# coding=utf-8 # Copyright 2024 The Google Research Authors. # # 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. """Default pretraining config values.""" import ml_collections def get_config(): """Returns default config.""" config = ml_collections.ConfigDict() # ============================================== # # General experiment params. # ============================================== # # The root directory where experiments will be saved. config.root_dir = "/tmp/xirl/pretrain_runs/" # Rng seed. Set this to `none` to disable seeding. config.seed = 1 # cudnn-related parameters that affect reproducibility. config.cudnn_deterministic = False config.cudnn_benchmark = True # Pretraining algorithm to use. config.algorithm = "tcc" # Number of steps between tensorboard logging. config.logging_frequency = 100 # Number of steps between consecutive checkpoints. config.checkpointing_frequency = 200 # ============================================== # # Dataset params. # ============================================== # config.data = ml_collections.ConfigDict() # Absolute path to the dataset root. config.data.root = "/tmp/xirl/datasets/xmagical/" # The mini-batch size. Note this only specifies the number of videos to # load frames from in a single batch. The effective batch size is actually # larger since we sample multiple frame sequences per video. config.data.batch_size = 4 # Which action classes to select for creating the pretraining dataset. Leave # it empty to load all action classes. config.data.pretrain_action_class = () # Which action classes to select for creating the dowstream dataset. Leave # it empty to load all action classes. config.data.downstream_action_class = () # Restrict the number of videos per class. This is useful for experiments # that test sample complexity based on the number of pretraining # demonstrations. config.data.max_vids_per_class = -1 # This controls how a video batch is created. If set to 'random', videos # are sampled randomly from different classes. If set to 'same_class', only # videos belonging to the same class folder are sampled within a batch. config.data.pretraining_video_sampler = "random" # ============================================== # # Frame sampling params. # ============================================== # config.frame_sampler = ml_collections.ConfigDict() # A wildcard specifying the file extension for images in each video folder. # This will usually be either "*.jpg" or "*.png". config.frame_sampler.image_ext = "*.png" # This controls the type of sampling we perform on video frames. config.frame_sampler.strategy = "uniform" # The number of frames to sample per video. config.frame_sampler.num_frames_per_sequence = 15 # The number of context frames to sample per frame. This is useful for # models that use 3D convolutions. config.frame_sampler.num_context_frames = 1 # The stride between sampled context frames. config.frame_sampler.context_stride = 3 config.frame_sampler.all_sampler = ml_collections.ConfigDict() config.frame_sampler.all_sampler.stride = 1 config.frame_sampler.strided_sampler = ml_collections.ConfigDict() config.frame_sampler.strided_sampler.stride = 3 config.frame_sampler.strided_sampler.offset = True config.frame_sampler.uniform_sampler = ml_collections.ConfigDict() config.frame_sampler.uniform_sampler.offset = 0 # Currently, this frame sampler has no additional kwargs. config.frame_sampler.window_sampler = ml_collections.ConfigDict() # ============================================== # # Data augmentation params. # ============================================== # config.data_augmentation = ml_collections.ConfigDict() # The image resolution to train on. config.data_augmentation.image_size = (112, 112) # A list of image augmentations to apply to the training dataset. note that # the order matters, e.g. normalize should be done last if you decide to # turn it on. config.data_augmentation.train_transforms = [ "random_resized_crop", "color_jitter", "grayscale", "gaussian_blur", # "normalize", ] # A list of image augmentations to apply to the evaluation dataset. config.data_augmentation.eval_transforms = [ "global_resize", # "normalize", ] # ============================================== # # Evaluator params. # ============================================== # config.eval = ml_collections.ConfigDict() # How many iterations of the downstream dataloaders to run. Set to None to # evaluate the entire dataloader. config.eval.val_iters = 20 # The number of steps in between every evaluation. config.eval.eval_frequency = 500 # A list of downstream task evaluators that will be run sequentially every # EVAL_FREQUENCY steps. config.eval.downstream_task_evaluators = [ "reward_visualizer", "kendalls_tau", ] # What distance metric to use in the embedding space. Should match what was # used in the loss computation. # Can be one of ['cosine', 'sqeuclidean']. config.eval.distance = "sqeuclidean" config.eval.kendalls_tau = ml_collections.ConfigDict() config.eval.kendalls_tau.stride = 3 config.eval.reward_visualizer = ml_collections.ConfigDict() config.eval.reward_visualizer.num_plots = 2 config.eval.cycle_consistency = ml_collections.ConfigDict() config.eval.cycle_consistency.stride = 1 config.eval.nearest_neighbour_visualizer = ml_collections.ConfigDict() config.eval.nearest_neighbour_visualizer.num_videos = 4 config.eval.embedding_visualizer = ml_collections.ConfigDict() config.eval.embedding_visualizer.num_seqs = 2 config.eval.reconstruction_visualizer = ml_collections.ConfigDict() config.eval.reconstruction_visualizer.num_frames = 2 # ============================================== # # Model params. # ============================================== # config.model = ml_collections.ConfigDict() config.model.model_type = "resnet18_linear" config.model.embedding_size = 32 config.model.normalize_embeddings = False config.model.learnable_temp = False # ============================================== # # Loss params. # ============================================== # config.loss = ml_collections.ConfigDict() ## TCC loss. config.loss.tcc = ml_collections.ConfigDict() config.loss.tcc.stochastic_matching = False config.loss.tcc.loss_type = "regression_mse" config.loss.tcc.cycle_length = 2 config.loss.tcc.label_smoothing = 0.1 config.loss.tcc.softmax_temperature = 0.1 config.loss.tcc.normalize_indices = True config.loss.tcc.variance_lambda = 0.001 config.loss.tcc.huber_delta = 0.1 config.loss.tcc.similarity_type = "l2" # cosine ## TCN loss. config.loss.tcn = ml_collections.ConfigDict() config.loss.tcn.pos_radius = 1 config.loss.tcn.neg_radius = 4 config.loss.tcn.num_pairs = 2 config.loss.tcn.margin = 1.0 config.loss.tcn.temperature = 0.1 ## LIFS loss. config.loss.lifs = ml_collections.ConfigDict() config.loss.lifs.temperature = 1.0 # ============================================== # # Optimizer params # ============================================== # config.optim = ml_collections.ConfigDict() config.optim.train_max_iters = 4_000 # L2 regularization. config.optim.weight_decay = 1e-4 # Learning rate. config.optim.lr = 1e-5 # ============================================== # # End of config file # ============================================== # return config