# -------------------------------------------------------- # mcan-vqa (Deep Modular Co-Attention Networks) # Licensed under The MIT License [see LICENSE for details] # Written by Yuhao Cui https://github.com/cuiyuhao1996 # -------------------------------------------------------- from cfgs.path_cfgs import PATH import os, torch, random import numpy as np from types import MethodType class Cfgs(PATH): def __init__(self, EXP_NAME, DATASET_PATH): super(Cfgs, self).__init__(EXP_NAME, DATASET_PATH) # Set Devices # If use multi-gpu training, set e.g.'0, 1, 2' instead self.GPU = '0' # Set RNG For CPU And GPUs self.SEED = random.randint(0, 99999999) # ------------------------- # ---- Version Control ---- # ------------------------- # Define a specific name to start new training # self.VERSION = 'Anonymous_' + str(self.SEED) self.VERSION = str(self.SEED) # Resume training self.RESUME = False # Used in Resume training and testing self.CKPT_VERSION = self.VERSION self.CKPT_EPOCH = 0 # Absolutely checkpoint path, 'CKPT_VERSION' and 'CKPT_EPOCH' will be overridden self.CKPT_PATH = None # Print loss every step self.VERBOSE = True # ------------------------------ # ---- Data Provider Params ---- # ------------------------------ # {'train', 'val', 'test'} self.RUN_MODE = 'train' # Set True to evaluate offline self.EVAL_EVERY_EPOCH = True # # Define the 'train' 'val' 'test' data split # # (EVAL_EVERY_EPOCH triggered when set {'train': 'train'}) # self.SPLIT = { # 'train': '', # 'val': 'val', # 'test': 'test', # } # # A external method to set train split # self.TRAIN_SPLIT = 'train+val+vg' # Set True to use pretrained word embedding # (GloVe: spaCy https://spacy.io/) self.USE_GLOVE = True # Word embedding matrix size # (token size x WORD_EMBED_SIZE) self.WORD_EMBED_SIZE = 300 # Max length of question sentences self.MAX_TOKEN = 15 # VGG 4096D features self.FRAME_FEAT_SIZE = 4096 # C3D 4096D features self.CLIP_FEAT_SIZE = 4096 self.NUM_ANS = 1000 # Default training batch size: 64 self.BATCH_SIZE = 64 # Multi-thread I/O self.NUM_WORKERS = 8 # Use pin memory # (Warning: pin memory can accelerate GPU loading but may # increase the CPU memory usage when NUM_WORKS is large) self.PIN_MEM = True # Large model can not training with batch size 64 # Gradient accumulate can split batch to reduce gpu memory usage # (Warning: BATCH_SIZE should be divided by GRAD_ACCU_STEPS) self.GRAD_ACCU_STEPS = 1 # Set 'external': use external shuffle method to implement training shuffle # Set 'internal': use pytorch dataloader default shuffle method self.SHUFFLE_MODE = 'external' # ------------------------ # ---- Network Params ---- # ------------------------ # Model deeps # (Encoder and Decoder will be same deeps) self.LAYER = 6 # Model hidden size # (512 as default, bigger will be a sharp increase of gpu memory usage) self.HIDDEN_SIZE = 512 # Multi-head number in MCA layers # (Warning: HIDDEN_SIZE should be divided by MULTI_HEAD) self.MULTI_HEAD = 8 # Dropout rate for all dropout layers # (dropout can prevent overfitting： [Dropout: a simple way to prevent neural networks from overfitting]) self.DROPOUT_R = 0.1 # MLP size in flatten layers self.FLAT_MLP_SIZE = 512 # Flatten the last hidden to vector with {n} attention glimpses self.FLAT_GLIMPSES = 1 self.FLAT_OUT_SIZE = 1024 # -------------------------- # ---- Optimizer Params ---- # -------------------------- # The base learning rate self.LR_BASE = 0.0001 # Learning rate decay ratio self.LR_DECAY_R = 0.2 # Learning rate decay at {x, y, z...} epoch self.LR_DECAY_LIST = [10, 12] # Max training epoch self.MAX_EPOCH = 30 # Gradient clip # (default: -1 means not using) self.GRAD_NORM_CLIP = -1 # Adam optimizer betas and eps self.OPT_BETAS = (0.9, 0.98) self.OPT_EPS = 1e-9 self.OPT_WEIGHT_DECAY = 1e-5 # -------------------------- # ---- DNC Hyper-Params ---- # -------------------------- self.IN_SIZE_DNC = self.HIDDEN_SIZE self.OUT_SIZE_DNC = self.HIDDEN_SIZE self.WORD_LENGTH_DNC = 512 self.CELL_COUNT_DNC = 64 self.MEM_HIDDEN_SIZE = self.CELL_COUNT_DNC * self.WORD_LENGTH_DNC self.N_READ_HEADS_DNC = 4 def parse_to_dict(self, args): args_dict = {} for arg in dir(args): if not arg.startswith('_') and not isinstance(getattr(args, arg), MethodType): if getattr(args, arg) is not None: args_dict[arg] = getattr(args, arg) return args_dict def add_args(self, args_dict): for arg in args_dict: setattr(self, arg, args_dict[arg]) def proc(self): assert self.RUN_MODE in ['train', 'val', 'test'] # ------------ Devices setup # os.environ['CUDA_VISIBLE_DEVICES'] = self.GPU self.N_GPU = len(self.GPU.split(',')) self.DEVICES = [_ for _ in range(self.N_GPU)] torch.set_num_threads(2) # ------------ Seed setup # fix pytorch seed torch.manual_seed(self.SEED) if self.N_GPU < 2: torch.cuda.manual_seed(self.SEED) else: torch.cuda.manual_seed_all(self.SEED) torch.backends.cudnn.deterministic = True # fix numpy seed np.random.seed(self.SEED) # fix random seed random.seed(self.SEED) if self.CKPT_PATH is not None: print('Warning: you are now using CKPT_PATH args, ' 'CKPT_VERSION and CKPT_EPOCH will not work') self.CKPT_VERSION = self.CKPT_PATH.split('/')[-1] + '_' + str(random.randint(0, 99999999)) # ------------ Split setup self.SPLIT['train'] = self.TRAIN_SPLIT if 'val' in self.SPLIT['train'].split('+') or self.RUN_MODE not in ['train']: self.EVAL_EVERY_EPOCH = False if self.RUN_MODE not in ['test']: self.TEST_SAVE_PRED = False # ------------ Gradient accumulate setup assert self.BATCH_SIZE % self.GRAD_ACCU_STEPS == 0 self.SUB_BATCH_SIZE = int(self.BATCH_SIZE / self.GRAD_ACCU_STEPS) # Use a small eval batch will reduce gpu memory usage self.EVAL_BATCH_SIZE = 32 # ------------ Networks setup # FeedForwardNet size in every MCA layer self.FF_SIZE = int(self.HIDDEN_SIZE * 4) self.FF_MEM_SIZE = int() # A pipe line hidden size in attention compute assert self.HIDDEN_SIZE % self.MULTI_HEAD == 0 self.HIDDEN_SIZE_HEAD = int(self.HIDDEN_SIZE / self.MULTI_HEAD) def __str__(self): for attr in dir(self): if not attr.startswith('__') and not isinstance(getattr(self, attr), MethodType): print('{ %-17s }->' % attr, getattr(self, attr)) return '' def check_path(self): print('Checking dataset ...') if not os.path.exists(self.FRAMES): print(self.FRAMES + 'NOT EXIST') exit(-1) if not os.path.exists(self.CLIPS): print(self.CLIPS + 'NOT EXIST') exit(-1) for mode in self.QA_PATH: if not os.path.exists(self.QA_PATH[mode]): print(self.QA_PATH[mode] + 'NOT EXIST') exit(-1) print('Finished') print('')