def test_quantized_input_range_mat_mul(self):
shapes = [[3, 2], [2, 4]]
inputs = []
for i, shape in enumerate(shapes):
node = quantize_graph.create_node("PlaceholderV2", "input_%s" % i, [])
quantize_graph.set_attr_dtype(node, "dtype", dtypes.float32)
quantize_graph.set_attr_shape(node, "shape", shape)
inputs.append(node)
mat_mul_node = quantize_graph.create_node("MatMul", "mat_mul",
[n.name for n in inputs])
quantize_graph.set_attr_dtype(mat_mul_node, "T", dtypes.float32)
float_graph_def = graph_pb2.GraphDef()
float_graph_def.node.extend(inputs + [mat_mul_node])
input_map = {
inputs[0].name + ":0":
np.reshape([1, 2, 3, 4, 5, 6], shapes[0]),
inputs[1].name + ":0":
np.reshape([.8, .7, .6, .5, .4, .3, .2, .1], shapes[1])
}
self._RunTestsForQuantizedInputRange(float_graph_def, input_map,
[mat_mul_node.name], [-1, 20.])
self._RunTestsForQuantizedInputRange(float_graph_def, input_map,
[mat_mul_node.name], [0, 6.])
def test_quantized_input_range_bias_add(self):
input_shape = [1, 1, 2, 6]
input_n = quantize_graph.create_node("Placeholder", "input", [])
quantize_graph.set_attr_dtype(input_n, "dtype", dtypes.float32)
quantize_graph.set_attr_shape(input_n, "shape", input_shape)
offset_n = quantize_graph.create_constant_node(
"offset",
value=[1, 2, 3, 4, 5, 6],
dtype=dtypes.float32,
shape=[6])
bias_add_n = quantize_graph.create_node("BiasAdd", "bias_add",
[input_n.name, offset_n.name])
quantize_graph.set_attr_dtype(bias_add_n, "T", dtypes.float32)
float_graph_def = graph_pb2.GraphDef()
float_graph_def.node.extend([input_n, offset_n, bias_add_n])
input_map = {
input_n.name + ":0":
np.reshape([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], input_shape)
}
self._RunTestsForQuantizedInputRange(float_graph_def, input_map,
[bias_add_n.name], [-1, 20.])
self._RunTestsForQuantizedInputRange(float_graph_def, input_map,
[bias_add_n.name], [0, 12.])
def test_quantized_input_range_bias_add(self):
input_shape = [1, 1, 2, 6]
input_n = quantize_graph.create_node("PlaceholderV2", "input", [])
quantize_graph.set_attr_dtype(input_n, "dtype", dtypes.float32)
quantize_graph.set_attr_shape(input_n, "shape", input_shape)
offset_n = quantize_graph.create_constant_node(
"offset", value=[1, 2, 3, 4, 5, 6], dtype=dtypes.float32, shape=[6])
bias_add_n = quantize_graph.create_node("BiasAdd", "bias_add",
[input_n.name, offset_n.name])
quantize_graph.set_attr_dtype(bias_add_n, "T", dtypes.float32)
float_graph_def = graph_pb2.GraphDef()
float_graph_def.node.extend([input_n, offset_n, bias_add_n])
input_map = {
input_n.name + ":0":
np.reshape([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], input_shape)
}
self._RunTestsForQuantizedInputRange(float_graph_def, input_map,
[bias_add_n.name], [-1, 20.])
self._RunTestsForQuantizedInputRange(float_graph_def, input_map,
[bias_add_n.name], [0, 12.])