added .copy() to weights in wexchange

1 files changed, 9 insertions, 9 deletions

diff --git a/dnn/torch/weight-exchange/wexchange/torch/torch.py b/dnn/torch/weight-exchange/wexchange/torch/torch.py
index 2f479a28..580ea3bf 100644
--- a/dnn/torch/weight-exchange/wexchange/torch/torch.py
+++ b/dnn/torch/weight-exchange/wexchange/torch/torch.py
@@ -39,14 +39,14 @@ def dump_torch_gru_weights(where, gru, name='gru', input_sparse=False, recurrent
     assert gru.num_layers == 1
     assert gru.bidirectional == False
 
-    w_ih = gru.weight_ih_l0.detach().cpu().numpy()
-    w_hh = gru.weight_hh_l0.detach().cpu().numpy()
+    w_ih = gru.weight_ih_l0.detach().cpu().numpy().copy()
+    w_hh = gru.weight_hh_l0.detach().cpu().numpy().copy()
     if hasattr(gru, 'bias_ih_l0'):
-        b_ih = gru.bias_ih_l0.detach().cpu().numpy()
+        b_ih = gru.bias_ih_l0.detach().cpu().numpy().copy()
     else:
         b_ih = None
     if hasattr(gru, 'bias_hh_l0'):
-        b_hh = gru.bias_hh_l0.detach().cpu().numpy()
+        b_hh = gru.bias_hh_l0.detach().cpu().numpy().copy()
     else:
         b_hh = None
 
@@ -81,11 +81,11 @@ def load_torch_gru_weights(where, gru):
 
 def dump_torch_dense_weights(where, dense, name='dense', scale=1/128, sparse=False, diagonal=False, quantize=False):
 
-    w = dense.weight.detach().cpu().numpy()
+    w = dense.weight.detach().cpu().numpy().copy()
     if dense.bias is None:
         b = np.zeros(dense.out_features, dtype=w.dtype)
     else:
-        b = dense.bias.detach().cpu().numpy()
+        b = dense.bias.detach().cpu().numpy().copy()
 
     if isinstance(where, CWriter):
         return print_dense_layer(where, name, w, b, scale=scale, format='torch', sparse=sparse, diagonal=diagonal, quantize=quantize)
@@ -110,11 +110,11 @@ def load_torch_dense_weights(where, dense):
 
 def dump_torch_conv1d_weights(where, conv, name='conv', scale=1/128, quantize=False):
 
-    w = conv.weight.detach().cpu().numpy()
+    w = conv.weight.detach().cpu().numpy().copy()
     if conv.bias is None:
         b = np.zeros(conv.out_channels, dtype=w.dtype)
     else:
-        b = conv.bias.detach().cpu().numpy()
+        b = conv.bias.detach().cpu().numpy().copy()
 
     if isinstance(where, CWriter):
 
@@ -141,7 +141,7 @@ def load_torch_conv1d_weights(where, conv):
 def dump_torch_embedding_weights(where, emb):
     os.makedirs(where, exist_ok=True)
 
-    w = emb.weight.detach().cpu().numpy()
+    w = emb.weight.detach().cpu().numpy().copy()
     np.save(os.path.join(where, 'weight.npy'), w)