Kaggle competition: Prostate cANcer graDe Assessment (PANDA) Challenge
<SOS> metric: QuadraticWeightedKappa Kaggle data source: prostate-cancer-grade-assessment-(panda)-challenge<merge>
DEBUG = False
test_peaks = pd.merge(test_peaks, meta_test_df[['signal_id', 'id_measurement']], on='signal_id', how='left')
 test_peaks.head()<feature_engineering>
sys.path = [
'.. /input/efficientnet-pytorch/EfficientNet-PyTorch/EfficientNet-PyTorch-master',
] + sys.path
X_test = calculate_features(test_peaks, meta_test_df )<predict_on_test>
import skimage.io
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from efficientnet_pytorch import model as enet
import matplotlib.pyplot as plt
from tqdm.notebook import tqdm
yp_test = np.zeros(X_test.shape[0])
 for j in range(len(models)) :
model = models[j]
yp_test += model.predict(X_test.values)/len(models )<data_type_conversions>
data_dir = '.. /input/prostate-cancer-grade-assessment'
df_train = pd.read_csv(os.path.join(data_dir, 'train.csv'))
df_test = pd.read_csv(os.path.join(data_dir, 'test.csv'))
df_sub = pd.read_csv(os.path.join(data_dir, 'sample_submission.csv'))
model_dir = '.. /input/my-effnet-model-17'
image_folder = os.path.join(data_dir, 'test_images')
 is_test = os.path.exists(image_folder)
 image_folder = image_folder if is_test else os.path.join(data_dir, 'train_images')
 df = df_test if is_test else df_train.loc[:16]
tile_size = 256
image_size = 256
n_tiles = 36
batch_size = 8
num_workers = 4
if torch.cuda.is_available() :
device = torch.device('cuda')
 map_loc = 'cuda'
else:
device = torch.device('cpu')
 map_loc='cpu'
print(map_loc)
 print(image_folder )
test_submission = pd.DataFrame(
 yp_test,
index=X_test.index,
columns=['probability'] 
)
 test_submission['target'] =(yp_test > best_thresh ).astype(np.int)
 test_submission = pd.merge(
 meta_test_df[['id_measurement', 'signal_id']],
test_submission,
on='id_measurement',
how='left'
)<count_values>
class enetv2(nn.Module):
def __init__(self, backbone, out_dim):
super(enetv2, self ).__init__()
self.enet = enet.EfficientNet.from_name(backbone)
 self.myfc = nn.Linear(self.enet._fc.in_features, out_dim)
 self.enet._fc = nn.Identity()
def extract(self, x):
return self.enet(x)
 def forward(self, x):
x = self.extract(x)
 x = self.myfc(x)
 return x
def load_models(model_files):
models = []
for model_f in model_files:
model_f = os.path.join(model_dir, model_f)
 backbone = 'efficientnet-b0'
model = enetv2(backbone, out_dim=5)
 model.load_state_dict(torch.load(model_f, map_location=map_loc), strict=True)
 model.eval()
model.to(device)
 models.append(model)
 print(f'{model_f} loaded!')
 return models
model_files = ['my_effnet_b0_model_state_dict_best_fold0(10 ).pth']
models = load_models(model_files )
test_submission.target.value_counts()<save_to_csv>
def get_tiles(img, mode=0):
result = []
h, w, c = img.shape
pad_h =(tile_size - h % tile_size)% tile_size +(( tile_size * mode)// 2)
 pad_w =(tile_size - w % tile_size)% tile_size +(( tile_size * mode)// 2)
 img2 = np.pad(img,[[pad_h // 2, pad_h - pad_h // 2], [pad_w // 2,pad_w - pad_w//2], [0,0]], constant_values=255)
 img3 = img2.reshape(
 img2.shape[0] // tile_size,
tile_size,
img2.shape[1] // tile_size,
tile_size,
3 
)
 img3 = img3.transpose(0,2,1,3,4 ).reshape(-1, tile_size, tile_size,3)
 n_tiles_with_info =(img3.reshape(img3.shape[0],-1 ).sum(1)< tile_size ** 2 * 3 * 255 ).sum()
if len(img)< n_tiles:
img3 = np.pad(img3,[[0,N-len(img3)],[0,0],[0,0],[0,0]], constant_values=255)
 idxs = np.argsort(img3.reshape(img3.shape[0],-1 ).sum(-1)) [:n_tiles]
img3 = img3[idxs]
for i in range(len(img3)) :
result.append({'img':img3[i], 'idx':i})
 return result, n_tiles_with_info >= n_tiles
class PANDADataset(Dataset):
def __init__(self,
df,
image_size,
n_tiles=n_tiles,
tile_mode=0,
rand=False,
sub_imgs=False 
):
self.df = df.reset_index(drop=True)
 self.image_size = image_size
self.n_tiles = n_tiles
self.tile_mode = tile_mode
self.rand = rand
self.sub_imgs = sub_imgs
def __len__(self):
return self.df.shape[0]
def __getitem__(self, index):
row = self.df.iloc[index]
img_id = row.image_id
tiff_file = os.path.join(image_folder, f'{img_id}.tiff')
 image = skimage.io.MultiImage(tiff_file)[1]
tiles, OK = get_tiles(image, self.tile_mode)
 if self.rand:
idxes = np.random.choice(list(range(self.n_tiles)) , self.n_tiles, replace=False)
 else:
idxes = list(range(self.n_tiles))
idxes = np.asarray(idxes)+ self.n_tiles if self.sub_imgs else idxes
n_row_tiles = int(np.sqrt(self.n_tiles))
images = np.zeros(( image_size * n_row_tiles, image_size * n_row_tiles, 3))
for h in range(n_row_tiles):
for w in range(n_row_tiles):
i = h * n_row_tiles + w
if len(tiles)> idxes[i]:
this_img = tiles[idxes[i]]['img']
else:
this_img = np.ones(( self.image_size, self.image_size, 3)).astype(np.uint8)* 255
this_img = 255 - this_img
h1 = h * image_size
w1 = w * image_size
images[h1:h1+image_size, w1:w1+image_size] = this_img
images = images.astype(np.float32)
 images /= 255
images = images.transpose(2, 0, 1)
 return torch.tensor(images)
submission = test_submission[['signal_id', 'target']]
submission.to_csv('submission.csv', index=False )<define_variables>
dataset = PANDADataset(df, image_size, n_tiles, 0)
 loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
 dataset2 = PANDADataset(df, image_size, n_tiles, 2)
 loader2 = DataLoader(dataset2, batch_size=batch_size, num_workers=num_workers, shuffle=False)
RECALCULATE_PEAK_FEATURES = True
PREPROCESSED_DIR = '.. /input/vsb-1st-place-solution'<define_variables>
LOGITS = []
LOGITS2 = []
with torch.no_grad() :
for data in tqdm(loader):
data = data.to(device)
 logits = models[0](data)
 LOGITS.append(logits)
 for data in tqdm(loader2):
data = data.to(device)
 logits = models[0](data)
 LOGITS2.append(logits)
 LOGITS =(torch.cat(LOGITS ).sigmoid().cpu() + torch.cat(LOGITS2 ).sigmoid().cpu())/ 2
PREDS = LOGITS.sum(1 ).round().numpy()
df['isup_grade'] = PREDS.astype(int)
 df[['image_id', 'isup_grade']].to_csv('submission.csv', index=False)
 print(df.head())
 print()
print(df.isup_grade.value_counts() )
USE_SIMPLIFIED_VERSION = False<import_modules>
<import_modules><EOS>