test for both cpu and gpu

HawkAaron · May 29, 2018 · 5e16b3d · 5e16b3d
1 parent b7a5b48
commit 5e16b3d
Showing 1 changed file with 56 additions and 78 deletions.
diff --git a/pytorch_binding/test/test.py b/pytorch_binding/test/test.py
@@ -18,14 +18,10 @@
 from transducer_np import RNNTLoss as rnntloss
 
 parser = argparse.ArgumentParser(description='MXNet RNN Transducer Test.')
-parser.add_argument('B', type=int, default=1, help='batch size')
-parser.add_argument('T', type=int, default=300, help='time step')
-parser.add_argument('U', type=int, default=100, help='prediction step')
-parser.add_argument('V', type=int, default=60, help='vocab size')
 parser.add_argument('--np', default=False, action='store_true', help='numpy loss')
 args = parser.parse_args()
 
-fn = rnntloss() if args.np else RNNTLoss() 
+fn = rnntloss() if args.np else RNNTLoss(size_average=False) 
 
 def wrap_and_call(acts, labels):
  acts = torch.FloatTensor(acts)
@@ -38,48 +34,47 @@ def wrap_and_call(acts, labels):
  labels = autograd.Variable(torch.IntTensor(labels))
  lengths = autograd.Variable(torch.IntTensor(lengths))
  label_lengths = autograd.Variable(torch.IntTensor(label_lengths))
+ if use_cuda:
+ labels = labels.cuda()
+ lengths = lengths.cuda()
+ label_lengths = label_lengths.cuda()
 
  log_probs = nn.functional.log_softmax(acts, dim=3)
- def grad_hook(grad):
- log_probs.saved_grad = grad.clone()
- log_probs.register_hook(grad_hook)
 
  costs = fn(log_probs, labels, lengths, label_lengths)
  cost = torch.sum(costs)
  cost.backward()
- grads = log_probs.saved_grad
- if use_cuda:
- costs = costs.cpu()
- grads = grads.cpu()
- for i, a in enumerate(acts.grad.data.numpy().reshape(-1)):
- if i % 6 == 0: print(end='\n')
- print('{:.6f}, '.format(a), end='')
- print()
- # print(acts.grad.data.numpy())
- return costs.data.numpy(), grads.data.numpy()
+ print(repr(acts.grad.data.cpu().numpy()))
+ return costs.data.cpu().numpy(), acts.grad.data.cpu().numpy()
 
 
 def small_test():
- acts = np.array([[[0.1, 0.6, 0.1, 0.1, 0.1],
+ acts = np.array([[[[0.1, 0.6, 0.1, 0.1, 0.1],
  [0.1, 0.1, 0.6, 0.1, 0.1],
  [0.1, 0.1, 0.2, 0.8, 0.1]],
  [[0.1, 0.6, 0.1, 0.1, 0.1],
  [0.1, 0.1, 0.2, 0.1, 0.1],
- [0.7, 0.1, 0.2, 0.1, 0.1]]])
+ [0.7, 0.1, 0.2, 0.1, 0.1]]]])
  labels = [[1, 2]]
 
- acts = acts[None, ...]
-
  cost, grads = wrap_and_call(acts, labels)
  expected_cost = 4.495666
- expected_grads = np.array([[[-0.308198071906, -0.6918019280939998, 0.0, 0.0, 0.0],
- [-0.308198071906, 0.0, -0.3836038561880001, 0.0, 0.0],
- [-0.3836038561880001, 0.0, 0.0, 0.0, 0.0]],
- [[0.0, -0.308198071906, 0.0, 0.0, 0.0],
- [0.0, 0.0, -0.6163961438119995, 0.0, 0.0],
- [-0.9999999999999991, 0.0, 0.0, 0.0, 0.0]]])
+ expected_grads = np.array([[[[-0.13116688, -0.3999269 , 0.17703125, 0.17703125,
+ 0.17703125],
+ [-0.18572757, 0.12247056, -0.18168412, 0.12247056,
+ 0.12247056],
+ [-0.32091254, 0.06269141, 0.06928472, 0.12624499,
+ 0.06269141]],
+
+ [[ 0.05456069, -0.21824276, 0.05456069, 0.05456069,
+ 0.05456069],
+ [ 0.12073959, 0.12073959, -0.48295835, 0.12073959,
+ 0.12073959],
+ [-0.6925882 , 0.16871116, 0.18645467, 0.16871116,
+ 0.16871116]]]])
  assert np.allclose(cost, expected_cost, rtol=1e-6), \
  "small_test costs mismatch."
+ print(grads) # TODO change this exptected grad to actis.
  assert np.allclose(grads, expected_grads), \
  "small_test gradient mismatch."
 
@@ -121,38 +116,38 @@ def big_test():
  [0.6607698886038497, 0.3771277082495921, 0.3580209022231813]]]]
 
  expected_costs = [4.2806528590890736, 3.9384369822503591]
- expected_grads = [
- [[[-0.4322264564338117, -0.5677735435661883, 0.0],
- [-0.36565009313836844, 0.0, -0.20212345042782007],
- [-0.20212345042782007, 0.0, 0.0]],
+ expected_grads = [[[[-1.86843902e-01, -6.25548810e-02, 2.49398798e-01],
+ [-2.03376666e-01, 2.02399328e-01, 9.77333169e-04],
+ [-1.41016081e-01, 7.91234672e-02, 6.18926100e-02]],
 
- [[-0.16521672442463506, -0.2670097320091765, 0.0],
- [-0.3943653886107811, 0.0, -0.2382944365367636],
- [-0.44041788696458367, 0.0, 0.0]],
+  [[-1.15517676e-02, -8.12802389e-02, 9.28319991e-02],
+  [-1.54257029e-01, 2.29432687e-01, -7.51756504e-02],
+  [-2.46593088e-01, 1.46404594e-01, 1.00188486e-01]],
 
- [[-0.052129794015740985, -0.11308693040889405, 0.0],
- [-0.18313786985332664, 0.0, -0.3243144491663483],
- [-0.7647323361309323, 0.0, 0.0]],
+  [[-1.29182907e-02, -6.15932420e-02, 7.45115355e-02],
+  [-5.59857301e-02, 2.19830811e-01, -1.63845062e-01],
+  [-4.97626871e-01, 2.09239945e-01, 2.88386941e-01]],
 
- [[0.0, -0.052129794015740985, 0.0],
- [0.0, 0.0, -0.23526766386906767],
- [-1.0, 0.0, 0.0]]],
+  [[ 1.36048580e-02, -3.02196294e-02, 1.66147724e-02],
+  [ 1.13924511e-01, 6.27811998e-02, -1.76705718e-01],
+  [-6.67078257e-01, 3.67658824e-01, 2.99419403e-01]]],
 
- [[[-0.7161424128232795, -0.2838575871767207, 0.0],
- [-0.18382932237365335, -0.10002826480306751, 0.0],
- [-0.10002826480306751, 0.0, 0.0]],
 
- [[-0.41121794618117213, -0.3049244666421072, 0.0],
- [-0.3295759402552584, -0.15917784876050195, 0.0],
- [-0.2592061135635692, 0.0, 0.0]],
+  [[[-3.56343776e-01, -5.53474613e-02, 4.11691219e-01],
+  [-9.69219357e-02, 2.94591039e-02, 6.74628317e-02],
+  [-6.35175705e-02, 2.76544970e-02, 3.58630717e-02]],
 
- [[-0.11607642141651396, -0.29514152476465827, 0.0],
- [-0.2865333615432337, -0.3381841034766833, 0.0],
- [-0.5973902170402529, 0.0, 0.0]],
+  [[-1.54499024e-01, -7.39420280e-02, 2.28441030e-01],
+  [-1.66789949e-01, -8.78955179e-05, 1.66877866e-01],
+  [-1.72369644e-01, 1.05565332e-01, 6.68043196e-02]],
 
- [[0.0, -0.11607642141651396, 0.0],
- [0.0, -0.4026097829597475, 0.0],
- [-1.0, 0.0, 0.0]]]]
+ [[ 2.38748826e-02, -1.18255816e-01, 9.43809375e-02],
+ [-1.04707085e-01, -1.08934477e-01, 2.13641584e-01],
+ [-3.69844258e-01, 1.80118099e-01, 1.89726159e-01]],
+
+ [[ 2.57137045e-02, -7.94617534e-02, 5.37480488e-02],
+ [ 1.22328237e-01, -2.38788679e-01, 1.16460443e-01],
+ [-5.98686993e-01, 3.02203178e-01, 2.96483815e-01]]]]
 
  activations = np.array(activations)
  labels = [[1, 2],
@@ -163,33 +158,16 @@ def big_test():
  assert np.allclose(costs, sum(expected_costs)), \
  "big_test average costs mismatch."
 
- assert np.allclose(grads, expected_grads), \
+ assert np.allclose(grads, expected_grads, rtol=1e-3), \
  "big_test grads for average cost mismatch."
 
-def time_test(blank=0):
- batch_size = args.B
- vocab_size = args.V
- input_len = args.T
- output_len = args.U
- acts = np.random.rand(batch_size, input_len, output_len + 1, vocab_size)
- labels = np.random.randint(1, vocab_size, (batch_size, output_len))
-
- start = time.time()
- iters = 10
- for _ in range(iters):
- wrap_and_call(acts, labels)
- end = time.time()
-
- print("Time per iteration: {:.3f}(s)".format((end-start)/iters))
-
-
 if __name__ == "__main__":
  use_cuda = False
- # small_test()
+ small_test()
  big_test()
- # print("CPU Tests passed!")
- # if torch.cuda.is_available():
- #  use_cuda = True
- #  small_test()
- # print("GPU Tests passed!")
- # time_test()
+ print("CPU Tests passed!")
+ if torch.cuda.is_available():
+ use_cuda = True
+ small_test()
+  big_test()
+  print("GPU Tests passed!")