Set funsor backend in backend-specific examples (#360)

examples/discrete_hmm.py

@@ -15,6 +15,8 @@
 
 
 def main(args):
+    funsor.set_backend("torch")
+
     # Declare parameters.
     trans_probs = torch.tensor([[0.2, 0.8], [0.7, 0.3]], requires_grad=True)
     emit_probs = torch.tensor([[0.4, 0.6], [0.1, 0.9]], requires_grad=True)

examples/eeg_slds.py

@@ -244,6 +244,8 @@ def filter_and_predict(self, data, smoothing=False):
 
 
 def main(args):
+    funsor.set_backend("torch")
+
     # download and pre-process EEG data if not in test mode
     if not args.test:
         download_data()

examples/kalman_filter.py

@@ -14,6 +14,8 @@
 
 
 def main(args):
+    funsor.set_backend("torch")
+
     # Declare parameters.
     trans_noise = torch.tensor(0.1, requires_grad=True)
     emit_noise = torch.tensor(0.5, requires_grad=True)

examples/minipyro.py

@@ -8,11 +8,14 @@
 from pyro.generic import infer, optim, pyro, pyro_backend
 from torch.distributions import constraints
 
+import funsor
 from funsor.interpreter import interpretation
 from funsor.montecarlo import monte_carlo
 
 
 def main(args):
+    funsor.set_backend("torch")
+
     # Define a basic model with a single Normal latent random variable `loc`
     # and a batch of Normally distributed observations.
     def model(data):

examples/pcfg.py

@@ -7,6 +7,7 @@
 
 import torch
 
+import funsor
 import funsor.ops as ops
 from funsor.delta import Delta
 from funsor.domains import Bint
@@ -36,6 +37,7 @@ def model(size, position=0):
 
 
 def main(args):
+    funsor.set_backend("torch")
     torch.manual_seed(args.seed)
 
     print_ = print if args.verbose else lambda msg: None

examples/sensor.py

@@ -11,6 +11,7 @@
 import torch.nn as nn
 from torch.optim import Adam
 
+import funsor
 import funsor.torch.distributions as f_dist
 import funsor.ops as ops
 from funsor.domains import Reals
@@ -203,6 +204,7 @@ def track(args):
 
 
 def main(args):
+    funsor.set_backend("torch")
     if args.force or not args.metrics_filename or not os.path.exists(args.metrics_filename):
         results = track(args)
     else:

examples/slds.py

@@ -11,6 +11,8 @@
 
 
 def main(args):
+    funsor.set_backend("torch")
+
     # Declare parameters.
     trans_probs = funsor.Tensor(torch.tensor([[0.9, 0.1],
                                               [0.1, 0.9]], requires_grad=True))

examples/vae.py

@@ -48,6 +48,8 @@ def forward(self, z):
 
 
 def main(args):
+    funsor.set_backend("torch")
+
     encoder = Encoder()
     decoder = Decoder()