import matplotlib matplotlib.use('Agg') import os import sys import json import argparse import time import numpy as np import tensorflow as tf from tensorflow.python import debug as tf_debug from args import argument_parser, prepare_args, model_kwards, learn_kwards parser = argument_parser() args = parser.parse_args() args = prepare_args(args) if args.dataset_name == 'gpsamples': from data.gpsampler import GPSampler data = np.load("gpsamples_var05.npz") train_data = {"xs": data['xs'][:50000], "ys": data['ys'][:50000]} val_data = {"xs": data['xs'][50000:60000], "ys": data['ys'][50000:60000]} train_set = GPSampler(input_range=[-2., 2.], var_range=[0.5, 0.5], max_num_samples=200, data=train_data) val_set = GPSampler(input_range=[-2., 2.], var_range=[0.5, 0.5], max_num_samples=200, data=val_data) elif args.dataset_name == 'sinusoid': from data.sinusoid import Sinusoid
import omni_torch.visualize.basic as vb from omni_torch.networks.optimizer import * import time import torch from torch.autograd import Variable import torch.nn as nn import torch.optim as optim import torch.backends.cudnn as cudnn import torch.nn.init as init import torch.utils.data as data import numpy as np from args import prepare_args import mmdet.ops.dcn as dcn from layers.visualization import * args = prepare_args(VOC_ROOT) TMPJPG = os.path.expanduser("~/Pictures/tmp.jpg") torch.set_default_tensor_type('torch.cuda.FloatTensor') dt = datetime.datetime.now().strftime("%Y-%m-%d_%H:%M") def avg(list): return sum(list) / len(list) def old_fit(args, cfg, net, train_set, optimizer, criterion): step_index = 0 train_loader = data.DataLoader(train_set, args.batch_size, num_workers=args.num_workers,