(
f"Enter only compatible backbones from {', '.join(self.supported_backbones)}"
)
if hasattr(self, '_orig_backbone'):
_backbone_meta = cnn_config(self._orig_backbone)
backbone_cut = _backbone_meta['cut']
backbone_split = _backbone_meta['split']
if not _isnotebook() and arcgis_os.name == 'posix':
_set_ddp_multigpu(self)
if self._multigpu_training:
self.learn = unet_learner(
data,
arch=self._backbone,
metrics=accuracy,
wd=1e-2,
bottle=True,
last_cross=True,
cut=backbone_cut,
split_on=backbone_split).to_distributed(
self._rank_distributed)
else:
self.learn = unet_learner(data,
arch=self._backbone,
metrics=accuracy,
wd=1e-2,
bottle=True,
last_cross=True,
cut=backbone_cut,
split_on=backbone_split)
else:
self.learn = unet_learner(data,
arch=self._backbone,
metrics=accuracy,
wd=1e-2,
bottle=True,
last_cross=True,
cut=backbone_cut,
split_on=backbone_split)
if self.class_balancing:
if data.class_weight is not None:
class_weight = torch.tensor(
[data.class_weight.mean()] +
data.class_weight.tolist()).float().to(self._device)
else:
if getattr(data, 'overflow_encountered', False):
logger.warning(
"Overflow Encountered. Ignoring `class_balancing` parameter."
)
class_weight = [1] * len(data.classes)
else:
logger.warning(
"Could not find 'NumPixelsPerClass' in 'esri_accumulated_stats.json'. Ignoring `class_balancing` parameter."
)
if self._ignore_classes != []:
if not self.class_balancing:
class_weight = torch.tensor([1] * data.c).float().to(
self._device)
class_weight[self._ignore_mapped_class] = 0.
else:
class_weight = None
self.learn.loss_func = CrossEntropyFlat(class_weight, axis=1)
if self.focal_loss:
self.learn.loss_func = FocalLoss(self.learn.loss_func)
if self.mixup:
self.learn.callbacks.append(MixUpCallback(self.learn))
self._arcgis_init_callback() # make first conv weights learnable
self.learn.callbacks.append(LabelCallback(
self.learn)) #appending label callback
self.learn.model = self.learn.model.to(self._device)
# _set_multigpu_callback(self) # MultiGPU doesn't work for U-Net. (Fastai-Forums)
if pretrained_path is not None:
self.load(pretrained_path)
