def test_multiple_outputs(self): input_constant_name = "input_constant" split_constant_name = "split_constant" split_name = "split" concat_constant_name = "concat_constant" concat_name = "concat" float_graph_def = graph_pb2.GraphDef() input_constant = quantize_graph.create_constant_node( input_constant_name, value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtypes.float32, shape=[2, 6]) float_graph_def.node.extend([input_constant]) split_constant = quantize_graph.create_constant_node( split_constant_name, value=1, dtype=dtypes.int32, shape=[]) float_graph_def.node.extend([split_constant]) split_node = quantize_graph.create_node( "Split", split_name, [split_constant_name, input_constant_name]) quantize_graph.set_attr_int(split_node, "num_split", 2) quantize_graph.set_attr_dtype(split_node, "T", dtypes.float32) float_graph_def.node.extend([split_node]) concat_constant = quantize_graph.create_constant_node( concat_constant_name, value=1, dtype=dtypes.int32, shape=[]) float_graph_def.node.extend([concat_constant]) concat_node = quantize_graph.create_node( "Concat", concat_name, [concat_constant_name, split_name + ":0", split_name + ":1"]) quantize_graph.set_attr_int(concat_node, "N", 2) quantize_graph.set_attr_dtype(concat_node, "T", dtypes.float32) float_graph_def.node.extend([concat_node]) graph_test(float_graph_def, {}, [concat_name])
def conv_test(depth, image_width, image_height, image_batch_count, filter_size, filter_count, stride, padding, input_values, filter_values): """Tests a Conv replacement.""" input_constant_name = "input_constant" filter_constant_name = "filter_constant" conv_name = "conv" float_graph_def = graph_pb2.GraphDef() input_constant = quantize_graph.create_constant_node( input_constant_name, value=input_values, dtype=dtypes.float32, shape=[image_batch_count, image_height, image_width, depth]) float_graph_def.node.extend([input_constant]) filter_constant = quantize_graph.create_constant_node( filter_constant_name, value=filter_values, dtype=dtypes.float32, shape=[filter_size, filter_size, depth, filter_count]) float_graph_def.node.extend([filter_constant]) conv_node = quantize_graph.create_node( "Conv2D", conv_name, [input_constant_name, filter_constant_name]) quantize_graph.set_attr_dtype(conv_node, "T", dtypes.float32) quantize_graph.set_attr_int_list(conv_node, "strides", [1, stride, stride, 1]) quantize_graph.set_attr_string(conv_node, "padding", padding) float_graph_def.node.extend([conv_node]) graph_test(float_graph_def, {}, [conv_name])
def mat_mul_test(m, n, k, a, b): """Tests a MatMul replacement.""" a_constant_name = "a_constant" b_constant_name = "b_constant" mat_mul_name = "mat_mul" float_graph_def = graph_pb2.GraphDef() a_constant = quantize_graph.create_constant_node(a_constant_name, value=a, dtype=dtypes.float32, shape=[m, k]) float_graph_def.node.extend([a_constant]) b_constant = quantize_graph.create_constant_node(b_constant_name, value=b, dtype=dtypes.float32, shape=[k, n]) float_graph_def.node.extend([b_constant]) mat_mul_node = quantize_graph.create_node( "MatMul", mat_mul_name, [a_constant_name, b_constant_name]) quantize_graph.set_attr_dtype(mat_mul_node, "T", dtypes.float32) quantize_graph.set_attr_bool(mat_mul_node, "transpose_a", False) quantize_graph.set_attr_bool(mat_mul_node, "transpose_b", False) float_graph_def.node.extend([mat_mul_node]) graph_test(float_graph_def, {}, [mat_mul_name])
def test_reshape(self): """Tests that MatMul->Reshape->MatMul avoids extra quantize/dequantize.""" def make_matmul(name, a, b): n = quantize_graph.create_node("MatMul", name, [a.name, b.name]) quantize_graph.set_attr_dtype(n, "T", dtypes.float32) quantize_graph.set_attr_bool(n, "transpose_a", False) quantize_graph.set_attr_bool(n, "transpose_b", False) return n # matmul_1 = input*weight_1 input_node = quantize_graph.create_constant_node("input", value=[0, 1, 2, 3], dtype=dtypes.float32, shape=[4, 1]) weight_1_node = quantize_graph.create_constant_node( "weight_1", value=[.5, .6, .7, .8, .9], dtype=dtypes.float32, shape=[1, 5]) matmul_1_node = make_matmul("matmul_1", input_node, weight_1_node) # Reshape 4x5 to 10x2. new_shape_node = quantize_graph.create_constant_node( "new_shape_node", value=[10, 2], dtype=dtypes.int32, shape=[2]) reshape_node = quantize_graph.create_node( "Reshape", "reshape", [matmul_1_node.name, new_shape_node.name]) quantize_graph.set_attr_dtype(reshape_node, "T", dtypes.float32) # matmul_2_node = reshape*weight_2 weight_2_node = quantize_graph.create_constant_node( "weight_2", value=[1.5, 2.5], dtype=dtypes.float32, shape=[2, 1]) matmul_2_node = make_matmul("matmul_2", reshape_node, weight_2_node) g = graph_pb2.GraphDef() g.node.extend([ input_node, weight_1_node, matmul_1_node, new_shape_node, reshape_node, weight_2_node, matmul_2_node ]) # Test the graph graph_test(g, {}, ["matmul_2"]) # Verify there is only one Quantize and one Requantize op. eightbit_rewriter = quantize_graph.GraphRewriter( g, "eightbit", quantized_input_range=None) eightbit_graph_def = eightbit_rewriter.rewrite(["matmul_2"]) ops = [node.op for node in eightbit_graph_def.node] # No quantize since all inputs are const and can be quantized up-front. self.assertEqual(0, ops.count("QuantizeV2") + ops.count("Quantize")) self.assertEqual(1, ops.count("QuantizedReshape")) # One dequantize at the end. self.assertEqual(1, ops.count("Dequantize"))
def test_bias_add_w_fake_quant_w_min_max_vars(self): input_node = quantize_graph.create_constant_node( "input", value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=dtypes.float32, shape=[1, 1, 2, 5]) offset_node = quantize_graph.create_constant_node( "offset", value=[1, 2, 3, 4, 5], dtype=dtypes.float32, shape=[5]) bias_add_node = quantize_graph.create_node( "BiasAdd", "bias_add", [input_node.name, offset_node.name]) quantize_graph.set_attr_dtype(bias_add_node, "T", dtypes.float32) min_node = quantize_graph.create_constant_node("min_bias_add", value=-.5, dtype=dtypes.float32, shape=[]) max_node = quantize_graph.create_constant_node("max_bias_add", value=15.5, dtype=dtypes.float32, shape=[]) fake_quant_node = quantize_graph.create_node( "FakeQuantWithMinMaxVars", "fake_quant", [bias_add_node.name, min_node.name, max_node.name]) float_graph_def = graph_pb2.GraphDef() float_graph_def.node.extend([ input_node, offset_node, bias_add_node, min_node, max_node, fake_quant_node ]) graph_test(float_graph_def, {}, [fake_quant_node.name], log_graph=True) # Verify there is only one Quantize and one Requantize op. # Pass in fallback_quantization_range, although it will have no effect # because the FakeQuantWithMinMaxVars are used instead. eightbit_rewriter = quantize_graph.GraphRewriter( float_graph_def, "eightbit", quantized_input_range=None, fallback_quantization_range=[-100, 100]) eightbit_graph_def = eightbit_rewriter.rewrite([fake_quant_node.name]) ops = [node.op for node in eightbit_graph_def.node] node_names = [node.name for node in eightbit_graph_def.node] # No quantize since all inputs are const and can be quantized up-front. self.assertEqual(0, ops.count("QuantizeV2") + ops.count("Quantize")) # One dequantize at the end. self.assertEqual(1, ops.count("Dequantize")) # The fallback constants are not in the graph. self.assertEqual(0, node_names.count("fallback_quantization_min_value")) self.assertEqual(0, node_names.count("fallback_quantization_max_value"))
def test_negative_const_problem(self): shape_constant_name = "shape_constant" shape_constant = quantize_graph.create_constant_node( shape_constant_name, value=-0.8, dtype=dtypes.float32, shape=[1]) quantization_result = quantize_graph.quantize_weight_eightbit( shape_constant, b"MIN_COMBINED") self.assertEqual(4, len(quantization_result))
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_batch_norm(self): input_constant_name = "input_constant" mean_constant_name = "mean_constant" variance_constant_name = "variance_constant" beta_constant_name = "beta_constant" gamma_constant_name = "gamma_constant" batch_norm_name = "batch_norm" float_graph_def = graph_pb2.GraphDef() input_constant = quantize_graph.create_constant_node( input_constant_name, value=[1, 4, 2, 5, 3, 6, -1, -4, -2, -5, -3, -6], dtype=dtypes.float32, shape=[1, 1, 6, 2]) float_graph_def.node.extend([input_constant]) mean_constant = quantize_graph.create_constant_node( mean_constant_name, value=[10, 20], dtype=dtypes.float32, shape=[2]) float_graph_def.node.extend([mean_constant]) variance_constant = quantize_graph.create_constant_node( variance_constant_name, value=[0.25, 0.5], dtype=dtypes.float32, shape=[2]) float_graph_def.node.extend([variance_constant]) beta_constant = quantize_graph.create_constant_node( beta_constant_name, value=[0.1, 0.6], dtype=dtypes.float32, shape=[2]) float_graph_def.node.extend([beta_constant]) gamma_constant = quantize_graph.create_constant_node( gamma_constant_name, value=[0, 0], dtype=dtypes.float32, shape=[2]) float_graph_def.node.extend([gamma_constant]) batch_norm_node = quantize_graph.create_node( "BatchNormWithGlobalNormalization", batch_norm_name, [ input_constant_name, mean_constant_name, variance_constant_name, beta_constant_name, gamma_constant_name ]) quantize_graph.set_attr_dtype(batch_norm_node, "T", dtypes.float32) quantize_graph.set_attr_bool(batch_norm_node, "scale_after_normalization", False) quantize_graph.set_attr_float(batch_norm_node, "variance_epsilon", 0.001) float_graph_def.node.extend([batch_norm_node]) graph_test(float_graph_def, {}, [batch_norm_name])
def test_non_float_reshape(self): a = quantize_graph.create_constant_node( "a", value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtypes.int32, shape=[2, 2, 3]) shape = quantize_graph.create_constant_node("shape", value=[12], dtype=dtypes.int32, shape=[1]) reshape = quantize_graph.create_node("Reshape", "reshape", [a.name, shape.name]) quantize_graph.set_attr_dtype(reshape, "T", dtypes.int32) g = graph_pb2.GraphDef() g.node.extend([a, shape, reshape]) graph_test(g, {}, [reshape.name])
def test_bias_add_w_fallback_min_max_vars(self): input_node = quantize_graph.create_constant_node( "input", value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=dtypes.float32, shape=[1, 1, 2, 5]) offset_node = quantize_graph.create_constant_node( "offset", value=[1, 2, 3, 4, 5], dtype=dtypes.float32, shape=[5]) bias_add_node = quantize_graph.create_node( "BiasAdd", "bias_add", [input_node.name, offset_node.name]) quantize_graph.set_attr_dtype(bias_add_node, "T", dtypes.float32) float_graph_def = graph_pb2.GraphDef() float_graph_def.node.extend([input_node, offset_node, bias_add_node]) graph_test(float_graph_def, {}, [bias_add_node.name], log_graph=True) # Verify there is only one Quantize, one Requantize op, and no # RequantizationRange op. eightbit_rewriter = quantize_graph.GraphRewriter( float_graph_def, "eightbit", quantized_input_range=None, fallback_quantization_range=[-.5, 15.5]) eightbit_graph_def = eightbit_rewriter.rewrite([bias_add_node.name]) ops = [node.op for node in eightbit_graph_def.node] node_names = [node.name for node in eightbit_graph_def.node] # No quantize since all inputs are const and can be quantized up-front. self.assertEqual(0, ops.count("QuantizeV2") + ops.count("Quantize")) # One dequantize at the end. self.assertEqual(1, ops.count("Dequantize")) # No RequantizationRange self.assertEqual(0, ops.count("RequantizationRange")) # The fallback constants are in the graph. self.assertEqual(1, node_names.count("fallback_quantization_min_value")) self.assertEqual(1, node_names.count("fallback_quantization_max_value"))
def test_relu_w_fake_quant_w_min_max_vars(self): input_node = quantize_graph.create_constant_node( "input", value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtypes.float32, shape=[1, 2, 6, 1]) relu_node = quantize_graph.create_node("Relu", "relu", [input_node.name]) quantize_graph.set_attr_dtype(relu_node, "T", dtypes.float32) min_node = quantize_graph.create_constant_node("min_bias_add", value=0, dtype=dtypes.float32, shape=[]) max_node = quantize_graph.create_constant_node("max_bias_add", value=12, dtype=dtypes.float32, shape=[]) fake_quant_node = quantize_graph.create_node( "FakeQuantWithMinMaxVars", "fake_quant", [relu_node.name, min_node.name, max_node.name]) float_graph_def = graph_pb2.GraphDef() float_graph_def.node.extend( [input_node, relu_node, min_node, max_node, fake_quant_node]) graph_test(float_graph_def, {}, [fake_quant_node.name], log_graph=True) # Verify there is only one Quantize and one Requantize op. eightbit_rewriter = quantize_graph.GraphRewriter( float_graph_def, "eightbit", quantized_input_range=None) eightbit_graph_def = eightbit_rewriter.rewrite([fake_quant_node.name]) ops = [node.op for node in eightbit_graph_def.node] # No quantize since all inputs are const and can be quantized up-front. self.assertEqual(0, ops.count("QuantizeV2") + ops.count("Quantize")) # One dequantize at the end. self.assertEqual(1, ops.count("Dequantize"))
def test_concat(self): shape_constant_name = "shape_constant" a_constant_name = "a_constant" b_constant_name = "b_constant" concat_name = "concat" float_graph_def = graph_pb2.GraphDef() shape_constant = quantize_graph.create_constant_node( shape_constant_name, value=0, dtype=dtypes.int32, shape=[]) float_graph_def.node.extend([shape_constant]) a_constant = quantize_graph.create_constant_node( a_constant_name, value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtypes.float32, shape=[2, 2, 3]) float_graph_def.node.extend([a_constant]) b_constant = quantize_graph.create_constant_node( b_constant_name, value=[13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], dtype=dtypes.float32, shape=[2, 2, 3]) float_graph_def.node.extend([b_constant]) concat_node = quantize_graph.create_node( "Concat", concat_name, [shape_constant_name, a_constant_name, b_constant_name]) quantize_graph.set_attr_int(concat_node, "N", 2) quantize_graph.set_attr_dtype(concat_node, "T", dtypes.float32) float_graph_def.node.extend([concat_node]) graph_test(float_graph_def, {}, [concat_name]) # Verify the concat is quantized. eightbit_rewriter = quantize_graph.GraphRewriter( float_graph_def, "eightbit", quantized_input_range=None) eightbit_graph_def = eightbit_rewriter.rewrite([concat_name]) ops = [node.op for node in eightbit_graph_def.node] self.assertEqual(1, ops.count("QuantizedConcat"))
def test_bias_add(self): input_constant_name = "input_constant" offset_constant_name = "offset_constant" bias_add_name = "bias_add" float_graph_def = graph_pb2.GraphDef() input_constant = quantize_graph.create_constant_node( input_constant_name, value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtypes.float32, shape=[1, 1, 2, 6]) float_graph_def.node.extend([input_constant]) offset_constant = quantize_graph.create_constant_node( offset_constant_name, value=[1, 2, 3, 4, 5, 6], dtype=dtypes.float32, shape=[6]) float_graph_def.node.extend([offset_constant]) bias_add_node = quantize_graph.create_node( "BiasAdd", bias_add_name, [input_constant_name, offset_constant_name]) quantize_graph.set_attr_dtype(bias_add_node, "T", dtypes.float32) float_graph_def.node.extend([bias_add_node]) graph_test(float_graph_def, {}, [bias_add_name])
def test_non_float_concat(self): concat_dim = quantize_graph.create_constant_node("concat_dim", value=0, dtype=dtypes.int32, shape=[]) a = quantize_graph.create_constant_node( "a", value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtypes.int32, shape=[2, 2, 3]) b = quantize_graph.create_constant_node( "b", value=[13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], dtype=dtypes.int32, shape=[2, 2, 3]) concat = quantize_graph.create_node("Concat", "concat", [concat_dim.name, a.name, b.name]) quantize_graph.set_attr_int(concat, "N", 2) quantize_graph.set_attr_dtype(concat, "T", dtypes.int32) g = graph_pb2.GraphDef() g.node.extend([concat_dim, a, b, concat]) graph_test(g, {}, [concat.name])
def test_relu6(self): input_constant_name = "input_constant" relu6_name = "relu6" float_graph_def = graph_pb2.GraphDef() input_constant = quantize_graph.create_constant_node( input_constant_name, value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtypes.float32, shape=[1, 2, 6, 1]) float_graph_def.node.extend([input_constant]) relu6_node = quantize_graph.create_node("Relu6", relu6_name, [input_constant_name]) quantize_graph.set_attr_dtype(relu6_node, "T", dtypes.float32) float_graph_def.node.extend([relu6_node]) graph_test(float_graph_def, {}, [relu6_name])
def test_max_pool(self): input_constant_name = "input_constant" max_pool_name = "max_pool" float_graph_def = graph_pb2.GraphDef() input_constant = quantize_graph.create_constant_node( input_constant_name, value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtypes.float32, shape=[1, 2, 6, 1]) float_graph_def.node.extend([input_constant]) max_pool_node = quantize_graph.create_node("MaxPool", max_pool_name, [input_constant_name]) quantize_graph.set_attr_int_list(max_pool_node, "ksize", [1, 2, 2, 1]) quantize_graph.set_attr_int_list(max_pool_node, "strides", [1, 1, 1, 1]) quantize_graph.set_attr_string(max_pool_node, "padding", b"SAME") float_graph_def.node.extend([max_pool_node]) graph_test(float_graph_def, {}, [max_pool_name])
def test_identity(self): input_constant_name = "input_constant" identity_name = "identity" float_graph_def = graph_pb2.GraphDef() input_constant = quantize_graph.create_constant_node( input_constant_name, value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtypes.float32, shape=[2, 6]) float_graph_def.node.extend([input_constant]) identity_node = quantize_graph.create_node("Identity", identity_name, [input_constant_name]) quantize_graph.set_attr_dtype(identity_node, "T", dtypes.float32) float_graph_def.node.extend([identity_node]) mul_name = "mul" mul_node = quantize_graph.create_node("Mul", mul_name, [identity_name, identity_name]) quantize_graph.set_attr_dtype(mul_node, "T", dtypes.float32) float_graph_def.node.extend([mul_node]) graph_test(float_graph_def, {}, [mul_name])
def test_remove_redundant_quantization(self): a_constant_name = "a_constant" a_constant_min_name = "a_constant_min" a_constant_max_name = "a_constant_max" a_dequantize_name = "a_dequantize" a_quantize_name = "a_quantize" b_constant_name = "b_constant" b_constant_min_name = "b_constant_min" b_constant_max_name = "b_constant_max" b_dequantize_name = "b_dequantize" b_quantize_name = "b_quantize" mat_mul_name = "mat_mul" graph_def = graph_pb2.GraphDef() a_constant = quantize_graph.create_constant_node(a_constant_name, value=(0, ), dtype=dtypes.quint8, shape=[]) graph_def.node.extend([a_constant]) a_constant_min = quantize_graph.create_constant_node( a_constant_min_name, value=2, dtype=dtypes.float32, shape=[]) graph_def.node.extend([a_constant_min]) a_constant_max = quantize_graph.create_constant_node( a_constant_max_name, value=2, dtype=dtypes.float32, shape=[]) graph_def.node.extend([a_constant_max]) a_dequantize_node = quantize_graph.create_node( "Dequantize", a_dequantize_name, [a_constant_name, a_constant_min_name, a_constant_max_name]) quantize_graph.set_attr_dtype(a_dequantize_node, "T", dtypes.uint8) graph_def.node.extend([a_dequantize_node]) a_quantize_node = quantize_graph.create_node( "QuantizeV2", a_quantize_name, [ a_dequantize_name, a_dequantize_name + ":1", a_dequantize_name + ":2" ]) quantize_graph.set_attr_dtype(a_quantize_node, "T", dtypes.uint8) graph_def.node.extend([a_quantize_node]) b_constant = quantize_graph.create_constant_node(b_constant_name, value=(0, ), dtype=dtypes.quint8, shape=[]) graph_def.node.extend([b_constant]) b_constant_min = quantize_graph.create_constant_node( b_constant_min_name, value=3, dtype=dtypes.float32, shape=[]) graph_def.node.extend([b_constant_min]) b_constant_max = quantize_graph.create_constant_node( b_constant_max_name, value=3, dtype=dtypes.float32, shape=[]) graph_def.node.extend([b_constant_max]) b_dequantize_node = quantize_graph.create_node( "Dequantize", b_dequantize_name, [b_constant_name, b_constant_min_name, b_constant_max_name]) quantize_graph.set_attr_dtype(b_dequantize_node, "T", dtypes.uint8) graph_def.node.extend([b_dequantize_node]) b_quantize_node = quantize_graph.create_node( "QuantizeV2", b_quantize_name, [ b_dequantize_name, b_dequantize_name + ":1", b_dequantize_name + ":2" ]) quantize_graph.set_attr_dtype(b_quantize_node, "T", dtypes.uint8) graph_def.node.extend([b_quantize_node]) mat_mul_node = quantize_graph.create_node( "QuantizedMatMul", mat_mul_name, [ a_quantize_name, b_quantize_name, a_quantize_name + ":1", a_quantize_name + ":2", b_quantize_name + ":1", b_quantize_name + ":2" ]) quantize_graph.set_attr_dtype(mat_mul_node, "T1", dtypes.uint8) quantize_graph.set_attr_dtype(mat_mul_node, "T2", dtypes.int32) graph_def.node.extend([mat_mul_node]) expected_output = graph_pb2.GraphDef() a_constant = quantize_graph.create_constant_node(a_constant_name, value=(0, ), dtype=dtypes.quint8, shape=[]) expected_output.node.extend([a_constant]) a_constant_min = quantize_graph.create_constant_node( a_constant_min_name, value=2, dtype=dtypes.float32, shape=[]) expected_output.node.extend([a_constant_min]) a_constant_max = quantize_graph.create_constant_node( a_constant_max_name, value=2, dtype=dtypes.float32, shape=[]) expected_output.node.extend([a_constant_max]) b_constant = quantize_graph.create_constant_node(b_constant_name, value=(0, ), dtype=dtypes.quint8, shape=[]) expected_output.node.extend([b_constant]) b_constant_min = quantize_graph.create_constant_node( b_constant_min_name, value=3, dtype=dtypes.float32, shape=[]) expected_output.node.extend([b_constant_min]) b_constant_max = quantize_graph.create_constant_node( b_constant_max_name, value=3, dtype=dtypes.float32, shape=[]) expected_output.node.extend([b_constant_max]) mat_mul_node = quantize_graph.create_node( "QuantizedMatMul", mat_mul_name, [ a_constant_name, b_constant_name, a_constant_min_name, a_constant_max_name, b_constant_min_name, b_constant_max_name ]) quantize_graph.set_attr_dtype(mat_mul_node, "T1", dtypes.uint8) quantize_graph.set_attr_dtype(mat_mul_node, "T2", dtypes.int32) expected_output.node.extend([mat_mul_node]) expected_output.versions.CopyFrom(graph_def.versions) expected_output.library.CopyFrom(graph_def.library) rewriter = quantize_graph.GraphRewriter(graph_def, [mat_mul_name], quantized_input_range=None) output = rewriter.remove_redundant_quantization(graph_def) stripped_output = graph_util.extract_sub_graph(output, [mat_mul_name]) self.assertProtoEquals(expected_output, stripped_output)
def test_keep_control_edges(self): no_op_name = "no_op" a_constant_name = "a_constant" b_constant_name = "b_constant" a_check_name = "a_check" b_check_name = "b_check" a_identity_name = "a_identity" b_identity_name = "b_identity" add_name = "add" graph_def = graph_pb2.GraphDef() no_op = quantize_graph.create_node("NoOp", no_op_name, []) graph_def.node.extend([no_op]) a_constant = quantize_graph.create_constant_node(a_constant_name, value=1, dtype=dtypes.float32, shape=[]) graph_def.node.extend([a_constant]) a_check_node = quantize_graph.create_node("CheckNumerics", a_check_name, [a_constant_name]) graph_def.node.extend([a_check_node]) a_identity_node = quantize_graph.create_node( "Identity", a_identity_name, [a_constant_name, "^" + a_check_name, "^" + no_op_name]) graph_def.node.extend([a_identity_node]) b_constant = quantize_graph.create_constant_node(b_constant_name, value=1, dtype=dtypes.float32, shape=[]) graph_def.node.extend([b_constant]) b_check_node = quantize_graph.create_node("CheckNumerics", b_check_name, [b_constant_name]) graph_def.node.extend([b_check_node]) b_identity_node = quantize_graph.create_node( "Identity", b_identity_name, [b_constant_name, "^" + b_check_name]) graph_def.node.extend([b_identity_node]) add_node = quantize_graph.create_node( "Add", add_name, [a_identity_name, b_identity_name]) quantize_graph.set_attr_dtype(add_node, "T", dtypes.float32) graph_def.node.extend([add_node]) expected_output = graph_pb2.GraphDef() no_op = quantize_graph.create_node("NoOp", no_op_name, []) expected_output.node.extend([no_op]) a_constant = quantize_graph.create_constant_node(a_constant_name, value=1, dtype=dtypes.float32, shape=[]) expected_output.node.extend([a_constant]) a_identity_node = quantize_graph.create_node( "Identity", a_identity_name, [a_constant_name, "^" + no_op_name]) expected_output.node.extend([a_identity_node]) b_constant = quantize_graph.create_constant_node(b_constant_name, value=1, dtype=dtypes.float32, shape=[]) expected_output.node.extend([b_constant]) add_node = quantize_graph.create_node( "Add", add_name, [a_identity_name, b_constant_name]) quantize_graph.set_attr_dtype(add_node, "T", dtypes.float32) expected_output.node.extend([add_node]) expected_output.versions.CopyFrom(graph_def.versions) expected_output.library.CopyFrom(graph_def.library) output = graph_util.remove_training_nodes(graph_def) stripped_output = graph_util.extract_sub_graph(output, [add_name]) self.assertProtoEquals(expected_output, stripped_output)