def predict(learn: Learner, name: str):
# submission.csv
preds, _ = learn.get_preds(ds_type=DatasetType.Test)
test['has_cactus'] = preds.numpy()[:, 0]
test.to_csv('submission_{}.csv'.format(name), index=False)
print('Finish creating submission_{}.csv'.format(name))
# loss.csv
id_ = range(len(learn.recorder.losses))
loss_df = pd.DataFrame({
'id': id_,
'loss': np.array(learn.recorder.losses)
})
loss_df.to_csv('loss_{}.csv'.format(name), index=False)
print('Finish creating loss_{}.csv'.format(name))
# Calculate some metrics on the training set
preds, targets = learn.get_preds(ds_type=DatasetType.Train)
preds_label = np.argmax(preds.numpy(), axis=1)
id_ = range(len(preds))
train_pred_df = pd.DataFrame({
'id': id_,
'preds': preds.numpy()[:, 0],
'preds_label': preds_label,
'targets': targets.numpy()
})
train_pred_df.to_csv('./train_pred_{}.csv'.format(name))
print('Finish creating train_pred_{}.csv'.format(name))
correct_count = np.equal(preds_label, targets.numpy()).sum()
len_preds = len(preds)
incorrect_count = len_preds - correct_count
fpr, tpr = metrics.roc_curve(preds[:, 0], targets)
fpr, tpr = fpr.numpy(), tpr.numpy()
FP = np.floor(fpr * len_preds)
FN = incorrect_count - FP
TP = np.floor(tpr * len_preds)
TN = correct_count - TP
id_ = range(len(fpr))
train_index_df = pd.DataFrame({
'id': id_,
'fpr': fpr,
'tpr': tpr,
'TP': TP,
'TN': TN,
'FP': FP,
'FN': FN
})
train_index_df.to_csv('./train_index_{}.csv'.format(name))
print('Finish creating train_index_{}.csv'.format(name))
# Destroy learn and save the model
learn.export('./model_{}.pth'.format(name), destroy=True)
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.')
