kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
15,203,842 | optimizer = RMSprop(lr=0.001,rho=0.9,epsilon=1e-08,decay=0.0 )<choose_model_class> | if not torch.cuda.is_available() :
device = torch.device("cpu")
else:
device = torch.device("cuda")
print(device ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,203,842 | model.compile(optimizer=optimizer,loss='categorical_crossentropy',metrics=['accuracy'] )<choose_model_class> | 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=64)
test_preds_arr = np.zeros(( N_FOLD, len(smpl_sub), N_CLASSES))
for fold_id in FOLD... | RANZCR CLiP - Catheter and Line Position Challenge |
15,203,842 | learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',patience=3,verbose=1,factor=0.5,min_lir=0.00001 )<define_variables> | sub = smpl_sub.copy()
sub[CLASSES] = test_preds_arr.mean(axis=0 ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,203,842 | epochs=30
batch_size=86<choose_model_class> | Final_Submission = smpl_sub.copy()
Final_Submission[CLASSES] =.50 * sub[CLASSES] +.50 * submission[CLASSES] | RANZCR CLiP - Catheter and Line Position Challenge |
15,203,842 | <train_model><EOS> | Final_Submission.to_csv("submission.csv", index=False ) | RANZCR CLiP - Catheter and Line Position Challenge |
14,767,736 | <SOS> metric: MCAUC Kaggle data source: ranzcr-clip-catheter-and-line-position-challenge<train_model> | 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 |
14,767,736 | history = model.fit_generator(datagen.flow(X_train,Y_train,batch_size=batch_size),
epochs=epochs,
validation_data=(X_test,Y_test),
verbose=2,
steps_per_epoch=X_train.shape[0]//batch_size,
callbacks=[learning_rate_reduction]
)<load_from_csv> | IMAGE_SIZE = 640
BATCH_SIZE = 128
TEST_PATH = '.. /input/ranzcr-clip-catheter-line-classification/test'
MODEL_PATH = '.. /input/resnet200d-public/resnet200d_320_CV9632.pth' | RANZCR CLiP - Catheter and Line Position Challenge |
14,767,736 | test = pd.read_csv('.. /input/Kannada-MNIST/test.csv' )<define_variables> | test = pd.read_csv('.. /input/ranzcr-clip-catheter-line-classification/sample_submission.csv' ) | RANZCR CLiP - Catheter and Line Position Challenge |
14,767,736 | test_ids = test['id']<drop_column> | def get_transforms() :
return Compose([
Resize(IMAGE_SIZE, IMAGE_SIZE),
Normalize(
),
ToTensorV2() ,
] ) | RANZCR CLiP - Catheter and Line Position Challenge |
14,767,736 | test = test.drop(['id'],axis=1 )<feature_engineering> | class ResNet200D(nn.Module):
def __init__(self, model_name='resnet200d_320'):
super().__init__()
self.model = timm.create_model(model_name, pretrained=False)
n_features = self.model.fc.in_features
self.model.global_pool = nn.Identity()
self.model.fc = nn.Identity()
self.pooling = nn.AdaptiveAvgPool2d(1)
self.fc = nn.... | RANZCR CLiP - Catheter and Line Position Challenge |
14,767,736 | test = test/255.0<predict_on_test> | 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 |
14,767,736 | y_pre = model.predict(test )<prepare_output> | model = ResNet200D()
model.load_state_dict(torch.load(MODEL_PATH)['model'])
model.eval()
models = [model.to(device)] | RANZCR CLiP - Catheter and Line Position Challenge |
14,767,736 | y_pre = np.argmax(y_pre,axis=1 )<create_dataframe> | 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 |
14,767,736 | <prepare_output><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 |
15,074,438 | <SOS> metric: MCAUC Kaggle data source: ranzcr-clip-catheter-and-line-position-challenge<concatenate> | batch_size = 1
image_size = 512
tta = True
submit = True
enet_type = ['resnet200d'] * 5
model_path = ['.. /input/resnet200d-baseline-benchmark-public/resnet200d_fold0_cv953.pth',
'.. /input/resnet200d-baseline-benchmark-public/resnet200d_fold1_cv955.pth',
'.. /input/resnet200d-baseline-benchmark-public/resnet200d_fold2... | RANZCR CLiP - Catheter and Line Position Challenge |
15,074,438 | sub1 = pd.concat([test_ids,sub1],axis=1 )<rename_columns> | sys.path.append('.. /input/pytorch-image-models/pytorch-image-models-master')
sys.path.append('.. /input/timm-pytorch-image-models/pytorch-image-models-master')
DEBUG = False
%matplotlib inline
device = torch.device('cuda')if not DEBUG else torch.device('cpu' ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,074,438 | sub1 = sub1.rename(columns={0:'label'} )<save_to_csv> | class RANZCRResNet200D(nn.Module):
def __init__(self, model_name='resnet200d', out_dim=11, pretrained=False):
super().__init__()
self.model = timm.create_model(model_name, pretrained=False)
n_features = self.model.fc.in_features
self.model.global_pool = nn.Identity()
self.model.fc = nn.Identity()
self.pooling = nn.Ada... | RANZCR CLiP - Catheter and Line Position Challenge |
15,074,438 | sub1.to_csv('submission.csv',index=False )<set_options> | transforms_test = albumentations.Compose([
Resize(image_size, image_size),
Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
ToTensorV2()
] ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,074,438 | plt.style.use('ggplot')
%matplotlib inline<load_from_csv> | test = pd.read_csv('.. /input/ranzcr-clip-catheter-line-classification/sample_submission.csv')
test['file_path'] = test.StudyInstanceUID.apply(lambda x: os.path.join('.. /input/ranzcr-clip-catheter-line-classification/test', f'{x}.jpg'))
target_cols = test.iloc[:, 1:12].columns.tolist()
test_dataset = RANZCRDataset(te... | RANZCR CLiP - Catheter and Line Position Challenge |
15,074,438 | test = pd.read_csv('.. /input/Kannada-MNIST/test.csv')
train = pd.read_csv('.. /input/Kannada-MNIST/train.csv')
dig_df = pd.read_csv('.. /input/Kannada-MNIST/Dig-MNIST.csv')
sample_df = pd.read_csv('.. /input/Kannada-MNIST/sample_submission.csv' )<split> | if submit:
test_preds = []
for i in range(len(enet_type)) :
if enet_type[i] == 'resnet200d':
print('resnet200d loaded')
model = RANZCRResNet200D(enet_type[i], out_dim=len(target_cols))
model = model.to(device)
model.load_state_dict(torch.load(model_path[i], map_location='cuda:0'))
if tta:
test_preds += [tta_inference... | RANZCR CLiP - Catheter and Line Position Challenge |
15,074,438 | <choose_model_class><EOS> | if fast_sub:
pd.read_csv('.. /input/ranzcr-clip-catheter-line-classification/sample_submission.csv',usecols=[0],index_col=0 ).join(pd.read_csv(fast_sub_path ).set_index('StudyInstanceUID')).fillna(0 ).to_csv('submission.csv' ) | RANZCR CLiP - Catheter and Line Position Challenge |
14,456,355 | <SOS> metric: MCAUC Kaggle data source: ranzcr-clip-catheter-and-line-position-challenge<choose_model_class> | 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 |
14,456,355 | learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy',
patience=7,
verbose=1,
factor=0.1,
min_lr=1e-8 )<choose_model_class> | IMAGE_SIZE = 640
BATCH_SIZE = 128
TEST_PATH = '.. /input/ranzcr-clip-catheter-line-classification/test'
MODEL_PATH = '.. /input/seresnet152d-cv9615/seresnet152d_320_CV96.15.pth' | RANZCR CLiP - Catheter and Line Position Challenge |
14,456,355 | def build_norm_model() :
inputs = layers.Input(shape=(28, 28, 1))
x = layers.Conv2D(filters=64, kernel_size=(5, 5), strides=(1, 1), padding='same', input_shape=(28, 28, 1))(inputs)
x = layers.LeakyReLU(alpha=0.3 )(x)
x = layers.BatchNormalization()(x)
x = layers.Conv2D(filters=128, kernel_size=(3, 3), strides=(1, 1)... | test = pd.read_csv('.. /input/ranzcr-clip-catheter-line-classification/sample_submission.csv' ) | RANZCR CLiP - Catheter and Line Position Challenge |
14,456,355 | def make_prediction(model, x):
y_pred = model.predict(x)
return np.argmax(y_pred, axis=1 )<categorify> | def get_transforms() :
return Compose([
Resize(IMAGE_SIZE, IMAGE_SIZE),
Normalize(
),
ToTensorV2() ,
] ) | RANZCR CLiP - Catheter and Line Position Challenge |
14,456,355 |
<train_model> | class SeResNet152D(nn.Module):
def __init__(self, model_name='seresnet152d_320'):
super().__init__()
self.model = timm.create_model(model_name, pretrained=False)
n_features = self.model.fc.in_features
self.model.global_pool = nn.Identity()
self.model.fc = nn.Identity()
self.pooling = nn.AdaptiveAvgPool2d(1)
self.fc =... | RANZCR CLiP - Catheter and Line Position Challenge |
14,456,355 | final_model = build_norm_model()
final_model.compile(optimizer=optimizers.RMSprop(lr=0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
history_final = final_model.fit_generator(train_datagen.flow(X_train, y_train, batch_size=1024),
steps_per_epoch=100,
epochs=120,
validation_data=(X_valid, y_valid),
callb... | 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 |
14,456,355 | y_result = make_prediction(final_model, X_test)
sample_df['label'] = y_result
sample_df.to_csv('submission.csv',index=False )<set_options> | model = SeResNet152D()
model.load_state_dict(torch.load(MODEL_PATH)['model'])
model.eval()
models = [model.to(device)] | RANZCR CLiP - Catheter and Line Position Challenge |
14,456,355 | plt.style.use('ggplot')
%matplotlib inline
np.random.RandomState(42)
<load_from_csv> | 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 |
14,456,355 | <choose_model_class><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 |
14,814,599 | <SOS> metric: MCAUC Kaggle data source: ranzcr-clip-catheter-and-line-position-challenge<set_options> | warnings.filterwarnings("ignore")
| RANZCR CLiP - Catheter and Line Position Challenge |
14,814,599 | optimizers = {
'sgd': opt.SGD() ,
'sgd+momentum': opt.SGD(nesterov=True),
'rmsprop': opt.RMSprop() ,
'adam': opt.Adam() ,
}
<choose_model_class> | RANZCR CLiP - Catheter and Line Position Challenge | |
14,814,599 | batch_size = 1024
epochs = 5
datagen_train = ImageDataGenerator(
rotation_range = 10,
width_shift_range = 0.25,
height_shift_range = 0.25,
shear_range = 0.1,
zoom_range = 0.4,
horizontal_flip = False
)
datagen_val = ImageDataGenerator()
learning_rate_reduction = ReduceLROnPlateau(
monitor='loss',
factor=0.25,
patie... | RANZCR CLiP - Catheter and Line Position Challenge | |
14,814,599 | %matplotlib inline<load_from_csv> | RANZCR CLiP - Catheter and Line Position Challenge | |
14,814,599 | train = pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')
test = pd.read_csv('/kaggle/input/Kannada-MNIST/test.csv' )<prepare_x_and_y> | RANZCR CLiP - Catheter and Line Position Challenge | |
14,814,599 | X = train.drop(['label'],axis=1)
y = train['label']
display(X.head() ,y.head() )<filter> | RANZCR CLiP - Catheter and Line Position Challenge | |
14,814,599 | <init_hyperparams><EOS> | img_size = 600
def auto_select_accelerator() :
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
print("Running on TPU:", tpu.master())
except ValueError:... | RANZCR CLiP - Catheter and Line Position Challenge |
13,702,907 | <SOS> metric: MCAUC Kaggle data source: ranzcr-clip-catheter-and-line-position-challenge<train_model> | !pip install /kaggle/input/kerasapplications -q
!pip install /kaggle/input/efficientnet-keras-source-code/ -q --no-deps | RANZCR CLiP - Catheter and Line Position Challenge |
13,702,907 | def train_mnist() :
Reached 99.9% accuracy so cancelling training!")
callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)
X_train, X_val, y_train, y_val = train_test_split(X,y,random_state=0,test_size=0.2,shuffle=True)
X_val, X_test, y_val, y_test = train_test_split(X_val,y_val,random_state=0... | import os
import efficientnet.tfkeras as efn
import numpy as np
import pandas as pd
import tensorflow as tf | RANZCR CLiP - Catheter and Line Position Challenge |
13,702,907 |
<prepare_x_and_y> | def auto_select_accelerator() :
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
print("Running on TPU:", tpu.master())
except ValueError:
strategy = tf.... | RANZCR CLiP - Catheter and Line Position Challenge |
13,702,907 | history, model, X_test, y_test = train_mnist()<prepare_x_and_y> | COMPETITION_NAME = "ranzcr-clip-catheter-line-classification"
strategy = auto_select_accelerator()
BATCH_SIZE = strategy.num_replicas_in_sync * 16 | RANZCR CLiP - Catheter and Line Position Challenge |
13,702,907 | X_test =(X_test / 255.0 ).reshape(len(X_test),28,28,1)
test =(test / 255.0 ).reshape(len(test),28,28,1 )<compute_test_metric> | IMSIZE =(224, 240, 260, 300, 380, 456, 528, 600, 512)
load_dir = f"/kaggle/input/{COMPETITION_NAME}/"
sub_df = pd.read_csv(load_dir + 'sample_submission.csv')
test_paths = load_dir + "test/" + sub_df['StudyInstanceUID'] + '.jpg'
label_cols = sub_df.columns[1:]
test_decoder = build_decoder(with_labels=False, target_si... | RANZCR CLiP - Catheter and Line Position Challenge |
13,702,907 | loss, acc = model.evaluate(X_test,y_test)
print(f"Accuracy: {acc}")
print(f"Loss: {loss}" )<predict_on_test> | def create_model(w, input_shape=[IMSIZE[-2], IMSIZE[-2],3], classes=11):
base_model = efn.EfficientNetB7(include_top=False,
weights=None,
input_shape=input_shape)
inputs = tf.keras.Input(shape=input_shape)
x = base_model(inputs)
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(classes )(x)
... | RANZCR CLiP - Catheter and Line Position Challenge |
13,702,907 | <create_dataframe><EOS> | sub_df[label_cols] = model.predict(dtest, verbose=1)
sub_df.to_csv('submission.csv', index=False)
sub_df.head() | RANZCR CLiP - Catheter and Line Position Challenge |
13,676,994 | <SOS> metric: MCAUC Kaggle data source: ranzcr-clip-catheter-and-line-position-challenge<save_to_csv> | import tensorflow as tf
from tensorflow.keras import layers
import os
import re
import math
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd | RANZCR CLiP - Catheter and Line Position Challenge |
13,676,994 | df.to_csv('submission.csv', index=False )<load_from_csv> | GCS_DS_PATH = ".. /input/ranzcr-clip-catheter-line-classification" | RANZCR CLiP - Catheter and Line Position Challenge |
13,676,994 | base_path = '/kaggle/input/Kannada-MNIST/'
train = pd.read_csv(base_path + 'train.csv')
test = pd.read_csv(base_path + 'test.csv')
dig_mnist = pd.read_csv(base_path + 'Dig-MNIST.csv')
train_label = train['label']
train_data_raw = train.drop('label', axis=1, inplace=False)
train_data = []
train_data_raw = np.array(t... | train_df = pd.read_csv(GCS_DS_PATH+"/train.csv")
train_df.index = train_df["StudyInstanceUID"]
del train_df["StudyInstanceUID"]
train_annot_df = pd.read_csv(GCS_DS_PATH+"/train_annotations.csv")
train_annot_df.index = train_annot_df["StudyInstanceUID"]
del train_annot_df["StudyInstanceUID"] | RANZCR CLiP - Catheter and Line Position Challenge |
13,676,994 | train_data = np.array(train_data)/255.0
test_data = np.array(test_data)/255.0
train_label = np.array(train_label ).reshape(60000,-1 )<choose_model_class> | classes = list(train_df.columns[:-1])
classes_normal= [name for name in classes[:-1] if name.split(" - ")[1] == "Normal"]
classes_abnormal= [name for name in classes[:-1] if name.split(" - ")[1] == "Abnormal"]
classes_borderline = [name for name in classes[:-1] if name.split(" - ")[1] == "Borderline"]
classes_count = ... | RANZCR CLiP - Catheter and Line Position Challenge |
13,676,994 | es = EarlyStopping(monitor='accuracy', mode='min', verbose=1, patience=5,baseline=0.99)
def define_model() :
model2 = keras.models.Sequential([
keras.layers.Conv2D(16,(3,3), input_shape=(28,28,1), activation='relu'),
keras.layers.BatchNormalization() ,
keras.layers.Conv2D(16,(5,5), activation='relu', padding='same'),
... | class_weights = {}
ls = list(classes_count.values)
tot_samples = sum(ls)
for i in range(num_classes):
class_weights[i] = tot_samples/(num_classes*ls[i])
class_weights | RANZCR CLiP - Catheter and Line Position Challenge |
13,676,994 | def define_model_ResNet50() :
model = keras.models.Sequential()
input_layer = keras.layers.Input(shape=(224, 224, 3), name='image_input')
model.add(DenseNet121(weights=None, include_top=False, input_tensor=input_layer))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(128))
model.add(keras.layers.BatchN... | patient_ids = train_df["PatientID"].unique()
patientwise_count = train_df['PatientID'].value_counts()
num_patients = len(patientwise_count)
print("Number of patients: ",num_patients)
patientwise_count | RANZCR CLiP - Catheter and Line Position Challenge |
13,676,994 | EPOCHS =100
X_train ,X_test,Y_train,Y_test= train_test_split(train_data,train_label,test_size=0.1)
print(np.array(Y_train ).shape)
es = keras.callbacks.EarlyStopping(monitor='accuracy', mode='min', patience=3, baseline=0.99)
checkpoint = ModelCheckpoint('best_weights.h5', monitor='val_loss', sava_best_only=True, mod... | IMAGE_SIZE = [600,600]
AUTO = tf.data.experimental.AUTOTUNE
TEST_FILENAMES = tf.io.gfile.glob(GCS_DS_PATH + '/test_tfrecords/*.tfrec' ) | RANZCR CLiP - Catheter and Line Position Challenge |
13,676,994 |
<prepare_x_and_y> | def decode_image(image_data):
image = tf.image.decode_jpeg(image_data, channels=3)
image = tf.cast(image, tf.float32)/ 255.0
image = tf.image.resize(image, [*IMAGE_SIZE])
return image
def read_labeled_tfrecord(example):
LABELED_TFREC_FORMAT = {
"StudyInstanceUID" : tf.io.FixedLenFeature([], tf.string),
"image" : tf.i... | RANZCR CLiP - Catheter and Line Position Challenge |
13,676,994 |
<load_from_csv> | def data_augment(image, label):
image = tf.image.random_flip_left_right(image)
return image,label
def get_test_dataset(ordered=False):
dataset = load_dataset(TEST_FILENAMES, labeled=False, ordered=ordered)
dataset = dataset.map(data_augment, num_parallel_calls=AUTO)
dataset = dataset.batch(BATCH_SIZE)
dataset = dat... | RANZCR CLiP - Catheter and Line Position Challenge |
13,676,994 |
<prepare_x_and_y> | BATCH_SIZE = 16 * strategy.num_replicas_in_sync
test_ds = get_test_dataset()
print("Test:", test_ds ) | RANZCR CLiP - Catheter and Line Position Challenge |
13,676,994 | print(np.array(X_train ).shape)
print(np.array(Y_train ).shape)
datagen = ImageDataGenerator(featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=20,
zoom_range = 0.2,
width_shift_range=0.20,
height_shift_range=... | !pip install /kaggle/input/kerasapplications -q
!pip install /kaggle/input/efficientnet-keras-source-code/ -q --no-deps
| RANZCR CLiP - Catheter and Line Position Challenge |
13,676,994 | model_prediction = define_model()
model_prediction.load_weights('best_weights.h5' )<data_type_conversions> | model = tf.keras.models.load_model(".. /input/ranzcr-clip-tpu/model.h5" ) | RANZCR CLiP - Catheter and Line Position Challenge |
13,676,994 | <save_to_csv><EOS> | test_ids=[]
test_pred = []
j=0
for batch in test_ds:
images,ids_batch = batch
pred_batch = model.predict(images)
for i,ids in enumerate(ids_batch):
j+=1
if j%500 == 0:
print(str(j),"Test Images Done")
test_ids.append(ids)
test_pred.append(pred_batch[i])
test_ids = [np.array(i ).astype("str" ).tolist() for i in test... | RANZCR CLiP - Catheter and Line Position Challenge |
13,542,132 | <SOS> metric: MCAUC Kaggle data source: ranzcr-clip-catheter-and-line-position-challenge<install_modules> | warnings.simplefilter("ignore")
| RANZCR CLiP - Catheter and Line Position Challenge |
13,542,132 | !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<init_hyperparams> | print('Train images: %d' %len(os.listdir(os.path.join(WORK_DIR, "train")))) | RANZCR CLiP - Catheter and Line Position Challenge |
13,542,132 | %%time
def detect(save_img=False):
weights, imgsz = opt.weights,opt.img_size
source = '.. /input/global-wheat-detection/test/'
device = torch_utils.select_device(opt.device)
half = False
models = []
for w in weights:
models.append(torch.load(w, map_location=device)['model'].to(device ).float().eval())
dataset = LoadI... | train = pd.read_csv(os.path.join(WORK_DIR, "train.csv"))
train_images = WORK_DIR + "/train/" + train['StudyInstanceUID'] + '.jpg'
ss = pd.read_csv(os.path.join(WORK_DIR, 'sample_submission.csv'))
test_images = WORK_DIR + "/test/" + ss['StudyInstanceUID'] + '.jpg'
label_cols = ss.columns[1:]
labels = train[label_cols].v... | RANZCR CLiP - Catheter and Line Position Challenge |
13,542,132 | def run_wbf_yolo(boxes,scores, image_size=1024, iou_thr=0.4, skip_box_thr=0.34, weights=None):
labels0 = [np.ones(len(scores[idx])) for idx in range(len(scores)) ]
boxes, scores, labels = weighted_boxes_fusion(boxes, scores, labels0, weights=None, iou_thr=iou_thr, skip_box_thr=skip_box_thr)
return boxes, scores, label... | BATCH_SIZE = 8 * 1
STEPS_PER_EPOCH = len(train)* 0.85 / BATCH_SIZE
VALIDATION_STEPS = len(train)* 0.15 / BATCH_SIZE
EPOCHS = 30
TARGET_SIZE = 750 | RANZCR CLiP - Catheter and Line Position Challenge |
13,542,132 | all_path,all_score,all_bboxex = res
yolov5preds = {}
for row in range(len(all_path)) :
preds = {}
image_id = all_path[row].split("/")[-1].split(".")[0]
boxes = all_bboxex[row]
scores = all_score[row]
boxes, scores, labels = run_wbf_yolo(boxes,scores)
yolov5preds[image_id] = [boxes,scores,labels]<choose_model_class> | def build_decoder(with_labels = True,
target_size =(TARGET_SIZE, TARGET_SIZE),
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 ValueErr... | RANZCR CLiP - Catheter and Line Position Challenge |
13,542,132 | 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... | test_df = build_dataset(
test_images, bsize = BATCH_SIZE, repeat = False,
shuffle = False, augment = False, cache = False)
test_df | RANZCR CLiP - Catheter and Line Position Challenge |
13,542,132 | best_iou_thr = 0.432
best_skip_box_thr = 0.397<load_pretrained> | RANZCR CLiP - Catheter and Line Position Challenge | |
13,542,132 | models = [
load_net('.. /input/kernel5c5dc38533/effdet5-cutmix-augmix-bboxaug-0/last-checkpoint.bin'),
load_net('.. /input/fold-1-global-wheat/effdet5-cutmix-augmix-bboxaug-1/last-checkpoint.bin'),
load_net('.. /input/fold-2-global-wheat/effdet5-cutmix-augmix-bboxaug-2/last-checkpoint.bin'),
load_net('.. /input/fold-3-... | print('Our Xception CNN has %d layers' %len(model.layers)) | RANZCR CLiP - Catheter and Line Position Challenge |
13,542,132 | DATA_ROOT_PATH = '.. /input/global-wheat-detection/test'
class TestDatasetRetriever(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_PAT... | img_tensor = build_dataset(
pd.Series(test_images[0]), bsize = 1,repeat = False,
shuffle = False, augment = False, cache = False ) | RANZCR CLiP - Catheter and Line Position Challenge |
13,542,132 | <define_variables><EOS> | ss[label_cols] = model.predict(test_df, verbose = 1)
ss.to_csv('submission.csv', index = False ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | <SOS> metric: MCAUC Kaggle data source: ranzcr-clip-catheter-and-line-position-challenge<create_dataframe> | class CFG:
device = 'GPU'
cpu_workers = 2
debug = True
seed = 13353
batch_size = 50
num_tta = 2
num_folds = 3
fold_idx = False
fold_blend = 'pmean'
model_blend = 'pmean'
power = 1/11
w_public = 0.25
lgb_folds = 5
label_features = False
sort_targets = True
pred_as_feature = True
lgb_stop_rounds = 200
lgb_params = {'obje... | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | y = effdetpreds.copy()
x = yolov5preds.copy()<statistical_test> | CFG = dict(vars(CFG))
for key in ['__dict__', '__doc__', '__module__', '__weakref__']:
del CFG[key] | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | def run_last_wbf(yolo,eff,weights=[2,1],iou_thr=0.5,skip_box_thr=0.397):
box1,scores1,labels1 = yolo
box2,scores2,labels2 = eff
box1 = box1/1023
box2 = box2/1023
boxes, scores, labels = weighted_boxes_fusion([box1,box2], [scores1,scores2],
[labels1,labels2],weights=[2,1],
iou_thr=iou_thr, skip_box_thr=skip_box_thr)
re... | CFGs = []
for model in CFG['models']:
model_cfg = pickle.load(open(model + 'configuration.pkl', 'rb'))
CFGs.append(model_cfg)
print('Numer of models:', len(CFGs)) | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | iou_thr = 0.5
skip_box_thr = 0.0001
results = []
for row in range(len(all_path)) :
image_id = all_path[row].split("/")[-1].split(".")[0]
boxes,scores = run_last_wbf(yolov5preds[image_id],effdetpreds[image_id])
boxes =(boxes*1023 ).astype(np.int32 ).clip(min=0, max=1023)
boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
boxes[:... | pd.set_option('display.max_columns', 100)
ImageFile.LOAD_TRUNCATED_IMAGES = True
%matplotlib inline
warnings.filterwarnings('ignore')
sys.path.append('.. /input/timm-pytorch-image-models/pytorch-image-models-master')
| RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | test_df.to_csv('submission.csv', index=False)
test_df.head()<install_modules> | if CFG['device'] == 'GPU':
print('Training on GPU...')
device = torch.device('cuda:0')
if CFG['device'] == 'CPU':
print('Training on CPU...')
device = torch.device('cpu' ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | ! pip install.. /input/mmdetectionv260/addict-2.4.0-py3-none-any.whl
! pip install.. /input/mmdetectionv260/mmcv_full-latesttorch1.6.0cu102-cp37-cp37m-manylinux1_x86_64.whl
! pip install.. /input/mmdetectionv260/mmpycocotools-12.0.3-cp37-cp37m-linux_x86_64.whl
! pip install.. /input/mmdetection-package/mmdet-2.7.0-py3-... | 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 compute_blend(df, preds, blend, CFG, weights = None):
if weights is None:
weights = np.ones(len(preds)) / len(preds... | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | ! pip install.. /input/mmdetection-package/torch-1.6.0-cp37-cp37m-linux_x86_64.whl<install_modules> | df = pd.read_csv(CFG['data_path'] + 'sample_submission.csv')
CFG['targets'] = ['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']
CFG['num_classes'] ... | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 |
<install_modules> | for m in CFG['models']:
tmp_train_preds = pd.read_csv(m + '/oof.csv')
tmp_train_preds.columns = ['StudyInstanceUID'] + CFG['targets'] + ['PatientID', 'fold'] + [m + ' ' + c for c in CFG['targets']]
if m == CFG['models'][0]:
train_preds = tmp_train_preds
else:
train_preds = train_preds.merge(tmp_train_preds[['StudyInst... | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | ! cp -r.. /input/mmdetection-wheat-models/attention_stage1/faster_rcnn_r50_fpn_attention_0010_dcn_albu_1x4_1x_bWheat_kaggle.py./config.py
! cp -r.. /input/mmdetection-wheat-models/attention_stage1/epoch_12.pth./model.pth<define_variables> | for c in CFG['targets']:
class_preds = train_preds.filter(like = 'kaggle' ).filter(like = c ).columns
for blend in ['amean', 'median', 'gmean', 'pmean', 'rmean']:
train_preds[blend + ' ' + c] = compute_blend(train_preds, class_preds, blend, CFG)
for blend in ['amean', 'median', 'gmean', 'pmean', 'rmean']:
train_preds[... | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | CONFIG_FILE = './config.py'
CHECKPOINT_PATH = './model.pth'
TEST_IMG_DIR = '.. /input/global-wheat-detection/test'<import_modules> | def get_dataset(CFG):
class ImageData(Dataset):
def __init__(self,
df,
path,
transform = None,
labeled = False,
indexed = False):
self.df = df
self.path = path
self.transform = transform
self.labeled = labeled
self.indexed = indexed
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
path = os.path.join... | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | import os
import json
import pandas as pd
import numpy as np
import cv2
from tqdm import tqdm
import torch
import mmcv
from mmdet.apis import init_detector, inference_detector<define_variables> | def get_model(CFG, device, num_classes):
if CFG['weights'] != 'public':
model = timm.create_model(model_name = CFG['backbone'],
pretrained = False,
in_chans = CFG['channels'])
if 'efficient' in CFG['backbone']:
model.classifier = nn.Linear(model.classifier.in_features, num_classes)
else:
model.fc = nn.Linear(model.fc... | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | TEST_ANN_FILE = './annotation_test.json'
anns = []
for img_name in tqdm(os.listdir(TEST_IMG_DIR)) :
if not img_name.endswith('.jpg'):
continue
anns.append(dict(filename=img_name, boxes=[]))<load_pretrained> | cv_start = time.time()
gc.collect()
all_counter = 0
fold_counter = 0 if not CFG['fold_idx'] else CFG['fold_idx']
all_cnn_preds = None
for model_idx in range(len(CFG['models'])) :
ImageData = get_dataset(CFGs[model_idx])
test_dataset = ImageData(df = df,
path = CFG['data_path'] + 'test/',
transform = get_augs(CFGs[mode... | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | with open('./result.bbox.json', 'r')as f1, open(TEST_ANN_FILE, 'r')as f2:
result_info = json.load(f1)
annotations_info = json.load(f2)
for i, ann in tqdm(enumerate(result_info)) :
if ann['score'] < 0.5:
continue
annotation = ann
annotation['id'] = i
annotation['area'] = ann['bbox'][2] * ann['bbox'][3]
annotation['isc... | print('Blending fold predictions with: ' + CFG['fold_blend'])
for m in CFG['models']:
for c in CFG['targets']:
class_preds = all_cnn_preds.filter(like = m ).filter(like = c ).columns
all_cnn_preds[m + c] = compute_blend(all_cnn_preds, class_preds, CFG['fold_blend'], CFG)
all_cnn_preds.drop(class_preds, axis = 1, inpl... | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | train_config = './train_config.py'
cfg = mmcv.Config.fromfile(CONFIG_FILE)
cfg.data.samples_per_gpu = 8
cfg.data.workers_per_gpu = 4
cfg.data.train.ann_file = './annotation_new.json'
cfg.data.train.img_prefix = TEST_IMG_DIR
cfg.data.train.pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_b... | for m in CFG['models']:
tmp_train_preds = pd.read_csv(m + '/oof.csv')
tmp_train_preds.columns = ['StudyInstanceUID'] + CFG['targets'] + ['PatientID', 'fold'] + [m + '' + c for c in CFG['targets']]
if m == CFG['models'][0]:
train_preds = tmp_train_preds
else:
train_preds = train_preds.merge(tmp_train_preds[['StudyInsta... | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | ! python./mmdetection/tools/train.py {train_config} --no-validate --work-dir./pseudo<define_variables> | test_preds = all_cnn_preds.copy()
test_preds = pd.concat([df['StudyInstanceUID'], test_preds], axis = 1)
test_preds.head() | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | 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 )<set_options> | X = train_preds.copy()
X_test = test_preds.copy()
drop_features = ['StudyInstanceUID', 'PatientID', 'fold'] + CFG['targets']
features = [f for f in X.columns if f not in drop_features]
print(len(features), 'features')
display(features ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | device = 'cuda:0' if torch.cuda.is_available() else 'cpu'<load_pretrained> | folds = pd.read_csv('/kaggle/input/how-to-properly-split-folds/train_folds.csv')
del X['fold']
X = X.merge(folds[['StudyInstanceUID', 'fold']], how = 'left', on = 'StudyInstanceUID' ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | config = mmcv.Config.fromfile(CONFIG_FILE)
config.model.pretrained = None
config.data.test.pipeline[1]['img_scale'] = [(1280, 1280),(1408, 1408)]
model = init_detector(config, './pseudo/epoch_1.pth', device=device)
model.eval()<predict_on_test> | if CFG['sort_targets']:
sorted_targets = ['Swan Ganz Catheter Present',
'ETT - Normal',
'ETT - Abnormal',
'ETT - Borderline',
'NGT - Abnormal',
'NGT - Normal',
'NGT - Incompletely Imaged',
'NGT - Borderline',
'CVC - Abnormal',
'CVC - Normal',
'CVC - Borderline'] | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | results = []
score_threshold = 0.3
with torch.no_grad() :
for img_name in tqdm(os.listdir(TEST_IMG_DIR)) :
img_pth = os.path.join(TEST_IMG_DIR, img_name)
image = mmcv.imread(img_pth)
result = inference_detector(model, image)
boxes = result[0][:, :4]
scores = result[0][:, 4]
if len(boxes)> 0:
boxes[:, 2] = boxes[:, 2... | cnn_oof = np.zeros(( len(X), CFG['num_classes']))
lgb_oof = np.zeros(( len(X), CFG['num_classes']))
lgb_tst = np.zeros(( len(X_test), CFG['lgb_folds'], CFG['num_classes']))
all_lgb_preds = None
cv_start = time.time()
print('-' * 45)
print('{:<28}{:<7}{:>5}'.format('Label', 'Model', 'AUC'))
print('-' * 45)
for label i... | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | test_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])
test_df.to_csv('submission.csv', index=False)
test_df.head()<install_modules> | print('Blending fold predictions with: ' + CFG['fold_blend'])
for c in CFG['targets']:
class_preds = all_lgb_preds.filter(like = c ).columns
all_lgb_preds[c] = compute_blend(all_lgb_preds, class_preds, CFG['fold_blend'], CFG)
all_lgb_preds.drop(class_preds, axis = 1, inplace = True)
all_lgb_preds.head() | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | !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> | if CFG['w_public'] > 0:
gc.collect()
BATCH_SIZE = 96
IMAGE_SIZE = 640
TEST_PATH = '.. /input/ranzcr-clip-catheter-line-classification/test'
MODEL_PATH_resnet200d = '.. /input/resnet200d-public/resnet200d_320_CV9632.pth'
MODEL_PATH_seresnet152d = '.. /input/seresnet152d-cv9615/seresnet152d_320_CV96.15.pth'
class TestDat... | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | 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> | if CFG['w_public'] == 0:
df_pub = all_lgb_preds.copy()
else:
for c in CFG['targets']:
class_preds = df_pub.filter(like = c ).columns
df_pub[c] = compute_blend(df_pub, class_preds, CFG['model_blend'], CFG, weights = np.array([2/3, 1/3]))
df_pub.drop(class_preds, axis = 1, inplace = True)
df_pub.head() | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | 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}/{... | all_preds = all_lgb_preds.copy()
all_preds.columns = ['my/' + c for c in all_preds.columns]
df_pub.columns = ['public/' + c for c in df_pub.columns]
preds = pd.concat([all_preds, df_pub], axis = 1)
for c in CFG['targets']:
class_preds = preds.filter(like = c ).columns
preds[c] = compute_blend(preds, class_preds, CFG['... | RANZCR CLiP - Catheter and Line Position Challenge |
15,587,798 | <choose_model_class><EOS> | if all_counter == len(CFG['models'] * CFG['num_folds']):
for c in CFG['targets']:
df[c] = preds[c].rank(pct = True)
df.to_csv('submission.csv', index = False)
display(df.head() ) | RANZCR CLiP - Catheter and Line Position Challenge |
15,559,926 | <SOS> metric: MCAUC Kaggle data source: ranzcr-clip-catheter-and-line-position-challenge<categorify> | !pip install /kaggle/input/kerasapplications -q
!pip install /kaggle/input/efficientnet-keras-source-code/ -q --no-deps | RANZCR CLiP - Catheter and Line Position Challenge |
15,559,926 | 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... | import os, gc
import efficientnet.tfkeras as efn
import numpy as np
import pandas as pd
import tensorflow as tf | RANZCR CLiP - Catheter and Line Position Challenge |
15,559,926 | 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... | def auto_select_accelerator() :
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
print("Running on TPU:", tpu.master())
except ValueError:
strategy = tf.... | RANZCR CLiP - Catheter and Line Position Challenge |
15,559,926 | 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> | COMPETITION_NAME = "ranzcr-clip-catheter-line-classification"
strategy = auto_select_accelerator()
BATCH_SIZE = strategy.num_replicas_in_sync * 16 | RANZCR CLiP - Catheter and Line Position Challenge |
15,559,926 | 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... | model_paths = [
'.. /input/ranzcr-last-models/0.952_model_640_47.h5',
'.. /input/ranzcr-last-models/0.953_model_616_51.h5',
'.. /input/ranzcr-last-models/0.953_model_640_43.h5',
'.. /input/ranzcr-last-models/0.954_model_640_42.h5',
'.. /input/ranzcr-last-models/0.954_model_632_48.h5',
]
subs = []
for model_path in mode... | RANZCR CLiP - Catheter and Line Position Challenge |
15,559,926 | <categorify><EOS> | submission = pd.concat(subs)
submission = submission.groupby('StudyInstanceUID' ).mean()
submission.to_csv('submission.csv')
submission | RANZCR CLiP - Catheter and Line Position Challenge |
15,517,725 | <SOS> metric: MCAUC Kaggle data source: ranzcr-clip-catheter-and-line-position-challenge<save_to_csv> | !pip install.. /input/timm-repo/pytorch-image-models-master/ > /dev/null | 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> | !pip install.. /input/pretrainedmodels-pytorch/pretrained-models.pytorch-master/ > /dev/null | 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> | !pip install.. /input/efficientnet-pyotrch/EfficientNet-PyTorch-master/ > /dev/null | RANZCR CLiP - Catheter and Line Position Challenge |
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