def run_predict_one_fold(fold: int):
test_data_loader = get_test_data_loader()
model = load_trained_model(f"checkpoint-f{fold}.pt")
results = []
for images, image_ids in test_data_loader:
images = list(image.to(config.DEVICE) for image in images)
outputs = model(images)
for i, image in enumerate(images):
boxes = outputs[i]['boxes'].cpu().detach().numpy()
scores = outputs[i]['scores'].cpu().detach().numpy()
boxes = boxes[scores > config.SCORE_THRESHOLD]
scores = scores[scores > config.SCORE_THRESHOLD]
image_id = image_ids[i]
result = {
'image_id': image_id,
'PredictionString': format_prediction_string(boxes, scores)
}
results.append(result)
save_labeled_image(image, boxes, image_id)
test_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])
test_df.to_csv('submission.csv', index=False)
def run_predict():
test_data_loader = get_test_data_loader()
for fold in range(0, config.NUM_FOLDS):
models.append(load_trained_model(f"checkpoint-f{fold}.pt"))
results = []
for images, image_ids in test_data_loader:
predictions = make_ensemble_predictions(images, models)
for i, image in enumerate(images):
boxes, scores, labels = run_wbf(predictions, image_index=i)
boxes = boxes.astype(np.int32)
image_id = image_ids[i]
result = {
'image_id': image_id,
'PredictionString': format_prediction_string(boxes, scores)
}
results.append(result)
save_labeled_image(image, boxes, image_id)
test_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])
test_df.to_csv('submission.csv', index=False)
print("Error: You need to specify an existing preprocessing json file.")
print(preproc_file,": this file doesn't exist!")
print(usage)
sys.exit(1)
if save_feature == -1:
print("Error: You need to specify an existing features json file.")
print(feature_file,": this file doesn't exist!")
print(usage)
sys.exit(1)
print("Using %d core(s)"%cores)
print("Using batches of %d files"%batch_quantity)
print("Total number of files to process: %d"%num_files)
print("Loading trained module...")
model, model_path = mo.load_trained_model(args, config_model, metadata )
if model is None:
print("Some error ocurred during the loading of the trained module.")
print("Please check if the module file exists and if it's valid.")
print("Failed to load model: ",model_path)
print(usage)
sys.exit(1)
print("Initiating the extraction of features and classification process...")
files = []
for d in doc_ids:
files.append(join(pdf_path,d+'.pdf'))
over_batch = batch_quantity
under_batch = 0
num_batch = 0
def softmax_to_age(predictions):
return np.sum(predictions * classes, axis=1)
if __name__ == "__main__":
BASE_PATH = os.path.dirname(os.path.abspath(__file__))
os.chdir(BASE_PATH)
import sys
sys.path.insert(0, os.path.dirname(__file__))
sys.path.insert(0, os.getcwd())
DATA_NAME = 'wiki' # or imdb
# load model
vgg_face_age_model = load_trained_model()
# load data
data_df = None
base_path = '.'
if DATA_NAME == 'wiki':
data_df = load_data.load_wiki_df_from_csv(
'../data/processed/wiki_df.csv')
base_path = '../data/raw/wiki_crop/'
print("= " + str(len(data_df)) + " ROWS OF WIKI DATA LOADED\n")
if DATA_NAME == 'imdb':
data_df = load_data.load_imdb_df_from_pkl(
'../data/processed/imdb_meta_df.pkl')
data_df['full_path'] = data_df['full_path'].map(lambda x: x[0])
base_path = '../data/raw/imdb_crop/'
print("= " + str(len(data_df)) + " ROWS OF IMDB DATA LOADED\n")