Kaggle competition: Rainforest Connection Species Audio Detection
<SOS> metric: WeightedLabelRankingAveragePrecision Kaggle data source: rainforest-connection-species-audio-detection<filter>
def read_train_csv(n_trains,path):
base_df=pd.DataFrame()
for i in range(n_trains):
base_df=base_df.append(pd.read_csv(path+f'train_n_{i}.csv'), ignore_index = True)
 return base_df
def find_best_train_csv(n_trains,path,n_bests):
base_df=pd.DataFrame()
for i in range(n_trains):
df=pd.read_csv(path+f'train_n_{i}.csv')
 base_df=base_df.append(df.nlargest(n_bests, ['best_score']), ignore_index = True)
 return base_df
def find_worst_train_csv(n_trains,path,n_worst):
base_df=pd.DataFrame()
for i in range(n_trains):
df=pd.read_csv(path+f'train_n_{i}.csv')
 base_df=base_df.append(df.nsmallest(n_worst, ['best_score']), ignore_index = True)
 return base_df
def average_csv(csv_list):
BLEND=pd.read_csv('.. /input/resnet-wavenet-my-best-single-model-ensemble/submission.csv')
 BLEND.iloc[:,1:]=pd.read_csv(csv_list[0])
 for sub_csv in csv_list[1:]:
BLEND.iloc[:,1:]+=pd.read_csv(sub_csv)
 BLEND.iloc[:,1:]=BLEND.iloc[:,1:]/len(csv_list)
 return BLEND
def create_csv_list(df,path):
df['CSV'] = path + df['CSV'].astype(str)
 return df["CSV"].tolist()
df_sub.iloc[5847:5880] 
<install_modules>
print(KaggleDatasets().get_gcs_path('rfcx-species-audio-detection'))
n_trains=10
path='.. /input/rainforest-connection-species-audio-detectionv1/KAggle/'
train_data=read_train_csv(n_trains,path)
 print(train_data)
 BLEND_all=create_csv_list(train_data,path)
 BLEND_all=average_csv(BLEND_all)
 print(BLEND_all)
 BLEND_all.to_csv('submission.csv',index=False )
package_path = '.. /input/efficientnet-pytorch-07/efficientnet_pytorch-0.7.0'
!cp.. /input/gdcm-conda-install/gdcm.tar.
 !tar -xvzf gdcm.tar
!conda install --offline./gdcm/gdcm-2.8.9-py37h71b2a6d_0.tar.bz2<define_variables>
sub2 = pd.read_csv('.. /input/automl-inference-audio-detection-soliset/submission.csv')
 sub3 = pd.read_csv('.. /input/fork-of-bagging-rainforest/submission.csv')
 BLEND_all.iloc[:,1:]=(BLEND_all.iloc[:,1:]*0.30+sub3.iloc[:,1:]*0.70)*0.40+sub2.iloc[:,1:]*0.60/5
BLEND_all.to_csv('submission_ensemble.csv',index=False )
CFG = {
'train': False,
'train_img_path': '.. /input/rsna-str-pulmonary-embolism-detection/train',
'test_img_path': '.. /input/rsna-str-pulmonary-embolism-detection/test',
'cv_fold_path': '.. /input/stratified-validation-strategy/rsna_train_splits_fold_20.csv',
'train_path': '.. /input/rsna-str-pulmonary-embolism-detection/train.csv',
'test_path': '.. /input/rsna-str-pulmonary-embolism-detection/test.csv',
'image_target_cols': [
'pe_present_on_image',
],
'exam_target_cols': [
'negative_exam_for_pe',
'rv_lv_ratio_gte_1',
'rv_lv_ratio_lt_1',
'leftsided_pe',
'chronic_pe',
'rightsided_pe',
'acute_and_chronic_pe',
'central_pe',
'indeterminate'
],
'img_num': 200,
'img_size': 256,
'lr': 0.0005,
'epochs': 2,
'device': 'cuda',
'train_bs': 2,
'accum_iter': 8,
'verbose_step': 1,
'num_workers': 4,
'efbnet': 'efficientnet-b0',
'train_folds': [np.arange(0,16),
np.concatenate([np.arange(0,12), np.arange(16,20)]),
np.concatenate([np.arange(0,8), np.arange(12,20)]),
np.concatenate([np.arange(0,4), np.arange(8,20)]),
np.arange(4,20),
],
'valid_folds': [np.arange(16,20),
np.arange(12,16),
np.arange(8,12),
np.arange(4,8),
np.arange(0,4)
 ],
'model_path': '.. /input/kh-rsna-model',
'tag': 'efb0_stage2_multilabel'
}
SEED = 42321
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
def window(img, WL=50, WW=350):
upper, lower = WL+WW//2, WL-WW//2
X = np.clip(img.copy() , lower, upper)
 X = X - np.min(X)
 X = X / np.max(X)
 return X
def get_img(path):
d = pydicom.read_file(path)


 img =(d.pixel_array * d.RescaleSlope)+ d.RescaleIntercept
r = window(img, -600, 1500)
 g = window(img, 100, 700)
 b = window(img, 40, 400)
 res = np.concatenate([r[:, :, np.newaxis],
g[:, :, np.newaxis],
b[:, :, np.newaxis]], axis=-1)
 res = zoom(res, [CFG['img_size']/res.shape[0], CFG['img_size']/res.shape[1], 1.], prefilter=False, order=1)
 return res
class RSNADatasetStage1(Dataset):
def __init__(
 self, df, label_smoothing, data_root,
image_subsampling=True, transforms=None, output_label=True 
):
super().__init__()
self.df = df.reset_index(drop=True ).copy()
self.label_smoothing = label_smoothing
self.transforms = transforms
self.data_root = data_root
self.output_label = output_label
def __len__(self):
return self.df.shape[0]
def __getitem__(self, index: int):
if self.output_label:
target = self.df[CFG['image_target_cols']].values[index]
path = "{}/{}/{}/{}.dcm".format(self.data_root,
self.df.iloc[index]['StudyInstanceUID'],
self.df.iloc[index]['SeriesInstanceUID'],
self.df.iloc[index]['SOPInstanceUID'])
 img = get_img(path)
 if self.transforms:
img = self.transforms(image=img)['image']
if self.output_label == True:
target = np.clip(target, self.label_smoothing, 1 - self.label_smoothing)
 return img, target
else:
return img
class RSNADataset(Dataset):
def __init__(
 self, df, label_smoothing, data_root,
image_subsampling=True, transforms=None, output_label=True 
):
super().__init__()
self.df = df
self.patients = self.df['StudyInstanceUID'].unique()
self.image_subsampling = image_subsampling
self.label_smoothing = label_smoothing
self.transforms = transforms
self.data_root = data_root
self.output_label = output_label
def get_patients(self):
return self.patients
def __len__(self):
return len(self.patients)
 def __getitem__(self, index: int):
patient = self.patients[index]
df_ = self.df.loc[self.df.StudyInstanceUID == patient]
per_image_feats = get_stage1_columns()
if self.image_subsampling:
img_num = min(CFG['img_num'], df_.shape[0])
 img_ix = np.random.choice(np.arange(df_.shape[0]), replace=False, size=img_num)
 imgs = np.zeros(( CFG['img_num'],), np.float32)
 per_image_preds = np.zeros(( CFG['img_num'], len(per_image_feats)) , np.float32)
 locs = np.zeros(( CFG['img_num'],), np.float32)
 image_masks = np.zeros(( CFG['img_num'],), np.float32)
 image_masks[:img_num] = 1.
 if self.output_label:
exam_label = df_[CFG['exam_target_cols']].values[0]
image_labels = np.zeros(( CFG['img_num'], len(CFG['image_target_cols'])) , np.float32)
 else:
img_num = df_.shape[0]
img_ix = np.arange(df_.shape[0])
 imgs = np.zeros(( img_num,), np.float32)
 per_image_preds = np.zeros(( img_num, len(per_image_feats)) , np.float32)
 locs = np.zeros(( img_num,), np.float32)
 image_masks = np.zeros(( img_num,), np.float32)
 image_masks[:img_num] = 1.
 if self.output_label:
exam_label = df_[CFG['exam_target_cols']].values[0]
image_labels = np.zeros(( img_num, len(CFG['image_target_cols'])) , np.float32)
 for i, im_ix in enumerate(img_ix):
path = "{}/{}/{}/{}.dcm".format(self.data_root,
df_['StudyInstanceUID'].values[im_ix],
df_['SeriesInstanceUID'].values[im_ix],
df_['SOPInstanceUID'].values[im_ix])
 d = pydicom.read_file(path)
 locs[i] = d.ImagePositionPatient[2]
per_image_preds[i,:] = df_[per_image_feats].values[im_ix,:]
if self.output_label == True:
image_labels[i] = df_[CFG['image_target_cols']].values[im_ix]
seq_ix = np.argsort(locs)

locs = locs[seq_ix]
locs[1:img_num] = locs[1:img_num]-locs[0:img_num-1]
locs[0] = 0
per_image_preds = per_image_preds[seq_ix]
if self.output_label == True:
image_labels = image_labels[seq_ix]
image_labels = np.clip(image_labels, self.label_smoothing, 1 - self.label_smoothing)
 exam_label = np.clip(exam_label, self.label_smoothing, 1 - self.label_smoothing)
 return imgs, per_image_preds, locs, image_labels, exam_label, image_masks
else:
return imgs, per_image_preds, locs, img_num, index, seq_ix
HorizontalFlip, VerticalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine,
IAASharpen, IAAEmboss, RandomBrightnessContrast, Flip, OneOf, Compose, Normalize, Cutout 
)
 def get_train_transforms() :
return Compose([
ToTensorV2(p=1.0),
], p=1.)

 def get_valid_transforms() :
return Compose([
ToTensorV2(p=1.0),
], p=1.)

 class RNSAImageFeatureExtractor(nn.Module):
def __init__(self):
super().__init__()
self.cnn_model = EfficientNet.from_name(CFG['efbnet'])
 self.pooling = nn.AdaptiveAvgPool2d(1)
 def get_dim(self):
return self.cnn_model._fc.in_features
def forward(self, x):
feats = self.cnn_model.extract_features(x)
 return self.pooling(feats ).view(x.shape[0], -1)
 class RSNAImgClassifierSingle(nn.Module):
def __init__(self):
super().__init__()
self.cnn_model = RNSAImageFeatureExtractor()
self.image_predictors = nn.Linear(self.cnn_model.get_dim() , 1)
 def forward(self, imgs):
imgs_embdes = self.cnn_model(imgs)
 image_preds = self.image_predictors(imgs_embdes)
 return image_preds
class RSNAImgClassifier(nn.Module):
def __init__(self):
super().__init__()
self.cnn_model = RNSAImageFeatureExtractor()
self.image_predictors = nn.Linear(self.cnn_model.get_dim() , 9)
 def forward(self, imgs):
imgs_embdes = self.cnn_model(imgs)
 image_preds = self.image_predictors(imgs_embdes)
 return image_preds
class TimeDistributed(nn.Module):
def __init__(self, module, batch_first=True):
super(TimeDistributed, self ).__init__()
self.module = module
self.batch_first = batch_first
def forward(self, x):

 x_size= x.size()
c_in = x.contiguous().view(x_size[0] * x_size[1], *x_size[2:])
 c_out = self.module(c_in)
 r_in = c_out.view(x_size[0], x_size[1], -1)
 if self.batch_first is False:
r_in = r_in.permute(1, 0, 2)
 return r_in
class RSNAClassifier(nn.Module):
def __init__(self, hidden_size=64):
super().__init__()
self.gru = nn.GRU(len(get_stage1_columns())+1, hidden_size, bidirectional=True, batch_first=True, num_layers=2)
 self.image_predictors = TimeDistributed(nn.Linear(hidden_size*2, 1))
self.exam_predictor = nn.Linear(hidden_size*2*2, 9)
 def forward(self, img_preds, locs):
embeds = torch.cat([img_preds, locs.view(locs.shape[0], locs.shape[1], 1)], dim=2)
 embeds, _ = self.gru(embeds)
 image_preds = self.image_predictors(embeds)
 avg_pool = torch.mean(embeds, 1)
 max_pool, _ = torch.max(embeds, 1)
 conc = torch.cat([avg_pool, max_pool], 1)
 exam_pred = self.exam_predictor(conc)
 return image_preds, exam_pred
def rsna_wloss_inference(y_true_img, y_true_exam, y_pred_img, y_pred_exam, chunk_sizes):

 y_true_img, y_true_exam, y_pred_img, y_pred_exam = torch.tensor(y_true_img, dtype=torch.float32), torch.tensor(y_true_exam, dtype=torch.float32), torch.tensor(y_pred_img, dtype=torch.float32), torch.tensor(y_pred_exam, dtype=torch.float32)
 y_true_img_chunks, y_true_exam_chunks, y_pred_img_chunks, y_pred_exam_chunks = torch.split(y_true_img, chunk_sizes, dim=0), torch.split(y_true_exam, chunk_sizes, dim=0), torch.split(y_pred_img, chunk_sizes, dim=0), torch.split(y_pred_exam, chunk_sizes, dim=0)
 label_w = torch.tensor([0.0736196319, 0.2346625767, 0.0782208589, 0.06257668712, 0.1042944785, 0.06257668712, 0.1042944785, 0.1877300613, 0.09202453988] ).view(1, -1)
 img_w = 0.07361963
bce_func = torch.nn.BCELoss(reduction='none')
 total_loss = torch.tensor(0, dtype=torch.float32)
 total_weights = torch.tensor(0, dtype=torch.float32)
 for i,(y_true_img_, y_true_exam_, y_pred_img_, y_pred_exam_)in enumerate(zip(y_true_img_chunks, y_true_exam_chunks, y_pred_img_chunks, y_pred_exam_chunks)) :
exam_loss = bce_func(y_pred_exam_[0, :], y_true_exam_[0, :])
 exam_loss = torch.sum(exam_loss*label_w, 1)[0]
image_loss = bce_func(y_pred_img_, y_true_img_)
 img_num = chunk_sizes[i]
qi = torch.sum(y_true_img_)/img_num
image_loss = torch.sum(img_w*qi*image_loss)
 total_loss += exam_loss+image_loss
total_weights += label_w.sum() + img_w*qi*img_num
final_loss = total_loss/total_weights
return final_loss
def rsna_wloss_train(y_true_img, y_true_exam, y_pred_img, y_pred_exam, image_masks, device):
label_w = torch.tensor([0.0736196319, 0.2346625767, 0.0782208589, 0.06257668712, 0.1042944785, 0.06257668712, 0.1042944785, 0.1877300613, 0.09202453988] ).view(1, -1 ).to(device)
 img_w = 0.07361963
bce_func = torch.nn.BCEWithLogitsLoss(reduction='none' ).to(device)
 total_loss = torch.tensor(0, dtype=torch.float32 ).to(device)
 total_weights = torch.tensor(0, dtype=torch.float32 ).to(device)
 for i in range(y_true_img.shape[0]):
exam_loss = bce_func(y_pred_exam[i, :], y_true_exam[i, :])
 exam_loss = torch.sum(exam_loss*label_w, 1)[0]
img_mask = image_masks[i]
image_loss = bce_func(y_pred_img[i,:], y_true_img[i,:] ).flatten()
image_loss = image_loss*img_mask
img_num = torch.sum(img_mask)
 qi = torch.sum(y_true_img[i,:])/img_num
image_loss = torch.sum(img_w*qi*image_loss)
 total_loss += exam_loss+image_loss
total_weights += label_w.sum() + img_w*qi*img_num
final_loss = total_loss/total_weights
return final_loss, total_loss, total_weights
def rsna_wloss_valid(y_true_img, y_true_exam, y_pred_img, y_pred_exam, image_masks, device):
label_w = torch.tensor([0.0736196319, 0.2346625767, 0.0782208589, 0.06257668712, 0.1042944785, 0.06257668712, 0.1042944785, 0.1877300613, 0.09202453988] ).view(1, -1 ).to(device)
 img_w = 0.07361963
bce_func = torch.nn.BCEWithLogitsLoss(reduction='none' ).to(device)
 total_loss = torch.tensor(0, dtype=torch.float32 ).to(device)
 total_weights = torch.tensor(0, dtype=torch.float32 ).to(device)
 for i in range(y_true_img.shape[0]):
exam_loss = bce_func(y_pred_exam[i, :], y_true_exam[i, :])
 exam_loss = torch.sum(exam_loss*label_w, 1)[0]
img_mask = image_masks[i]
image_loss = bce_func(y_pred_img[i,:], y_true_img[i,:] ).flatten()
image_loss = image_loss*img_mask
img_num = torch.sum(img_mask)
 qi = torch.sum(y_true_img[i,:])/img_num
image_loss = torch.sum(img_w*qi*image_loss)
 total_loss += exam_loss+image_loss
total_weights += label_w.sum() + img_w*qi*img_num
final_loss = total_loss/total_weights
return final_loss, total_loss, total_weights
def prepare_train_dataloader(train, cv_df, train_fold, valid_fold):
train_patients = cv_df.loc[cv_df.fold.isin(train_fold), 'StudyInstanceUID'].unique()
valid_patients = cv_df.loc[cv_df.fold.isin(valid_fold), 'StudyInstanceUID'].unique()
train_ = train.loc[train.StudyInstanceUID.isin(train_patients),:].reset_index(drop=True)
 valid_ = train.loc[train.StudyInstanceUID.isin(valid_patients),:].reset_index(drop=True)
 train_ds = RSNADataset(train_, 0.0, CFG['train_img_path'], image_subsampling=True, transforms=get_train_transforms() , output_label=True)
 valid_ds = RSNADataset(valid_, 0.0, CFG['train_img_path'], image_subsampling=False, transforms=get_valid_transforms() , output_label=True)
 train_loader = torch.utils.data.DataLoader(
 train_ds,
batch_size=CFG['train_bs'],
pin_memory=True,
drop_last=False,
shuffle=True,
num_workers=CFG['num_workers'], 
)
 val_loader = torch.utils.data.DataLoader(
 valid_ds,
batch_size=1,
num_workers=CFG['num_workers'],
shuffle=False,
pin_memory=True, 
)
 return train_loader, val_loader
def train_one_epoch(epoch, model, device, scaler, optimizer, train_loader):
model.train()
t = time.time()
loss_sum = 0
loss_w_sum = 0
for step,(imgs, per_image_preds, locs, image_labels, exam_label, image_masks)in enumerate(train_loader):
imgs = imgs.to(device ).float()
per_image_preds = per_image_preds.to(device ).float()
locs = locs.to(device ).float()
image_masks = image_masks.to(device ).float()
image_labels = image_labels.to(device ).float()
exam_label = exam_label.to(device ).float()
with autocast() :
image_preds, exam_pred = model(per_image_preds, locs)
 loss, total_loss, total_weights = rsna_wloss_train(image_labels, exam_label, image_preds, exam_pred, image_masks, device)
 scaler.scale(loss ).backward()
loss_sum += total_loss.detach().item()
loss_w_sum += total_weights.detach().item()
if(( step + 1)% CFG['accum_iter'] == 0)or(( step + 1)== len(train_loader)) :
scaler.step(optimizer)
 scaler.update()
optimizer.zero_grad()
if(( step + 1)% CFG['verbose_step'] == 0)or(( step + 1)== len(train_loader)) :
print(
 f'epoch {epoch} train step {step+1}/{len(train_loader)}, ' + \
f'loss: {loss_sum/loss_w_sum:.4f}, ' + \
f'time: {(time.time() - t):.4f}', end= '\r' if(step + 1)!= len(train_loader)else '
' 
)
 def post_process(exam_pred, image_pred):
rv_lv_ratio_lt_1_ix = CFG['exam_target_cols'].index('rv_lv_ratio_lt_1')
 rv_lv_ratio_gte_1_ix = CFG['exam_target_cols'].index('rv_lv_ratio_gte_1')
 central_pe_ix = CFG['exam_target_cols'].index('central_pe')
 rightsided_pe_ix = CFG['exam_target_cols'].index('rightsided_pe')
 leftsided_pe_ix = CFG['exam_target_cols'].index('leftsided_pe')
 acute_and_chronic_pe_ix = CFG['exam_target_cols'].index('acute_and_chronic_pe')
 chronic_pe_ix = CFG['exam_target_cols'].index('chronic_pe')
 negative_exam_for_pe_ix = CFG['exam_target_cols'].index('negative_exam_for_pe')
 indeterminate_ix = CFG['exam_target_cols'].index('indeterminate')
 has_pe_image = torch.max(image_pred, 1)[0][0] > 0
rv_lv_ratios = exam_pred[:, [rv_lv_ratio_lt_1_ix, rv_lv_ratio_gte_1_ix]]
rv_lv_ratios_1_a = nn.functional.softmax(rv_lv_ratios, dim=1)
 rv_lv_ratios_1_a = torch.log(rv_lv_ratios_1_a/(1-rv_lv_ratios_1_a))
exam_pred[:, [rv_lv_ratio_lt_1_ix, rv_lv_ratio_gte_1_ix]] = torch.where(has_pe_image, rv_lv_ratios_1_a, rv_lv_ratios)
 crl_pe = exam_pred[:, [central_pe_ix, rightsided_pe_ix, leftsided_pe_ix]]
has_no_pe = torch.max(crl_pe ,1)[0] <= 0
max_val = torch.max(crl_pe, 1)[0]
crl_pe_1_b = torch.where(crl_pe==max_val, 0.0001-crl_pe+crl_pe, crl_pe)
 exam_pred[:, [central_pe_ix, rightsided_pe_ix, leftsided_pe_ix]] = torch.where(has_pe_image*has_no_pe, crl_pe_1_b, crl_pe)
 ac_pe = exam_pred[:, [acute_and_chronic_pe_ix, chronic_pe_ix]]
both_ac_ch = torch.min(ac_pe ,1)[0] > 0
ac_pe_1_c = nn.functional.softmax(ac_pe, dim=1)
 ac_pe_1_c = torch.log(ac_pe_1_c/(1-ac_pe_1_c))
exam_pred[:, [acute_and_chronic_pe_ix, chronic_pe_ix]] = torch.where(has_pe_image*both_ac_ch, ac_pe_1_c, ac_pe)
 neg_ind = exam_pred[:, [negative_exam_for_pe_ix, indeterminate_ix]]
neg_ind_1d = torch.clamp(neg_ind, max=0)
 exam_pred[:, [negative_exam_for_pe_ix, indeterminate_ix]] = torch.where(has_pe_image, neg_ind_1d, neg_ind)
 ne_inde = exam_pred[:, [negative_exam_for_pe_ix, indeterminate_ix]]
ne_inde_2_a = nn.functional.softmax(ne_inde, dim=1)
 ne_inde_2_a = torch.log(ne_inde_2_a/(1-ne_inde_2_a))
exam_pred[:, [negative_exam_for_pe_ix, indeterminate_ix]] = torch.where(~has_pe_image, ne_inde_2_a, ne_inde)
 all_other_exam_labels = exam_pred[:, [rv_lv_ratio_lt_1_ix, rv_lv_ratio_gte_1_ix,
central_pe_ix, rightsided_pe_ix, leftsided_pe_ix,
acute_and_chronic_pe_ix, chronic_pe_ix]]
all_other_exam_labels_2_b = torch.clamp(all_other_exam_labels, max=0)
 exam_pred[:, [rv_lv_ratio_lt_1_ix, rv_lv_ratio_gte_1_ix,
central_pe_ix, rightsided_pe_ix, leftsided_pe_ix,
acute_and_chronic_pe_ix, chronic_pe_ix]] = torch.where(~has_pe_image, all_other_exam_labels_2_b, all_other_exam_labels)
 return exam_pred, image_pred
def valid_one_epoch(epoch, model, device, scheduler, val_loader, schd_loss_update=False):
model.eval()
t = time.time()
loss_sum = 0
loss_w_sum = 0
for step,(imgs, per_image_preds, locs, image_labels, exam_label, image_masks)in enumerate(val_loader):
imgs = imgs.to(device ).float()
per_image_preds = per_image_preds.to(device ).float()
locs = locs.to(device ).float()
image_masks = image_masks.to(device ).float()
image_labels = image_labels.to(device ).float()
exam_label = exam_label.to(device ).float()
image_preds, exam_pred = model(per_image_preds, locs)
 exam_pred, image_preds= post_process(exam_pred, image_preds)
 loss, total_loss, total_weights = rsna_wloss_valid(image_labels, exam_label, image_preds, exam_pred, image_masks, device)
 loss_sum += total_loss.detach().item()
loss_w_sum += total_weights.detach().item()
if(( step + 1)% CFG['verbose_step'] == 0)or(( step + 1)== len(val_loader)) :
print(
 f'epoch {epoch} valid Step {step+1}/{len(val_loader)}, ' + \
f'loss: {loss_sum/loss_w_sum:.4f}, ' + \
f'time: {(time.time() - t):.4f}', end='\r' if(step + 1)!= len(val_loader)else '
' 
)
 if schd_loss_update:
scheduler.step(loss_sum/loss_w_sum)
 else:
scheduler.step()
def check_label_consistency(checking_df):
df = checking_df.copy()
print(df.shape)
 df['positive_images_in_exam'] = df['StudyInstanceUID'].map(df.groupby(['StudyInstanceUID'] ).pe_present_on_image.max())
 df_pos = df.loc[df.positive_images_in_exam > 0.5]
df_neg = df.loc[df.positive_images_in_exam <= 0.5]
rule1a = df_pos.loc[(( df_pos.rv_lv_ratio_lt_1 > 0.5)& 
(df_pos.rv_lv_ratio_gte_1 > 0.5)) | 
(( df_pos.rv_lv_ratio_lt_1 <= 0.5)& 
(df_pos.rv_lv_ratio_gte_1 <= 0.5)) ].reset_index(drop = True)
 rule1a['broken_rule'] = '1a'
rule1b = df_pos.loc[(df_pos.central_pe <= 0.5)& 
(df_pos.rightsided_pe <= 0.5)& 
(df_pos.leftsided_pe <= 0.5)].reset_index(drop = True)
 rule1b['broken_rule'] = '1b'
rule1c = df_pos.loc[(df_pos.acute_and_chronic_pe > 0.5)& 
(df_pos.chronic_pe > 0.5)].reset_index(drop = True)
 rule1c['broken_rule'] = '1c'
rule1d = df_pos.loc[(df_pos.indeterminate > 0.5)| 
(df_pos.negative_exam_for_pe > 0.5)].reset_index(drop = True)
 rule1d['broken_rule'] = '1d'
rule2a = df_neg.loc[(( df_neg.indeterminate > 0.5)& 
(df_neg.negative_exam_for_pe > 0.5)) | 
(( df_neg.indeterminate <= 0.5)& 
(df_neg.negative_exam_for_pe <= 0.5)) ].reset_index(drop = True)
 rule2a['broken_rule'] = '2a'
rule2b = df_neg.loc[(df_neg.rv_lv_ratio_lt_1 > 0.5)| 
(df_neg.rv_lv_ratio_gte_1 > 0.5)| 
(df_neg.central_pe > 0.5)| 
(df_neg.rightsided_pe > 0.5)| 
(df_neg.leftsided_pe > 0.5)| 
(df_neg.acute_and_chronic_pe > 0.5)| 
(df_neg.chronic_pe > 0.5)].reset_index(drop = True)
 rule2b['broken_rule'] = '2b'
errors = pd.concat([rule1a, rule1b, rule1c, rule1d, rule2a, rule2b], axis = 0)
 print('label in-consistency counts:', errors.shape)
 if errors.shape[0] > 0:
print(errors.broken_rule.value_counts())
 print(errors)
 assert False
def inference(model, device, df, root_path):
model.eval()
t = time.time()
ds = RSNADataset(df, 0.0, root_path, image_subsampling=False, transforms=get_valid_transforms() , output_label=False)
 dataloader = torch.utils.data.DataLoader(
 ds,
batch_size=1,
num_workers=CFG['num_workers'],
shuffle=False,
pin_memory=True, 
)
 patients = ds.get_patients()
res_dfs = []
for step,(imgs, per_image_preds, locs, img_num, index, seq_ix)in enumerate(dataloader):
imgs = imgs.to(device ).float()
per_image_preds = per_image_preds.to(device ).float()
locs = locs.to(device ).float()
index = index.detach().numpy() [0]
seq_ix = seq_ix.detach().numpy() [0,:]
patient_filt =(df.StudyInstanceUID == patients[index])
 patient_df = pd.DataFrame()
patient_df['SOPInstanceUID'] = df.loc[patient_filt, 'SOPInstanceUID'].values[seq_ix]
patient_df['SeriesInstanceUID'] = df.loc[patient_filt, 'SeriesInstanceUID'].values
patient_df['StudyInstanceUID'] = patients[index]
for c in CFG['image_target_cols']+CFG['exam_target_cols']:
patient_df[c] = 0.0
image_preds, exam_pred = model(per_image_preds, locs)
 exam_pred, image_preds = post_process(exam_pred, image_preds)
 exam_pred = torch.sigmoid(exam_pred ).cpu().detach().numpy()
image_preds = torch.sigmoid(image_preds ).cpu().detach().numpy()
patient_df[CFG['exam_target_cols']] = exam_pred[0]
patient_df[CFG['image_target_cols']] = image_preds[0,:]
res_dfs += [patient_df]


if(( step + 1)% CFG['verbose_step'] == 0)or(( step + 1)== len(dataloader)) :
print(
 f'Inference Step {step+1}/{len(dataloader)}, ' + \
f'time: {(time.time() - t):.4f}', end='\r' if(step + 1)!= len(dataloader)else '
' 
)
 res_dfs = pd.concat(res_dfs, axis=0 ).reset_index(drop=True)
 res_dfs = df[['SOPInstanceUID', 'SeriesInstanceUID', 'StudyInstanceUID']].merge(res_dfs, on=['SOPInstanceUID', 'SeriesInstanceUID', 'StudyInstanceUID'], how='left')
 print(res_dfs[CFG['image_target_cols']+CFG['exam_target_cols']].head(5))
print(res_dfs[CFG['image_target_cols']+CFG['exam_target_cols']].tail(5))
assert res_dfs.shape[0] == df.shape[0]
check_label_consistency(res_dfs)
 return res_dfs
STAGE1_CFGS = [
{
'tag': 'efb0_stage1',
'model_constructor': RSNAImgClassifierSingle,
'dataset_constructor': RSNADatasetStage1,
'output_len': 1
},
{
'tag': 'efb0_stage1_multilabel',
'model_constructor': RSNAImgClassifier,
'dataset_constructor': RSNADatasetStage1,
'output_len': 9
},
]
STAGE1_CFGS_TAG = 'efb0-stage1-single-multi-label'
def get_stage1_columns() :
new_feats = []
for cfg in STAGE1_CFGS:
for i in range(cfg['output_len']):
f = cfg['tag']+'_'+str(i)
 new_feats += [f]
return new_feats
def update_stage1_oof_preds(df, cv_df):
res_file_name = STAGE1_CFGS_TAG+"-train.csv"
new_feats = get_stage1_columns()
for f in new_feats:
df[f] = 0
if os.path.isfile(res_file_name):
df = pd.read_csv(res_file_name)
 print('img acc:',(( df[new_feats[0]]>0)==df[CFG['image_target_cols'][0]] ).mean())
 return df
for fold,(train_fold, valid_fold)in enumerate(zip(CFG['train_folds'], CFG['valid_folds'])) :
if fold < 0:
continue
valid_patients = cv_df.loc[cv_df.fold.isin(valid_fold), 'StudyInstanceUID'].unique()
filt = df.StudyInstanceUID.isin(valid_patients)
 valid_ = df.loc[filt,:].reset_index(drop=True)
 image_preds_all_list = []
for cfg in STAGE1_CFGS:
valid_ds = cfg['dataset_constructor'](valid_, 0.0, CFG['train_img_path'], image_subsampling=False, transforms=get_valid_transforms() , output_label=True)
 val_loader = torch.utils.data.DataLoader(
 valid_ds,
batch_size=256,
num_workers=CFG['num_workers'],
shuffle=False,
pin_memory=False,
sampler=SequentialSampler(valid_ds)
)
 device = torch.device(CFG['device'])
 model = cfg['model_constructor']().to(device)
 model.load_state_dict(torch.load('{}/model_fold_{}_{}'.format(CFG['model_path'], fold, cfg['tag'])))
 model.eval()
image_preds_all = []
correct_count = 0
count = 0
for step,(imgs, target)in enumerate(val_loader):
imgs = imgs.to(device ).float()
target = target.to(device ).float()
image_preds = model(imgs)
 if len(image_preds.shape)== 1:
image_preds = image_preds.view(-1, 1)
 correct_count +=(( image_preds[:,0]>0)== target[:,0] ).sum().detach().item()
count += imgs.shape[0]
image_preds_all += [image_preds.cpu().detach().numpy() ]
print('acc: {:.4f}, {}, {}, {}/{}'.format(correct_count/count, correct_count, count, step+1, len(val_loader)) , end='\r')
 print()
image_preds_all = np.concatenate(image_preds_all, axis=0)
 image_preds_all_list += [image_preds_all]
del model, val_loader
torch.cuda.empty_cache()
image_preds_all_list = np.concatenate(image_preds_all_list, axis=1)
 df.loc[filt, new_feats] = image_preds_all_list
df.to_csv(res_file_name, index=False)
 return df
def update_stage1_test_preds(df):
new_feats = get_stage1_columns()
for f in new_feats:
df[f] = 0
image_preds_all_list = []
for cfg in STAGE1_CFGS:
test_ds = cfg['dataset_constructor'](df, 0.0, CFG['test_img_path'], image_subsampling=False, transforms=get_valid_transforms() , output_label=False)
 test_loader = torch.utils.data.DataLoader(
 test_ds,
batch_size=256,
num_workers=CFG['num_workers'],
shuffle=False,
pin_memory=False,
sampler=SequentialSampler(test_ds)
)
 device = torch.device(CFG['device'])
 model = cfg['model_constructor']().to(device)
 model.load_state_dict(torch.load('{}/model_{}'.format(CFG['model_path'], cfg['tag'])))
 model.eval()
image_preds_all = []
for step, imgs in enumerate(tqdm(test_loader)) :
imgs = imgs.to(device ).float()
image_preds = model(imgs)
 image_preds_all += [image_preds.cpu().detach().numpy() ]
image_preds_all = np.concatenate(image_preds_all, axis=0)
 image_preds_all_list += [image_preds_all]
del model, test_loader
torch.cuda.empty_cache()
image_preds_all_list = np.concatenate(image_preds_all_list, axis=1)
 df.loc[:,new_feats] = image_preds_all_list
return df
if __name__ == '__main__':
if CFG['train']:
seed_everything(SEED)
 if CFG['train']:
train_df = pd.read_csv(CFG['train_path'])
 cv_df = pd.read_csv(CFG['cv_fold_path'])
 with torch.no_grad() :
train_df = update_stage1_oof_preds(train_df,cv_df)
 else:
test_df = pd.read_csv(CFG['test_path'])
 with torch.no_grad() :
test_df = update_stage1_test_preds(test_df)
 if CFG['train']:
for fold,(train_fold, valid_fold)in enumerate(zip(CFG['train_folds'], CFG['valid_folds'])) :
train_loader, val_loader = prepare_train_dataloader(train_df, cv_df, train_fold, valid_fold)
 device = torch.device(CFG['device'])
 model = RSNAClassifier().to(device)
 scaler = GradScaler()
optimizer = torch.optim.Adam(model.parameters() , lr=CFG['lr'])
 scheduler = torch.optim.lr_scheduler.StepLR(optimizer, gamma=0.1, step_size=1); schd_loss_update=False
for epoch in range(CFG['epochs']):
train_one_epoch(epoch, model, device, scaler, optimizer, train_loader)
 with torch.no_grad() :
valid_one_epoch(epoch, model, device, scheduler, val_loader, schd_loss_update=schd_loss_update)
 torch.save(model.state_dict() ,'{}/model_fold_{}_{}'.format(CFG['model_path'], fold, CFG['tag']))
model.load_state_dict(torch.load('{}/model_fold_{}_{}'.format(CFG['model_path'], fold, CFG['tag'])))
 valid_patients = cv_df.loc[cv_df.fold.isin(valid_fold), 'StudyInstanceUID'].unique()
valid_ = train_df.loc[train_df.StudyInstanceUID.isin(valid_patients),:].reset_index(drop=True)
 with torch.no_grad() :
val_pred_df = inference(model, device, valid_, CFG['train_img_path'])
 target = valid_[CFG['image_target_cols']].values
pred =(val_pred_df[CFG['image_target_cols']].values > 0.5 ).astype(int)
 print('Image PE Accuracy: {:.3f}'.format(( target==pred ).mean() *100))
loss = rsna_wloss_inference(valid_[CFG['image_target_cols']].values, valid_[CFG['exam_target_cols']].values,
val_pred_df[CFG['image_target_cols']].values, val_pred_df[CFG['exam_target_cols']].values,
list(valid_.groupby('StudyInstanceUID', sort=False)['SOPInstanceUID'].count()))
print('Validation loss = {:.4f}'.format(loss.detach().item()))
del model, optimizer, train_loader, val_loader, scaler, scheduler
torch.cuda.empty_cache()
train_loader, val_loader = prepare_train_dataloader(train_df, cv_df, np.arange(0, 20), np.array([]))
device = torch.device(CFG['device'])
 model = RSNAClassifier().to(device)
 scaler = GradScaler()
optimizer = torch.optim.Adam(model.parameters() , lr=CFG['lr'])
 scheduler = torch.optim.lr_scheduler.StepLR(optimizer, gamma=0.1, step_size=1); schd_loss_update=False
for epoch in range(CFG['epochs']):
train_one_epoch(epoch, model, device, scaler, optimizer, train_loader)
 torch.save(model.state_dict() ,'{}/model_{}'.format(CFG['model_path'], CFG['tag']))
else:
device = torch.device(CFG['device'])
 model = RSNAClassifier().to(device)
 model.load_state_dict(torch.load('{}/model_{}'.format(CFG['model_path'], CFG['tag'])))
 test_pred_df = inference(model, device, test_df, CFG['test_img_path'])
 test_pred_df.to_csv('kh_submission_raw.csv')
 ids = []
labels = []
gp_mean = test_pred_df.loc[:, ['StudyInstanceUID']+CFG['exam_target_cols']].groupby('StudyInstanceUID', sort=False ).mean()
for col in CFG['exam_target_cols']:
ids += [[patient+'_'+col for patient in gp_mean.index]]
labels += [gp_mean[col].values]
ids += [test_pred_df.SOPInstanceUID.values]
labels += [test_pred_df[CFG['image_target_cols']].values[:,0]]
ids = np.concatenate(ids)
 labels = np.concatenate(labels)
 assert len(ids)== len(labels)
 submission = pd.DataFrame()
submission['id'] = ids
submission['label'] = labels
print(submission.head(3))
print(submission.tail(3))
print(submission.shape)
 submission.to_csv('submission.csv', index=False )<define_variables>
train_best=find_best_train_csv(n_trains,path,3)
 print(train_best)
 BLEND_best=create_csv_list(train_best,path)
 BLEND_best=average_csv(BLEND_best)
 print(BLEND_best)
 BLEND_best.to_csv('submission_best.csv',index=False )
sub_path = ".. /input/ieeesubmissions5"
all_files = os.listdir(sub_path)
 all_files
all_files.remove('blend01.csv')
 all_files.remove('blend02.csv')
 all_files<feature_engineering>
train_worst=find_worst_train_csv(n_trains,path,2)
 print(train_worst)
 BLEND_worst=create_csv_list(train_worst,path)
 BLEND_worst=average_csv(BLEND_worst)
 print(BLEND_worst)
 BLEND_worst.to_csv('submission_worst.csv',index=False )
<define_variables><EOS> 
train_data=read_train_csv(n_trains,path)
 print(train_data)
 BLEND_all=create_csv_list(train_data.nlargest(5, ['best_score']),path)
 BLEND_all=average_csv(BLEND_all)
 print(BLEND_all)
 BLEND_all.to_csv('submission_best_5.csv',index=False )