def setUp(self):
def myhelper(model, val=-1):
return val
if not brew.has_helper(myhelper):
brew.Register(myhelper)
self.myhelper = myhelper
def myhelper2(model, val=-1):
return val
if not brew.has_helper(myhelper2):
brew.Register(myhelper2)
self.myhelper2 = myhelper2
def __init__(
self,
name: str,
cnn_parameters: CNNParameters,
) -> None:
"""
:param name: A unique name for this trainer used to create the data on the
caffe2 workspace
:param parameters: The set of training parameters
"""
if not CNN.registered:
brew.Register(conv_explicit_param_names) # type: ignore
CNN.registered = True
self.model_id = name
self.init_height = cnn_parameters.input_height
self.init_width = cnn_parameters.input_width
self.dims = cnn_parameters.conv_dims
self.conv_height_kernels = cnn_parameters.conv_height_kernels
self.conv_width_kernels = cnn_parameters.conv_width_kernels
self.pool_kernels_strides = cnn_parameters.pool_kernels_strides
self.pool_types = cnn_parameters.pool_types
self._validate_inputs()
self._setup_initial_blobs()
def fc_explicit_param_names(self):
brew.Register(fc_explicit_param_names)
model = model_helper.ModelHelper(name="test_model")
dim_in = 10
dim_out = 100
weight_name = "test_weight_name"
bias_name = "test_bias_name"
inputs_name = "test_inputs"
output_name = "test_output"
input_distribution = stats.norm()
inputs = input_distribution.rvs(size=(1, dim_in)).astype(np.float32)
workspace.FeedBlob(inputs_name, inputs)
weights = np.random.normal(size=(dim_out, dim_in)).astype(np.float32)
bias = np.transpose(
np.random.normal(size=(dim_out)).astype(np.float32))
brew.fc_explicit_param_names(
model,
inputs_name,
output_name,
dim_in=dim_in,
dim_out=dim_out,
bias_name=bias_name,
weight_name=weight_name,
weight_init=("GivenTensorFill", {
"values": weights
}),
bias_init=("GivenTensorFill", {
"values": bias
}),
)
workspace.RunNetOnce(model.param_init_net)
model.net.Proto().type = "async_scheduling"
workspace.CreateNet(model.net)
workspace.RunNet(model.net)
expected_output = np.dot(inputs, np.transpose(weights)) + bias
outputs_diff = expected_output - workspace.FetchBlob(output_name)
self.assertEqual(np.linalg.norm(outputs_diff), 0)
def __init__(self, name: str, parameters: TrainingParameters) -> None:
"""
:param name: A unique name for this trainer used to create the data on the
caffe2 workspace
:param parameters: The set of training parameters
"""
if not DNN.registered:
brew.Register(fc_explicit_param_names) # type: ignore
DNN.registered = True
self.model_id = name
self.layers = parameters.layers
self.activations = parameters.activations
self.dropout_ratio = parameters.dropout_ratio
self.skip_random_weight_init = parameters.warm_start_model_path is not None
self._validate_inputs()
self._setup_initial_blobs()
