def evaluation( # evaluation loop for use during training
model_dir,
tpu_name,
bucket_name_prefix,
once=False,
dump_samples_only=False,
total_bs=256,
tfds_data_dir='tensorflow_datasets',
load_ckpt=None):
region = utils.get_gcp_region()
tfds_data_dir = 'gs://{}-{}/{}'.format(bucket_name_prefix, region,
tfds_data_dir)
kwargs = tpu_utils.load_train_kwargs(model_dir)
print('loaded kwargs:', kwargs)
ds = datasets.get_dataset(kwargs['dataset'], tfds_data_dir=tfds_data_dir)
worker = tpu_utils.EvalWorker(
tpu_name=tpu_name,
model_constructor=functools.partial(_load_model, kwargs=kwargs, ds=ds),
total_bs=total_bs,
inception_bs=total_bs,
num_inception_samples=50000,
dataset=ds,
)
worker.run(logdir=model_dir,
once=once,
skip_non_ema_pass=True,
dump_samples_only=dump_samples_only,
load_ckpt=load_ckpt)
def evaluation(
model_dir, tpu_name, bucket_name_prefix, once=False, dump_samples_only=False, total_bs=128,
tfr_file='tensorflow_datasets/lsun/church-r08.tfrecords', samples_dir=None, num_inception_samples=2048,
):
#region = utils.get_gcp_region()
tfr_file = 'gs://{}/{}'.format(bucket_name_prefix, tfr_file)
kwargs = tpu_utils.load_train_kwargs(model_dir)
print('loaded kwargs:', kwargs)
ds = datasets.get_dataset(kwargs['dataset'], tfr_file=tfr_file)
worker = tpu_utils.EvalWorker(
tpu_name=tpu_name,
model_constructor=lambda: Model(
model_name=kwargs['model_name'],
betas=get_beta_schedule(
kwargs['beta_schedule'], beta_start=kwargs['beta_start'], beta_end=kwargs['beta_end'],
num_diffusion_timesteps=kwargs['num_diffusion_timesteps']
),
loss_type=kwargs['loss_type'],
num_classes=ds.num_classes,
dropout=kwargs['dropout'],
randflip=kwargs['randflip'],
block_size=kwargs['block_size']
),
total_bs=total_bs, inception_bs=total_bs, num_inception_samples=num_inception_samples,
dataset=ds,
limit_dataset_size=30000 # limit size of dataset for computing Inception features, for memory reasons
)
worker.run(logdir=model_dir, once=once, skip_non_ema_pass=True, dump_samples_only=dump_samples_only,
samples_dir=samples_dir)
def evaluation(
model_dir, tpu_name, bucket_name_prefix, once=False, dump_samples_only=False, total_bs=128,
tfds_data_dir='tensorflow_datasets',
):
region = utils.get_gcp_region()
tfds_data_dir = 'gs://{}-{}/{}'.format(bucket_name_prefix, region, tfds_data_dir)
kwargs = tpu_utils.load_train_kwargs(model_dir)
print('loaded kwargs:', kwargs)
ds = datasets.get_dataset(kwargs['dataset'], tfds_data_dir=tfds_data_dir)
worker = tpu_utils.EvalWorker(
tpu_name=tpu_name,
model_constructor=lambda: Model(
model_name=kwargs['model_name'],
betas=get_beta_schedule(
kwargs['beta_schedule'], beta_start=kwargs['beta_start'], beta_end=kwargs['beta_end'],
num_diffusion_timesteps=kwargs['num_diffusion_timesteps']
),
loss_type=kwargs['loss_type'],
num_classes=ds.num_classes,
dropout=kwargs['dropout'],
randflip=kwargs['randflip'],
block_size=kwargs['block_size']
),
total_bs=total_bs, inception_bs=total_bs, num_inception_samples=2048,
dataset=ds,
)
worker.run(logdir=model_dir, once=once, skip_non_ema_pass=True, dump_samples_only=dump_samples_only)
def simple_eval(model_dir, tpu_name, bucket_name_prefix, mode, load_ckpt=None, total_bs=16, tfr_file='tensorflow_datasets/lsun/church-r08.tfrecords'):
#region = utils.get_gcp_region()
tfr_file = 'gs://{}/{}'.format(bucket_name_prefix, tfr_file)
kwargs = tpu_utils.load_train_kwargs(model_dir)
print('loaded kwargs:', kwargs)
ds = datasets.get_dataset(kwargs['dataset'], tfr_file=tfr_file)
worker = simple_eval_worker.SimpleEvalWorker(
tpu_name=tpu_name, model_constructor=functools.partial(_load_model, kwargs=kwargs, ds=ds),
total_bs=total_bs, dataset=ds)
worker.run(mode=mode, logdir=model_dir, load_ckpt=load_ckpt)
def simple_eval(model_dir, tpu_name, bucket_name_prefix, mode, load_ckpt=None, total_bs=16, tfr_file='tensorflow_datasets/lsun/church-r08.tfrecords', samples_dir='gs://tensorfork-arfa-euw4/ddpm-samples'):
#region = utils.get_gcp_region()
tfr_file = 'gs://{}/{}'.format(bucket_name_prefix, tfr_file)
kwargs = tpu_utils.load_train_kwargs(model_dir)
if 'NUM_DIFFUSION_TIMESTEPS' in os.environ:
kwargs['num_diffusion_timesteps'] = int(os.environ['NUM_DIFFUSION_TIMESTEPS'])
if 'BETA_SCHEDULE' in os.environ:
kwargs['beta_schedule'] = os.environ['BETA_SCHEDULE']
print('loaded kwargs:', kwargs)
ds = datasets.get_dataset(kwargs['dataset'], tfr_file=tfr_file)
worker = simple_eval_worker.SimpleEvalWorker(
tpu_name=tpu_name, model_constructor=functools.partial(_load_model, kwargs=kwargs, ds=ds),
total_bs=total_bs, dataset=ds)
worker.run(mode=mode, logdir=model_dir, load_ckpt=load_ckpt, samples_dir=samples_dir)
def simple_eval(model_dir,
tpu_name,
bucket_name_prefix,
mode,
load_ckpt,
total_bs=256,
tfds_data_dir='tensorflow_datasets'):
region = utils.get_gcp_region()
tfds_data_dir = 'gs://{}-{}/{}'.format(bucket_name_prefix, region,
tfds_data_dir)
kwargs = tpu_utils.load_train_kwargs(model_dir)
print('loaded kwargs:', kwargs)
ds = datasets.get_dataset(kwargs['dataset'], tfds_data_dir=tfds_data_dir)
worker = simple_eval_worker.SimpleEvalWorker(
tpu_name=tpu_name,
model_constructor=functools.partial(_load_model, kwargs=kwargs, ds=ds),
total_bs=total_bs,
dataset=ds)
worker.run(mode=mode, logdir=model_dir, load_ckpt=load_ckpt)