`input_fn_builder` WIP

run_classifier_pytorch.py

@@ -23,6 +23,7 @@ import os
 # import modeling_pytorch
 # import optimization
 import tokenization_pytorch
+import torch
 
 import logging
 logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s', 
@@ -381,4 +382,64 @@ def _truncate_seq_pair(tokens_a, tokens_b, max_length):
         if len(tokens_a) > len(tokens_b):
             tokens_a.pop()
         else:
-            tokens_b.pop()
+            tokens_b.pop()
+            
+
+def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
+                                 labels, num_labels, use_one_hot_embeddings):
+    raise NotImplementedError()
+
+
+def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
+                                         num_train_steps, num_warmup_steps,
+                                         use_one_hot_embeddings):
+    raise NotImplementedError()
+    ### ATTENTION - I removed the `use_tpu` argument
+    
+
+def input_fn_builder(features, seq_length, is_training, drop_remainder):
+    """Creates an `input_fn` closure to be passed to TPUEstimator.""" ### ATTENTION - To rewrite ###
+
+    all_input_ids = []
+    all_input_mask = []
+    all_segment_ids = []
+    all_label_ids = []
+
+    for feature in features:
+        all_input_ids.append(feature.input_ids)
+        all_input_mask.append(feature.input_mask)
+        all_segment_ids.append(feature.segment_ids)
+        all_label_ids.append(feature.label_id)
+
+    def input_fn(params):
+        """The actual input function."""
+        batch_size = params["batch_size"]
+
+        num_examples = len(features)
+
+        # This is for demo purposes and does NOT scale to large data sets. We do
+        # not use Dataset.from_generator() because that uses tf.py_func which is
+        # not TPU compatible. The right way to load data is with TFRecordReader.
+        d = tf.data.Dataset.from_tensor_slices({
+                "input_ids":
+                        torch.Tensor(all_input_ids, size=[num_examples, seq_length],
+                                    dtype=torch.int32, requires_grad=False),
+                "input_mask":
+                        torch.Tensor(all_input_mask, size=[num_examples, seq_length],
+                                    dtype=torch.int32, requires_grad=False),
+                "segment_ids":
+                        torch.Tensor(all_segment_ids, size=[num_examples, seq_length],
+                                    dtype=torch.int32, requires_grad=False),
+                "label_ids":
+                        torch.Tensor(all_label_ids, size=[num_examples], 
+                                    dtype=torch.int32, requires_grad=False)
+        })
+
+        if is_training:
+            d = d.repeat()
+            d = d.shuffle(buffer_size=100)
+
+        d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
+        return d
+
+    return input_fn