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integration_tests/model_visualization_test.py
802 строки
30 KB
Samuel Marks
[*.py] Use f-strings and `os.path.join` throughout (#21343)
21 авг 2025, 21:36
Не верифицирован
21 авг 2025, 21:36
bf85450
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import re import keras from keras.src import testing from keras.src.utils import model_to_dot from keras.src.utils import plot_model class SubclassModel(keras.models.Model): def __init__(self, name): super().__init__(name=name) def call(self, x): return x def parse_text_from_html(html): pattern = r"<font[^>]*>(.*?)</font>" matches = re.findall(pattern, html) for match in matches: clean_text = re.sub(r"<[^>]*>", "", match) return clean_text return "" def get_node_text(node): attributes = node.get_attributes() if "label" in attributes: html = node.get_attributes()["label"] return parse_text_from_html(html) else: return None def get_edge_dict(dot): def get_node_dict(graph, path=""): nodes = { node.get_name(): path + get_node_text(node) for node in graph.get_nodes() if node.get_name() != "node" # Dummy node inserted by pydot? } for subgraph in graph.get_subgraphs(): sub_nodes = get_node_dict( subgraph, path=f"{path}{subgraph.get_label()} > " ) nodes.update(sub_nodes) return nodes node_dict = get_node_dict(dot) def get_edges(graph): edges = list(graph.get_edges()) for subgraph in graph.get_subgraphs(): edges.extend(get_edges(subgraph)) return edges edge_dict = dict() dangling_edges = [] for edge in get_edges(dot): source_node = node_dict.get(edge.get_source(), None) destination_node = node_dict.get(edge.get_destination(), None) if source_node is None or destination_node is None: dangling_edges.append( f"from '{source_node}'/'{edge.get_source()}' " f"to '{destination_node}'/'{edge.get_destination()}'" ) if source_node in edge_dict: destination_nodes = edge_dict[source_node] if not isinstance(destination_nodes, set): destination_nodes = set([destination_nodes]) edge_dict[source_node] = destination_nodes destination_nodes.add(destination_node) else: edge_dict[source_node] = destination_node if dangling_edges: raise ValueError(f"Dangling edges found: {dangling_edges}") return edge_dict class ModelVisualizationTest(testing.TestCase): def multi_plot_model(self, model, name, expand_nested=False): if expand_nested: name = f"{name}-expand_nested" TEST_CASES = [ {}, { "show_shapes": True, }, { "show_shapes": True, "show_dtype": True, }, { "show_shapes": True, "show_dtype": True, "show_layer_names": True, }, { "show_shapes": True, "show_dtype": True, "show_layer_names": True, "show_layer_activations": True, }, { "show_shapes": True, "show_dtype": True, "show_layer_names": True, "show_layer_activations": True, "show_trainable": True, }, { "show_shapes": True, "show_dtype": True, "show_layer_names": True, "show_layer_activations": True, "show_trainable": True, "rankdir": "LR", }, { "show_layer_activations": True, "show_trainable": True, }, ] for test_case in TEST_CASES: tags = [v if k == "rankdir" else k for k, v in test_case.items()] file_name = f"{'-'.join([name] + tags)}.png" plot_model( model, file_name, expand_nested=expand_nested, **test_case ) self.assertFileExists(file_name) def test_plot_sequential_model(self): model = keras.Sequential( [ keras.Input((3,), name="input"), keras.layers.Dense(4, activation="relu", name="dense"), keras.layers.Dense(1, activation="sigmoid", name="dense_1"), ] ) edge_dict = get_edge_dict(model_to_dot(model)) self.assertEqual( edge_dict, { "dense (Dense)": "dense_1 (Dense)", }, ) self.multi_plot_model(model, "sequential") def test_plot_functional_model(self): inputs = keras.Input((3,), name="input") x = keras.layers.Dense( 4, activation="relu", trainable=False, name="dense" )(inputs) residual = x x = keras.layers.Dense(4, activation="relu", name="dense_1")(x) x = keras.layers.Dense(4, activation="relu", name="dense_2")(x) x = keras.layers.Dense(4, activation="relu", name="dense_3")(x) x += residual residual = x x = keras.layers.Dense(4, activation="relu", name="dense_4")(x) x = keras.layers.Dense(4, activation="relu", name="dense_5")(x) x = keras.layers.Dense(4, activation="relu", name="dense_6")(x) x += residual x = keras.layers.Dropout(0.5, name="dropout")(x) outputs = keras.layers.Dense(1, activation="sigmoid", name="dense_7")(x) model = keras.Model(inputs, outputs) edge_dict = get_edge_dict(model_to_dot(model)) self.assertEqual( edge_dict, { "input (InputLayer)": "dense (Dense)", "dense (Dense)": {"dense_1 (Dense)", "add (Add)"}, "dense_1 (Dense)": "dense_2 (Dense)", "dense_2 (Dense)": "dense_3 (Dense)", "dense_3 (Dense)": "add (Add)", "add (Add)": {"dense_4 (Dense)", "add_1 (Add)"}, "dense_4 (Dense)": "dense_5 (Dense)", "dense_5 (Dense)": "dense_6 (Dense)", "dense_6 (Dense)": "add_1 (Add)", "add_1 (Add)": "dropout (Dropout)", "dropout (Dropout)": "dense_7 (Dense)", }, ) self.multi_plot_model(model, "functional") def test_plot_subclassed_model(self): model = SubclassModel(name="subclass") model.build((None, 3)) self.multi_plot_model(model, "subclassed") def test_plot_nested_functional_model(self): inputs = keras.Input((3,), name="input") x = keras.layers.Dense(4, activation="relu", name="dense")(inputs) x = keras.layers.Dense(4, activation="relu", name="dense_1")(x) outputs = keras.layers.Dense(3, activation="relu", name="dense_2")(x) inner_model = keras.Model(inputs, outputs, name="inner_model") inputs = keras.Input((3,), name="input_1") x = keras.layers.Dense( 3, activation="relu", trainable=False, name="dense_3" )(inputs) residual = x x = inner_model(x) x = keras.layers.Add(name="add")([x, residual]) residual = x x = keras.layers.Dense(4, activation="relu", name="dense_4")(x) x = keras.layers.Dense(4, activation="relu", name="dense_5")(x) x = keras.layers.Dense(3, activation="relu", name="dense_6")(x) x = keras.layers.Add(name="add_1")([x, residual]) x = keras.layers.Dropout(0.5, name="dropout")(x) outputs = keras.layers.Dense(1, activation="sigmoid", name="dense_7")(x) model = keras.Model(inputs, outputs) edge_dict = get_edge_dict(model_to_dot(model)) self.assertEqual( edge_dict, { "input_1 (InputLayer)": "dense_3 (Dense)", "dense_3 (Dense)": {"inner_model (Functional)", "add (Add)"}, "inner_model (Functional)": "add (Add)", "add (Add)": {"dense_4 (Dense)", "add_1 (Add)"}, "dense_4 (Dense)": "dense_5 (Dense)", "dense_5 (Dense)": "dense_6 (Dense)", "dense_6 (Dense)": "add_1 (Add)", "add_1 (Add)": "dropout (Dropout)", "dropout (Dropout)": "dense_7 (Dense)", }, ) self.multi_plot_model(model, "nested-functional") edge_dict = get_edge_dict(model_to_dot(model, expand_nested=True)) self.assertEqual( edge_dict, { "input_1 (InputLayer)": "dense_3 (Dense)", "dense_3 (Dense)": { "inner_model > input (InputLayer)", "add (Add)", }, "inner_model > input (InputLayer)": "inner_model > dense (Dense)", # noqa: E501 "inner_model > dense (Dense)": "inner_model > dense_1 (Dense)", # noqa: E501 "inner_model > dense_1 (Dense)": "inner_model > dense_2 (Dense)", # noqa: E501 "inner_model > dense_2 (Dense)": "add (Add)", "add (Add)": {"dense_4 (Dense)", "add_1 (Add)"}, "dense_4 (Dense)": "dense_5 (Dense)", "dense_5 (Dense)": "dense_6 (Dense)", "dense_6 (Dense)": "add_1 (Add)", "add_1 (Add)": "dropout (Dropout)", "dropout (Dropout)": "dense_7 (Dense)", }, ) self.multi_plot_model(model, "nested-functional", expand_nested=True) def test_plot_functional_model_with_splits_and_merges(self): class SplitLayer(keras.Layer): def call(self, x): return list(keras.ops.split(x, 2, axis=1)) class ConcatLayer(keras.Layer): def call(self, xs): return keras.ops.concatenate(xs, axis=1) inputs = keras.Input((2,), name="input") a, b = SplitLayer()(inputs) a = keras.layers.Dense(2, name="dense")(a) b = keras.layers.Dense(2, name="dense_1")(b) outputs = ConcatLayer(name="concat_layer")([a, b]) model = keras.Model(inputs, outputs) edge_dict = get_edge_dict(model_to_dot(model)) self.assertEqual( edge_dict, { "input (InputLayer)": "split_layer (SplitLayer)", "split_layer (SplitLayer)": { "dense (Dense)", "dense_1 (Dense)", }, "dense (Dense)": "concat_layer (ConcatLayer)", "dense_1 (Dense)": "concat_layer (ConcatLayer)", }, ) self.multi_plot_model(model, "split-functional") def test_plot_sequential_in_sequential(self): inner_model = keras.models.Sequential( [ keras.layers.Dense(10, name="dense2"), keras.layers.Dense(10, name="dense3"), ], name="sub", ) model = keras.models.Sequential( [ keras.layers.Dense(10, name="dense1"), inner_model, ], ) model.build((1, 10)) # # +-------------------------+ # | dense1 (Dense) | # +-------------------------+ # | # v # +-------------------------+ # | sub (Sequential) | # +-------------------------+ # edge_dict = get_edge_dict(model_to_dot(model)) self.assertEqual( edge_dict, { "dense1 (Dense)": "sub (Sequential)", }, ) self.multi_plot_model(model, "sequential_in_sequential") # # +-------------------------+ # | dense1 (Dense) | # +-------------------------+ # | # +--------------|--------------+ # | sub v | # | +-------------------------+ | # | | dense2 (Dense) | | # | +-------------------------+ | # | | | # | v | # | +-------------------------+ | # | | dense3 (Dense) | | # | +-------------------------+ | # +-----------------------------+ # edge_dict = get_edge_dict(model_to_dot(model, expand_nested=True)) self.assertEqual( edge_dict, { "dense1 (Dense)": "sub > dense2 (Dense)", "sub > dense2 (Dense)": "sub > dense3 (Dense)", }, ) self.multi_plot_model( model, "sequential_in_sequential", expand_nested=True ) def test_plot_functional_in_functional(self): inner_input = keras.layers.Input((10,), name="inner_input") x = keras.layers.Dense(10, name="dense1")(inner_input) x = keras.layers.Dense(10, name="dense2")(x) inner_model = keras.models.Model(inner_input, x, name="inner") outer_input = keras.layers.Input((10,), name="outer_input") model = keras.models.Model(outer_input, inner_model(outer_input)) # # +-------------------------+ # |outer_input (InputLayer) | # +-------------------------+ # | # v # +-------------------------+ # | inner (Functional) | # +-------------------------+ # edge_dict = get_edge_dict(model_to_dot(model)) self.assertEqual( edge_dict, { "outer_input (InputLayer)": "inner (Functional)", }, ) self.multi_plot_model(model, "functional_in_functional") # # +-------------------------+ # |outer_input (InputLayer) | # +-------------------------+ # | # +--------------|--------------+ # | inner v | # | +-------------------------+ | # | |inner_input (InputLayer) | | # | +-------------------------+ | # | | | # | v | # | +-------------------------+ | # | | dense1 (Dense) | | # | +-------------------------+ | # | | | # | v | # | +-------------------------+ | # | | dense2 (Dense) | | # | +-------------------------+ | # +-----------------------------+ # edge_dict = get_edge_dict(model_to_dot(model, expand_nested=True)) self.assertEqual( edge_dict, { "outer_input (InputLayer)": "inner > inner_input (InputLayer)", "inner > inner_input (InputLayer)": "inner > dense1 (Dense)", "inner > dense1 (Dense)": "inner > dense2 (Dense)", }, ) self.multi_plot_model( model, "functional_in_functional", expand_nested=True ) def test_plot_sequential_in_sequential_in_sequential(self): inner_model = keras.models.Sequential( [ keras.layers.Dense(10, name="dense2"), keras.layers.Dense(10, name="dense3"), ], name="inner", ) mid_model = keras.models.Sequential( [ inner_model, ], name="mid", ) model = keras.models.Sequential( [ keras.layers.Dense(10, name="dense1"), mid_model, keras.layers.Dense(10, name="dense4"), ], ) model.build((1, 10)) # # +-------------------------+ # | dense1 (Dense) | # +-------------------------+ # | # v # +-------------------------+ # | mid (Sequential) | # +-------------------------+ # | # v # +-------------------------+ # | dense4 (Dense) | # +-------------------------+ # edge_dict = get_edge_dict(model_to_dot(model)) self.assertEqual( edge_dict, { "dense1 (Dense)": "mid (Sequential)", "mid (Sequential)": "dense4 (Dense)", }, ) self.multi_plot_model(model, "sequential_in_sequential_in_sequential") # # +-------------------------+ # | dense1 (Dense) | # +-------------------------+ # | # +----------------|----------------+ # | mid | | # | +--------------|--------------+ | # | | inner v | | # | | +-------------------------+ | | # | | | dense2 (Dense) | | | # | | +-------------------------+ | | # | | | | | # | | v | | # | | +-------------------------+ | | # | | | dense3 (Dense) | | | # | | +-------------------------+ | | # | +--------------|--------------+ | # +----------------|----------------+ # v # +-------------------------+ # | dense4 (Dense) | # +-------------------------+ # edge_dict = get_edge_dict(model_to_dot(model, expand_nested=True)) self.assertEqual( edge_dict, { "dense1 (Dense)": "mid > inner > dense2 (Dense)", "mid > inner > dense2 (Dense)": "mid > inner > dense3 (Dense)", "mid > inner > dense3 (Dense)": "dense4 (Dense)", }, ) self.multi_plot_model( model, "sequential_in_sequential_in_sequential", expand_nested=True ) def test_plot_functional_in_sequential_in_sequential(self): input1 = keras.layers.Input((10,), name="input1") x = keras.layers.Dense(10, name="dense2")(input1) inner_model = keras.models.Model(input1, x, name="inner") mid_model = keras.models.Sequential( [ inner_model, ], name="mid", ) model = keras.models.Sequential( [ keras.layers.Dense(10, name="dense1"), mid_model, keras.layers.Dense(10, name="dense3"), ], ) model.build((1, 10)) # # +-------------------------+ # | dense1 (Dense) | # +-------------------------+ # | # v # +-------------------------+ # | mid (Sequential) | # +-------------------------+ # | # v # +-------------------------+ # | dense3 (Dense) | # +-------------------------+ # edge_dict = get_edge_dict(model_to_dot(model)) self.assertEqual( edge_dict, { "dense1 (Dense)": "mid (Sequential)", "mid (Sequential)": "dense3 (Dense)", }, ) self.multi_plot_model(model, "functional_in_sequential_in_sequential") # # +-------------------------+ # | dense1 (Dense) | # +-------------------------+ # | # +----------------|----------------+ # | mid | | # | +--------------|--------------+ | # | | inner v | | # | | +-------------------------+ | | # | | | input1 (Inputlayer) | | | # | | +-------------------------+ | | # | | | | | # | | v | | # | | +-------------------------+ | | # | | | dense2 (Dense) | | | # | | +-------------------------+ | | # | +--------------|--------------+ | # +----------------|----------------+ # v # +-------------------------+ # | dense3 (Dense) | # +-------------------------+ # edge_dict = get_edge_dict(model_to_dot(model, expand_nested=True)) self.assertEqual( edge_dict, { "dense1 (Dense)": "mid > inner > input1 (InputLayer)", "mid > inner > input1 (InputLayer)": "mid > inner > dense2 (Dense)", # noqa: E501 "mid > inner > dense2 (Dense)": "dense3 (Dense)", }, ) self.multi_plot_model( model, "functional_in_sequential_in_sequential", expand_nested=True ) def test_plot_functional_in_functional_in_functional(self): # From https://github.com/keras-team/keras/issues/21119 inner_input = keras.layers.Input((10,), name="inner_input") x = keras.layers.Dense(10, name="dense1")(inner_input) inner_model = keras.models.Model(inner_input, x, name="inner") mid_input = keras.layers.Input((10,), name="mid_input") mid_output = inner_model(mid_input) mid_model = keras.models.Model(mid_input, mid_output, name="mid") outer_input = keras.layers.Input((10,), name="outer_input") x = mid_model(outer_input) x = keras.layers.Dense(10, name="dense2")(x) model = keras.models.Model(outer_input, x) # # +-------------------------+ # |outer_input (InputLayer) | # +-------------------------+ # | # v # +-------------------------+ # | mid (Functional) | # +-------------------------+ # | # v # +-------------------------+ # | dense2 (Dense) | # +-------------------------+ # edge_dict = get_edge_dict(model_to_dot(model)) self.assertEqual( edge_dict, { "outer_input (InputLayer)": "mid (Functional)", "mid (Functional)": "dense2 (Dense)", }, ) self.multi_plot_model(model, "functional_in_functional_in_functional") # # +-------------------------+ # |outer_input (InputLayer) | # +-------------------------+ # | # +----------------|----------------+ # | mid | | # | +-------------------------+ | # | | mid_input (Inputlayer) | | # | +-------------------------+ | # | +--------------|--------------+ | # | | inner v | | # | | +-------------------------+ | | # | | |inner_input (Inputlayer) | | | # | | +-------------------------+ | | # | | | | | # | | v | | # | | +-------------------------+ | | # | | | dense1 (Dense) | | | # | | +-------------------------+ | | # | +--------------|--------------+ | # +----------------|----------------+ # v # +-------------------------+ # | dense2 (Dense) | # +-------------------------+ # edge_dict = get_edge_dict(model_to_dot(model, expand_nested=True)) self.assertEqual( edge_dict, { "outer_input (InputLayer)": "mid > mid_input (InputLayer)", "mid > mid_input (InputLayer)": "mid > inner > inner_input (InputLayer)", # noqa: E501 "mid > inner > inner_input (InputLayer)": "mid > inner > dense1 (Dense)", # noqa: E501 "mid > inner > dense1 (Dense)": "dense2 (Dense)", }, ) self.multi_plot_model( model, "functional_in_functional_in_functional", expand_nested=True ) def test_plot_complex(self): # Note: this test exercises the case when `output_index` is not 0 and # changes when going deeply in nested models to resolve the destination # of an edge. inner_inpt1 = keras.layers.Input(shape=(10,), name="inner_inpt1") inner_inpt2 = keras.layers.Input(shape=(10,), name="inner_inpt2") inner_model = keras.models.Model( [inner_inpt1, inner_inpt2], [ keras.layers.Dense(10, name="dense1")(inner_inpt1), keras.layers.Dense(10, name="dense2")(inner_inpt2), ], name="inner", ) input0 = keras.layers.Input(shape=(10,), name="input0") input1 = keras.layers.Input(shape=(10,), name="input1") input2 = keras.layers.Input(shape=(10,), name="input2") input3 = keras.layers.Input(shape=(10,), name="input3") mid_sequential = keras.models.Sequential( [ keras.layers.Dense(10, name="dense0"), SubclassModel(name="subclass0"), ], name="seq", ) mid_subclass = SubclassModel(name="subclass3") mid_model = keras.models.Model( [input0, input1, input2, input3], [ mid_sequential(input0), *inner_model([input1, input2]), mid_subclass(input3), ], name="mid", ) outer_input = keras.layers.Input((10,), name="outer_input") mid_outputs = mid_model( [outer_input, outer_input, outer_input, outer_input] ) model = keras.models.Model( outer_input, [ keras.layers.Add(name="add1")([mid_outputs[0], mid_outputs[1]]), keras.layers.Add(name="add2")([mid_outputs[2], mid_outputs[3]]), ], ) # # +-------------------------+ # |outer_input (InputLayer) | # +-------------------------+ # | # v # +-------------------------+ # | mid (Functional) | # +-------------------------+ # | | # v v # +-------------------------+ +-------------------------+ # | add1 (Add) | | add2 (Add) | # +-------------------------+ +-------------------------+ # edge_dict = get_edge_dict(model_to_dot(model)) self.assertEqual( edge_dict, { "outer_input (InputLayer)": "mid (Functional)", "mid (Functional)": {"add1 (Add)", "add2 (Add)"}, }, ) self.multi_plot_model(model, "complex") # # +-----------+ # +------------------|outer_input|-----------------+ # | +-----------+ | # | | | | # +---------|-------------------|---------|------------------|-------+ # | mid v v v v | # | +-----------+ +-----------+ +-----------+ +-----------+ | # | | input0 | | input1 | | input2 | | input3 | | # | +-----------+ +-----------+ +-----------+ +-----------+ | # | +-------|-------+ +-------|-------------|-------+ | | # | | seq v | | inner v v | | | # | | +-----------+ | | +-----------+ +-----------+ | +-----------+ | # | | | dense0 | | | |inner_inp1t| |inner_inp2t| | | subclass3 | | # | | +-----------+ | | +-----------+ +-----------+ | +-----------+ | # | | | | | | | | | | # | | v | | v v | | | # | | +-----------+ | | +-----------+ +-----------+ | | | # | | | subclass0 | | | | dense1 | | dense2 | | | | # | | +-----------+ | | +-----------+ +-----------+ | | | # | +-----------|---+ +---|---------------------|---+ | | # +-------------|---------|---------------------|--------|-----------+ # v v v v # +-----------+ +-----------+ # | add1 | | add2 | # +-----------+ +-----------+ # edge_dict = get_edge_dict(model_to_dot(model, expand_nested=True)) self.assertEqual( edge_dict, { # 1st row "outer_input (InputLayer)": { "mid > input0 (InputLayer)", "mid > input1 (InputLayer)", "mid > input2 (InputLayer)", "mid > input3 (InputLayer)", }, # 2nd row "mid > input0 (InputLayer)": "mid > seq > dense0 (Dense)", "mid > input1 (InputLayer)": "mid > inner > inner_inpt1 (InputLayer)", # noqa: E501 "mid > input2 (InputLayer)": "mid > inner > inner_inpt2 (InputLayer)", # noqa: E501 "mid > input3 (InputLayer)": "mid > subclass3 (SubclassModel)", # 3rd row "mid > seq > dense0 (Dense)": "mid > seq > subclass0 (SubclassModel)", # noqa: E501 "mid > inner > inner_inpt1 (InputLayer)": "mid > inner > dense1 (Dense)", # noqa: E501 "mid > inner > inner_inpt2 (InputLayer)": "mid > inner > dense2 (Dense)", # noqa: E501 # 4th row "mid > seq > subclass0 (SubclassModel)": "add1 (Add)", "mid > inner > dense1 (Dense)": "add1 (Add)", "mid > inner > dense2 (Dense)": "add2 (Add)", "mid > subclass3 (SubclassModel)": "add2 (Add)", }, ) self.multi_plot_model(model, "complex", expand_nested=True)