wip

run_classifier_pytorch.py

@@ -20,7 +20,7 @@ from __future__ import print_function
 
 import csv
 import os
-# import modeling_pytorch
+import modeling_pytorch
 # import optimization
 import tokenization_pytorch
 import torch
@@ -212,6 +212,39 @@ class DataProcessor(object):
             return lines
     
             
+class MrpcProcessor(DataProcessor):
+    """Processor for the MRPC data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        print("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "%s-%s" % (set_type, i)
+            text_a = tokenization_pytorch.convert_to_unicode(line[3])
+            text_b = tokenization_pytorch.convert_to_unicode(line[4])
+            label = tokenization_pytorch.convert_to_unicode(line[0])
+            examples.append(
+                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+
 class MnliProcessor(DataProcessor):
     """Processor for the MultiNLI data set (GLUE version)."""
 
@@ -395,13 +428,13 @@ def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
 
 
 def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
-                                         num_train_steps, num_warmup_steps,
+                    num_train_steps, num_warmup_steps, use_gpu,
                     use_one_hot_embeddings):
     raise NotImplementedError()
     ### ATTENTION - I removed the `use_tpu` argument
     
 
-def input_fn_builder(features, seq_length, is_training, drop_remainder):
+def input_fn_builder(features, seq_length, is_training, eval_drop_remainder):
     """Creates an `input_fn` closure to be passed to TPUEstimator.""" ### ATTENTION - To rewrite ###
 
     all_input_ids = []
@@ -422,21 +455,17 @@ def input_fn_builder(features, seq_length, is_training, drop_remainder):
         num_examples = len(features)
         
         device = torch.device("cuda") if args.use_gpu else torch.device("cpu")
-        d = {"input_ids":
-                        torch.IntTensor(all_input_ids, device = device), #Requires_grad=False by default
-            "input_mask":
-                        torch.IntTensor(all_input_mask, device = device),
-            "segment_ids":
-                        torch.IntTensor(all_segment_ids, device = device),
-            "label_ids":
-                        torch.IntTensor(all_label_ids, device = device)
-            }
-                        
-        if is_training:
-            d = d.repeat()
-            d = d.shuffle(buffer_size=100)
-
-        d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
+        d = torch.utils.data.TensorDataset({ ## BUG THIS IS NOT WORKING.... ###
+            "input_ids": torch.IntTensor(all_input_ids, device=device), #Requires_grad=False by default
+            "input_mask": torch.IntTensor(all_input_mask, device=device),
+            "segment_ids": torch.IntTensor(all_segment_ids, device=device),
+            "label_ids": torch.IntTensor(all_label_ids, device=device)
+            })
+
+        shuffle = True if training else False
+        d = torch.utils.data.DataLoader(dataset=d, batch_size=batch_size, 
+                                        shuffle=shuffle,drop_last=drop_remainder)       
+        # Cf https://pytorch.org/tutorials/beginner/data_loading_tutorial.html
         return d
 
     return input_fn
@@ -452,7 +481,7 @@ def main(_):
     if not args.do_train and not args.do_eval:
         raise ValueError("At least one of `do_train` or `do_eval` must be True.")
         
-    bert_config = modeling.BertConfig.from_json_file(args.bert_config_file)
+    bert_config = modeling_pytorch.BertConfig.from_json_file(args.bert_config_file)
     
     if args.max_seq_length > bert_config.max_position_embeddings:
         raise ValueError(
@@ -461,7 +490,7 @@ def main(_):
             (args.max_seq_length, bert_config.max_position_embeddings))
     
     if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
-                    raise ConfigurationError(f"Output directory ({args.output_dir}) already exists and is "
+                    raise ValueError(f"Output directory ({args.output_dir}) already exists and is "
                                      f"not empty.")
     os.makedirs(args.output_dir, exist_ok=True)
 
@@ -474,7 +503,7 @@ def main(_):
 
     label_list = processor.get_labels()
 
-    tokenizer = tokenization.FullTokenizer(
+    tokenizer = tokenization_pytorch.FullTokenizer(
         vocab_file=args.vocab_file, do_lower_case=args.do_lower_case)
         
     # tpu_cluster_resolver = None
@@ -514,13 +543,12 @@ def main(_):
     
     # If TPU is not available, this will fall back to normal Estimator on CPU
     # or GPU. - TO DO
-    for batch in 
-    estimator = tf.contrib.tpu.TPUEstimator(
-        use_tpu=args.use_tpu,
-        model_fn=model_fn,
-        config=run_config,
-        train_batch_size=args.train_batch_size,
-        eval_batch_size=args.eval_batch_size)
+    # estimator = tf.contrib.tpu.TPUEstimator(
+    #     use_tpu=args.use_tpu,
+    #     model_fn=model_fn,
+    #     config=run_config,
+    #     train_batch_size=args.train_batch_size,
+    #     eval_batch_size=args.eval_batch_size)
     
     if args.do_train:
         train_features = convert_examples_to_features(
@@ -529,21 +557,27 @@ def main(_):
         logger.info("  Num examples = %d", len(train_examples))
         logger.info("  Batch size = %d", args.train_batch_size)
         logger.info("  Num steps = %d", num_train_steps)
-        train_input_fn = input_fn_builder(
+        train_input = input_fn_builder(
             features=train_features,
             seq_length=args.max_seq_length,
             is_training=True,
             drop_remainder=True)
-        estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
+        # estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
+        for batch_ix, batch in train_input:
+            output = model_fn(batch)
+            loss = output["loss"]
+            loss.backward()
+            
+            
     
     if args.do_eval:
         eval_examples = processor.get_dev_examples(args.data_dir)
         eval_features = convert_examples_to_features(
             eval_examples, label_list, args.max_seq_length, tokenizer)
 
-        tf.logging.info("***** Running evaluation *****")
-        tf.logging.info("  Num examples = %d", len(eval_examples))
-        tf.logging.info("  Batch size = %d", args.eval_batch_size)
+        logger.info("***** Running evaluation *****")
+        logger.info("  Num examples = %d", len(eval_examples))
+        logger.info("  Batch size = %d", args.eval_batch_size)
 
         # This tells the estimator to run through the entire set.
         eval_steps = None
@@ -564,10 +598,10 @@ def main(_):
         result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
 
         output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
-        with tf.gfile.GFile(output_eval_file, "w") as writer:
-            tf.logging.info("***** Eval results *****")
+        with open(output_eval_file, "w") as writer:
+            logger.info("***** Eval results *****")
             for key in sorted(result.keys()):
-                tf.logging.info("  %s = %s", key, str(result[key]))
+                logger.info("  %s = %s", key, str(result[key]))
                 writer.write("%s = %s\n" % (key, str(result[key])))
     
 if __name__ == "__main__":