kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
18,398,566 | def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
set_seed(SEED )<load_from_csv> | train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
print(train.shape, test.shape)
train.sample(10, random_state=26 ) | Natural Language Processing with Disaster Tweets |
18,398,566 | marking = pd.read_csv('.. /input/global-wheat-detection/train.csv')
bboxs = np.stack(marking['bbox'].apply(lambda x: np.fromstring(x[1:-1], sep=',')))
for i, column in enumerate(['x', 'y', 'w', 'h']):
marking[column] = bboxs[:,i]
marking.drop(columns=['bbox'], inplace=True )<feature_engineering> | def preprocess(df):
df_new = df.copy(deep=True)
df_new['text'] = df.apply(lambda row: re.sub('@[A-z0-9]', '', row['text'] ).lower() , axis=1)
df_new['text_w_kword'] = df_new.apply(lambda row: 'keyword: ' + str(row['keyword'])+ '.'+ str(row['text']), axis=1)
return df_new
train_prep = preprocess(train)
test_prep = p... | Natural Language Processing with Disaster Tweets |
18,398,566 | 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... | X_train, X_valid, y_train, y_valid = train_test_split(train_prep['text_w_kword'],
train_prep['target'],
test_size=0.1,
random_state=1 ) | Natural Language Processing with Disaster Tweets |
18,398,566 | def run_wbf(boxes, scores, image_size=1024, 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... | tokenizer = DistilBertTokenizerFast.from_pretrained('/kaggle/input/huggingface-bert-variants/distilbert-base-uncased/distilbert-base-uncased/')
train_encodings = tokenizer(list(X_train), truncation=True, padding='max_length', max_length=100)
valid_encodings = tokenizer(list(X_valid), truncation=True, padding='max_len... | Natural Language Processing with Disaster Tweets |
18,398,566 | @jit(nopython=True)
def calculate_iou(gt, pr, form='pascal_voc')-> float:
if form == 'coco':
gt = gt.copy()
pr = pr.copy()
gt[2] = gt[0] + gt[2]
gt[3] = gt[1] + gt[3]
pr[2] = pr[0] + pr[2]
pr[3] = pr[1] + pr[3]
dx = min(gt[2], pr[2])- max(gt[0], pr[0])+ 1
if dx < 0:
return 0.0
dy = min(gt[3], pr[3])- max(gt[1], pr[1... | train_dataset = tf.data.Dataset.from_tensor_slices((
dict(train_encodings),
y_train.values.astype('float32' ).reshape(( -1,1))
))
valid_dataset = tf.data.Dataset.from_tensor_slices((
dict(valid_encodings),
y_valid.values.astype('float32' ).reshape(( -1,1))
))
train_dataset | Natural Language Processing with Disaster Tweets |
18,398,566 | def log(text):
print(text)
def optimize(space, all_predictions, n_calls=10):
@use_named_args(space)
def score(**params):
log('-'*10)
log(params)
final_score = calculate_final_score(all_predictions, **params)
log(f'final_score = {final_score}')
log('-'*10)
return -final_score
return gp_minimize(func=score, dimens... | es = EarlyStopping(monitor='val_loss',
verbose=1,
patience=4,
restore_best_weights=True ) | Natural Language Processing with Disaster Tweets |
18,398,566 | def makePseudolabel() :
source = '.. /input/global-wheat-detection/test/'
weights = WEIGHTS
imagenames = os.listdir(source)
device = torch.device('cuda')if torch.cuda.is_available() else torch.device('cpu')
model = torch.load(weights, map_location=device)['model'].float()
model.to(device ).eval()
dataset = LoadImages... | batch_size = 64
num_epochs = 15
num_train_steps =(X_train.shape[0] // batch_size)* num_epochs
lr_scheduler = PolynomialDecay(
initial_learning_rate=5e-5,
end_learning_rate=1e-5,
decay_steps=num_train_steps
)
new_opt = Adam(learning_rate=lr_scheduler ) | Natural Language Processing with Disaster Tweets |
18,398,566 | if PSEUDO or VALIDATE:
convertTrainLabel()<find_best_params> | def f1_score(true, pred):
ground_positives = K.sum(true, axis=0)+ K.epsilon()
pred_positives = K.sum(pred, axis=0)+ K.epsilon()
true_positives = K.sum(true * pred, axis=0)+ K.epsilon()
precision = true_positives / pred_positives
recall = true_positives / ground_positives
f1 = 2 *(precision * recall)/(precision + recall... | Natural Language Processing with Disaster Tweets |
18,398,566 | if VALIDATE and is_TEST:
all_predictions = validate()
for score_threshold in tqdm(np.arange(0, 1, 0.01), total=np.arange(0, 1, 0.01 ).shape[0]):
final_score = calculate_final_score(all_predictions, best_iou_thr, best_skip_box_thr, score_threshold)
if final_score > best_final_score:
best_final_score = final_score
bes... | model = TFDistilBertForSequenceClassification.from_pretrained('/kaggle/input/huggingface-bert-variants/distilbert-base-uncased/distilbert-base-uncased/',
num_labels=2)
model.compile(
optimizer=new_opt,
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
)
history = model.fit(train_dataset.batch(batc... | Natural Language Processing with Disaster Tweets |
18,398,566 | 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)
def detect() :
source = '.. /input/global-wheat-detection/test/'
weights = 'weights/best.pt'
if not o... | test_encodings = tokenizer(list(test_prep['text_w_kword']), truncation=True, padding='max_length', max_length=100)
test_dataset = tf.data.Dataset.from_tensor_slices((
dict(test_encodings)
)) | Natural Language Processing with Disaster Tweets |
18,398,566 | results = detect()
test_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])
test_df.to_csv('submission.csv', index=False)
test_df.head()<import_modules> | test_preds = model.predict(test_dataset.batch(1)) | Natural Language Processing with Disaster Tweets |
18,398,566 | 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> | class_preds = np.argmax(test_preds.logits, axis=1)
class_preds | Natural Language Processing with Disaster Tweets |
18,398,566 | !pip install.. /input/pytorch-16/torch-1.6.0cu101-cp37-cp37m-linux_x86_64.whl<install_modules> | valid_preds = model.predict(valid_dataset.batch(batch_size)) | Natural Language Processing with Disaster Tweets |
18,398,566 | !pip install.. /input/pytorch-16/torchvision-0.7.0cu101-cp37-cp37m-linux_x86_64.whl<install_modules> | valid_class_preds = np.argmax(valid_preds.logits, axis=1 ) | Natural Language Processing with Disaster Tweets |
18,398,566 | !pip install.. /input/pretrainedmodels/pretrainedmodels-0.7.4/pretrainedmodels-0.7.4/ > /dev/null<install_modules> | print(classification_report(y_valid, valid_class_preds)) | Natural Language Processing with Disaster Tweets |
18,398,566 | !pip install.. /input/wheat-pkgs/EfficientNet-PyTorch-master/EfficientNet-PyTorch-master/ > /dev/null<install_modules> | df_submission = pd.DataFrame({'id':test['id'].values,
'target':class_preds})
df_submission | Natural Language Processing with Disaster Tweets |
18,398,566 | <install_modules><EOS> | df_submission.to_csv('submission.csv', index=False ) | Natural Language Processing with Disaster Tweets |
20,927,228 | <SOS> metric: meanfscore Kaggle data source: natural-language-processing-with-disaster-tweets<import_modules> | print(tf.__version__ ) | Natural Language Processing with Disaster Tweets |
20,927,228 | set_seed,
create_logging,
WheatDataset,
FastDataLoader,
collate,
ModleWithLoss,
CtdetLoss,
ModelEMA,
get_constant_schedule_with_warmup,
train_one_epoch,
get_train_transforms,
freeze_bn
)<define_variables> | df = pd.read_csv('.. /input/disaster-tweets-cleaned/df.csv')
test_df = pd.read_csv('.. /input/disaster-tweets-cleaned/test_df.csv')
print(df.shape, test_df.shape ) | Natural Language Processing with Disaster Tweets |
20,927,228 | 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> | train_txts, val_txts, y_train, y_val = train_test_split(
df[col].values, df['target'].values,
shuffle = True, test_size = 0.15,
stratify = df['target'].values,
)
test_txts = test_df[col].values
y_test = test_df['target'].values
print('Train size:', train_txts.shape)
print('Validation size:', val_txts.shape)
print(... | Natural Language Processing with Disaster Tweets |
20,927,228 | 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_... | tokenizer = BertTokenizer.from_pretrained('bert-base-uncased' ) | Natural Language Processing with Disaster Tweets |
20,927,228 | def change_key(d):
for _ in range(len(d)) :
k, v = d.popitem(False)
d['.'.join(k.split('.')[1:])] = v<load_from_csv> | def tokenize_txts(txts, max_len = 40):
res = tokenizer(
text = [tokenizer.tokenize(txt)for txt in txts],
max_length = max_len,
padding = 'max_length',
truncation = True,
is_split_into_words = True,
)
return {
'input_word_ids': res['input_ids'],
'input_mask': res['attention_mask'],
'input_type_ids': res['token_type_i... | Natural Language Processing with Disaster Tweets |
20,927,228 | 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> | MAX_LEN = 35
BATCH_SIZE = 32 | Natural Language Processing with Disaster Tweets |
20,927,228 | 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... | train_tokens = tokenize_txts(train_txts, MAX_LEN)
val_tokens = tokenize_txts(val_txts, MAX_LEN)
test_tokens = tokenize_txts(test_txts, MAX_LEN)
| Natural Language Processing with Disaster Tweets |
20,927,228 | 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... | print('Orginal txt: ', train_txts[0])
print()
sample = train_tokens['input_word_ids'][0]
print('Tokenized txt:', sample)
print()
print('Detokenizd txt:', detokenize_txt(sample)) | Natural Language Processing with Disaster Tweets |
20,927,228 | 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... | train_ds = tf.data.Dataset.from_tensor_slices(( train_tokens, y_train))
val_ds = tf.data.Dataset.from_tensor_slices(( val_tokens, y_val))
test_ds = tf.data.Dataset.from_tensor_slices(( test_tokens, y_test))
train_ds = train_ds.batch(BATCH_SIZE)
val_ds = val_ds.batch(BATCH_SIZE)
test_ds = test_ds.batch(BATCH_SIZE ) | Natural Language Processing with Disaster Tweets |
20,927,228 | 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> | class MyF1(tf.keras.metrics.Metric):
def __init__(self, name = 'mf1_score'):
super(MyF1, self ).__init__(name)
self.p = tf.metrics.Precision()
self.r = tf.metrics.Recall()
self.f1 = self.add_weight(name="f1", initializer="zeros")
def update_state(self, actual, predicted, sample_weight = None):
self.p.update_state(act... | Natural Language Processing with Disaster Tweets |
20,927,228 | def do_predict(opt, model, threshold, flip_type=0, return_ids=False, return_shapes=False):
if flip_type == 0:
test_dataset = testdataset
deaug_transform = None
elif flip_type == 1:
test_dataset = testdataset_lr
deaug_transform = deaug_lr
elif flip_type == 2:
test_dataset = testdataset_ud
deaug_transform = deaug_ud
dete... | class CSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
def __init__(self, lr, freeze_epoch ,batch_size, data_size):
super(CSchedule, self ).__init__()
self.lr = lr
self.bs = batch_size
self.ds = data_size
self.freeze_epoch = freeze_epoch
def __call__(self, step):
epoch = step /(self.ds / self.bs)+ 1
if no... | Natural Language Processing with Disaster Tweets |
20,927,228 | bifpn_model = PoseBiFPNNet(opt.arch, opt.heads, opt.head_conv)
checkpoint = torch.load(bifpn_path_0, 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> | bert_handler = 'https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/3' | Natural Language Processing with Disaster Tweets |
20,927,228 | 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... | class MClassifier(tf.keras.Model):
def __init__(self, dropout_rate):
super(MClassifier, self ).__init__()
self.bert_layer = hub.KerasLayer(
bert_handler,
name = 'feature_ext',
trainable = True,
)
self.dropout = tf.keras.layers.Dropout(dropout_rate)
self.proba = tf.keras.layers.Dense(1, activation = 'sigmoid')
def ... | Natural Language Processing with Disaster Tweets |
20,927,228 | del bifpn_model
gc.collect()
torch.cuda.empty_cache()<load_pretrained> | LEARNING_RATE = 2e-5
EPOCHS = 5
DP_RATE = 0.3
loss_objective = tf.keras.losses.BinaryCrossentropy() | Natural Language Processing with Disaster Tweets |
20,927,228 | 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 model_evaluation(model, ds, name):
acc = tf.keras.metrics.BinaryAccuracy()
f1 = MyF1()
total_loss = []
y_hats = []
for X, y in ds:
y_hat = model(X, training = False)
loss = loss_objective(y, y_hat)
acc.update_state(y, y_hat)
f1.update_state(y, y_hat)
total_loss.append(loss.numpy())
y_hats.append(y_hat)
y_hats... | Natural Language Processing with Disaster Tweets |
20,927,228 | 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... | @tf.function
def train_step(model, tr_vars, X, y):
with tf.GradientTape() as tape:
y_hat = model(X, training = True)
loss = loss_objective(y, y_hat)
grads = tape.gradient(loss, tr_vars)
return loss, grads, y_hat
def train_model(model, epochs, freeze_bert_on_epoch = None):
clr = CSchedule(LEARNING_RATE, freeze_bert_o... | Natural Language Processing with Disaster Tweets |
20,927,228 | del bifpn_model
gc.collect()
torch.cuda.empty_cache()<load_pretrained> | model = MClassifier(DP_RATE,)
y_test_hat = train_model(model, EPOCHS, freeze_bert_on_epoch = 3 ) | Natural Language Processing with Disaster Tweets |
20,927,228 | 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> | y_model_hat = np.array([1 if x[0] >0.5 else 0 for x in y_test_hat])
print(classification_report(y_test, y_model_hat)) | Natural Language Processing with Disaster Tweets |
20,927,228 | 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... | def train_gbm_cls(X_tr, y_tr, X_val, y_val, X_test, y_test):
gbm_cls = LGBMClassifier(
objective = 'binary',
class_weight = 'balanced'
)
gbm_cls.fit(
X_tr, y_tr,
eval_set =(X_val, y_val),
early_stopping_rounds = 20,
verbose = 0,
)
print('Train')
print(classification_report(y_train, gbm_cls.predict(X_tr)))
print... | Natural Language Processing with Disaster Tweets |
20,927,228 | del bifpn_model
gc.collect()
torch.cuda.empty_cache()<categorify> | sub = pd.DataFrame(columns = ['id', 'target'])
sub['id'] = test_df.id
sub['target'] = gbm_y_hat
| Natural Language Processing with Disaster Tweets |
20,927,228 | def normalize_boxes(boxes, h0, w0):
boxes[:, 0] = boxes[:, 0] / w0
boxes[:, 1] = boxes[:, 1] / h0
boxes[:, 2] = boxes[:, 2] / w0
boxes[:, 3] = boxes[:, 3] / h0
return boxes
def denormalize_clip_boxes(boxes, h0, w0):
boxes[:, 0] = np.clip(boxes[:, 0] * w0, 0, w0-1)
boxes[:, 1] = np.clip(boxes[:, 1] * h0, 0, h0-1)
boxe... | nsub = pd.DataFrame(columns = ['id', 'target'])
nsub['id'] = test_df.id
nsub['target'] = y_model_hat
nsub.to_csv('submission.csv', index = False ) | Natural Language Processing with Disaster Tweets |
20,267,711 | 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,
b10, b11, b12, b13, b14, b15,
b20, b21, b22, b23, b24, b25,
s00, s01, s02, s03, s04, s05,
s10, s11, s12, s13, s14, s15,
s20, s21, s22, s23, s24,... | !pip install tweet_preprocessor
!pip install datasets | Natural Language Processing with Disaster Tweets |
20,267,711 | 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> | train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv' ) | Natural Language Processing with Disaster Tweets |
20,267,711 | data_dict_pseudo = []
id_generator = 20201000000
for idx,(bboxes, h0, w0)in enumerate(zip(tqdm(pred_boxes_ensemble), h0_list, w0_list)) :
img_dict = {
'file_name': os.path.join(DIR_TEST, testdataset[idx][1]),
'height': h0,
'width': w0,
'id': id_generator,
}
annotations = []
for bbox in bboxes:
xywh = np.round([bbox[0],... | sample_submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
sample_submission.head(20 ) | Natural Language Processing with Disaster Tweets |
20,267,711 | data_dict_pseudo = [d for d in data_dict_pseudo if len(d['annotations'])> 0]<init_hyperparams> | train.dropna() | Natural Language Processing with Disaster Tweets |
20,267,711 | class TrainConfig:
seed = 2519
arch = 'timm-efficientnet-b5'
heads = {
'hm': 1,
'wh': 2,
'reg': 2}
head_conv = 64
reg_offset = True
data_root = '.. /input/global-wheat-detection'
crop_size = 896
scale = 0.
shift = 0.
rotate = 15.
shear = 5.
down_ratio = 4
debug = False
hm_weight = 1
off_weight = 1
wh_weight = 0.1
b... | print('train positive samples: %d' % train[train['target'] == 1].shape[0])
print('train negative samples: %d' % train[train['target'] == 0].shape[0] ) | Natural Language Processing with Disaster Tweets |
20,267,711 | with open('.. /input/wheat-splits/wheat_train_3.json', 'r')as f:
data_dict_train = json.load(f)
with open('.. /input/wheat-splits/wheat_valid_3.json', 'r')as f:
data_dict_valid = json.load(f )<create_dataframe> | def preprocess(text):
text = text.replace("
text = p.clean(text)
return text
train['text'] = train['text'].apply(lambda x: preprocess(x))
test['text'] = test['text'].apply(lambda x: preprocess(x))
print(train['text'].values.tolist() [:5])
print(test['text'].values.tolist() [:5] ) | Natural Language Processing with Disaster Tweets |
20,267,711 | def main(opt):
set_seed(opt.seed)
torch.backends.cudnn.benchmark = True
create_logging(opt.logs_dir, 'w')
train_dataset = WheatDataset(
opt,
opt.data_root,
data_dict_train,
data_dict_pseudo=data_dict_pseudo,
img_size=1024,
transforms=get_train_transforms(opt.crop_size),
is_train=True,
load_to_ram=False)
logging.inf... | X_train, X_val, y_train, y_val = train_test_split(train['text'], train['target'], test_size=.1, random_state=42)
X_test = test['text'].values.tolist()
X_train = X_train.tolist()
X_val = X_val.tolist()
y_train = y_train.tolist()
y_val = y_val.tolist() | Natural Language Processing with Disaster Tweets |
20,267,711 | gc.collect()
torch.cuda.empty_cache()
pseudo_model = PoseBiFPNNet(train_opt.arch, train_opt.heads, train_opt.head_conv)
if len(os.listdir(DIR_TEST)) < 20:
train_opt.total_epochs = 2
train_opt.stage_epochs = 2
data_dict_train = data_dict_train[:300]
state_dict = main(train_opt)
change_key(state_dict)
pseudo_model.loa... | model = AutoModelForSequenceClassification.from_pretrained('vinai/bertweet-large')
tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-large", use_fast=False ) | Natural Language Processing with Disaster Tweets |
20,267,711 | opt.pad = 63
opt.test_scales = [1.1, ]
threshold = 0.32
pseudo_pred_boxes_0 , pseudo_pred_scores_0, h0_list, w0_list, img_ids = do_predict(opt, pseudo_model, threshold=threshold, flip_type=0, return_ids=True, return_shapes=True)
pseudo_pred_boxes_0_lr, pseudo_pred_scores_0_lr = do_predict(opt, pseudo_model, threshold=... | train_encodings = tokenizer(X_train, truncation=True, padding=True)
val_encodings = tokenizer(X_val, truncation=True, padding=True ) | Natural Language Processing with Disaster Tweets |
20,267,711 | pred_boxes_pseudo = [denormalize_clip_boxes(a, h0, w0)for a, h0, w0 in zip(pred_boxes_pseudo, h0_list, w0_list)]
pred_scores_pseudo = [a for a in pred_scores_pseudo]<categorify> | class DisasterDataset(torch.utils.data.Dataset):
def __init__(self, encodings, labels):
self.encodings = encodings
self.labels = labels
def __getitem__(self, idx):
item = {key: torch.tensor(val[idx])for key, val in self.encodings.items() }
item['labels'] = torch.tensor(self.labels[idx])
return item
def __len__(self):
... | Natural Language Processing with Disaster Tweets |
20,267,711 | 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> | acc = load_metric('accuracy')
precision = load_metric('precision')
recall = load_metric('recall')
f1 = load_metric('f1')
def compute_metrics(eval_pred):
predictions, labels = eval_pred
predictions = np.argmax(predictions, axis=1)
acc_result = acc.compute(predictions=predictions, references=labels)
precision_resul... | Natural Language Processing with Disaster Tweets |
20,267,711 | pred_strs = []
for bboxes, scores in zip(pred_boxes_pseudo, pred_scores_pseudo):
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> | nlp=pipeline("sentiment-analysis", model=model.to('cpu'), tokenizer=tokenizer)
test_preds = []
for index, text in enumerate(test['text'].values.tolist()):
if index % 10 == 0:
print(index)
if nlp(text)[0]['label'] == 'LABEL_0':
test_preds.append(0)
else:
test_preds.append(1)
test['target'] = test_preds | Natural Language Processing with Disaster Tweets |
20,267,711 | <save_to_csv><EOS> | submissions = test.drop(labels = ["keyword", "location", "text"], axis = 1)
submissions.to_csv("submissions.csv", index = False ) | Natural Language Processing with Disaster Tweets |
19,958,768 | <SOS> metric: meanfscore Kaggle data source: natural-language-processing-with-disaster-tweets<init_hyperparams> | warnings.simplefilter(action='ignore', category=FutureWarning)
| Natural Language Processing with Disaster Tweets |
19,958,768 | TIME_LIMIT = 6.9*60*60
Origin = False
p_HF = True
p_ROT = False
eimg_size = 1024
pesudo_thres = 0.4
pesudo_iou = 0.35
pesudo_score_threshold = 0
img_size = 1024
NMS_IOU_THR = 0.6
NMS_CONF_THR = 0.35
best_iou_thr = 0.35
best_skip_box_thr = 0.35
best_final_score = 0
best_score_threshold = 0.1
EPO = 8
WEIGHTS = ".. /input... | try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print('Running on TPU ', tpu.master())
except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
else:
strategy = tf.distrib... | Natural Language Processing with Disaster Tweets |
19,958,768 | !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
sys.path.insert(0, ".. /input/timm-efficientdet-pytorch")
sys.path.insert(0, ".. /input/omegaconf")
<feature_engineering> | train_dir = ".. /input/nlp-getting-started/train.csv"
df = pd.read_csv(train_dir)
x =(df['text']
.str.lower()
.str.replace('\x89Ûª|Ûª', "'")
.str.replace('
|\x89.|\x9d *', ' ')
.str.replace('>', ">")
.str.replace('<', "<")
.str.replace('&', " and ")
.str.replace('won't', 'will not')
.str.replace('can't', ... | Natural Language Processing with Disaster Tweets |
19,958,768 | def convertTrainLabel(max_label_numbers):
df = pd.read_csv(csv_file)
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['x1'] = df['x'] + df['w']
df['y1'] = df['y'] + df['h'... | tokenizer = AutoTokenizer.from_pretrained('vinai/bertweet-base',
normalization=True,
use_fast = False,
add_special_tokens=True,
pad_to_max_length=True,
return_attention_mask=True)
train_token = tokenizer(x_train.tolist() ,
padding="max_length",
truncation=True,
return_tensors = 'tf' ).data
val_token = tokenizer(x_val.... | Natural Language Processing with Disaster Tweets |
19,958,768 | def run_wbf(boxes, scores, image_size=1024, 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... | with strategy.scope() :
bert_model = TFRobertaModel.from_pretrained("vinai/bertweet-base")
def build_model(hidden_n, drop = 0.3, lr = 1e-5, weight_decay = 1e-6):
with strategy.scope() :
input_ids = tf.keras.Input(shape=(128,),dtype='int32', name = 'input_ids')
attention_masks = tf.keras.Input(shape=(128,),dtype='int3... | Natural Language Processing with Disaster Tweets |
19,958,768 | @jit(nopython=True)
def calculate_iou(gt, pr, form='pascal_voc')-> float:
if form == 'coco':
gt = gt.copy()
pr = pr.copy()
gt[2] = gt[0] + gt[2]
gt[3] = gt[1] + gt[3]
pr[2] = pr[0] + pr[2]
pr[3] = pr[1] + pr[3]
dx = min(gt[2], pr[2])- max(gt[0], pr[0])+ 1
if dx < 0:
return 0.0
dy = min(gt[3], pr[3])- max(gt[1], pr[1... | grid = [{'hidden_n': 16, 'drop': 0.3, 'lr': 1e-5, 'weight_decay': 1e-6},
{'hidden_n': 32, 'drop': 0.3, 'lr': 1e-5, 'weight_decay': 1e-6},
{'hidden_n': 32, 'drop': 0.3, 'lr': 5e-6, 'weight_decay': 1e-6},
{'hidden_n': 32, 'drop': 0.25, 'lr': 1e-5, 'weight_decay': 5e-6},
{'hidden_n': 32, 'drop': 0.35, 'lr': 1e-5, 'weight_... | Natural Language Processing with Disaster Tweets |
19,958,768 | def log(text):
print(text)
def optimize(space, all_predictions, n_calls=10):
@use_named_args(space)
def score(**params):
log('-'*10)
log(params)
final_score = calculate_final_score(all_predictions, **params)
log(f'final_score = {final_score}')
log('-'*10)
return -final_score
return gp_minimize(func=score, dimens... | TEST_PATH = ".. /input/nlp-getting-started/test.csv"
df = pd.read_csv(TEST_PATH)
x =(df['text']
.str.lower()
.str.replace('\x89Ûª|Ûª', "'")
.str.replace('
|\x89.|\x9d *', ' ')
.str.replace('>', ">")
.str.replace('<', "<")
.str.replace('&', " and ")
.str.replace('won't', 'will not')
.str.replace('can't', '... | Natural Language Processing with Disaster Tweets |
19,958,768 | <categorify><EOS> | df[['id', 'target']].to_csv("submissions.csv", index = False ) | Natural Language Processing with Disaster Tweets |
15,639,884 | <SOS> metric: meanfscore Kaggle data source: natural-language-processing-with-disaster-tweets<choose_model_class> | pip install -U lightautoml | Natural Language Processing with Disaster Tweets |
15,639,884 | model_struct = 'tf_efficientdet_d7'
def get_valnet_file(Get_Path):
config = get_efficientdet_config(model_struct)
net = EfficientDet(config, pretrained_backbone=False)
config.num_classes = 1
config.image_size = img_size
net.class_net = HeadNet(config, num_outputs=config.num_classes, norm_kwargs=dict(eps=.001, momentu... | pip install -U transformers | Natural Language Processing with Disaster Tweets |
15,639,884 | if is_TEST:
if PSEUDO:
remain_times = 60
!python train.py -TRAIN_EPOCHS {EPO} -cfgfile {CONFIG} -op {OPTIMIZER} -dir /kaggle/working/convertor -pretrained {WEIGHTS} -train_label_path convertor/train.txt -val_label_path convertor/val.txt -optimizer radam -iou-type ciou -l 0.0001 -g 0 -classes 1 -maxboxes {max_label_numb... | import os
import time
import numpy as np
import pandas as pd
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
import torch
import matplotlib.pyplot as plt
from lightautoml.automl.presets.text_presets import TabularNLPAutoML
from lightautoml.dataset.roles import DatetimeRole
from... | Natural Language Processing with Disaster Tweets |
15,639,884 | if VALIDATE and is_TEST:
all_predictions = validate()
for score_threshold in tqdm(np.arange(0.1, 0.25, 0.01), total=np.arange(0.1, 0.25, 0.01 ).shape[0]):
final_score = calculate_final_score(all_predictions, best_iou_thr, best_skip_box_thr, score_threshold)
if final_score > best_final_score:
best_final_score = final... | N_THREADS = 4
RANDOM_STATE = 42
TEST_SIZE = 0.2
TIMEOUT = 6 * 3600
TARGET_NAME = 'target' | Natural Language Processing with Disaster Tweets |
15,639,884 | 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)
def detect_cv2(pesudo_result):
use_cuda = True
DATA_ROOT_PATH = '.. /input/global-wheat-detection/t... | np.random.seed(RANDOM_STATE)
torch.set_num_threads(N_THREADS ) | Natural Language Processing with Disaster Tweets |
15,639,884 | results = detect_cv2(pesudo_result)
test_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])
test_df.to_csv('submission.csv', index=False)
test_df.head()<import_modules> | %%time
train_data = pd.read_csv('.. /input/nlp-getting-started/train.csv')
train_data.head() | Natural Language Processing with Disaster Tweets |
15,639,884 | sys.path.insert(0, ".. /input/weightedboxesfusion")
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
<choose_model_class> | test_data = pd.read_csv('.. /input/nlp-getting-started/test.csv')
test_data.head() | Natural Language Processing with Disaster Tweets |
15,639,884 | def load_resnet101_model(checkpoint_path):
num_classes = 2
backbone = resnet_fpn_backbone('resnet101', pretrained= False)
model_faster = FasterRCNN(backbone, num_classes)
in_features = model_faster.roi_heads.box_predictor.cls_score.in_features
model_faster.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_... | submission = pd.read_csv('.. /input/nlp-getting-started/sample_submission.csv')
submission.head() | Natural Language Processing with Disaster Tweets |
15,639,884 | resnet101_33e = ".. /input/resnet10133e-1024/cp33.pt"
resnet50 = ".. /input/resnet50-40e/resnet50_40e.pt"
resnet101_25e = ".. /input/resnet101mymodel/cp25.pt"
resnet152_20e = ".. /input/resnet152-20e/cp20.pt"
resnet152_19e =".. /input/resnet152-19e/cp19(1 ).pt"<load_pretrained> | train_data.target.value_counts() | Natural Language Processing with Disaster Tweets |
15,639,884 | models = [
load_resnet50_model(resnet50),
load_resnet152_model(resnet152_20e),
load_resnet152_model(resnet152_19e)
]<data_type_conversions> | train_data['keyword'].value_counts(dropna = False ) | Natural Language Processing with Disaster Tweets |
15,639,884 | 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}/... | train_data['location'].value_counts(dropna = False ) | Natural Language Processing with Disaster Tweets |
15,639,884 | def get_valid_transforms() :
return A.Compose([
ToTensorV2(p=1.0),
], p=1.0 )<define_variables> | def clean_text(text):
return text | Natural Language Processing with Disaster Tweets |
15,639,884 | 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> | all_data = pd.concat([
train_data.drop(TARGET_NAME, axis = 1),
test_data
] ).reset_index(drop = True)
all_data['location'] = all_data['location'].astype(str)
all_data.loc[all_data['location'].value_counts() [all_data['location']].values < 5, 'location'] = "RARE_VALUE"
all_data.loc[all_data['location'] == 'nan', 'loca... | Natural Language Processing with Disaster Tweets |
15,639,884 | dataset = DatasetRetriever(np.array([path.split('/')[-1][:-4] for path in glob.glob(f'{DATA_ROOT_PATH}/*.jpg')]),get_valid_transforms())
def collate_fn(batch):
return tuple(zip(*batch))
data_loader = DataLoader(
dataset,
batch_size=1,
shuffle=False,
num_workers=2,
drop_last=False,
collate_fn=collate_fn
)<categorify> | y_train = train_data.target.values
train_data = all_data[:len(train_data)]
train_data[TARGET_NAME] = y_train
test_data = all_data[len(train_data):] | Natural Language Processing with Disaster Tweets |
15,639,884 | class BaseWheatTTA:
image_size = 1024
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(se... | %%time
roles = {'target': TARGET_NAME,
'text': ['text'],
'drop': ['id']} | Natural Language Processing with Disaster Tweets |
15,639,884 | def make_tta_predictions(model,images, score_threshold=0.35):
model.eval()
model.to(device)
with torch.no_grad() :
images = torch.stack(images ).float().cuda()
predictions = []
for tta_transform in tta_transforms:
result = []
det = model(tta_transform.batch_augment(images.clone()))
for i in range(images.shape[0]):
box... | %%time
automl = TabularNLPAutoML(task = task,
timeout = TIMEOUT,
cpu_limit = N_THREADS,
reader_params = {'cv': 5},
general_params = {'nested_cv': False, 'use_algos': [['linear_l2', 'lgb', 'nn']]},
text_params = {'lang': 'en'},
nn_params = {'lang': 'en',
'bert_name': 'vinai/bertweet-base',
'opt_params': { 'lr': 1e-5},
'... | Natural Language Processing with Disaster Tweets |
15,639,884 | tta_transforms = []
for tta_combination in product([TTAHorizontalFlip() , None],
[TTAVerticalFlip() , None],
[TTARotate90() , TTARotate180() , TTARotate270() , None]):
tta_transforms.append(TTACompose([tta_transform for tta_transform in tta_combination if tta_transform]))
<define_variables> | automl.collect_used_feats() | Natural Language Processing with Disaster Tweets |
15,639,884 | validation_image_precisions = []
iou_thresholds = [x for x in np.arange(0.5, 0.76, 0.05)]
results = []
ts = 0.147
for images, image_ids in data_loader:
predictions = make_ensemble_predictions(images)
for i, image in enumerate(images):
boxes, scores, labels = run_wbf(predictions, image_index=i, iou_thr=0.35, skip_box_t... | test_pred = automl.predict(test_data)
print('Prediction for test data:
{}
Shape = {}'.format(test_pred, test_pred.shape)) | Natural Language Processing with Disaster Tweets |
15,639,884 | test_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])
test_df.to_csv('submission.csv', index=False)
test_df.head(10 )<install_modules> | def select_threshold_f1(y_true, y_pred):
best_score = -1
best_thr = None
for thr in np.arange(0, 1.01, 0.01):
score = f1_score(y_true,(y_pred > thr ).astype(int))
if score > best_score:
best_score = score
best_thr = thr
print('Best score: {}
Best selected threshold: {:.2f}'.format(best_score, best_thr))
return best_thr... | Natural Language Processing with Disaster Tweets |
15,639,884 | !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<set_options> | submission['target'] =(test_pred.data[:, 0] > best_thr ).astype(int)
submission | Natural Language Processing with Disaster Tweets |
15,639,884 | SEED = 42
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
seed_everything(SEED )<load_from_csv> | submission['target'].value_counts() | Natural Language Processing with Disaster Tweets |
15,639,884 | marking = pd.read_csv('.. /input/global-wheat-detection/train.csv')
bboxs = np.stack(marking['bbox'].apply(lambda x: np.fromstring(x[1:-1], sep=',')))
for i, column in enumerate(['x', 'y', 'w', 'h']):
marking[column] = bboxs[:,i]
marking.drop(columns=['bbox'], inplace=True)
marking.sample(10 )<feature_engineering> | submission.to_csv('LightAutoML_preds_without_id.csv', index = False ) | Natural Language Processing with Disaster Tweets |
14,591,530 | skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
df_folds = marking[['image_id']].copy()
df_folds.loc[:, 'bbox_count'] = 1
df_folds = df_folds.groupby('image_id' ).count()
df_folds.loc[:, 'source'] = marking[['image_id', 'source']].groupby('image_id' ).min() ['source']
df_folds.loc[:, 'stratify_group']... | device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("Using %s" %(device))
seed_val = 42
random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val ) | Natural Language Processing with Disaster Tweets |
14,591,530 | TRAIN_ROOT_PATH = '.. /input/global-wheat-detection/train'
class WheatDataset(Dataset):
def __init__(self, marking, image_ids, transforms=None, test=False):
super().__init__()
self.image_ids = image_ids
self.marking = marking
self.transforms = transforms
self.test = test
def __getitem__(self, index: int):
image_id = se... | def clean_text(text):
text = text.lower()
text = re.sub(r'[!]+', '!', text)
text = re.sub(r'[?]+', '?', text)
text = re.sub(r'[.]+', '.', text)
text = re.sub(r"'", "", text)
text = re.sub('\s+', ' ', text ).strip()
text = re.sub(r'&?', r'and', text)
text = re.sub(r"https?:\/\/t.co\/[A-Za-z0-9]+", "", text)
te... | Natural Language Processing with Disaster Tweets |
14,591,530 | fold_number = 0
train_dataset = WheatDataset(
image_ids=df_folds[df_folds['fold'] != fold_number].index.values,
marking=marking,
transforms=get_train_transforms() ,
test=False,
)
validation_dataset = WheatDataset(
image_ids=df_folds[df_folds['fold'] == fold_number].index.values,
marking=marking,
transforms=get_vali... | train = pd.read_csv("/kaggle/input/nlp-getting-started/train.csv")
test = pd.read_csv("/kaggle/input/nlp-getting-started/test.csv")
train['text_new'] = train['text'].apply(clean_text)
test['text'] = test['text'].apply(clean_text)
train_texts = list(train["text"])
train_labels = list(train["target"])
res_texts = l... | Natural Language Processing with Disaster Tweets |
14,591,530 | class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count<train_model> | tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
train_encoding = tokenizer(x_train, truncation=True, padding=True)
test_encoding = tokenizer(x_test, truncation=True, padding=True)
res_encoding = tokenizer(res_texts, truncation=True, padding=True ) | Natural Language Processing with Disaster Tweets |
14,591,530 | class Fitter:
def __init__(self, model, device, config):
self.config = config
self.epoch = 0
self.base_dir = f'./{config.folder}'
if not os.path.exists(self.base_dir):
os.makedirs(self.base_dir)
self.log_path = f'{self.base_dir}/log.txt'
self.best_summary_loss = 10**5
self.model = model
self.device = device
param_opti... | class TwitterDataset(Dataset):
def __init__(self, encodings, labels):
self.encodings = encodings
self.labels = labels
def __getitem__(self, idx):
item = {key: torch.tensor(val[idx])for key, val in self.encodings.items() }
item['labels'] = torch.tensor(self.labels[idx])
return item
def __len__(self):
return len(self.la... | Natural Language Processing with Disaster Tweets |
14,591,530 | class TrainGlobalConfig:
num_workers = 2
batch_size = 4
n_epochs = 3
lr = 0.0002
folder = 'effdet5-cutmix-augmix'
verbose = True
verbose_step = 1
step_scheduler = False
validation_scheduler = True
SchedulerClass = torch.optim.lr_scheduler.ReduceLROnPlateau
scheduler_params = dict(
mode='min',
factor=0.5,
patience=1,
v... | def flat_accuracy(preds, labels):
pred_flat = np.argmax(preds, axis=1 ).flatten()
labels_flat = labels.flatten()
return np.sum(pred_flat == labels_flat)/ len(labels_flat ) | Natural Language Processing with Disaster Tweets |
14,591,530 | def collate_fn(batch):
return tuple(zip(*batch))<load_pretrained> | step_nums = 30
model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
model.to(device)
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
test_dataloader = DataLoader(test_dataset, batch_size=16, shuffle=True)
optim = AdamW(model.parameters() , lr=2e-5)
scheduler ... | Natural Language Processing with Disaster Tweets |
14,591,530 | def run_training() :
device = torch.device('cuda:0')
net.to(device)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=TrainGlobalConfig.batch_size,
sampler=RandomSampler(train_dataset),
pin_memory=False,
drop_last=True,
num_workers=TrainGlobalConfig.num_workers,
collate_fn=collate_fn,
)
val_loa... | epoth_num=5
model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
model.to(device)
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
test_dataloader = DataLoader(test_dataset, batch_size=16, shuffle=True)
optim = AdamW(model.parameters() , lr=2e-5)
total_steps =... | Natural Language Processing with Disaster Tweets |
14,591,530 | def get_net() :
config = get_efficientdet_config('tf_efficientdet_d5')
net = EfficientDet(config, pretrained_backbone=False)
checkpoint = torch.load('.. /input/efficientdet/efficientdet_d5-ef44aea8.pth')
net.load_state_dict(checkpoint)
config.num_classes = 1
config.image_size = 512
net.class_net = HeadNet(config, n... | print("Restoring the best model weights.")
model.load_state_dict(torch.load("./model.weights"))
model.eval()
class TwitterValDataset(Dataset):
def __init__(self, encodings):
self.encodings = encodings
def __getitem__(self, idx):
item = {key: torch.tensor(val[idx])for key, val in self.encodings.items() }
return item
de... | Natural Language Processing with Disaster Tweets |
15,318,234 | run_training()<categorify> | train_set = pd.read_csv('.. /input/nlp-getting-started/train.csv')
test_set = pd.read_csv('.. /input/nlp-getting-started/test.csv' ) | Natural Language Processing with Disaster Tweets |
15,318,234 | def get_test_transforms() :
return A.Compose([
A.Resize(height=512, width=512, p=1.0),
ToTensorV2(p=1.0),
], p=1.0 )<data_type_conversions> | import re | Natural Language Processing with Disaster Tweets |
15,318,234 | 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... | def clean_url(x):
cleaned_x = re.sub(r'http\S{0,}', r'', x)
return cleaned_x | Natural Language Processing with Disaster Tweets |
15,318,234 | dataset = TestDatasetRetriever(
image_ids=np.array([path.split('/')[-1][:-4] for path in glob(f'{DATA_ROOT_PATH}/*.jpg')]),
transforms=get_test_transforms()
)
test_data_loader = DataLoader(
dataset,
batch_size=1,
shuffle=False,
num_workers=4,
drop_last=False,
collate_fn=collate_fn
)<choose_model_class> | train_set['text'] = train_set['text'].apply(clean_url)
test_set['text'] = test_set['text'].apply(clean_url ) | Natural Language Processing with Disaster Tweets |
15,318,234 | 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 clean_new_line(x):
return re.sub(r"
", " ", x ) | Natural Language Processing with Disaster Tweets |
15,318,234 | 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(self, i... | def remove_emoji(text):
emoji_pattern = re.compile("["
u"\U0001F600-\U0001F64F"
u"\U0001F300-\U0001F5FF"
u"\U0001F680-\U0001F6FF"
u"\U0001F1E0-\U0001F1FF"
u"\U00002702-\U000027B0"
u"\U000024C2-\U0001F251"
"]+", flags=re.UNICODE)
return emoji_pattern.sub(r'', text ) | Natural Language Processing with Disaster Tweets |
15,318,234 | 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]))<find_best_model_class> | train_set['text'] = train_set['text'].apply(clean_new_line)
train_set['text'] = train_set['text'].apply(remove_emoji)
train_set['text'] = train_set['text'].apply(lower_case ) | Natural Language Processing with Disaster Tweets |
15,318,234 | 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_set['text'] = test_set['text'].apply(clean_new_line)
test_set['text'] = test_set['text'].apply(remove_emoji)
test_set['text'] = test_set['text'].apply(lower_case ) | Natural Language Processing with Disaster Tweets |
15,318,234 | def run_wbf(predictions, image_index, image_size=512, iou_thr=0.44, skip_box_thr=0.43, weights=None):
boxes = [(prediction[image_index]['boxes']/(image_size-1)).tolist() for prediction in predictions]
scores = [prediction[image_index]['scores'].tolist() for prediction in predictions]
labels = [np.ones(prediction[image_... | !wget --quiet https://raw.githubusercontent.com/tensorflow/models/master/official/nlp/bert/tokenization.py | Natural Language Processing with Disaster Tweets |
15,318,234 | 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> | import tokenization
from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit
from sklearn.metrics import precision_score, recall_score, f1_score
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow import keras
from tensorflow.keras.optimizers import Adam, SGD
from tensorflow.keras.la... | Natural Language Processing with Disaster Tweets |
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