def __init__(self, backend, networks, backend_opts):
super(Frontend, self).__init__(networks)
self.backend_opts = backend_opts
self.backend = backend
if backend == 'plaid':
try:
self.configuration['plaid'] = importlib.import_module('plaidml').__version__
importlib.import_module('plaidml.keras').install_backend()
except ImportError:
raise core.ExtrasNeeded(['plaidml-keras'])
elif backend == 'tc':
try:
importlib.import_module('tensor_comprehensions')
except ImportError:
raise core.ExtrasNeeded(['torch', 'tensor_comprehensions'])
if backend_opts['tc_cachedir']:
try:
os.makedirs(backend_opts['tc_cachedir'])
except OSError:
pass
elif backend == 'tvm':
try:
importlib.import_module('tvm')
except ImportError:
raise core.ExtrasNeeded(['tvm', 'topi'])
def __init__(self, backend, fp16, train):
super(Frontend, self).__init__(Frontend.NETWORK_NAMES)
self.backend = backend
if backend == 'plaid':
try:
self.configuration['plaid'] = importlib.import_module('plaidml').__version__
importlib.import_module('plaidml.keras').install_backend()
except ImportError:
raise core.ExtrasNeeded(['plaidml-keras'])
if backend == 'plaid_edsl':
try:
self.configuration['plaid'] = importlib.import_module('plaidml').__version__
except ImportError:
raise RuntimeError("Failed to import plaidml module")
try:
importlib.import_module('plaidml2.bridge.keras')
except ImportError:
raise RuntimeError("The PlaidML2 EDSL Keras bridge was requested but not found.")
elif backend == 'tensorflow':
try:
importlib.import_module('keras.backend')
except ImportError:
raise core.ExtrasNeeded(['keras', 'tensorflow'])
if fp16:
importlib.import_module('keras.backend').set_floatx('float16')
if train:
self.configuration['train'] = True
self.fp16 = fp16
self.train = train
def __init__(self, backend, fp16, train):
super(Frontend, self).__init__(Frontend.NETWORK_NAMES)
self.backend = backend
if backend == 'plaid':
try:
self.configuration['plaid'] = importlib.import_module(
'plaidml').__version__
importlib.import_module('plaidml.keras').install_backend()
except ImportError:
raise core.ExtrasNeeded(['plaidml-keras'])
elif backend == 'tensorflow':
try:
importlib.import_module('keras.backend')
except ImportError:
raise core.ExtrasNeeded(['keras', 'tensorflow'])
if fp16:
importlib.import_module('keras.backend').set_floatx('float16')
if train:
self.configuration['train'] = True
self.fp16 = fp16
self.train = train
def cli(ctx, backend, fp16, train, networks):
"""Benchmarks Keras neural networks."""
runner = ctx.ensure_object(core.Runner)
frontend = Frontend(backend, fp16, train)
if backend == 'plaid':
try:
runner.reporter.configuration['plaid'] = importlib.import_module(
'plaidml').__version__
importlib.import_module('plaidml.keras').install_backend()
except ImportError:
raise core.ExtrasNeeded(['plaidml-keras'])
elif backend == 'tensorflow':
try:
importlib.import_module('keras.backend')
except ImportError:
raise core.ExtrasNeeded(['keras', 'tensorflow'])
if fp16:
importlib.import_module('keras.backend').set_floatx('float16')
if train:
runner.reporter.configuration['train'] = True
return runner.run(frontend, networks)
def cli(ctx, backend, cpu, use_cached_data, networks):
"""Benchmarks ONNX models."""
runner = ctx.ensure_object(core.Runner)
try:
importlib.import_module(backend.module_name)
except ImportError:
six.raise_from(core.ExtrasNeeded(backend.requirements), None)
if backend.is_plaidml:
runner.reporter.configuration['plaid'] = plaidml.__version__
onnx = importlib.import_module('onnx')
importlib.import_module('onnx.numpy_helper')
frontend = Frontend(cpu, use_cached_data, backend, onnx)
return runner.run(frontend, networks)
def __init__(self, backend, cpu, use_cached_data):
super(Frontend, self).__init__(Frontend.NETWORK_NAMES)
self.cpu = cpu
self.use_cached_data = use_cached_data
self.backend_info = backend
try:
importlib.import_module(backend.module_name)
except ImportError:
six.raise_from(core.ExtrasNeeded(backend.requirements), None)
if backend.is_plaidml:
self.configuration['plaid'] = plaidml.__version__
self.onnx = importlib.import_module('onnx')
importlib.import_module('onnx.numpy_helper')
def setup(self):
try:
self.backend = importlib.import_module(self.frontend.backend_info.module_name)
except ImportError:
raise core.ExtrasNeeded(self.frontend.backend_info.requirements)
try:
data_path = download_onnx_data(self.params.network_name, 'test_data_0.npz',
self.frontend.use_cached_data)
self.x = np.load(data_path)['inputs'][0]
except DataNotFoundError:
# See if we can access it as a proto.
data_path = download_onnx_data(self.params.network_name,
os.path.join('test_data_set_0', 'input_0.pb'))
tensor = self.onnx.TensorProto()
with open(data_path, 'rb') as f:
tensor.ParseFromString(f.read())
self.x = self.onnx.numpy_helper.to_array(tensor)
model_path = download_onnx_data(self.params.network_name, 'model.onnx')
self.model = self.onnx.load(model_path)
