def get_databunch():
df = get_decorated_df()
data = (ImageDataBunch.from_df(df, size=224)
.random_split_by_pct()
.transform())
return data
def export_to_fastAI(self, imagedatabunch_args={}):
# https://docs.fast.ai/vision.data.html#ImageDataBunch.from_df
# from fastai.vision import ImageDataBunch
# data = ImageDataBunch.from_df(path, df, ds_tfms=tfms, size=24)
# return data
try:
from fastai.vision import ImageDataBunch, ImageList
from fastai.vision.transform import get_transforms
except ModuleNotFoundError as e:
ERROR['missing_module']('fastai')
df = self.filesnames_to_ml_df(withAbsPath=False)
fastai_idb = ImageDataBunch.from_df(path='', df=df, **imagedatabunch_args)
return fastai_idb
def save_preds(input_csv, output_csv):
df = pd.read_csv(input_csv)
try:
df = df[['Study']]
except:
try:
df = df[['Path']]
except:
raise ValueError('csv has no attribute for path/study.')
for lbl in ALL_LBLS:
df[lbl] = np.zeros(len(df))
test = ImageDataBunch.from_df(
path=folder_path,
df=df,
folder=chexpert_folder,
seed=0,
label_col=ALL_LBLS,
suffix='',
valid_pct=1,
ds_tfms=data_tfms,
bs=BS,
size=IMG_SZ) #.normalize([IMG_MEAN, IMG_STD])
IDs, outputs = test.valid_ds.x.items, []
learn = cnn_learner(test,
models.densenet121,
model_dir=model_path,
pretrained=False)
learn.load(model_names[0])
output, y, _ = learn.get_preds(ds_type=DatasetType.Valid, with_loss=True)
outputs.append(output)
learn.load(model_names[1])
output, y, _ = learn.get_preds(ds_type=DatasetType.Valid, with_loss=True)
outputs.append(output)
learn = cnn_learner(test,
models.resnet152,
model_dir=model_path,
pretrained=False)
learn.load(model_names[2])
output, y, _ = learn.get_preds(ds_type=DatasetType.Valid, with_loss=True)
outputs.append(output)
learn.load(model_names[3])
output, y, _ = learn.get_preds(ds_type=DatasetType.Valid, with_loss=True)
outputs.append(output)
model = resnext101_64x4d(pretrained=None)
model.last_linear = nn.Sequential(nn.Linear(32768, 2048), nn.ReLU(True),
nn.Dropout(), nn.Linear(2048, 14))
learn = Learner(test, model, model_dir=model_path)
learn.load(model_names[4])
output, y, _ = learn.get_preds(ds_type=DatasetType.Valid, with_loss=True)
outputs.append(output)
learn = cnn_learner(test,
models.vgg19_bn,
model_dir=model_path,
pretrained=False)
learn.load(model_names[5])
output, y, _ = learn.get_preds(ds_type=DatasetType.Valid, with_loss=True)
outputs.append(output)
learn.load(model_names[6])
output, y, _ = learn.get_preds(ds_type=DatasetType.Valid, with_loss=True)
outputs.append(output)
learn = cnn_learner(test,
models.densenet121,
model_dir=model_path,
pretrained=False)
learn.load(model_names[7])
output, y, _ = learn.get_preds(ds_type=DatasetType.Valid, with_loss=True)
outputs.append(output)
output = ensemble_method(outputs, mode='avg')
if torch.cuda.is_available():
output = output.cpu()
output = output.numpy()
df = pd.DataFrame({
'Path': IDs,
EVAL_LBLS[0]: output[:, 1],
EVAL_LBLS[1]: output[:, 2],
EVAL_LBLS[2]: output[:, 3],
EVAL_LBLS[3]: output[:, 4],
EVAL_LBLS[4]: output[:, 5]
})
df.to_csv(output_csv, index=False)
print('submission saved.')
