return _resnetlstm('resnet50', pretrained=pretrained,
block=Bottleneck, layers=[3, 4, 6, 3], num_classes=num_classes)
def ResNet101LSTM(num_classes=101, pretrained=True):
return _resnetlstm('resnet101', pretrained=pretrained,
block=Bottleneck, layers=[3, 4, 23, 3], num_classes=num_classes)
EPOCHS = 100
BATCH_SIZE = 4
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--folder", type=str, nargs="?", default=None)
args = vars(parser.parse_args())
ds_shape = d5ds.dataset_shape('ucf101')
ds_classes, sample_shape = ds_shape[0], ds_shape[1:]
train_set, validation_set = d5ds.load_ucf101('0', 'label', folder=args['folder'],
normalize=True, max_length=700, skip_frames=10)
seq_lengths = [av.open(path).streams.video[0].frames for path in train_set.data]
train_sampler = d5.BucketSampler(train_set, BATCH_SIZE, seq_lengths, max_length=500,
transformations=[d5ref.Crop((224, 224)),])
validation_sampler = d5.OrderedSampler(validation_set, BATCH_SIZE,
transformations=[d5ref.Crop((224, 224)),])
model = ResNet50LSTM(num_classes=ds_classes, pretrained=True)
loss = torch.nn.CrossEntropyLoss()
executor = d5fw.PyTorchNativeGraphExecutor(model, loss, device=d5.GPUDevice())
BATCH_SIZE = 64
MAX_EPOCHS = 10
if __name__ == '__main__':
if len(sys.argv) > 4:
print(
'USAGE: train_until.py [NETWORK NAME] [DATASET NAME] [DESIRED ACCURACY]'
)
print('Defaults: simple_cnn on MNIST, desired accuracy 98%')
sys.exit(1)
netname = 'simple_cnn' if len(sys.argv) < 2 else sys.argv[1]
dsname = 'mnist' if len(sys.argv) < 3 else sys.argv[2]
accuracy = 98.0 if len(sys.argv) < 4 else float(sys.argv[3])
# Create CNN using ONNX
ds_cls, ds_c, ds_h, ds_w = d5ds.dataset_shape(dsname)
onnx_file = d5net.export_network(netname,
BATCH_SIZE,
classes=ds_cls,
shape=(ds_c, ds_h, ds_w))
model = d5.parser.load_and_parse_model(onnx_file)
# Recover input and output nodes (assuming only one input and one output)
INPUT_NODE = model.get_input_nodes()[0].name
OUTPUT_NODE = model.get_output_nodes()[0].name
# Create dataset and add loss function to model
train_set, test_set = d5ds.load_dataset(dsname, INPUT_NODE, LABEL_NODE)
model.add_operation(
d5.ops.SoftmaxCrossEntropy([OUTPUT_NODE, LABEL_NODE], 'loss'))
def run_recipe(fixed: Dict[str, Any], mutable: Dict[str, Any],
metrics: List[Tuple[d5.TestMetric, Any]]) -> bool:
""" Runs a Deep500 recipe (see file documentation). Returns True on success
and False on failure, printing the unacceptable metrics. """
# Argument validation
if any(k in mutable for k in fixed.keys()):
raise RuntimeError('Fixed and mutable components cannot overlap')
# Create unified dictionary
comps = dict(fixed, **mutable)
# Add missing arguments and keyword arguments
old_keys = list(comps.keys())
for k in old_keys:
if (k not in ['batch_size', 'epochs', 'events']
and not (k.endswith('_args') or k.endswith('_kwargs'))):
if ('%s_args' % k) not in comps:
comps['%s_args' % k] = tuple()
if ('%s_kwargs' % k) not in comps:
comps['%s_kwargs' % k] = {}
########################################################################
# Obtain dataset metadata
if 'dataset' not in comps:
raise SyntaxError('Dataset must be specified in training recipe')
if isinstance(comps['dataset'], str):
loss_op = d5ds.dataset_loss(comps['dataset'])
ds_shape = d5ds.dataset_shape(comps['dataset'])
else:
loss_op = comps['dataset'].loss
ds_shape = comps['dataset'].shape
ds_classes, sample_shape = ds_shape[0], ds_shape[1:]
# Construct network
if 'model' not in comps:
raise SyntaxError('Model must be specified in recipe')
if 'batch_size' not in comps:
raise SyntaxError('Batch size must be specified in training recipe')
batch = comps['batch_size']
if isinstance(comps['model'], str):
# ONNX file
if os.path.isfile(comps['model']):
network = d5.parser.load_and_parse_model(comps['model'])
input_node = network.get_input_nodes()[0].name
output_node = network.get_output_nodes()[0].name
else: # Standard model
network, input_node, output_node = \
d5nt.create_model(comps['model'], batch, *comps['model_args'],
classes=ds_classes, shape=sample_shape,
**comps['model_kwargs'])
else: # Callable
network, input_node, output_node = comps['model'](
batch,
*comps['model_args'],
classes=ds_classes,
shape=sample_shape,
**comps['model_kwargs'])
# Add loss function to model
network.add_operation(loss_op([output_node, 'label'], 'loss'))
# Construct dataset
if isinstance(comps['dataset'], str):
train_set, validation_set = d5ds.load_dataset(
comps['dataset'], input_node, 'label', *comps['dataset_args'],
**comps['dataset_kwargs'])
else:
train_set, validation_set = comps['dataset'](input_node, 'label',
*comps['dataset_args'],
**comps['dataset_kwargs'])
# Construct samplers
if 'train_sampler' in comps:
if isinstance(comps['train_sampler'], d5.Sampler):
train_sampler = comps['train_sampler']
else:
train_sampler = comps['train_sampler'](
train_set, batch, *comps['train_sampler_args'],
**comps['train_sampler_kwargs'])
else:
train_sampler = train_set
if 'validation_sampler' in comps:
if isinstance(comps['validation_sampler'], d5.Sampler):
validation_sampler = comps['validation_sampler']
else:
validation_sampler = comps['validation_sampler'](
validation_set, batch, *comps['validation_sampler_args'],
**comps['validation_sampler_kwargs'])
else:
validation_sampler = validation_set
# Construct executor
if 'executor' not in comps:
raise SyntaxError('Executor must be specified in recipe')
if isinstance(comps['executor'], d5.GraphExecutor):
executor = comps['executor']
else:
executor = comps['executor'](network, *comps['executor_args'],
**comps['executor_kwargs'])
# Construct optimizer
if 'optimizer' not in comps:
raise SyntaxError('Optimizer must be specified in training recipe')
optimizer = comps['optimizer'](executor, 'loss', *comps['optimizer_args'],
**comps['optimizer_kwargs'])
# Add total time to metrics
metrics.append((d5.WallclockTime(reruns=0, avg_over=1), None))
########################################################################
# Create trainer and run
if 'epochs' not in comps:
raise SyntaxError('Epochs must be specified in training recipe')
if 'events' not in comps:
comps['events'] = None
results = d5.test_training(executor,
train_sampler,
validation_sampler,
optimizer,
comps['epochs'],
batch,
output_node,
metrics=[m[0] for m in metrics],
events=comps['events'])
# Verify results
ok = True
for (metric, acceptable), result in zip(metrics, results):
if acceptable is not None:
if result < acceptable:
print('FAIL %s: %s (Acceptable: %s)' %
(type(metric).__name__, result, acceptable))
ok = False
if not ok:
return False
else:
print('PASSED')
return True