Allow any input in to_onnx and to_torchscript

pytorch_lightning/core/hooks.py

@@ -14,7 +14,7 @@
 
 """Various hooks to be used in the Lightning code."""
 
-from typing import Any, Dict, List, Union
+from typing import Any, Dict, List, Optional, Union
 
 import torch
 from pytorch_lightning.utilities import AMPType, move_data_to_device, rank_zero_warn
@@ -508,7 +508,7 @@ def val_dataloader(self):
             will have an argument ``dataloader_idx`` which matches the order here.
         """
 
-    def transfer_batch_to_device(self, batch: Any, device: torch.device) -> Any:
+    def transfer_batch_to_device(self, batch: Any, device: Optional[torch.device] = None) -> Any:
         """
         Override this hook if your :class:`~torch.utils.data.DataLoader` returns tensors
         wrapped in a custom data structure.
@@ -556,6 +556,7 @@ def transfer_batch_to_device(self, batch, device)
             - :func:`~pytorch_lightning.utilities.apply_func.move_data_to_device`
             - :func:`~pytorch_lightning.utilities.apply_func.apply_to_collection`
         """
+        device = device or self.device
         return move_data_to_device(batch, device)
 
 

pytorch_lightning/core/lightning.py

@@ -23,7 +23,8 @@
 import types
 from abc import ABC
 from argparse import Namespace
-from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union, Mapping
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
 
 import torch
 from pytorch_lightning import _logger as log
@@ -1682,12 +1683,19 @@ def _set_hparams(self, hp: Union[dict, Namespace, str]) -> None:
         else:
             self._hparams = hp
 
-    def to_onnx(self, file_path: str, input_sample: Optional[Tensor] = None, **kwargs):
-        """Saves the model in ONNX format
+    @torch.no_grad()
+    def to_onnx(
+        self,
+        file_path: Union[str, Path],
+        input_sample: Optional[Any] = None,
+        **kwargs,
+    ):
+        """
+        Saves the model in ONNX format
 
         Args:
-            file_path: The path of the file the model should be saved to.
-            input_sample: A sample of an input tensor for tracing.
+            file_path: The path of the file the onnx model should be saved to.
+            input_sample: An input for tracing. Default: None (Use self.example_input_array)
             **kwargs: Will be passed to torch.onnx.export function.
 
         Example:
@@ -1706,32 +1714,32 @@ def to_onnx(self, file_path: str, input_sample: Optional[Tensor] = None, **kwarg
             ...     os.path.isfile(tmpfile.name)
             True
         """
+        mode = self.training
 
-        if isinstance(input_sample, Tensor):
-            input_data = input_sample
-        elif self.example_input_array is not None:
-            input_data = self.example_input_array
-        else:
-            if input_sample is not None:
-                raise ValueError(
-                    f"Received `input_sample` of type {type(input_sample)}. Expected type is `Tensor`"
-                )
-            else:
+        if input_sample is None:
+            if self.example_input_array is None:
                 raise ValueError(
                     "Could not export to ONNX since neither `input_sample` nor"
                     " `model.example_input_array` attribute is set."
                 )
-        input_data = input_data.to(self.device)
+            input_sample = self.example_input_array
+
+        input_sample = self.transfer_batch_to_device(input_sample)
+
         if "example_outputs" not in kwargs:
             self.eval()
-            with torch.no_grad():
-                kwargs["example_outputs"] = self(input_data)
+            kwargs["example_outputs"] = self(input_sample)
 
-        torch.onnx.export(self, input_data, file_path, **kwargs)
+        torch.onnx.export(self, input_sample, file_path, **kwargs)
+        self.train(mode)
 
+    @torch.no_grad()
     def to_torchscript(
-        self, file_path: Optional[str] = None, method: Optional[str] = 'script',
-            example_inputs: Optional[Union[torch.Tensor, Tuple[torch.Tensor]]] = None, **kwargs
+        self,
+        file_path: Optional[Union[str, Path]] = None,
+        method: Optional[str] = 'script',
+        example_inputs: Optional[Any] = None,
+        **kwargs,
     ) -> Union[ScriptModule, Dict[str, ScriptModule]]:
         """
         By default compiles the whole model to a :class:`~torch.jit.ScriptModule`.
@@ -1743,7 +1751,7 @@ def to_torchscript(
         Args:
             file_path: Path where to save the torchscript. Default: None (no file saved).
             method: Whether to use TorchScript's script or trace method. Default: 'script'
-            example_inputs: Tensor to be used to do tracing when method is set to 'trace'.
+            example_inputs: An input to be used to do tracing when method is set to 'trace'.
               Default: None (Use self.example_input_array)
             **kwargs: Additional arguments that will be passed to the :func:`torch.jit.script` or
               :func:`torch.jit.trace` function.
@@ -1777,21 +1785,27 @@ def to_torchscript(
             This LightningModule as a torchscript, regardless of whether file_path is
             defined or not.
         """
-
         mode = self.training
-        with torch.no_grad():
-            if method == 'script':
-                torchscript_module = torch.jit.script(self.eval(), **kwargs)
-            elif method == 'trace':
-                # if no example inputs are provided, try to see if model has example_input_array set
-                if example_inputs is None:
-                    example_inputs = self.example_input_array
-                # automatically send example inputs to the right device and use trace
-                example_inputs = self.transfer_batch_to_device(example_inputs, device=self.device)
-                torchscript_module = torch.jit.trace(func=self.eval(), example_inputs=example_inputs, **kwargs)
-            else:
-                raise ValueError(f"The 'method' parameter only supports 'script' or 'trace', but value given was:"
-                                 f"{method}")
+
+        if method == 'script':
+            torchscript_module = torch.jit.script(self.eval(), **kwargs)
+        elif method == 'trace':
+            # if no example inputs are provided, try to see if model has example_input_array set
+            if example_inputs is None:
+                if self.example_input_array is None:
+                    raise ValueError(
+                        'Choosing method=`trace` requires either `example_inputs`'
+                        ' or `model.example_input_array` to be defined'
+                    )
+                example_inputs = self.example_input_array
+
+            # automatically send example inputs to the right device and use trace
+            example_inputs = self.transfer_batch_to_device(example_inputs)
+            torchscript_module = torch.jit.trace(func=self.eval(), example_inputs=example_inputs, **kwargs)
+        else:
+            raise ValueError("The 'method' parameter only supports 'script' or 'trace',"
+                             f" but value given was: {method}")
+
         self.train(mode)
 
         if file_path is not None:

tests/base/boring_model.py

@@ -76,6 +76,7 @@ def training_step(...):
         """
         super().__init__()
         self.layer = torch.nn.Linear(32, 2)
+        self.example_input_array = torch.rand(5, 32)
 
     def forward(self, x):
         return self.layer(x)

tests/models/test_onnx.py

@@ -21,44 +21,44 @@
 import tests.base.develop_pipelines as tpipes
 import tests.base.develop_utils as tutils
 from pytorch_lightning import Trainer
-from tests.base import EvalModelTemplate
+from tests.base import BoringModel, EvalModelTemplate
 
 
 def test_model_saves_with_input_sample(tmpdir):
     """Test that ONNX model saves with input sample and size is greater than 3 MB"""
-    model = EvalModelTemplate()
+    model = BoringModel()
     trainer = Trainer(max_epochs=1)
     trainer.fit(model)
 
     file_path = os.path.join(tmpdir, "model.onnx")
-    input_sample = torch.randn((1, 28 * 28))
+    input_sample = torch.randn((1, 32))
     model.to_onnx(file_path, input_sample)
     assert os.path.isfile(file_path)
-    assert os.path.getsize(file_path) > 3e+06
+    assert os.path.getsize(file_path) > 4e2
 
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
 def test_model_saves_on_gpu(tmpdir):
     """Test that model saves on gpu"""
-    model = EvalModelTemplate()
+    model = BoringModel()
     trainer = Trainer(gpus=1, max_epochs=1)
     trainer.fit(model)
 
     file_path = os.path.join(tmpdir, "model.onnx")
-    input_sample = torch.randn((1, 28 * 28))
+    input_sample = torch.randn((1, 32))
     model.to_onnx(file_path, input_sample)
     assert os.path.isfile(file_path)
-    assert os.path.getsize(file_path) > 3e+06
+    assert os.path.getsize(file_path) > 4e2
 
 
 def test_model_saves_with_example_output(tmpdir):
     """Test that ONNX model saves when provided with example output"""
-    model = EvalModelTemplate()
+    model = BoringModel()
     trainer = Trainer(max_epochs=1)
     trainer.fit(model)
 
     file_path = os.path.join(tmpdir, "model.onnx")
-    input_sample = torch.randn((1, 28 * 28))
+    input_sample = torch.randn((1, 32))
     model.eval()
     example_outputs = model.forward(input_sample)
     model.to_onnx(file_path, input_sample, example_outputs=example_outputs)
@@ -67,11 +67,11 @@ def test_model_saves_with_example_output(tmpdir):
 
 def test_model_saves_with_example_input_array(tmpdir):
     """Test that ONNX model saves with_example_input_array and size is greater than 3 MB"""
-    model = EvalModelTemplate()
+    model = BoringModel()
     file_path = os.path.join(tmpdir, "model.onnx")
     model.to_onnx(file_path)
     assert os.path.exists(file_path) is True
-    assert os.path.getsize(file_path) > 3e+06
+    assert os.path.getsize(file_path) > 4e2
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
@@ -100,38 +100,27 @@ def test_model_saves_on_multi_gpu(tmpdir):
 
 def test_verbose_param(tmpdir, capsys):
     """Test that output is present when verbose parameter is set"""
-    model = EvalModelTemplate()
+    model = BoringModel()
     file_path = os.path.join(tmpdir, "model.onnx")
     model.to_onnx(file_path, verbose=True)
     captured = capsys.readouterr()
     assert "graph(%" in captured.out
 
 
 def test_error_if_no_input(tmpdir):
-    """Test that an exception is thrown when there is no input tensor"""
-    model = EvalModelTemplate()
+    """Test that an error is thrown when there is no input tensor"""
+    model = BoringModel()
     model.example_input_array = None
     file_path = os.path.join(tmpdir, "model.onnx")
     with pytest.raises(ValueError, match=r'Could not export to ONNX since neither `input_sample` nor'
                                          r' `model.example_input_array` attribute is set.'):
         model.to_onnx(file_path)
 
 
-def test_error_if_input_sample_is_not_tensor(tmpdir):
-    """Test that an exception is thrown when there is no input tensor"""
-    model = EvalModelTemplate()
-    model.example_input_array = None
-    file_path = os.path.join(tmpdir, "model.onnx")
-    input_sample = np.random.randn(1, 28 * 28)
-    with pytest.raises(ValueError, match=f'Received `input_sample` of type {type(input_sample)}. Expected type is '
-                                         f'`Tensor`'):
-        model.to_onnx(file_path, input_sample)
-
-
 def test_if_inference_output_is_valid(tmpdir):
     """Test that the output inferred from ONNX model is same as from PyTorch"""
-    model = EvalModelTemplate()
-    trainer = Trainer(max_epochs=5)
+    model = BoringModel()
+    trainer = Trainer(max_epochs=2)
     trainer.fit(model)
 
     model.eval()