def validate(args):
rng = jax.random.PRNGKey(0)
model, variables = create_model(args.model, pretrained=True, rng=rng)
print(f'Created {args.model} model. Validating...')
if args.no_jit:
eval_step = lambda images, labels: eval_forward(
model, variables, images, labels)
else:
eval_step = jax.jit(lambda images, labels: eval_forward(
model, variables, images, labels))
if os.path.splitext(args.data)[1] == '.tar' and os.path.isfile(args.data):
dataset = DatasetTar(args.data)
else:
dataset = Dataset(args.data)
data_config = resolve_data_config(vars(args), model=model)
loader = create_loader(dataset,
input_size=data_config['input_size'],
batch_size=args.batch_size,
use_prefetcher=False,
interpolation=data_config['interpolation'],
mean=data_config['mean'],
std=data_config['std'],
num_workers=8,
crop_pct=data_config['crop_pct'])
batch_time = AverageMeter()
correct_top1, correct_top5 = 0, 0
total_examples = 0
start_time = prev_time = time.time()
for batch_index, (images, labels) in enumerate(loader):
images = images.numpy().transpose(0, 2, 3, 1)
labels = labels.numpy()
top1_count, top5_count = eval_step(images, labels)
correct_top1 += top1_count
correct_top5 += top5_count
total_examples += images.shape[0]
batch_time.update(time.time() - prev_time)
if batch_index % 20 == 0 and batch_index > 0:
print(
f'Test: [{batch_index:>4d}/{len(loader)}] '
f'Rate: {images.shape[0] / batch_time.val:>5.2f}/s ({images.shape[0] / batch_time.avg:>5.2f}/s) '
f'Acc@1: {100 * correct_top1 / total_examples:>7.3f} '
f'Acc@5: {100 * correct_top5 / total_examples:>7.3f}')
prev_time = time.time()
acc_1 = 100 * correct_top1 / total_examples
acc_5 = 100 * correct_top5 / total_examples
print(
f'Validation complete. {total_examples / (prev_time - start_time):>5.2f} img/s. '
f'Acc@1 {acc_1:>7.3f}, Acc@5 {acc_5:>7.3f}')
return dict(top1=float(acc_1), top5=float(acc_5))
def validate(args):
rng = jax.random.PRNGKey(0)
platform = jax.local_devices()[0].platform
if args.half_precision:
if platform == 'tpu':
model_dtype = jax.numpy.bfloat16
else:
model_dtype = jax.numpy.float16
else:
model_dtype = jax.numpy.float32
model, variables = create_model(args.model, pretrained=True, dtype=model_dtype, rng=rng)
print(f'Created {args.model} model. Validating...')
if args.no_jit:
eval_step = lambda images, labels: eval_forward(model.apply, variables, images, labels)
else:
eval_step = jax.jit(lambda images, labels: eval_forward(model.apply, variables, images, labels))
"""Runs evaluation and returns top-1 accuracy."""
image_size = model.default_cfg['input_size'][-1]
eval_iter, num_batches = create_eval_iter(
args.data, args.batch_size, image_size,
half_precision=args.half_precision,
mean=tuple([x * 255 for x in model.default_cfg['mean']]),
std=tuple([x * 255 for x in model.default_cfg['std']]),
interpolation=model.default_cfg['interpolation'],
)
batch_time = AverageMeter()
correct_top1, correct_top5 = 0, 0
total_examples = 0
start_time = prev_time = time.time()
for batch_index, batch in enumerate(eval_iter):
images, labels = batch['image'], batch['label']
top1_count, top5_count = eval_step(images, labels)
correct_top1 += int(top1_count)
correct_top5 += int(top5_count)
total_examples += images.shape[0]
batch_time.update(time.time() - prev_time)
if batch_index % 20 == 0 and batch_index > 0:
print(
f'Test: [{batch_index:>4d}/{num_batches}] '
f'Rate: {images.shape[0] / batch_time.val:>5.2f}/s ({images.shape[0] / batch_time.avg:>5.2f}/s) '
f'Acc@1: {100 * correct_top1 / total_examples:>7.3f} '
f'Acc@5: {100 * correct_top5 / total_examples:>7.3f}')
prev_time = time.time()
acc_1 = 100 * correct_top1 / total_examples
acc_5 = 100 * correct_top5 / total_examples
print(f'Validation complete. {total_examples / (prev_time - start_time):>5.2f} img/s. '
f'Acc@1 {acc_1:>7.3f}, Acc@5 {acc_5:>7.3f}')
return dict(top1=acc_1, top5=acc_5)
def validate(args):
model = create_model(args.model, pretrained=True)
print(f'Created {args.model} model. Validating...')
eval_step = objax.Jit(
lambda images, labels: eval_forward(model, images, labels),
model.vars())
dataset = create_dataset('imagenet', args.data)
data_config = resolve_data_config(vars(args), model=model)
loader = create_loader(
dataset,
input_size=data_config['input_size'],
batch_size=args.batch_size,
use_prefetcher=False,
interpolation=data_config['interpolation'],
mean=data_config['mean'],
std=data_config['std'],
num_workers=8,
crop_pct=data_config['crop_pct'])
batch_time = AverageMeter()
correct_top1, correct_top5 = 0, 0
total_examples = 0
start_time = prev_time = time.time()
for batch_index, (images, labels) in enumerate(loader):
images = images.numpy()
labels = labels.numpy()
top1_count, top5_count = eval_step(images, labels)
correct_top1 += int(top1_count)
correct_top5 += int(top5_count)
total_examples += images.shape[0]
batch_time.update(time.time() - prev_time)
if batch_index % 20 == 0 and batch_index > 0:
print(
f'Test: [{batch_index:>4d}/{len(loader)}] '
f'Rate: {images.shape[0] / batch_time.val:>5.2f}/s ({images.shape[0] / batch_time.avg:>5.2f}/s) '
f'Acc@1: {100 * correct_top1 / total_examples:>7.3f} '
f'Acc@5: {100 * correct_top5 / total_examples:>7.3f}')
prev_time = time.time()
acc_1 = 100 * correct_top1 / total_examples
acc_5 = 100 * correct_top5 / total_examples
print(f'Validation complete. {total_examples / (prev_time - start_time):>5.2f} img/s. '
f'Acc@1 {acc_1:>7.3f}, Acc@5 {acc_5:>7.3f}')
return dict(top1=float(acc_1), top5=float(acc_5))
def validate(args):
rng = jax.random.PRNGKey(0)
model, variables = create_model(args.model, pretrained=True, rng=rng)
print(f'Created {args.model} model. Validating...')
if args.no_jit:
eval_step = lambda images, labels: eval_forward(
model, variables, images, labels)
else:
eval_step = jax.jit(lambda images, labels: eval_forward(
model, variables, images, labels))
"""Runs evaluation and returns top-1 accuracy."""
image_size = model.default_cfg['input_size'][-1]
test_ds, num_batches = imagenet_data.load(
imagenet_data.Split.TEST,
is_training=False,
image_size=image_size,
batch_dims=[args.batch_size],
mean=tuple([x * 255 for x in model.default_cfg['mean']]),
std=tuple([x * 255 for x in model.default_cfg['std']]),
tfds_data_dir=args.data)
batch_time = AverageMeter()
correct_top1, correct_top5 = 0, 0
total_examples = 0
start_time = prev_time = time.time()
for batch_index, batch in enumerate(test_ds):
images, labels = batch['images'], batch['labels']
top1_count, top5_count = eval_step(images, labels)
correct_top1 += top1_count
correct_top5 += top5_count
total_examples += images.shape[0]
batch_time.update(time.time() - prev_time)
if batch_index % 20 == 0 and batch_index > 0:
print(
f'Test: [{batch_index:>4d}/{num_batches}] '
f'Rate: {images.shape[0] / batch_time.val:>5.2f}/s ({images.shape[0] / batch_time.avg:>5.2f}/s) '
f'Acc@1: {100 * correct_top1 / total_examples:>7.3f} '
f'Acc@5: {100 * correct_top5 / total_examples:>7.3f}')
prev_time = time.time()
acc_1 = 100 * correct_top1 / total_examples
acc_5 = 100 * correct_top5 / total_examples
print(
f'Validation complete. {total_examples / (prev_time - start_time):>5.2f} img/s. '
f'Acc@1 {acc_1:>7.3f}, Acc@5 {acc_5:>7.3f}')
return dict(top1=acc_1, top5=acc_5)