from flask import send_from_directory, render_template
from werkzeug.utils import secure_filename
from image_model import ImageModel
from datetime import datetime
sys.path.append(os.curdir) # カレントファイルをインポートするための設定
UPLOAD_FOLDER = '/tmp/uploads'
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg', 'gif'])
app = Flask(__name__, static_url_path='/static', static_folder='assets/static')
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
model = ImageModel()
model.load('image_model.h5')
model.prepare_train_data()
def allowed_file(filename):
return '.' in filename and \
filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
@app.route('/flask/uploader', methods=['POST'])
def upload_file():
# check if the post request has the file part
# create a special subfolder for the files uploaded this time
# to avoid overwrite
)
best_file = f'{config.neural_backbone}_best_fold{fold}_dropout_{config.dropout}.pth'
if config.regression:
best_file = 'regression_' + best_file
log_filename = best_file.replace('.pth', '.txt')
model = ImageModel(model_name=config.neural_backbone,
device=device,
dropout=config.dropout,
neurons=0,
num_classes=config.num_classes,
extras_inputs=[],
base_model_pretrained_weights=None)
model.load(directory=config.output_dir, filename=best_file)
model = model.to(device)
if fine_tune:
best_file = 'finetune_' + best_file
checkpoint_path = os.path.join(config.output_dir, best_file)
if os.path.exists(checkpoint_path):
print(
f"WARNING: TRAINED CHECKPOINT ALREADY EXISTS IN {checkpoint_path}. "
f"SKIPPING TRAINING FOR THIS MODEL/FOLD")
else:
print("Training model!")
perform_train(model, device, train_df, val_df, config,
best_file, fine_tune)