dygraph_seq2seq_fix_infer (#4191)

dygraph/seq2seq/attention_model.py

@@ -269,9 +269,8 @@ class AttentionModel(fluid.dygraph.Layer):
             enc_outputs = self.tile_beam_merge_with_batch(enc_outputs)
             enc_padding_mask = self.tile_beam_merge_with_batch(enc_padding_mask)
             batch_beam_shape = (self.batch_size, self.beam_size)
-            batch_beam_shape_1 = (self.batch_size, self.beam_size, 1)
             vocab_size_tensor = to_variable(np.full((1), self.tar_vocab_size))
-            start_token_tensor = to_variable(np.full(batch_beam_shape_1, self.beam_start_token, dtype='int64')) # remove last dim 1 in v1.7
+            start_token_tensor = to_variable(np.full(batch_beam_shape, self.beam_start_token, dtype='int64'))
             end_token_tensor = to_variable(np.full(batch_beam_shape, self.beam_end_token, dtype='int64'))
             step_input = self.tar_embeder(start_token_tensor)
             input_feed = to_variable(np.zeros((self.batch_size, self.hidden_size), dtype='float32'))
@@ -348,7 +347,7 @@ class AttentionModel(fluid.dygraph.Layer):
                 dec_hidden, dec_cell = new_dec_hidden, new_dec_cell
                 beam_finished = next_finished
                 beam_state_log_probs = next_log_probs
-                step_input = self.tar_embeder(fluid.layers.unsqueeze(token_indices, 2)) # remove unsqueeze in v1.7
+                step_input = self.tar_embeder(token_indices)
                 predicted_ids.append(token_indices)
                 parent_ids.append(beam_indices)
 
@@ -359,4 +358,4 @@ class AttentionModel(fluid.dygraph.Layer):
             return predicted_ids
         else:
             print("not support mode ", self.mode)
-            raise Exception("not support mode: " + self.mode)
\ No newline at end of file
+            raise Exception("not support mode: " + self.mode)

dygraph/seq2seq/base_model.py

@@ -202,7 +202,7 @@ class BaseModel(fluid.dygraph.Layer):
             batch_beam_shape = (self.batch_size, self.beam_size)
             #batch_beam_shape_1 = (self.batch_size, self.beam_size, 1)
             vocab_size_tensor = to_variable(np.full((1), self.tar_vocab_size))
-            start_token_tensor = to_variable(np.full(batch_beam_shape, self.beam_start_token, dtype='int64')) # remove last dim 1 in v1.7
+            start_token_tensor = to_variable(np.full(batch_beam_shape, self.beam_start_token, dtype='int64')) 
             end_token_tensor = to_variable(np.full(batch_beam_shape, self.beam_end_token, dtype='int64'))
             step_input = self.tar_embeder(start_token_tensor)
             beam_finished = to_variable(np.full(batch_beam_shape, 0, dtype='float32'))
@@ -271,7 +271,7 @@ class BaseModel(fluid.dygraph.Layer):
                 dec_hidden, dec_cell = new_dec_hidden, new_dec_cell
                 beam_finished = next_finished
                 beam_state_log_probs = next_log_probs
-                step_input = self.tar_embeder(fluid.layers.unsqueeze(token_indices, 2)) # remove unsqueeze in v1.7
+                step_input = self.tar_embeder(token_indices) # remove unsqueeze in v1.7
                 predicted_ids.append(token_indices)
                 parent_ids.append(beam_indices)
 
@@ -282,4 +282,4 @@ class BaseModel(fluid.dygraph.Layer):
             return predicted_ids
         else:
             print("not support mode ", self.mode)
-            raise Exception("not support mode: " + self.mode)
\ No newline at end of file
+            raise Exception("not support mode: " + self.mode)

dygraph/seq2seq/infer.py

@@ -70,7 +70,6 @@ def infer():
         # So we can set dropout to 0
         if args.attention:
             model = AttentionModel(
-                "attention_model",
                 hidden_size,
                 src_vocab_size,
                 tar_vocab_size,
@@ -82,7 +81,6 @@ def infer():
                 mode='beam_search')
         else:
             model = BaseModel(
-                "base_model",
                 hidden_size,
                 src_vocab_size,
                 tar_vocab_size,
@@ -134,11 +132,11 @@ def infer():
 
             for batch_id, batch in enumerate(train_data_iter):
                 input_data_feed, word_num = prepare_input(batch, epoch_id=0)
+                # import ipdb; ipdb.set_trace()
                 outputs = model(input_data_feed)
-         
                 for i in range(outputs.shape[0]):
-                    ins = fluid.Variable.numpy(outputs[i])
-                    res = [tar_id2vocab[e] for e in ins[:, 0].reshape(-1)]
+                    ins = outputs[i].numpy()
+                    res = [tar_id2vocab[int(e)] for e in ins[:, 0].reshape(-1)]
                     new_res = []
                     for ele in res:
                         if ele == "</s>":