remove debugging lines

Thinklab-SJTU · Aug 13, 2023 · 92d9fd5 · 92d9fd5
1 parent 468b9d2
commit 92d9fd5
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Showing 3 changed files with 3 additions and 14 deletions.
diff --git a/pygmtools/mindspore_backend.py b/pygmtools/mindspore_backend.py
@@ -223,19 +223,14 @@ def rrwm(K: mindspore.Tensor, n1: mindspore.Tensor, n2: mindspore.Tensor, n1max,
     """
     mindspore implementation of RRWM algorithm.
     """
-    import time
-    lp1 = sk = 0
-    t1 = time.time()
     batch_num, n1, n2, n1max, n2max, n1n2, v0 = _check_and_init_gm(K, n1, n2, n1max, n2max, x0)
     # rescale the values in K
     d = K.sum(axis=2, keepdims=True)
     dmax = d.max(axis=1, keepdims=True)
     K = K / (dmax + d.min() * 1e-5)
     v = v0
-    t2 = time.time()
     for i in range(max_iter):
         # random walk
-        t3 = time.time()
         v = mindspore.ops.BatchMatMul()(K, v)
         last_v = v
         n = mindspore.ops.norm(v, axis=1, p=1, keep_dims=True)
@@ -244,20 +239,14 @@ def rrwm(K: mindspore.Tensor, n1: mindspore.Tensor, n2: mindspore.Tensor, n1max,
         # reweighted jump
         s = v.view(batch_num, int(n2max), int(n1max)).swapaxes(1, 2)
         s = beta * s / s.max(axis=1, keepdims=True).max(axis=2, keepdims=True)
-        t4 = time.time()
-        lp1 += (t4 - t3)
-        # print(n1, n2)
         v = alpha * sinkhorn(s, n1, n2, max_iter=sk_iter, batched_operation=True).swapaxes(1, 2).reshape(batch_num, n1n2, 1) + \
             (1 - alpha) * v
-        t5 = time.time()
-        # print(s.shape)
-        sk += (t5 - t4)
         n = mindspore.ops.norm(v, axis=1, p=1, keep_dims=True)
         v = mindspore.ops.matmul(v, 1 / n)
 
         if (v - last_v).sum().sqrt() < 1e-5:
             break
-    # print(f'pre:{t2-t1:.4f}, lp1:{lp1:.4f}, sk:{sk:.4f}')
+
     return v.view(batch_num, int(n2max), int(n1max)).swapaxes(1, 2)
 
 

diff --git a/pygmtools/paddle_backend.py b/pygmtools/paddle_backend.py
@@ -309,7 +309,7 @@ def _check_and_init_gm(K, n1, n2, n1max, n2max, x0):
         n1max = paddle.max(n1)
     if n2max is None:
         n2max = paddle.max(n2)
-        
+
     assert n1max * n2max == n1n2, 'the input size of K does not match with n1max * n2max!'
 
     # initialize x0 (also v0)

diff --git a/pygmtools/pytorch_backend.py b/pygmtools/pytorch_backend.py
@@ -20,7 +20,7 @@
 from .pytorch_astar_modules import GCNConv, AttentionModule, TensorNetworkModule, GraphPair, \
     VERY_LARGE_INT, to_dense_adj, to_dense_batch, default_parameter, check_layer_parameter, node_metric
 from torch import Tensor
-# from pygmtools.a_star import a_star
+from pygmtools.a_star import a_star
 
 #############################################
 #     Linear Assignment Problem Solvers     #