Spaces:
Runtime error
Runtime error
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import os | |
| import torch | |
| from detectron2.utils.env import TORCH_VERSION | |
| from detectron2.utils.file_io import PathManager | |
| from .torchscript_patch import freeze_training_mode, patch_instances | |
| __all__ = ["scripting_with_instances", "dump_torchscript_IR"] | |
| def scripting_with_instances(model, fields): | |
| """ | |
| Run :func:`torch.jit.script` on a model that uses the :class:`Instances` class. Since | |
| attributes of :class:`Instances` are "dynamically" added in eager mode，it is difficult | |
| for scripting to support it out of the box. This function is made to support scripting | |
| a model that uses :class:`Instances`. It does the following: | |
| 1. Create a scriptable ``new_Instances`` class which behaves similarly to ``Instances``, | |
| but with all attributes been "static". | |
| The attributes need to be statically declared in the ``fields`` argument. | |
| 2. Register ``new_Instances``, and force scripting compiler to | |
| use it when trying to compile ``Instances``. | |
| After this function, the process will be reverted. User should be able to script another model | |
| using different fields. | |
| Example: | |
| Assume that ``Instances`` in the model consist of two attributes named | |
| ``proposal_boxes`` and ``objectness_logits`` with type :class:`Boxes` and | |
| :class:`Tensor` respectively during inference. You can call this function like: | |
| :: | |
| fields = {"proposal_boxes": Boxes, "objectness_logits": torch.Tensor} | |
| torchscipt_model = scripting_with_instances(model, fields) | |
| Note: | |
| It only support models in evaluation mode. | |
| Args: | |
| model (nn.Module): The input model to be exported by scripting. | |
| fields (Dict[str, type]): Attribute names and corresponding type that | |
| ``Instances`` will use in the model. Note that all attributes used in ``Instances`` | |
| need to be added, regardless of whether they are inputs/outputs of the model. | |
| Data type not defined in detectron2 is not supported for now. | |
| Returns: | |
| torch.jit.ScriptModule: the model in torchscript format | |
| """ | |
| assert TORCH_VERSION >= (1, 8), "This feature is not available in PyTorch < 1.8" | |
| assert ( | |
| not model.training | |
| ), "Currently we only support exporting models in evaluation mode to torchscript" | |
| with freeze_training_mode(model), patch_instances(fields): | |
| scripted_model = torch.jit.script(model) | |
| return scripted_model | |
| # alias for old name | |
| export_torchscript_with_instances = scripting_with_instances | |
| def dump_torchscript_IR(model, dir): | |
| """ | |
| Dump IR of a TracedModule/ScriptModule/Function in various format (code, graph, | |
| inlined graph). Useful for debugging. | |
| Args: | |
| model (TracedModule/ScriptModule/ScriptFUnction): traced or scripted module | |
| dir (str): output directory to dump files. | |
| """ | |
| PathManager.mkdirs(dir) | |
| def _get_script_mod(mod): | |
| if isinstance(mod, torch.jit.TracedModule): | |
| return mod._actual_script_module | |
| return mod | |
| # Dump pretty-printed code: https://pytorch.org/docs/stable/jit.html#inspecting-code | |
| with PathManager.open(os.path.join(dir, "model_ts_code.txt"), "w") as f: | |
| def get_code(mod): | |
| # Try a few ways to get code using private attributes. | |
| try: | |
| # This contains more information than just `mod.code` | |
| return _get_script_mod(mod)._c.code | |
| except AttributeError: | |
| pass | |
| try: | |
| return mod.code | |
| except AttributeError: | |
| return None | |
| def dump_code(prefix, mod): | |
| code = get_code(mod) | |
| name = prefix or "root model" | |
| if code is None: | |
| f.write(f"Could not found code for {name} (type={mod.original_name})\n") | |
| f.write("\n") | |
| else: | |
| f.write(f"\nCode for {name}, type={mod.original_name}:\n") | |
| f.write(code) | |
| f.write("\n") | |
| f.write("-" * 80) | |
| for name, m in mod.named_children(): | |
| dump_code(prefix + "." + name, m) | |
| if isinstance(model, torch.jit.ScriptFunction): | |
| f.write(get_code(model)) | |
| else: | |
| dump_code("", model) | |
| def _get_graph(model): | |
| try: | |
| # Recursively dump IR of all modules | |
| return _get_script_mod(model)._c.dump_to_str(True, False, False) | |
| except AttributeError: | |
| return model.graph.str() | |
| with PathManager.open(os.path.join(dir, "model_ts_IR.txt"), "w") as f: | |
| f.write(_get_graph(model)) | |
| # Dump IR of the entire graph (all submodules inlined) | |
| with PathManager.open(os.path.join(dir, "model_ts_IR_inlined.txt"), "w") as f: | |
| f.write(str(model.inlined_graph)) | |
| if not isinstance(model, torch.jit.ScriptFunction): | |
| # Dump the model structure in pytorch style | |
| with PathManager.open(os.path.join(dir, "model.txt"), "w") as f: | |
| f.write(str(model)) | |