kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
14,158,600 | sub_files = [
'.. /input/siamese/sub_ens_777.csv',
'.. /input/siamese/sub_822.csv',
]
sub_weight = [
0.777**2,
0.822**2
]
<save_to_csv> | img_shapes = {}
for image_name in os.listdir(os.path.join(BASE_DIR, "train_images")) [:300]:
image = cv2.imread(os.path.join(BASE_DIR, "train_images", image_name))
img_shapes[image.shape] = img_shapes.get(image.shape, 0)+ 1
print(img_shapes ) | Cassava Leaf Disease Classification |
14,158,600 | Hlabel = 'Image'
Htarget = 'Id'
npt = 5
place_weights = {}
for i in range(npt):
place_weights[i] =(1 /(i + 1))
print(place_weights)
lg = len(sub_files)
sub = [None]*lg
for i, file in enumerate(sub_files):
print("Reading {}: w={} - {}".format(i, sub_weight[i], file))
reader = csv.DictReader(open(file,"r"))
sub[i] = so... | df_train = pd.read_csv(os.path.join(BASE_DIR, "train.csv"))
df_train["class_name"] = df_train["label"].map(map_classes)
df_train | Cassava Leaf Disease Classification |
14,158,600 | !pip install lapjv
Lambda, MaxPooling2D, Reshape
<load_from_csv> | As we can see, the dataset has a fairly large **imbalance**.
| Cassava Leaf Disease Classification |
14,158,600 | TRAIN_DF = '.. /input/humpback-whale-identification/train.csv'
SUB_Df = '.. /input/humpback-whale-identification/sample_submission.csv'
TRAIN = '.. /input/humpback-whale-identification/train/'
TEST = '.. /input/humpback-whale-identification/test/'
P2H = '.. /input/metadata/p2h.pickle'
P2SIZE = '.. /input/metadata/p2siz... | tmp_df = df_train[df_train["label"] == 4]
print(f"Total train images for class 4: {tmp_df.shape[0]}")
tmp_df = tmp_df.sample(9)
image_ids = tmp_df["image_id"].values
labels = tmp_df["label"].values
visualize_batch(image_ids, labels ) | Cassava Leaf Disease Classification |
14,158,600 | if isfile(P2SIZE):
print("P2SIZE exists.")
with open(P2SIZE, 'rb')as f:
p2size = pickle.load(f)
else:
p2size = {}
for p in tqdm(join):
size = pil_image.open(expand_path(p)).size
p2size[p] = size<compute_test_metric> | package_path = '.. /input/pytorch-image-models/pytorch-image-models-master'
| Cassava Leaf Disease Classification |
14,158,600 | def match(h1, h2):
for p1 in h2ps[h1]:
for p2 in h2ps[h2]:
i1 = pil_image.open(expand_path(p1))
i2 = pil_image.open(expand_path(p2))
if i1.mode != i2.mode or i1.size != i2.size: return False
a1 = np.array(i1)
a1 = a1 - a1.mean()
a1 = a1 / sqrt(( a1 ** 2 ).mean())
a2 = np.array(i2)
a2 = a2 - a2.mean()
a2 = a2 / sqrt(... | from glob import glob
from sklearn.model_selection import GroupKFold, StratifiedKFold
import cv2
from skimage import io
import torch
from torch import nn
import os
from datetime import datetime
import time
import random
import cv2
import torchvision
from torchvision import transforms
import pandas as pd
import numpy as... | Cassava Leaf Disease Classification |
14,158,600 | def prefer(ps):
if len(ps)== 1: return ps[0]
best_p = ps[0]
best_s = p2size[best_p]
for i in range(1, len(ps)) :
p = ps[i]
s = p2size[p]
if s[0] * s[1] > best_s[0] * best_s[1]:
best_p = p
best_s = s
return best_p
h2p = {}
for h, ps in h2ps.items() :
h2p[h] = prefer(ps)
len(h2p), list(h2p.items())[:5]<set_options> | CFG = {
'fold_num': 5,
'seed': 719,
'model_arch': 'tf_efficientnet_b4_ns',
'img_size': 512,
'epochs': 10,
'train_bs': 32,
'valid_bs': 32,
'lr': 1e-4,
'num_workers': 4,
'accum_iter': 1,
'verbose_step': 1,
'device': 'cuda:0',
'tta': 3,
'used_epochs': [6,7,8,9],
'weights': [1,1,1,1]
} | Cassava Leaf Disease Classification |
14,158,600 | p2bb = pd.read_csv(BB_DF ).set_index("Image")
old_stderr = sys.stderr
sys.stderr = open('/dev/null' if platform.system() != 'Windows' else 'nul', 'w')
sys.stderr = old_stderr
img_shape =(384, 384, 1)
anisotropy = 2.15
crop_margin = 0.05<normalization> | train = pd.read_csv('.. /input/cassava-leaf-disease-classification/train.csv')
train.head() | Cassava Leaf Disease Classification |
14,158,600 | def build_transform(rotation, shear, height_zoom, width_zoom, height_shift, width_shift):
rotation = np.deg2rad(rotation)
shear = np.deg2rad(shear)
rotation_matrix = np.array(
[[np.cos(rotation), np.sin(rotation), 0], [-np.sin(rotation), np.cos(rotation), 0], [0, 0, 1]])
shift_matrix = np.array([[1, 0, height_shi... | train.label.value_counts() | Cassava Leaf Disease Classification |
14,158,600 | def read_cropped_image(p, augment):
if p in h2p:
p = h2p[p]
size_x, size_y = p2size[p]
row = p2bb.loc[p]
x0, y0, x1, y1 = row['x0'], row['y0'], row['x1'], row['y1']
dx = x1 - x0
dy = y1 - y0
x0 -= dx * crop_margin
x1 += dx * crop_margin + 1
y0 -= dy * crop_margin
y1 += dy * crop_margin + 1
if x0 < 0:
x0 = 0
if x1 > s... | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
submission.head() | Cassava Leaf Disease Classification |
14,158,600 | def subblock(x, filter, **kwargs):
x = BatchNormalization()(x)
y = x
y = Conv2D(filter,(1, 1), activation='relu', **kwargs )(y)
y = BatchNormalization()(y)
y = Conv2D(filter,(3, 3), activation='relu', **kwargs )(y)
y = BatchNormalization()(y)
y = Conv2D(K.int_shape(x)[-1],(1, 1), **kwargs )(y)
y = Add()([x, y])
... | class CassavaDataset(Dataset):
def __init__(
self, df, data_root, transforms=None, output_label=True
):
super().__init__()
self.df = df.reset_index(drop=True ).copy()
self.transforms = transforms
self.data_root = data_root
self.output_label = output_label
def __len__(self):
return self.df.shape[0]
def __getitem__(sel... | Cassava Leaf Disease Classification |
14,158,600 | h2ws = {}
new_whale = 'new_whale'
for p, w in tagged.items() :
if w != new_whale:
h = p2h[p]
if h not in h2ws: h2ws[h] = []
if w not in h2ws[h]: h2ws[h].append(w)
for h, ws in h2ws.items() :
if len(ws)> 1:
h2ws[h] = sorted(ws)
w2hs = {}
for h, ws in h2ws.items() :
if len(ws)== 1:
w = ws[0]
if w not in w2hs: w2hs[w] =... | HorizontalFlip, VerticalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine, RandomResizedCrop,
IAASharpen, IAAEmboss, RandomBrightnessCon... | Cassava Leaf Disease Classification |
14,158,600 | train = []
for hs in w2hs.values() :
if len(hs)> 1:
train += hs
random.shuffle(train)
train_set = set(train)
w2ts = {}
for w, hs in w2hs.items() :
for h in hs:
if h in train_set:
if w not in w2ts:
w2ts[w] = []
if h not in w2ts[w]:
w2ts[w].append(h)
for w, ts in w2ts.items() :
w2ts[w] = np.array(ts)
t2i = {}
for i, ... | class CassvaImgClassifier(nn.Module):
def __init__(self, model_arch, n_class, pretrained=False):
super().__init__()
self.model = timm.create_model(model_arch, pretrained=pretrained)
n_features = self.model.classifier.in_features
self.model.classifier = nn.Linear(n_features, n_class)
def forward(self, x):
x = self.mod... | Cassava Leaf Disease Classification |
14,158,600 | class TrainingData(Sequence):
def __init__(self, score, steps=1000, batch_size=32):
super(TrainingData, self ).__init__()
self.score = -score
self.steps = steps
self.batch_size = batch_size
for ts in w2ts.values() :
idxs = [t2i[t] for t in ts]
for i in idxs:
for j in idxs:
self.score[
i, j] = 10000.0
self.on_epoch_en... | if __name__ == '__main__':
seed_everything(CFG['seed'])
folds = StratifiedKFold(n_splits=CFG['fold_num'] ).split(np.arange(train.shape[0]), train.label.values)
for fold,(trn_idx, val_idx)in enumerate(folds):
if fold > 0:
break
print('Inference fold {} started'.format(fold))
valid_ = train.loc[val_idx,:].reset_index(d... | Cassava Leaf Disease Classification |
14,158,600 | def set_lr(model, lr):
K.set_value(model.optimizer.lr, float(lr))
def get_lr(model):
return K.get_value(model.optimizer.lr)
def score_reshape(score, x, y=None):
if y is None:
m = np.zeros(( x.shape[0], x.shape[0]), dtype=K.floatx())
m[np.triu_indices(x.shape[0], 1)] = score.squeeze()
m += m.transpose()
else:
m = np... | test['label'] = np.argmax(tst_preds, axis=1)
test.head() | Cassava Leaf Disease Classification |
14,158,600 | <load_pretrained><EOS> | test.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
14,334,187 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<feature_engineering> | !pip3 install --no-dependencies.. /input/efficientnetcassava/Keras_Applications-1.0.8-py3-none-any.whl
!pip3 install --no-dependencies.. /input/efficientnetcassava/efficientnet-1.1.1-py3-none-any.whl | Cassava Leaf Disease Classification |
14,334,187 | h2ws = {}
for p, w in tagged.items() :
if w != new_whale:
h = p2h[p]
if h not in h2ws: h2ws[h] = []
if w not in h2ws[h]: h2ws[h].append(w)
known = sorted(list(h2ws.keys()))
h2i = {}
for i, h in enumerate(known): h2i[h] = i
fknown = branch_model.predict_generator(FeatureGen(known), max_queue_size=20, workers=10, verbos... | import re
import numpy as np
import pandas as pd
import os
import json | Cassava Leaf Disease Classification |
14,334,187 | !pip install fastai==0.7.0 --no-deps
!pip install torch==0.4.1 torchvision==0.2.1
!pip install torchtext==0.2.3<define_variables> | import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras import models
from tensorflow.keras import layers
from tensorflow.keras import losses
from sklearn.model_selection import train_test_split
from efficientnet.keras import EfficientNetB3 as EfficientNet | Cassava Leaf Disease Classification |
14,334,187 | MODEL_NAME = 'Densenet201'
TRAIN = '.. /input/train/'
TEST = '.. /input/test/'
LABELS = '.. /input/train.csv'
SAMPLE_SUB = '.. /input/sample_submission.csv'
arch = dn201
num_workers = 8<feature_engineering> | AUTO = tf.data.experimental.AUTOTUNE
SIZE = 600
ORIGINAL_WIDTH = 800
ORIGINAL_HEIGHT = 600
CHANNELS = 3
BATCH_SIZE = 32 | Cassava Leaf Disease Classification |
14,334,187 | df = pd.read_csv(LABELS ).set_index('Image')
new_whale_df = df[df.Id == "new_whale"]
train_df = df[~(df.Id == "new_whale")]
unique_labels = np.unique(train_df.Id.values)
labels_dict = dict()
labels_list = []
for i in range(len(unique_labels)) :
labels_dict[unique_labels[i]] = i
labels_list.append(unique_labels[i])
p... | def decode_image(path):
image = tf.io.read_file(path)
image = tf.image.decode_jpeg(image, channels=3)
image =(tf.cast(image, tf.float32)/ 255.0)
image = tf.image.resize(image, [ORIGINAL_HEIGHT, ORIGINAL_WIDTH])
image = tf.reshape(image, [ORIGINAL_HEIGHT, ORIGINAL_WIDTH , CHANNELS])
return image
def normalize(x):
x... | Cassava Leaf Disease Classification |
14,334,187 | train_df['image_name'] = train_df.index
rs = np.random.RandomState(42)
perm = rs.permutation(len(train_df))
tr_n = train_df['image_name'].values
val_n = train_df['image_name'].values[perm][:1000]
print('Train/val:', len(tr_n), len(val_n))
print('Train classes', len(train_df.loc[tr_n].Id.unique()))
print('Val classes',... | load_dir = "/kaggle/input/cassava-leaf-disease-classification"
sub_df = pd.read_csv(load_dir + '/sample_submission.csv')
sub_df['paths'] = load_dir + "/test_images/" + sub_df.image_id | Cassava Leaf Disease Classification |
14,334,187 | class HWIDataset(FilesDataset):
def __init__(self, fnames, path, transform):
self.train_df = train_df
super().__init__(fnames, transform, path)
def get_x(self, i):
img = open_image(os.path.join(self.path, self.fnames[i]))
img = cv2.resize(img,(self.sz, self.sz))
return img
def get_y(self, i):
if(self.path == TEST): re... | def load_dataset(augment=False):
test_dataset =tf.data.Dataset.from_tensor_slices(sub_df.paths.values ).map(decode_image, num_parallel_calls=AUTO)
if augment:
test_dataset = test_dataset.map(lambda x: data_aug(x), num_parallel_calls=AUTO)
else:
test_dataset = test_dataset.map(lambda x:normalize(x))
return test_datase... | Cassava Leaf Disease Classification |
14,334,187 | class RandomLighting(Transform):
def __init__(self, b, c, tfm_y=TfmType.NO):
super().__init__(tfm_y)
self.b, self.c = b, c
def set_state(self):
self.store.b_rand = rand0(self.b)
self.store.c_rand = rand0(self.c)
def do_transform(self, x, is_y):
if is_y and self.tfm_y != TfmType.PIXEL: return x
b = self.store.b_rand
... | def load_model(i):
inputs = layers.Input(shape=(ORIGINAL_HEIGHT, ORIGINAL_WIDTH, 3))
model = Sequential([
EfficientNet(include_top=False,weights=None, input_tensor=inputs),
layers.GlobalAveragePooling2D(name="avg_pool"),
layers.BatchNormalization() ,
layers.Dropout(0.3, name="top_dropout"),
layers.Dense(5, activation="... | Cassava Leaf Disease Classification |
14,334,187 | image_size = 224
batch_size = 48
md = get_data(image_size, batch_size)
extra_fc_layers_size = []
learn = ConvLearner.pretrained(arch, md, xtra_fc=extra_fc_layers_size)
learn.opt_fn = optim.Adam<init_hyperparams> | n_models = 5
models = []
for i in range(n_models):
models.append(load_model(i)) | Cassava Leaf Disease Classification |
14,334,187 | print('Number of layer groups:', len(learn.get_layer_groups()), '\t(first 2 groups is pretrained backbone)')
print('This is our extra thin on top of the backbone Resnet50 architecture:')
learn.get_layer_groups() [2]<train_model> | preds = []
test_dataset = load_dataset()
for i in range(n_models):
preds.append(models[i].predict(test_dataset, verbose=1))
for i in range(10):
test_dataset_augmented = load_dataset(augment=True)
for i in range(n_models):
preds.append(models[i].predict(test_dataset_augmented, verbose=1))
preds = np.mean(preds, axis=0)... | Cassava Leaf Disease Classification |
14,334,187 | <define_variables><EOS> | sub_df['label'] = preds.argmax(axis=1)
sub_df.drop(columns='paths' ).to_csv('submission.csv', index=False)
!head submission.csv | Cassava Leaf Disease Classification |
14,319,149 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<train_model> | warnings.filterwarnings('ignore')
| Cassava Leaf Disease Classification |
14,319,149 | base_lr = 1e-5
fc_lr = 1e-3
lrs = [base_lr, base_lr, fc_lr]
learn.fit(lrs, n_cycle=6, cycle_len=1)
learn.save('weights_v2' )<prepare_output> | training_folder = '.. /input/cassava-leaf-disease-classification/train_images/' | Cassava Leaf Disease Classification |
14,319,149 | best_th = 0.38
preds_t,y_t = learn.TTA(is_test=True,n_aug=8)
preds_t = np.stack(preds_t, axis=-1)
preds_t = np.exp(preds_t)
preds_t = preds_t.mean(axis=-1)
preds_t = np.concatenate([np.zeros(( preds_t.shape[0],1)) +best_th, preds_t],axis=1)
np.save('preds_dn201.npy', preds_t )<save_to_csv> | samples_df = pd.read_csv(".. /input/cassava-leaf-disease-classification/train.csv")
samples_df = shuffle(samples_df, random_state=42)
samples_df["filepath"] = training_folder+samples_df["image_id"]
samples_df.head()
| Cassava Leaf Disease Classification |
14,319,149 | sample_df = pd.read_csv(SAMPLE_SUB)
sample_list = list(sample_df.Image)
labels_list = ["new_whale"]+labels_list
pred_list = [[labels_list[i] for i in p.argsort() [-5:][::-1]] for p in preds_t]
pred_dic = dict(( key, value)for(key, value)in zip(learn.data.test_ds.fnames,pred_list))
pred_list_cor = [' '.join(pred_dic[i... | training_percentage = 0.8
training_item_count = int(len(samples_df)*training_percentage)
validation_item_count = len(samples_df)-int(len(samples_df)*training_percentage)
training_df = samples_df[:training_item_count]
validation_df = samples_df[training_item_count:]
| Cassava Leaf Disease Classification |
14,319,149 | sub_files = [
'.. /input/publickernel/sub_766.csv',
'.. /input/publickernel/sub_771.csv'
]
sub_weight = [
0.766**2,
0.771**2
]<save_to_csv> | batch_size = 8
image_size = 512
input_shape =(image_size, image_size, 3)
dropout_rate = 0.4
classes_to_predict = sorted(training_df.label.unique() ) | Cassava Leaf Disease Classification |
14,319,149 | Hlabel = 'Image'
Htarget = 'Id'
npt = 5
place_weights = {}
for i in range(npt):
place_weights[i] =(1 /(i + 1))
print(place_weights)
lg = len(sub_files)
sub = [None]*lg
for i, file in enumerate(sub_files):
print("Reading {}: w={} - {}".format(i, sub_weight[i], file))
reader = csv.DictReader(open(file,"r"))
sub[i] = so... | training_data = tf.data.Dataset.from_tensor_slices(( training_df.filepath.values, training_df.label.values))
validation_data = tf.data.Dataset.from_tensor_slices(( validation_df.filepath.values, validation_df.label.values)) | Cassava Leaf Disease Classification |
14,319,149 | !pip install fastai==0.7.0 --no-deps
!pip install torch==0.4.1 torchvision==0.2.1<define_variables> | def load_image_and_label_from_path(image_path, label):
img = tf.io.read_file(image_path)
img = tf.image.decode_jpeg(img, channels=3)
return img, label
AUTOTUNE = tf.data.experimental.AUTOTUNE
training_data = training_data.map(load_image_and_label_from_path, num_parallel_calls=AUTOTUNE)
validation_data = validation_d... | Cassava Leaf Disease Classification |
14,319,149 | MODEL_NAME = 'Resnext50'
TRAIN = '.. /input/humpback-whale-identification/train/'
TEST = '.. /input/humpback-whale-identification/test/'
LABELS = '.. /input/humpback-whale-identification/train.csv'
SAMPLE_SUB = '.. /input/humpback-whale-identification/sample_submission.csv'
arch = resnext50
num_workers = 4<feature_engi... | adapt_data = tf.data.Dataset.from_tensor_slices(training_df.filepath.values)
def adapt_mode(image_path):
img = tf.io.read_file(image_path)
img = tf.image.decode_jpeg(img, channels=3)
img = layers.experimental.preprocessing.Rescaling(1.0 / 255 )(img)
return img
adapt_data = adapt_data.map(adapt_mode, num_parallel_ca... | Cassava Leaf Disease Classification |
14,319,149 | df = pd.read_csv(LABELS ).set_index('Image')
new_whale_df = df[df.Id == "new_whale"]
train_df = df[~(df.Id == "new_whale")]
unique_labels = np.unique(train_df.Id.values)
labels_dict = dict()
labels_list = []
for i in range(len(unique_labels)) :
labels_dict[unique_labels[i]] = i
labels_list.append(unique_labels[i])
p... | data_augmentation_layers = tf.keras.Sequential(
[
layers.experimental.preprocessing.RandomCrop(height=image_size, width=image_size),
layers.experimental.preprocessing.RandomFlip("horizontal_and_vertical"),
layers.experimental.preprocessing.RandomRotation(0.25),
layers.experimental.preprocessing.RandomZoom(( -0.2, 0)) ... | Cassava Leaf Disease Classification |
14,319,149 | train_df['image_name'] = train_df.index
rs = np.random.RandomState(42)
perm = rs.permutation(len(train_df))
tr_n = train_df['image_name'].values
val_n = train_df['image_name'].values[perm][:1000]
print('Train/val:', len(tr_n), len(val_n))
print('Train classes', len(train_df.loc[tr_n].Id.unique()))
print('Val classes',... | efficientnet = EfficientNetB3(weights=".. /input/keras-efficientnetb3-no-top-weights/efficientnetb3_notop.h5",
include_top=False,
input_shape=input_shape,
drop_connect_rate=dropout_rate)
inputs = Input(shape=input_shape)
augmented = data_augmentation_layers(inputs)
efficientnet = efficientnet(augmented)
pooling = l... | Cassava Leaf Disease Classification |
14,319,149 | PICKL = '.. /input/cropimg/bounding-box.pickle'
with open(PICKL, 'rb')as f:
crop_boxs = pickle.load(f )<data_type_conversions> | epochs = 20 | Cassava Leaf Disease Classification |
14,319,149 | pil2tensor = transforms.ToTensor()
class HWIDataset(FilesDataset):
def __init__(self, fnames, path, transform):
self.train_df = train_df
super().__init__(fnames, transform, path)
def get_x(self, i):
img_cr = make_bbox_image(os.path.join(self.path, self.fnames[i])).convert("RGB")
img = pil2tensor(img_cr ).numpy().tran... | decay_steps = int(round(len(training_df)/batch_size)) *epochs
cosine_decay = CosineDecay(initial_learning_rate=1e-4, decay_steps=decay_steps, alpha=0.3)
callbacks = [ModelCheckpoint(filepath='best_model.h5', monitor='val_loss', save_best_only=True)]
model.compile(loss="sparse_categorical_crossentropy", optimizer=tf.ke... | Cassava Leaf Disease Classification |
14,319,149 | class RandomLighting(Transform):
def __init__(self, b, c, tfm_y=TfmType.NO):
super().__init__(tfm_y)
self.b, self.c = b, c
def set_state(self):
self.store.b_rand = rand0(self.b)
self.store.c_rand = rand0(self.c)
def do_transform(self, x, is_y):
if is_y and self.tfm_y != TfmType.PIXEL: return x
b = self.store.b_rand
... | history = model.fit(training_data_batches,
epochs = epochs,
validation_data=validation_data_batches,
callbacks=callbacks ) | Cassava Leaf Disease Classification |
14,319,149 | !wget http://files.fast.ai/models/weights.tgz
!tar -zxvf weights.tgz
!mkdir /opt/conda/lib/python3.6/site-packages/fastai/weights/
!cp weights/resnext_50_32x4d.pth /opt/conda/lib/python3.6/site-packages/fastai/weights/
!rm -rf weights weights.tgz<load_pretrained> | model.load_weights("./best_model.h5" ) | Cassava Leaf Disease Classification |
14,319,149 | image_size = 448
batch_size = 8
md = get_data(image_size, batch_size)
extra_fc_layers_size = []
learn = ConvLearner.pretrained(arch, md, xtra_fc=extra_fc_layers_size)
learn.opt_fn = optim.Adam<set_options> | submission_df.to_csv("submission.csv", index=False ) | Cassava Leaf Disease Classification |
13,755,992 | md.is_multi, md.is_reg<init_hyperparams> | HorizontalFlip, VerticalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine, RandomResizedCrop,
IAASharpen, IAAEmboss, RandomBrightnessCon... | Cassava Leaf Disease Classification |
13,755,992 | print('Number of layer groups:', len(learn.get_layer_groups()), '\t(first 2 groups is pretrained backbone)')
print('This is our extra thin on top of the backbone Resnet50 architecture:')
learn.get_layer_groups() [2]<train_model> | CFG = {
'model_arch': 'tf_efficientnet_b3_ns',
'img_size': 512,
'valid_bs': 16,
'device': 'cuda' if torch.cuda.is_available() else 'cpu'
} | Cassava Leaf Disease Classification |
13,755,992 | base_lr = 1e-4
fc_lr = 1e-3
lrs = [base_lr, base_lr, fc_lr]
learn.fit(lrs=lrs, n_cycle=2, cycle_len=None)
learn.unfreeze()
learn.fit(lrs, n_cycle=9, cycle_len=None)
learn.save(MODEL_PATH )<define_variables> | df = pd.read_csv('/kaggle/input/cassava-leaf-disease-classification/sample_submission.csv')
df_torch = df.copy()
df_tf = df.copy()
| Cassava Leaf Disease Classification |
13,755,992 | best_th = 0.4
<prepare_output> | PATH = '/kaggle/input/cassava-leaf-disease-classification/test_images/' | Cassava Leaf Disease Classification |
13,755,992 | preds_t,y_t = learn.TTA(is_test=True,n_aug=8)
preds_t = np.stack(preds_t, axis=-1)
preds_t = np.exp(preds_t)
preds_t = preds_t.mean(axis=-1)
preds_t = np.concatenate([np.zeros(( preds_t.shape[0],1)) +best_th, preds_t],axis=1)
np.save("rx50_480_preds.npy",preds_t )<save_to_csv> | class CassavaImageClassifier(nn.Module):
def __init__(self, model_arch, n_class, pretrained=False):
super().__init__()
self.model = timm.create_model(model_arch, pretrained=pretrained)
n_features = self.model.classifier.in_features
self.model.classifier = nn.Linear(n_features, n_class)
def forward(self, x):
x = self.... | Cassava Leaf Disease Classification |
13,755,992 | sample_df = pd.read_csv(SAMPLE_SUB)
sample_list = list(sample_df.Image)
labels_list = ["new_whale"]+labels_list
pred_list = [[labels_list[i] for i in p.argsort() [-5:][::-1]] for p in preds_t]
pred_dic = dict(( key, value)for(key, value)in zip(learn.data.test_ds.fnames,pred_list))
pred_list_cor = [' '.join(pred_dic[i... | class DiseaseDatasetInference(torch.utils.data.Dataset):
def __init__(self, df, transform=None, opt_label=True):
self.df = df.reset_index(drop=True ).copy()
self.transform = transform
self.opt_label = opt_label
if self.opt_label:
self.data = [(row['image_id'], row['label'])for _, row in self.df.iterrows() ]
else:
self.... | Cassava Leaf Disease Classification |
13,755,992 | !pip install fastai==0.7.0 --no-deps
!pip install torch==0.4.1 torchvision==0.2.1<define_variables> | trained_model = torch.load('/kaggle/input/cassava-effecientnet-b3/model_6.pt', map_location=torch.device(CFG['device'])) | Cassava Leaf Disease Classification |
13,755,992 | sub_files = [
'.. /input/results/sub_725.csv',
'.. /input/results/sub_728.csv',
'.. /input/results-/sub_719.csv',
]
sub_weight = [
0.719**2,
0.728**2,
0.725**2]<save_to_csv> | test_csv = df.copy()
test_csv['image_id'] = PATH + test_csv['image_id']
test_ds = DiseaseDatasetInference(test_csv, transform=get_inference_transforms() , opt_label=False)
test_loader = torch.utils.data.DataLoader(test_ds, batch_size=CFG['valid_bs'], shuffle=False, pin_memory=False ) | Cassava Leaf Disease Classification |
13,755,992 | Hlabel = 'Image'
Htarget = 'Id'
npt = 5
place_weights = {}
for i in range(npt):
place_weights[i] =(1 /(i + 1))
print(place_weights)
lg = len(sub_files)
sub = [None]*lg
for i, file in enumerate(sub_files):
print("Reading {}: w={} - {}".format(i, sub_weight[i], file))
reader = csv.DictReader(open(file,"r"))
sub[i] = so... | torch_outcomes = pd.concat([df_torch['image_id'], pd.DataFrame(preds_torch)], axis=1 ).sort_values(['image_id'] ) | Cassava Leaf Disease Classification |
13,755,992 | MODEL_NAME = 'Resnext50'
TRAIN = '.. /input/humpback-whale-identification/train/'
TEST = '.. /input/humpback-whale-identification/test/'
LABELS = '.. /input/humpback-whale-identification/train.csv'
SAMPLE_SUB = '.. /input/humpback-whale-identification/sample_submission.csv'
arch = resnext50
num_workers = 4<feature_engi... | import tensorflow as tf
import pandas as pd
import numpy as np
import os
from PIL import Image | Cassava Leaf Disease Classification |
13,755,992 | df = pd.read_csv(LABELS ).set_index('Image')
new_whale_df = df[df.Id == "new_whale"]
train_df = df[~(df.Id == "new_whale")]
unique_labels = np.unique(train_df.Id.values)
labels_dict = dict()
labels_list = []
for i in range(len(unique_labels)) :
labels_dict[unique_labels[i]] = i
labels_list.append(unique_labels[i])
p... | model = tf.keras.models.load_model('/kaggle/input/plantdiseaseresnet50/resnet50.h5' ) | Cassava Leaf Disease Classification |
13,755,992 | train_df['image_name'] = train_df.index
rs = np.random.RandomState(42)
perm = rs.permutation(len(train_df))
tr_n = train_df['image_name'].values
val_n = train_df['image_name'].values[perm][:1000]
print('Train/val:', len(tr_n), len(val_n))
print('Train classes', len(train_df.loc[tr_n].Id.unique()))
print('Val classes',... | tf_outcomes = pd.concat([pd.DataFrame(test_images, columns=['image_id']), pd.DataFrame(preds_tf)], axis=1 ).sort_values(['image_id'] ) | Cassava Leaf Disease Classification |
13,755,992 | PICKL = '.. /input/cropimg/bounding-box.pickle'
with open(PICKL, 'rb')as f:
crop_boxs = pickle.load(f )<data_type_conversions> | final_preds =(torch_outcomes.drop('image_id', axis=1)*0.7 + tf_outcomes.drop('image_id', axis=1)*0.3 ).to_numpy().argmax(1 ) | Cassava Leaf Disease Classification |
13,755,992 | <categorify><EOS> | submit = pd.DataFrame({'image_id': torch_outcomes['image_id'].values, 'label': final_preds})
submit.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
13,724,678 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<load_from_zip> | !pip install.. /input/easydict/easydict-1.9-py2.py3-none-any.whl
| Cassava Leaf Disease Classification |
13,724,678 | !wget http://files.fast.ai/models/weights.tgz
!tar -zxvf weights.tgz
!mkdir /opt/conda/lib/python3.6/site-packages/fastai/weights/
!cp weights/resnext_50_32x4d.pth /opt/conda/lib/python3.6/site-packages/fastai/weights/
!rm -rf weights weights.tgz<load_pretrained> | sys.path.insert(1, '.. /input/snapmix/')
def set_env(seed=0):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def predict(model,testloader,midlevel=False):
model.eval()
time_start = time.time()
pbar = tqdm(testloader, dynamic_ncols=True,... | Cassava Leaf Disease Classification |
13,724,678 | <set_options><EOS> | set_env(seed=0)
foldid = 2
conf = edict({
'depth':50,
'pretrained':True,
'num_class':5,
'midlevel':False,
'datadir':'.. /input/cassava-leaf-disease-classification',
'dataset':'cassava',
'testing':False,
'tta': None,
'foldid':foldid,
'cropsize':448,
'netname':'resnet50',
'net_type':'resnet_ft',
'prams_group':['ftlayer'... | Cassava Leaf Disease Classification |
13,703,884 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<init_hyperparams> | !pip install --quiet /kaggle/input/kerasapplications
!pip install --quiet /kaggle/input/efficientnet-git | Cassava Leaf Disease Classification |
13,703,884 | print('Number of layer groups:', len(learn.get_layer_groups()), '\t(first 2 groups is pretrained backbone)')
print('This is our extra thin on top of the backbone Resnet50 architecture:')
learn.get_layer_groups() [2]<train_model> | def seed_everything(seed=0):
random.seed(seed)
np.random.seed(seed)
tf.random.set_seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
os.environ['TF_DETERMINISTIC_OPS'] = '1'
seed = 0
seed_everything(seed)
warnings.filterwarnings('ignore' ) | Cassava Leaf Disease Classification |
13,703,884 | base_lr = 1e-4
fc_lr = 1e-3
lrs = [base_lr, base_lr, fc_lr]
learn.fit(lrs=lrs, n_cycle=2, cycle_len=None)
learn.unfreeze()
learn.fit(lrs, n_cycle=9, cycle_len=None)
learn.save(MODEL_PATH )<define_variables> | BATCH_SIZE = 4 * REPLICAS
HEIGHT = 512
WIDTH = 512
CHANNELS = 3
N_CLASSES = 5
TTA_STEPS = 3 | Cassava Leaf Disease Classification |
13,703,884 | best_th = 0.4<prepare_output> | def get_name(file_path):
parts = tf.strings.split(file_path, os.path.sep)
name = parts[-1]
return name
def decode_image(image_data):
image = tf.image.decode_jpeg(image_data, channels=3)
image = tf.cast(image, tf.float32)/ 255.0
return image
def center_crop(image):
image = tf.reshape(image, [600, 800, CHANNELS])
h, w... | Cassava Leaf Disease Classification |
13,703,884 | preds_t,y_t = learn.TTA(is_test=True,n_aug=8)
preds_t = np.stack(preds_t, axis=-1)
preds_t = np.exp(preds_t)
preds_t = preds_t.mean(axis=-1)
preds_t = np.concatenate([np.zeros(( preds_t.shape[0],1)) +best_th, preds_t],axis=1 )<save_to_csv> | model_path_list = glob.glob('/kaggle/input/cassava-leaf-disease-tpu-tensorflow-training/*.h5')
model_path_list.sort()
print('Models to predict:')
print(*model_path_list, sep='
' ) | Cassava Leaf Disease Classification |
13,703,884 | sample_df = pd.read_csv(SAMPLE_SUB)
sample_list = list(sample_df.Image)
labels_list = ["new_whale"]+labels_list
pred_list = [[labels_list[i] for i in p.argsort() [-5:][::-1]] for p in preds_t]
pred_dic = dict(( key, value)for(key, value)in zip(learn.data.test_ds.fnames,pred_list))
pred_list_cor = [' '.join(pred_dic[i... | model_path_list_2 = glob.glob('/kaggle/input/cassava-leaf-disease-training-with-tpu-v2-pods/*.h5')
model_path_list_2.sort()
print('Models to predict:')
print(*model_path_list_2, sep='
' ) | Cassava Leaf Disease Classification |
13,703,884 | MODEL_NAME = 'Resnext50'
TRAIN = '.. /input/humpback-whale-identification/train/'
TEST = '.. /input/humpback-whale-identification/test/'
LABELS = '.. /input/humpback-whale-identification/train.csv'
SAMPLE_SUB = '.. /input/humpback-whale-identification/sample_submission.csv'
arch = resnext50
num_workers = 4<feature_engi... | def model_fn(input_shape, N_CLASSES):
inputs = L.Input(shape=input_shape, name='inputs')
base_model = efn.EfficientNetB3(input_tensor=inputs,
include_top=False,
weights=None,
pooling='avg')
model = tf.keras.Sequential([
base_model,
L.Dropout (.25),
L.Dense(N_CLASSES, activation='softmax', name='output')
])
return m... | Cassava Leaf Disease Classification |
13,703,884 | df = pd.read_csv(LABELS ).set_index('Image')
new_whale_df = df[df.Id == "new_whale"]
train_df = df[~(df.Id == "new_whale")]
unique_labels = np.unique(train_df.Id.values)
labels_dict = dict()
labels_list = []
for i in range(len(unique_labels)) :
labels_dict[unique_labels[i]] = i
labels_list.append(unique_labels[i])
p... | files_path = f'{database_base_path}test_images/'
test_preds = np.zeros(( len(os.listdir(files_path)) , N_CLASSES))
print('First model')
for model_path in model_path_list:
print(model_path)
K.clear_session()
model.load_weights(model_path)
if TTA_STEPS > 0:
test_ds = get_dataset(files_path, tta=True)
for step in rang... | Cassava Leaf Disease Classification |
13,703,884 | <load_pretrained><EOS> | submission = pd.DataFrame({'image_id': image_names, 'label': test_preds})
submission.to_csv('submission.csv', index=False)
display(submission.head() ) | Cassava Leaf Disease Classification |
13,629,501 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<data_type_conversions> | !pip install.. /input/timm031/timm-0.3.1-py3-none-any.whl
sub = 0
if sub==0:
!pip install --upgrade pip adabound | Cassava Leaf Disease Classification |
13,629,501 | pil2tensor = transforms.ToTensor()
class HWIDataset(FilesDataset):
def __init__(self, fnames, path, transform):
self.train_df = train_df
super().__init__(fnames, transform, path)
def get_x(self, i):
img_cr = make_bbox_image(os.path.join(self.path, self.fnames[i])).convert("RGB")
img = pil2tensor(img_cr ).numpy().tran... | import numpy as np
import pandas as pd
import timm
import os
import matplotlib.pyplot as plt
import cv2
import sys
from sklearn import model_selection, metrics
import torch
from PIL import Image
from tensorflow.keras import models, layers
from PIL import ImageEnhance, ImageOps
import pdb
import torchvision.transforms a... | Cassava Leaf Disease Classification |
13,629,501 | class RandomLighting(Transform):
def __init__(self, b, c, tfm_y=TfmType.NO):
super().__init__(tfm_y)
self.b, self.c = b, c
def set_state(self):
self.store.b_rand = rand0(self.b)
self.store.c_rand = rand0(self.c)
def do_transform(self, x, is_y):
if is_y and self.tfm_y != TfmType.PIXEL: return x
b = self.store.b_rand
... | BASE_DIR = '.. /input/cassava-leaf-disease-classification'
TRAIN_PATH = BASE_DIR+"/train_images/"
TEST_PATH = BASE_DIR+"/test_images/" | Cassava Leaf Disease Classification |
13,629,501 | !wget http://files.fast.ai/models/weights.tgz
!tar -zxvf weights.tgz
!mkdir /opt/conda/lib/python3.6/site-packages/fastai/weights/
!cp weights/resnext_50_32x4d.pth /opt/conda/lib/python3.6/site-packages/fastai/weights/
!rm -rf weights weights.tgz<load_pretrained> | def read_image(path,label):
image_data = cv2.imread(path)
plt.title('label:{}'.format(label))
plt.imshow(image_data)
return image_data
class CassavaDataset(torch.utils.data.Dataset):
def __init__(self, df, data_path, mode="train", transforms=None):
super().__init__()
self.df_data = df.values
self.data_path = data_p... | Cassava Leaf Disease Classification |
13,629,501 | image_size = 384
batch_size = 16
md = get_data(image_size, batch_size)
extra_fc_layers_size = []
learn = ConvLearner.pretrained(arch, md, xtra_fc=extra_fc_layers_size)
learn.opt_fn = optim.Adam<set_options> | def train(net, epoch, trainLoader, optimizer, criterion):
sum_loss = 0
total = 0
correct = 0
net.train()
for i, data in enumerate(trainLoader, 0):
length = len(data)
input, target = data
input, target = input.to(device), target.to(device)
optimizer.zero_grad()
output= net(input)
loss = criterion(output, target)
los... | Cassava Leaf Disease Classification |
13,629,501 | md.is_multi, md.is_reg<init_hyperparams> | lr = 1e-4
BATCH_SIZE=8
IMG_SIZE=512
EPOCH=10
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
df = pd.read_csv(os.path.join(BASE_DIR,'train.csv'))
df.image_id = df.image_id.apply(lambda x : TRAIN_PATH+x)
LOG_FOUT = open(os.path.join('./', 'log_train.txt'), 'w')
if os.path.exists('./models')==0:
... | Cassava Leaf Disease Classification |
13,629,501 | print('Number of layer groups:', len(learn.get_layer_groups()), '\t(first 2 groups is pretrained backbone)')
print('This is our extra thin on top of the backbone Resnet50 architecture:')
learn.get_layer_groups() [2]<train_model> | def one_epoch(fold):
log_string('fold:%d, batch_size:%d,lr:%f,fold_epech:%d,device:%s' %(fold , BATCH_SIZE,lr,2,device))
folds= StratifiedKFold(n_splits=fold ).split(df['image_id'],df['label'])
acc_train_all= []
acc_valid_all= []
best_acc = 0
for i,(train_index,valid_index)in enumerate(folds):
train_df = df.loc[train_... | Cassava Leaf Disease Classification |
13,629,501 | base_lr = 1e-4
fc_lr = 1e-3
lrs = [base_lr, base_lr, fc_lr]
learn.fit(lrs=lrs, n_cycle=2, cycle_len=None)
learn.unfreeze()
learn.fit(lrs, n_cycle=9, cycle_len=None)
learn.save(MODEL_PATH )<define_variables> | Cassava Leaf Disease Classification | |
13,629,501 | best_th = 0.4<prepare_output> | def test_one(model_name , file_name):
net = CassvaImgClassifier(model_name,5, False)
net.to(device)
net.load_state_dict(torch.load(filename ).state_dict())
net.eval()
preds = []
submit = pd.read_csv(os.path.join(BASE_DIR, "sample_submission.csv"))
for image_id in submit.image_id:
img = Image.open(BASE_DIR+"/test_ima... | Cassava Leaf Disease Classification |
13,629,501 | <save_to_csv><EOS> | file_name = ['a','b','c']
model_name = ['a','b','c']
transform1 = transforms.Compose([
transforms.CenterCrop(IMG_SIZE),
transforms.ToTensor() ,
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
all_preds = []
for i in range(3):
all_preds.append(test_one(model_name[i],file_name[i]))
submit... | Cassava Leaf Disease Classification |
13,417,467 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<define_variables> | package_path = '.. /input/pytorch-image-models/pytorch-image-models-master'
| Cassava Leaf Disease Classification |
13,417,467 | !pip install fastai==0.7.0 --no-deps
!pip install torch==0.4.1 torchvision==0.2.1
MODEL_NAME = 'Resnet50'
TRAIN = '.. /input/humpback-whale-identification/train/'
TEST = '.. /input/humpback-whale-identification/test/'
LABELS = '.. /input/humpback-whale-identification/train.csv'
SAMPLE_SUB = '.. /input/humpback-whale-id... | from glob import glob
from sklearn.model_selection import GroupKFold, StratifiedKFold
import cv2
from skimage import io
import torch
from torch import nn
import os
from datetime import datetime
import time
import random
import cv2
import torchvision
from torchvision import transforms
import pandas as pd
import numpy as... | Cassava Leaf Disease Classification |
13,417,467 | df = pd.read_csv(LABELS ).set_index('Image')
new_whale_df = df[df.Id == "new_whale"]
train_df = df[~(df.Id == "new_whale")]
unique_labels = np.unique(train_df.Id.values)
labels_dict = dict()
labels_list = []
for i in range(len(unique_labels)) :
labels_dict[unique_labels[i]] = i
labels_list.append(unique_labels[i])
p... | CFG = {
'fold_num': 10,
'seed': 719,
'model_arch': 'tf_efficientnet_b3_ns',
'img_size': 512,
'epochs': 32,
'train_bs': 32,
'valid_bs': 32,
'lr': 0.03*1e-4,
'num_workers': 4,
'accum_iter': 2,
'verbose_step': 1,
'device': 'cuda:0',
'tta': 1,
'used_epochs': [6,7,8,9],
'weights': [1,1,1,1],
'PseEpochs':3,
'weight_decay':1e... | Cassava Leaf Disease Classification |
13,417,467 | train_df['image_name'] = train_df.index
bbox_df = pd.read_csv(BBOX ).set_index('Image')
rs = np.random.RandomState(42)
perm = rs.permutation(len(train_df))
tr_n = train_df['image_name'].values
val_n = train_df['image_name'].values[perm][:1000]
print('Train/val:', len(tr_n), len(val_n))
print('Train classes', len(trai... | train = pd.read_csv('.. /input/cassava-leaf-disease-classification/train.csv')
train | Cassava Leaf Disease Classification |
13,417,467 | class RandomLighting(Transform):
def __init__(self, b, c, tfm_y=TfmType.NO):
super().__init__(tfm_y)
self.b, self.c = b, c
def set_state(self):
self.store.b_rand = rand0(self.b)
self.store.c_rand = rand0(self.c)
def do_transform(self, x, is_y):
if is_y and self.tfm_y != TfmType.PIXEL: return x
b = self.store.b_rand
... | train.label.value_counts() | Cassava Leaf Disease Classification |
13,417,467 | image_size = 384
batch_size = 32
md = get_data(image_size, batch_size)
extra_fc_layers_size = []
learn = ConvLearner.pretrained(arch, md, xtra_fc=extra_fc_layers_size)
learn.opt_fn = optim.Adam<init_hyperparams> | submission = pd.read_csv('.. /input/cassava-leaf-disease-classification/sample_submission.csv')
submission.head() | Cassava Leaf Disease Classification |
13,417,467 | print('Number of layer groups:', len(learn.get_layer_groups()), '\t(first 2 groups is pretrained backbone)')
print('This is our extra thin on top of the backbone Resnet50 architecture:')
learn.get_layer_groups() [2]<train_model> | class CassavaDataset(Dataset):
def __init__(
self, df, data_root, transforms=None, output_label=True
):
super().__init__()
self.df = df.reset_index(drop=True ).copy()
self.transforms = transforms
self.data_root = data_root
self.output_label = output_label
def __len__(self):
return self.df.shape[0]
def __getitem__(sel... | Cassava Leaf Disease Classification |
13,417,467 | base_lr = 1e-4
fc_lr = 1e-3
lrs = [base_lr, base_lr, fc_lr]
learn.fit(lrs=lrs, n_cycle=2, cycle_len=None)
learn.unfreeze()
learn.fit(lrs, n_cycle=16, cycle_len=None )<define_variables> | HorizontalFlip, VerticalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine, RandomResizedCrop,
IAASharpen, IAAEmboss, RandomBrightnessCon... | Cassava Leaf Disease Classification |
13,417,467 | best_th = 0.38<prepare_output> | class CassvaImgClassifier(nn.Module):
def __init__(self, model_arch, n_class, pretrained=False):
super().__init__()
self.model = timm.create_model(model_arch, pretrained=pretrained)
n_features = self.model.classifier.in_features
self.model.classifier = nn.Linear(n_features, n_class)
def forward(self, x):
x = self.mod... | Cassava Leaf Disease Classification |
13,417,467 | preds_t,y_t = learn.TTA(is_test=True,n_aug=8)
preds_t = np.stack(preds_t, axis=-1)
preds_t = np.exp(preds_t)
preds_t = preds_t.mean(axis=-1)
preds_t = np.concatenate([np.zeros(( preds_t.shape[0],1)) +best_th, preds_t],axis=1)
np.save("preds.npy",preds_t )<save_to_csv> | if __name__ == '__main__':
seed_everything(CFG['seed'])
folds = StratifiedKFold(n_splits=CFG['fold_num'] ).split(np.arange(train.shape[0]), train.label.values)
for fold,(trn_idx, val_idx)in enumerate(folds):
if fold > 0:
break
print('Inference fold {} started'.format(fold))
valid_ = train.loc[val_idx,:].reset_index(d... | Cassava Leaf Disease Classification |
13,417,467 | sample_df = pd.read_csv(SAMPLE_SUB)
sample_list = list(sample_df.Image)
labels_list = ["new_whale"]+labels_list
pred_list = [[labels_list[i] for i in p.argsort() [-5:][::-1]] for p in preds_t]
pred_dic = dict(( key, value)for(key, value)in zip(learn.data.test_ds.fnames,pred_list))
pred_list_cor = [' '.join(pred_dic[i... | test['label'] = np.argmax(tst_preds, axis=1)
| Cassava Leaf Disease Classification |
13,417,467 | from fastai.conv_learner import *
from fastai.dataset import *
from tqdm import tqdm
import pandas as pd
import numpy as np
import os
from sklearn.model_selection import train_test_split, StratifiedShuffleSplit
import matplotlib.pyplot as plt
import math<define_variables> | def rect_path(path):
return '.. /input/cassava-leaf-disease-classification/train_images/'+path
train['image_id'] = train['image_id'].apply(rect_path ) | Cassava Leaf Disease Classification |
13,417,467 | MODEL_PATH = 'Resnet18_v1'
TRAIN = '.. /input/train/'
TEST = '.. /input/test/'
LABELS = '.. /input/train.csv'
SAMPLE_SUB = '.. /input/sample_submission.csv'
<choose_model_class> | train = pd.concat([train,test] ).reset_index()
train | Cassava Leaf Disease Classification |
13,417,467 | arch = resnet34
nw = 4<feature_engineering> | class CassavaPseDataset(Dataset):
def __init__(self, df, data_root,
transforms=None,
output_label=True,
one_hot_label=False,
do_fmix=False,
fmix_params={
'alpha': 1.,
'decay_power': 3.,
'shape':(CFG['img_size'], CFG['img_size']),
'max_soft': True,
'reformulate': False
},
do_cutmix=False,
cutmix_params={
'alpha': 1,
}
... | Cassava Leaf Disease Classification |
13,417,467 | df = pd.read_csv(LABELS ).set_index('Image')
new_whale_df = df[df.Id == "new_whale"]
train_df = df[~(df.Id == "new_whale")]
unique_labels = np.unique(train_df.Id.values)
labels_dict = dict()
labels_list = []
for i in range(len(unique_labels)) :
labels_dict[unique_labels[i]] = i
labels_list.append(unique_labels[i])
p... | def prepare_dataloader(df, trn_idx, val_idx, data_root='.. /input/cassava-leaf-disease-classification/train_images/'):
train_ = df.loc[trn_idx,:].reset_index(drop=True)
valid_ = df.loc[val_idx,:].reset_index(drop=True)
train_ds = CassavaPseDataset(train_, data_root, transforms=get_train_transforms() , output_label=Tr... | Cassava Leaf Disease Classification |
13,417,467 | dup = []
for idx,row in train_df.iterrows() :
if labels_count[row['Id']] < 5:
dup.extend([idx]*math.ceil(( 5 - labels_count[row['Id']])/labels_count[row['Id']]))
train_names = np.concatenate([train_names, dup])
train_names = train_names[np.random.RandomState(seed=42 ).permutation(train_names.shape[0])]
len(train_names... | if __name__ == '__main__':
seed_everything(CFG['seed'])
folds = StratifiedKFold(n_splits=CFG['fold_num'], shuffle=True, random_state=CFG['seed'] ).split(np.arange(train.shape[0]), train.label.values)
for fold,(trn_idx, val_idx)in enumerate(folds):
if fold>0:
break
test = pd.DataFrame()
test['image_id'] = list(os.list... | Cassava Leaf Disease Classification |
13,417,467 | sss = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42069)
for train_idx, val_idx in sss.split(train_names, np.zeros(train_names.shape)) :
tr_n, val_n = train_names[train_idx], train_names[val_idx]
print(len(tr_n), len(val_n))<set_options> | test['label'] = np.argmax(tst_preds, axis=1)
test.head() | Cassava Leaf Disease Classification |
13,417,467 | <prepare_x_and_y><EOS> | test.to_csv('submission.csv', index=False ) | Cassava Leaf Disease Classification |
13,464,807 | <SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<categorify> | !pip install --quiet /kaggle/input/kerasapplications
!pip install --quiet /kaggle/input/efficientnet-git | Cassava Leaf Disease Classification |
13,464,807 | class RandomLighting(Transform):
def __init__(self, b, c, tfm_y=TfmType.NO):
super().__init__(tfm_y)
self.b, self.c = b, c
def set_state(self):
self.store.b_rand = rand0(self.b)
self.store.c_rand = rand0(self.c)
def do_transform(self, x, is_y):
if is_y and self.tfm_y != TfmType.PIXEL: return x
b = self.store.b_rand
... | print("Tensorflow version " + tf.__version__ ) | Cassava Leaf Disease Classification |
13,464,807 | batch_size = 64
md = get_data(384, batch_size)
learn = ConvLearner.pretrained(arch, md)
learn.opt_fn = optim.Adam<train_model> | strategy = tf.distribute.get_strategy()
AUTOTUNE = tf.data.experimental.AUTOTUNE
GCS_PATH = ".. /input/cassava-leaf-disease-classification"
IMAGE_SIZE = [512, 512]
RESIZE_IMAGE_SIZE = [512, 512]
CLASSES = ['0', '1', '2', '3', '4']
WEIGHTS_PATH = ".. /input/cassava-leaf-disease-resnet-weights/EfficientNetB4-best-08-0.88... | Cassava Leaf Disease Classification |
13,464,807 | learn.fit(lr, 2, cycle_len=3)
learn.unfreeze()
lrs = np.array([lr/10, lr/20, lr/40])
learn.fit(lrs, 4, cycle_len=4, use_clr=(20, 16))
learn.save(MODEL_PATH )<prepare_output> | def seed_everything(seed=0):
random.seed(seed)
np.random.seed(seed)
tf.random.set_seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
os.environ['TF_DETERMINISTIC_OPS'] = '1'
seed = 0
seed_everything(seed)
warnings.filterwarnings('ignore' ) | Cassava Leaf Disease Classification |
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