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virtex-redcaps / virtex /config.py
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from typing import Any, List, Optional
from fvcore.common.config import CfgNode as CN
class Config(object):
r"""
This class provides package-wide configuration management. It is a
nested dict-like structure with nested keys accessible as attributes. It
contains sensible default values, which can be modified by (first) a YAML
file and (second) a list of attributes and values.
An instantiated object is immutable: modifying any attribute is illegal.
You must override required parameter values either through ``config_file``
or ``override_list`` arguments. For adding more parameters at runtime
(based on existing parameters), modify :meth:`add_derived_params`.
Parameters
----------
config_file: str
Path to a YAML file containing configuration parameters to override.
config_override: List[Any], optional (default = [])
A list of sequential attributes and values of parameters to override.
This happens after overriding from YAML file.
Examples
--------
Let a YAML file named "config.yaml" specify these parameters to override::
OPTIM:
BATCH_SIZE: 512
LR: 0.01
>>> _C = Config("config.yaml", ["OPTIM.BATCH_SIZE", 1024])
>>> _C.LR # default: 0.001
0.01
>>> _C.OPTIM.BATCH_SIZE # default: 256, file: 512
1024
"""
def __init__(
self, config_file: Optional[str] = None, override_list: List[Any] = []
):
_C = CN()
# Random seed for NumPy and PyTorch, important for reproducibility.
_C.RANDOM_SEED = 0
# Train with Automatic Mixed Precision (native PyTorch).
_C.AMP = True
# Set CUDNN deterministic flag (torch.backends.cudnn.deterministic).
# Setting this will ensure exact results on every run at the cost of
# little slowdown. Good for debugging.
_C.CUDNN_DETERMINISTIC = False
# Set CUDNN benchmark flag (torch.backends.cudnn.benchmark). Enables
# CUDNN to select fastest implementation for operations based on GPU.
# May change results (in decimals) on different hardware, but faster
# to train. Turn off while debugging.
_C.CUDNN_BENCHMARK = True
# ---------------------------------------------------------------------
# Data paths and parameters related to dataloading.
# ---------------------------------------------------------------------
_C.DATA = CN()
# Path to the dataset root, which structure as per README. Path is
# assumed to be relative to project root.
_C.DATA.ROOT = "datasets/coco"
# Path to .model file generated by ``sentencepiece``.
_C.DATA.TOKENIZER_MODEL = "datasets/vocab/coco_10k.model"
# Handy config params for vocab size and indices of special tokens.
# While these can be picked up from the tokenizer, having these in
# the config makes it easy to create a model without instantiating too
# many tokenizer instances (especially when not needed, e.g. model zoo).
# These must match according to what's present in ``TOKENIZER_VOCAB``
# and ``TOKENIZER_MODEL`` above.
_C.DATA.VOCAB_SIZE = 10000
# Index of out-of-vocabulary (and padding) token.
_C.DATA.UNK_INDEX = 0
# Index of the start-of-sentence [SOS] token.
_C.DATA.SOS_INDEX = 1
# Index of the end-of-sentence [EOS] token.
_C.DATA.EOS_INDEX = 2
# Index of the word masking token. While not used for captioning, having
# this extra token makes it possible to train an MLM model without
# re-creating a new vocab mapping.
_C.DATA.MASK_INDEX = 3
# Size of the image (square) to crop from original input image.
_C.DATA.IMAGE_CROP_SIZE = 224
# Maximum length of input caption (number of tokens).
# Longer captions will be truncated up to this length.
_C.DATA.MAX_CAPTION_LENGTH = 30
# COCO Captions has five captions per image. If ``True``, training will
# use one random caption per image (data efficiency ablations).
_C.DATA.USE_SINGLE_CAPTION = False
# Percentage of dataset to use for training (data efficiency ablations).
_C.DATA.USE_PERCENTAGE = 100.0
# List of image transforms (pre-processing and data augmentation) to be
# applied sequentially (always or randomly) during training and
# validation. Refer ``virtex/facetories.py`` for all possible transforms.
_C.DATA.IMAGE_TRANSFORM_TRAIN = [
"random_resized_crop",
"horizontal_flip",
"color_jitter",
"normalize",
]
_C.DATA.IMAGE_TRANSFORM_VAL = [
"smallest_resize",
"center_crop",
"normalize",
]
# Hyper-parameters for masked LM pretraining task. These are only used
# when ``MODEL.NAME`` is "masked_lm".
_C.DATA.MASKED_LM = CN()
# Fraction of tokens to choose for masking, this must be less than 1.
_C.DATA.MASKED_LM.MASK_PROPORTION = 0.15
# Probability to replace chosen tokens with [MASK] token.
_C.DATA.MASKED_LM.MASK_PROBABILITY = 0.85
# Probability to replace chosen tokens with a random token.
_C.DATA.MASKED_LM.REPLACE_PROBABILITY = 0.10
# ---------------------------------------------------------------------
# Model architecture: visual backbone and textual head.
# ---------------------------------------------------------------------
_C.MODEL = CN()
# Name of model, based on pretraining task.
# Possible choices: {"token_classification", "multilabel_classification",
# "captioning", "bicaptioning", "masked_lm", "virtex"}
_C.MODEL.NAME = "virtex"
_C.MODEL.VISUAL = CN()
# Name of visual backbone. Possible choices: {"blind", "torchvision"}
# Models from torchvision can be specified as shown below.
_C.MODEL.VISUAL.NAME = "torchvision::resnet50"
# Number of channels in pooled spatial features of visual backbone.
_C.MODEL.VISUAL.FEATURE_SIZE = 2048
# Whether to load ImageNet pretrained weights into visual backbone.
_C.MODEL.VISUAL.PRETRAINED = False
# Whether to keep visual backbone frozen and train only textual head.
_C.MODEL.VISUAL.FROZEN = False
_C.MODEL.TEXTUAL = CN()
# Name of textual head. Set to "none" for MODEL.NAME = "*_classification".
# Possible choices: {"transdec_postnorm", "transdec_prenorm"}.
# Architectural hyper-parameters are specified as shown above.
_C.MODEL.TEXTUAL.NAME = "transdec_postnorm::L1_H2048_A32_F8192"
# L = Number of layers in the transformer.
# H = Hidden size of the transformer (embeddings, attention features).
# A = Number of attention heads in the transformer.
# F = Size of feedforward layers in the transformer.
# Typically, we have (A = H / 64) and (F = 4 * H).
# Dropout probability for embedding, hidden features in textual head.
_C.MODEL.TEXTUAL.DROPOUT = 0.1
# Apply label smoothing to targets for (cross entropy) loss computation.
_C.MODEL.LABEL_SMOOTHING = 0.0
_C.MODEL.DECODER = CN()
# What algorithm to use for decoding. Supported values: {"beam_search",
# "nucleus_sampling"}.
_C.MODEL.DECODER.NAME = "beam_search"
# Number of beams to decode (1 = greedy decoding). Ignored when decoding
# through nucleus sampling.
_C.MODEL.DECODER.BEAM_SIZE = 5
# Size of nucleus for sampling predictions. Ignored when decoding through
# beam search.
_C.MODEL.DECODER.NUCLEUS_SIZE = 0.9
# Maximum length of decoded caption. Decoding may end earlier when [EOS]
# token is sampled.
_C.MODEL.DECODER.MAX_DECODING_STEPS = _C.DATA.MAX_CAPTION_LENGTH
# ---------------------------------------------------------------------
# Optimization hyper-parameters, default values are for pretraining
# our best model on bicaptioning task (COCO Captions).
# ---------------------------------------------------------------------
_C.OPTIM = CN()
# Name of optimizer to use. Supported values: {"sgd", "adamw"}.
# AdamW uses default (beta1, beta2) values from PyTorch.
_C.OPTIM.OPTIMIZER_NAME = "sgd"
# Momentum co-efficient for SGD. Ignored for AdamW.
_C.OPTIM.SGD_MOMENTUM = 0.9
# Weight decay co-efficient for the optimizer.
_C.OPTIM.WEIGHT_DECAY = 0.0001
# Regex pattern of params for which there will be no weight decay.
_C.OPTIM.NO_DECAY = ".*textual.(embedding|transformer).*(norm.*|bias)"
# Max gradient norm for clipping to avoid exploding gradients.
_C.OPTIM.CLIP_GRAD_NORM = 10.0
# Wrap our optimizer with Lookahead (https://arxiv.org/abs/1907.08610).
_C.OPTIM.LOOKAHEAD = CN()
_C.OPTIM.LOOKAHEAD.USE = True
_C.OPTIM.LOOKAHEAD.ALPHA = 0.5
_C.OPTIM.LOOKAHEAD.STEPS = 5
# We set different learning rates for CNN (visual backbone) and rest of
# the model. CNN LR is typically much higher for training from scratch.
# Both LRs undergo same warmup-decay schedules.
# Total batch size (will be distributed evenly across GPUs).
_C.OPTIM.BATCH_SIZE = 256
# Max learning rate for CNN (visual backbone).
_C.OPTIM.CNN_LR = 0.2
# Max learning rate for rest of the model.
_C.OPTIM.LR = 0.001
# Number of iterations to train for, batches are randomly sampled.
_C.OPTIM.NUM_ITERATIONS = 500000
# Number of steps at the start of training for linear LR warmup.
_C.OPTIM.WARMUP_STEPS = 10000
# Learning rate annealing schedule for decay after warmup.
# Possible choices: {"none", "linear", "cosine", "multistep"}.
_C.OPTIM.LR_DECAY_NAME = "cosine"
# Steps to decay LR for "multistep" schedule.
_C.OPTIM.LR_STEPS = []
# Factor to multiply with LR for "multistep" schedule.
_C.OPTIM.LR_GAMMA = 0.1
# Override parameter values from YAML file first, then from override
# list, then add derived params.
self._C = _C
if config_file is not None:
self._C.merge_from_file(config_file)
self._C.merge_from_list(override_list)
self.add_derived_params()
# Make an instantiated object of this class immutable.
self._C.freeze()
def add_derived_params(self):
r"""Add parameters with values derived from existing parameters."""
# We don't have any such cases so far.
pass
def dump(self, file_path: str):
r"""Save config at the specified file path.
Parameters
----------
file_path: str
(YAML) path to save config at.
"""
self._C.dump(stream=open(file_path, "w"))
def __getattr__(self, attr: str):
return self._C.__getattr__(attr)
def __str__(self):
return self._C.__str__()
def __repr__(self):
return self._C.__repr__()