def trainLoop(num_epochs, batch_size, gen_lr, disc_lr, gen_train_freq,
disc_train_freq, logger):
# Create run directory for current training run
os.chdir(os.path.expanduser('~') + '/Research/psig-gan/runs/')
run_dir = str(datetime.datetime.now()).replace(
' ',
'',
) + "--" + "disc_lr" + str(disc_lr) + " " + "gen_lr" + str(
gen_lr) + " " + "gen_train_freq" + str(
gen_train_freq) + " " + "disc_train_freq" + str(
disc_train_freq) + " " + "num_epochs" + str(num_epochs)
os.mkdir(run_dir)
# Instantiate GAN
gan = DCGAN(latent_shape=100,
output_image_shape=512,
num_gen_images=batch_size,
gen_filter_size=6,
discrim_filter_size=5,
gen_num_channels=128,
discrim_num_channels=64)
# Load real grassweeds image data
data_path = '/home/data/GrassClover/'
data_batch = util.createDataBatch(data_path, batch_size, 512)
# Execute training loop
train(gan, data_batch, num_epochs, run_dir, gen_lr, disc_lr,
gen_train_freq, disc_train_freq, logger)
def trainLoop(num_epochs, batch_size, gen_lr, disc_lr, gen_train_freq, disc_train_freq, logger):
# Create run directory for current training run
os.chdir(os.path.expanduser('~') + '/psig-gan/runs/')
run_dir = str(datetime.datetime.now()).replace(' ','',) + "--" + "disc_lr"+ str(disc_lr) + " " + "gen_lr"+ str(gen_lr) + " " + "gen_train_freq" + str(gen_train_freq) + " " + "disc_train_freq" + str(disc_train_freq) + " " + "num_epochs"+ str(num_epochs)
os.mkdir(run_dir)
# Instantiate GAN
gan = DCGAN(latent_shape=100, output_image_shape=256, num_gen_images=batch_size, gen_filter_size=5, discrim_filter_size=5, gen_num_channels=128, discrim_num_channels=64)
# Load real grassweeds image data
data_path = '/home/data/dcgan-data/'
data_batch = util.createDataBatch(data_path,batch_size)
# Define Comet-ML API key here for error logging
comet_api_key = 'Gdy4QDrOmu0P01XuBI33rPuIS'
#Define Comet Project Name
logger = Experiment(comet_api_key, project_name="psig-gan")
#Define experiment name
logger.set_name("disc_lr"+ str(disc_lr) + " " + "gen_lr"+ str(gen_lr) + " " + "gen_train_freq"+ str(gen_train_freq) + " " + "disc_train_freq" + str(disc_train_freq) + " " + "num_epochs"+ str(num_epochs))
# Execute training loop
train(gan, data_batch, num_epochs, run_dir, gen_lr, disc_lr, gen_train_freq, disc_train_freq, logger)
if __name__ == "__main__":
# set GPU here
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# Define Comet-ML API key here for error logging
comet_api_key = 'Gdy4QDrOmu0P01XuBI33rPuIS'
#Define Comet Project Name
logger = Experiment(comet_api_key, project_name="psig-gan")
#Define experiment name
logger.set_name("discrim-only training") #+ str( datetime.datetime.now().replace(' ','',) ))
#define epochs and lr here
num_epochs = 15
batch_size = 64
disc_lr = 0.01
# Load real grassweeds image data
data_path = '/home/data/dcgan-data/'
data_batch = util.createDataBatch(data_path,batch_size)
#make gan object here
gan = DCGAN(latent_shape=100, output_image_shape=256, num_gen_images=batch_size, gen_filter_size=5, discrim_filter_size=5, gen_num_channels=128, discrim_num_channels=64)
# call discrim training loop
train_discrim(gan,disc_lr, data_batch, num_epochs, logger, batch_size)