Add files using upload-large-folder tool

fairseq/docs/models.rst

@@ -0,0 +1,104 @@
+.. role:: hidden
+    :class: hidden-section
+
+.. module:: fairseq.models
+
+.. _Models:
+
+Models
+======
+
+A Model defines the neural network's ``forward()`` method and encapsulates all
+of the learnable parameters in the network. Each model also provides a set of
+named *architectures* that define the precise network configuration (e.g.,
+embedding dimension, number of layers, etc.).
+
+Both the model type and architecture are selected via the ``--arch``
+command-line argument. Once selected, a model may expose additional command-line
+arguments for further configuration.
+
+.. note::
+
+    All fairseq Models extend :class:`BaseFairseqModel`, which in turn extends
+    :class:`torch.nn.Module`. Thus any fairseq Model can be used as a
+    stand-alone Module in other PyTorch code.
+
+
+Convolutional Neural Networks (CNN)
+-----------------------------------
+
+.. module:: fairseq.models.fconv
+.. autoclass:: fairseq.models.fconv.FConvModel
+    :members:
+.. autoclass:: fairseq.models.fconv.FConvEncoder
+    :members:
+    :undoc-members:
+.. autoclass:: fairseq.models.fconv.FConvDecoder
+    :members:
+
+
+Long Short-Term Memory (LSTM) networks
+--------------------------------------
+
+.. module:: fairseq.models.lstm
+.. autoclass:: fairseq.models.lstm.LSTMModel
+    :members:
+.. autoclass:: fairseq.models.lstm.LSTMEncoder
+    :members:
+.. autoclass:: fairseq.models.lstm.LSTMDecoder
+    :members:
+
+
+Transformer (self-attention) networks
+-------------------------------------
+
+.. module:: fairseq.models.transformer
+.. autoclass:: fairseq.models.transformer.TransformerModel
+    :members:
+.. autoclass:: fairseq.models.transformer.TransformerEncoder
+    :members:
+.. autoclass:: fairseq.models.transformer.TransformerEncoderLayer
+    :members:
+.. autoclass:: fairseq.models.transformer.TransformerDecoder
+    :members:
+.. autoclass:: fairseq.models.transformer.TransformerDecoderLayer
+    :members:
+
+
+Adding new models
+-----------------
+
+.. currentmodule:: fairseq.models
+.. autofunction:: fairseq.models.register_model
+.. autofunction:: fairseq.models.register_model_architecture
+.. autoclass:: fairseq.models.BaseFairseqModel
+    :members:
+    :undoc-members:
+.. autoclass:: fairseq.models.FairseqEncoderDecoderModel
+    :members:
+    :undoc-members:
+.. autoclass:: fairseq.models.FairseqEncoderModel
+    :members:
+    :undoc-members:
+.. autoclass:: fairseq.models.FairseqLanguageModel
+    :members:
+    :undoc-members:
+.. autoclass:: fairseq.models.FairseqMultiModel
+    :members:
+    :undoc-members:
+.. autoclass:: fairseq.models.FairseqEncoder
+    :members:
+.. autoclass:: fairseq.models.CompositeEncoder
+    :members:
+.. autoclass:: fairseq.models.FairseqDecoder
+    :members:
+
+
+.. _Incremental decoding:
+
+Incremental decoding
+--------------------
+
+.. autoclass:: fairseq.models.FairseqIncrementalDecoder
+    :members:
+    :undoc-members:

fairseq/docs/tutorial_classifying_names.rst

@@ -0,0 +1,415 @@
+Tutorial: Classifying Names with a Character-Level RNN
+======================================================
+
+In this tutorial we will extend fairseq to support *classification* tasks. In
+particular we will re-implement the PyTorch tutorial for `Classifying Names with
+a Character-Level RNN <https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html>`_
+in fairseq. It is recommended to quickly skim that tutorial before beginning
+this one.
+
+This tutorial covers:
+
+1. **Preprocessing the data** to create dictionaries.
+2. **Registering a new Model** that encodes an input sentence with a simple RNN
+   and predicts the output label.
+3. **Registering a new Task** that loads our dictionaries and dataset.
+4. **Training the Model** using the existing command-line tools.
+5. **Writing an evaluation script** that imports fairseq and allows us to
+   interactively evaluate our model on new inputs.
+
+
+1. Preprocessing the data
+-------------------------
+
+The original tutorial provides raw data, but we'll work with a modified version
+of the data that is already tokenized into characters and split into separate
+train, valid and test sets.
+
+Download and extract the data from here:
+`tutorial_names.tar.gz <https://dl.fbaipublicfiles.com/fairseq/data/tutorial_names.tar.gz>`_
+
+Once extracted, let's preprocess the data using the :ref:`fairseq-preprocess`
+command-line tool to create the dictionaries. While this tool is primarily
+intended for sequence-to-sequence problems, we're able to reuse it here by
+treating the label as a "target" sequence of length 1. We'll also output the
+preprocessed files in "raw" format using the ``--dataset-impl`` option to
+enhance readability:
+
+.. code-block:: console
+
+  > fairseq-preprocess \
+    --trainpref names/train --validpref names/valid --testpref names/test \
+    --source-lang input --target-lang label \
+    --destdir names-bin --dataset-impl raw
+
+After running the above command you should see a new directory,
+:file:`names-bin/`, containing the dictionaries for *inputs* and *labels*.
+
+
+2. Registering a new Model
+--------------------------
+
+Next we'll register a new model in fairseq that will encode an input sentence
+with a simple RNN and predict the output label. Compared to the original PyTorch
+tutorial, our version will also work with batches of data and GPU Tensors.
+
+First let's copy the simple RNN module implemented in the `PyTorch tutorial
+<https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html#creating-the-network>`_.
+Create a new file named :file:`fairseq/models/rnn_classifier.py` with the
+following contents::
+
+    import torch
+    import torch.nn as nn
+
+    class RNN(nn.Module):
+
+        def __init__(self, input_size, hidden_size, output_size):
+            super(RNN, self).__init__()
+
+            self.hidden_size = hidden_size
+
+            self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
+            self.i2o = nn.Linear(input_size + hidden_size, output_size)
+            self.softmax = nn.LogSoftmax(dim=1)
+
+        def forward(self, input, hidden):
+            combined = torch.cat((input, hidden), 1)
+            hidden = self.i2h(combined)
+            output = self.i2o(combined)
+            output = self.softmax(output)
+            return output, hidden
+
+        def initHidden(self):
+            return torch.zeros(1, self.hidden_size)
+
+We must also *register* this model with fairseq using the
+:func:`~fairseq.models.register_model` function decorator. Once the model is
+registered we'll be able to use it with the existing :ref:`Command-line Tools`.
+
+All registered models must implement the :class:`~fairseq.models.BaseFairseqModel`
+interface, so we'll create a small wrapper class in the same file and register
+it in fairseq with the name ``'rnn_classifier'``::
+
+    from fairseq.models import BaseFairseqModel, register_model
+
+    # Note: the register_model "decorator" should immediately precede the
+    # definition of the Model class.
+
+    @register_model('rnn_classifier')
+    class FairseqRNNClassifier(BaseFairseqModel):
+
+        @staticmethod
+        def add_args(parser):
+            # Models can override this method to add new command-line arguments.
+            # Here we'll add a new command-line argument to configure the
+            # dimensionality of the hidden state.
+            parser.add_argument(
+                '--hidden-dim', type=int, metavar='N',
+                help='dimensionality of the hidden state',
+            )
+
+        @classmethod
+        def build_model(cls, args, task):
+            # Fairseq initializes models by calling the ``build_model()``
+            # function. This provides more flexibility, since the returned model
+            # instance can be of a different type than the one that was called.
+            # In this case we'll just return a FairseqRNNClassifier instance.
+
+            # Initialize our RNN module
+            rnn = RNN(
+                # We'll define the Task in the next section, but for now just
+                # notice that the task holds the dictionaries for the "source"
+                # (i.e., the input sentence) and "target" (i.e., the label).
+                input_size=len(task.source_dictionary),
+                hidden_size=args.hidden_dim,
+                output_size=len(task.target_dictionary),
+            )
+
+            # Return the wrapped version of the module
+            return FairseqRNNClassifier(
+                rnn=rnn,
+                input_vocab=task.source_dictionary,
+            )
+
+        def __init__(self, rnn, input_vocab):
+            super(FairseqRNNClassifier, self).__init__()
+
+            self.rnn = rnn
+            self.input_vocab = input_vocab
+
+            # The RNN module in the tutorial expects one-hot inputs, so we can
+            # precompute the identity matrix to help convert from indices to
+            # one-hot vectors. We register it as a buffer so that it is moved to
+            # the GPU when ``cuda()`` is called.
+            self.register_buffer('one_hot_inputs', torch.eye(len(input_vocab)))
+
+        def forward(self, src_tokens, src_lengths):
+            # The inputs to the ``forward()`` function are determined by the
+            # Task, and in particular the ``'net_input'`` key in each
+            # mini-batch. We'll define the Task in the next section, but for
+            # now just know that *src_tokens* has shape `(batch, src_len)` and
+            # *src_lengths* has shape `(batch)`.
+            bsz, max_src_len = src_tokens.size()
+
+            # Initialize the RNN hidden state. Compared to the original PyTorch
+            # tutorial we'll also handle batched inputs and work on the GPU.
+            hidden = self.rnn.initHidden()
+            hidden = hidden.repeat(bsz, 1)  # expand for batched inputs
+            hidden = hidden.to(src_tokens.device)  # move to GPU
+
+            for i in range(max_src_len):
+                # WARNING: The inputs have padding, so we should mask those
+                # elements here so that padding doesn't affect the results.
+                # This is left as an exercise for the reader. The padding symbol
+                # is given by ``self.input_vocab.pad()`` and the unpadded length
+                # of each input is given by *src_lengths*.
+
+                # One-hot encode a batch of input characters.
+                input = self.one_hot_inputs[src_tokens[:, i].long()]
+
+                # Feed the input to our RNN.
+                output, hidden = self.rnn(input, hidden)
+
+            # Return the final output state for making a prediction
+            return output
+
+Finally let's define a *named architecture* with the configuration for our
+model. This is done with the :func:`~fairseq.models.register_model_architecture`
+function decorator. Thereafter this named architecture can be used with the
+``--arch`` command-line argument, e.g., ``--arch pytorch_tutorial_rnn``::
+
+    from fairseq.models import register_model_architecture
+
+    # The first argument to ``register_model_architecture()`` should be the name
+    # of the model we registered above (i.e., 'rnn_classifier'). The function we
+    # register here should take a single argument *args* and modify it in-place
+    # to match the desired architecture.
+
+    @register_model_architecture('rnn_classifier', 'pytorch_tutorial_rnn')
+    def pytorch_tutorial_rnn(args):
+        # We use ``getattr()`` to prioritize arguments that are explicitly given
+        # on the command-line, so that the defaults defined below are only used
+        # when no other value has been specified.
+        args.hidden_dim = getattr(args, 'hidden_dim', 128)
+
+
+3. Registering a new Task
+-------------------------
+
+Now we'll register a new :class:`~fairseq.tasks.FairseqTask` that will load our
+dictionaries and dataset. Tasks can also control how the data is batched into
+mini-batches, but in this tutorial we'll reuse the batching provided by
+:class:`fairseq.data.LanguagePairDataset`.
+
+Create a new file named :file:`fairseq/tasks/simple_classification.py` with the
+following contents::
+
+  import os
+  import torch
+
+  from fairseq.data import Dictionary, LanguagePairDataset
+  from fairseq.tasks import LegacyFairseqTask, register_task
+
+
+  @register_task('simple_classification')
+  class SimpleClassificationTask(LegacyFairseqTask):
+
+      @staticmethod
+      def add_args(parser):
+          # Add some command-line arguments for specifying where the data is
+          # located and the maximum supported input length.
+          parser.add_argument('data', metavar='FILE',
+                              help='file prefix for data')
+          parser.add_argument('--max-positions', default=1024, type=int,
+                              help='max input length')
+
+      @classmethod
+      def setup_task(cls, args, **kwargs):
+          # Here we can perform any setup required for the task. This may include
+          # loading Dictionaries, initializing shared Embedding layers, etc.
+          # In this case we'll just load the Dictionaries.
+          input_vocab = Dictionary.load(os.path.join(args.data, 'dict.input.txt'))
+          label_vocab = Dictionary.load(os.path.join(args.data, 'dict.label.txt'))
+          print('| [input] dictionary: {} types'.format(len(input_vocab)))
+          print('| [label] dictionary: {} types'.format(len(label_vocab)))
+
+          return SimpleClassificationTask(args, input_vocab, label_vocab)
+
+      def __init__(self, args, input_vocab, label_vocab):
+          super().__init__(args)
+          self.input_vocab = input_vocab
+          self.label_vocab = label_vocab
+
+      def load_dataset(self, split, **kwargs):
+          """Load a given dataset split (e.g., train, valid, test)."""
+
+          prefix = os.path.join(self.args.data, '{}.input-label'.format(split))
+
+          # Read input sentences.
+          sentences, lengths = [], []
+          with open(prefix + '.input', encoding='utf-8') as file:
+              for line in file:
+                  sentence = line.strip()
+
+                  # Tokenize the sentence, splitting on spaces
+                  tokens = self.input_vocab.encode_line(
+                      sentence, add_if_not_exist=False,
+                  )
+
+                  sentences.append(tokens)
+                  lengths.append(tokens.numel())
+
+          # Read labels.
+          labels = []
+          with open(prefix + '.label', encoding='utf-8') as file:
+              for line in file:
+                  label = line.strip()
+                  labels.append(
+                      # Convert label to a numeric ID.
+                      torch.LongTensor([self.label_vocab.add_symbol(label)])
+                  )
+
+          assert len(sentences) == len(labels)
+          print('| {} {} {} examples'.format(self.args.data, split, len(sentences)))
+
+          # We reuse LanguagePairDataset since classification can be modeled as a
+          # sequence-to-sequence task where the target sequence has length 1.
+          self.datasets[split] = LanguagePairDataset(
+              src=sentences,
+              src_sizes=lengths,
+              src_dict=self.input_vocab,
+              tgt=labels,
+              tgt_sizes=torch.ones(len(labels)),  # targets have length 1
+              tgt_dict=self.label_vocab,
+              left_pad_source=False,
+              # Since our target is a single class label, there's no need for
+              # teacher forcing. If we set this to ``True`` then our Model's
+              # ``forward()`` method would receive an additional argument called
+              # *prev_output_tokens* that would contain a shifted version of the
+              # target sequence.
+              input_feeding=False,
+          )
+
+      def max_positions(self):
+          """Return the max input length allowed by the task."""
+          # The source should be less than *args.max_positions* and the "target"
+          # has max length 1.
+          return (self.args.max_positions, 1)
+
+      @property
+      def source_dictionary(self):
+          """Return the source :class:`~fairseq.data.Dictionary`."""
+          return self.input_vocab
+
+      @property
+      def target_dictionary(self):
+          """Return the target :class:`~fairseq.data.Dictionary`."""
+          return self.label_vocab
+
+      # We could override this method if we wanted more control over how batches
+      # are constructed, but it's not necessary for this tutorial since we can
+      # reuse the batching provided by LanguagePairDataset.
+      #
+      # def get_batch_iterator(
+      #     self, dataset, max_tokens=None, max_sentences=None, max_positions=None,
+      #     ignore_invalid_inputs=False, required_batch_size_multiple=1,
+      #     seed=1, num_shards=1, shard_id=0, num_workers=0, epoch=1,
+      #     data_buffer_size=0, disable_iterator_cache=False,
+      # ):
+      #     (...)
+
+
+4. Training the Model
+---------------------
+
+Now we're ready to train the model. We can use the existing :ref:`fairseq-train`
+command-line tool for this, making sure to specify our new Task (``--task
+simple_classification``) and Model architecture (``--arch
+pytorch_tutorial_rnn``):
+
+.. note::
+
+  You can also configure the dimensionality of the hidden state by passing the
+  ``--hidden-dim`` argument to :ref:`fairseq-train`.
+
+.. code-block:: console
+
+  > fairseq-train names-bin \
+    --task simple_classification \
+    --arch pytorch_tutorial_rnn \
+    --optimizer adam --lr 0.001 --lr-shrink 0.5 \
+    --max-tokens 1000
+  (...)
+  | epoch 027 | loss 1.200 | ppl 2.30 | wps 15728 | ups 119.4 | wpb 116 | bsz 116 | num_updates 3726 | lr 1.5625e-05 | gnorm 1.290 | clip 0% | oom 0 | wall 32 | train_wall 21
+  | epoch 027 | valid on 'valid' subset | valid_loss 1.41304 | valid_ppl 2.66 | num_updates 3726 | best 1.41208
+  | done training in 31.6 seconds
+
+The model files should appear in the :file:`checkpoints/` directory.
+
+
+5. Writing an evaluation script
+-------------------------------
+
+Finally we can write a short script to evaluate our model on new inputs. Create
+a new file named :file:`eval_classifier.py` with the following contents::
+
+  from fairseq import checkpoint_utils, data, options, tasks
+
+  # Parse command-line arguments for generation
+  parser = options.get_generation_parser(default_task='simple_classification')
+  args = options.parse_args_and_arch(parser)
+
+  # Setup task
+  task = tasks.setup_task(args)
+
+  # Load model
+  print('| loading model from {}'.format(args.path))
+  models, _model_args = checkpoint_utils.load_model_ensemble([args.path], task=task)
+  model = models[0]
+
+  while True:
+      sentence = input('\nInput: ')
+
+      # Tokenize into characters
+      chars = ' '.join(list(sentence.strip()))
+      tokens = task.source_dictionary.encode_line(
+          chars, add_if_not_exist=False,
+      )
+
+      # Build mini-batch to feed to the model
+      batch = data.language_pair_dataset.collate(
+          samples=[{'id': -1, 'source': tokens}],  # bsz = 1
+          pad_idx=task.source_dictionary.pad(),
+          eos_idx=task.source_dictionary.eos(),
+          left_pad_source=False,
+          input_feeding=False,
+      )
+
+      # Feed batch to the model and get predictions
+      preds = model(**batch['net_input'])
+
+      # Print top 3 predictions and their log-probabilities
+      top_scores, top_labels = preds[0].topk(k=3)
+      for score, label_idx in zip(top_scores, top_labels):
+          label_name = task.target_dictionary.string([label_idx])
+          print('({:.2f})\t{}'.format(score, label_name))
+
+Now we can evaluate our model interactively. Note that we have included the
+original data path (:file:`names-bin/`) so that the dictionaries can be loaded:
+
+.. code-block:: console
+
+  > python eval_classifier.py names-bin --path checkpoints/checkpoint_best.pt
+  | [input] dictionary: 64 types
+  | [label] dictionary: 24 types
+  | loading model from checkpoints/checkpoint_best.pt
+
+  Input: Satoshi
+  (-0.61) Japanese
+  (-1.20) Arabic
+  (-2.86) Italian
+
+  Input: Sinbad
+  (-0.30) Arabic
+  (-1.76) English
+  (-4.08) Russian

fairseq/examples/MMPT/.gitignore

@@ -0,0 +1,139 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+runs
+data
+pretrained_models
+projects/mmfusion_*
+log_test
+third-party
+python_log
+slurm_snapshot_code
+lightning_logs
+demos

fairseq/examples/MMPT/README.md

@@ -0,0 +1,166 @@
+# VideoCLIP and VLM
+
+You just find this toolkit for multimodal video understanding! It contains implementation of two recent multi-modal video understanding papers [VideoCLIP](https://arxiv.org/pdf/2109.14084.pdf) (EMNLP, 2021) and [VLM](https://aclanthology.org/2021.findings-acl.370.pdf) (ACL Findings, 2021), along with high-performance toolkits that are typically lacking in existing codebase. The toolkit is desigend to contain generic performance-tuned components that can be potentially adapted to other frameworks (we initially use fairseq). 
+
+VideoCLIP is a contrastive learning model for zero-shot transfer to retrieval/classification/sequence labeling style tasks.
+
+<img src="videoclip.png" width="350" class="center">
+
+VLM is a masked language model style pre-training using only one encoder with masked modality model (MMM) for retrieval/generation/sequence labeling style tasks.
+
+<img src="vlm.png" width="350" class="center">
+
+### News
+[Oct. 2021] Initial release of implementation for the following papers:  
+[VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding](https://arxiv.org/pdf/2109.14084.pdf) (Xu et. al., EMNLP 2021)  
+[VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding](https://aclanthology.org/2021.findings-acl.370.pdf) (Xu et. al., ACL Findings 2021)  
+
+
+### Installation
+We aim to minimize the dependency of this repo on other packages.  
+We use fairseq as the main trainer (no models/datasets dependency on fairseq. We will support other trainer in future):  
+```
+git clone https://github.com/pytorch/fairseq
+cd fairseq
+pip install -e .  # also optionally follow fairseq README for apex installation for fp16 training.
+export MKL_THREADING_LAYER=GNU  # fairseq may need this for numpy.
+```
+
+Then install this toolkit:
+```
+cd examples/MMPT  # MMPT can be in any folder, not necessarily under fairseq/examples.
+pip install -e .
+```
+
+The code is developed under Python=3.8.8, Pytorch=1.8, cuda=11.0 with fairseq=1.0.0a0+af0389f and tested under Python=3.8.8 pytorch=1.9 cuda=11.0 fairseq=1.0.0a0+8e7bc73 during code release.
+Most models require `transformers==3.4` for API compatibility `pip install transformers==3.4`. 
+In addition, some downstream tasks may need `conda install pandas`.  
+
+
+### Usage
+#### Download Checkpoints
+We use pre-trained [S3D](https://github.com/antoine77340/S3D_HowTo100M) for video feature extraction. Please place the models as `pretrained_models/s3d_dict.npy` and `pretrained_models/s3d_howto100m.pth`.
+
+Download VideoCLIP checkpoint `https://dl.fbaipublicfiles.com/MMPT/retri/videoclip/checkpoint_best.pt` to `runs/retri/videoclip` or VLM checkpoint `https://dl.fbaipublicfiles.com/MMPT/mtm/vlm/checkpoint_best.pt` to `runs/mtm/vlm`.
+
+#### Demo of Inference
+run `python locallaunch.py projects/retri/videoclip.yaml --dryrun` to get all `.yaml`s for VideoCLIP.
+
+```python
+import torch
+
+from mmpt.models import MMPTModel
+
+
+model, tokenizer, aligner = MMPTModel.from_pretrained(
+    "projects/retri/videoclip/how2.yaml")
+
+model.eval()
+
+
+# B, T, FPS, H, W, C (VideoCLIP is trained on 30 fps of s3d)
+video_frames = torch.randn(1, 2, 30, 224, 224, 3)
+caps, cmasks = aligner._build_text_seq(
+    tokenizer("some text", add_special_tokens=False)["input_ids"]
+)
+
+caps, cmasks = caps[None, :], cmasks[None, :]  # bsz=1
+
+with torch.no_grad():
+    output = model(video_frames, caps, cmasks, return_score=True)
+print(output["score"])  # dot-product
+```
+
+#### Data Preparation
+See [dataset](DATASET.md) for each dataset.
+
+#### Global Config for Training Pipeline
+We organize a global config file for a training/testing pipeline under projects (see a detailed [explanation](CONFIG.md)). For example, VideoCLIP in `projects/retri/videoclip.yaml` and VLM is in `projects/mtm/vlm.yaml`.
+
+We wrap all cmds into `locallaunch.py` and `mmpt_cli/localjob.py`. You can check concrete cmds by `--dryrun` and then drop it for actual run.  
+
+First, run `python locallaunch.py projects/retri/videoclip.yaml --dryrun` will generate configs for all configs of pre-training, zero-shot evaluation, fine-tuning and testing, for VideoCLIP under `projects/retri/videoclip`.  
+
+Then each (either training or evaluation) process will be configed by a concrete config file (we save all complex arguments into the concrete config file for reproducibility, including fairseq args). For example, run zero-shot evaluation on youcook,
+```
+python locallaunch.py projects/retri/videoclip/test_youcook_zs.yaml --jobtype local_predict  # zero-shot evaluation.
+python locallaunch.py projects/retri/videoclip/youcook_videoclip.yaml --jobtype local_single --dryrun  # fine-tuning: use --dryrun to check cmds and drop it to make an actual run; local_small will run on two gpus (as in paper).
+python locallaunch.py projects/retri/videoclip/test_youcook_videoclip.yaml --jobtype local_predict  # testing on fine-tuned model.
+```
+
+Pretraining can be run as:  
+```
+python locallaunch.py projects/retri/videoclip/how2.yaml --jobtype local_single --dryrun # check then drop dryrun; paper is ran on local_big as 8 gpus.
+```
+You may need to change `--jobtype`, check/extend `LocalJob` in `mmpt_cli/localjob.py` for multi-gpu/multi-node pre-training.
+
+The detailed instructions of pretraining and fine-tuning can be found at [pretraining instruction](pretraining.md) and [finetuning instruction](endtask.md).
+
+
+### Development
+Several components of this toolkit can be re-used for future research (and also our ongoing research).
+
+#### Framework Wrapper
+We currently only support fairseq, but most components can be easily fit into other frameworks like huggingface. This repo is a `--user-dir` of fairseq with fairseq wrapper. For example, `mmpt/tasks` includes a `FairseqMMTTask`, which manages `mmpt/datasets` with `FairseqDataset`, `mmpt/models` with `FairseqModel`, `mmpt/losses` with `FairseqCriterion`.  
+
+#### Processors
+**Multi**modal research introduces the complexity on modality alignment from different input sources to losses. Inspired by [MMF](https://github.com/facebookresearch/mmf), this toolkit leverages `mmpt/processors` to handle various needs of data preprocessing and loading, **alleviating** the needs of multiple `torch.data.utils.Dataset` (that can be tricky for ablation study).  
+Processors can also be decoupled from `torch.data.utils.Dataset` for offline preprocessing instead of on-the-fly data preprocessing.
+
+We decouple a `mmpt.MMDataset` as 3 types of processors: `MetaProcessor`, `VideoProcessor`, `TextProcessor` and `Aligner`. They can be configed in `dataset` field of a config file (e.g., see `projects/task/how2.yaml`).  
+`MetaProcessor` is used to load the meta data about a dataset, aka, all video_ids of how2 dataset.  
+`VideoProcessor` is used to load the video features about a dataset. For example, S3D features for each second of a video.  
+`TextProcessor` is used to load the text (feature). For example, BERT pre-tokenized text clips for how2 dataset (with `start`s, `end`s of timestamps and `cap` for `token_ids`).  
+`Aligner` is the core class for different baselines that prepares the training data. For example, sampling a clip, masking tokens for MLM, etc.
+
+#### Performance-tuned Components
+To speed up pre-training, this toolkit uses sharded features stored in mmaped numpy, backed by `ShardedTensor` in `mmpt/utils/shardedtensor.py` (adopted from MARGE paper). This reduces the loads of IO for multi-GPU training without loading all features for a video into the memory each time and `ShardedTensor` ensure features are stored in continuous disk space for near random access. This is used for both How2 video features and texts in `mmpt/processors/how2processor.py`.
+
+
+### Citation
+If this codebase is useful for your work, please cite the following papers:
+
+```BibTeX
+@inproceedings{xu-etal-2021-videoclip,
+    title = "{VideoCLIP}: Contrastive Pre-training for\\Zero-shot Video-Text Understanding",
+    author = "Xu, Hu  and
+      Ghosh, Gargi  and
+      Huang, Po-Yao  and
+      Okhonko, Dmytro  and
+      Aghajanyan, Armen  and
+      Metze, Florian  and
+      Zettlemoyer, Luke  and
+      Feichtenhofer, Christoph",
+    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+}
+
+@inproceedings{xu-etal-2021-vlm,
+    title = "{VLM}: Task-agnostic Video-Language Model Pre-training for Video Understanding",
+    author = "Xu, Hu  and
+      Ghosh, Gargi  and
+      Huang, Po-Yao  and
+      Arora, Prahal  and
+      Aminzadeh, Masoumeh  and
+      Feichtenhofer, Christoph  and
+      Metze, Florian  and
+      Zettlemoyer, Luke",
+    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
+    month = aug,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.findings-acl.370",
+    doi = "10.18653/v1/2021.findings-acl.370",
+    pages = "4227--4239",
+}
+```
+
+### Bug Reports
+This repo is in its initial stage, welcome bug reports to [email protected]
+
+### Copyright
+The majority of Multimodal Pre-training (MMPT) is licensed under CC-BY-NC, however portions of the project are available under separate license terms: Evaluation Codes/Models: Howto100M and HuggingFace Transformers are licensed under the Apache2.0 license; COIN and NLG-eval are licensed under the MIT license; CrossTask is licensed under the BSD-3; DiDeMo is licensed under the BSD-2 license.

fairseq/examples/MMPT/endtask.md

@@ -0,0 +1,41 @@
+# Zero-shot Transfer and Finetuning
+
+(If you are new to the ideas of `mmpt.processors`, see [README](README.md) first.)
+All finetuning datasets (specifically `processors`) are defined in `mmpt.processors.dsprocessor`.
+Given the complexity of different types of finetuning tasks, each task may have their own meta/video/text/aligner processors and `mmpt/evaluators/{Predictor,Metric}`.
+
+### Tasks
+
+Currently, we support 5 end datasets: `MSRVTT`, `Youcook`, `COIN`, `Crosstask` and `DiDeMo` with the following tasks:  
+text-video retrieval: `MSRVTT`, `Youcook`, `DiDeMo`;   
+video captioning: `Youcook`;  
+Video Question and Answering: `MSRVTT-QA`.  
+
+To add your own dataset, you can specify the corresponding processors and config them in the `dataset` field of a config file, such as `projects/task/vtt.yaml`.
+
+### Zero-shot Transfer (no Training)
+Zero-shot transfer will run the pre-trained model (e.g., VideoCLIP) directly on testing data. Configs with pattern: `projects/task/*_zs_*.yaml` are dedicated for zero-shot transfer.
+
+### Fine-tuning
+
+The training of a downstream task is similar to pretraining, execept you may need to specify the `restore_file` in `fairseq.checkpoint` and reset optimizers, see `projects/task/ft.yaml` that is included by `projects/task/vtt.yaml`.
+
+We typically do finetuning on 2 gpus (`local_small`).
+
+### Testing
+For each finetuning dataset, you may need to specify a testing config, similar to `projects/task/test_vtt.yaml`.  
+
+We define `mmpt.evaluators.Predictor` for different types of prediction. For example, `MSRVTT` and `Youcook` are video-retrieval tasks and expecting to use `RetrievalPredictor`. You may need to define your new type of predictors and specify that in `predictor` field of a testing config.
+
+Each task may also have their own metric for evaluation. This can be created in `mmpt.evaluators.Metric` and specified in the `metric` field of a testing config.
+
+Launching a testing is as simple as training by specifying the path of a testing config:
+```python locallaunch.py projects/mfmmlm/test_vtt.yaml```
+Testing will be launched locally by default since prediction is computationally less expensive.
+
+### Third-party Libraries
+We list the following finetuning tasks that require third-party libraries.
+
+Youcook captioning: `https://github.com/Maluuba/nlg-eval`  
+
+CrossTask: `https://github.com/DmZhukov/CrossTask`'s `dp` under `third-party/CrossTask` (`python setup.py build_ext --inplace`)

fairseq/examples/MMPT/mmpt/datasets/fairseqmmdataset.py

@@ -0,0 +1,57 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+"""
+TODO (huxu): fairseq wrapper class for all dataset you defined: mostly MMDataset.
+"""
+
+from collections import OrderedDict
+
+from torch.utils.data import Dataset
+from torch.utils.data.dataloader import default_collate
+from fairseq.data import FairseqDataset, data_utils
+
+
+class FairseqMMDataset(FairseqDataset):
+    """
+    A wrapper class for MMDataset for fairseq.
+    """
+
+    def __init__(self, mmdataset):
+        if not isinstance(mmdataset, Dataset):
+            raise TypeError("mmdataset must be of type `torch.utils.data.dataset`.")
+        self.mmdataset = mmdataset
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    def __getitem__(self, idx):
+        with data_utils.numpy_seed(43211, self.epoch, idx):
+            return self.mmdataset[idx]
+
+    def __len__(self):
+        return len(self.mmdataset)
+
+    def collater(self, samples):
+        if hasattr(self.mmdataset, "collator"):
+            return self.mmdataset.collator(samples)
+        if len(samples) == 0:
+            return {}
+        if isinstance(samples[0], dict):
+            batch = OrderedDict()
+            for key in samples[0]:
+                if samples[0][key] is not None:
+                    batch[key] = default_collate([sample[key] for sample in samples])
+            return batch
+        else:
+            return default_collate(samples)
+
+    def size(self, index):
+        """dummy implementation: we don't use --max-tokens"""
+        return 1
+
+    def num_tokens(self, index):
+        """dummy implementation: we don't use --max-tokens"""
+        return 1

fairseq/examples/MMPT/mmpt/datasets/mmdataset.py

@@ -0,0 +1,111 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+
+from collections import OrderedDict
+
+from torch.utils.data import Dataset
+from torch.utils.data.dataloader import default_collate
+
+from ..utils import set_seed
+
+
+class MMDataset(Dataset):
+    """
+    A generic multi-modal dataset.
+        Args:
+            `meta_processor`: a meta processor,
+                handling loading meta data and return video_id and text_id.
+            `video_processor`: a video processor,
+                handling e.g., decoding, loading .np files.
+            `text_processor`: a text processor,
+                handling e.g., tokenization.
+            `aligner`: combine the video and text feature
+                as one training example.
+    """
+
+    def __init__(
+        self,
+        meta_processor,
+        video_processor,
+        text_processor,
+        align_processor,
+    ):
+        self.split = meta_processor.split
+        self.meta_processor = meta_processor
+        self.video_processor = video_processor
+        self.text_processor = text_processor
+        self.align_processor = align_processor
+
+    def __len__(self):
+        return len(self.meta_processor)
+
+    def __getitem__(self, idx):
+        if self.split == "test":
+            set_seed(idx)
+        video_id, text_id = self.meta_processor[idx]
+        video_feature = self.video_processor(video_id)
+        text_feature = self.text_processor(text_id)
+        output = self.align_processor(video_id, video_feature, text_feature)
+        # TODO (huxu): the following is for debug purpose.
+        output.update({"idx": idx})
+        return output
+
+    def collater(self, samples):
+        """This collator is deprecated.
+        set self.collator = MMDataset.collater.
+        see collator in FairseqMMDataset.
+        """
+
+        if len(samples) == 0:
+            return {}
+        if isinstance(samples[0], dict):
+            batch = OrderedDict()
+            for key in samples[0]:
+                if samples[0][key] is not None:
+                    batch[key] = default_collate(
+                        [sample[key] for sample in samples])
+                # if torch.is_tensor(batch[key]):
+                #    print(key, batch[key].size())
+                # else:
+                #    print(key, len(batch[key]))
+            return batch
+        else:
+            return default_collate(samples)
+
+    def print_example(self, output):
+        print("[one example]", output["video_id"])
+        if (
+            hasattr(self.align_processor, "subsampling")
+            and self.align_processor.subsampling is not None
+            and self.align_processor.subsampling > 1
+        ):
+            for key in output:
+                if torch.is_tensor(output[key]):
+                    output[key] = output[key][0]
+
+        # search tokenizer to translate ids back.
+        tokenizer = None
+        if hasattr(self.text_processor, "tokenizer"):
+            tokenizer = self.text_processor.tokenizer
+        elif hasattr(self.align_processor, "tokenizer"):
+            tokenizer = self.align_processor.tokenizer
+        if tokenizer is not None:
+            caps = output["caps"].tolist()
+            if isinstance(caps[0], list):
+                caps = caps[0]
+            print("caps", tokenizer.decode(caps))
+            print("caps", tokenizer.convert_ids_to_tokens(caps))
+
+        for key, value in output.items():
+            if torch.is_tensor(value):
+                if len(value.size()) >= 3:  # attention_mask.
+                    print(key, value.size())
+                    print(key, "first", value[0, :, :])
+                    print(key, "last", value[-1, :, :])
+                else:
+                    print(key, value)
+        print("[end of one example]")

fairseq/examples/MMPT/mmpt/evaluators/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+from .metric import *
+from .evaluator import *
+
+
+# experimental.
+try:
+    from .expmetric import *
+except ImportError:
+    pass

fairseq/examples/MMPT/mmpt/evaluators/evaluator.py

@@ -0,0 +1,54 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+import glob
+import numpy as np
+
+from . import metric as metric_path
+from . import predictor as predictor_path
+
+
+class Evaluator(object):
+    """
+    perform evaluation on a single (downstream) task.
+    make this both offline and online.
+    TODO(huxu) saving evaluation results.
+    """
+
+    def __init__(self, config, eval_dataloader=None):
+        if config.metric is None:
+            raise ValueError("config.metric is", config.metric)
+        metric_cls = getattr(metric_path, config.metric)
+        self.metric = metric_cls(config)
+        if config.predictor is None:
+            raise ValueError("config.predictor is", config.predictor)
+        predictor_cls = getattr(predictor_path, config.predictor)
+        self.predictor = predictor_cls(config)
+        self.eval_dataloader = eval_dataloader
+
+    def __call__(self):
+        try:
+            print(self.predictor.pred_dir)
+            for pred_file in glob.glob(
+                    self.predictor.pred_dir + "/*_merged.npy"):
+                outputs = np.load(pred_file)
+                results = self.metric.compute_metrics(outputs)
+                self.metric.print_computed_metrics(results)
+
+            outputs = np.load(os.path.join(
+                    self.predictor.pred_dir, "merged.npy"))
+            results = self.metric.compute_metrics(outputs)
+            return {"results": results, "metric": self.metric}
+        except FileNotFoundError:
+            print("\n[missing]", self.predictor.pred_dir)
+            return {}
+
+    def evaluate(self, model, eval_dataloader=None, output_file="merged"):
+        if eval_dataloader is None:
+            eval_dataloader = self.eval_dataloader
+        outputs = self.predictor.predict_loop(
+            model, eval_dataloader, output_file)
+        results = self.metric.compute_metrics(**outputs)
+        return results

fairseq/examples/MMPT/mmpt/models/transformermodel.py

@@ -0,0 +1,734 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Copyright (c) Facebook, Inc. All Rights Reserved
+
+import torch
+
+from torch import nn
+
+try:
+    from transformers.modeling_bert import (
+        BertPreTrainedModel,
+        BertModel,
+        BertEncoder,
+        BertPredictionHeadTransform,
+    )
+except ImportError:
+    pass
+
+from ..modules import VideoTokenMLP, MMBertEmbeddings
+
+
+# --------------- fine-tuning models ---------------
+class MMBertForJoint(BertPreTrainedModel):
+    """A BertModel with isolated attention mask to separate modality."""
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.videomlp = VideoTokenMLP(config)
+        self.bert = MMBertModel(config)
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids=None,
+        input_video_embeds=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        next_sentence_label=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        separate_forward_split=None,
+    ):
+        return_dict = (
+            return_dict if return_dict is not None
+            else self.config.use_return_dict
+        )
+        video_tokens = self.videomlp(input_video_embeds)
+
+        outputs = self.bert(
+            input_ids,
+            video_tokens,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            separate_forward_split=separate_forward_split,
+        )
+
+        return outputs
+
+
+class MMBertForTokenClassification(BertPreTrainedModel):
+    """A BertModel similar to MMJointUni, with extra wrapper layer
+    to be fine-tuned from other pretrained MMFusion model."""
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.videomlp = VideoTokenMLP(config)
+        self.bert = MMBertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        # TODO(huxu): 779 is the number of classes for COIN: move to config?
+        self.classifier = nn.Linear(config.hidden_size, 779)
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids=None,
+        input_video_embeds=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        next_sentence_label=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        separate_forward_split=None,
+    ):
+        return_dict = (
+            return_dict if return_dict is not None
+            else self.config.use_return_dict
+        )
+
+        video_tokens = self.videomlp(input_video_embeds)
+        outputs = self.bert(
+            input_ids,
+            video_tokens,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            separate_forward_split=separate_forward_split,
+        )
+
+        return (self.classifier(outputs[0]),)
+
+
+# ------------ pre-training models ----------------
+
+class MMBertForEncoder(BertPreTrainedModel):
+    """A BertModel for Contrastive Learning."""
+    def __init__(self, config):
+        super().__init__(config)
+        self.videomlp = VideoTokenMLP(config)
+        self.bert = MMBertModel(config)
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids=None,
+        input_video_embeds=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        return_dict = (
+            return_dict if return_dict is not None
+            else self.config.use_return_dict
+        )
+        if input_video_embeds is not None:
+            video_tokens = self.videomlp(input_video_embeds)
+        else:
+            video_tokens = None
+
+        outputs = self.bert(
+            input_ids,
+            video_tokens,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        return outputs
+
+
+class MMBertForMFMMLM(BertPreTrainedModel):
+    """A BertModel with shared prediction head on MFM-MLM."""
+    def __init__(self, config):
+        super().__init__(config)
+        self.videomlp = VideoTokenMLP(config)
+        self.bert = MMBertModel(config)
+        self.cls = MFMMLMHead(config)
+        self.hidden_size = config.hidden_size
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def forward(
+        self,
+        input_ids=None,
+        input_video_embeds=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        masked_frame_labels=None,
+        target_video_hidden_states=None,
+        non_masked_frame_mask=None,
+        masked_lm_labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        return_dict = (
+            return_dict if return_dict is not None
+            else self.config.use_return_dict
+        )
+        if input_video_embeds is not None:
+            video_tokens = self.videomlp(input_video_embeds)
+        else:
+            video_tokens = None
+
+        if target_video_hidden_states is not None:
+            target_video_hidden_states = self.videomlp(
+                target_video_hidden_states)
+
+            non_masked_frame_hidden_states = video_tokens.masked_select(
+                non_masked_frame_mask.unsqueeze(-1)
+            ).view(-1, self.hidden_size)
+
+        outputs = self.bert(
+            input_ids,
+            video_tokens,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        mfm_scores, prediction_scores = None, None
+        if masked_frame_labels is not None and masked_lm_labels is not None:
+            # split the sequence.
+            text_offset = masked_frame_labels.size(1) + 1  # [CLS]
+            video_sequence_output = sequence_output[
+                :, 1:text_offset
+            ]  # remove [SEP] as not in video_label.
+            text_sequence_output = torch.cat(
+                [sequence_output[:, :1], sequence_output[:, text_offset:]],
+                dim=1
+            )
+
+            hidden_size = video_sequence_output.size(-1)
+            selected_video_output = video_sequence_output.masked_select(
+                masked_frame_labels.unsqueeze(-1)
+            ).view(-1, hidden_size)
+
+            # only compute select tokens to training to speed up.
+            hidden_size = text_sequence_output.size(-1)
+            # masked_lm_labels = masked_lm_labels.reshape(-1)
+            labels_mask = masked_lm_labels != -100
+
+            selected_text_output = text_sequence_output.masked_select(
+                labels_mask.unsqueeze(-1)
+            ).view(-1, hidden_size)
+            mfm_scores, prediction_scores = self.cls(
+                selected_video_output,
+                target_video_hidden_states,
+                non_masked_frame_hidden_states,
+                selected_text_output,
+            )
+
+        output = (
+            mfm_scores,
+            prediction_scores,
+        ) + outputs
+        return output
+
+
+class BertMFMMLMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.transform = BertPredictionHeadTransform(config)
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(
+            config.hidden_size, config.vocab_size, bias=False)
+
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+        # Need a link between the two variables so that the bias is correctly
+        # resized with `resize_token_embeddings`
+        self.decoder.bias = self.bias
+
+    def forward(
+        self,
+        video_hidden_states=None,
+        target_video_hidden_states=None,
+        non_masked_frame_hidden_states=None,
+        text_hidden_states=None,
+    ):
+        video_logits, text_logits = None, None
+        if video_hidden_states is not None:
+            video_hidden_states = self.transform(video_hidden_states)
+            non_masked_frame_logits = torch.mm(
+                video_hidden_states,
+                non_masked_frame_hidden_states.transpose(1, 0)
+            )
+            masked_frame_logits = torch.bmm(
+                video_hidden_states.unsqueeze(1),
+                target_video_hidden_states.unsqueeze(-1),
+            ).squeeze(-1)
+            video_logits = torch.cat(
+                [masked_frame_logits, non_masked_frame_logits], dim=1
+            )
+
+        if text_hidden_states is not None:
+            text_hidden_states = self.transform(text_hidden_states)
+            text_logits = self.decoder(text_hidden_states)
+        return video_logits, text_logits
+
+
+class MFMMLMHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = BertMFMMLMPredictionHead(config)
+
+    def forward(
+        self,
+        video_hidden_states=None,
+        target_video_hidden_states=None,
+        non_masked_frame_hidden_states=None,
+        text_hidden_states=None,
+    ):
+        video_logits, text_logits = self.predictions(
+            video_hidden_states,
+            target_video_hidden_states,
+            non_masked_frame_hidden_states,
+            text_hidden_states,
+        )
+        return video_logits, text_logits
+
+
+class MMBertForMTM(MMBertForMFMMLM):
+    def __init__(self, config):
+        BertPreTrainedModel.__init__(self, config)
+        self.videomlp = VideoTokenMLP(config)
+        self.bert = MMBertModel(config)
+        self.cls = MTMHead(config)
+        self.hidden_size = config.hidden_size
+        self.init_weights()
+
+
+class BertMTMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.transform = BertPredictionHeadTransform(config)
+        self.decoder = nn.Linear(
+            config.hidden_size, config.vocab_size, bias=False)
+
+    def forward(
+        self,
+        video_hidden_states=None,
+        target_video_hidden_states=None,
+        non_masked_frame_hidden_states=None,
+        text_hidden_states=None,
+    ):
+        non_masked_frame_hidden_states = non_masked_frame_hidden_states.transpose(1, 0)
+        video_logits, text_logits = None, None
+        if video_hidden_states is not None:
+            video_hidden_states = self.transform(video_hidden_states)
+
+            masked_frame_logits = torch.bmm(
+                video_hidden_states.unsqueeze(1),
+                target_video_hidden_states.unsqueeze(-1),
+            ).squeeze(-1)
+
+            non_masked_frame_logits = torch.mm(
+                video_hidden_states,
+                non_masked_frame_hidden_states
+            )
+            video_on_vocab_logits = self.decoder(video_hidden_states)
+            video_logits = torch.cat([
+                masked_frame_logits,
+                non_masked_frame_logits,
+                video_on_vocab_logits], dim=1)
+
+        if text_hidden_states is not None:
+            text_hidden_states = self.transform(text_hidden_states)
+            # text first so label does not need to be shifted.
+            text_on_vocab_logits = self.decoder(text_hidden_states)
+            text_on_video_logits = torch.mm(
+                text_hidden_states,
+                non_masked_frame_hidden_states
+            )
+            text_logits = torch.cat([
+                text_on_vocab_logits,
+                text_on_video_logits
+            ], dim=1)
+
+        return video_logits, text_logits
+
+
+class MTMHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = BertMTMPredictionHead(config)
+
+    def forward(
+        self,
+        video_hidden_states=None,
+        target_video_hidden_states=None,
+        non_masked_frame_hidden_states=None,
+        text_hidden_states=None,
+    ):
+        video_logits, text_logits = self.predictions(
+            video_hidden_states,
+            target_video_hidden_states,
+            non_masked_frame_hidden_states,
+            text_hidden_states,
+        )
+        return video_logits, text_logits
+
+
+class MMBertModel(BertModel):
+    """MMBertModel has MMBertEmbedding to support video tokens."""
+
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        # overwrite embedding
+        self.embeddings = MMBertEmbeddings(config)
+        self.encoder = MultiLayerAttentionMaskBertEncoder(config)
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids=None,
+        input_video_embeds=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        separate_forward_split=None,
+    ):
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None
+            else self.config.use_return_dict
+        )
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both input_ids "
+                "and inputs_embeds at the same time"
+            )
+        elif input_ids is not None:
+            if input_video_embeds is not None:
+                input_shape = (
+                    input_ids.size(0),
+                    input_ids.size(1) + input_video_embeds.size(1),
+                )
+            else:
+                input_shape = (
+                    input_ids.size(0),
+                    input_ids.size(1),
+                )
+        elif inputs_embeds is not None:
+            if input_video_embeds is not None:
+                input_shape = (
+                    inputs_embeds.size(0),
+                    inputs_embeds.size(1) + input_video_embeds.size(1),
+                )
+            else:
+                input_shape = (
+                    input_ids.size(0),
+                    input_ids.size(1),
+                )
+        else:
+            raise ValueError(
+                "You have to specify either input_ids or inputs_embeds")
+
+        device = input_ids.device if input_ids is not None \
+            else inputs_embeds.device
+
+        if attention_mask is None:
+            attention_mask = torch.ones(input_shape, device=device)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(
+                input_shape, dtype=torch.long, device=device)
+
+        # We can provide a self-attention mask of dimensions
+        # [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case
+        # we just need to make it broadcastable to all heads.
+        extended_attention_mask: torch.Tensor = \
+            self.get_extended_attention_mask(
+                attention_mask, input_shape, device)
+
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to
+        # [batch_size, num_heads, seq_length, seq_length]
+        if self.config.is_decoder and encoder_hidden_states is not None:
+            (
+                encoder_batch_size,
+                encoder_sequence_length,
+                _,
+            ) = encoder_hidden_states.size()
+            encoder_hidden_shape = (
+                encoder_batch_size, encoder_sequence_length)
+            if encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(
+                    encoder_hidden_shape, device=device)
+            encoder_extended_attention_mask = self.invert_attention_mask(
+                encoder_attention_mask
+            )
+        else:
+            encoder_extended_attention_mask = None
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or
+        # [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape
+        # [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+
+        head_mask = self.get_head_mask(
+            head_mask, self.config.num_hidden_layers)
+
+        embedding_output = self.embeddings(
+            input_ids,
+            input_video_embeds,
+            position_ids=position_ids,
+            token_type_ids=token_type_ids,
+            inputs_embeds=inputs_embeds,
+        )
+
+        if separate_forward_split is not None:
+            split_embedding_output = \
+                embedding_output[:, :separate_forward_split]
+            split_extended_attention_mask = extended_attention_mask[
+                :, :, :, :separate_forward_split, :separate_forward_split
+            ]
+            split_encoder_outputs = self.encoder(
+                split_embedding_output,
+                attention_mask=split_extended_attention_mask,
+                head_mask=head_mask,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_attention_mask=encoder_extended_attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            assert (
+                len(split_encoder_outputs) <= 2
+            ), "we do not support merge on attention for now."
+            encoder_outputs = []
+            encoder_outputs.append([split_encoder_outputs[0]])
+            if len(split_encoder_outputs) == 2:
+                encoder_outputs.append([])
+                for _all_hidden_states in split_encoder_outputs[1]:
+                    encoder_outputs[-1].append([_all_hidden_states])
+
+            split_embedding_output = \
+                embedding_output[:, separate_forward_split:]
+            split_extended_attention_mask = extended_attention_mask[
+                :, :, :, separate_forward_split:, separate_forward_split:
+            ]
+
+            split_encoder_outputs = self.encoder(
+                split_embedding_output,
+                attention_mask=split_extended_attention_mask,
+                head_mask=head_mask,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_attention_mask=encoder_extended_attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+            assert (
+                len(split_encoder_outputs) <= 2
+            ), "we do not support merge on attention for now."
+            encoder_outputs[0].append(split_encoder_outputs[0])
+            encoder_outputs[0] = torch.cat(encoder_outputs[0], dim=1)
+            if len(split_encoder_outputs) == 2:
+                for layer_idx, _all_hidden_states in enumerate(
+                    split_encoder_outputs[1]
+                ):
+                    encoder_outputs[1][layer_idx].append(_all_hidden_states)
+                    encoder_outputs[1][layer_idx] = torch.cat(
+                        encoder_outputs[1][layer_idx], dim=1
+                    )
+            encoder_outputs = tuple(encoder_outputs)
+        else:
+            encoder_outputs = self.encoder(
+                embedding_output,
+                attention_mask=extended_attention_mask,
+                head_mask=head_mask,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_attention_mask=encoder_extended_attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+
+        sequence_output = encoder_outputs[0]
+        pooled_output = (
+            self.pooler(sequence_output) if self.pooler is not None else None
+        )
+
+        return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+    def get_extended_attention_mask(self, attention_mask, input_shape, device):
+        """This is borrowed from `modeling_utils.py` with the support of
+        multi-layer attention masks.
+        The second dim is expected to be number of layers.
+        See `MMAttentionMaskProcessor`.
+        Makes broadcastable attention and causal masks so that future
+        and masked tokens are ignored.
+
+        Arguments:
+            attention_mask (:obj:`torch.Tensor`):
+                Mask with ones indicating tokens to attend to,
+                zeros for tokens to ignore.
+            input_shape (:obj:`Tuple[int]`):
+                The shape of the input to the model.
+            device: (:obj:`torch.device`):
+                The device of the input to the model.
+
+        Returns:
+            :obj:`torch.Tensor` The extended attention mask, \
+                with a the same dtype as :obj:`attention_mask.dtype`.
+        """
+        # We can provide a self-attention mask of dimensions
+        # [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable
+        # to all heads.
+        if attention_mask.dim() == 4:
+            extended_attention_mask = attention_mask[:, :, None, :, :]
+            extended_attention_mask = extended_attention_mask.to(
+                dtype=self.dtype
+            )  # fp16 compatibility
+            extended_attention_mask = (1.0 - extended_attention_mask) \
+                * -10000.0
+            return extended_attention_mask
+        else:
+            return super().get_extended_attention_mask(
+                attention_mask, input_shape, device
+            )
+
+
+class MultiLayerAttentionMaskBertEncoder(BertEncoder):
+    """extend BertEncoder with the capability of
+    multiple layers of attention mask."""
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=False,
+        output_hidden_states=False,
+        return_dict=False,
+    ):
+        all_hidden_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+
+            layer_attention_mask = (
+                attention_mask[:, i, :, :, :]
+                if attention_mask.dim() == 5
+                else attention_mask
+            )
+
+            if getattr(self.config, "gradient_checkpointing", False):
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    layer_attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    layer_attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    output_attentions,
+                )
+            hidden_states = layer_outputs[0]
+            if output_attentions:
+                all_attentions = all_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        return tuple(
+            v
+            for v in [hidden_states, all_hidden_states, all_attentions]
+            if v is not None
+        )

fairseq/examples/MMPT/mmpt/modules/__init__.py

@@ -0,0 +1,10 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+from .mm import *
+
+try:
+    from .expmm import *
+except ImportError:
+    pass

fairseq/examples/MMPT/mmpt/modules/mm.py

@@ -0,0 +1,145 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Copyright (c) Facebook, Inc. All Rights Reserved
+
+
+import torch
+
+from torch import nn
+
+try:
+    from transformers.modeling_bert import (
+        BertEmbeddings,
+        ACT2FN,
+    )
+except ImportError:
+    pass
+
+
+class VideoTokenMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        input_dim = config.input_dim if hasattr(config, "input_dim") else 512
+        self.linear1 = nn.Linear(input_dim, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size)
+        self.activation = ACT2FN[config.hidden_act]
+        self.linear2 = nn.Linear(config.hidden_size, config.hidden_size)
+
+    def forward(self, hidden_states):
+        hidden_states = self.linear1(hidden_states)
+        hidden_states = self.activation(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        hidden_states = self.linear2(hidden_states)
+        return hidden_states
+
+
+class MMBertEmbeddings(BertEmbeddings):
+    def __init__(self, config):
+        super().__init__(config)
+        self.max_video_len = config.max_video_len
+        if hasattr(config, "use_seg_emb") and config.use_seg_emb:
+            """the original VLM paper uses seg_embeddings for temporal space.
+            although not used it changed the randomness of initialization.
+            we keep it for reproducibility.
+            """
+            self.seg_embeddings = nn.Embedding(256, config.hidden_size)
+
+    def forward(
+        self,
+        input_ids,
+        input_video_embeds,
+        token_type_ids=None,
+        position_ids=None,
+        inputs_embeds=None,
+    ):
+        input_tensor = input_ids if input_ids is not None else inputs_embeds
+        if input_video_embeds is not None:
+            input_shape = (
+                input_tensor.size(0),
+                input_tensor.size(1) + input_video_embeds.size(1),
+            )
+        else:
+            input_shape = (input_tensor.size(0), input_tensor.size(1))
+
+        if position_ids is None:
+            """
+            Auto skip position embeddings for text only case.
+            use cases:
+            (1) action localization and segmentation:
+                feed in len-1 dummy video token needs text part to
+                skip input_video_embeds.size(1) for the right
+                position_ids for video [SEP] and rest text tokens.
+            (2) MMFusionShare for two forward passings:
+                in `forward_text`: input_video_embeds is None.
+                    need to skip video [SEP] token.
+
+            # video_len + 1: [CLS] + video_embed
+            # self.max_video_len + 1: [SEP] for video.
+            # self.max_video_len + 2: [SEP] for video.
+            # self.max_video_len + input_ids.size(1): rest for text.
+            """
+            if input_video_embeds is not None:
+                video_len = input_video_embeds.size(1)
+                starting_offset = self.max_video_len + 1  # video [SEP]
+                ending_offset = self.max_video_len + input_ids.size(1)
+            else:
+                video_len = 0
+                starting_offset = self.max_video_len + 2  # first text token.
+                ending_offset = self.max_video_len + input_ids.size(1) + 1
+            position_ids = torch.cat([
+                self.position_ids[:, :video_len + 1],
+                self.position_ids[:, starting_offset:ending_offset]
+                ], dim=1)
+
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(
+                input_shape, dtype=torch.long, device=self.position_ids.device
+            )
+
+        """
+        the format of input_ids is [CLS] [SEP] caption [SEP] padding.
+        the goal is to build [CLS] video tokens [SEP] caption [SEP] .
+        """
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+        if input_video_embeds is not None:
+            inputs_mm_embeds = torch.cat([
+                inputs_embeds[:, :1], input_video_embeds, inputs_embeds[:, 1:]
+            ], dim=1)
+        else:
+            # text only for `MMFusionShare`.
+            inputs_mm_embeds = inputs_embeds
+
+        position_embeddings = self.position_embeddings(position_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+        embeddings = inputs_mm_embeds + position_embeddings
+        embeddings += token_type_embeddings
+
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class AlignHead(nn.Module):
+    """this will load pre-trained weights for NSP, which is desirable."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, dropout_pooled_output):
+        logits = self.seq_relationship(dropout_pooled_output)
+        return logits

fairseq/examples/MMPT/mmpt/modules/retri.py

@@ -0,0 +1,429 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+import numpy as np
+import pickle
+import time
+
+try:
+    import faiss
+except ImportError:
+    pass
+
+from collections import defaultdict
+
+from ..utils import get_local_rank, print_on_rank0
+
+
+class VectorRetriever(object):
+    """
+    How2 Video Retriver.
+    Reference usage of FAISS:
+    https://github.com/fairinternal/fairseq-py/blob/paraphrase_pretraining/fairseq/data/multilingual_faiss_dataset.py
+    """
+
+    def __init__(self, hidden_size, cent, db_type, examples_per_cent_to_train):
+        if db_type == "flatl2":
+            quantizer = faiss.IndexFlatL2(hidden_size)  # the other index
+            self.db = faiss.IndexIVFFlat(
+                quantizer, hidden_size, cent, faiss.METRIC_L2)
+        elif db_type == "pq":
+            self.db = faiss.index_factory(
+                    hidden_size, f"IVF{cent}_HNSW32,PQ32"
+            )
+        else:
+            raise ValueError("unknown type of db", db_type)
+        self.train_thres = cent * examples_per_cent_to_train
+        self.train_cache = []
+        self.train_len = 0
+        self.videoid_to_vectoridx = {}
+        self.vectoridx_to_videoid = None
+        self.make_direct_maps_done = False
+
+    def make_direct_maps(self):
+        faiss.downcast_index(self.db).make_direct_map()
+
+    def __len__(self):
+        return self.db.ntotal
+
+    def save(self, out_dir):
+        faiss.write_index(
+            self.db,
+            os.path.join(out_dir, "faiss_idx")
+        )
+        with open(
+                os.path.join(
+                    out_dir, "videoid_to_vectoridx.pkl"),
+                "wb") as fw:
+            pickle.dump(
+                self.videoid_to_vectoridx, fw,
+                protocol=pickle.HIGHEST_PROTOCOL
+            )
+
+    def load(self, out_dir):
+        fn = os.path.join(out_dir, "faiss_idx")
+        self.db = faiss.read_index(fn)
+        with open(
+                os.path.join(out_dir, "videoid_to_vectoridx.pkl"), "rb") as fr:
+            self.videoid_to_vectoridx = pickle.load(fr)
+
+    def add(self, hidden_states, video_ids, last=False):
+        assert len(hidden_states) == len(video_ids), "{}, {}".format(
+            str(len(hidden_states)), str(len(video_ids)))
+        assert len(hidden_states.shape) == 2
+        assert hidden_states.dtype == np.float32
+
+        valid_idx = []
+        for idx, video_id in enumerate(video_ids):
+            if video_id not in self.videoid_to_vectoridx:
+                valid_idx.append(idx)
+                self.videoid_to_vectoridx[video_id] = \
+                    len(self.videoid_to_vectoridx)
+
+        hidden_states = hidden_states[valid_idx]
+        if not self.db.is_trained:
+            self.train_cache.append(hidden_states)
+            self.train_len += hidden_states.shape[0]
+            if self.train_len < self.train_thres:
+                return
+            self.finalize_training()
+        else:
+            self.db.add(hidden_states)
+
+    def finalize_training(self):
+        hidden_states = np.concatenate(self.train_cache, axis=0)
+        del self.train_cache
+        local_rank = get_local_rank()
+        if local_rank == 0:
+            start = time.time()
+            print("training db on", self.train_thres, "/", self.train_len)
+        self.db.train(hidden_states[:self.train_thres])
+        if local_rank == 0:
+            print("training db for", time.time() - start)
+        self.db.add(hidden_states)
+
+    def search(
+        self,
+        query_hidden_states,
+        orig_dist,
+    ):
+        if len(self.videoid_to_vectoridx) != self.db.ntotal:
+            raise ValueError(
+                "cannot search: size mismatch in-between index and db",
+                len(self.videoid_to_vectoridx),
+                self.db.ntotal
+            )
+
+        if self.vectoridx_to_videoid is None:
+            self.vectoridx_to_videoid = {
+                self.videoid_to_vectoridx[videoid]: videoid
+                for videoid in self.videoid_to_vectoridx
+            }
+            assert len(self.vectoridx_to_videoid) \
+                == len(self.videoid_to_vectoridx)
+
+        # MultilingualFaissDataset uses the following; not sure the purpose.
+        # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10)
+        queried_dist, index = self.db.search(query_hidden_states, 1)
+        queried_dist, index = queried_dist[:, 0], index[:, 0]
+
+        outputs = np.array(
+            [self.vectoridx_to_videoid[_index]
+                if _index != -1 else (-1, -1, -1) for _index in index],
+            dtype=np.int32)
+        outputs[queried_dist <= orig_dist] = -1
+        return outputs
+
+    def search_by_video_ids(
+        self,
+        video_ids,
+        retri_factor
+    ):
+        if len(self.videoid_to_vectoridx) != self.db.ntotal:
+            raise ValueError(
+                len(self.videoid_to_vectoridx),
+                self.db.ntotal
+            )
+
+        if not self.make_direct_maps_done:
+            self.make_direct_maps()
+
+        if self.vectoridx_to_videoid is None:
+            self.vectoridx_to_videoid = {
+                self.videoid_to_vectoridx[videoid]: videoid
+                for videoid in self.videoid_to_vectoridx
+            }
+            assert len(self.vectoridx_to_videoid) \
+                == len(self.videoid_to_vectoridx)
+
+        query_hidden_states = []
+        vector_ids = []
+        for video_id in video_ids:
+            vector_id = self.videoid_to_vectoridx[video_id]
+            vector_ids.append(vector_id)
+            query_hidden_state = self.db.reconstruct(vector_id)
+            query_hidden_states.append(query_hidden_state)
+        query_hidden_states = np.stack(query_hidden_states)
+
+        # MultilingualFaissDataset uses the following; not sure the reason.
+        # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10)
+        _, index = self.db.search(query_hidden_states, retri_factor)
+        outputs = []
+        for sample_idx, sample in enumerate(index):
+            # the first video_id is always the video itself.
+            cands = [video_ids[sample_idx]]
+            for vector_idx in sample:
+                if vector_idx >= 0 \
+                        and vector_ids[sample_idx] != vector_idx:
+                    cands.append(
+                        self.vectoridx_to_videoid[vector_idx]
+                    )
+            outputs.append(cands)
+        return outputs
+
+
+class VectorRetrieverDM(VectorRetriever):
+    """
+    with direct map.
+    How2 Video Retriver.
+    Reference usage of FAISS:
+    https://github.com/fairinternal/fairseq-py/blob/paraphrase_pretraining/fairseq/data/multilingual_faiss_dataset.py
+    """
+
+    def __init__(
+        self,
+        hidden_size,
+        cent,
+        db_type,
+        examples_per_cent_to_train
+    ):
+        super().__init__(
+            hidden_size, cent, db_type, examples_per_cent_to_train)
+        self.make_direct_maps_done = False
+
+    def make_direct_maps(self):
+        faiss.downcast_index(self.db).make_direct_map()
+        self.make_direct_maps_done = True
+
+    def search(
+        self,
+        query_hidden_states,
+        orig_dist,
+    ):
+        if len(self.videoid_to_vectoridx) != self.db.ntotal:
+            raise ValueError(
+                len(self.videoid_to_vectoridx),
+                self.db.ntotal
+            )
+
+        if not self.make_direct_maps_done:
+            self.make_direct_maps()
+        if self.vectoridx_to_videoid is None:
+            self.vectoridx_to_videoid = {
+                self.videoid_to_vectoridx[videoid]: videoid
+                for videoid in self.videoid_to_vectoridx
+            }
+            assert len(self.vectoridx_to_videoid) \
+                == len(self.videoid_to_vectoridx)
+
+        # MultilingualFaissDataset uses the following; not sure the reason.
+        # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10)
+        queried_dist, index = self.db.search(query_hidden_states, 1)
+        outputs = []
+        for sample_idx, sample in enumerate(index):
+            # and queried_dist[sample_idx] < thres \
+            if sample >= 0 \
+                    and queried_dist[sample_idx] < orig_dist[sample_idx]:
+                outputs.append(self.vectoridx_to_videoid[sample])
+            else:
+                outputs.append(None)
+        return outputs
+
+    def search_by_video_ids(
+        self,
+        video_ids,
+        retri_factor=8
+    ):
+        if len(self.videoid_to_vectoridx) != self.db.ntotal:
+            raise ValueError(
+                len(self.videoid_to_vectoridx),
+                self.db.ntotal
+            )
+
+        if not self.make_direct_maps_done:
+            self.make_direct_maps()
+        if self.vectoridx_to_videoid is None:
+            self.vectoridx_to_videoid = {
+                self.videoid_to_vectoridx[videoid]: videoid
+                for videoid in self.videoid_to_vectoridx
+            }
+            assert len(self.vectoridx_to_videoid) \
+                == len(self.videoid_to_vectoridx)
+
+        query_hidden_states = []
+        vector_ids = []
+        for video_id in video_ids:
+            vector_id = self.videoid_to_vectoridx[video_id]
+            vector_ids.append(vector_id)
+            query_hidden_state = self.db.reconstruct(vector_id)
+            query_hidden_states.append(query_hidden_state)
+        query_hidden_states = np.stack(query_hidden_states)
+
+        # MultilingualFaissDataset uses the following; not sure the reason.
+        # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10)
+        _, index = self.db.search(query_hidden_states, retri_factor)
+        outputs = []
+        for sample_idx, sample in enumerate(index):
+            # the first video_id is always the video itself.
+            cands = [video_ids[sample_idx]]
+            for vector_idx in sample:
+                if vector_idx >= 0 \
+                        and vector_ids[sample_idx] != vector_idx:
+                    cands.append(
+                        self.vectoridx_to_videoid[vector_idx]
+                    )
+            outputs.append(cands)
+        return outputs
+
+
+class MMVectorRetriever(VectorRetrieverDM):
+    """
+    multimodal vector retriver:
+    text retrieve video or video retrieve text.
+    """
+
+    def __init__(self, hidden_size, cent, db_type, examples_per_cent_to_train):
+        super().__init__(
+            hidden_size, cent, db_type, examples_per_cent_to_train)
+        video_db = self.db
+        super().__init__(
+            hidden_size, cent, db_type, examples_per_cent_to_train)
+        text_db = self.db
+        self.db = {"video": video_db, "text": text_db}
+        self.video_to_videoid = defaultdict(list)
+
+    def __len__(self):
+        assert self.db["video"].ntotal == self.db["text"].ntotal
+        return self.db["video"].ntotal
+
+    def make_direct_maps(self):
+        faiss.downcast_index(self.db["video"]).make_direct_map()
+        faiss.downcast_index(self.db["text"]).make_direct_map()
+
+    def save(self, out_dir):
+        faiss.write_index(
+            self.db["video"],
+            os.path.join(out_dir, "video_faiss_idx")
+        )
+        faiss.write_index(
+            self.db["text"],
+            os.path.join(out_dir, "text_faiss_idx")
+        )
+
+        with open(
+                os.path.join(
+                    out_dir, "videoid_to_vectoridx.pkl"),
+                "wb") as fw:
+            pickle.dump(
+                self.videoid_to_vectoridx, fw,
+                protocol=pickle.HIGHEST_PROTOCOL
+            )
+
+    def load(self, out_dir):
+        fn = os.path.join(out_dir, "video_faiss_idx")
+        video_db = faiss.read_index(fn)
+        fn = os.path.join(out_dir, "text_faiss_idx")
+        text_db = faiss.read_index(fn)
+        self.db = {"video": video_db, "text": text_db}
+        with open(
+                os.path.join(out_dir, "videoid_to_vectoridx.pkl"), "rb") as fr:
+            self.videoid_to_vectoridx = pickle.load(fr)
+        self.video_to_videoid = defaultdict(list)
+
+    def add(self, hidden_states, video_ids):
+        """hidden_states is a pair `(video, text)`"""
+        assert len(hidden_states) == len(video_ids), "{}, {}".format(
+            str(len(hidden_states)), str(len(video_ids)))
+        assert len(hidden_states.shape) == 3
+        assert len(self.video_to_videoid) == 0
+
+        valid_idx = []
+        for idx, video_id in enumerate(video_ids):
+            if video_id not in self.videoid_to_vectoridx:
+                valid_idx.append(idx)
+                self.videoid_to_vectoridx[video_id] = \
+                    len(self.videoid_to_vectoridx)
+
+        batch_size = hidden_states.shape[0]
+        hidden_states = hidden_states[valid_idx]
+
+        hidden_states = np.transpose(hidden_states, (1, 0, 2)).copy()
+        if not self.db["video"].is_trained:
+            self.train_cache.append(hidden_states)
+            train_len = batch_size * len(self.train_cache)
+            if train_len < self.train_thres:
+                return
+
+            hidden_states = np.concatenate(self.train_cache, axis=1)
+            del self.train_cache
+            self.db["video"].train(hidden_states[0, :self.train_thres])
+            self.db["text"].train(hidden_states[1, :self.train_thres])
+        self.db["video"].add(hidden_states[0])
+        self.db["text"].add(hidden_states[1])
+
+    def get_clips_by_video_id(self, video_id):
+        if not self.video_to_videoid:
+            for video_id, video_clip, text_clip in self.videoid_to_vectoridx:
+                self.video_to_videoid[video_id].append(
+                    (video_id, video_clip, text_clip))
+        return self.video_to_videoid[video_id]
+
+    def search(
+        self,
+        video_ids,
+        target_modality,
+        retri_factor=8
+    ):
+        if len(self.videoid_to_vectoridx) != len(self):
+            raise ValueError(
+                len(self.videoid_to_vectoridx),
+                len(self)
+            )
+
+        if not self.make_direct_maps_done:
+            self.make_direct_maps()
+        if self.vectoridx_to_videoid is None:
+            self.vectoridx_to_videoid = {
+                self.videoid_to_vectoridx[videoid]: videoid
+                for videoid in self.videoid_to_vectoridx
+            }
+            assert len(self.vectoridx_to_videoid) \
+                == len(self.videoid_to_vectoridx)
+
+        src_modality = "text" if target_modality == "video" else "video"
+
+        query_hidden_states = []
+        vector_ids = []
+        for video_id in video_ids:
+            vector_id = self.videoid_to_vectoridx[video_id]
+            vector_ids.append(vector_id)
+            query_hidden_state = self.db[src_modality].reconstruct(vector_id)
+            query_hidden_states.append(query_hidden_state)
+        query_hidden_states = np.stack(query_hidden_states)
+
+        # MultilingualFaissDataset uses the following; not sure the reason.
+        # faiss.ParameterSpace().set_index_parameter(self.db, "nprobe", 10)
+        _, index = self.db[target_modality].search(
+            query_hidden_states, retri_factor)
+        outputs = []
+        for sample_idx, sample in enumerate(index):
+            cands = []
+            for vector_idx in sample:
+                if vector_idx >= 0:
+                    cands.append(
+                        self.vectoridx_to_videoid[vector_idx]
+                    )
+            outputs.append(cands)
+        return outputs

fairseq/examples/MMPT/mmpt/modules/vectorpool.py

@@ -0,0 +1,246 @@
+# Copyright (c) Facebook, Inc. All Rights Reserved
+
+import torch
+import os
+import numpy as np
+import pickle
+
+from . import retri
+from ..utils import get_local_rank
+
+
+class VectorPool(object):
+    """
+    Base class of retrieval space.
+    """
+
+    def __init__(self, config):
+        from transformers import AutoConfig
+        self.hidden_size = AutoConfig.from_pretrained(
+            config.dataset.bert_name).hidden_size
+        self.retriever_cls = getattr(retri, config.retriever_cls)
+
+    def __call__(self, sample, **kwargs):
+        raise NotImplementedError
+
+    def build_retriver(
+        self,
+        retriever_cls=None,
+        hidden_size=None,
+        centroids=512,
+        db_type="flatl2",
+        examples_per_cent_to_train=48
+    ):
+
+        """merge results from multiple gpus and return a retriver.."""
+        self.retriver = retriever_cls(
+            hidden_size, centroids, db_type, examples_per_cent_to_train)
+        return self.retriver
+
+    def __repr__(self):
+        if hasattr(self, "retriver"):
+            retriver_name = str(len(self.retriver))
+        else:
+            retriver_name = "no retriver field yet"
+        return self.__class__.__name__ \
+            + "(" + retriver_name + ")"
+
+
+class VideoVectorPool(VectorPool):
+    """
+    average clips of a video as video representation.
+    """
+    def __init__(self, config):
+        super().__init__(config)
+        self.build_retriver(self.retriever_cls, self.hidden_size)
+
+    def __call__(self, sample, subsampling, **kwargs):
+        hidden_states = (
+            sample["pooled_video"] + sample["pooled_text"]) / 2.
+        hidden_states = hidden_states.view(
+            -1, subsampling,
+            hidden_states.size(-1))
+        hidden_states = torch.mean(hidden_states, dim=1)
+        hidden_states = hidden_states.cpu().detach().numpy()
+        video_ids = []
+        for offset_idx, video_id in enumerate(sample["video_id"]):
+            if isinstance(video_id, tuple) and len(video_id) == 3:
+                # a sharded video_id.
+                video_id = video_id[0]
+            video_ids.append(video_id)
+        assert len(video_ids) == len(hidden_states)
+        self.retriver.add(
+            hidden_states.astype("float32"),
+            video_ids
+        )
+
+
+class DistributedVectorPool(VectorPool):
+    """
+    support sync of multiple gpus/nodes.
+    """
+    def __init__(self, config):
+        super().__init__(config)
+        self.out_dir = os.path.join(
+            config.fairseq.checkpoint.save_dir,
+            "retri")
+        os.makedirs(self.out_dir, exist_ok=True)
+        self.hidden_states = []
+        self.video_ids = []
+
+    def build_retriver(
+        self,
+        retriever_cls=None,
+        hidden_size=None,
+        centroids=4096,
+        db_type="flatl2",
+        examples_per_cent_to_train=48
+    ):
+        if retriever_cls is None:
+            retriever_cls = self.retriever_cls
+        if hidden_size is None:
+            hidden_size = self.hidden_size
+        """merge results from multiple gpus and return a retriver.."""
+        if torch.distributed.is_initialized():
+            self.save()
+            # sync saving.
+            torch.distributed.barrier()
+            world_size = torch.distributed.get_world_size()
+        else:
+            world_size = 1
+        self.retriver = retriever_cls(
+            hidden_size, centroids, db_type, examples_per_cent_to_train)
+        # each gpu process has its own retriever.
+        for local_rank in range(world_size):
+            if get_local_rank() == 0:
+                print("load local_rank", local_rank)
+            hidden_states, video_ids = self.load(local_rank)
+            hidden_states = hidden_states.astype("float32")
+            self.retriver.add(hidden_states, video_ids)
+        return self.retriver
+
+    def load(self, local_rank):
+        hidden_states = np.load(
+            os.path.join(
+                self.out_dir,
+                "hidden_state" + str(local_rank) + ".npy"
+            )
+        )
+
+        with open(
+            os.path.join(
+                self.out_dir, "video_id" + str(local_rank) + ".pkl"),
+                "rb") as fr:
+            video_ids = pickle.load(fr)
+        return hidden_states, video_ids
+
+    def save(self):
+        hidden_states = np.vstack(self.hidden_states)
+        assert len(hidden_states) == len(self.video_ids), "{}, {}".format(
+            len(hidden_states),
+            len(self.video_ids)
+        )
+        local_rank = torch.distributed.get_rank() \
+            if torch.distributed.is_initialized() else 0
+
+        np.save(
+            os.path.join(
+                self.out_dir,
+                "hidden_state" + str(local_rank) + ".npy"),
+            hidden_states)
+
+        with open(
+            os.path.join(
+                self.out_dir,
+                "video_id" + str(local_rank) + ".pkl"),
+                "wb") as fw:
+            pickle.dump(
+                self.video_ids,
+                fw,
+                protocol=pickle.HIGHEST_PROTOCOL
+            )
+
+
+class DistributedVideoVectorPool(DistributedVectorPool):
+    """
+    average clips of a video as video representation.
+    """
+    def __call__(self, sample, subsampling, **kwargs):
+        hidden_states = (
+            sample["pooled_video"] + sample["pooled_text"]) / 2.
+        hidden_states = hidden_states.view(
+            -1, subsampling,
+            hidden_states.size(-1))
+        hidden_states = torch.mean(hidden_states, dim=1)
+        hidden_states = hidden_states.cpu().detach().numpy()
+        video_ids = []
+        for offset_idx, video_id in enumerate(sample["video_id"]):
+            if isinstance(video_id, tuple) and len(video_id) == 3:
+                # a sharded video_id.
+                video_id = video_id[0]
+            video_ids.append(video_id)
+        assert len(video_ids) == len(hidden_states)
+        self.hidden_states.append(hidden_states)
+        self.video_ids.extend(video_ids)
+
+
+# ------------ the following are deprecated --------------
+
+class TextClipVectorPool(VectorPool):
+    def __init__(self, config):
+        from transformers import AutoConfig
+        hidden_size = AutoConfig.from_pretrained(
+            config.dataset.bert_name).hidden_size
+        retriever_cls = getattr(retri, config.retriever_cls)
+        self.build_retriver(retriever_cls, hidden_size)
+
+    def __call__(self, sample, **kwargs):
+        clip_meta = sample["clip_meta"].cpu()
+        assert torch.all(torch.le(clip_meta[:, 4], clip_meta[:, 5]))
+        text_meta = [tuple(item.tolist()) for item in clip_meta[:, 3:]]
+
+        if hasattr(self, "retriver"):
+            # build_retriver is called.
+            self.retriver.add(
+                sample["pooled_text"].cpu().numpy().astype("float32"),
+                text_meta
+            )
+        else:
+            raise NotImplementedError
+
+
+class MMClipVectorPool(VectorPool):
+    """
+    Multimodal Clip-level vector pool.
+    """
+    def __init__(self, out_dir):
+        """use hidden_states to store `(video, text)`."""
+        """use video_ids to store `(video_id, start, end)`."""
+        super().__init__(out_dir)
+
+    def __call__(self, sample, **kwargs):
+        pooled_video = sample["pooled_video"].cpu().unsqueeze(1).numpy()
+        pooled_text = sample["pooled_text"].cpu().unsqueeze(1).numpy()
+
+        self.hidden_states.append(
+            np.concatenate([pooled_video, pooled_text], axis=1)
+        )
+
+        video_starts = sample["video_start"].cpu()
+        video_ends = sample["video_end"].cpu()
+        assert torch.all(torch.le(video_starts, video_ends))
+
+        text_starts = sample["text_start"].cpu()
+        text_ends = sample["text_end"].cpu()
+        assert torch.all(torch.le(text_starts, text_ends))
+        subsample_size = sample["pooled_video"].size(0) // len(sample["video_id"])
+        video_ids = [video_id for video_id in sample["video_id"]
+                    for _ in range(subsample_size)
+        ]
+        for video_id, video_start, video_end, text_start, text_end in zip(
+                video_ids, video_starts, video_ends, text_starts, text_ends):
+            self.video_ids.append((
+                video_id,
+                (int(video_start), int(video_end)),
+                (int(text_start), int(text_end))
+            ))

fairseq/examples/MMPT/mmpt/processors/dedupprocessor.py

@@ -0,0 +1,242 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import random
+import json
+import pickle
+from tqdm import tqdm
+import os
+import numpy as np
+
+
+class CaptionDedupProcessor(object):
+    """remove overlapping of caption sentences(clip).
+    Some statistics:
+    caption:
+    {'t_clip_len': 246.6448431320854,
+    'video_len': 281.09174795676245,
+    'clip_tps': 0.8841283727427481,
+    'video_tps': 0.7821156477732097,
+    'min_clip_len': 0.0,
+    'max_clip_len': 398.3,
+    'mean_clip_len': 3.196580003006861,
+    'num_clip': 77.15897706301081}
+
+    raw_caption:
+    {'t_clip_len': 238.95908778424115,
+    'video_len': 267.5914859862507,
+    'clip_tps': 2.4941363624267963,
+    'video_tps': 2.258989769647173,
+    'min_clip_len': 0.0,
+    'max_clip_len': 398.3,
+    'mean_clip_len': 3.0537954186814265,
+    'num_clip': 78.24986779481756}
+    """
+
+    def __init__(self, pkl_file):
+        with open(pkl_file, "rb") as fd:
+            self.data = pickle.load(fd)
+        self.stat = {
+            "t_clip_len": [],
+            "video_len": [],
+            "clip_tps": [],
+            "video_tps": [],
+            "clip_len": [],
+        }
+
+    def __call__(self):
+        for idx, video_id in enumerate(tqdm(self.data)):
+            caption = json.loads(self.data[video_id])
+            caption = self._dedup(caption)
+            if idx < 4096:  # for the first 4096 examples, compute the statistics.
+                self.save_stat(video_id, caption)
+            self.data[video_id] = json.dumps(caption)
+        self.print_stat()
+
+    def single(self, video_id):
+        caption = json.loads(self.data[video_id])
+        for clip_idx, (start, end, text) in enumerate(
+            zip(caption["start"], caption["end"], caption["text"])
+        ):
+            print(start, end, text)
+        print("@" * 100)
+        caption = self._dedup(caption)
+        for clip_idx, (start, end, text) in enumerate(
+            zip(caption["start"], caption["end"], caption["text"])
+        ):
+            print(start, end, text)
+        print("#" * 100)
+        self.save_stat(video_id, caption)
+        self.print_stat()
+
+    def finalize(self, tgt_fn):
+        with open(tgt_fn, "wb") as fw:
+            pickle.dump(self.data, fw, pickle.HIGHEST_PROTOCOL)
+
+    def save_stat(self, video_id, caption):
+        video_fn = os.path.join(
+            "data/feat/feat_how2_s3d", video_id + ".npy"
+        )
+        if os.path.isfile(video_fn):
+            with open(video_fn, "rb", 1) as fr:  # 24 is the buffer size. buffered
+                version = np.lib.format.read_magic(fr)
+                shape, fortran, dtype = np.lib.format._read_array_header(fr, version)
+                video_len = shape[0]
+
+            t_clip_len = 0.0
+            t_tokens = 0
+            for idx, (start, end, text) in enumerate(
+                zip(caption["start"], caption["end"], caption["text"])
+            ):
+                clip_len = (
+                    (end - max(caption["end"][idx - 1], start))
+                    if idx > 0
+                    else end - start
+                )
+                t_clip_len += clip_len
+                t_tokens += len(text.split(" "))
+                self.stat["clip_len"].append(clip_len)
+            self.stat["t_clip_len"].append(t_clip_len)
+            self.stat["video_len"].append(video_len)
+            self.stat["clip_tps"].append(t_tokens / t_clip_len)
+            self.stat["video_tps"].append(t_tokens / video_len)
+
+    def print_stat(self):
+        result = {
+            "t_clip_len": np.mean(self.stat["t_clip_len"]),
+            "video_len": np.mean(self.stat["video_len"]),
+            "clip_tps": np.mean(self.stat["clip_tps"]),
+            "video_tps": np.mean(self.stat["video_tps"]),
+            "min_clip_len": min(self.stat["clip_len"]),
+            "max_clip_len": max(self.stat["clip_len"]),
+            "mean_clip_len": np.mean(self.stat["clip_len"]),
+            "num_clip": len(self.stat["clip_len"]) / len(self.stat["video_tps"]),
+        }
+        print(result)
+
+    def _dedup(self, caption):
+        def random_merge(end_idx, start, end, text, starts, ends, texts):
+            if random.random() > 0.5:
+                # print(clip_idx, "[PARTIAL INTO PREV]", end_idx)
+                # overlapped part goes to the end of previous.
+                ends[-1] = max(ends[-1], start)  # ?
+                rest_text = text[end_idx:].strip()
+                if rest_text:
+                    starts.append(max(ends[-1], start))
+                    ends.append(max(end, starts[-1]))
+                    texts.append(rest_text)
+            else:  # goes to the beginning of the current.
+                # strip the previous.
+                left_text = texts[-1][:-end_idx].strip()
+                if left_text:
+                    # print(clip_idx, "[PREV PARTIAL INTO CUR]", end_idx)
+                    ends[-1] = min(ends[-1], start)
+                    texts[-1] = left_text
+                else:
+                    # print(clip_idx, "[PREV LEFT NOTHING ALL INTO CUR]", end_idx)
+                    starts.pop(-1)
+                    ends.pop(-1)
+                    texts.pop(-1)
+                starts.append(start)
+                ends.append(end)
+                texts.append(text)
+
+        starts, ends, texts = [], [], []
+        for clip_idx, (start, end, text) in enumerate(
+            zip(caption["start"], caption["end"], caption["text"])
+        ):
+            if not isinstance(text, str):
+                continue
+            text = text.replace("\n", " ").strip()
+            if len(text) == 0:
+                continue
+            starts.append(start)
+            ends.append(end)
+            texts.append(text)
+            break
+
+        for clip_idx, (start, end, text) in enumerate(
+            zip(
+                caption["start"][clip_idx + 1:],
+                caption["end"][clip_idx + 1:],
+                caption["text"][clip_idx + 1:],
+            )
+        ):
+            if not isinstance(text, str):
+                continue
+            text = text.replace("\n", " ").strip()
+            if len(text) == 0:
+                continue
+
+            # print(clip_idx, texts[-5:])
+            # print(clip_idx, start, end, text)
+            if texts[-1].endswith(text):  # subset of prev caption -> merge
+                # print(clip_idx, "[MERGE INTO PREV]")
+                ends[-1] = max(ends[-1], end)
+            elif text.startswith(texts[-1]):  # superset of prev caption -> merge
+                # print(clip_idx, "[PREV MERGE INTO CUR]")
+                texts[-1] = text
+                starts[-1] = min(starts[-1], start)
+                ends[-1] = max(ends[-1], end)
+            else:  # overlapping or non-overlapping.
+                for end_idx in range(1, len(text) + 1):
+                    if texts[-1].endswith(text[:end_idx]):
+                        random_merge(end_idx, start, end, text, starts, ends, texts)
+                        break
+                else:
+                    starts.append(start)
+                    ends.append(end)
+                    texts.append(text)
+
+            assert (ends[-1] + 0.001) >= starts[-1] and len(
+                texts[-1]
+            ) > 0, "{} {} {} <- {} {} {}, {} {} {}".format(
+                str(starts[-1]),
+                str(ends[-1]),
+                texts[-1],
+                caption["start"][clip_idx - 1],
+                caption["end"][clip_idx - 1],
+                caption["text"][clip_idx - 1],
+                str(start),
+                str(end),
+                text,
+            )
+
+        return {"start": starts, "end": ends, "text": texts}
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="dedup how2 caption")
+    parser.add_argument('--how2dir', default="data/how2")
+    args = parser.parse_args()
+
+    raw_caption_json = os.path.join(args.how2dir, "raw_caption.json")
+    raw_caption_pickle = os.path.join(args.how2dir, "raw_caption.pkl")
+    raw_caption_dedup_pickle = os.path.join(args.how2dir, "raw_caption_dedup.pkl")
+
+    def convert_to_pickle(src_fn, tgt_fn):
+        with open(src_fn) as fd:
+            captions = json.load(fd)
+
+        for video_id in captions:
+            captions[video_id] = json.dumps(captions[video_id])
+
+        with open(tgt_fn, "wb") as fw:
+            pickle.dump(captions, fw, pickle.HIGHEST_PROTOCOL)
+
+    if not os.path.isfile(raw_caption_pickle):
+        convert_to_pickle(raw_caption_json, raw_caption_pickle)
+
+    deduper = CaptionDedupProcessor(raw_caption_pickle)
+    deduper()
+    deduper.finalize(raw_caption_dedup_pickle)
+
+    """
+    # demo
+    deduper = CaptionDedupProcessor("data/how2/raw_caption.pkl")
+    deduper.single("HfIeQ9pzL5U")
+    """

fairseq/examples/MMPT/mmpt/processors/how2processor.py

@@ -0,0 +1,887 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Copyright (c) Facebook, Inc. All Rights Reserved
+
+
+import torch
+import math
+import pickle
+import random
+import os
+import numpy as np
+
+from collections import deque
+from typing import Optional, Tuple, List
+from .processor import (
+    Processor,
+    MetaProcessor,
+    TextProcessor,
+    Aligner,
+    MMAttentionMask2DProcessor
+)
+
+from ..utils import ShardedTensor
+
+
+class How2MetaProcessor(MetaProcessor):
+    def __init__(self, config):
+        super().__init__(config)
+        path = self._get_split_path(config)
+        with open(path) as fd:
+            self.data = [line.strip() for line in fd]
+
+    def __getitem__(self, idx):
+        video_id = self.data[idx]
+        return video_id, video_id
+
+
+class ShardedHow2MetaProcessor(How2MetaProcessor):
+    def __init__(self, config):
+        super().__init__(config)
+        self.split = str(config.split)
+        self.vfeat_dir = config.vfeat_dir
+        self._init_shard()
+
+    def _init_shard(self):
+        if self.split == "train":
+            meta_fn = os.path.join(self.vfeat_dir, "train" + "_meta.pkl")
+            with open(meta_fn, "rb") as fr:
+                meta = pickle.load(fr)
+        elif self.split == "valid":
+            meta_fn = os.path.join(self.vfeat_dir, "val" + "_meta.pkl")
+            with open(meta_fn, "rb") as fr:
+                meta = pickle.load(fr)
+        elif self.split == "test":
+            print("use how2 val as test.")
+            meta_fn = os.path.join(self.vfeat_dir, "val" + "_meta.pkl")
+            with open(meta_fn, "rb") as fr:
+                meta = pickle.load(fr)
+        else:
+            raise ValueError("unsupported for MetaProcessor:", self.split)
+        video_id_to_shard = {}
+        for shard_id in meta:
+            for video_idx, video_id in enumerate(meta[shard_id]):
+                video_id_to_shard[video_id] = (shard_id, video_idx)
+        self.video_id_to_shard = video_id_to_shard
+
+    def __getitem__(self, idx):
+        video_id, video_id = super().__getitem__(idx)
+        shard_id, shard_idx = self.video_id_to_shard[video_id]
+        meta = (video_id, idx, shard_id, shard_idx)
+        return meta, meta
+
+
+class ShardedVideoProcessor(Processor):
+    """
+    mmaped shards of numpy video features.
+    """
+
+    def __init__(self, config):
+        self.split = str(config.split)
+        self.vfeat_dir = config.vfeat_dir
+
+    def __call__(self, video_id):
+        _, _, shard_id, video_idx = video_id
+        if self.split == "train":
+            shard = ShardedTensor.load(
+                os.path.join(self.vfeat_dir, "train" + "_" + str(shard_id)),
+                "r"
+            )
+        elif self.split == "valid":
+            shard = ShardedTensor.load(
+                os.path.join(self.vfeat_dir, "val" + "_" + str(shard_id)),
+                "r"
+            )
+        elif self.split == "test":
+            shard = ShardedTensor.load(
+                os.path.join(self.vfeat_dir, "val" + "_" + str(shard_id)),
+                "r"
+            )
+        else:
+            raise ValueError("unknown split", self.split)
+        feat = shard[video_idx]
+        return feat
+
+
+class ShardedTextProcessor(Processor):
+    def __init__(self, config):
+        self.tfeat_dir = str(config.tfeat_dir)
+        self.split = str(config.split)
+
+    def __call__(self, video_id):
+        _, _, shard_id, shard_idx = video_id
+        if self.split == "train":
+            target_path = self.tfeat_dir + "train" + "_" + str(shard_id)
+        elif self.split == "valid":
+            target_path = self.tfeat_dir + "val" + "_" + str(shard_id)
+        elif self.split == "test":
+            target_path = self.tfeat_dir + "val" + "_" + str(shard_id)
+        else:
+            raise ValueError("unknown split", self.split)
+
+        startend = ShardedTensor.load(
+            target_path + ".startends", "r")[shard_idx]
+        cap_ids = ShardedTensor.load(
+            target_path + ".caps_ids", "r")[shard_idx]
+        cap = []
+        for clip_idx in range(len(cap_ids)):
+            clip = cap_ids[clip_idx]
+            cap.append(clip[clip != -1].tolist())
+        start, end = startend[:, 0].tolist(), startend[:, 1].tolist()
+        return {"start": start, "end": end, "cap": cap}
+
+
+class FixedLenAligner(Aligner):
+    """
+    In the model we assume text is on the left (closer to BERT formulation)
+    and video is on the right.
+    We fix the total length of text + video.
+    max_video_len is in number of secs.
+    max_text_len is in number of tokens.
+
+    special tokens formats:
+    we use the format [CLS] [SEP] text tokens [SEP] [PAD] ...
+    [CLS] will be splitted out into:
+    [CLS] video tokens [SEP] text tokens [SEP] [PAD] ...
+    token_type_ids will be generated by the model (for now).
+    0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+    | first sequence    | second sequence |
+    so each sequence owns a [SEP] token for no-ops.
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.text_clip_sampler = TextClipSamplingProcessor(
+            self.max_len - self.max_video_len - 3
+        )
+        """
+        decide subsampling:
+        `config.subsampling` will change batch_size in trainer.
+        `config.clip_per_video` (used by RetriTask) doesn't
+            change batch_size in trainer.
+        """
+        subsampling = config.subsampling \
+            if config.subsampling is not None else None
+        if config.clip_per_video is not None:
+            subsampling = config.clip_per_video
+        self.subsampling = subsampling
+
+    def _get_text_maxlen(self):
+        # use max text len
+        return self.text_clip_sampler.max_text_len
+
+    def __call__(self, video_id, video_feature, text_feature):
+        from transformers import default_data_collator
+        video_idx = video_id[1]
+        if self.subsampling is not None and self.subsampling >= 1:
+            batch = []
+            for _ in range(self.subsampling):
+                centerclip_idx = random.randint(
+                                    0, len(text_feature["start"]) - 1)
+                batch.append(
+                    self.sampling(
+                        video_idx,
+                        video_feature,
+                        text_feature,
+                        centerclip_idx,
+                        self._get_text_maxlen()
+                    ))
+            batch = self.batch_post_processing(batch, video_feature)
+            batch = default_data_collator(batch)
+        else:
+            raise ValueError(
+                "dataset.subsampling must be >= 1 for efficient video loading.")
+            batch = self.sampling(video_idx, video_feature, text_feature)
+            batch = self.batch_post_processing(batch, video_feature)
+
+        batch["video_id"] = video_id if isinstance(video_id, str) \
+            else video_id[0]
+        # e2e: make sure frame ids is into tensor.
+        assert torch.is_tensor(batch["vfeats"])
+        return batch
+
+    def sampling(
+        self,
+        video_idx,
+        video_feature,
+        text_feature,
+        centerclip_idx=None,
+        sampled_max_text_len=None,
+    ):
+        text_clip_indexs = self.text_clip_sampler(
+            text_feature, centerclip_idx,
+            sampled_max_text_len
+        )
+        if isinstance(video_feature, np.ndarray):
+            video_len = len(video_feature)
+        else:
+            video_len = math.ceil(text_feature["end"][-1])
+
+        video_end = min(
+            math.ceil(text_feature["end"][text_clip_indexs[-1]]),
+            video_len
+        )
+        video_start = max(
+            min(
+                math.floor(text_feature["start"][text_clip_indexs[0]]),
+                video_end),
+            0
+        )
+
+        video_clips = {"start": [video_start], "end": [video_end]}
+
+        # tensorize.
+        vfeats, vmasks = self._build_video_seq(
+            video_feature, video_clips
+        )
+        caps, cmasks = self._build_text_seq(
+            text_feature, text_clip_indexs
+        )
+
+        text_start = text_clip_indexs[0]
+        text_end = text_clip_indexs[-1] + 1
+
+        return {
+            "caps": caps,
+            "cmasks": cmasks,
+            "vfeats": vfeats,
+            "vmasks": vmasks,
+            "video_start": video_start,
+            "video_end": video_end,
+            "text_start": text_start,
+            "text_end": text_end,
+        }
+
+
+class VariedLenAligner(FixedLenAligner):
+    def __init__(self, config):
+        super().__init__(config)
+        self.sampled_min_len = config.sampled_min_len
+        self.sampled_max_len = config.sampled_max_len
+
+    def _get_text_maxlen(self):
+        return random.randint(self.sampled_min_len, self.sampled_max_len)
+
+
+class StartClipAligner(VariedLenAligner):
+    def sampling(
+        self,
+        video_idx,
+        video_feature,
+        text_feature,
+        centerclip_idx=None,
+        sampled_max_text_len=None,
+    ):
+        return super().sampling(
+            video_idx, video_feature, text_feature, 0)
+
+
+class OverlappedAligner(VariedLenAligner):
+    """video clip and text clip has overlappings
+    but may not be the same start/end."""
+    def __init__(self, config):
+        super().__init__(config)
+        self.sampled_video_min_len = config.sampled_video_min_len
+        self.sampled_video_max_len = config.sampled_video_max_len
+
+        self.video_clip_sampler = VideoClipSamplingProcessor()
+
+    def _get_video_maxlen(self):
+        return random.randint(
+            self.sampled_video_min_len, self.sampled_video_max_len)
+
+    def sampling(
+        self,
+        video_idx,
+        video_feature,
+        text_feature,
+        centerclip_idx=None,
+        sampled_max_text_len=None,
+    ):
+        text_clip_indexs = self.text_clip_sampler(
+            text_feature, centerclip_idx,
+            sampled_max_text_len
+        )
+        if isinstance(video_feature, np.ndarray):
+            video_len = len(video_feature)
+        else:
+            video_len = math.ceil(text_feature["end"][-1])
+        low = math.floor(text_feature["start"][text_clip_indexs[0]])
+        high = math.ceil(text_feature["end"][text_clip_indexs[-1]])
+        if low < high:
+            center = random.randint(low, high)
+        else:
+            center = int((low + high) // 2)
+        center = max(0, min(video_feature.shape[0] - 1, center))
+
+        assert 0 <= center < video_feature.shape[0]
+
+        video_clips = self.video_clip_sampler(
+            video_len, self._get_video_maxlen(), center
+        )
+        video_start = video_clips["start"][0]
+        video_end = video_clips["end"][0]
+
+        # tensorize.
+        vfeats, vmasks = self._build_video_seq(
+            video_feature, video_clips
+        )
+        caps, cmasks = self._build_text_seq(
+            text_feature, text_clip_indexs
+        )
+
+        text_start = text_clip_indexs[0]
+        text_end = text_clip_indexs[-1] + 1
+
+        return {
+            "caps": caps,
+            "cmasks": cmasks,
+            "vfeats": vfeats,
+            "vmasks": vmasks,
+            "video_start": video_start,
+            "video_end": video_end,
+            "text_start": text_start,
+            "text_end": text_end,
+        }
+
+
+class MFMMLMAligner(FixedLenAligner):
+    """
+    `FixedLenAligner` with Masked Language Model and Masked Frame Model.
+    """
+
+    def __init__(self, config):
+        super().__init__(config)
+        keep_prob = config.keep_prob if config.keep_prob is not None else 1.0
+        self.text_clip_sampler = TextClipSamplingProcessor(
+            self.max_len - self.max_video_len - 3, keep_prob
+        )
+        self.sampled_min_len = config.sampled_min_len
+        self.sampled_max_len = config.sampled_max_len
+        self.masked_token_sampler = TextMaskingProcessor(config)
+        self.mm_type = config.mm_type \
+            if config.mm_type is not None else "full"
+        self.attnmasker = MMAttentionMask2DProcessor() \
+            if self.mm_type == "textgen" else None
+        self.masked_frame_sampler = FrameMaskingProcessor(config)
+        self.lazy_vfeat_mask = (
+            False if config.lazy_vfeat_mask is None else config.lazy_vfeat_mask
+        )
+        self.mm_prob = config.mm_prob if config.mm_prob is not None else 0.
+
+    def __call__(self, video_id, video_feature, text_feature):
+        from transformers import default_data_collator
+        if self.subsampling is not None and self.subsampling > 1:
+            batch = []
+            for _ in range(self.subsampling):
+                centerclip_idx = random.randint(
+                                    0, len(text_feature["start"]) - 1)
+                sampled_max_text_len = random.randint(
+                    self.sampled_min_len, self.sampled_max_len
+                )
+                batch.append(
+                    self.sampling(
+                        video_id,
+                        video_feature,
+                        text_feature,
+                        centerclip_idx,
+                        sampled_max_text_len,
+                    )
+                )
+            batch = self.batch_post_processing(batch, video_feature)
+            batch = default_data_collator(batch)
+        else:
+            batch = self.sampling(video_id, video_feature, text_feature)
+            batch = self.batch_post_processing(batch, video_feature)
+        batch["video_id"] = video_id if isinstance(video_id, str) \
+            else video_id[0]
+        return batch
+
+    def sampling(
+        self,
+        video_id,
+        video_feature,
+        text_feature,
+        centerclip_idx=None,
+        sampled_max_text_len=None,
+    ):
+        output = FixedLenAligner.sampling(self,
+            video_id, video_feature, text_feature,
+            centerclip_idx, sampled_max_text_len)
+
+        masking_text, masking_video = None, None
+        if random.random() < self.mm_prob:
+            if random.random() > 0.5:
+                masking_text, masking_video = self.mm_type, "no"
+            else:
+                masking_text, masking_video = "no", "full"
+        video_feats = output["vfeats"] if not self.lazy_vfeat_mask else None
+        video_label = self.masked_frame_sampler(
+            output["vmasks"], masking_video, vfeats=video_feats)
+        caps, text_label = self.masked_token_sampler(
+            output["caps"], masking_text)
+
+        output.update({
+            "caps": caps,
+            "video_label": video_label,
+            "text_label": text_label,
+        })
+
+        if self.attnmasker is not None:
+            attention_mask = self.attnmasker(
+                output["vmasks"], output["cmasks"], masking_text)
+            output.update({
+                "attention_mask": attention_mask
+            })
+        return output
+
+
+class FrameMaskingProcessor(Processor):
+    def __init__(self, config):
+        self.mfm_probability = 0.15
+        if config.mfm_probability is not None:
+            self.mfm_probability = config.mfm_probability
+
+    def __call__(self, vmasks, modality_masking=None, vfeats=None):
+        """
+        We perform lazy masking to save data transfer time.
+        It only generates video_labels by default and MFM model
+        will do actualy masking.
+        Return: `video_label` is a binary mask.
+        """
+        video_label = vmasks.clone()
+        if modality_masking is not None:
+            if modality_masking == "full":
+                probability_matrix = torch.full(video_label.shape, 1.)
+            elif modality_masking == "no":
+                probability_matrix = torch.full(video_label.shape, 0.)
+            elif modality_masking == "inverse":
+                probability_matrix = torch.full(
+                    video_label.shape, 1. - self.mfm_probability)
+            else:
+                raise ValueError("unknown modality masking.", modality_masking)
+        else:
+            probability_matrix = torch.full(
+                video_label.shape, self.mfm_probability)
+        masked_indices = torch.bernoulli(probability_matrix).bool()
+        # We only compute loss on masked tokens
+        video_label[~masked_indices] = 0
+        if vfeats is not None:
+            vfeats[video_label, :] = 0.0
+        return video_label
+
+
+class TextGenerationProcessor(Processor):
+    def __init__(self, tokenizer):
+        self.bos_token_id = tokenizer.bos_token_id
+        self.pad_token_id = tokenizer.pad_token_id
+
+    def __call__(self, inputs):
+        labels = inputs.clone()
+        # [CLS] [SEP] for video
+        labels[:2] = -100
+        # keep [SEP] for text.
+        pad_mask = labels == self.pad_token_id
+        labels[pad_mask] = -100
+        inputs[2:] = torch.cat([
+            torch.LongTensor([self.bos_token_id]),
+            inputs[2:-1]])
+        inputs[pad_mask] = self.pad_token_id
+        assert len(inputs) == len(labels)
+        return inputs, labels
+
+
+class TextMaskingProcessor(Processor):
+    def __init__(self, config):
+        """this function is borrowed from
+        `transformers/data/data_collator.DataCollatorForLanguageModeling`"""
+        self.mlm_probability = 0.15
+        if config.mlm_probability is not None:
+            self.mlm_probability = config.mlm_probability
+        self.bert_name = config.bert_name
+        # [CLS] is used as bos_token and [SEP] is used as eos_token.
+        # https://huggingface.co/transformers/master/model_doc/bertgeneration.html
+        from transformers import AutoTokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.bert_name, bos_token="[CLS]", eos_token="[SEP]")
+        self.textgen = TextGenerationProcessor(self.tokenizer)
+
+    def __call__(
+        self, inputs: torch.Tensor,
+        modality_masking=None,
+        special_tokens_mask: Optional[torch.Tensor] = None
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        expand modality_masking into
+            None: traditional bert masking.
+            "no": no masking.
+            "full": all [MASK] token for generation.
+            "gen": autoregressive generation.
+        """
+        """
+        Prepare masked tokens inputs/labels for masked language modeling:
+        80% MASK, 10% random, 10% original.
+        """
+        labels = inputs.clone()
+        # We sample a few tokens in each sequence for MLM training
+        # (with probability `self.mlm_probability`)
+        if modality_masking is not None:
+            if modality_masking == "full":
+                probability_matrix = torch.full(labels.shape, 1.)
+            elif modality_masking == "no":
+                probability_matrix = torch.full(labels.shape, 0.)
+            elif modality_masking.startswith("textgen"):
+                # [CLS] [SEP] <s> ...
+                inputs, labels = self.textgen(inputs)
+                if "mask" not in modality_masking:
+                    return inputs, labels
+                inputs = self.mask_input(inputs, special_tokens_mask)
+                return inputs, labels
+            elif modality_masking == "mask":
+                inputs = self.mask_input(inputs, special_tokens_mask)
+                labels = torch.full(inputs.shape, -100)
+                return inputs, labels
+            elif modality_masking == "inverse":
+                probability_matrix = torch.full(labels.shape, 1. - self.mlm_probability)
+            else:
+                raise ValueError("unknown modality masking.", modality_masking)
+        else:
+            probability_matrix = torch.full(labels.shape, self.mlm_probability)
+
+        if special_tokens_mask is None:
+            special_tokens_mask = self.get_special_tokens_mask(
+                labels.tolist(), already_has_special_tokens=True
+            )
+            special_tokens_mask = torch.tensor(
+                special_tokens_mask, dtype=torch.bool)
+        else:
+            special_tokens_mask = special_tokens_mask.bool()
+
+        probability_matrix.masked_fill_(special_tokens_mask, value=0.0)
+        masked_indices = torch.bernoulli(probability_matrix).bool()
+        labels[~masked_indices] = -100  # We only compute loss on masked tokens
+
+        # 80% of the time,
+        # we replace masked input tokens with tokenizer.mask_token ([MASK])
+        indices_replaced = (
+            torch.bernoulli(
+                torch.full(labels.shape, 0.8)).bool() & masked_indices
+        )
+        inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(
+            self.tokenizer.mask_token
+        )
+
+        # 10% of the time, we replace masked input tokens with random word
+        indices_random = (
+            torch.bernoulli(torch.full(labels.shape, 0.5)).bool()
+            & masked_indices
+            & ~indices_replaced
+        )
+        random_words = torch.randint(
+            len(self.tokenizer), labels.shape, dtype=torch.long
+        )
+        inputs[indices_random] = random_words[indices_random]
+
+        # The rest of the time (10% of the time) we keep the masked input
+        # tokens unchanged
+        return inputs, labels
+
+    def mask_input(self, inputs, special_tokens_mask=None):
+        # the following is new with masked autoregressive.
+        probability_matrix = torch.full(
+            inputs.shape, self.mlm_probability)
+        if special_tokens_mask is None:
+            special_tokens_mask = self.get_special_tokens_mask(
+                inputs.tolist(), already_has_special_tokens=True
+            )
+            special_tokens_mask = torch.tensor(
+                special_tokens_mask, dtype=torch.bool)
+        else:
+            special_tokens_mask = special_tokens_mask.bool()
+        probability_matrix.masked_fill_(special_tokens_mask, value=0.0)
+        masked_indices = torch.bernoulli(probability_matrix).bool()
+        indices_replaced = (
+            torch.bernoulli(
+                torch.full(inputs.shape, 0.8)).bool() & masked_indices
+        )
+        inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(
+            self.tokenizer.mask_token
+        )
+
+        # 10% of the time, we replace masked input tokens with random word
+        indices_random = (
+            torch.bernoulli(torch.full(inputs.shape, 0.5)).bool()
+            & masked_indices
+            & ~indices_replaced
+        )
+        random_words = torch.randint(
+            len(self.tokenizer), inputs.shape, dtype=torch.long
+        )
+        inputs[indices_random] = random_words[indices_random]
+        return inputs
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int],
+        token_ids_1: Optional[List[int]] = None,
+        already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Note: the version from transformers do not consider pad
+        as special tokens.
+        """
+
+        if already_has_special_tokens:
+            if token_ids_1 is not None:
+                raise ValueError(
+                    "You should not supply a second sequence if"
+                    "the provided sequence of "
+                    "ids is already formated with special tokens "
+                    "for the model."
+                )
+            return list(map(lambda x: 1 if x in [
+                self.tokenizer.sep_token_id,
+                self.tokenizer.cls_token_id,
+                self.tokenizer.pad_token_id] else 0, token_ids_0))
+
+        if token_ids_1 is not None:
+            return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1]
+
+
+class TextClipSamplingProcessor(Processor):
+    def __init__(self, max_text_len, keep_prob=1.0):
+        self.max_text_len = max_text_len
+        self.max_video_len = 256  # always hold.
+        self.keep_prob = keep_prob
+
+    def __call__(
+        self,
+        text_feature,
+        centerclip_idx=None,
+        sampled_max_text_len=None,
+        sampled_max_video_len=None,
+    ):
+        # Let's use all caps for now and see if 256 can cover all of them.
+        if sampled_max_text_len is not None:
+            max_text_len = sampled_max_text_len
+        else:
+            max_text_len = self.max_text_len
+        if sampled_max_video_len is not None:
+            max_video_len = sampled_max_video_len
+        else:
+            max_video_len = self.max_video_len
+
+        t_num_clips = len(text_feature["start"])
+
+        if centerclip_idx is None:
+            centerclip_idx = random.randint(0, t_num_clips - 1)
+
+        start_idx, end_idx = centerclip_idx, centerclip_idx + 1
+        text_clip_indexs = deque()
+        text_clip_indexs.append(start_idx)
+        text_len = len(text_feature["cap"][start_idx])
+
+        video_len = max(
+            0,
+            text_feature["end"][start_idx]
+            - text_feature["start"][start_idx],
+        )
+
+        while (
+            (start_idx > 0 or end_idx < t_num_clips)
+            and text_len < max_text_len
+            and video_len < max_video_len
+        ):
+            if random.random() > 0.5 and end_idx < t_num_clips:
+                # skip the next one?
+                if random.random() > self.keep_prob and (end_idx + 1) < t_num_clips:
+                    end_idx = end_idx + 1
+                text_clip_indexs.append(end_idx)
+                text_len += len(text_feature["cap"][end_idx])
+                end_idx += 1
+            elif start_idx > 0:
+                if random.random() > self.keep_prob and (start_idx - 1) > 0:
+                    start_idx = start_idx - 1
+                start_idx -= 1
+                text_clip_indexs.insert(0, start_idx)
+                text_len += len(text_feature["cap"][start_idx])
+            else:
+                if end_idx < t_num_clips:
+                    if random.random() > self.keep_prob and (end_idx + 1) < t_num_clips:
+                        end_idx = end_idx + 1
+                    text_clip_indexs.append(end_idx)
+                    text_len += len(text_feature["cap"][end_idx])
+                    end_idx += 1
+                else:
+                    return text_clip_indexs
+            video_len = max(
+                0,
+                text_feature["end"][text_clip_indexs[-1]]
+                - text_feature["start"][text_clip_indexs[0]],
+            )
+        return text_clip_indexs
+
+
+class VideoClipSamplingProcessor(Processor):
+    def __call__(self, video_len, max_video_len, center):
+        """
+        `video_len`: length of the video.
+        `max_video_len`: maximum video tokens allowd in a sequence.
+        `center`: initial starting index.
+        """
+        assert center >= 0 and center < video_len
+        t_clip_len = 0
+        start, end = center, center
+        while (start > 0 or end < video_len) and t_clip_len < max_video_len:
+            # decide the direction to grow.
+            if start <= 0:
+                end += 1
+            elif end >= video_len:
+                start -= 1
+            elif random.random() > 0.5:
+                end += 1
+            else:
+                start -= 1
+            t_clip_len += 1
+        return {"start": [start], "end": [end]}
+
+
+class How2MILNCEAligner(FixedLenAligner):
+    """reference: `antoine77340/MIL-NCE_HowTo100M/video_loader.py`"""
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_candidates = 4
+        self.min_time = 5.0
+        self.num_sec = 3.2
+        # self.num_sec = self.num_frames / float(self.fps)  num_frames=16 / fps = 5
+        # self.num_frames = 16
+
+    def sampling(
+        self,
+        video_id,
+        video_feature,
+        text_feature,
+        centerclip_idx=None,  # will be ignored.
+        sampled_max_text_len=None  # will be ignored.
+    ):
+        text, start, end = self._get_text(text_feature)
+        video = self._get_video(video_feature, start, end)
+
+        vfeats = torch.zeros((self.max_video_len, video_feature.shape[1]))
+        vmasks = torch.zeros((self.max_video_len,), dtype=torch.bool)
+        vfeats[: video.shape[0]] = torch.from_numpy(np.array(video))
+        vmasks[: video.shape[0]] = 1
+
+        caps, cmasks = [], []
+        for words in text:
+            cap, cmask = self._build_text_seq(text_feature, words)
+            caps.append(cap)
+            cmasks.append(cmask)
+        caps = torch.stack(caps)
+        cmasks = torch.stack(cmasks)
+        # video of shape: (video_len)
+        # text of shape (num_candidates, max_text_len)
+
+        return {
+            "caps": caps,
+            "cmasks": cmasks,
+            "vfeats": vfeats,
+            "vmasks": vmasks,
+            # "video_id": video_id,
+        }
+
+    def _get_video(self, video_feature, start, end):
+        start_seek = random.randint(start, int(max(start, end - self.num_sec)))
+        # duration = self.num_sec + 0.1
+        return video_feature[start_seek : int(start_seek + self.num_sec)]
+
+    def _get_text(self, cap):
+        ind = random.randint(0, len(cap["start"]) - 1)
+        if self.num_candidates == 1:
+            words = [ind]
+        else:
+            words = []
+            cap_start = self._find_nearest_candidates(cap, ind)
+            for i in range(self.num_candidates):
+                words.append([max(0, min(len(cap["cap"]) - 1, cap_start + i))])
+
+        start, end = cap["start"][ind], cap["end"][ind]
+        # TODO: May need to be improved for edge cases.
+        # expand the min time.
+        if end - start < self.min_time:
+            diff = self.min_time - end + start
+            start = max(0, start - diff / 2)
+            end = start + self.min_time
+        return words, int(start), int(end)
+
+    def _find_nearest_candidates(self, caption, ind):
+        """find the range of the clips."""
+        start, end = ind, ind
+        #diff = caption["end"][end] - caption["start"][start]
+        n_candidate = 1
+        while n_candidate < self.num_candidates:
+            # the first clip
+            if start == 0:
+                return 0
+            # we add () in the following condition to fix the bug.
+            elif end == (len(caption["start"]) - 1):
+                return start - (self.num_candidates - n_candidate)
+            elif (caption["end"][end] - caption["start"][start - 1]) < (
+                caption["end"][end + 1] - caption["start"][start]
+            ):
+                start -= 1
+            else:
+                end += 1
+            n_candidate += 1
+        return start
+
+
+class PKLJSONStrTextProcessor(TextProcessor):
+    """`caption.json` from howto100m are preprocessed as a
+    dict `[video_id, json_str]`.
+    Json parsing tokenization are conducted on-the-fly and cached into dict.
+    """
+
+    def __init__(self, config, max_clip_text_len=96):
+        print("[Warning] PKLJSONStrTextProcessor is slow for num_workers > 0.")
+        self.caption_pkl_path = str(config.caption_pkl_path)
+        with open(self.caption_pkl_path, "rb") as fd:
+            self.data = pickle.load(fd)
+        self.max_clip_text_len = max_clip_text_len
+        from transformers import AutoTokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            str(config.bert_name), use_fast=config.use_fast
+        )
+
+    def __call__(self, video_id):
+        caption = self.data[video_id]
+        if isinstance(caption, str):
+            import json
+            caption = json.loads(caption)
+            cap = []
+            for clip_idx, text_clip in enumerate(caption["text"]):
+                clip_ids = []
+                if isinstance(text_clip, str):
+                    clip_ids = self.tokenizer(
+                        text_clip[: self.max_clip_text_len],
+                        add_special_tokens=False
+                    )["input_ids"]
+                cap.append(clip_ids)
+            caption["cap"] = cap
+            caption.pop("text")  # save space.
+            self.data[video_id] = caption
+        return caption

fairseq/examples/MMPT/mmpt/processors/models/s3dg.py

@@ -0,0 +1,336 @@
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Contains a PyTorch definition for Gated Separable 3D network (S3D-G)
+with a text module for computing joint text-video embedding from raw text
+and video input. The following code will enable you to load the HowTo100M
+pretrained S3D Text-Video model from:
+  A. Miech, J.-B. Alayrac, L. Smaira, I. Laptev, J. Sivic and A. Zisserman,
+  End-to-End Learning of Visual Representations from Uncurated Instructional Videos.
+  https://arxiv.org/abs/1912.06430.
+
+S3D-G was proposed by:
+  S. Xie, C. Sun, J. Huang, Z. Tu and K. Murphy,
+  Rethinking Spatiotemporal Feature Learning For Video Understanding.
+  https://arxiv.org/abs/1712.04851.
+  Tensorflow code: https://github.com/tensorflow/models/blob/master/research/slim/nets/s3dg.py
+
+The S3D architecture was slightly modified with a space to depth trick for TPU
+optimization.
+"""
+
+import torch as th
+import torch.nn.functional as F
+import torch.nn as nn
+import os
+import numpy as np
+import re
+
+
+class InceptionBlock(nn.Module):
+    def __init__(
+        self,
+        input_dim,
+        num_outputs_0_0a,
+        num_outputs_1_0a,
+        num_outputs_1_0b,
+        num_outputs_2_0a,
+        num_outputs_2_0b,
+        num_outputs_3_0b,
+        gating=True,
+    ):
+        super(InceptionBlock, self).__init__()
+        self.conv_b0 = STConv3D(input_dim, num_outputs_0_0a, [1, 1, 1])
+        self.conv_b1_a = STConv3D(input_dim, num_outputs_1_0a, [1, 1, 1])
+        self.conv_b1_b = STConv3D(
+            num_outputs_1_0a, num_outputs_1_0b, [3, 3, 3], padding=1, separable=True
+        )
+        self.conv_b2_a = STConv3D(input_dim, num_outputs_2_0a, [1, 1, 1])
+        self.conv_b2_b = STConv3D(
+            num_outputs_2_0a, num_outputs_2_0b, [3, 3, 3], padding=1, separable=True
+        )
+        self.maxpool_b3 = th.nn.MaxPool3d((3, 3, 3), stride=1, padding=1)
+        self.conv_b3_b = STConv3D(input_dim, num_outputs_3_0b, [1, 1, 1])
+        self.gating = gating
+        self.output_dim = (
+            num_outputs_0_0a + num_outputs_1_0b + num_outputs_2_0b + num_outputs_3_0b
+        )
+        if gating:
+            self.gating_b0 = SelfGating(num_outputs_0_0a)
+            self.gating_b1 = SelfGating(num_outputs_1_0b)
+            self.gating_b2 = SelfGating(num_outputs_2_0b)
+            self.gating_b3 = SelfGating(num_outputs_3_0b)
+
+    def forward(self, input):
+        """Inception block
+      """
+        b0 = self.conv_b0(input)
+        b1 = self.conv_b1_a(input)
+        b1 = self.conv_b1_b(b1)
+        b2 = self.conv_b2_a(input)
+        b2 = self.conv_b2_b(b2)
+        b3 = self.maxpool_b3(input)
+        b3 = self.conv_b3_b(b3)
+        if self.gating:
+            b0 = self.gating_b0(b0)
+            b1 = self.gating_b1(b1)
+            b2 = self.gating_b2(b2)
+            b3 = self.gating_b3(b3)
+        return th.cat((b0, b1, b2, b3), dim=1)
+
+
+class SelfGating(nn.Module):
+    def __init__(self, input_dim):
+        super(SelfGating, self).__init__()
+        self.fc = nn.Linear(input_dim, input_dim)
+
+    def forward(self, input_tensor):
+        """Feature gating as used in S3D-G.
+      """
+        spatiotemporal_average = th.mean(input_tensor, dim=[2, 3, 4])
+        weights = self.fc(spatiotemporal_average)
+        weights = th.sigmoid(weights)
+        return weights[:, :, None, None, None] * input_tensor
+
+
+class STConv3D(nn.Module):
+    def __init__(
+        self, input_dim, output_dim, kernel_size, stride=1, padding=0, separable=False
+    ):
+        super(STConv3D, self).__init__()
+        self.separable = separable
+        self.relu = nn.ReLU(inplace=True)
+        assert len(kernel_size) == 3
+        if separable and kernel_size[0] != 1:
+            spatial_kernel_size = [1, kernel_size[1], kernel_size[2]]
+            temporal_kernel_size = [kernel_size[0], 1, 1]
+            if isinstance(stride, list) and len(stride) == 3:
+                spatial_stride = [1, stride[1], stride[2]]
+                temporal_stride = [stride[0], 1, 1]
+            else:
+                spatial_stride = [1, stride, stride]
+                temporal_stride = [stride, 1, 1]
+            if isinstance(padding, list) and len(padding) == 3:
+                spatial_padding = [0, padding[1], padding[2]]
+                temporal_padding = [padding[0], 0, 0]
+            else:
+                spatial_padding = [0, padding, padding]
+                temporal_padding = [padding, 0, 0]
+        if separable:
+            self.conv1 = nn.Conv3d(
+                input_dim,
+                output_dim,
+                kernel_size=spatial_kernel_size,
+                stride=spatial_stride,
+                padding=spatial_padding,
+                bias=False,
+            )
+            self.bn1 = nn.BatchNorm3d(output_dim)
+            self.conv2 = nn.Conv3d(
+                output_dim,
+                output_dim,
+                kernel_size=temporal_kernel_size,
+                stride=temporal_stride,
+                padding=temporal_padding,
+                bias=False,
+            )
+            self.bn2 = nn.BatchNorm3d(output_dim)
+        else:
+            self.conv1 = nn.Conv3d(
+                input_dim,
+                output_dim,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=padding,
+                bias=False,
+            )
+            self.bn1 = nn.BatchNorm3d(output_dim)
+
+    def forward(self, input):
+        out = self.relu(self.bn1(self.conv1(input)))
+        if self.separable:
+            out = self.relu(self.bn2(self.conv2(out)))
+        return out
+
+
+class MaxPool3dTFPadding(th.nn.Module):
+    def __init__(self, kernel_size, stride=None, padding="SAME"):
+        super(MaxPool3dTFPadding, self).__init__()
+        if padding == "SAME":
+            padding_shape = self._get_padding_shape(kernel_size, stride)
+            self.padding_shape = padding_shape
+            self.pad = th.nn.ConstantPad3d(padding_shape, 0)
+        self.pool = th.nn.MaxPool3d(kernel_size, stride, ceil_mode=True)
+
+    def _get_padding_shape(self, filter_shape, stride):
+        def _pad_top_bottom(filter_dim, stride_val):
+            pad_along = max(filter_dim - stride_val, 0)
+            pad_top = pad_along // 2
+            pad_bottom = pad_along - pad_top
+            return pad_top, pad_bottom
+
+        padding_shape = []
+        for filter_dim, stride_val in zip(filter_shape, stride):
+            pad_top, pad_bottom = _pad_top_bottom(filter_dim, stride_val)
+            padding_shape.append(pad_top)
+            padding_shape.append(pad_bottom)
+        depth_top = padding_shape.pop(0)
+        depth_bottom = padding_shape.pop(0)
+        padding_shape.append(depth_top)
+        padding_shape.append(depth_bottom)
+        return tuple(padding_shape)
+
+    def forward(self, inp):
+        inp = self.pad(inp)
+        out = self.pool(inp)
+        return out
+
+
+class Sentence_Embedding(nn.Module):
+    def __init__(
+        self,
+        embd_dim,
+        num_embeddings=66250,
+        word_embedding_dim=300,
+        token_to_word_path="dict.npy",
+        max_words=16,
+        output_dim=2048,
+    ):
+        super(Sentence_Embedding, self).__init__()
+        self.word_embd = nn.Embedding(num_embeddings, word_embedding_dim)
+        self.fc1 = nn.Linear(word_embedding_dim, output_dim)
+        self.fc2 = nn.Linear(output_dim, embd_dim)
+        self.word_to_token = {}
+        self.max_words = max_words
+        token_to_word = np.load(token_to_word_path)
+        for i, t in enumerate(token_to_word):
+            self.word_to_token[t] = i + 1
+
+    def _zero_pad_tensor_token(self, tensor, size):
+        if len(tensor) >= size:
+            return tensor[:size]
+        else:
+            zero = th.zeros(size - len(tensor)).long()
+            return th.cat((tensor, zero), dim=0)
+
+    def _split_text(self, sentence):
+        w = re.findall(r"[\w']+", str(sentence))
+        return w
+
+    def _words_to_token(self, words):
+        words = [
+            self.word_to_token[word] for word in words if word in self.word_to_token
+        ]
+        if words:
+            we = self._zero_pad_tensor_token(th.LongTensor(words), self.max_words)
+            return we
+        else:
+            return th.zeros(self.max_words).long()
+
+    def _words_to_ids(self, x):
+        split_x = [self._words_to_token(self._split_text(sent.lower())) for sent in x]
+        return th.stack(split_x, dim=0)
+
+    def forward(self, x):
+        x = self._words_to_ids(x)
+        x = self.word_embd(x)
+        x = F.relu(self.fc1(x))
+        x = th.max(x, dim=1)[0]
+        x = self.fc2(x)
+        return {'text_embedding': x}
+
+
+class S3D(nn.Module):
+    def __init__(self, dict_path, num_classes=512, gating=True, space_to_depth=True):
+        super(S3D, self).__init__()
+        self.num_classes = num_classes
+        self.gating = gating
+        self.space_to_depth = space_to_depth
+        if space_to_depth:
+            self.conv1 = STConv3D(
+                24, 64, [2, 4, 4], stride=1, padding=(1, 2, 2), separable=False
+            )
+        else:
+            self.conv1 = STConv3D(
+                3, 64, [3, 7, 7], stride=2, padding=(1, 3, 3), separable=False
+            )
+        self.conv_2b = STConv3D(64, 64, [1, 1, 1], separable=False)
+        self.conv_2c = STConv3D(64, 192, [3, 3, 3], padding=1, separable=True)
+        self.gating = SelfGating(192)
+        self.maxpool_2a = MaxPool3dTFPadding(
+            kernel_size=(1, 3, 3), stride=(1, 2, 2), padding="SAME"
+        )
+        self.maxpool_3a = MaxPool3dTFPadding(
+            kernel_size=(1, 3, 3), stride=(1, 2, 2), padding="SAME"
+        )
+        self.mixed_3b = InceptionBlock(192, 64, 96, 128, 16, 32, 32)
+        self.mixed_3c = InceptionBlock(
+            self.mixed_3b.output_dim, 128, 128, 192, 32, 96, 64
+        )
+        self.maxpool_4a = MaxPool3dTFPadding(
+            kernel_size=(3, 3, 3), stride=(2, 2, 2), padding="SAME"
+        )
+        self.mixed_4b = InceptionBlock(
+            self.mixed_3c.output_dim, 192, 96, 208, 16, 48, 64
+        )
+        self.mixed_4c = InceptionBlock(
+            self.mixed_4b.output_dim, 160, 112, 224, 24, 64, 64
+        )
+        self.mixed_4d = InceptionBlock(
+            self.mixed_4c.output_dim, 128, 128, 256, 24, 64, 64
+        )
+        self.mixed_4e = InceptionBlock(
+            self.mixed_4d.output_dim, 112, 144, 288, 32, 64, 64
+        )
+        self.mixed_4f = InceptionBlock(
+            self.mixed_4e.output_dim, 256, 160, 320, 32, 128, 128
+        )
+        self.maxpool_5a = self.maxPool3d_5a_2x2 = MaxPool3dTFPadding(
+            kernel_size=(2, 2, 2), stride=(2, 2, 2), padding="SAME"
+        )
+        self.mixed_5b = InceptionBlock(
+            self.mixed_4f.output_dim, 256, 160, 320, 32, 128, 128
+        )
+        self.mixed_5c = InceptionBlock(
+            self.mixed_5b.output_dim, 384, 192, 384, 48, 128, 128
+        )
+        self.fc = nn.Linear(self.mixed_5c.output_dim, num_classes)
+        self.text_module = Sentence_Embedding(num_classes,
+            token_to_word_path=dict_path)
+
+    def _space_to_depth(self, input):
+        """3D space to depth trick for TPU optimization.
+      """
+        B, C, T, H, W = input.shape
+        input = input.view(B, C, T // 2, 2, H // 2, 2, W // 2, 2)
+        input = input.permute(0, 3, 5, 7, 1, 2, 4, 6)
+        input = input.contiguous().view(B, 8 * C, T // 2, H // 2, W // 2)
+        return input
+
+    def forward(self, inputs):
+        """Defines the S3DG base architecture."""
+        if self.space_to_depth:
+            inputs = self._space_to_depth(inputs)
+        net = self.conv1(inputs)
+        if self.space_to_depth:
+            # we need to replicate 'SAME' tensorflow padding
+            net = net[:, :, 1:, 1:, 1:]
+        net = self.maxpool_2a(net)
+        net = self.conv_2b(net)
+        net = self.conv_2c(net)
+        if self.gating:
+            net = self.gating(net)
+        net = self.maxpool_3a(net)
+        net = self.mixed_3b(net)
+        net = self.mixed_3c(net)
+        net = self.maxpool_4a(net)
+        net = self.mixed_4b(net)
+        net = self.mixed_4c(net)
+        net = self.mixed_4d(net)
+        net = self.mixed_4e(net)
+        net = self.mixed_4f(net)
+        net = self.maxpool_5a(net)
+        net = self.mixed_5b(net)
+        net = self.mixed_5c(net)
+        net = th.mean(net, dim=[2, 3, 4])
+        return {'video_embedding': self.fc(net), 'mixed_5c': net}

fairseq/examples/MMPT/mmpt/processors/processor.py

@@ -0,0 +1,274 @@
+# Copyright (c) Facebook, Inc. All Rights Reserved
+
+import numpy as np
+import os
+import torch
+
+
+class Processor(object):
+    """
+    A generic processor for video (codec, feature etc.) and text.
+    """
+
+    def __call__(self, **kwargs):
+        raise NotImplementedError
+
+
+class MetaProcessor(Processor):
+    """
+    A meta processor is expected to load the metadata of a dataset:
+        (e.g., video_ids, or captions).
+    You must implement the `__getitem__` (meta datasets are rather diverse.).
+    """
+
+    def __init__(self, config):
+        self.split = config.split
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        raise NotImplementedError
+
+    def _get_split_path(self, config):
+        splits = {
+            "train": config.train_path,
+            "valid": config.val_path,
+            "test": config.test_path,
+        }
+        if config.split is not None:
+            return splits[config.split]
+        return config.train_path
+
+
+class TextProcessor(Processor):
+    """
+    A generic Text processor: rename this as `withTokenizer`.
+    tokenize a string of text on-the-fly.
+    Warning: mostly used for end tasks.
+        (on-the-fly tokenization is slow for how2.)
+    TODO(huxu): move this class as a subclass.
+    """
+
+    def __init__(self, config):
+        self.bert_name = str(config.bert_name)
+        self.use_fast = config.use_fast
+        from transformers import AutoTokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.bert_name, use_fast=self.use_fast
+        )
+
+    def __call__(self, text_id):
+        caption = self.tokenizer(text_id, add_special_tokens=False)
+        return caption["input_ids"]
+
+
+class VideoProcessor(Processor):
+    """
+    A generic video processor: load a numpy video tokens by default.
+    """
+
+    def __init__(self, config):
+        self.vfeat_dir = config.vfeat_dir
+
+    def __call__(self, video_fn):
+        if isinstance(video_fn, tuple):
+            video_fn = video_fn[0]
+        assert isinstance(video_fn, str)
+        video_fn = os.path.join(self.vfeat_dir, video_fn + ".npy")
+        feat = np.load(video_fn)
+        return feat
+
+
+class Aligner(object):
+    """
+    An alignprocessor align video and text and output a dict of tensors (for a model).
+    """
+    def __init__(self, config):
+        """__init__ needs to be light weight for more workers/threads."""
+        self.split = config.split
+        self.max_video_len = config.max_video_len
+        self.max_len = config.max_len
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(
+            str(config.bert_name), use_fast=config.use_fast
+        )
+        self.cls_token_id = tokenizer.cls_token_id
+        self.sep_token_id = tokenizer.sep_token_id
+        self.pad_token_id = tokenizer.pad_token_id
+        self.mask_token_id = tokenizer.mask_token_id
+
+    def __call__(self, video_id, video_feature, text_feature):
+        raise NotImplementedError
+
+    def _build_video_seq(self, video_feature, video_clips=None):
+        """
+        `video_feature`: available video tokens.
+        `video_clips`: video clip sequence to build.
+        """
+        if not isinstance(video_feature, np.ndarray):
+            raise ValueError(
+                "unsupported type of video_feature", type(video_feature)
+            )
+
+        if video_clips is None:
+            # this is borrowed from DSAligner
+            video_start = 0
+            video_end = min(len(video_feature), self.max_video_len)
+            # the whole sequence is a single clip.
+            video_clips = {"start": [video_start], "end": [video_end]}
+
+        vfeats = np.zeros(
+            (self.max_video_len, video_feature.shape[1]), dtype=np.float32
+        )
+        vmasks = torch.zeros((self.max_video_len,), dtype=torch.bool)
+        video_len = 0
+        for start, end in zip(video_clips["start"], video_clips["end"]):
+            clip_len = min(self.max_video_len - video_len, (end - start))
+            if clip_len > 0:
+                vfeats[video_len: video_len + clip_len] = video_feature[
+                    start: start + clip_len
+                ]
+                vmasks[video_len: video_len + clip_len] = 1
+                video_len += clip_len
+        vfeats = torch.from_numpy(vfeats)
+
+        return vfeats, vmasks
+
+    def _build_text_seq(self, text_feature, text_clip_indexs=None):
+        """
+        `text_feature`: all available clips.
+        `text_clip_indexes`: clip sequence to build.
+        """
+        if text_clip_indexs is None:
+            text_clip_indexs = [0]
+
+        full_caps = []
+        if isinstance(text_feature, dict):
+            for clip_idx in text_clip_indexs:
+                full_caps.extend(text_feature["cap"][clip_idx])
+        else:
+            full_caps = text_feature
+        max_text_len = self.max_len - self.max_video_len - 3
+        full_caps = full_caps[:max_text_len]
+        full_caps = (
+            [self.cls_token_id, self.sep_token_id] + full_caps + [self.sep_token_id]
+        )
+        text_pad_len = self.max_len - len(full_caps) - self.max_video_len
+        padded_full_caps = full_caps + [self.pad_token_id] * text_pad_len
+        caps = torch.LongTensor(padded_full_caps)
+        cmasks = torch.zeros((len(padded_full_caps),), dtype=torch.bool)
+        cmasks[: len(full_caps)] = 1
+
+        return caps, cmasks
+
+    def batch_post_processing(self, batch, video_feature):
+        return batch
+
+
+class MMAttentionMask2DProcessor(Processor):
+    """text generation requires 2d mask
+    that is harder to generate by GPU at this stage."""
+
+    def __call__(self, vmask, cmask, mtype):
+        if mtype == "textgen":
+            return self._build_textgeneration_mask(vmask, cmask)
+        elif mtype == "videogen":
+            return self._build_videogeneration_mask(vmask, cmask)
+        else:
+            return self._build_mm_mask(vmask, cmask)
+
+    def _build_mm_mask(self, vmask, cmask):
+        mask_1d = torch.cat([cmask[:1], vmask, cmask[1:]], dim=0)
+        return mask_1d[None, :].repeat(mask_1d.size(0), 1)
+
+    def _build_videogeneration_mask(self, vmask, cmask):
+        # cls_mask is only about text otherwise it will leak generation.
+        cls_text_mask = torch.cat([
+            # [CLS]
+            torch.ones(
+                (1,), dtype=torch.bool, device=cmask.device),
+            # video tokens and [SEP] for video.
+            torch.zeros(
+                (vmask.size(0) + 1,), dtype=torch.bool, device=cmask.device),
+            cmask[2:]
+            ], dim=0)
+
+        # concat horizontially.
+        video_len = int(vmask.sum())
+        video_masks = torch.cat([
+            # [CLS]
+            torch.ones(
+                (video_len, 1), dtype=torch.bool, device=cmask.device
+            ),
+            torch.tril(
+                torch.ones(
+                    (video_len, video_len),
+                    dtype=torch.bool, device=cmask.device)),
+            # video_padding
+            torch.zeros(
+                (video_len, vmask.size(0) - video_len),
+                dtype=torch.bool, device=cmask.device
+            ),
+            # [SEP] for video (unused).
+            torch.zeros(
+                (video_len, 1), dtype=torch.bool, device=cmask.device
+            ),
+            cmask[2:].unsqueeze(0).repeat(video_len, 1)
+            ], dim=1)
+
+        text_masks = cls_text_mask[None, :].repeat(
+            cmask.size(0) - 2, 1)
+        video_padding_masks = cls_text_mask[None, :].repeat(
+            vmask.size(0) - video_len, 1)
+
+        return torch.cat([
+            cls_text_mask[None, :],
+            video_masks,
+            video_padding_masks,
+            torch.cat([cmask[:1], vmask, cmask[1:]], dim=0)[None,:],
+            text_masks
+            ], dim=0)
+
+    def _build_textgeneration_mask(self, vmask, cmask):
+        # cls_mask is only about video otherwise it will leak generation.
+        cls_video_mask = torch.cat([
+            # [CLS]
+            torch.ones(
+                (1,), dtype=torch.bool, device=cmask.device),
+            vmask,
+            # [SEP]
+            torch.ones((1,), dtype=torch.bool, device=cmask.device),
+            torch.zeros(
+                (cmask.size(0)-2,), dtype=torch.bool, device=cmask.device)
+        ], dim=0)
+
+        # concat horizontially.
+        text_len = int(cmask[2:].sum())
+        text_masks = torch.cat([
+            # [CLS]
+            torch.ones(
+                (text_len, 1), dtype=torch.bool, device=cmask.device
+            ),
+            vmask.unsqueeze(0).repeat(text_len, 1),
+            # [SEP] for video.
+            torch.ones(
+                (text_len, 1), dtype=torch.bool, device=cmask.device
+            ),
+            torch.tril(
+                torch.ones(
+                    (text_len, text_len),
+                    dtype=torch.bool, device=cmask.device)),
+            # padding.
+            torch.zeros(
+                (text_len, cmask.size(0) - text_len - 2),
+                dtype=torch.bool, device=cmask.device
+            )
+        ], dim=1)
+
+        cls_video_masks = cls_video_mask[None, :].repeat(
+            vmask.size(0) + 2, 1)
+        text_padding_masks = cls_video_mask[None, :].repeat(
+            cmask.size(0) - text_len - 2, 1)
+        return torch.cat([
+            cls_video_masks, text_masks, text_padding_masks], dim=0)

fairseq/examples/MMPT/mmpt/tasks/fairseqmmtask.py

@@ -0,0 +1,104 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+"""
+make a general fairseq task for MM pretraining.
+"""
+
+import random
+
+from fairseq.tasks import LegacyFairseqTask, register_task
+
+from .task import Task
+from .retritask import RetriTask
+from ..datasets import FairseqMMDataset
+from .. import utils
+
+
+@register_task("mmtask")
+class FairseqMMTask(LegacyFairseqTask):
+    @staticmethod
+    def add_args(parser):
+        # Add some command-line arguments for specifying where the data is
+        # located and the maximum supported input length.
+        parser.add_argument(
+            "taskconfig",
+            metavar="FILE",
+            help=("taskconfig to load all configurations" "outside fairseq parser."),
+        )
+
+    @classmethod
+    def setup_task(cls, args, **kwargs):
+        return FairseqMMTask(args)
+
+    def __init__(self, args):
+        super().__init__(args)
+        config = utils.load_config(args)
+        self.mmtask = Task.config_task(config)
+        self.mmtask.build_dataset()
+        self.mmtask.build_model()
+        self.mmtask.build_loss()
+
+    def load_dataset(self, split, **kwargs):
+        split_map = {
+            "train": self.mmtask.train_data,
+            "valid": self.mmtask.val_data,
+            "test": self.mmtask.test_data,
+        }
+        if split not in split_map:
+            raise ValueError("unknown split type.")
+        if split_map[split] is not None:
+            self.datasets[split] = FairseqMMDataset(split_map[split])
+
+    def get_batch_iterator(
+        self,
+        dataset,
+        max_tokens=None,
+        max_sentences=None,
+        max_positions=None,
+        ignore_invalid_inputs=False,
+        required_batch_size_multiple=1,
+        seed=1,
+        num_shards=1,
+        shard_id=0,
+        num_workers=0,
+        epoch=1,
+        data_buffer_size=0,
+        disable_iterator_cache=False,
+        skip_remainder_batch=False,
+        grouped_shuffling=False,
+        update_epoch_batch_itr=False,
+    ):
+        random.seed(epoch)
+        if dataset.mmdataset.split == "train" and isinstance(self.mmtask, RetriTask):
+            if epoch >= self.mmtask.config.retri_epoch:
+                if not hasattr(self.mmtask, "retri_dataloader"):
+                    self.mmtask.build_dataloader()
+                self.mmtask.retrive_candidates(epoch)
+
+        return super().get_batch_iterator(
+            dataset,
+            max_tokens,
+            max_sentences,
+            max_positions,
+            ignore_invalid_inputs,
+            required_batch_size_multiple,
+            seed,
+            num_shards,
+            shard_id,
+            num_workers,
+            epoch,
+            data_buffer_size,
+            disable_iterator_cache,
+            grouped_shuffling,
+            update_epoch_batch_itr,
+        )
+
+    @property
+    def source_dictionary(self):
+        return None
+
+    @property
+    def target_dictionary(self):
+        return None

fairseq/examples/MMPT/mmpt/tasks/milncetask.py

@@ -0,0 +1,27 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+
+from .task import Task
+
+
+class MILNCETask(Task):
+    def reshape_subsample(self, sample):
+        if (
+            hasattr(self.config.dataset, "subsampling")
+            and self.config.dataset.subsampling is not None
+            and self.config.dataset.subsampling > 1
+        ):
+            for key in sample:
+                if torch.is_tensor(sample[key]):
+                    tensor = self.flat_subsample(sample[key])
+                    if key in ["caps", "cmasks"]:
+                        size = tensor.size()
+                        batch_size = size[0] * size[1]
+                        expanded_size = (batch_size,) + size[2:]
+                        tensor = tensor.view(expanded_size)
+                    sample[key] = tensor
+        return sample

fairseq/examples/MMPT/mmpt/tasks/retritask.py

@@ -0,0 +1,253 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+import torch
+import pickle
+import random
+
+from tqdm import tqdm
+from torch.utils.data import DataLoader
+from torch.utils.data.distributed import DistributedSampler
+
+from ..processors import (
+    ShardedHow2MetaProcessor,
+    ShardedVideoProcessor,
+    ShardedTextProcessor,
+    VariedLenAligner,
+)
+
+from ..datasets import MMDataset
+from .task import Task
+from ..modules import vectorpool
+from ..evaluators.predictor import Predictor
+from ..utils import set_seed, get_local_rank, get_world_size
+
+
+class RetriTask(Task):
+    """abstract class for task with retrival."""
+
+    def reshape_subsample(self, sample):
+        for key in sample:
+            if torch.is_tensor(sample[key]):
+                sample[key] = self.flat_subsample(sample[key])
+        return sample
+
+    def flat_subsample(self, tensor):
+        if tensor.size(0) == 1:
+            tensor = tensor.squeeze(0)
+        return tensor
+
+    def build_dataloader(self):
+        """called by `get_batch_iterator` in fairseqmmtask. """
+        # TODO: hard-code dataloader for retri for now and configurable in .yaml.
+        # reuse the `train.lst`.
+        self.config.dataset.split = "train"
+        meta_processor = ShardedHow2MetaProcessor(self.config.dataset)
+        video_processor = ShardedVideoProcessor(self.config.dataset)
+        text_processor = ShardedTextProcessor(self.config.dataset)
+
+        aligner = VariedLenAligner(self.config.dataset)
+        aligner.subsampling = self.config.dataset.clip_per_video
+
+        self.retri_data = MMDataset(
+            meta_processor, video_processor, text_processor, aligner
+        )
+
+        retri_sampler = DistributedSampler(self.retri_data)
+        infer_scale = 16
+        batch_size = self.config.dataset.num_video_per_batch \
+            * infer_scale
+
+        self.retri_dataloader = DataLoader(
+            self.retri_data,
+            collate_fn=self.retri_data.collater,
+            batch_size=batch_size,
+            shuffle=False,
+            sampler=retri_sampler,
+            num_workers=self.config.fairseq.dataset.num_workers
+        )
+        return self.retri_dataloader
+
+    def retrive_candidates(self, epoch, dataloader=None):
+        if get_local_rank() == 0:
+            print("running retrieval model.")
+        out_dir = os.path.join(
+            self.config.fairseq.checkpoint.save_dir, "retri")
+        os.makedirs(out_dir, exist_ok=True)
+
+        if not os.path.isfile(
+                os.path.join(
+                    out_dir, "batched_e" + str(epoch) + "_videos0.pkl")
+        ):
+            if dataloader is None:
+                dataloader = self.retri_dataloader
+
+            self.model.eval()
+            self.model.is_train = False
+
+            assert self.retri_data.meta_processor.data == \
+                self.train_data.meta_processor.data  # video_ids not mutated.
+
+            self._retri_predict(epoch, dataloader)
+
+            self.model.train()
+            self.model.is_train = True
+
+        torch.distributed.barrier()
+        output = self._retri_sync(epoch, out_dir)
+        torch.distributed.barrier()
+        self.train_data.meta_processor.set_candidates(output)
+        return output
+
+
+class VideoRetriTask(RetriTask):
+    """RetriTask on video level."""
+
+    def reshape_subsample(self, sample):
+        if (
+            hasattr(self.config.dataset, "clip_per_video")
+            and self.config.dataset.clip_per_video is not None
+            and self.config.dataset.clip_per_video > 1
+        ):
+            for key in sample:
+                if torch.is_tensor(sample[key]):
+                    sample[key] = self.flat_subsample(sample[key])
+        return sample
+
+    def flat_subsample(self, tensor):
+        if tensor.size(0) == 1:
+            tensor = tensor.squeeze(0)
+        return Task.flat_subsample(self, tensor)
+
+    def _retri_predict(self, epoch, dataloader):
+        set_seed(epoch)
+        # save for retrival.
+        predictor = VideoPredictor(self.config)
+        predictor.predict_loop(
+            self.model, dataloader)
+        set_seed(epoch)  # get the same text clips.
+        # retrival.
+        retri_predictor = VideoRetriPredictor(
+            self.config)
+        retri_predictor.predict_loop(
+            self.model, predictor.vecpool.retriver, epoch)
+        del predictor
+        del retri_predictor
+
+    def _retri_sync(self, epoch, out_dir):
+        # gpu do the same merge.
+        batched_videos = []
+        for local_rank in range(get_world_size()):
+            fn = os.path.join(
+                out_dir,
+                "batched_e" + str(epoch) + "_videos" + str(local_rank) + ".pkl")
+            with open(fn, "rb") as fr:
+                batched_videos.extend(pickle.load(fr))
+        print(
+            "[INFO] batched_videos",
+            len(batched_videos), len(batched_videos[0]))
+        return batched_videos
+
+
+class VideoPredictor(Predictor):
+    def __init__(self, config):
+        vectorpool_cls = getattr(vectorpool, config.vectorpool_cls)
+        self.vecpool = vectorpool_cls(config)
+
+    def predict_loop(
+        self,
+        model,
+        dataloader,
+        early_stop=-1,
+    ):
+        with torch.no_grad():
+            if get_local_rank() == 0:
+                dataloader = tqdm(dataloader)
+            for batch_idx, batch in enumerate(dataloader):
+                if batch_idx == early_stop:
+                    break
+                self(batch, model)
+        return self.finalize()
+
+    def __call__(self, sample, model, **kwargs):
+        param = next(model.parameters())
+        dtype = param.dtype
+        device = param.device
+        subsample = sample["vfeats"].size(1)
+        sample = self.to_ctx(sample, device, dtype)
+        for key in sample:
+            if torch.is_tensor(sample[key]):
+                size = sample[key].size()
+                if len(size) >= 2:
+                    batch_size = size[0] * size[1]
+                    expanded_size = (
+                        (batch_size,) + size[2:] if len(size) > 2
+                        else (batch_size,)
+                    )
+                    sample[key] = sample[key].view(expanded_size)
+
+        outputs = model(**sample)
+        sample.update(outputs)
+        self.vecpool(sample, subsample)
+
+    def finalize(self):
+        print("[INFO]", self.vecpool)
+        if not self.vecpool.retriver.db.is_trained:
+            self.vecpool.retriver.finalize_training()
+        return self.vecpool.retriver
+
+
+class VideoRetriPredictor(Predictor):
+    """
+    Online Retrieval Predictor for Clips (used by RetriTask).
+    TODO: merge this with VisPredictor?
+    """
+
+    def __init__(self, config):
+        self.pred_dir = os.path.join(
+            config.fairseq.checkpoint.save_dir,
+            "retri")
+        self.num_cands = config.num_cands
+        self.num_video_per_batch = config.dataset.num_video_per_batch
+
+    def predict_loop(
+        self,
+        model,
+        retriver,
+        epoch,
+        early_stop=-1
+    ):
+        # a fake loop that only try to recover video vector
+        # from video_id.
+        batched_videos = []
+        # obtain available video_ids.
+        video_ids = list(retriver.videoid_to_vectoridx.keys())
+
+        dataloader = random.sample(
+            video_ids,
+            len(video_ids) // self.num_video_per_batch
+        )
+
+        if get_local_rank() == 0:
+            dataloader = tqdm(dataloader)
+        for batch_idx, batch in enumerate(dataloader):
+            # batch is one video id.
+            if batch_idx == early_stop:
+                break
+            video_ids = retriver.search_by_video_ids(
+                [batch], self.num_cands)[0]
+            if len(video_ids) > self.num_video_per_batch:
+                # we moved the center to make cluster robust.
+                video_ids = random.sample(video_ids, self.num_video_per_batch)
+            batched_videos.append(video_ids)
+        return self.finalize(batched_videos, epoch)
+
+    def finalize(self, batched_videos, epoch):
+        fn = os.path.join(
+            self.pred_dir,
+            "batched_e" + str(epoch) + "_videos" + str(get_local_rank()) + ".pkl")
+        with open(fn, "wb") as fw:
+            pickle.dump(batched_videos, fw, pickle.HIGHEST_PROTOCOL)
+        return batched_videos

fairseq/examples/MMPT/mmpt/tasks/task.py

@@ -0,0 +1,184 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import torch
+
+from .. import tasks
+from .. import models
+from .. import losses
+from ..datasets import MMDataset
+from .. import processors
+
+
+class Task(object):
+    """
+    A task refers to one generic training task (e.g., training one model).
+    """
+
+    @classmethod
+    def config_task(cls, config):
+        """
+        determine whether to load a hard-coded task or config from a generic one.
+        via if a task string is available in config.
+        """
+        if config.task is not None:
+            # TODO (huxu): expand the search scope.
+            task_cls = getattr(tasks, config.task)
+            return task_cls(config)
+        else:
+            return Task(config)
+
+    def __init__(self, config):
+        self.config = config
+        self.train_data = None
+        self.val_data = None
+        self.test_data = None
+
+        self.model = None
+        self.loss_fn = None
+        self.eval_fn = None
+
+    def build_dataset(self):
+        """TODO (huxu): move processor breakdown to MMDataset."""
+        """fill-in `self.train_data`, `self.val_data` and `self.test_data`."""
+
+        meta_processor_cls = getattr(
+            processors, self.config.dataset.meta_processor)
+        video_processor_cls = getattr(
+            processors, self.config.dataset.video_processor)
+        text_processor_cls = getattr(
+            processors, self.config.dataset.text_processor)
+        aligner_cls = getattr(
+            processors, self.config.dataset.aligner)
+
+        if self.config.dataset.train_path is not None:
+            self.config.dataset.split = "train"
+            # may be used by meta processor.
+            # meta_processor controls different dataset.
+            meta_processor = meta_processor_cls(self.config.dataset)
+            video_processor = video_processor_cls(self.config.dataset)
+            text_processor = text_processor_cls(self.config.dataset)
+            aligner = aligner_cls(self.config.dataset)
+            self.train_data = MMDataset(
+                meta_processor, video_processor, text_processor, aligner
+            )
+            print("train_len", len(self.train_data))
+            output = self.train_data[0]
+            self.train_data.print_example(output)
+        if self.config.dataset.val_path is not None:
+            self.config.dataset.split = "valid"
+            # may be used by meta processor.
+            meta_processor = meta_processor_cls(self.config.dataset)
+            video_processor = video_processor_cls(self.config.dataset)
+            text_processor = text_processor_cls(self.config.dataset)
+            aligner = aligner_cls(self.config.dataset)
+            self.val_data = MMDataset(
+                meta_processor, video_processor, text_processor, aligner
+            )
+            print("val_len", len(self.val_data))
+            output = self.val_data[0]
+            self.val_data.print_example(output)
+
+        if self.config.dataset.split == "test":
+            # the following is run via lauching fairseq-validate.
+            meta_processor = meta_processor_cls(self.config.dataset)
+            video_processor = video_processor_cls(self.config.dataset)
+            text_processor = text_processor_cls(self.config.dataset)
+
+            self.test_data = MMDataset(
+                meta_processor, video_processor, text_processor, aligner
+            )
+            print("test_len", len(self.test_data))
+            output = self.test_data[0]
+            self.test_data.print_example(output)
+
+    def build_model(self, checkpoint=None):
+        if self.model is None:
+            model_cls = getattr(models, self.config.model.model_cls)
+            self.model = model_cls(self.config)
+        if checkpoint is not None:
+            self.load_checkpoint(checkpoint)
+        return self.model
+
+    def load_checkpoint(self, checkpoint):
+        if self.model is None:
+            raise ValueError("model is not initialized.")
+        state_dict = torch.load(checkpoint)
+        state_dict = self._trim_state_dict(state_dict)
+        self.model.load_state_dict(state_dict, strict=False)
+        # if it's a fp16 model, turn it back.
+        if next(self.model.parameters()).dtype == torch.float16:
+            self.model = self.model.float()
+        return self.model
+
+    def _trim_state_dict(self, state_dict):
+        from collections import OrderedDict
+
+        if "state_dict" in state_dict:
+            state_dict = state_dict["state_dict"]
+        if "model" in state_dict:  # fairseq checkpoint format.
+            state_dict = state_dict["model"]
+        ret_state_dict = OrderedDict()
+        for (
+            key,
+            value,
+        ) in state_dict.items():
+            # remove fairseq wrapper since this is a task.
+            if key.startswith("mmmodel"):
+                key = key[len("mmmodel."):]
+            ret_state_dict[key] = value
+        return ret_state_dict
+
+    def build_loss(self):
+        if self.loss_fn is None and self.config.loss is not None:
+            loss_cls = getattr(losses, self.config.loss.loss_cls)
+            self.loss_fn = loss_cls()
+        return self.loss_fn
+
+    def flat_subsample(self, tensor):
+        size = tensor.size()
+        if len(size) >= 2:
+            batch_size = size[0] * size[1]
+            expanded_size = (
+                (batch_size,) + size[2:] if len(size) > 2
+                else (batch_size,)
+            )
+            tensor = tensor.view(expanded_size)
+        return tensor
+
+    def reshape_subsample(self, sample):
+        if (
+            hasattr(self.config.dataset, "subsampling")
+            and self.config.dataset.subsampling is not None
+            and self.config.dataset.subsampling > 1
+        ):
+            for key in sample:
+                if torch.is_tensor(sample[key]):
+                    sample[key] = self.flat_subsample(sample[key])
+        return sample
+
+    def __call__(self, model, sample):
+        loss = None
+        loss_scalar = float("inf")
+
+        sample = self.reshape_subsample(sample)
+        outputs = self.model(**sample)
+        sample.update(outputs)
+        if self.loss_fn is not None:
+            loss = self.loss_fn(**sample)
+            loss_scalar = loss.item()
+
+        batch_size = sample["caps"].size(0)
+        sample_size = 1
+        return {
+            "loss": loss,
+            "loss_scalar": loss_scalar,
+            "max_len": self.config.dataset.max_len,
+            "batch_size": batch_size,
+            "sample_size": sample_size,
+        }
+
+    def build_dataloader(self):
+        """only used for trainer that lacks building loaders."""
+        raise NotImplementedError

fairseq/examples/MMPT/mmpt/tasks/vlmtask.py

@@ -0,0 +1,27 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import torch
+
+from .task import Task
+
+
+class VLMTask(Task):
+    """A VLM task for reproducibility.
+    the collator split subsamples into two sub-batches.
+    This has should have no logic changes.
+    but changed the randomness in frame masking.
+    """
+
+    def flat_subsample(self, tensor):
+        size = tensor.size()
+        if len(size) >= 2:
+            batch_size = size[0] * (size[1] // 2)
+            expanded_size = (
+                (batch_size, 2) + size[2:] if len(size) > 2
+                else (batch_size, 2)
+            )
+            tensor = tensor.view(expanded_size)
+            tensor = torch.cat([tensor[:, 0], tensor[:, 1]], dim=0)
+        return tensor

fairseq/examples/MMPT/mmpt/utils/__init__.py

@@ -0,0 +1,68 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import random
+import numpy as np
+import torch
+
+from .shardedtensor import *
+from .load_config import *
+
+
+def set_seed(seed=43211):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    if torch.backends.cudnn.enabled:
+        torch.backends.cudnn.benchmark = False
+        torch.backends.cudnn.deterministic = True
+
+
+def get_world_size():
+    if torch.distributed.is_initialized():
+        world_size = torch.distributed.get_world_size()
+    else:
+        world_size = 1
+    return world_size
+
+
+def get_local_rank():
+    return torch.distributed.get_rank() \
+        if torch.distributed.is_initialized() else 0
+
+
+def print_on_rank0(func):
+    local_rank = get_local_rank()
+    if local_rank == 0:
+        print("[INFO]", func)
+
+
+class RetriMeter(object):
+    """
+    Statistics on whether retrieval yields a better pair.
+    """
+    def __init__(self, freq=1024):
+        self.freq = freq
+        self.total = 0
+        self.replace = 0
+        self.updates = 0
+
+    def __call__(self, data):
+        if isinstance(data, np.ndarray):
+            self.replace += data.shape[0] - int((data[:, 0] == -1).sum())
+            self.total += data.shape[0]
+        elif torch.is_tensor(data):
+            self.replace += int(data.sum())
+            self.total += data.size(0)
+        else:
+            raise ValueError("unsupported RetriMeter data type.", type(data))
+
+        self.updates += 1
+        if get_local_rank() == 0 and self.updates % self.freq == 0:
+            print("[INFO]", self)
+
+    def __repr__(self):
+        return "RetriMeter (" + str(self.replace / self.total) \
+            + "/" + str(self.replace) + "/" + str(self.total) + ")"

fairseq/examples/MMPT/mmpt/utils/load_config.py

@@ -0,0 +1,81 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+import omegaconf
+from omegaconf import OmegaConf
+
+
+def load_config(args=None, config_file=None, overwrite_fairseq=False):
+    """TODO (huxu): move fairseq overwrite to another function."""
+    if args is not None:
+        config_file = args.taskconfig
+    config = recursive_config(config_file)
+
+    if config.dataset.subsampling is not None:
+        batch_size = config.fairseq.dataset.batch_size // config.dataset.subsampling
+        print(
+            "adjusting batch_size to {} due to subsampling {}.".format(
+                batch_size, config.dataset.subsampling
+            )
+        )
+        config.fairseq.dataset.batch_size = batch_size
+
+    is_test = config.dataset.split is not None and config.dataset.split == "test"
+    if not is_test:
+        if (
+            config.fairseq.checkpoint is None
+            or config.fairseq.checkpoint.save_dir is None
+        ):
+            raise ValueError("fairseq save_dir or save_path must be specified.")
+
+        save_dir = config.fairseq.checkpoint.save_dir
+        os.makedirs(save_dir, exist_ok=True)
+        if config.fairseq.common.tensorboard_logdir is not None:
+            tb_run_dir = suffix_rundir(
+                save_dir, config.fairseq.common.tensorboard_logdir
+            )
+            config.fairseq.common.tensorboard_logdir = tb_run_dir
+            print(
+                "update tensorboard_logdir as", config.fairseq.common.tensorboard_logdir
+            )
+        os.makedirs(save_dir, exist_ok=True)
+        OmegaConf.save(config=config, f=os.path.join(save_dir, "config.yaml"))
+
+    if overwrite_fairseq and config.fairseq is not None and args is not None:
+        # flatten fields.
+        for group in config.fairseq:
+            for field in config.fairseq[group]:
+                print("overwrite args." + field, "as", config.fairseq[group][field])
+                setattr(args, field, config.fairseq[group][field])
+    return config
+
+
+def recursive_config(config_path):
+    """allows for stacking of configs in any depth."""
+    config = OmegaConf.load(config_path)
+    if config.includes is not None:
+        includes = config.includes
+        config.pop("includes")
+        base_config = recursive_config(includes)
+        config = OmegaConf.merge(base_config, config)
+    return config
+
+
+def suffix_rundir(save_dir, run_dir):
+    max_id = -1
+    for search_dir in os.listdir(save_dir):
+        if search_dir.startswith(run_dir):
+            splits = search_dir.split("_")
+            cur_id = int(splits[1]) if len(splits) > 1 else 0
+            max_id = max(max_id, cur_id)
+    return os.path.join(save_dir, run_dir + "_" + str(max_id + 1))
+
+
+def overwrite_dir(config, replace, basedir):
+    for key in config:
+        if isinstance(config[key], str) and config[key].startswith(basedir):
+            config[key] = config[key].replace(basedir, replace)
+        if isinstance(config[key], omegaconf.dictconfig.DictConfig):
+            overwrite_dir(config[key], replace, basedir)

fairseq/examples/MMPT/mmpt_cli/localjob.py

@@ -0,0 +1,117 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+
+from mmpt.utils import recursive_config
+
+
+class BaseJob(object):
+    def __init__(self, yaml_file, dryrun=False):
+        self.yaml_file = yaml_file
+        self.config = recursive_config(yaml_file)
+        self.dryrun = dryrun
+
+    def submit(self, **kwargs):
+        raise NotImplementedError
+
+    def _normalize_cmd(self, cmd_list):
+        cmd_list = list(cmd_list)
+        yaml_index = cmd_list.index("[yaml]")
+        cmd_list[yaml_index] = self.yaml_file
+        return cmd_list
+
+
+class LocalJob(BaseJob):
+
+    CMD_CONFIG = {
+        "local_single": [
+            "fairseq-train", "[yaml]", "--user-dir", "mmpt",
+            "--task", "mmtask", "--arch", "mmarch",
+            "--criterion", "mmloss",
+        ],
+        "local_small": [
+            "fairseq-train", "[yaml]", "--user-dir", "mmpt",
+            "--task", "mmtask", "--arch", "mmarch",
+            "--criterion", "mmloss",
+            "--distributed-world-size", "2"
+        ],
+        "local_big": [
+            "fairseq-train", "[yaml]", "--user-dir", "mmpt",
+            "--task", "mmtask", "--arch", "mmarch",
+            "--criterion", "mmloss",
+            "--distributed-world-size", "8"
+        ],
+        "local_predict": ["python", "mmpt_cli/predict.py", "[yaml]"],
+    }
+
+    def __init__(self, yaml_file, job_type=None, dryrun=False):
+        super().__init__(yaml_file, dryrun)
+        if job_type is None:
+            self.job_type = "local_single"
+            if self.config.task_type is not None:
+                self.job_type = self.config.task_type
+        else:
+            self.job_type = job_type
+        if self.job_type in ["local_single", "local_small"]:
+            if self.config.fairseq.dataset.batch_size > 32:
+                print("decreasing batch_size to 32 for local testing?")
+
+    def submit(self):
+        cmd_list = self._normalize_cmd(LocalJob.CMD_CONFIG[self.job_type])
+        if "predict" not in self.job_type:
+            # append fairseq args.
+            from mmpt.utils import load_config
+
+            config = load_config(config_file=self.yaml_file)
+            for field in config.fairseq:
+                for key in config.fairseq[field]:
+                    if key in ["fp16", "reset_optimizer", "reset_dataloader", "reset_meters"]:  # a list of binary flag.
+                        param = ["--" + key.replace("_", "-")]
+                    else:
+                        if key == "lr":
+                            value = str(config.fairseq[field][key][0])
+                        elif key == "adam_betas":
+                            value = "'"+str(config.fairseq[field][key])+"'"
+                        else:
+                            value = str(config.fairseq[field][key])
+                        param = [
+                            "--" + key.replace("_", "-"),
+                            value
+                        ]
+                    cmd_list.extend(param)
+
+        print("launching", " ".join(cmd_list))
+        if not self.dryrun:
+            os.system(" ".join(cmd_list))
+        return JobStatus("12345678")
+
+
+class JobStatus(object):
+    def __init__(self, job_id):
+        self.job_id = job_id
+
+    def __repr__(self):
+        return self.job_id
+
+    def __str__(self):
+        return self.job_id
+
+    def done(self):
+        return False
+
+    def running(self):
+        return False
+
+    def result(self):
+        if self.done():
+            return "{} is done.".format(self.job_id)
+        else:
+            return "{} is running.".format(self.job_id)
+
+    def stderr(self):
+        return self.result()
+
+    def stdout(self):
+        return self.result()

fairseq/examples/MMPT/mmpt_cli/predict.py

@@ -0,0 +1,113 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+import glob
+import argparse
+import pprint
+import omegaconf
+
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+
+from mmpt.utils import load_config, set_seed
+from mmpt.evaluators import Evaluator
+from mmpt.evaluators import predictor as predictor_path
+from mmpt.tasks import Task
+from mmpt import processors
+from mmpt.datasets import MMDataset
+
+
+def get_dataloader(config):
+    meta_processor_cls = getattr(processors, config.dataset.meta_processor)
+    video_processor_cls = getattr(processors, config.dataset.video_processor)
+    text_processor_cls = getattr(processors, config.dataset.text_processor)
+    aligner_cls = getattr(processors, config.dataset.aligner)
+
+    meta_processor = meta_processor_cls(config.dataset)
+    video_processor = video_processor_cls(config.dataset)
+    text_processor = text_processor_cls(config.dataset)
+    aligner = aligner_cls(config.dataset)
+
+    test_data = MMDataset(
+        meta_processor,
+        video_processor,
+        text_processor,
+        aligner,
+    )
+    print("test_len", len(test_data))
+    output = test_data[0]
+    test_data.print_example(output)
+
+    test_dataloader = DataLoader(
+        test_data,
+        batch_size=config.fairseq.dataset.batch_size,
+        shuffle=False,
+        num_workers=6,
+        collate_fn=test_data.collater,
+    )
+    return test_dataloader
+
+
+def main(args):
+    config = load_config(args)
+
+    if isinstance(config, omegaconf.dictconfig.DictConfig):
+        print(OmegaConf.to_yaml(config))
+    else:
+        pp = pprint.PrettyPrinter(indent=4)
+        pp.print(config)
+
+    mmtask = Task.config_task(config)
+    mmtask.build_model()
+
+    test_dataloader = get_dataloader(config)
+    checkpoint_search_path = os.path.dirname(config.eval.save_path)
+    results = []
+
+    prefix = os.path.basename(args.taskconfig)
+    if prefix.startswith("test"):
+        # loop all checkpoint for datasets without validation set.
+        if "best" not in config.fairseq.common_eval.path:
+            print("eval each epoch.")
+            for checkpoint in glob.glob(checkpoint_search_path + "/checkpoint*"):
+                model = mmtask.load_checkpoint(checkpoint)
+                ckpt = os.path.basename(checkpoint)
+                evaluator = Evaluator(config)
+                output = evaluator.evaluate(
+                    model, test_dataloader, ckpt + "_merged")
+                results.append((checkpoint, output))
+        # use the one specified by the config lastly.
+        model = mmtask.load_checkpoint(config.fairseq.common_eval.path)
+        evaluator = Evaluator(config)
+        output = evaluator.evaluate(model, test_dataloader)
+        results.append((config.fairseq.common_eval.path, output))
+
+        best_result = None
+        best_metric = 0.
+        for checkpoint, result in results:
+            print(checkpoint)
+            evaluator.metric.print_computed_metrics(result)
+            best_score = evaluator.metric.best_metric(result)
+            if best_score > best_metric:
+                best_result = (checkpoint, result)
+                best_metric = best_score
+        print("best results:")
+        print(best_result[0])
+        evaluator.metric.print_computed_metrics(best_result[1])
+
+    elif prefix.startswith("vis"):
+        model = mmtask.load_checkpoint(config.fairseq.common_eval.path)
+        predictor_cls = getattr(predictor_path, config.predictor)
+        predictor = predictor_cls(config)
+        predictor.predict_loop(model, test_dataloader, mmtask, None)
+    else:
+        raise ValueError("unknown prefix of the config file", args.taskconfig)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("taskconfig", type=str)
+    args = parser.parse_args()
+    main(args)

fairseq/examples/MMPT/projects/mtm/mmfusionmtm.yaml

@@ -0,0 +1,19 @@
+includes: projects/mfmmlm.yaml
+project_dir: mtm/mmfusionmtm
+task_group:
+  pretrain:
+    task: VLMTask  # reproducible
+    dataset:
+      aligner: MFMMLMAligner
+    model:
+      use_seg_emb: True  # reproducible
+      model_cls: MMFusionMTM
+      mm_encoder_cls: MMBertForMFMMLM
+    loss:
+      loss_cls: MTM
+  finetune:
+    model:
+      use_seg_emb: True  # reproducible
+  test:
+    model:
+      use_seg_emb: True  # reproducible

fairseq/examples/MMPT/projects/mtm/vlm/coin.yaml

@@ -0,0 +1,47 @@
+dataset:
+  video_processor: VideoProcessor
+  bert_name: bert-base-uncased
+  meta_processor: COINActionSegmentationMetaProcessor
+  train_path: data/coin/COIN.json
+  val_path: data/coin/COIN.json
+  vfeat_dir: data/feat/feat_coin_s3d
+  text_processor: COINActionSegmentationTextProcessor
+  aligner: COINActionSegmentationAligner
+  num_iso_layer: 12
+  sliding_window: 8
+  sliding_window_size: 32
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  common:
+    tensorboard_logdir: run
+    log_interval: 1000
+    fp16: true
+  dataset:
+    num_workers: 4
+    batch_size: 1
+  optimization:
+    lr:
+    - 5.0e-05
+    clip_norm: 2.0
+    optimizer: adam
+    adam_betas: (0.9, 0.98)
+    lr_scheduler: polynomial_decay
+    total_num_update: 1000000
+    warmup_updates: 122
+    weight_decay: 0.0
+    ddp_backend: no_c10d
+    max_epoch: 8
+  checkpoint:
+    restore_file: runs/mtm/vlm/checkpoint_best.pt
+    reset_optimizer: true
+    reset_dataloader: true
+    reset_meters: true
+    save_dir: runs/mtm/vlm/coin
+task_type: sweep_big
+model:
+  model_cls: MMFusionActionSegmentation
+  mm_encoder_cls: MMBertForTokenClassification
+  use_seg_emb: true
+loss:
+  loss_cls: CrossEntropy

fairseq/examples/MMPT/projects/mtm/vlm/how2.yaml

@@ -0,0 +1,55 @@
+dataset:
+  video_processor: ShardedVideoProcessor
+  bert_name: bert-base-uncased
+  meta_processor: ShardedHow2MetaProcessor
+  train_path: data/how2/how2_s3d_train.lst
+  val_path: data/how2/how2_s3d_val.lst
+  vfeat_dir: data/feat/feat_how2_s3d_shard_small
+  text_processor: ShardedTextProcessor
+  tfeat_dir: data/feat/feat_how2_s3d_shard_small/raw_caption_dedup.bert-base-uncased.
+  aligner: MFMMLMAligner
+  subsampling: 32
+  sampled_min_len: 8
+  sampled_max_len: 64
+  max_video_len: 32
+  max_len: 96
+  lazy_vfeat_mask: true
+  mfm_probability: 0.15
+  mlm_probability: 0.15
+  mm_prob: 0.5
+fairseq:
+  common:
+    tensorboard_logdir: run
+    log_interval: 1000
+    fp16: true
+  dataset:
+    num_workers: 4
+    batch_size: 256
+  optimization:
+    lr:
+    - 5.0e-05
+    clip_norm: 2.0
+    optimizer: adam
+    adam_betas: (0.9, 0.98)
+    lr_scheduler: polynomial_decay
+    total_num_update: 1000000
+    warmup_updates: 1000
+    weight_decay: 0.0
+    ddp_backend: no_c10d
+    max_epoch: 15
+  checkpoint:
+    save_dir: runs/mtm/vlm
+    save_interval_updates: 1024
+    keep_interval_updates: 2
+    keep_last_epochs: 30
+task_type: sweep_big
+slurm_config: big
+eval:
+  save_path: runs/mtm/vlm
+model:
+  model_cls: MMFusionMTM
+  mm_encoder_cls: MMBertForMFMMLM
+  use_seg_emb: true
+loss:
+  loss_cls: MTM
+task: VLMTask

fairseq/examples/MMPT/projects/mtm/vlm/test_crosstask.yaml

@@ -0,0 +1,38 @@
+slurm_config: big
+task_type: local_predict
+dataset:
+  split: test
+  video_processor: CrossTaskVideoProcessor
+  aligner: CrossTaskAligner
+  bert_name: bert-base-uncased
+  meta_processor: CrossTaskMetaProcessor
+  test_path: data/crosstask/crosstask_release/videos_val.csv
+  train_csv_path: data/crosstask/crosstask_release/videos.csv
+  val_path: data/crosstask/crosstask_release/videos_val.csv
+  val_csv_path: data/crosstask/crosstask_release/videos_val.csv
+  primary_path: data/crosstask/crosstask_release/tasks_primary.txt
+  related_path: data/crosstask/crosstask_release/tasks_related.txt
+  vfeat_dir: data/feat/feat_crosstask_s3d
+  annotation_path: data/crosstask/crosstask_release/annotations
+  n_train: 30
+  text_processor: CrossTaskTextProcessor
+  num_iso_layer: 12
+  sliding_window: 16
+  sliding_window_size: 32
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  dataset:
+    batch_size: 1
+    valid_subset: test
+    num_workers: 2
+  common_eval:
+    path: runs/mtm/vlm/crosstask/checkpoint_best.pt
+model:
+  model_cls: MMFusionActionLocalization
+  mm_encoder_cls: MMBertForJoint
+  use_seg_emb: true
+eval:
+  save_path: runs/mtm/vlm/crosstask/eval
+metric: CrossTaskMetric
+predictor: CrossTaskPredictor

fairseq/examples/MMPT/projects/mtm/vlm/test_vtt.yaml

@@ -0,0 +1,29 @@
+slurm_config: big
+task_type: local_predict
+dataset:
+  split: test
+  video_processor: VideoProcessor
+  aligner: DSAligner
+  bert_name: bert-base-uncased
+  meta_processor: MSRVTTMetaProcessor
+  test_path: data/msrvtt/MSRVTT_JSFUSION_test.csv
+  vfeat_dir: data/feat/feat_vtt_s3d
+  text_processor: MSRVTTTextProcessor
+  num_iso_layer: 12
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  dataset:
+    batch_size: 256
+    valid_subset: test
+    num_workers: 2
+  common_eval:
+    path: runs/mtm/vlm/vtt/checkpoint_last.pt
+model:
+  model_cls: MMFusionJoint
+  mm_encoder_cls: MMBertForJoint
+  use_seg_emb: true
+eval:
+  save_path: runs/mtm/vlm/vtt/eval
+metric: RetrievalMetric
+predictor: RetrievalPredictor

fairseq/examples/MMPT/projects/mtm/vlm/test_vttqa.yaml

@@ -0,0 +1,29 @@
+slurm_config: big
+task_type: local_predict
+dataset:
+  split: test
+  video_processor: VideoProcessor
+  aligner: MSRVTTQAAligner
+  bert_name: bert-base-uncased
+  meta_processor: MSRVTTQAMetaProcessor
+  test_path: data/msrvtt-qa/MSR_MC_test.csv
+  vfeat_dir: data/feat/feat_vtt_s3d
+  text_processor: MSRVTTQATextProcessor
+  num_iso_layer: 12
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  dataset:
+    batch_size: 256
+    valid_subset: test
+    num_workers: 2
+  common_eval:
+    path: runs/mtm/vlm/vttqa/checkpoint_last.pt
+model:
+  model_cls: MMFusionJoint
+  mm_encoder_cls: MMBertForJoint
+  use_seg_emb: true
+eval:
+  save_path: runs/mtm/vlm/vttqa/eval
+metric: QAMetric
+predictor: QAPredictor

fairseq/examples/MMPT/projects/mtm/vlm/test_youcookcap.yaml

@@ -0,0 +1,32 @@
+slurm_config: big
+task_type: local_predict
+dataset:
+  split: test
+  video_processor: YoucookVideoProcessor
+  aligner: DSNLGAligner
+  bert_name: bert-base-uncased
+  meta_processor: YoucookNLGMetaProcessor
+  test_path: data/youcook/val_list.txt
+  trainval_annotation: data/youcook/youcookii_annotations_trainval.json
+  vfeat_dir: data/feat/feat_youcook_s3d
+  text_processor: NLGTextProcessor
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  dataset:
+    batch_size: 256
+    valid_subset: test
+    num_workers: 2
+  common_eval:
+    path: runs/mtm/vlm/youcookcap/checkpoint_best.pt
+model:
+  model_cls: MMFusionNLG
+  mm_encoder_cls: MMBertForNLG
+  max_decode_length: 24
+  use_seg_emb: true
+eval:
+  save_path: runs/mtm/vlm/youcookcap/eval
+metric: NLGMetric
+predictor: NLGPredictor
+gen_param:
+  num_beams: 5

fairseq/examples/MMPT/projects/mtm/vlm/vtt.yaml

@@ -0,0 +1,49 @@
+dataset:
+  video_processor: VideoProcessor
+  bert_name: bert-base-uncased
+  meta_processor: MSRVTTMetaProcessor
+  train_path: data/msrvtt/MSRVTT_train.csv
+  jsfusion_path: data/msrvtt/MSRVTT_JSFUSION_test.csv
+  full_test_path: data/msrvtt/MSRVTT_FULL_test.csv
+  dup: 20
+  val_path: data/msrvtt/MSRVTT_JSFUSION_test.csv
+  vfeat_dir: data/feat/feat_vtt_s3d
+  text_processor: MSRVTTTextProcessor
+  json_path: data/msrvtt/MSRVTT_data.json
+  aligner: DSAligner
+  num_iso_layer: 12
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  common:
+    tensorboard_logdir: run
+    log_interval: 1000
+    fp16: true
+  dataset:
+    num_workers: 4
+    batch_size: 256
+  optimization:
+    lr:
+    - 5.0e-05
+    clip_norm: 2.0
+    optimizer: adam
+    adam_betas: (0.9, 0.98)
+    lr_scheduler: polynomial_decay
+    total_num_update: 1000000
+    warmup_updates: 122
+    weight_decay: 0.0
+    ddp_backend: no_c10d
+    max_epoch: 10
+  checkpoint:
+    restore_file: runs/mtm/vlm/checkpoint_best.pt
+    reset_optimizer: true
+    reset_dataloader: true
+    reset_meters: true
+    save_dir: runs/mtm/vlm/vtt
+task_type: sweep_small
+model:
+  model_cls: MMFusionJoint
+  mm_encoder_cls: MMBertForJoint
+  use_seg_emb: true
+loss:
+  loss_cls: T2VContraLoss

fairseq/examples/MMPT/projects/mtm/vlm/vttqa.yaml

@@ -0,0 +1,47 @@
+dataset:
+  video_processor: VideoProcessor
+  bert_name: bert-base-uncased
+  meta_processor: MSRVTTMetaProcessor
+  train_path: data/msrvtt/MSRVTT_train.csv
+  dup: 20
+  val_path: data/msrvtt/MSRVTT_JSFUSION_test.csv
+  vfeat_dir: data/feat/feat_vtt_s3d
+  text_processor: MSRVTTTextProcessor
+  json_path: data/msrvtt/MSRVTT_data.json
+  aligner: DSAligner
+  num_iso_layer: 12
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  common:
+    tensorboard_logdir: run
+    log_interval: 1000
+    fp16: true
+  dataset:
+    num_workers: 4
+    batch_size: 128
+  optimization:
+    lr:
+    - 5.0e-05
+    clip_norm: 2.0
+    optimizer: adam
+    adam_betas: (0.9, 0.98)
+    lr_scheduler: polynomial_decay
+    total_num_update: 1000000
+    warmup_updates: 122
+    weight_decay: 0.0
+    ddp_backend: no_c10d
+    max_epoch: 5
+  checkpoint:
+    restore_file: runs/mtm/vlm/checkpoint_best.pt
+    reset_optimizer: true
+    reset_dataloader: true
+    reset_meters: true
+    save_dir: runs/mtm/vlm/vttqa
+task_type: sweep_small
+model:
+  model_cls: MMFusionJoint
+  mm_encoder_cls: MMBertForJoint
+  use_seg_emb: true
+loss:
+  loss_cls: V2TContraLoss

fairseq/examples/MMPT/projects/mtm/vlm/youcook.yaml

@@ -0,0 +1,47 @@
+dataset:
+  video_processor: YoucookVideoProcessor
+  bert_name: bert-base-uncased
+  meta_processor: YoucookMetaProcessor
+  train_path: data/youcook/youcook_train.pkl
+  val_path: data/youcook/youcook_val.pkl
+  trainval_annotation: data/youcook/youcookii_annotations_trainval.json
+  use_annotation_text: true
+  vfeat_dir: data/feat/feat_youcook_s3d
+  text_processor: TextProcessor
+  aligner: DSAligner
+  num_iso_layer: 12
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  common:
+    tensorboard_logdir: run
+    log_interval: 1000
+    fp16: true
+  dataset:
+    num_workers: 4
+    batch_size: 128
+  optimization:
+    lr:
+    - 5.0e-05
+    clip_norm: 2.0
+    optimizer: adam
+    adam_betas: (0.9, 0.98)
+    lr_scheduler: polynomial_decay
+    total_num_update: 1000000
+    warmup_updates: 122
+    weight_decay: 0.0
+    ddp_backend: no_c10d
+    max_epoch: 10
+  checkpoint:
+    restore_file: runs/mtm/vlm/checkpoint_best.pt
+    reset_optimizer: true
+    reset_dataloader: true
+    reset_meters: true
+    save_dir: runs/mtm/vlm/youcook
+task_type: sweep_small
+model:
+  model_cls: MMFusionJoint
+  mm_encoder_cls: MMBertForJoint
+  use_seg_emb: true
+loss:
+  loss_cls: T2VContraLoss

fairseq/examples/MMPT/projects/retri/videoclip.yaml

@@ -0,0 +1,10 @@
+includes: projects/retri/videoretri.yaml
+project_dir: retri/videoclip
+task_group:
+  pretrain:
+    model:
+      model_cls: MMFusionSeparate
+      mm_encoder_cls: 
+      video_encoder_cls: MMBertForEncoder
+      text_encoder_cls: BertModel
+      num_hidden_video_layers: 6

fairseq/examples/MMPT/projects/retri/videoclip/coin_videoclip.yaml

@@ -0,0 +1,49 @@
+dataset:
+  video_processor: VideoProcessor
+  bert_name: bert-base-uncased
+  meta_processor: COINActionSegmentationMetaProcessor
+  train_path: data/coin/COIN.json
+  val_path: data/coin/COIN.json
+  vfeat_dir: data/feat/feat_coin_s3d
+  text_processor: COINActionSegmentationTextProcessor
+  aligner: COINActionSegmentationAligner
+  num_iso_layer: 12
+  sliding_window: 8
+  sliding_window_size: 32
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  common:
+    tensorboard_logdir: run
+    log_interval: 1000
+    fp16: true
+  dataset:
+    num_workers: 4
+    batch_size: 1
+  optimization:
+    lr:
+    - 5.0e-05
+    clip_norm: 2.0
+    optimizer: adam
+    adam_betas: (0.9, 0.98)
+    lr_scheduler: polynomial_decay
+    total_num_update: 1000000
+    warmup_updates: 122
+    weight_decay: 0.0
+    ddp_backend: no_c10d
+    max_epoch: 8
+  checkpoint:
+    restore_file: runs/retri/videoclip/checkpoint_best.pt
+    reset_optimizer: true
+    reset_dataloader: true
+    reset_meters: true
+    save_dir: runs/retri/videoclip/coin
+task_type: sweep_big
+model:
+  model_cls: MMFusionSeparateActionSegmentation
+  mm_encoder_cls: null
+  video_encoder_cls: MMBertForTokenClassification
+  text_encoder_cls: BertModel
+  num_hidden_video_layers: 6
+loss:
+  loss_cls: CrossEntropy

fairseq/examples/MMPT/projects/retri/videoclip/crosstask_videoclip.yaml

@@ -0,0 +1,55 @@
+dataset:
+  video_processor: CrossTaskVideoProcessor
+  bert_name: bert-base-uncased
+  meta_processor: CrossTaskMetaProcessor
+  train_path: data/crosstask/crosstask_release/videos.csv
+  train_csv_path: data/crosstask/crosstask_release/videos.csv
+  val_path: data/crosstask/crosstask_release/videos_val.csv
+  val_csv_path: data/crosstask/crosstask_release/videos_val.csv
+  primary_path: data/crosstask/crosstask_release/tasks_primary.txt
+  related_path: data/crosstask/crosstask_release/tasks_related.txt
+  vfeat_dir: data/feat/feat_crosstask_s3d
+  annotation_path: data/crosstask/crosstask_release/annotations
+  n_train: 30
+  text_processor: CrossTaskTextProcessor
+  aligner: CrossTaskAligner
+  num_iso_layer: 12
+  sliding_window: 16
+  sliding_window_size: 32
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  common:
+    tensorboard_logdir: run
+    log_interval: 1000
+    fp16: true
+  dataset:
+    num_workers: 4
+    batch_size: 1
+  optimization:
+    lr:
+    - 5.0e-05
+    clip_norm: 2.0
+    optimizer: adam
+    adam_betas: (0.9, 0.98)
+    lr_scheduler: polynomial_decay
+    total_num_update: 1000000
+    warmup_updates: 122
+    weight_decay: 0.0
+    ddp_backend: no_c10d
+    max_epoch: 5
+  checkpoint:
+    restore_file: runs/retri/videoclip/checkpoint_best.pt
+    reset_optimizer: true
+    reset_dataloader: true
+    reset_meters: true
+    save_dir: runs/retri/videoclip/crosstask
+task_type: sweep_small
+model:
+  model_cls: MMFusionSeparateActionLocalization
+  mm_encoder_cls: null
+  video_encoder_cls: MMBertForEncoder
+  text_encoder_cls: BertModel
+  num_hidden_video_layers: 6
+loss:
+  loss_cls: BCE

fairseq/examples/MMPT/projects/retri/videoclip/how2.yaml

@@ -0,0 +1,65 @@
+dataset:
+  video_processor: ShardedVideoRetriVideoProcessor
+  bert_name: bert-base-uncased
+  meta_processor: ShardedHow2VideoRetriMetaProcessor
+  train_path: data/how2/how2_s3d_train.lst
+  val_path: data/how2/how2_s3d_val.lst
+  vfeat_dir: data/feat/feat_how2_s3d_shard_small
+  text_processor: ShardedVideoRetriTextProcessor
+  tfeat_dir: data/feat/feat_how2_s3d_shard_small/raw_caption_dedup.bert-base-uncased.
+  aligner: VideoRetriOverlappedAligner
+  subsampling: 1
+  sampled_min_len: 8
+  sampled_max_len: 64
+  max_video_len: 32
+  max_len: 96
+  lazy_vfeat_mask: true
+  mfm_probability: 0.15
+  mlm_probability: 0.15
+  mm_prob: 0.5
+  sampled_video_min_len: 3
+  sampled_video_max_len: 32
+  num_video_per_batch: 32
+  clip_per_video: 16
+fairseq:
+  common:
+    tensorboard_logdir: run
+    log_interval: 1000
+    fp16: true
+  dataset:
+    num_workers: 4
+    batch_size: 1
+  optimization:
+    lr:
+    - 5.0e-05
+    clip_norm: 2.0
+    optimizer: adam
+    adam_betas: (0.9, 0.98)
+    lr_scheduler: polynomial_decay
+    total_num_update: 1000000
+    warmup_updates: 1000
+    weight_decay: 0.0
+    ddp_backend: no_c10d
+    max_epoch: 25
+  checkpoint:
+    save_dir: runs/retri/videoclip
+    save_interval_updates: 1024
+    keep_interval_updates: 2
+    keep_last_epochs: 30
+task_type: sweep_big
+slurm_config: big
+eval:
+  save_path: runs/retri/videoclip
+model:
+  model_cls: MMFusionSeparate
+  mm_encoder_cls: null
+  video_encoder_cls: MMBertForEncoder
+  text_encoder_cls: BertModel
+  num_hidden_video_layers: 6
+loss:
+  loss_cls: MMContraLoss
+task: VideoRetriTask
+retri_epoch: 1
+vectorpool_cls: VideoVectorPool
+retriever_cls: VectorRetriever
+num_cands: 64

fairseq/examples/MMPT/projects/retri/videoclip/test_coin_videoclip.yaml

@@ -0,0 +1,33 @@
+slurm_config: big
+task_type: local_predict
+dataset:
+  split: test
+  video_processor: VideoProcessor
+  aligner: COINActionSegmentationAligner
+  bert_name: bert-base-uncased
+  test_path: data/coin/COIN.json
+  meta_processor: COINActionSegmentationMetaProcessor
+  vfeat_dir: data/feat/feat_coin_s3d
+  text_processor: COINActionSegmentationTextProcessor
+  num_iso_layer: 12
+  sliding_window: 16
+  sliding_window_size: 32
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  dataset:
+    batch_size: 1
+    valid_subset: test
+    num_workers: 2
+  common_eval:
+    path: runs/retri/videoclip/coin/checkpoint_best.pt
+model:
+  model_cls: MMFusionSeparateActionSegmentation
+  mm_encoder_cls: null
+  video_encoder_cls: MMBertForTokenClassification
+  text_encoder_cls: BertModel
+  num_hidden_video_layers: 6
+eval:
+  save_path: runs/retri/videoclip/coin/eval
+metric: COINActionSegmentationMetric
+predictor: COINPredictor

fairseq/examples/MMPT/projects/retri/videoclip/test_coin_zs.yaml

@@ -0,0 +1,33 @@
+slurm_config: big
+task_type: local_predict
+dataset:
+  split: test
+  video_processor: VideoProcessor
+  aligner: COINActionSegmentationAligner
+  bert_name: bert-base-uncased
+  test_path: data/coin/COIN.json
+  meta_processor: COINActionSegmentationMetaProcessor
+  vfeat_dir: data/feat/feat_coin_s3d
+  text_processor: COINActionSegmentationTextProcessor
+  num_iso_layer: 12
+  sliding_window: 16
+  sliding_window_size: 32
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  dataset:
+    batch_size: 1
+    valid_subset: test
+    num_workers: 2
+  common_eval:
+    path: runs/retri/videoclip/checkpoint_best.pt
+model:
+  model_cls: MMFusionSeparate
+  mm_encoder_cls: null
+  video_encoder_cls: MMBertForEncoder
+  text_encoder_cls: BertModel
+  num_hidden_video_layers: 6
+eval:
+  save_path: runs/retri/videoclip/coin_zs/eval
+metric: COINActionSegmentationMetric
+predictor: COINZSPredictor

fairseq/examples/MMPT/projects/retri/videoclip/test_crosstask_videoclip.yaml

@@ -0,0 +1,40 @@
+slurm_config: big
+task_type: local_predict
+dataset:
+  split: test
+  video_processor: CrossTaskVideoProcessor
+  aligner: CrossTaskAligner
+  bert_name: bert-base-uncased
+  meta_processor: CrossTaskMetaProcessor
+  test_path: data/crosstask/crosstask_release/videos_val.csv
+  train_csv_path: data/crosstask/crosstask_release/videos.csv
+  val_path: data/crosstask/crosstask_release/videos_val.csv
+  val_csv_path: data/crosstask/crosstask_release/videos_val.csv
+  primary_path: data/crosstask/crosstask_release/tasks_primary.txt
+  related_path: data/crosstask/crosstask_release/tasks_related.txt
+  vfeat_dir: data/feat/feat_crosstask_s3d
+  annotation_path: data/crosstask/crosstask_release/annotations
+  n_train: 30
+  text_processor: CrossTaskTextProcessor
+  num_iso_layer: 12
+  sliding_window: 16
+  sliding_window_size: 32
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  dataset:
+    batch_size: 1
+    valid_subset: test
+    num_workers: 2
+  common_eval:
+    path: runs/retri/videoclip/crosstask/checkpoint_best.pt
+model:
+  model_cls: MMFusionSeparateActionLocalization
+  mm_encoder_cls: null
+  video_encoder_cls: MMBertForEncoder
+  text_encoder_cls: BertModel
+  num_hidden_video_layers: 6
+eval:
+  save_path: runs/retri/videoclip/crosstask/eval
+metric: CrossTaskMetric
+predictor: CrossTaskPredictor

fairseq/examples/MMPT/projects/retri/videoclip/test_crosstask_zs_videoclip.yaml

@@ -0,0 +1,40 @@
+slurm_config: big
+task_type: local_predict
+dataset:
+  split: test
+  video_processor: CrossTaskVideoProcessor
+  aligner: CrossTaskAligner
+  bert_name: bert-base-uncased
+  meta_processor: CrossTaskMetaProcessor
+  test_path: data/crosstask/crosstask_release/videos_val.csv
+  train_csv_path: data/crosstask/crosstask_release/videos.csv
+  val_path: data/crosstask/crosstask_release/videos_val.csv
+  val_csv_path: data/crosstask/crosstask_release/videos_val.csv
+  primary_path: data/crosstask/crosstask_release/tasks_primary.txt
+  related_path: data/crosstask/crosstask_release/tasks_related.txt
+  vfeat_dir: data/feat/feat_crosstask_s3d
+  annotation_path: data/crosstask/crosstask_release/annotations
+  n_train: 30
+  text_processor: CrossTaskTextProcessor
+  num_iso_layer: 12
+  sliding_window: 16
+  sliding_window_size: 32
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  dataset:
+    batch_size: 1
+    valid_subset: test
+    num_workers: 2
+  common_eval:
+    path: runs/retri/videoclip/checkpoint_best.pt
+model:
+  model_cls: MMFusionSeparateActionLocalization
+  mm_encoder_cls: null
+  video_encoder_cls: MMBertForEncoder
+  text_encoder_cls: BertModel
+  num_hidden_video_layers: 6
+eval:
+  save_path: runs/retri/videoclip/crosstask_zs/eval
+metric: CrossTaskMetric
+predictor: CrossTaskPredictor

fairseq/examples/MMPT/projects/retri/videoclip/test_vtt_videoclip.yaml

@@ -0,0 +1,31 @@
+slurm_config: big
+task_type: local_predict
+dataset:
+  split: test
+  video_processor: VideoProcessor
+  aligner: DSAligner
+  bert_name: bert-base-uncased
+  meta_processor: MSRVTTMetaProcessor
+  test_path: data/msrvtt/MSRVTT_JSFUSION_test.csv
+  vfeat_dir: data/feat/feat_vtt_s3d
+  text_processor: MSRVTTTextProcessor
+  num_iso_layer: 12
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  dataset:
+    batch_size: 256
+    valid_subset: test
+    num_workers: 2
+  common_eval:
+    path: runs/retri/videoclip/vtt/checkpoint_last.pt
+model:
+  model_cls: MMFusionSeparate
+  mm_encoder_cls: null
+  video_encoder_cls: MMBertForEncoder
+  text_encoder_cls: BertModel
+  num_hidden_video_layers: 6
+eval:
+  save_path: runs/retri/videoclip/vtt/eval
+metric: RetrievalMetric
+predictor: RetrievalPredictor

fairseq/examples/MMPT/projects/retri/videoclip/test_vtt_zs.yaml

@@ -0,0 +1,31 @@
+slurm_config: big
+task_type: local_predict
+dataset:
+  split: test
+  video_processor: VideoProcessor
+  aligner: DSAligner
+  bert_name: bert-base-uncased
+  meta_processor: MSRVTTMetaProcessor
+  test_path: data/msrvtt/MSRVTT_JSFUSION_test.csv
+  vfeat_dir: data/feat/feat_vtt_s3d
+  text_processor: MSRVTTTextProcessor
+  num_iso_layer: 12
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  dataset:
+    batch_size: 256
+    valid_subset: test
+    num_workers: 2
+  common_eval:
+    path: runs/retri/videoclip/checkpoint_best.pt
+model:
+  model_cls: MMFusionSeparate
+  mm_encoder_cls: null
+  video_encoder_cls: MMBertForEncoder
+  text_encoder_cls: BertModel
+  num_hidden_video_layers: 6
+eval:
+  save_path: runs/retri/videoclip/vtt_zs/eval
+metric: RetrievalMetric
+predictor: RetrievalPredictor

fairseq/examples/MMPT/projects/retri/videoclip/test_vttqa_videoclip.yaml

@@ -0,0 +1,31 @@
+slurm_config: big
+task_type: local_predict
+dataset:
+  split: test
+  video_processor: VideoProcessor
+  aligner: MSRVTTQAAligner
+  bert_name: bert-base-uncased
+  meta_processor: MSRVTTQAMetaProcessor
+  test_path: data/msrvtt-qa/MSR_MC_test.csv
+  vfeat_dir: data/feat/feat_vtt_s3d
+  text_processor: MSRVTTQATextProcessor
+  num_iso_layer: 12
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  dataset:
+    batch_size: 256
+    valid_subset: test
+    num_workers: 2
+  common_eval:
+    path: runs/retri/videoclip/vttqa/checkpoint_last.pt
+model:
+  model_cls: MMFusionSeparate
+  mm_encoder_cls: null
+  video_encoder_cls: MMBertForEncoder
+  text_encoder_cls: BertModel
+  num_hidden_video_layers: 6
+eval:
+  save_path: runs/retri/videoclip/vttqa/eval
+metric: QAMetric
+predictor: QAPredictor

fairseq/examples/MMPT/projects/retri/videoclip/test_vttqa_zs.yaml

@@ -0,0 +1,31 @@
+slurm_config: big
+task_type: local_predict
+dataset:
+  split: test
+  video_processor: VideoProcessor
+  aligner: MSRVTTQAAligner
+  bert_name: bert-base-uncased
+  meta_processor: MSRVTTQAMetaProcessor
+  test_path: data/msrvtt-qa/MSR_MC_test.csv
+  vfeat_dir: data/feat/feat_vtt_s3d
+  text_processor: MSRVTTQATextProcessor
+  num_iso_layer: 12
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  dataset:
+    batch_size: 256
+    valid_subset: test
+    num_workers: 2
+  common_eval:
+    path: runs/retri/videoclip/checkpoint_best.pt
+model:
+  model_cls: MMFusionSeparate
+  mm_encoder_cls: null
+  video_encoder_cls: MMBertForEncoder
+  text_encoder_cls: BertModel
+  num_hidden_video_layers: 6
+eval:
+  save_path: runs/retri/videoclip/vttqa_zs/eval
+metric: QAMetric
+predictor: QAPredictor

fairseq/examples/MMPT/projects/retri/videoclip/test_youcook_videoclip.yaml

@@ -0,0 +1,33 @@
+slurm_config: big
+task_type: local_predict
+dataset:
+  split: test
+  video_processor: YoucookVideoProcessor
+  aligner: DSAligner
+  bert_name: bert-base-uncased
+  meta_processor: YoucookMetaProcessor
+  test_path: data/youcook/youcook_val.pkl
+  trainval_annotation: data/youcook/youcookii_annotations_trainval.json
+  use_annotation_text: true
+  vfeat_dir: data/feat/feat_youcook_s3d
+  text_processor: TextProcessor
+  num_iso_layer: 12
+  max_video_len: 32
+  max_len: 96
+fairseq:
+  dataset:
+    batch_size: 256
+    valid_subset: test
+    num_workers: 2
+  common_eval:
+    path: runs/retri/videoclip/youcook/checkpoint_last.pt
+model:
+  model_cls: MMFusionSeparate
+  mm_encoder_cls: null
+  video_encoder_cls: MMBertForEncoder
+  text_encoder_cls: BertModel
+  num_hidden_video_layers: 6
+eval:
+  save_path: runs/retri/videoclip/youcook/eval
+metric: RetrievalMetric
+predictor: RetrievalPredictor