]
train_dataset = AmbiLocalFeatDataset(
iccv_res_dir=iccv_res_dir,
image_dir=cambridge_img_dir,
lmdb_paths=lmdb_img_cache,
dataset_list=dataset_list,
downsample_scale=0.5,
sampling_num=30,
sample_res_cache='/mnt/Exp_5/AmbiguousData_pg/temp_cache.bin',
sub_graph_nodes=24)
# set train parameters
train_params = TrainParameters()
train_params.MAX_EPOCHS = 20
train_params.START_LR = 1.0e-4
train_params.DEV_IDS = [0, 1]
train_params.LOADER_BATCH_SIZE = 1
train_params.LOADER_NUM_THREADS = 0
train_params.VALID_STEPS = 5000
train_params.MAX_VALID_BATCHES_NUM = 20
train_params.CHECKPOINT_STEPS = 6000
train_params.VERBOSE_MODE = True
train_params.NAME_TAG = 'test_gat_cluster'
box = LocalGlobalGATTrainBox(train_params=train_params,
ckpt_path_dict=checkpoint_dict)
train_loader = dataloader.DataLoader(train_dataset,
batch_size=1,
shuffle=False,
import pickle import torch import torchtext from trainbox import DPCNNTrainBox import numpy as np # [1] """ Train Parameters --------------------------------------------------------------------------------------------------- """ # toggle `DEBUG` to disable logger (won't dump to disk) DEBUG = False # set train parameters train_params = TrainParameters() train_params.MAX_EPOCHS = 10 train_params.START_LR = 0.01 train_params.DEV_IDS = [0] train_params.LOADER_BATCH_SIZE = 100 train_params.LOADER_NUM_THREADS = 0 train_params.VALID_STEPS = 250 train_params.MAX_VALID_BATCHES_NUM = 50 train_params.CHECKPOINT_STEPS = 3000 train_params.VERBOSE_MODE = True train_params.LOADER_VALID_BATCH_SIZE = train_params.LOADER_BATCH_SIZE train_params.LR_DECAY_FACTOR = 0.1 train_params.LR_DECAY_STEPS = 8 # specific unique description for current training experiments train_params.NAME_TAG = 'dpcnn' train_params.DESCRIPTION = 'Initial eval'