import os
import sys
import tensorflow as tf
from tensorflow.keras.callbacks import ModelCheckpoint
root_path = os.path.abspath(os.path.join('..'))
if root_path not in sys.path:
sys.path.append(root_path)
import config
from cars_loader import load_cars, build_dataset
from ssd_utils.ssd_loss import SSDLoss
from utils import import_by_name, train_test_split_tensors, MeanAveragePrecisionCallback
# Load train and validation data
train_image_paths, train_bnd_boxes = load_cars(split='train')
valid_image_paths, valid_bnd_boxes = load_cars(split='valid')
valid_data = build_dataset(valid_image_paths,
valid_bnd_boxes,
image_size=config.IMAGE_SIZE,
batch_size=config.BATCH_SIZE)
for run in range(1, config.NUM_RUNS + 1):
weights_dir = 'weights_{}'.format(run)
history_dir = 'history_{}'.format(run)
os.makedirs(weights_dir, exist_ok=True)
os.makedirs(history_dir, exist_ok=True)
for architecture in config.ARCHITECTURES:
import pandas as pd
import os
import sys
import tensorflow as tf
root_path = os.path.abspath('..')
if root_path not in sys.path:
sys.path.append(root_path)
import config_naive_pasting_ablation as config
from cars_loader import load_cars, build_dataset
from ssd_utils import output_encoder
from ssd_utils.metrics import MeanAveragePrecision
from utils import import_by_name, MeanAveragePrecisionCallback
test_image_paths, test_bnd_boxes = load_cars(split='test')
test_data = build_dataset(test_image_paths,
test_bnd_boxes,
image_size=config.IMAGE_SIZE,
batch_size=config.BATCH_SIZE)
meanAP_metric = MeanAveragePrecision()
results = {'architecture': []}
for run in range(1, config.NUM_RUNS + 1):
results['run_{}'.format(run)] = []
for architecture in config.ARCHITECTURES:
model_class = import_by_name('ssd_utils.networks.' + architecture)
model = model_class(num_classes=len(config.CLASSES))