Python load_cars Examples

Example #1

File: run_training.py Project: Leonardo-Blanger/synthesis_pretraining_object_detection

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:

Example #2

File: generate_results_naive_pasting_ablation.py Project: Leonardo-Blanger/synthesis_pretraining_object_detection

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))