kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
15,517,725 | def get_valid_transforms() :
return A.Compose([
A.Resize(height=512, width=512, p=1.0),
ToTensorV2(p=1.0),
], p=1.0 )<data_type_conversions> | !pip install.. /input/segmentation-models-pytorch/segmentation_models.pytorch-master/ > /dev/null | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | DATA_ROOT_PATH = '.. /input/global-wheat-detection/test'
class DatasetRetriever(Dataset):
def __init__(self, image_ids, transforms=None):
super().__init__()
self.image_ids = image_ids
self.transforms = transforms
def __getitem__(self, index: int):
image_id = self.image_ids[index]
image = cv2.imread(f'{DATA_ROOT_PATH}/{... | class RANZCRDataset(torch.utils.data.Dataset):
def __init__(
self,
df,
root,
ext,
path_col,
use_timm_aug=False,
transforms=None,
augmentations=None,
):
super().__init__()
df = df.reset_index(drop=True ).copy()
self.transforms = transforms
self.augmentations = augmentations
self.root = root
self.use_timm_aug = use_tim... | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | dataset = DatasetRetriever(
image_ids=np.array([path.split('/')[-1][:-4] for path in glob(f'{DATA_ROOT_PATH}/*.jpg')]),
transforms=get_valid_transforms()
)
def collate_fn(batch):
return tuple(zip(*batch))
data_loader = DataLoader(
dataset,
batch_size=2,
shuffle=False,
num_workers=4,
drop_last=False,
collate_fn=coll... | EFFNETB6_EMB_DIM = 2304
EFFNETB5_EMB_DIM = 2048
EFFNETB4_EMB_DIM = 1792
EFFNETB3_EMB_DIM = 1536
EFFNETB1_EMB_DIM = 1280
RESNET50_EMB_DIM = 2048
REXNET200_EMB_DIM = 2560
VIT_EMB_DIM = 768
NF_RESNET50_EMB_DIM = 2048
EPS = 1e-6
class TaylorSoftmax(nn.Module):
def __init__(self, dim=1, n=2):
super(TaylorSoftmax, self ).__i... | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | def load_net(checkpoint_path):
config = get_efficientdet_config('tf_efficientdet_d5')
net = EfficientDet(config, pretrained_backbone=False)
config.num_classes = 1
config.image_size=512
net.class_net = HeadNet(config, num_outputs=config.num_classes, norm_kwargs=dict(eps=.001, momentum=.01))
checkpoint = torch.load(che... | def get_validation_models(
model_initilizer: Callable,
model_config: Mapping[str, Any],
model_ckp_dicts: List[OrderedDict],
device: str,
):
t_models = []
for mcd in model_ckp_dicts:
t_model = model_initilizer(**model_config, device=device)
t_model.load_state_dict(mcd)
t_model = t_model.to(device)
t_model.eval()
t_... | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | def make_predictions(images, score_threshold=0.22):
images = torch.stack(images ).cuda().float()
predictions = []
with torch.no_grad() :
det = net(images, torch.tensor([1]*images.shape[0] ).float().cuda())
for i in range(images.shape[0]):
boxes = det[i].detach().cpu().numpy() [:,:4]
scores = det[i].detach().cpu().nump... | %matplotlib inline | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | def format_prediction_string(boxes, scores):
pred_strings = []
for j in zip(scores, boxes):
pred_strings.append("{0:.4f} {1} {2} {3} {4}".format(j[0], j[1][0], j[1][1], j[1][2], j[1][3]))
return " ".join(pred_strings )<predict_on_test> | SKIP_VAL = True | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | results = []
for images, image_ids in data_loader:
predictions = make_predictions(images)
for i, image in enumerate(images):
boxes, scores, labels = run_wbf(predictions, image_index=i)
boxes =(boxes*2 ).astype(np.int32 ).clip(min=0, max=1023)
image_id = image_ids[i]
boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
boxes[:, 3... | def public_notebook() :
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
BATCH_SIZE = 64
TEST_PATH = '.. /input/ranzcr-clip-catheter-line-classification/test'
test = pd.read_csv('.. /input/ranzcr-clip-catheter-line-classification/sample_submission.csv')
class TestDataset(Dataset):
def __init__(se... | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | test_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])
test_df.to_csv('submission.csv', index=False)
test_df.head()<install_modules> | RANZCR CLiP - Catheter and Line Position Challenge | |
15,517,725 | !pip install --no-deps '.. /input/timm-package/timm-0.1.26-py3-none-any.whl' > /dev/null
!pip install --no-deps '.. /input/pycocotools/pycocotools-2.0-cp37-cp37m-linux_x86_64.whl' > /dev/null<categorify> | RESIZE_SIZE = 640 | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | def get_valid_transforms() :
return A.Compose([
A.Resize(height=512, width=512, p=1.0),
ToTensorV2(p=1.0),
], p=1.0 )<data_type_conversions> | PATH2DIR = '.. /input/ranzcr-clip-catheter-line-classification/'
os.listdir(PATH2DIR)
train = pd.read_csv(pjoin(PATH2DIR, 'train.csv'))
sample_sub = pd.read_csv(pjoin(PATH2DIR, 'sample_submission.csv'))
split = np.load('.. /input/ranzcr-models/naive_cv_split.npy', allow_pickle=True ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | DATA_ROOT_PATH = '.. /input/global-wheat-detection/test'
class DatasetRetriever(Dataset):
def __init__(self, image_ids, transforms=None):
super().__init__()
self.image_ids = image_ids
self.transforms = transforms
def __getitem__(self, index: int):
image_id = self.image_ids[index]
image = cv2.imread(f'{DATA_ROOT_PATH}/{... | DEVICE = 'cuda' | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | dataset = DatasetRetriever(
image_ids=np.array([path.split('/')[-1][:-4] for path in glob(f'{DATA_ROOT_PATH}/*.jpg')]),
transforms=get_valid_transforms()
)
def collate_fn(batch):
return tuple(zip(*batch))
data_loader = DataLoader(
dataset,
batch_size=4,
shuffle=False,
num_workers=2,
drop_last=False,
collate_fn=coll... | models_512 = []
ckp_names = glob('.. /input/ranzcr-models/timm_efficientnet_b5_unet_32bs_640res_lesslaugs_ls005_shedchanged_startpoint_difflrs_segbranch_125coefs_1e4noseg_bigholes_firstpseudo_swa_roc_auc_score/timm_efficientnet_b5_unet_32bs_640res_lesslaugs_ls005_shedchanged_startpoint_difflrs_segbranch_125coefs_1e4nos... | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | def load_net(checkpoint_path):
config = get_efficientdet_config('tf_efficientdet_d5')
net = EfficientDet(config, pretrained_backbone=False)
config.num_classes = 1
config.image_size=512
net.class_net = HeadNet(config, num_outputs=config.num_classes, norm_kwargs=dict(eps=.001, momentum=.01))
checkpoint = torch.load(che... | if not SKIP_VAL:
val_dfs = [
train.iloc[split[i][1]] for i in range(5)
]
val_loaders = create_val_loaders(
loader_initilizer=RANZCRDataset,
loader_config={
"root":'train_images_512_512',
"path_col": "StudyInstanceUID",
"ext": ".jpeg",
"transforms":T.ToTensor()
},
dfs=val_dfs,
batch_size=32
)
train_logits = predict_... | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | class BaseWheatTTA:
image_size = 512
def augment(self, image):
raise NotImplementedError
def batch_augment(self, images):
raise NotImplementedError
def deaugment_boxes(self, boxes):
raise NotImplementedError
class TTAHorizontalFlip(BaseWheatTTA):
def augment(self, image):
return image.flip(1)
def batch_augment(sel... | def predict_test_with_multiple_models(
my_models: List[List[torch.nn.Module]],
my_loaders: List[torch.utils.data.DataLoader],
predict_func: Callable,
device: str,
):
logits = []
for my_loader in my_loaders:
temp_logits = []
for batch in tqdm(my_loader):
temp_logits_inner = []
for exp_models in my_models:
logit = np.s... | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | def process_det(index, det, score_threshold=0.25):
boxes = det[index].detach().cpu().numpy() [:,:4]
scores = det[index].detach().cpu().numpy() [:,4]
boxes[:, 2] = boxes[:, 2] + boxes[:, 0]
boxes[:, 3] = boxes[:, 3] + boxes[:, 1]
boxes =(boxes ).clip(min=0, max=511 ).astype(int)
indexes = np.where(scores>score_threshol... | INF_BS = 32 | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | tta_transforms = []
for tta_combination in product([TTAHorizontalFlip() , None],
[TTAVerticalFlip() , None],
[TTARotate90() , None]):
tta_transforms.append(TTACompose([tta_transform for tta_transform in tta_combination if tta_transform]))<categorify> | all_test_loaders_512 = []
test_original = RANZCRDataset(**{
"df":sample_sub,
"root":'test_images_512_512',
"path_col": "StudyInstanceUID",
"ext": ".jpeg",
"transforms":T.ToTensor()
})
all_test_loaders_512.append(torch.utils.data.DataLoader(
test_original,
batch_size=INF_BS,
drop_last=False,
shuffle=False,
num_workers... | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | def make_tta_predictions(images, score_threshold=0.25):
with torch.no_grad() :
images = torch.stack(images ).float().cuda()
predictions = []
for tta_transform in tta_transforms:
result = []
det = net(tta_transform.batch_augment(images.clone()), torch.tensor([1]*images.shape[0] ).float().cuda())
for i in range(images.s... | test_logits_512 = predict_test_with_multiple_models(
models_512,
all_test_loaders_512,
cnn_model_predict,
DEVICE
)
| RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | def format_prediction_string(boxes, scores):
pred_strings = []
for j in zip(scores, boxes):
pred_strings.append("{0:.4f} {1} {2} {3} {4}".format(j[0], j[1][0], j[1][1], j[1][2], j[1][3]))
return " ".join(pred_strings )<categorify> | from scipy.special import expit | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | results = []
for images, image_ids in data_loader:
predictions = make_tta_predictions(images)
for i, image in enumerate(images):
boxes, scores, labels = run_wbf(predictions, image_index=i)
boxes =(boxes*2 ).round().astype(np.int32 ).clip(min=0, max=1023)
image_id = image_ids[i]
boxes[:, 2] = boxes[:, 2] - boxes[:, 0... | test_logits = expit(test_logits_512 ).mean(0 ).mean(1 ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | test_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])
test_df.to_csv('submission.csv', index=False)
test_df.head()<load_from_csv> | my_exp_1 = test_logits[0]
my_exp_2 = test_logits[1]
my_exp_3 = test_logits[2]
my_exp_4 = test_logits[3] | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | def load_dataset(root):
csv = pd.read_csv(os.path.join(root, "train.csv"))
data = {}
for i in csv.index:
key = csv["image_id"][i]
bbox = json.loads(csv["bbox"][i])
bbox = [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3], 0.0]
if key in data:
data[key].append(bbox)
else:
data[key] = [bbox]
return sorted(
[(k, ... | blend =(
my_exp_1**0.5 +
my_exp_2**0.5 +
my_exp_3**0.5 +
my_exp_3**0.5
) | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | def load_model(path, ctx=mx.cpu()):
net = gcv.model_zoo.yolo3_darknet53_custom(["wheat"], pretrained_base=False)
net.set_nms(post_nms=150)
net.load_parameters(path, ctx=ctx)
return net
<normalization> | sample_sub.iloc[:,1:] = blend
sample_sub | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | def inference(path):
raw = load_image(path)
rh, rw, _ = raw.shape
classes_list = []
scores_list = []
bboxes_list = []
for _ in range(5):
img, flips = gcv.data.transforms.image.random_flip(raw, px=0.5, py=0.5)
x, _ = gcv.data.transforms.presets.yolo.transform_test(img, short=img_s)
_, _, xh, xw = x.shape
rot = random... | sample_sub.nunique(axis=0 ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | import numpy as np
import pandas as pd
import os
from tqdm.auto import tqdm
import shutil as sh<install_modules> | !rm -rf test_images_512_512
sample_sub.to_csv('submission.csv', index=False)
os.listdir('./' ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | !cp -r.. /input/yolov5train/* .<load_from_csv> | ROOT = Path.cwd().parent
INPUT = ROOT / "input"
OUTPUT = ROOT / "output"
DATA = INPUT / "ranzcr-clip-catheter-line-classification"
TRAIN = DATA / "train"
TEST = DATA / "test"
TRAINED_MODEL = INPUT/ 'ranzer-models'
TMP = ROOT / "tmp"
TMP.mkdir(exist_ok=True)
RANDAM_SEED = 1086
N_CLASSES = 11
FOLDS = [0, 1, 2, 3, 4]
N_F... | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | with open(".. /input/valdata/val4.txt")as f:
content = f.readlines()
` at the end of each line
content = [x.strip().split('/')[-1].split('.')[0] for x in content]
content<install_modules> | for p in DATA.iterdir() :
print(p.name)
train = pd.read_csv(DATA / "train.csv")
smpl_sub = pd.read_csv(DATA / "sample_submission.csv" ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | !pip install --no-deps '.. /input/weightedboxesfusion/' > /dev/null<feature_engineering> | if FAST_COMMIT and len(smpl_sub)== 3582:
smpl_sub = smpl_sub.iloc[:64 * 2].reset_index(drop=True ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | def convertTrainLabel() :
df = pd.read_csv('.. /input/global-wheat-detection/train.csv')
bboxs = np.stack(df['bbox'].apply(lambda x: np.fromstring(x[1:-1], sep=',')))
for i, column in enumerate(['x', 'y', 'w', 'h']):
df[column] = bboxs[:,i]
df.drop(columns=['bbox'], inplace=True)
df['x_center'] = df['x'] + df['w']/2... | def multi_label_stratified_group_k_fold(label_arr: np.array, gid_arr: np.array, n_fold: int, seed: int=42):
np.random.seed(seed)
random.seed(seed)
start_time = time.time()
n_train, n_class = label_arr.shape
gid_unique = sorted(set(gid_arr))
n_group = len(gid_unique)
gid2aid = dict(zip(gid_unique, range(n_group)))
... | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | def run_wbf(boxes, scores, image_size=1023, iou_thr=0.5, skip_box_thr=0.7, weights=None):
labels = [np.zeros(score.shape[0])for score in scores]
boxes = [box/(image_size)for box in boxes]
boxes, scores, labels = weighted_boxes_fusion(boxes, scores, labels, weights=None, iou_thr=iou_thr, skip_box_thr=skip_box_thr)
boxe... | label_arr = train[CLASSES].values
group_id = train.PatientID.values
train_val_indexs = list(
multi_label_stratified_group_k_fold(label_arr, group_id, N_FOLD, RANDAM_SEED)) | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | def makePseudolabel() :
source = '.. /input/global-wheat-detection/test/'
weights = '.. /input/ckpts41/best_fold4.pt'
imgsz = 1024
conf_thres = 0.5
iou_thres = 0.6
is_TTA = True
imagenames = os.listdir(source)
device = torch.device('cuda')if torch.cuda.is_available() else torch.device('cpu')
model = torch.load(weight... | train["fold"] = -1
for fold_id,(trn_idx, val_idx)in enumerate(train_val_indexs):
train.loc[val_idx, "fold"] = fold_id
train.groupby("fold")[CLASSES].sum() | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | !cp 'weights/best.pt' 'best_psuedolblf4.pt'<define_variables> | def resize_images(img_id, input_dir, output_dir, resize_to=(640, 640), ext="png"):
img_path = input_dir / f"{img_id}.jpg"
save_path = output_dir / f"{img_id}.{ext}"
img = cv2.imread(str(img_path), cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, resize_to)
cv2.imwrite(str(save_path), img,)
TEST_RESIZED = TMP / "test_{0}x... | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | def format_prediction_string(boxes, scores):
pred_strings = []
for j in zip(scores, boxes):
pred_strings.append("{0:.4f} {1} {2} {3} {4}".format(j[0], j[1][0], j[1][1], j[1][2], j[1][3]))
return " ".join(pred_strings )<load_pretrained> | def get_activation(activ_name: str="relu"):
act_dict = {
"relu": nn.ReLU(inplace=True),
"tanh": nn.Tanh() ,
"sigmoid": nn.Sigmoid() ,
"identity": nn.Identity() }
if activ_name in act_dict:
return act_dict[activ_name]
else:
raise NotImplementedError
class Conv2dBNActiv(nn.Module):
def __init__(
self, in_channels: i... | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | def detect() :
source = '.. /input/global-wheat-detection/test/'
weights = 'weights/best.pt'
if not os.path.exists(weights):
weights = '.. /input/ckpts41/best_fold4.pt'
imgsz = 1024
conf_thres = 0.5
iou_thres = 0.6
is_TTA = True
imagenames = os.listdir(source)
device = torch.device('cuda')if torch.cuda.is_available() ... | class MultiHeadResNet200D(nn.Module):
def __init__(
self, out_dims_head: tp.List[int]=[3, 4, 3, 1], pretrained=False
):
self.base_name = "resnet200d_320"
self.n_heads = len(out_dims_head)
super(MultiHeadResNet200D, self ).__init__()
base_model = timm.create_model(
self.base_name, num_classes=sum(out_dims_head), p... | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | results = detect()
test_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])
test_df.to_csv('submission.csv', index=False)
test_df.head()<install_modules> | class LabeledImageDataset(data.Dataset):
def __init__(
self,
file_list: tp.List[
tp.Tuple[tp.Union[str, Path], tp.Union[int, float, np.ndarray]]],
transform_list: tp.List[tp.Dict],
):
self.file_list = file_list
self.transform = ImageTransformForCls(transform_list)
def __len__(self):
return len(self.file_list)
... | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | !pip install --no-deps '.. /input/timm-package/timm-0.1.26-py3-none-any.whl' > /dev/null
!pip install --no-deps '.. /input/pycocotools/pycocotools-2.0-cp37-cp37m-linux_x86_64.whl' > /dev/null<define_variables> | def get_dataloaders_for_inference(
file_list: tp.List[tp.List], batch_size=64,
):
dataset = LabeledImageDataset(
file_list,
transform_list=[
["Normalize", {
"always_apply": True, "max_pixel_value": 255.0,
"mean": ["0.4887381077884414"], "std": ["0.23064819430546407"]}],
["ToTensorV2", {"always_apply": True}],
])
... | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | img_sz = 640
device = 'cuda' if torch.cuda.is_available() else 'cpu'
device<normalization> | class ImageTransformBase:
def __init__(self, data_augmentations: tp.List[tp.Tuple[str, tp.Dict]]):
augmentations_list = [
self._get_augmentation(aug_name )(**params)
for aug_name, params in data_augmentations]
self.data_aug = albumentations.Compose(augmentations_list)
def __call__(self, pair: tp.Tuple[np.ndarray]... | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | def get_valid_transforms() :
return A.Compose([A.Resize(height=img_sz, width=img_sz, p=1.0),
ToTensorV2(p=1.0)], p=1.0 )<data_type_conversions> | def load_setting_file(path: str):
with open(path)as f:
settings = yaml.safe_load(f)
return settings
def set_random_seed(seed: int = 42, deterministic: bool = False):
random.seed(seed)
np.random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backe... | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | DATA_ROOT_PATH = '.. /input/global-wheat-detection/test'
class DatasetRetriever(Dataset):
def __init__(self, image_ids, transforms=None):
super().__init__()
self.image_ids = image_ids
self.transforms = transforms
def __getitem__(self, index: int):
image_id = self.image_ids[index]
image = cv2.imread(f'{DATA_ROOT_PATH}/{... | if not torch.cuda.is_available() :
device = torch.device("cpu")
else:
device = torch.device("cuda")
print(device ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | dataset = DatasetRetriever(image_ids=np.array([path.split('/')[-1][:-4] for path in glob(f'{DATA_ROOT_PATH}/*.jpg')]),
transforms=get_valid_transforms())
def collate_fn(batch):
return tuple(zip(*batch))
data_loader = DataLoader(dataset, batch_size=2, shuffle=False, num_workers=4,
drop_last=False, collate_fn=collate_fn... | model_dir = TRAINED_MODEL
test_dir = TEST_RESIZED
test_file_list = [
(test_dir / f"{img_id}.png", [-1] * 11)
for img_id in smpl_sub["StudyInstanceUID"].values]
test_loader = get_dataloaders_for_inference(test_file_list, batch_size=32)
test_preds_arr = np.zeros(( N_FOLD , len(smpl_sub), N_CLASSES))
for fold_id in [0,... | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | def load_net(checkpoint_path):
config = get_efficientdet_config('tf_efficientdet_d5')
net = EfficientDet(config, pretrained_backbone=False)
config.num_classes = 1
config.image_size=img_sz
net.class_net = HeadNet(config, num_outputs=config.num_classes, norm_kwargs=dict(eps=.001, momentum=.01))
checkpoint = torch.load(... | if CONVERT_TO_RANK:
test_preds_arr = test_preds_arr.argsort(axis=1 ).argsort(axis=1)
sub = smpl_sub.copy()
sub[CLASSES] = test_preds_arr.mean(axis=0)
sub.to_csv("submission.csv", index=False ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | class BaseWheatTTA:
image_size = img_sz
def augment(self, image): raise NotImplementedError
def batch_augment(self, images): raise NotImplementedError
def deaugment_boxes(self, boxes): raise NotImplementedError
class TTAHorizontalFlip(BaseWheatTTA):
def augment(self, image): return image.flip(1)
def batch_augment(self... | model_dir = TRAINED_MODEL
test_dir = TEST_RESIZED
test_file_list = [
(test_dir / f"{img_id}.png", [-1] * 11)
for img_id in smpl_sub["StudyInstanceUID"].values]
test_loader = get_dataloaders_for_inference(test_file_list, batch_size=4)
N_FOLD = len([1024])
test_preds_arr = np.zeros(( N_FOLD , len(smpl_sub), N_CLASSES... | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | tta_transforms = []
for tta_combination in product([TTAHorizontalFlip() , None],
[TTAVerticalFlip() , None],
[TTARotate90() , None]):
tta_transforms.append(TTACompose([tta_transform for tta_transform in tta_combination if tta_transform]))<predict_on_test> | sub_2 = smpl_sub.copy()
sub_2[CLASSES] = test_preds_arr.mean(axis=0 ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | def make_predictions(net, images, score_threshold=0.22):
images = torch.stack(images ).cuda().float()
predictions = []
with torch.no_grad() :
det = net(images, torch.tensor([1]*images.shape[0] ).float().cuda())
for i in range(images.shape[0]):
boxes = det[i].detach().cpu().numpy() [:,:4]
scores = det[i].detach().cpu()... | sub[CLASSES] = 0.6 * sub[CLASSES] + 0.4 * sub_2[CLASSES] | RANZCR CLiP - Catheter and Line Position Challenge |
15,372,370 | <categorify><EOS> | sub.to_csv("submission.csv", index=False ) | RANZCR CLiP - Catheter and Line Position Challenge |
13,557,724 | <SOS> metric: MCAUC Kaggle data source: ranzcr-clip-catheter-and-line-position-challenge<save_to_csv> | !pip install /kaggle/input/kerasapplications -q
!pip install /kaggle/input/efficientnet-keras-source-code/ -q --no-deps | RANZCR CLiP - Catheter and Line Position Challenge |
13,557,724 | test_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])
test_df.to_csv('submission.csv', index=False)
test_df.head()<import_modules> | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
warnings.simplefilter('ignore' ) | RANZCR CLiP - Catheter and Line Position Challenge |
13,557,724 | import logging
import os
import re
import gc
import json
from tqdm.auto import tqdm
import numpy as np
import pandas as pd
import cv2
import matplotlib.pyplot as plt<install_modules> | MIXED_PRECISION = True
XLA_ACCELERATE = False
GPUS = tf.config.experimental.list_physical_devices('GPU')
if GPUS:
try:
for GPU in GPUS:
tf.config.experimental.set_memory_growth(GPU, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(GPUS), "Physical GPUs,", len(logical_gpus), "Logical ... | RANZCR CLiP - Catheter and Line Position Challenge |
13,557,724 | !pip install.. /input/pytorch-16/torch-1.6.0cu101-cp37-cp37m-linux_x86_64.whl<install_modules> | class BaseConfig(object):
SEED = 101
TRAIN_DF = '.. /input/ranzcr-clip-catheter-line-classification/train.csv'
TRAIN_IMG_PATH = '.. /input/ranzcr224/RANZCR_224/'
TEST_IMG_PATH = '.. /input/ranzcr-clip-catheter-line-classification/test/'
CLASS_MAP = '.. /input/ranzcr-clip-catheter-line-classification/train_annotations.c... | RANZCR CLiP - Catheter and Line Position Challenge |
13,557,724 | !pip install.. /input/pytorch-16/torchvision-0.7.0cu101-cp37-cp37m-linux_x86_64.whl<install_modules> | df = pd.read_csv(BaseConfig.TRAIN_DF)
submit = pd.read_csv(BaseConfig.SUBMIT)
target_cols = submit.columns[1:]
df.head() | RANZCR CLiP - Catheter and Line Position Challenge |
13,557,724 | !pip install.. /input/pretrainedmodels/pretrainedmodels-0.7.4/pretrainedmodels-0.7.4/ > /dev/null<install_modules> | target_cols = ['ETT - Abnormal', 'ETT - Borderline', 'ETT - Normal',
'NGT - Abnormal', 'NGT - Borderline', 'NGT - Incompletely Imaged',
'NGT - Normal', 'CVC - Abnormal', 'CVC - Borderline', 'CVC - Normal',
'Swan Ganz Catheter Present'] | RANZCR CLiP - Catheter and Line Position Challenge |
13,557,724 | !pip install.. /input/wheat-pkgs/EfficientNet-PyTorch-master/EfficientNet-PyTorch-master/ > /dev/null<install_modules> | def build_decoder(with_labels=True, target_size=(300, 300), ext='jpg'):
def decode(path):
file_bytes = tf.io.read_file(path)
if ext == 'png':
img = tf.image.decode_png(file_bytes, channels=3)
elif ext in ['jpg', 'jpeg']:
img = tf.image.decode_jpeg(file_bytes, channels=3)
else:
raise ValueError("Image extension not s... | RANZCR CLiP - Catheter and Line Position Challenge |
13,557,724 | !pip install.. /input/wheat-pkgs/timm-0.1.20-py3-none-any.whl > /dev/null<install_modules> | class TrainConfig(BaseConfig):
EPOCH = 3
FOLDS = 5
TTA = 5
VERBOSITY = 0
WORKERS = 2
LABEL_SMOOTH = 0.0
MULTIPROCESS = False
LR_RATE = {
'0' : 1e-3,
'1' : 1e-3,
'2' : 1e-4,
'3' : 1e-4,
'4' : 1e-4
}
BATCH_SIZE = {
'0' : 32,
'1' : 128,
'2' : 86,
'3' : 128,
'4' : 128
}
IMG_SIZE = {
'0': 850,
'1': 240,
'2': 260,
'3': 224,
... | RANZCR CLiP - Catheter and Line Position Challenge |
13,557,724 | !pip install.. /input/wheat-pkgs/segmentation_models.pytorch-master/segmentation_models.pytorch-master > /dev/null<import_modules> | class SpatialAttentionModule(tf.keras.layers.Layer):
def __init__(self, kernel_size=3):
super(SpatialAttentionModule, self ).__init__()
self.conv1 = tf.keras.layers.Conv2D(64, kernel_size=kernel_size,
use_bias=False,
kernel_initializer='he_normal',
strides=1, padding='same',
activation=tf.nn.relu6)
self.conv2 = tf.k... | RANZCR CLiP - Catheter and Line Position Challenge |
13,557,724 | set_seed,
create_logging,
WheatDataset,
FastDataLoader,
collate,
ModleWithLoss,
CtdetLoss,
ModelEMA,
get_cosine_schedule_with_warmup,
train_one_epoch,
get_train_transforms
)<define_variables> | class AttentionWeightedAverage2D(tf.keras.layers.Layer):
def __init__(self, **kwargs):
self.init = tf.keras.initializers.get('uniform')
super(AttentionWeightedAverage2D, self ).__init__(** kwargs)
def build(self, input_shape):
self.input_spec = [tf.keras.layers.InputSpec(ndim=4)]
assert len(input_shape)== 4
self.W = ... | RANZCR CLiP - Catheter and Line Position Challenge |
13,557,724 | bifpn_path_0 = '.. /input/wheat-weights/model_centernet_effnetb5_bifpn_00099.pth'
bifpn_path_1 = '.. /input/wheat-weights/model_centernet_effnetb5_bifpn_fold1_00099.pth'
bifpn_path_3 = '.. /input/wheat-weights/model_centernet_effnetb5_bifpn_fold3_lb_ema_00099.pth'<init_hyperparams> | class RANZCRClassifier(tf.keras.Model):
def __init__(self, dim):
super(RANZCRClassifier, self ).__init__()
self.Base = efn.EfficientNetB5(
input_shape=(TrainConfig.IMG_SIZE['0'],
TrainConfig.IMG_SIZE['0'], 3),
weights=None,
include_top=False)
self.GAP1 = tf.keras.layers.GlobalAveragePooling2D()
self.GAP2 = tf.keras.l... | RANZCR CLiP - Catheter and Line Position Challenge |
13,557,724 | class Config:
arch = 'timm-efficientnet-b5'
heads = {'hm': 1,
'wh': 2,
'reg': 2}
head_conv = 64
reg_offset = True
cat_spec_wh = False
img_size = 1024
in_scale = 1024 / img_size
down_ratio = 4
mean = [0.315290, 0.317253, 0.214556],
std = [0.245211, 0.238036, 0.193879]
num_classes = 1
pad = 63
batch_size = 8
K = 128
max_... | model = RANZCRClassifier(( TrainConfig.IMG_SIZE['0'],TrainConfig.IMG_SIZE['0'], 3))
model.build(( None, *(TrainConfig.IMG_SIZE['0'],TrainConfig.IMG_SIZE['0'], 3)))
model.load_weights('.. /input/multiattentioncheckwg/model.h5' ) | RANZCR CLiP - Catheter and Line Position Challenge |
13,557,724 | def change_key(d):
for _ in range(len(d)) :
k, v = d.popitem(False)
d['.'.join(k.split('.')[1:])] = v<load_from_csv> | test_paths = BaseConfig.TEST_IMG_PATH + submit['StudyInstanceUID'] + '.jpg'
test_decoder = build_decoder(with_labels=False,
target_size=(TrainConfig.IMG_SIZE['0'],
TrainConfig.IMG_SIZE['0']))
dtest = build_dataset(
test_paths, bsize=TrainConfig.BATCH_SIZE['0'],
repeat=False, shuffle=False, augment=False,
cache=False, ... | RANZCR CLiP - Catheter and Line Position Challenge |
13,557,724 | DIR_INPUT = '.. /input/global-wheat-detection'
DIR_TRAIN = f'{DIR_INPUT}/train'
DIR_TEST = f'{DIR_INPUT}/test'
train_df = pd.read_csv(f'{DIR_INPUT}/train.csv')
train_df.shape<data_type_conversions> | submit[target_cols] = model.predict(dtest, verbose=1)
submit.to_csv('submission.csv', index=False)
submit.head() | RANZCR CLiP - Catheter and Line Position Challenge |
14,570,708 | train_df['x'] = -1
train_df['y'] = -1
train_df['w'] = -1
train_df['h'] = -1
def expand_bbox(x):
r = np.array(re.findall("([0-9]+[.]?[0-9]*)", x))
if len(r)== 0:
r = [-1, -1, -1, -1]
return r
train_df[['x', 'y', 'w', 'h']] = np.stack(train_df['bbox'].apply(lambda x: expand_bbox(x)))
train_df.drop(columns=['bbox'], inpl... | class PAM_Module(nn.Module):
def __init__(self, in_dim):
super(PAM_Module, self ).__init__()
self.chanel_in = in_dim
self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)
self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)
self.value_conv = nn.Conv2d... | RANZCR CLiP - Catheter and Line Position Challenge |
14,570,708 | class WheatDatasetTest(torch.utils.data.Dataset):
def __init__(self, opt, image_dir, transforms=None,
mean=[0.315290, 0.317253, 0.214556],
std=[0.245211, 0.238036, 0.193879]):
self.opt = opt
self.image_dir = image_dir
self.img_id = os.listdir(self.image_dir)
self.transforms = transforms
self.mean = np.array(mean, dtyp... | class EffNetWLF(nn.Module):
def __init__(self, model_name, target_size=11):
super().__init__()
self.backbone = EfficientNet.from_name(model_name)
self.backbone._dropout = nn.Dropout(0.1)
n_features = self.backbone._fc.in_features
self.backbone._fc = nn.Linear(n_features, target_size)
self.local_fe = CBAM(n_features)... | RANZCR CLiP - Catheter and Line Position Challenge |
14,570,708 | def flip_lr(img):
return np.ascontiguousarray(img[:, ::-1, :])
def deaug_lr(img, boxes):
h, w = img.shape[:2]
boxes[:,(0, 2)] = w - boxes[:,(2, 0)]
return boxes
def flip_ud(img):
return np.ascontiguousarray(img[::-1, :, :])
def deaug_ud(img, boxes):
h, w = img.shape[:2]
boxes[:,(1, 3)] = w - boxes[:,(3, 1)]
return bo... | work_dir = ".. /input/ranzcr-clip-catheter-line-classification/"
df = pd.read_csv(os.path.join(work_dir, "sample_submission.csv"))
test_img_paths = glob.glob(os.path.join(work_dir, "test/*.jpg"))
print(len(test_img_paths))
model_weights = glob.glob(".. /input/effb2wlf/*.pth")
print(model_weights ) | RANZCR CLiP - Catheter and Line Position Challenge |
14,570,708 | testdataset = WheatDatasetTest(opt, DIR_TEST)
print('Total number of images in test set: {}'.format(len(testdataset)))
testdataset_lr = WheatDatasetTest(opt, DIR_TEST, transforms=flip_lr)
testdataset_ud = WheatDatasetTest(opt, DIR_TEST, transforms=flip_ud )<find_best_model_class> | normalize = a_transform.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225], p=1.0, max_pixel_value=255.0)
test_transform = a_transform.Compose([a_transform.Resize(512, 512),
normalize,
ToTensorV2() ], p=1.0)
test_ds = TestDataset(test_img_paths, test_transform)
dataloader = DataLoader(test_ds, batch_siz... | RANZCR CLiP - Catheter and Line Position Challenge |
14,570,708 | <load_pretrained><EOS> | final_pred = np.empty(( len(model_weights),len(test_ds), 11), dtype=np.float32)
for model_idx, each_w in enumerate(model_weights):
print(f"running idx {model_idx}")
model = EffNetWLF("efficientnet-b2")
model = model.to(device)
checkpoint = torch.load(f"{each_w}")
model.load_state_dict(checkpoint["model"])
uids = ... | RANZCR CLiP - Catheter and Line Position Challenge |
14,476,794 | <SOS> metric: MCAUC Kaggle data source: ranzcr-clip-catheter-and-line-position-challenge<predict_on_test> | sys.path.append('.. /input/pytorch-images-seresnet')
warnings.filterwarnings('ignore')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu' ) | RANZCR CLiP - Catheter and Line Position Challenge |
14,476,794 | opt.pad = 63
opt.test_scales = [1.1, ]
threshold = 0.30
bifpn0_pred_boxes_0 , bifpn0_pred_scores_0, h0_list, w0_list, img_ids = do_predict(opt, bifpn_model, threshold=threshold, flip_type=0, return_ids=True, return_shapes=True)
bifpn0_pred_boxes_0_lr, bifpn0_pred_scores_0_lr = do_predict(opt, bifpn_model, threshold=th... | IMAGE_SIZE = 640
BATCH_SIZE = 64
TEST_PATH = '.. /input/ranzcr-clip-catheter-line-classification/test'
MODEL_PATH = '.. /input/efficientnetb5cv9621/tf_efficientnet_b5_ns_CV96.21.pth' | RANZCR CLiP - Catheter and Line Position Challenge |
14,476,794 | del bifpn_model
gc.collect()
torch.cuda.empty_cache()<load_pretrained> | test = pd.read_csv('.. /input/ranzcr-clip-catheter-line-classification/sample_submission.csv' ) | RANZCR CLiP - Catheter and Line Position Challenge |
14,476,794 | bifpn_model = PoseBiFPNNet(opt.arch, opt.heads, opt.head_conv)
checkpoint = torch.load(bifpn_path_1, map_location=device)
change_key(checkpoint['model'])
bifpn_model.load_state_dict(checkpoint['model'])
bifpn_model.to(device)
del checkpoint
gc.collect()<predict_on_test> | def get_transforms() :
return Compose([
Resize(IMAGE_SIZE, IMAGE_SIZE),
Normalize(
),
ToTensorV2() ,
] ) | RANZCR CLiP - Catheter and Line Position Challenge |
14,476,794 | opt.pad = 63
opt.test_scales = [1.1, ]
threshold = 0.30
bifpn1_pred_boxes_0 , bifpn1_pred_scores_0 = do_predict(opt, bifpn_model, threshold=threshold, flip_type=0, return_ids=False, return_shapes=False)
bifpn1_pred_boxes_0_lr, bifpn1_pred_scores_0_lr = do_predict(opt, bifpn_model, threshold=threshold, flip_type=1, ret... | class EfficientNetB5(nn.Module):
def __init__(self, model_name='tf_efficientnet_b5_ns'):
super().__init__()
self.model = timm.create_model(model_name, pretrained=False)
n_features = self.model.classifier.in_features
self.model.global_pool = nn.Identity()
self.model.classifier = nn.Identity()
self.pooling = nn.Adaptive... | RANZCR CLiP - Catheter and Line Position Challenge |
14,476,794 | del bifpn_model
gc.collect()
torch.cuda.empty_cache()<load_pretrained> | def inference(models, test_loader, device):
tk0 = tqdm(enumerate(test_loader), total=len(test_loader))
probs = []
for i,(images)in tk0:
images = images.to(device)
avg_preds = []
for model in models:
with torch.no_grad() :
y_preds1 = model(images)
y_preds2 = model(images.flip(-1))
y_preds =(y_preds1.sigmoid().to('cpu'... | RANZCR CLiP - Catheter and Line Position Challenge |
14,476,794 | bifpn_model = PoseBiFPNNet(opt.arch, opt.heads, opt.head_conv)
checkpoint = torch.load(bifpn_path_3, map_location=device)
change_key(checkpoint['model'])
bifpn_model.load_state_dict(checkpoint['model'])
bifpn_model.to(device)
del checkpoint
gc.collect()<predict_on_test> | model = EfficientNetB5()
model.load_state_dict(torch.load(MODEL_PATH)['model'])
model.eval()
models = [model.to(device)] | RANZCR CLiP - Catheter and Line Position Challenge |
14,476,794 | opt.pad = 63
opt.test_scales = [1.1, ]
threshold = 0.30
bifpn3_pred_boxes_0 , bifpn3_pred_scores_0 = do_predict(opt, bifpn_model, threshold=threshold, flip_type=0, return_ids=False, return_shapes=False)
bifpn3_pred_boxes_0_lr, bifpn3_pred_scores_0_lr = do_predict(opt, bifpn_model, threshold=threshold, flip_type=1, ret... | test_dataset = TestDataset(test, transform=get_transforms())
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False,
num_workers=4 , pin_memory=True)
predictions = inference(models, test_loader, device ) | RANZCR CLiP - Catheter and Line Position Challenge |
14,476,794 | <categorify><EOS> | target_cols = test.iloc[:, 1:12].columns.tolist()
test[target_cols] = predictions
test[['StudyInstanceUID'] + target_cols].to_csv('submission.csv', index=False)
test.head() | RANZCR CLiP - Catheter and Line Position Challenge |
14,298,658 | <SOS> metric: MCAUC Kaggle data source: ranzcr-clip-catheter-and-line-position-challenge<define_variables> | effnet_path = '.. /input/efficientnet-pytorch/'
iterstrat_path = '.. /input/iterative-stratification/iterative-stratification-master'
sys.path.append(effnet_path)
sys.path.append(iterstrat_path ) | RANZCR CLiP - Catheter and Line Position Challenge |
14,298,658 | sys.path.insert(0, ".. /input/weightedboxesfusion")
iou_thr = 0.44
skip_box_thr = 0.00001
pred_boxes_ensemble = []
pred_scores_ensemble = []
for(b00, b01, b02, b03, b04, b05, b06, b07, b08,
b10, b11, b12, b13, b14, b15, b16, b17, b18,
b20, b21, b22, b23, b24, b25, b26, b27, b28,
s00, s01, s02, s03, s04, s05, s06, s07,... | warnings.filterwarnings("ignore")
| RANZCR CLiP - Catheter and Line Position Challenge |
14,298,658 | pred_boxes_ensemble = [denormalize_clip_boxes(a, h0, w0)for a, h0, w0 in zip(pred_boxes_ensemble, h0_list, w0_list)]
pred_scores_ensemble = [a for a in pred_scores_ensemble]<categorify> | IMAGE_SIZE =(512, 512)
PIL.ImageFile.LOAD_TRUNCATED_IMAGES = True
IMAGE_BACKEND = 'cv2'
FOLD = 0
def set_seed(seed=0):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
set_seed() | RANZCR CLiP - Catheter and Line Position Challenge |
14,298,658 | def format_prediction_string(boxes, scores):
pred_strings = []
for s, b in zip(scores, boxes.astype(int)) :
pred_strings.append(f'{s:.4f} {b[0]} {b[1]} {b[2]} {b[3]}')
return " ".join(pred_strings )<compute_test_metric> | data_dir = ".. /input/ranzcr-clip-catheter-line-classification/"
path_checkpoints_dir = "./checkpoints"
path_submissions_dir = "./"
path_trained_models = ".. /input/000-010-sgdr-ensemble-4"
path_test_dir= os.path.join(data_dir, 'test')
path_sample_submission_file= os.path.join(data_dir, 'sample_submission.csv')
submi... | RANZCR CLiP - Catheter and Line Position Challenge |
14,298,658 | pred_strs = []
for bboxes, scores in zip(pred_boxes_ensemble, pred_scores_ensemble):
if len(bboxes)> 0:
bboxes[:, 2] -= bboxes[:, 0]
bboxes[:, 3] -= bboxes[:, 1]
bboxes = bboxes.round()
pred_strs.append(format_prediction_string(bboxes, scores))
else:
pred_strs.append('' )<create_dataframe> | def resize_one_image(input_path, output_path, image_size):
image = cv2.imread(input_path)
image = cv2.resize(image, image_size)
cv2.imwrite(output_path, image)
def resize_image_batch(input_dir, output_dir, image_size):
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
input_paths = [os.path.join(input_dir, i... | RANZCR CLiP - Catheter and Line Position Challenge |
14,298,658 | test_df = pd.DataFrame({'image_id': img_ids, 'PredictionString':pred_strs})
test_df<save_to_csv> | path_resized_test_image_dir = os.path.join('./', "test_resized")
print(path_resized_test_image_dir ) | RANZCR CLiP - Catheter and Line Position Challenge |
14,298,658 | test_df.to_csv('submission.csv', index=False )<set_options> | submission_file = pd.read_csv(path_sample_submission_file)
path_test_images = [os.path.join(path_resized_test_image_dir, i + ".jpg")for i in submission_file.StudyInstanceUID.values] | RANZCR CLiP - Catheter and Line Position Challenge |
14,298,658 | sns.set(context="notebook", style="darkgrid", palette="deep", font="sans-serif", font_scale=1, color_codes=True)
<load_from_csv> | path_test_dir = ".. /input/ranzcr-clip-catheter-line-classification/test"
resize_image_batch(path_test_dir, path_resized_test_image_dir, IMAGE_SIZE ) | RANZCR CLiP - Catheter and Line Position Challenge |
14,298,658 | data= pd.read_csv(".. /input/covid19-global-forecasting-week-1/train.csv")
data["Date"] = data["Date"].apply(lambda x: x.replace("-",""))
data["Date"] = data["Date"].astype(int)
<count_missing_values> | class ImageDataset:
def __init__(
self,
image_paths,
targets=None,
augmentations=None,
backend="cv2",
channel_first=True,
grayscale=False,
grayscale_as_rgb=False,
):
if grayscale is False and grayscale_as_rgb is True:
raise Exception("Invalid combination of " "arguments 'grayscale=False' and 'grayscale_as_rgb=True'... | RANZCR CLiP - Catheter and Line Position Challenge |
14,298,658 | data = data.drop(['Province/State'],axis=1)
data = data.dropna()
data.isnull().sum()
<load_from_csv> | test_dataset = ImageDataset(
path_test_images,
None,
augmentations=test_augmentation,
backend=IMAGE_BACKEND,
channel_first=True,
grayscale=True,
grayscale_as_rgb=True,
) | RANZCR CLiP - Catheter and Line Position Challenge |
14,298,658 | test = pd.read_csv(".. /input/covid19-global-forecasting-week-1/test.csv")
test["Date"] = test["Date"].apply(lambda x: x.replace("-",""))
test["Date"] = test["Date"].astype(int)
test["Lat"] = test["Lat"].fillna(12.5211)
test["Long"] = test["Long"].fillna(69.9683)
test.isnull().sum()
<prepare_x_and_y> | class DataModule(object):
def __init__(self, train_dataset, valid_dataset, test_dataset):
self.train_dataset = train_dataset
self.valid_dataset = valid_dataset
self.test_dataset = test_dataset
def get_train_dataloader(self, **kwargs):
return torch.utils.data.DataLoader(self.train_dataset, **kwargs)
def get_valid_datal... | RANZCR CLiP - Catheter and Line Position Challenge |
14,298,658 | x =data[['Lat', 'Long', 'Date']]
y1 = data[['ConfirmedCases']]
y2 = data[['Fatalities']]
x_test = test[['Lat', 'Long', 'Date']]
<choose_model_class> | class EfficientNetModel(torch.nn.Module):
def __init__(self, num_labels=11, pretrained=True):
super().__init__()
self.num_labels = num_labels
if pretrained:
self.backbone = EfficientNet.from_pretrained("efficientnet-b5",)
else:
self.backbone = EfficientNet.from_name("efficientnet-b5",)
self.dropout = torch.nn.Dropout... | RANZCR CLiP - Catheter and Line Position Challenge |
14,298,658 | model = RandomForestClassifier(n_estimators=200 )<predict_on_test> | class Trainer:
def __init__(self, model, data_module, experiment_id, optimizer=None, scheduler=None, device='cuda'):
self.model = model
self.data_module = data_module
self.optimizer = optimizer
self.scheduler = scheduler
self.device = device
self.fp16 = False
self.step_scheduler_after = None
self.n_epochs = None
self.m... | RANZCR CLiP - Catheter and Line Position Challenge |
14,298,658 | model.fit(x,y1)
pred1 = model.predict(x_test)
pred1 = pd.DataFrame(pred1)
pred1.columns = ["ConfirmedCases_prediction"]<choose_model_class> | def ensemble_models(model_paths, output_file, **kwargs):
model = EfficientNetModel(pretrained=False)
data_module = DataModule(None, None, test_dataset)
preds_list = []
num_models = len(model_paths)
print(f"number of models to ensemble={num_models}")
for mpath in model_paths:
print(mpath)
trainer = Trainer(model,... | RANZCR CLiP - Catheter and Line Position Challenge |
14,298,658 | <predict_on_test><EOS> | model_paths = os.listdir(path_trained_models,)
model_paths = [os.path.join(path_trained_models, mpath)for mpath in model_paths]
ensemble_models(model_paths, "submission", test_batch_size=16)
print("done" ) | RANZCR CLiP - Catheter and Line Position Challenge |
13,898,599 | <SOS> metric: MCAUC Kaggle data source: ranzcr-clip-catheter-and-line-position-challenge<load_from_csv> | OUTPUT_DIR = './'
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
TEST_PATH = '.. /input/ranzcr-clip-catheter-line-classification/test' | RANZCR CLiP - Catheter and Line Position Challenge |
13,898,599 | Sub = pd.read_csv(".. /input/covid19-global-forecasting-week-1/submission.csv")
Sub.columns
sub_new = Sub[["ForecastId"]]<concatenate> | class CFG:
debug=False
num_workers=4
model_name='resnet200d_320'
size=512
batch_size=128
seed=416
target_size=11
target_cols=['ETT - Abnormal', 'ETT - Borderline', 'ETT - Normal',
'NGT - Abnormal', 'NGT - Borderline', 'NGT - Incompletely Imaged', 'NGT - Normal',
'CVC - Abnormal', 'CVC - Borderline', 'CVC - Normal',
'Sw... | RANZCR CLiP - Catheter and Line Position Challenge |
13,898,599 | OP = pd.concat([pred1,pred2,sub_new],axis=1)
OP.head()
OP.columns = ['ConfirmedCases', 'Fatalities', 'ForecastId']
OP = OP[['ForecastId','ConfirmedCases', 'Fatalities']]
<data_type_conversions> | def get_score(y_true, y_pred):
scores = []
for i in range(y_true.shape[1]):
score = roc_auc_score(y_true[:,i], y_pred[:,i])
scores.append(score)
avg_score = np.mean(scores)
return avg_score, scores
def get_result(result_df):
preds = result_df[[f'pred_{c}' for c in CFG.target_cols]].values
labels = result_df[CFG.targ... | RANZCR CLiP - Catheter and Line Position Challenge |
13,898,599 | OP["ConfirmedCases"] = OP["ConfirmedCases"].astype(int)
OP["Fatalities"] = OP["Fatalities"].astype(int)
<save_to_csv> | oof_df = pd.read_csv('.. /input/ranzcr-exp12-step3-fold0/oof_df.csv')
for fold in CFG.trn_fold:
fold_oof_df = oof_df[oof_df['fold']==fold].reset_index(drop=True)
LOGGER.info(f"========== fold: {fold} result ==========")
get_result(fold_oof_df ) | RANZCR CLiP - Catheter and Line Position Challenge |
13,898,599 | OP.to_csv("submission.csv",index=False )<import_modules> | if CFG.debug:
test = pd.read_csv('.. /input/ranzcr-clip-catheter-line-classification/sample_submission.csv', nrows=10)
else:
test = pd.read_csv('.. /input/ranzcr-clip-catheter-line-classification/sample_submission.csv')
print(test.shape)
test.head() | RANZCR CLiP - Catheter and Line Position Challenge |
13,898,599 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
import datetime as dt
import folium<load_pretrained> | train_dataset = TestDataset(test, transform=get_transforms(data='valid'))
for i in range(1):
image = train_dataset[i]
plt.imshow(image[0])
plt.show()
plt.imshow(image[0].flip(-1))
plt.show() | RANZCR CLiP - Catheter and Line Position Challenge |
13,898,599 | !kaggle kaggle competitions download -c covid19-global-forecasting-week-1<install_modules> | class CustomResNet200D(nn.Module):
def __init__(self, model_name='resnet200d_320', pretrained=False):
super().__init__()
self.model = timm.create_model(model_name, pretrained=False)
if pretrained:
pretrained_path = '.. /input/resnet200d-pretrained-weight/resnet200d_ra2-bdba9bf9.pth'
self.model.load_state_dict(torch.lo... | RANZCR CLiP - Catheter and Line Position Challenge |
13,898,599 | !pip install kaggle<set_options> | def inference(models, test_loader, device):
tk0 = tqdm(enumerate(test_loader), total=len(test_loader))
probs = []
for i,(images)in tk0:
images = images.to(device)
avg_preds = []
for model in models:
with torch.no_grad() :
y_preds1 = model(images)
y_preds2 = model(images.flip(-1))
y_preds =(y_preds1.sigmoid().to('cpu'... | RANZCR CLiP - Catheter and Line Position Challenge |
13,898,599 | !mkdir.kaggle<load_pretrained> | %%time
model = CustomResNet200D(CFG.model_name, pretrained=False)
model_path = '.. /input/ranzcr-exp12-step3-fold0/resnet200d_320_fold0_best_loss.pth'
model.load_state_dict(torch.load(model_path)['model'])
model.eval()
models = [model.to(device)] | RANZCR CLiP - Catheter and Line Position Challenge |
13,898,599 | token = {"username":'nitingrover425','key':'c22685e02df7d46edd199e441980c448'}
with open('/content/.kaggle/kaggle.json', 'w')as file:
json.dump(token, file )<install_modules> | test_dataset = TestDataset(test, transform=get_transforms(data='valid'))
test_loader = DataLoader(test_dataset, batch_size=CFG.batch_size, shuffle=False,
num_workers=CFG.num_workers, pin_memory=True)
predictions = inference(models, test_loader, device ) | RANZCR CLiP - Catheter and Line Position Challenge |
13,898,599 | <load_pretrained><EOS> | test[CFG.target_cols] = predictions
test[['StudyInstanceUID'] + CFG.target_cols].to_csv(OUTPUT_DIR+'submission.csv', index=False)
test.head() | RANZCR CLiP - Catheter and Line Position Challenge |
13,871,913 | <SOS> metric: MCAUC Kaggle data source: ranzcr-clip-catheter-and-line-position-challenge<load_from_csv> | import os
import random
import numpy as np
import pandas as pd
import cv2
import matplotlib.pyplot as plt
import torch
import torchvision
from torchvision import transforms
import tensorflow as tf
import albumentations as A | RANZCR CLiP - Catheter and Line Position Challenge |
13,871,913 | train_data = pd.read_csv(".. /input/covid19-global-forecasting-week-1/train.csv" )<count_missing_values> | DIR = ".. /input/ranzcr-clip-catheter-line-classification/" | RANZCR CLiP - Catheter and Line Position Challenge |
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