def FeedBlob(name, arr, device_option=None):
"""Feeds a blob into the workspace.
Inputs:
name: the name of the blob.
arr: either a TensorProto object or a numpy array object to be fed into
the workspace.
device_option (optional): the device option to feed the data with.
Returns:
True or False, stating whether the feed is successful.
"""
if type(arr) is caffe2_pb2.TensorProto:
arr = utils.Caffe2TensorToNumpyArray(arr)
if type(arr) is np.ndarray and arr.dtype.kind in 'SU':
# Plain NumPy strings are weird, let's use objects instead
arr = arr.astype(np.object)
if device_option is None:
device_option = scope.CurrentDeviceScope()
if device_option and device_option.device_type == caffe2_pb2.CUDA:
if arr.dtype == np.dtype('float64'):
logger.warning(
"CUDA operators do not support 64-bit doubles, " +
"please use arr.astype(np.float32) or np.int32 for ints." +
" Blob: {}".format(name) +
" type: {}".format(str(arr.dtype))
)
name = StringifyBlobName(name)
if device_option is not None:
return C.feed_blob(name, arr, StringifyProto(device_option))
else:
return C.feed_blob(name, arr)
def FeedBlob(name, arr, device_option=None):
"""Feeds a blob into the workspace.
Inputs:
name: the name of the blob.
arr: either a TensorProto object or a numpy array object to be fed into
the workspace.
device_option (optional): the device option to feed the data with.
Returns:
True or False, stating whether the feed is successful.
"""
if type(arr) is caffe2_pb2.TensorProto:
arr = utils.Caffe2TensorToNumpyArray(arr)
if type(arr) is np.ndarray and arr.dtype.kind in 'SU':
# Plain NumPy strings are weird, let's use objects instead
arr = arr.astype(np.object)
if device_option is None:
device_option = scope.CurrentDeviceScope()
if device_option and device_option.device_type == caffe2_pb2.CUDA:
if arr.dtype == np.dtype('float64'):
logger.warning(
"CUDA operators do not support 64-bit doubles, " +
"please use arr.astype(np.float32) or np.int32 for ints." +
" Blob: {}".format(name) + " type: {}".format(str(arr.dtype)))
name = StringifyBlobName(name)
if device_option is not None:
return C.feed_blob(name, arr, StringifyProto(device_option))
else:
return C.feed_blob(name, arr)
def FeedBlob(name, arr, device_option=None):
"""Feeds a blob into the workspace.
Inputs:
name: the name of the blob.
arr: either a TensorProto object or a numpy array object to be fed into
the workspace.
device_option (optional): the device option to feed the data with.
Returns:
True or False, stating whether the feed is successful.
"""
if type(arr) is caffe2_pb2.TensorProto:
arr = utils.Caffe2TensorToNumpyArray(arr)
if type(arr) is np.ndarray and arr.dtype.kind == 'S':
# Plain NumPy strings are weird, let's use objects instead
arr = arr.astype(np.object)
name = StringifyBlobName(name)
if device_option is not None:
return C.feed_blob(name, arr, StringfyProto(device_option))
elif scope.DEVICESCOPE is not None:
return C.feed_blob(name, arr, StringfyProto(scope.DEVICESCOPE))
else:
return C.feed_blob(name, arr)
# Create GPU device option
device_opts = caffe2_pb2.DeviceOption()
if use_gpu == 0:
device_opts.device_type = caffe2_pb2.CPU
print('Running on CPU')
else:
device_opts.device_type = caffe2_pb2.HIP
device_opts.hip_gpu_id = 0
print('Running on HIP')
if use_gpu == 2:
engine_list = ['MIOPEN', '']
C.set_global_engine_pref({caffe2_pb2.HIP : engine_list})
print('Using MIOPEN')
C.feed_blob('data', img, device_opts.SerializeToString())
init_def = caffe2_pb2.NetDef()
with open(INIT_NET, 'rb') as f:
init_def.ParseFromString(f.read())
init_def.device_option.CopyFrom(device_opts)
C.run_net_once(init_def.SerializeToString())
net_def = caffe2_pb2.NetDef()
with open(PREDICT_NET, 'rb') as f:
net_def.ParseFromString(f.read())
net_def.device_option.CopyFrom(device_opts)
C.create_net(net_def.SerializeToString())
C.feed_blob('data', img, device_opts.SerializeToString())
### Debug code
