def predict_all(data_name, X, use_cache=False, child_cv_index=None):
"""予測。"""
cache_path = CACHE_DIR / data_name / f'{MODEL_NAME}.pkl'
if use_cache and cache_path.is_file() and child_cv_index is None:
return joblib.load(cache_path)
if data_name == 'test' and child_cv_index is None:
pred_bin = np.median(joblib.load(CACHE_DIR / 'test' / 'bin_nas.pkl'), axis=0)
pred_reg = np.median(joblib.load(CACHE_DIR / 'test' / 'reg_nas.pkl'), axis=0)
ath_estimator = joblib.load(MODELS_DIR / 'ath_estimator.pkl')
pred = []
for cci in range(5):
y_child = []
for pc in predict_all(data_name, X, use_cache, cci):
threshold_X = _ath.create_input_data(pc, pred_bin, pred_reg)
thresholds = ath_estimator.predict(threshold_X)
y_child.append(pc > np.reshape(thresholds, (len(pc), 1, 1, 1)))
pred.append(np.mean(y_child, axis=0))
cache_path.parent.mkdir(parents=True, exist_ok=True)
joblib.dump(pred, cache_path, compress=3)
return pred
if data_name == 'val':
X_val = _mf.get_meta_features(data_name, X)
X_list, vi_list = [], []
split_seed = int((MODELS_DIR / 'split_seed.txt').read_text())
for cv_index in range(CV_COUNT):
_, vi = tk.ml.cv_indices(X_val, None, cv_count=CV_COUNT, cv_index=cv_index, split_seed=split_seed, stratify=False)
X_list.append(X_val[vi])
vi_list.append(vi)
else:
X_test = _mf.get_meta_features(data_name, X, child_cv_index)
X_list = [X_test] * CV_COUNT
gen = tk.generator.SimpleGenerator()
model = tk.dl.models.Model.load(MODELS_DIR / f'model.fold0.h5', gen, batch_size=BATCH_SIZE, multi_gpu=True)
pred_list = []
for cv_index in tk.tqdm(range(CV_COUNT), desc='predict'):
if cv_index != 0:
model.load_weights(MODELS_DIR / f'model.fold{cv_index}.h5')
X_t = X_list[cv_index]
pred = np.mean([
model.predict(X_t, verbose=0),
model.predict(X_t[:, :, ::-1, :], verbose=0)[:, :, ::-1, :],
], axis=0)
pred_list.append(pred)
if data_name == 'val':
pred = np.empty((len(X), 101, 101, 1), dtype=np.float32)
for vi, p in zip(vi_list, pred_list):
pred[vi] = p
else:
pred = pred_list
if data_name != 'test':
cache_path.parent.mkdir(parents=True, exist_ok=True)
joblib.dump(pred, cache_path, compress=3)
return pred
def _main():
base_dir = pathlib.Path(__file__).resolve().parent
vocdevkit_dir = base_dir / 'pytoolkit' / 'data' / 'VOCdevkit'
save_dir = base_dir / '___assign_check'
save_dir.mkdir(exist_ok=True)
X_val, y_val = tk.data.voc.load_07_test(vocdevkit_dir)
X_val, y_val = X_val[:1], y_val[:1]
od = tk.dl.od.ObjectDetector.load_voc(batch_size=1, use_multi_gpu=False)
gen = tk.dl.od.od_gen.create_generator((512, 512),
preprocess_input=lambda x: x,
encode_truth=od.pb.encode_truth)
g, _ = gen.flow(X_val, y_val, data_augmentation=True)
for i, (X_batch, y_batch) in zip(tk.tqdm(range(32)), g):
for rgb, y in zip(X_batch, y_batch):
obj_pb = y[:, 1] == 1 # assignされたもの全部
classes = np.argmax(y[obj_pb, 2:-4], axis=-1)
bboxes = od.pb.decode_locs(np.zeros((len(y), 4)),
xp=np)[obj_pb, :] # prior box自体の座標を取る
img = tk.ml.plot_objects(rgb, classes, None, bboxes,
tk.data.voc.CLASS_NAMES)
tk.ndimage.save(save_dir / f'{i}.jpg', img)
def _load_image(X):
X = np.array([
cv2.imread(str(p), cv2.IMREAD_GRAYSCALE).astype(np.float32)
for p in tk.tqdm(X, desc='load')
])
X = np.expand_dims(X, axis=-1)
return X
def create_data(y, pred, pred_bin, pred_reg):
"""Create adaptive threshold data."""
threshold_X = create_input_data(pred, pred_bin, pred_reg)
mask_neg = y.max(axis=(1, 2, 3)) == 0
mask_pos = np.logical_not(mask_neg)
y_pos = y > 0.5
threshold_y = np.empty((len(y), ))
th_neg = (pred[mask_neg].max(axis=(1, 2, 3)) + 1) / 2
threshold_y[mask_neg] = np.minimum(np.maximum(th_neg, MIN_THRESHOLD),
MAX_THRESHOLD)
for i in tk.tqdm(np.where(mask_pos)[0], desc='ath'):
threshold_list = np.linspace(MIN_THRESHOLD, MAX_THRESHOLD, 1000)
iou_list = []
for th in threshold_list:
pred_pos = pred[i] > th
inter = np.logical_and(pred_pos, y_pos[i])
union = np.logical_or(pred_pos, y_pos[i])
iou = np.sum(inter) / max(np.sum(union), 1)
iou_list.append(iou)
best_index = np.argmax(iou_list)
threshold_y[i] = threshold_list[best_index]
threshold_y = np.array(threshold_y)
return threshold_X, threshold_y
def predict_all(data_name, X, use_cache=False, child_cv_index=None):
"""予測。"""
cache_path = CACHE_DIR / data_name / f'{MODEL_NAME}.pkl'
if use_cache and cache_path.is_file() and child_cv_index is None:
return joblib.load(cache_path)
if data_name == 'test' and child_cv_index is None:
pred = [predict_all(data_name, X, use_cache, i) for i in range(5)]
cache_path.parent.mkdir(parents=True, exist_ok=True)
joblib.dump(pred, cache_path, compress=3)
return pred
if data_name == 'val':
X_val = get_meta_features(data_name, X)
X_list, vi_list = [], []
split_seed = int((MODELS_DIR / 'split_seed.txt').read_text())
for cv_index in range(CV_COUNT):
_, vi = tk.ml.cv_indices(X_val,
None,
cv_count=CV_COUNT,
cv_index=cv_index,
split_seed=split_seed,
stratify=False)
X_list.append(X_val[vi])
vi_list.append(vi)
else:
X_test = get_meta_features(data_name, X, child_cv_index)
X_list = [X_test] * CV_COUNT
gen = tk.generator.SimpleGenerator()
model = tk.dl.models.Model.load(MODELS_DIR / f'model.fold0.h5',
gen,
batch_size=BATCH_SIZE,
multi_gpu=True)
pred_list = []
for cv_index in tk.tqdm(range(CV_COUNT), desc='predict'):
if cv_index != 0:
model.load_weights(MODELS_DIR / f'model.fold{cv_index}.h5')
X_t = X_list[cv_index]
pred = model.predict(X_t, verbose=0)
pred_list.append(pred)
if data_name == 'val':
pred = np.empty((len(X), 101, 101, 1), dtype=np.float32)
for vi, p in zip(vi_list, pred_list):
pred[vi] = p
else:
pred = np.mean(pred_list, axis=0)
if data_name != 'test':
cache_path.parent.mkdir(parents=True, exist_ok=True)
joblib.dump(pred, cache_path, compress=3)
return pred
def get_score_fixed_threshold(y_val, pred_val, search_th=True):
"""適当スコア算出。"""
if search_th:
threshold_list = np.linspace(0.3, 0.7, 100)
score_list = []
for th in tk.tqdm(threshold_list, desc='threshold'):
score = compute_score(y_val > 0.5, pred_val > th)
score_list.append(score)
best_index = np.argmax(score_list)
threshold = threshold_list[best_index]
score = score_list[best_index]
else:
threshold = 0.5
score = compute_score(y_val > 0.5, pred_val > threshold)
return score, threshold
def predict_all(data_name, X, use_cache=False):
"""予測。"""
cache_path = CACHE_DIR / data_name / f'{MODEL_NAME}.pkl'
if use_cache and cache_path.is_file():
return joblib.load(cache_path)
if data_name == 'val':
X_list, vi_list = [], []
split_seed = int((MODELS_DIR / 'split_seed.txt').read_text())
for cv_index in range(CV_COUNT):
_, vi = tk.ml.cv_indices(X,
None,
cv_count=CV_COUNT,
cv_index=cv_index,
split_seed=split_seed,
stratify=False)
X_list.append(X[vi])
vi_list.append(vi)
else:
X = _data.load_test_data()
X_list = [X] * CV_COUNT
gen = tk.generator.SimpleGenerator()
model = tk.dl.models.Model.load(MODELS_DIR / f'model.fold0.h5',
gen,
batch_size=BATCH_SIZE,
multi_gpu=True)
pred_list = []
for cv_index in tk.tqdm(range(CV_COUNT), desc='predict'):
if cv_index != 0:
model.load_weights(MODELS_DIR / f'model.fold{cv_index}.h5')
X_t = X_list[cv_index]
pred = _evaluation.predict_tta(model, X_t, mode='bin')
pred_list.append(pred)
if data_name == 'val':
pred = np.empty((len(X), 1), dtype=np.float32)
for vi, p in zip(vi_list, pred_list):
pred[vi] = p
else:
pred = pred_list
cache_path.parent.mkdir(parents=True, exist_ok=True)
joblib.dump(pred, cache_path, compress=3)
return pred
def _main():
base_dir = pathlib.Path(__file__).resolve().parent
vocdevkit_dir = base_dir / 'pytoolkit' / 'data' / 'VOCdevkit'
save_dir = base_dir / '___generator_check'
save_dir.mkdir(exist_ok=True)
X_val, y_val = tk.data.voc.load_07_test(vocdevkit_dir)
X_val, y_val = X_val[:1], y_val[:1]
gen = tk.dl.od.od_gen.create_generator((512, 512),
preprocess_input=lambda x: x,
encode_truth=None)
g, _ = gen.flow(X_val, y_val, data_augmentation=True)
for i, (X_batch, y_batch) in zip(tk.tqdm(range(32)), g):
for rgb, y in zip(X_batch, y_batch):
img = tk.ml.plot_objects(rgb, y.classes, None, y.bboxes,
tk.data.voc.CLASS_NAMES)
tk.ndimage.save(save_dir / f'{i}.jpg', img)
def log_evaluation(y_val,
pred_val,
print_fn=None,
search_th=False,
threshold=None):
"""検証結果をログる。"""
print_fn = print_fn or tk.log.get(__name__).info
# 正解率とか
tk.ml.print_classification_metrics(np.ravel(y_val),
np.ravel(pred_val),
print_fn=print_fn)
# 閾値探索＆スコア表示
if search_th:
assert threshold is None
threshold_list = np.linspace(0.3, 0.7, 100)
score_list = []
for th in tk.tqdm(threshold_list, desc='threshold'):
score = compute_score(np.int32(y_val > 0.5),
np.int32(pred_val > th))
score_list.append(score)
best_index = np.argmax(score_list)
print_fn('scores:')
for th, score in zip(threshold_list[::10], score_list[::10]):
print_fn(f' threshold={th:.3f}: score={score:.3f}')
threshold = threshold_list[best_index]
score = score_list[best_index]
print_fn(f'max score: {score:.3f} (threshold: {threshold:.3f})')
else:
if threshold is None:
threshold = 0.5
else:
assert threshold.shape == (len(pred_val), )
threshold = threshold.reshape((len(pred_val), 1, 1, 1))
print_fn(f'mean threshold: {np.mean(threshold):.3f}')
score = compute_score(np.int32(y_val > 0.5),
np.int32(pred_val > threshold))
print_fn(f'score: {score:.3f}')
# オレオレ指標
print_metrics(y_val > 0.5, pred_val > threshold, print_fn=print_fn)
return threshold
def save_submission(save_path, pred):
"""投稿用ファイルを出力。ついでにちょっとだけ結果を分析。"""
# 投稿用ファイルを出力
id_list = pd.read_csv(TEST_PATH)['id'].values
pred_dict = {
id_: _encode_rl(pred[i])
for i, id_ in enumerate(tk.tqdm(id_list, desc='encode_rl'))
}
df = pd.DataFrame.from_dict(pred_dict, orient='index')
df.index.names = ['id']
df.columns = ['rle_mask']
df.to_csv(str(save_path))
# 結果を分析
pred_bin = np.expand_dims(np.max(pred, axis=(1, 2, 3)).astype(np.uint8),
axis=-1) # 0 or 1
empty_count = len(pred_bin) - pred_bin.sum()
logger = tk.log.get(__name__)
logger.info(
f'empty rate: {empty_count}/{len(pred_bin)} = {100 * empty_count / len(pred_bin):.1f}%'
)
