import tensorflow as tf
from datasets import data as dataset
from models.nn import GCN as ConvNet
from learning.evaluators import AccuracyEvaluator as Evaluator
from learning.utils import draw_pixel
""" 1. Load dataset """
root_dir = os.path.join('data/catdog/') # FIXME
test_dir = os.path.join(root_dir, 'test')
# Set image size and number of class
IM_SIZE = (512, 512)
NUM_CLASSES = 3
# Load test set
X_test, y_test = dataset.read_data(test_dir, IM_SIZE)
test_set = dataset.DataSet(X_test, y_test)
""" 2. Set test hyperparameters """
hp_d = dict()
# FIXME: Test hyperparameters
hp_d['batch_size'] = 8
""" 3. Build graph, load weights, initialize a session and start test """
# Initialize
graph = tf.get_default_graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
model = ConvNet([IM_SIZE[0], IM_SIZE[1], 3], NUM_CLASSES, **hp_d)
evaluator = Evaluator()
saver = tf.train.Saver()
from learning.optimizers import MomentumOptimizer as Optimizer
from learning.evaluators import AccuracyEvaluator as Evaluator
""" 1. Load and split datasets """
root_dir = os.path.join('data/catdog/') # FIXME
trainval_dir = os.path.join(root_dir, 'train')
# Set image size and number of class
IM_SIZE = (512, 512)
NUM_CLASSES = 3
# Load trainval set and split into train/val sets
X_trainval, y_trainval = dataset.read_data(trainval_dir, IM_SIZE)
trainval_size = X_trainval.shape[0]
val_size = int(trainval_size * 0.1) # FIXME
val_set = dataset.DataSet(X_trainval[:val_size], y_trainval[:val_size])
train_set = dataset.DataSet(X_trainval[val_size:], y_trainval[val_size:])
""" 2. Set training hyperparameters"""
hp_d = dict()
# FIXME: Training hyperparameters
hp_d['batch_size'] = 8
hp_d['num_epochs'] = 100
hp_d['init_learning_rate'] = 1e-3
hp_d['momentum'] = 0.9
hp_d['learning_rate_patience'] = 10
hp_d['learning_rate_decay'] = 0.1
hp_d['eps'] = 1e-8
hp_d['score_threshold'] = 1e-4
hp_d['pretrain'] = True
