From: https://www.kaggle.com/axel81/ensembling-imet
Author: Ram Ramrakhya
This kernel is an implementation for KFolding using the already trained models. Run time for the kernel is 1.5hrs which will work even on the huge test set.
In this Kernel I'll use 6 Folds
import pandas as pd
import gzip
import base64
import os
from pathlib import Path
from typing import Dict
# this is base64 encoded source code
file_data: Dict = {'imet/transforms.py': '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',
'imet/make_submission.py': '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',
'imet/models.py': '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',
'imet/__init__.py': 'aW1wb3J0IGN2MgoKCmN2Mi5zZXROdW1UaHJlYWRzKDApICAjIGZpeCBwb3RlbnRpYWwgcHl0b3JjaCB3b3JrZXIgaXNzdWVzCg==',
'imet/make_folds.py': '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',
'imet/dataset.py': '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',
'imet/utils.py': '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',
'imet/main.py': 'import argparse
from itertools import islice
import json
from pathlib import Path
import shutil
import warnings
from typing import Dict

import numpy as np
import pandas as pd
from sklearn.metrics import fbeta_score
from sklearn.exceptions import UndefinedMetricWarning
import torch
from torch import nn, cuda
from torch.optim import Adam
import tqdm

from . import models
from .dataset import TrainDataset, TTADataset, get_ids, N_CLASSES, DATA_ROOT
from .transforms import train_transform, test_transform
from .utils import (
    write_event, load_model, mean_df, ThreadingDataLoader as DataLoader,
    ON_KAGGLE)


def main():
    parser = argparse.ArgumentParser()
    arg = parser.add_argument
    arg('mode', choices=['train', 'validate', 'predict_valid', 'predict_test'])
    arg('run_root')
    arg('--model', default='resnet101')
    arg('--pretrained', type=int, default=1)
    arg('--batch-size', type=int, default=64)
    arg('--step', type=int, default=1)
    arg('--workers', type=int, default=2 if ON_KAGGLE else 4)
    arg('--lr', type=float, default=1e-4)
    arg('--patience', type=int, default=4)
    arg('--clean', action='store_true')
    arg('--n-epochs', type=int, default=100)
    arg('--epoch-size', type=int)
    arg('--tta', type=int, default=4)
    arg('--use-sample', action='store_true', help='use a sample of the dataset')
    arg('--debug', action='store_true')
    arg('--limit', type=int)
    arg('--fold', type=int, default=0)
    args = parser.parse_args()

    run_root = Path(args.run_root)
    folds = pd.read_csv('folds.csv')
    train_root = DATA_ROOT / ('train_sample' if args.use_sample else 'train')
    if args.use_sample:
        folds = folds[folds['Id'].isin(set(get_ids(train_root)))]
    train_fold = folds[folds['fold'] != args.fold]
    valid_fold = folds[folds['fold'] == args.fold]
    if args.limit:
        train_fold = train_fold[:args.limit]
        valid_fold = valid_fold[:args.limit]

    def make_loader(df: pd.DataFrame, image_transform) -> DataLoader:
        return DataLoader(
            TrainDataset(train_root, df, image_transform, debug=args.debug),
            shuffle=True,
            batch_size=args.batch_size,
            num_workers=args.workers,
        )
    criterion = nn.BCEWithLogitsLoss(reduction='none')
    model = getattr(models, args.model)(
        num_classes=N_CLASSES, pretrained=args.pretrained)
    use_cuda = cuda.is_available()
    fresh_params = list(model.fresh_params())
    all_params = list(model.parameters())
    if use_cuda:
        model = model.cuda()

    if args.mode == 'train':
        if run_root.exists() and args.clean:
            shutil.rmtree(run_root)
        run_root.mkdir(exist_ok=True, parents=True)
        (run_root / 'params.json').write_text(
            json.dumps(vars(args), indent=4, sort_keys=True))

        train_loader = make_loader(train_fold, train_transform)
        valid_loader = make_loader(valid_fold, test_transform)
        print(f'{len(train_loader.dataset):,} items in train, '
              f'{len(valid_loader.dataset):,} in valid')

        train_kwargs = dict(
            args=args,
            model=model,
            criterion=criterion,
            train_loader=train_loader,
            valid_loader=valid_loader,
            patience=args.patience,
            init_optimizer=lambda params, lr: Adam(params, lr),
            use_cuda=use_cuda,
        )

        if args.pretrained:
            if train(params=fresh_params, n_epochs=1, **train_kwargs):
                train(params=all_params, **train_kwargs)
        else:
            train(params=all_params, **train_kwargs)

    elif args.mode == 'validate':
        valid_loader = make_loader(valid_fold, test_transform)
        load_model(model, run_root / 'model.pt')
        validation(model, criterion, tqdm.tqdm(valid_loader, desc='Validation'),
                   use_cuda=use_cuda)

    elif args.mode.startswith('predict'):
        load_model(model, run_root / 'best-model.pt')
        predict_kwargs = dict(
            batch_size=args.batch_size,
            tta=args.tta,
            use_cuda=use_cuda,
            workers=args.workers,
        )
        if args.mode == 'predict_valid':
            predict(model, df=valid_fold, root=train_root, out_path=run_root / 'val.h5', **predict_kwargs)
        elif args.mode == 'predict_test':
            test_root = DATA_ROOT / (
                'test_sample' if args.use_sample else 'test')
            ss = pd.read_csv(DATA_ROOT / 'sample_submission.csv')
            if args.use_sample:
                ss = ss[ss['id'].isin(set(get_ids(test_root)))]
            if args.limit:
                ss = ss[:args.limit]
            predict(model, df=ss, root=test_root,
                    out_path=run_root / 'test.h5',
                    **predict_kwargs)


def predict(model, root: Path, df: pd.DataFrame, out_path: Path,
            batch_size: int, tta: int, workers: int, use_cuda: bool):
    loader = DataLoader(
        dataset=TTADataset(root, df, test_transform, tta=tta),
        shuffle=False,
        batch_size=batch_size,
        num_workers=workers,
    )
    model.eval()
    all_outputs, all_ids = [], []
    with torch.no_grad():
        for inputs, ids in tqdm.tqdm(loader, desc='Predict'):
            if use_cuda:
                inputs = inputs.cuda()
            outputs = torch.sigmoid(model(inputs))
            all_outputs.append(outputs.data.cpu().numpy())
            all_ids.extend(ids)
    df = pd.DataFrame(
        data=np.concatenate(all_outputs),
        index=all_ids,
        columns=map(str, range(N_CLASSES)))
    df = mean_df(df)
    df.to_hdf(out_path, 'prob', index_label='id')
    print(f'Saved predictions to {out_path}')


def train(args, model: nn.Module, criterion, *, params,
          train_loader, valid_loader, init_optimizer, use_cuda,
          n_epochs=None, patience=2, max_lr_changes=2) -> bool:
    lr = args.lr
    n_epochs = n_epochs or args.n_epochs
    params = list(params)
    optimizer = init_optimizer(params, lr)

    run_root = Path(args.run_root)
    model_path = run_root / 'model.pt'
    best_model_path = run_root / 'best-model.pt'
    if model_path.exists():
        state = load_model(model, model_path)
        epoch = state['epoch']
        step = state['step']
        best_valid_loss = state['best_valid_loss']
    else:
        epoch = 1
        step = 0
        best_valid_loss = float('inf')
    lr_changes = 0

    save = lambda ep: torch.save({
        'model': model.state_dict(),
        'epoch': ep,
        'step': step,
        'best_valid_loss': best_valid_loss
    }, str(model_path))

    report_each = 10000
    log = run_root.joinpath('train.log').open('at', encoding='utf8')
    valid_losses = []
    lr_reset_epoch = epoch
    for epoch in range(epoch, n_epochs + 1):
        model.train()
        tq = tqdm.tqdm(total=(args.epoch_size or
                              len(train_loader) * args.batch_size))
        tq.set_description(f'Epoch {epoch}, lr {lr}')
        losses = []
        tl = train_loader
        if args.epoch_size:
            tl = islice(tl, args.epoch_size // args.batch_size)
        try:
            mean_loss = 0
            for i, (inputs, targets) in enumerate(tl):
                if use_cuda:
                    inputs, targets = inputs.cuda(), targets.cuda()
                outputs = model(inputs)
                loss = _reduce_loss(criterion(outputs, targets))
                batch_size = inputs.size(0)
                (batch_size * loss).backward()
                if (i + 1) % args.step == 0:
                    optimizer.step()
                    optimizer.zero_grad()
                    step += 1
                tq.update(batch_size)
                losses.append(loss.item())
                mean_loss = np.mean(losses[-report_each:])
                tq.set_postfix(loss=f'{mean_loss:.3f}')
                if i and i % report_each == 0:
                    write_event(log, step, loss=mean_loss)
            write_event(log, step, loss=mean_loss)
            tq.close()
            save(epoch + 1)
            valid_metrics = validation(model, criterion, valid_loader, use_cuda)
            write_event(log, step, **valid_metrics)
            valid_loss = valid_metrics['valid_loss']
            valid_losses.append(valid_loss)
            if valid_loss < best_valid_loss:
                best_valid_loss = valid_loss
                shutil.copy(str(model_path), str(best_model_path))
            elif (patience and epoch - lr_reset_epoch > patience and
                  min(valid_losses[-patience:]) > best_valid_loss):
                # "patience" epochs without improvement
                lr_changes +=1
                if lr_changes > max_lr_changes:
                    break
                lr /= 5
                print(f'lr updated to {lr}')
                lr_reset_epoch = epoch
                optimizer = init_optimizer(params, lr)
        except KeyboardInterrupt:
            tq.close()
            print('Ctrl+C, saving snapshot')
            save(epoch)
            print('done.')
            return False
    return True


def validation(
        model: nn.Module, criterion, valid_loader, use_cuda,
        ) -> Dict[str, float]:
    model.eval()
    all_losses, all_predictions, all_targets = [], [], []
    with torch.no_grad():
        for inputs, targets in valid_loader:
            all_targets.append(targets.numpy().copy())
            if use_cuda:
                inputs, targets = inputs.cuda(), targets.cuda()
            outputs = model(inputs)
            loss = criterion(outputs, targets)
            all_losses.append(_reduce_loss(loss).item())
            predictions = torch.sigmoid(outputs)
            all_predictions.append(predictions.cpu().numpy())
    all_predictions = np.concatenate(all_predictions)
    all_targets = np.concatenate(all_targets)

    def get_score(y_pred):
        with warnings.catch_warnings():
            warnings.simplefilter('ignore', category=UndefinedMetricWarning)
            return fbeta_score(
                all_targets, y_pred, beta=2, average='samples')

    metrics = {}
    argsorted = all_predictions.argsort(axis=1)
    for threshold in [0.05, 0.06, 0.07, 0.08, 0.09, 0.10, 0.11, 0.12, 0.13, 0.14, 0.15, 0.20]:
        metrics[f'valid_f2_th_{threshold:.2f}'] = get_score(
            binarize_prediction(all_predictions, threshold, argsorted))
    metrics['valid_loss'] = np.mean(all_losses)
    print(' | '.join(f'{k} {v:.3f}' for k, v in sorted(
        metrics.items(), key=lambda kv: -kv[1])))

    return metrics


def binarize_prediction(probabilities, threshold: float, argsorted=None,
                        min_labels=1, max_labels=10):
    """ Return matrix of 0/1 predictions, same shape as probabilities.
    """
    assert probabilities.shape[1] == N_CLASSES
    if argsorted is None:
        argsorted = probabilities.argsort(axis=1)
    max_mask = _make_mask(argsorted, max_labels)
    min_mask = _make_mask(argsorted, min_labels)
    prob_mask = probabilities > threshold
    return (max_mask & prob_mask) | min_mask


def _make_mask(argsorted, top_n: int):
    mask = np.zeros_like(argsorted, dtype=np.uint8)
    col_indices = argsorted[:, -top_n:].reshape(-1)
    row_indices = [i // top_n for i in range(len(col_indices))]
    mask[row_indices, col_indices] = 1
    return mask


def _reduce_loss(loss):
    return loss.sum() / loss.shape[0]


if __name__ == '__main__':
    main()
',
'setup.py': 'ZnJvbSBzZXR1cHRvb2xzIGltcG9ydCBzZXR1cAoKc2V0dXAoCiAgICBuYW1lPSdpbWV0JywKICAgIHBhY2thZ2VzPVsnaW1ldCddLAop'
}
for path, encoded in file_data.items():
print(path)
path = Path(path)
path.parent.mkdir(exist_ok=True)
path.write_bytes(base64.b64decode(encoded))
def run(command):
os.system('export PYTHONPATH=${PYTHONPATH}:/kaggle/working && ' + command)
print(os.listdir('/kaggle/working/'))
run('python setup.py develop --install-dir /kaggle/working')
!cp '../input/public-0615/best-model.pt' '/kaggle/working/'
run('python setup.py develop --install-dir /kaggle/working')
run('python -m imet.make_folds --n-folds 40')
# run('python -m imet.main train model_1 --n-epochs 16')
run('python -m imet.main predict_test .')
run('python -m imet.make_submission ./test.h5 /kaggle/working/sub.csv --threshold 0.10')
print(os.listdir('.'))
try:
test_preds_kon = pd.read_csv('sub.csv')
attr_ids_kon = test_preds_kon['attribute_ids'].apply(lambda x: x.split()).values
attr_id_thr = 0.10
except Exception as e:
pass
len(attr_ids_kon)
!rm -rf /kaggle/working/*
import os
os.system('cp -r ../input/pretrained-models-cadene/pretrained_models_pytroch/ /kaggle/working/')
os.chdir('/kaggle/working/pretrained_models_pytroch/pretrained-models.pytorch-master')
!python setup.py install
import os
import pickle
import fastai
from fastai.vision import *
from fastai.vision.models.cadene_models import *
from shutil import copyfile
fastai.__version__
os.chdir('/kaggle/working')
os.listdir('.')
# Source: https://www.kaggle.com/c/human-protein-atlas-image-classification/discussion/78109
class FocalLoss(nn.Module):
def __init__(self, gamma=2):
super().__init__()
self.gamma = gamma
def forward(self, logit, target):
target = target.float()
max_val = (-logit).clamp(min=0)
loss = logit - logit * target + max_val + \
((-max_val).exp() + (-logit - max_val).exp()).log()
invprobs = F.logsigmoid(-logit * (target * 2.0 - 1.0))
loss = (invprobs * self.gamma).exp() * loss
if len(loss.size())==2:
loss = loss.sum(dim=1)
return loss.mean()
path = Path('../input/imet-2019-fgvc6/') # iMet data path
BATCH = 32
SIZE = 256
from torch.utils import model_zoo
Path('models').mkdir(exist_ok=True)
test_df = pd.read_csv(path/'sample_submission.csv')
test_df.head()
labels_df = pd.read_csv(path/'labels.csv')
labels_df.head()
train_df = pd.read_csv(path/'train.csv')
train_df.head()
t_preds = []
n_fold = 5
val_preds = np.zeros((2, 10923, 1103))
tfms = get_transforms(do_flip=True, flip_vert=False, max_rotate=0.10, max_zoom=1.5, max_warp=0.2, max_lighting=0.2,
xtra_tfms=[(symmetric_warp(magnitude=(-0,0), p=0)), rand_crop(p=0.75),])
train, test = [ImageList.from_df(df, path=path, cols='id', folder=folder, suffix='.png')
for df, folder in zip([train_df, test_df], ['train', 'test'])]
data = (train.split_by_rand_pct(0.1, seed=42)
.label_from_df(cols='attribute_ids', label_delim=' ')
.add_test(test)
.transform(tfms, size=SIZE, resize_method=ResizeMethod.PAD, padding_mode='border',)
.databunch(path=Path('.'), bs=BATCH).normalize(imagenet_stats))
# model dictionary
model = {
'seresnextkfolds_15': 'se_resnext50_32x4d-a260b3a4',
'seresnextkfolds_11': 'se_resnext50_32x4d-a260b3a4',
'resnet5022epochs': 'resnet50',
'seresnextkfolds_16': 'se_resnext50_32x4d-a260b3a4',
'seresnextkfolds_49': 'se_resnext50_32x4d-a260b3a4',
}
for model_path in model.keys():
m_path = 'stage-1.pth'
if len(model_path.split('_')) > 1:
m_path = 'seresnext-' + model_path.split('_')[1] + '.pth'
print(model_path, m_path)
copyfile('../input/' + model_path.split('_')[0] + '/' + m_path, 'models/'+ model[model_path] +'.pth')
def load_url(*args, **kwargs):
model_dir = Path('models')
filename = model[model_path] + '.pth'
if not (model_dir/filename).is_file(): raise FileNotFoundError
return torch.load(model_dir/filename)
model_zoo.load_url = load_url
arch = se_resnext50_32x4d
if model[model_path] == 'resnet50':
arch = models.resnet50
learn = cnn_learner(data, base_arch=arch, loss_func=FocalLoss(), metrics=fbeta, pretrained=False)
learn.load(model[model_path])
preds = learn.TTA(ds_type=DatasetType.Test)
np.save(model_path + '.npy', preds[0].numpy())
t_preds.append(preds[0].numpy())
attr_ids = []
thrs = [0.27, 0.27, 0.28, 0.27, 0.29]
def preds_ids(preds, thr):
preds = [torch.tensor(preds)]
return [i2c[np.where(t==1)[0],1].astype(str) for t in (preds[0].sigmoid()>thr).long()]
attr_ids = [preds_ids(t_preds[i], thrs[i]) for i in range(len(t_preds))]
I take all the preds generated by all models and for every sample I count the frequency of attribute ids.
So for example,
For sample 1 let's say 3 model predicted attr_id_1, and 1 model predicted attr_id_2. Then there is a high chance attr_id_1 must be right one.
In such a way I calculate frequency of all attr ids and then all the attr_id frequency which are greater than equal to a threshold thr I choose those for final preds
attr_ids.append(attr_ids_kon)
thrs.append(attr_id_thr)
res_attr_ids = []
thr = 4/6
for i in range(len(attr_ids[0])):
id_s = np.concatenate([p_attr_id[i] for p_attr_id in attr_ids],
axis=None)
id_s, counts = np.unique(list(id_s),
return_counts=True)
counts = counts/6
pids = ' '.join(id_s[np.where(counts >= thr)])
res_attr_ids.append(pids)
print(len(res_attr_ids), len(attr_ids[0]))
test_df.attribute_ids = res_attr_ids
test_df.to_csv('submission.csv', index=False)
test_df.head()
Please upvote if you find the kernel interesting.
Note: I haven't revealed my private datasets so I dont think this kernel should affect the standings. I have just revealed the approach of my solution.
!rm -rf /kaggle/working/pretrained_models_pytroch