Fix/no train steps (#566)

* Fix no train steps bug and add Mock model

* Update changelog

* Set test steps to 0 if None

CHANGELOG.md

@@ -6,12 +6,14 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ## [Unreleased]
 ### Added
 - Added ``with_loader`` trial method that allows running of custom batch loaders
+- Added a Mock Model which is set when None is passed as the model to a Trial. Mock Model always returns None. 
 ### Changed
 ### Deprecated
 ### Removed
 - Removed the variational sub-package, this will now be packaged separately
 ### Fixed
 - Fixed a bug where list or dictionary metrics would cause the tensorboard callback to error
+- Fixed a bug where running a trial without training steps would error
 
 ## [0.3.2] - 2019-05-28
 ### Added

tests/test_end_to_end.py

@@ -115,3 +115,10 @@ def test_no_model(self):
         with warnings.catch_warnings(record=True) as w:
             tbmodel.run()
             self.assertTrue(len(w) == 1)
+
+        self.assertTrue(torchbearer.trial.MockModel()(torch.rand(1)) is None)
+
+    def test_no_train_steps(self):
+        tbmodel = torchbearer.Trial(None)
+        tbmodel.for_val_steps(10)
+        tbmodel.run()

torchbearer/trial.py

@@ -17,6 +17,7 @@ def get_default(fcn, arg):
 import itertools
 
 import torch
+import torch.nn
 from torch.utils.data import DataLoader, TensorDataset
 from torch.optim import Optimizer
 
@@ -61,6 +62,11 @@ def zero_grad(self):
         pass  # Do Nothing
 
 
+class MockModel(torch.nn.Module):
+    def forward(self, x, state=None):
+        return None
+
+
 class CallbackListInjection(CallbackList):
     """This class allows for an callback to be injected into a callback list, without masking the methods available for
     mutating the list. In this way, callbacks (such as printers) can be injected seamlessly into the methods of the
@@ -719,11 +725,11 @@ def run(self, epochs=1, verbose=-1):
             torchbearer.STOP_TRAINING: False,
         })
 
-        state.update(self.state)  # TODO: Swap this for something which makes `self.state` still mutable
-
-        if state[torchbearer.MODEL] is None or not callable(state[torchbearer.MODEL]):
+        if self.state[torchbearer.MODEL] is None or not callable(self.state[torchbearer.MODEL]):
             warnings.warn('The Model is None or not callable which may cause issues if not deliberate')
-            state[torchbearer.MODEL] = lambda *args, **kwargs: None
+            self.state[torchbearer.MODEL] = MockModel()
+
+        state.update(self.state)  # TODO: Swap this for something which makes `self.state` still mutable
 
         if state[torchbearer.TRAIN_GENERATOR] is not None \
                 or state[torchbearer.TRAIN_STEPS] is not None \
@@ -773,6 +779,7 @@ def _fit_pass(self, state):
         state[torchbearer.METRIC_LIST].reset(state)
         state[torchbearer.METRICS] = {}
 
+        state[torchbearer.STEPS] = 0 if state[torchbearer.STEPS] is None else state[torchbearer.STEPS]
         state[torchbearer.CALLBACK_LIST].on_start_training(state)
         for state[torchbearer.BATCH] in (range(state[torchbearer.STEPS]) if state[torchbearer.STEPS] != -1 else itertools.count()):
             state[torchbearer.SAMPLER](state)
@@ -801,6 +808,7 @@ def _test_pass(self, state):
 
             state[torchbearer.CALLBACK_LIST].on_start_validation(state)
 
+            state[torchbearer.STEPS] = 0 if state[torchbearer.STEPS] is None else state[torchbearer.STEPS]
             for state[torchbearer.BATCH] in range(state[torchbearer.STEPS]):
                 state[torchbearer.SAMPLER](state)
                 state[torchbearer.CALLBACK_LIST].on_sample_validation(state)