Add ignite API helpers, also temporarily disable JIT and python2 compatibiltiy (#29)

codefresh.yml

@@ -12,4 +12,4 @@ steps:
     commands:
       - flake8
       - python setup.py test
-      - python2 setup.py test
\ No newline at end of file
+      # - python2 setup.py test
\ No newline at end of file

setup.py

@@ -4,7 +4,7 @@ cmdclass = {'build_sphinx': BuildDoc}
 
 setup(name='torchani',
       version='0.1',
-      description='ANI based on pytorch',
+      description='PyTorch implementation of ANI',
       url='https://github.com/zasdfgbnm/torchani',
       author='Xiang Gao',
       author_email='qasdfgtyuiop@ufl.edu',
@@ -13,6 +13,7 @@ setup(name='torchani',
       include_package_data=True,
       install_requires=[
           'torch',
+          'pytorch-ignite',
           'lark-parser',
           'h5py',
       ],

tests/test_ignite.py

+import sys
+
+if sys.version_info.major >= 3:
+    import os
+    import unittest
+    import torch
+    from ignite.engine import create_supervised_trainer
+    import torchani
+    import torchani.data
+
+    path = os.path.dirname(os.path.realpath(__file__))
+    path = os.path.join(path, 'dataset/ani_gdb_s01.h5')
+    chunksize = 32
+    batch_chunks = 32
+    dtype = torch.float32
+    device = torch.device('cpu')
+
+    class TestIgnite(unittest.TestCase):
+
+        def testIgnite(self):
+            ds = torchani.data.ANIDataset(path, chunksize)
+            loader = torchani.data.dataloader(ds, batch_chunks)
+            aev_computer = torchani.SortedAEV(dtype=dtype, device=device)
+            nnp = torchani.models.NeuroChemNNP(aev_computer)
+
+            class Flatten(torch.nn.Module):
+
+                def __init__(self, model):
+                    super(Flatten, self).__init__()
+                    self.model = model
+
+                def forward(self, *input):
+                    return self.model(*input).flatten()
+            nnp = Flatten(nnp)
+            batch_nnp = torchani.models.BatchModel(nnp)
+            container = torchani.ignite.Container({'energies': batch_nnp})
+            loss = torchani.ignite.DictLosses({'energies': torch.nn.MSELoss()})
+            optimizer = torch.optim.SGD(container.parameters(),
+                                        lr=0.001, momentum=0.8)
+            trainer = create_supervised_trainer(container, optimizer, loss)
+            trainer.run(loader, max_epochs=10)
+
+    if __name__ == '__main__':
+        unittest.main()

torchani/__init__.py

 from .energyshifter import EnergyShifter
 from . import models
+from . import data
+from . import ignite
 from .aev import SortedAEV
 from .env import buildin_const_file, buildin_sae_file, buildin_network_dir, \
     buildin_model_prefix, buildin_ensemble, default_dtype, default_device
 
-__all__ = ['SortedAEV', 'EnergyShifter', 'models', 'data',
+__all__ = ['SortedAEV', 'EnergyShifter', 'models', 'data', 'ignite',
            'buildin_const_file', 'buildin_sae_file', 'buildin_network_dir',
            'buildin_model_prefix', 'buildin_ensemble',
            'default_dtype', 'default_device']

torchani/benchmarked.py

@@ -3,7 +3,7 @@ import timeit
 import functools
 
 
-class BenchmarkedModule(torch.jit.ScriptModule):
+class BenchmarkedModule(torch.nn.Module):
     """Module with member function benchmarking support.
 
     The benchmarking is done by wrapping the original member function with

torchani/data.py

@@ -2,18 +2,20 @@ from torch.utils.data import Dataset, DataLoader
 from os.path import join, isfile, isdir
 from os import listdir
 from .pyanitools import anidataloader
+from .env import default_dtype
 import torch
 
 
 class ANIDataset(Dataset):
 
     def __init__(self, path, chunk_size, shuffle=True,
-                 properties=['energies']):
+                 properties=['energies'], dtype=default_dtype):
         super(ANIDataset, self).__init__()
         self.path = path
         self.chunks_size = chunk_size
         self.shuffle = shuffle
         self.properties = properties
+        self.dtype = dtype
 
         # get name of files storing data
         files = []
@@ -33,10 +35,11 @@ class ANIDataset(Dataset):
             for m in anidataloader(f):
                 full = {
                     'coordinates': torch.from_numpy(m['coordinates'])
+                                        .type(dtype)
                 }
                 conformations = full['coordinates'].shape[0]
                 for i in properties:
-                    full[i] = torch.from_numpy(m[i])
+                    full[i] = torch.from_numpy(m[i]).type(dtype)
                 species = m['species']
                 if shuffle:
                     indices = torch.randperm(conformations)

torchani/ignite/__init__.py

+from .container import Container
+from .dict_loss import DictLosses
+
+__all__ = ['Container', 'DictLosses']

torchani/ignite/container.py

+import torch
+from ..models import BatchModel
+
+
+class Container(torch.nn.Module):
+
+    def __init__(self, models):
+        super(Container, self).__init__()
+        self.keys = models.keys()
+        for i in models:
+            if not isinstance(models[i], BatchModel):
+                raise ValueError('Container must contain batch models')
+            setattr(self, 'model_' + i, models[i])
+
+    def forward(self, batch):
+        output = {}
+        for i in self.keys:
+            model = getattr(self, 'model_' + i)
+            output[i] = model(batch)
+        return output

torchani/ignite/dict_loss.py

+from torch.nn.modules.loss import _Loss
+
+
+class DictLosses(_Loss):
+
+    def __init__(self, losses):
+        super(DictLosses, self).__init__()
+        self.losses = losses
+
+    def forward(self, input, other):
+        total = 0
+        for i in self.losses:
+            total += self.losses[i](input[i], other[i])
+        return total

torchani/models/neurochem_atomic_network.py

@@ -7,7 +7,7 @@ import math
 import struct
 
 
-class NeuroChemAtomicNetwork(torch.jit.ScriptModule):
+class NeuroChemAtomicNetwork(torch.nn.Module):
     """Per atom aev->y transformation, loaded from NeuroChem network dir.
 
     Attributes