Patches for current PyTorch Release, fixes #3, #4, and #5

.gitignore

@@ -0,0 +1,110 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# npz
+*.npz
+
+# pth
+*.pth

dataset/dataset.py

@@ -41,7 +41,7 @@ def _process(self, file, train):
         """Data format: A list, [train data, test data]
         Each data sample: label, S1, S2, Images, in this order.
         """
-        with np.load(file) as f:
+        with np.load(file, mmap_mode='r') as f:
             if train:
                 images = f['arr_0']
                 S1 = f['arr_1']

dataset/make_training_data.py

@@ -89,7 +89,7 @@ def make_data(dom_size, n_domains, max_obs,
     return X_f, S1_f, S2_f, Labels_f
 
 
-def main(dom_size=[8,8], n_domains=15000, max_obs=30, max_obs_size=None, 
+def main(dom_size=[28,28], n_domains=5000, max_obs=50, max_obs_size=2, 
             n_traj=7, state_batch_size=1):
     # Get path to save dataset
     save_path = "dataset/gridworld_{0}x{1}".format(dom_size[0], dom_size[1])

download_weights_and_datasets.sh

@@ -1,9 +1,9 @@
 cd trained
-wget 'https://github.com/kentsommer/pytorch-value-iteration-networks/releases/download/v1.0/vin_8x8.pth'
-wget 'https://github.com/kentsommer/pytorch-value-iteration-networks/releases/download/v1.0/vin_16x16.pth'
-wget 'https://github.com/kentsommer/pytorch-value-iteration-networks/releases/download/v1.0/vin_28x28.pth'
+wget 'https://github.com/kentsommer/pytorch-value-iteration-networks/releases/download/v1.1/vin_8x8.pth'
+wget 'https://github.com/kentsommer/pytorch-value-iteration-networks/releases/download/v1.1/vin_16x16.pth'
+wget 'https://github.com/kentsommer/pytorch-value-iteration-networks/releases/download/v1.1/vin_28x28.pth'
 cd ../dataset
-wget 'https://github.com/kentsommer/pytorch-value-iteration-networks/releases/download/v1.0/gridworld_8x8.npz'
-wget 'https://github.com/kentsommer/pytorch-value-iteration-networks/releases/download/v1.0/gridworld_16x16.npz'
-wget 'https://github.com/kentsommer/pytorch-value-iteration-networks/releases/download/v1.0/gridworld_28x28.npz'
+wget 'https://github.com/kentsommer/pytorch-value-iteration-networks/releases/download/v1.1/gridworld_8x8.npz'
+wget 'https://github.com/kentsommer/pytorch-value-iteration-networks/releases/download/v1.1/gridworld_16x16.npz'
+wget 'https://github.com/kentsommer/pytorch-value-iteration-networks/releases/download/v1.1/gridworld_28x28.npz'
 cd ..

model.py

@@ -32,7 +32,7 @@ def __init__(self, config):
                             out_features=8,
                             bias=False)
         self.w = Parameter(torch.zeros(config.l_q,1,3,3), requires_grad=True)
-        self.sm = nn.Softmax()
+        self.sm = nn.Softmax(dim=1)
 
 
     def forward(self, X, S1, S2, config):

test.py

@@ -22,9 +22,13 @@ def main(config, n_domains=100, max_obs=30,
     correct, total = 0.0, 0.0
     # Automatic swith of GPU mode if available
     use_GPU = torch.cuda.is_available()
-    vin = torch.load(config.weights)
+    # Instantiate a VIN model
+    vin = VIN(config)
+    # Load model parameters
+    vin.load_state_dict(torch.load(config.weights))
+    # Use GPU if available
     if use_GPU:
-         vin = vin.cuda()
+         vin = vin.cuda() 
 
     for dom in range(n_domains):
         # Randomly select goal position

train.py

@@ -77,7 +77,7 @@ def test(net, testloader, config):
         # Unwrap autograd.Variable to Tensor
         predicted = predicted.data
         # Compute test accuracy
-        correct += (predicted == labels).sum()
+        correct += (torch.eq(torch.squeeze(predicted), labels)).sum()
         total += labels.size()[0]
     print('Test Accuracy: {:.2f}%'.format(100*(correct/total)))
 
@@ -147,5 +147,5 @@ def test(net, testloader, config):
     train(net, trainloader, config, criterion, optimizer, use_GPU)
     # Test accuracy
     test(net, testloader, config)
-    # Save the trained model
-    torch.save(net, save_path)
+    # Save the trained model parameters
+    torch.save(net.state_dict(), save_path)