Move MPCNN API to common module (#105)

common/__init__.py

@@ -0,0 +1,2 @@
+from .evaluators import *
+from .trainers import *

common/dataset.py

@@ -24,7 +24,7 @@ class UnknownWordVecCache(object):
         return cls.cache[size_tup]
 
 
-class MPCNNDatasetFactory(object):
+class DatasetFactory(object):
     """
     Get the corresponding Dataset class for a particular dataset.
     """

common/evaluation.py

@@ -1,11 +1,11 @@
-from mp_cnn.evaluators.sick_evaluator import SICKEvaluator
-from mp_cnn.evaluators.msrvid_evaluator import MSRVIDEvaluator
-from mp_cnn.evaluators.trecqa_evaluator import TRECQAEvaluator
-from mp_cnn.evaluators.wikiqa_evaluator import WikiQAEvaluator
+from .evaluators.sick_evaluator import SICKEvaluator
+from .evaluators.msrvid_evaluator import MSRVIDEvaluator
+from .evaluators.trecqa_evaluator import TRECQAEvaluator
+from .evaluators.wikiqa_evaluator import WikiQAEvaluator
 from nce.nce_pairwise_mp.evaluators.trecqa_evaluator import TRECQAEvaluatorNCE
 from nce.nce_pairwise_mp.evaluators.wikiqa_evaluator import WikiQAEvaluatorNCE
 
-class MPCNNEvaluatorFactory(object):
+class EvaluatorFactory(object):
     """
     Get the corresponding Evaluator class for a particular dataset.
     """
@@ -27,9 +27,9 @@ class MPCNNEvaluatorFactory(object):
             return None
 
         if nce:
-            evaluator_map = MPCNNEvaluatorFactory.evaluator_map_nce
+            evaluator_map = EvaluatorFactory.evaluator_map_nce
         else:
-            evaluator_map = MPCNNEvaluatorFactory.evaluator_map
+            evaluator_map = EvaluatorFactory.evaluator_map
 
         if not hasattr(dataset_cls, 'NAME'):
             raise ValueError('Invalid dataset. Dataset should have NAME attribute.')

common/evaluators/msrvid_evaluator.py

@@ -2,7 +2,7 @@ from scipy.stats import pearsonr
 import torch
 import torch.nn.functional as F
 
-from mp_cnn.evaluators.evaluator import Evaluator
+from .evaluator import Evaluator
 
 
 class MSRVIDEvaluator(Evaluator):

common/evaluators/qa_evaluator.py

@@ -1,6 +1,6 @@
 import torch.nn.functional as F
 
-from mp_cnn.evaluators.evaluator import Evaluator
+from .evaluator import Evaluator
 from utils.relevancy_metrics import get_map_mrr
 
 

common/evaluators/sick_evaluator.py

@@ -2,7 +2,7 @@ from scipy.stats import pearsonr, spearmanr
 import torch
 import torch.nn.functional as F
 
-from mp_cnn.evaluators.evaluator import Evaluator
+from .evaluator import Evaluator
 
 
 class SICKEvaluator(Evaluator):

common/evaluators/trecqa_evaluator.py

@@ -1,4 +1,4 @@
-from mp_cnn.evaluators.qa_evaluator import QAEvaluator
+from .qa_evaluator import QAEvaluator
 
 
 class TRECQAEvaluator(QAEvaluator):

common/evaluators/wikiqa_evaluator.py

@@ -1,4 +1,4 @@
-from mp_cnn.evaluators.qa_evaluator import QAEvaluator
+from .qa_evaluator import QAEvaluator
 
 
 class WikiQAEvaluator(QAEvaluator):

common/train.py

@@ -1,12 +1,12 @@
-from mp_cnn.trainers.sick_trainer import SICKTrainer
-from mp_cnn.trainers.msrvid_trainer import MSRVIDTrainer
-from mp_cnn.trainers.trecqa_trainer import TRECQATrainer
-from mp_cnn.trainers.wikiqa_trainer import WikiQATrainer
+from .trainers.sick_trainer import SICKTrainer
+from .trainers.msrvid_trainer import MSRVIDTrainer
+from .trainers.trecqa_trainer import TRECQATrainer
+from .trainers.wikiqa_trainer import WikiQATrainer
 from nce.nce_pairwise_mp.trainers.trecqa_trainer import TRECQATrainerNCE
 from nce.nce_pairwise_mp.trainers.wikiqa_trainer import WikiQATrainerNCE
 
 
-class MPCNNTrainerFactory(object):
+class TrainerFactory(object):
     """
     Get the corresponding Trainer class for a particular dataset.
     """
@@ -25,9 +25,9 @@ class MPCNNTrainerFactory(object):
     @staticmethod
     def get_trainer(dataset_name, model, train_loader, trainer_config, train_evaluator, test_evaluator, dev_evaluator=None, nce=False):
         if nce:
-            trainer_map = MPCNNTrainerFactory.trainer_map_nce
+            trainer_map = TrainerFactory.trainer_map_nce
         else:
-            trainer_map = MPCNNTrainerFactory.trainer_map
+            trainer_map = TrainerFactory.trainer_map
 
         if dataset_name not in trainer_map:
             raise ValueError('{} is not implemented.'.format(dataset_name))

common/trainers/msrvid_trainer.py

@@ -6,7 +6,7 @@ import torch.nn.functional as F
 from torch.optim.lr_scheduler import ReduceLROnPlateau
 from scipy.stats import pearsonr
 
-from mp_cnn.trainers.trainer import Trainer
+from .trainer import Trainer
 
 
 class MSRVIDTrainer(Trainer):

common/trainers/qa_trainer.py

@@ -4,7 +4,7 @@ import torch
 import torch.nn.functional as F
 from torch.optim.lr_scheduler import ReduceLROnPlateau
 
-from mp_cnn.trainers.trainer import Trainer
+from .trainer import Trainer
 
 
 class QATrainer(Trainer):

common/trainers/sick_trainer.py

@@ -4,7 +4,7 @@ import torch
 import torch.nn.functional as F
 from torch.optim.lr_scheduler import ReduceLROnPlateau
 
-from mp_cnn.trainers.trainer import Trainer
+from .trainer import Trainer
 
 
 class SICKTrainer(Trainer):

common/trainers/trecqa_trainer.py

@@ -1,4 +1,4 @@
-from mp_cnn.trainers.qa_trainer import QATrainer
+from .qa_trainer import QATrainer
 
 
 class TRECQATrainer(QATrainer):

common/trainers/wikiqa_trainer.py

@@ -1,4 +1,4 @@
-from mp_cnn.trainers.qa_trainer import QATrainer
+from .qa_trainer import QATrainer
 
 
 class WikiQATrainer(QATrainer):

mp_cnn/README.md

@@ -11,7 +11,7 @@ Please ensure you have followed instructions in the main [README](../README.md)
 To run MP-CNN on the SICK dataset, use the following command. `--dropout 0` is for mimicking the original paper, although adding dropout can improve performance. If you have any problems running it check the Troubleshooting section below.
 
 ```
-python main.py mpcnn.sick.model.castor --dataset sick --epochs 19 --epsilon 1e-7 --dropout 0
+python -m mp_cnn mpcnn.sick.model.castor --dataset sick --epochs 19 --epsilon 1e-7 --dropout 0
 ```
 
 | Implementation and config        | Pearson's r   | Spearman's p  |
@@ -23,7 +23,7 @@ python main.py mpcnn.sick.model.castor --dataset sick --epochs 19 --epsilon 1e-7
 
 To run MP-CNN on the MSRVID dataset, use the following command:
 ```
-python main.py mpcnn.msrvid.model.castor --dataset msrvid --batch-size 16 --epsilon 1e-7 --epochs 32 --dropout 0 --regularization 0.0025
+python -m mp_cnn mpcnn.msrvid.model.castor --dataset msrvid --batch-size 16 --epsilon 1e-7 --epochs 32 --dropout 0 --regularization 0.0025
 ```
 
 | Implementation and config        | Pearson's r   |
@@ -37,7 +37,7 @@ To run MP-CNN on (Raw) TrecQA, you first need to run `./get_trec_eval.sh` in `ut
 
 Then, you can run:
 ```
-python main.py mpcnn.trecqa.model --dataset trecqa --epochs 5 --regularization 0.0005 --dropout 0.5 --eps 0.1
+python -m mp_cnn mpcnn.trecqa.model --dataset trecqa --epochs 5 --regularization 0.0005 --dropout 0.5 --eps 0.1
 ```
 
 | Implementation and config        | map    | mrr    |
@@ -53,7 +53,7 @@ You also need `trec_eval` for this dataset, similar to TrecQA.
 
 Then, you can run:
 ```
-python main.py mpcnn.wikiqa.model --epochs 10 --dataset wikiqa --batch-size 64 --lr 0.0004 --regularization 0.02
+python -m mp_cnn mpcnn.wikiqa.model --epochs 10 --dataset wikiqa --batch-size 64 --lr 0.0004 --regularization 0.02
 ```
 | Implementation and config        | map    | mrr    |
 | -------------------------------- |:------:|:------:|
@@ -67,7 +67,7 @@ These are not the optimal hyperparameters but they are decent. This README will
 
 To see all options available, use
 ```
-python main.py --help
+python -m mp_cnn --help
 ```
 
 ## Troubleshooting
@@ -75,9 +75,9 @@ python main.py --help
 ### ModuleNotFoundError: datasets
 ```
 Traceback (most recent call last):
-  File "main.py", line 9, in <module>
-    from dataset import MPCNNDatasetFactory
-  File "/u/z3tu/castorini/Castor/mp_cnn/dataset.py", line 12, in <module>
+  File "__main__.py", line 9, in <module>
+    from common.dataset import DatasetFactory
+  File "/u/z3tu/castorini/Castor/common/dataset.py", line 12, in <module>
     from datasets.sick import SICK
 ModuleNotFoundError: No module named 'datasets'
 ```

mp_cnn/__main__.py

@@ -8,10 +8,10 @@ import numpy as np
 import torch
 import torch.optim as optim
 
-from mp_cnn.dataset import MPCNNDatasetFactory
-from mp_cnn.evaluation import MPCNNEvaluatorFactory
-from mp_cnn.model import MPCNN
-from mp_cnn.train import MPCNNTrainerFactory
+from common.dataset import DatasetFactory
+from common.evaluation import EvaluatorFactory
+from common.train import TrainerFactory
+from .model import MPCNN
 
 
 if __name__ == '__main__':
@@ -62,7 +62,7 @@ if __name__ == '__main__':
     logger.info(pprint.pformat(vars(args)))
 
     dataset_cls, embedding, train_loader, test_loader, dev_loader \
-        = MPCNNDatasetFactory.get_dataset(args.dataset, args.word_vectors_dir, args.word_vectors_file, args.batch_size, args.device)
+        = DatasetFactory.get_dataset(args.dataset, args.word_vectors_dir, args.word_vectors_file, args.batch_size, args.device)
 
     filter_widths = list(range(1, args.max_window_size + 1)) + [np.inf]
     model = MPCNN(embedding, args.holistic_filters, args.per_dim_filters, filter_widths,
@@ -80,9 +80,9 @@ if __name__ == '__main__':
     else:
         raise ValueError('optimizer not recognized: it should be either adam or sgd')
 
-    train_evaluator = MPCNNEvaluatorFactory.get_evaluator(dataset_cls, model, train_loader, args.batch_size, args.device)
-    test_evaluator = MPCNNEvaluatorFactory.get_evaluator(dataset_cls, model, test_loader, args.batch_size, args.device)
-    dev_evaluator = MPCNNEvaluatorFactory.get_evaluator(dataset_cls, model, dev_loader, args.batch_size, args.device)
+    train_evaluator = EvaluatorFactory.get_evaluator(dataset_cls, model, train_loader, args.batch_size, args.device)
+    test_evaluator = EvaluatorFactory.get_evaluator(dataset_cls, model, test_loader, args.batch_size, args.device)
+    dev_evaluator = EvaluatorFactory.get_evaluator(dataset_cls, model, dev_loader, args.batch_size, args.device)
 
     trainer_config = {
         'optimizer': optimizer,
@@ -95,7 +95,7 @@ if __name__ == '__main__':
         'run_label': args.run_label,
         'logger': logger
     }
-    trainer = MPCNNTrainerFactory.get_trainer(args.dataset, model, train_loader, trainer_config, train_evaluator, test_evaluator, dev_evaluator)
+    trainer = TrainerFactory.get_trainer(args.dataset, model, train_loader, trainer_config, train_evaluator, test_evaluator, dev_evaluator)
 
     if not args.skip_training:
         total_params = 0
@@ -106,7 +106,7 @@ if __name__ == '__main__':
         trainer.train(args.epochs)
 
     model = torch.load(args.model_outfile)
-    saved_model_evaluator = MPCNNEvaluatorFactory.get_evaluator(dataset_cls, model, test_loader, args.batch_size, args.device)
+    saved_model_evaluator = EvaluatorFactory.get_evaluator(dataset_cls, model, test_loader, args.batch_size, args.device)
     scores, metric_names = saved_model_evaluator.get_scores()
     logger.info('Evaluation metrics for test')
     logger.info('\t'.join([' '] + metric_names))

mp_cnn/model.py

@@ -91,7 +91,7 @@ class MPCNN(nn.Module):
                 x2 = sent2_block_a[ws][pool]
                 batch_size = x1.size()[0]
                 comparison_feats.append(F.cosine_similarity(x1, x2).contiguous().view(batch_size, 1))
-                comparison_feats.append(F.pairwise_distance(x1, x2))
+                comparison_feats.append(F.pairwise_distance(x1, x2).unsqueeze(-1))
         return torch.cat(comparison_feats, dim=1)
 
     def _algo_2_vert_comp(self, sent1_block_a, sent2_block_a, sent1_block_b, sent2_block_b):
@@ -105,7 +105,7 @@ class MPCNN(nn.Module):
                     x2 = sent2_block_a[ws2][pool]
                     if (not np.isinf(ws1) and not np.isinf(ws2)) or (np.isinf(ws1) and np.isinf(ws2)):
                         comparison_feats.append(F.cosine_similarity(x1, x2).contiguous().view(batch_size, 1))
-                        comparison_feats.append(F.pairwise_distance(x1, x2))
+                        comparison_feats.append(F.pairwise_distance(x1, x2).unsqueeze(-1))
                         comparison_feats.append(torch.abs(x1 - x2))
 
         for pool in ('max', 'min'):
@@ -117,7 +117,7 @@ class MPCNN(nn.Module):
                     x2 = oG_2B[:, :, i]
                     batch_size = x1.size()[0]
                     comparison_feats.append(F.cosine_similarity(x1, x2).contiguous().view(batch_size, 1))
-                    comparison_feats.append(F.pairwise_distance(x1, x2))
+                    comparison_feats.append(F.pairwise_distance(x1, x2).unsqueeze(-1))
                     comparison_feats.append(torch.abs(x1 - x2))
 
         return torch.cat(comparison_feats, dim=1)

nce/nce_pairwise_mp/evaluators/qa_evaluator.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from mp_cnn.evaluators.evaluator import Evaluator
+from common.evaluators.evaluator import Evaluator
 from utils.relevancy_metrics import get_map_mrr
 
 

nce/nce_pairwise_mp/main.py

@@ -8,10 +8,10 @@ import numpy as np
 import torch
 import torch.optim as optim
 
-from mp_cnn.dataset import MPCNNDatasetFactory
-from mp_cnn.evaluation import MPCNNEvaluatorFactory
+from common.dataset import MPCNNDatasetFactory
+from common.evaluation import MPCNNEvaluatorFactory
 from nce.nce_pairwise_mp.model import MPCNN4NCE, PairwiseConv
-from mp_cnn.train import MPCNNTrainerFactory
+from common.train import MPCNNTrainerFactory
 
 
 if __name__ == '__main__':

nce/nce_pairwise_mp/trainers/qa_trainer.py

@@ -5,7 +5,7 @@ import torch
 import torch.nn.functional as F
 from torch.optim.lr_scheduler import ReduceLROnPlateau
 
-from mp_cnn.trainers.trainer import Trainer
+from common.trainers.trainer import Trainer
 from utils.nce_neighbors import get_nearest_neg_id, get_random_neg_id, get_batch
 
 class QATrainer(Trainer):