def test_Mean1D(self):
npr = np.divide(np.add(self.np_a, self.np_b), 2.)
dcr = dc.mean(dc.vectorTensorFloat([self.dc_a, self.dc_b]))
np.testing.assert_allclose(npr,
np.array(dcr.data()).astype(np.float32),
rtol=1e-3,
atol=1e-3)
def test_Max1D(self):
npr = np.maximum(self.np_a, self.np_b)
dcr = dc.max(dc.vectorTensorFloat([self.dc_a, self.dc_b]))
np.testing.assert_allclose(npr,
np.array(dcr.data()).astype(np.float32),
rtol=1e-3,
atol=1e-3)
def test_Mean2D(self):
np_a = np.reshape(self.np_a, (6, 4))
np_b = np.reshape(self.np_b, (6, 4))
dc_a = dc.reshape(self.dc_a, (6, 4))
dc_b = dc.reshape(self.dc_b, (6, 4))
npr = np.divide(np.add(np_a, np_b), 2.)
dcr = dc.mean(dc.vectorTensorFloat([dc_a, dc_b]))
np.testing.assert_allclose(npr.flatten(),
np.array(dcr.data()).astype(np.float32),
rtol=1e-3,
atol=1e-3)
def test_Max4D(self):
np_a = np.reshape(self.np_a, (2, 2, 3, 2))
np_b = np.reshape(self.np_b, (2, 2, 3, 2))
dc_a = dc.reshape(self.dc_a, (2, 2, 3, 2))
dc_b = dc.reshape(self.dc_b, (2, 2, 3, 2))
npr = np.maximum(np_a, np_b)
dcr = dc.max(dc.vectorTensorFloat([dc_a, dc_b]))
np.testing.assert_allclose(npr.flatten(),
np.array(dcr.data()).astype(np.float32),
rtol=1e-3,
atol=1e-3)
def test_Maxof4(self):
np_a = np.reshape(self.np_a, (5, 4))
np_b = np.reshape(self.np_b, (2, 5, 4))
np_c = np.reshape(self.np_c, (5, 4))
np_d = np.reshape(self.np_d, (2, 5, 4))
npr = np.maximum(np.maximum(np_a, np_b), np.maximum(np_c, np_d))
dc_a = dc.reshape(self.dc_a, (5, 4))
dc_b = dc.reshape(self.dc_b, (2, 5, 4))
dc_c = dc.reshape(self.dc_c, (5, 4))
dc_d = dc.reshape(self.dc_d, (2, 5, 4))
dcr = dc.max(dc.vectorTensorFloat([dc_a, dc_b, dc_c, dc_d]))
np.testing.assert_allclose(npr.flatten(),
np.array(dcr.data()).astype(np.float32),
rtol=1e-3,
atol=1e-3)
def addOPNode(self, node):
op_type = dnnc.getOpCode(node.op_type)
if (op_type is dnnc.opInvalid):
print("ERROR (ONNX):" + node.op_type +
" is not a valid graph-node op type.")
return None
dcNode = self._dcGraph.addOPNode(node.name, op_type)
for nd in node.input:
dcNode.addInput(nd)
for nd in node.output:
dcNode.addOutput(nd)
for attr in node.attribute:
attr_type = dnnc.IR_DataType_NOTYPE
attr_vals = []
attr_vec = None
if attr.type == onnx.AttributeProto.INT:
attr_type = dnnc.IR_DataType_INT32
attr_vals.append(attr.i)
attr_vec = dnnc.vectorInt(attr_vals)
elif attr.type == onnx.AttributeProto.INTS:
attr_type = dnnc.IR_DataType_INT32
for val in attr.ints:
attr_vals.append(int(val))
attr_vec = dnnc.vectorInt(attr_vals)
elif attr.type == onnx.AttributeProto.FLOAT:
attr_type = dnnc.IR_DataType_FLOAT
attr_vals.append(attr.f)
attr_vec = dnnc.vectorFloat(attr_vals)
elif attr.type == onnx.AttributeProto.FLOATS:
attr_type = dnnc.IR_DataType_FLOAT
for val in attr.floats:
attr_vals.append(float(val))
attr_vec = dnnc.vectorFloat(attr_vals)
elif attr.type == onnx.AttributeProto.STRING:
attr_type = dnnc.IR_DataType_STRING
attr_vals.append(str(attr.s))
attr_vec = dnnc.vectorStr(attr_vals)
elif attr.type == onnx.AttributeProto.STRINGS:
attr_type = dnnc.IR_DataType_STRING
for val in attr.strings:
attr_vals.append(str(val))
attr_vec = dnnc.vectorStr(attr_vals)
elif attr.type == onnx.AttributeProto.TENSOR:
if (attr.t.data_type == onnx.TensorProto.INT8
or attr.t.data_type == onnx.TensorProto.INT16
or attr.t.data_type == onnx.TensorProto.INT32
or attr.t.data_type == onnx.TensorProto.INT64):
attr_type = attr.t.data_type
attr_data = None
pack_format = 'P'
if (attr.t.data_type == onnx.TensorProto.INT8):
pack_format = 'b'
if (attr.t.data_type == onnx.TensorProto.INT16):
pack_format = 'h'
if (attr.t.data_type == onnx.TensorProto.INT32):
if (attr.t.int32_data):
attr_data = attr.t.int32_data
pack_format = 'i'
if (attr.t.data_type == onnx.TensorProto.INT64):
if (attr.t.int64_data):
attr_data = attr.t.int64_data
pack_format = 'q'
if (attr_data is None):
len = 1
for d in attr.t.dims:
len *= d
attr_data = struct.unpack(pack_format * len,
attr.t.raw_data)
if (attr_data is not None):
attr_tensor = dnnc.intTensor(attr.t.dims, attr.name)
attr_tensor.load(attr_data)
attr_vec = dnnc.vectorTensorInt()
attr_vec.push_back(attr_tensor)
else:
print("ERROR (ONNX): could not extract data for graph-node " + \
node.name + "\'s attribute " + attr.name + ".\n")
elif (attr.t.data_type == onnx.TensorProto.FLOAT16
or attr.t.data_type == onnx.TensorProto.FLOAT
or attr.t.data_type == onnx.TensorProto.DOUBLE):
attr_type = attr.t.data_type
attr_data = None
pack_format = 'P'
if (attr.t.data_type == onnx.TensorProto.FLOAT16):
if (attr.t.float_data):
attr_data = attr.t.float_data
pack_format = 'e'
if (attr.t.data_type == onnx.TensorProto.FLOAT):
if (attr.t.float_data):
attr_data = attr.t.float_data
pack_format = 'f'
if (attr.t.data_type == onnx.TensorProto.DOUBLE):
if (attr.t.double_data):
attr_data = attr.t.double_data
pack_format = 'd'
if (attr_data is None):
len = 1
for d in attr.t.dims:
len *= d
attr_data = struct.unpack(pack_format * len,
attr.t.raw_data)
if (attr_data is not None):
attr_tensor = dnnc.floatTensor(attr.t.dims, attr.name)
attr_tensor.load(attr_data)
attr_vec = dnnc.vectorTensorFloat()
attr_vec.push_back(attr_tensor)
else:
print("ERROR (ONNX): could not extract data for graph-node " + \
node.name + "\'s attribute " + attr.name + ".\n")
else:
print("ERROR (ONNX): attribute tensor's datatype " +
str(attr.t.data_type) + " isn't understood.")
elif attr.type == onnx.AttributeProto.TENSORS:
attr_type = dnnc.IR_DataType_TENSORS
attr_vals.append(attr.tensors)
attr_vec = dnnc.vectorTensorFloat(dnnc.floatTensor(attr_vals))
elif attr.type == onnx.AttributeProto.GRAPH:
attr_type = dnnc.IR_DataType_GRAPH
attr_vals.append(attr.g)
print(
"ERROR (ONNX): sub-graph in graph-node is not yet supported."
)
elif attr.type == onnx.AttributeProto.GRAPHS:
attr_type = dnnc.IR_DataType_GRAPH
attr_vals.append(attr.graphs)
print(
"ERROR (ONNX): sub-graph in graph-node is not yet supported."
)
else:
print("ERROR (ONNX): graph-node " + node.name + "\'s attribute " + \
attr.name + " type " + str(attr.type) + " is not valid.")
continue
if (attr_type is dnnc.IR_DataType_NOTYPE or attr_vec is None
or attr_vec.size() == 0):
print("ERROR (ONNX): graph-node " + node.name + "\'s attribute " + \
attr.name + " has no data.")
continue
attr_code = dnnc.getAttrName(attr.name)
if (attr_code is dnnc.attr_invalid):
print("WARN (ONNX): " + attr.name +
" is not a valid graph-node attribute.")
print(" operator " + node.op_type +
" will be added without this attribute.")
cAttrData = dnnc.irTypeData(attr_type, attr_vec)
cAttr = dnnc.nodeAttribute(attr_code, cAttrData)
dcNode.addAttribute(cAttr)
return dcNode
