def test_conv(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 = tf.GraphDef() input_constant = quantize_graph.create_constant_node( input_constant_name, value=input_values, dtype=tf.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=tf.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", tf.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]) test_graph(float_graph_def, {}, [conv_name])
def test_conv(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 = tf.GraphDef() input_constant = quantize_graph.create_constant_node( input_constant_name, value=input_values, dtype=tf.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=tf.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", tf.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]) test_graph(float_graph_def, {}, [conv_name])
def test_max_pool(self): input_constant_name = "input_constant" max_pool_name = "max_pool" float_graph_def = tf.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=tf.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]) test_graph(float_graph_def, {}, [max_pool_name])
def test_max_pool(self): input_constant_name = "input_constant" max_pool_name = "max_pool" float_graph_def = tf.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=tf.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]) test_graph(float_graph_def, {}, [max_pool_name])