Datasets:
jblitzar
/
github-python-corpus

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progress_bar.update(1)
progress_bar.set_postfix({
'avg_reward_score': f"{avg_reward_score:.3f}",
'lr': f"{current_lr:.5e}",
})
# wandb logging
if accelerator.is_main_process and total_steps_count % log_steps == 0:
lr_scale = current_lr / learning_rate
metrics.update({
"train/learning_rate": current_lr,
"train/lr_scale": lr_scale,
"train/epoch": epoch + (step + 1) / len(dataloader),
"train/global_step": total_steps_count,
"train/avg_reward_score": avg_reward_score,
})
wandb.log(metrics, step=total_steps_count)
if avg_reward_score > best_reward and total_steps_count % save_steps == 0:
best_reward = avg_reward_score
metrics["best_reward"] = best_reward
save_manager(epoch + 1, total_steps_count, avg_reward_score, max_save=max_save, prefix="step")
if __name__ == "__main__":
main()
# <FILESEP>
from __future__ import print_function
import argparse
import os
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
from fbpn_sr_rbpn_v1 import Net as FBPNSR_RBPN_V1
from fbpn_sr_rbpn_v2 import Net as FBPNSR_RBPN_V2
from fbpn_sr_rbpn_v3 import Net as FBPNSR_RBPN_V3
from fbpn_sr_rbpn_v4 import Net as FBPNSR_RBPN_V4
from fbpn_sr_rbpn_v1_ref import Net as FBPNSR_RBPN_V1_REF
from fbpn_sr_rbpn_v2_ref import Net as FBPNSR_RBPN_V2_REF
from fbpn_sr_rbpn_v3_ref import Net as FBPNSR_RBPN_V3_REF
from fbpn_sr_rbpn_v4_ref import Net as FBPNSR_RBPN_V4_REF, FeatureExtractor
from data import get_test_set_interp
from functools import reduce
import numpy as np
import utils
import time
import cv2
import math
import pdb
# Training settings
parser = argparse.ArgumentParser(description='PyTorch Super Res Example')
parser.add_argument('--upscale_factor', type=int, default=4, help="super resolution upscale factor")
parser.add_argument('--testBatchSize', type=int, default=1, help='testing batch size')
parser.add_argument('--gpu_mode', type=bool, default=True)
parser.add_argument('--chop_forward', type=bool, default=False)
parser.add_argument('--threads', type=int, default=1, help='number of threads for data loader to use')
parser.add_argument('--seed', type=int, default=123, help='random seed to use. Default=123')
parser.add_argument('--gpus', default=1, type=float, help='number of gpu')
parser.add_argument('--data_dir', type=str, default='vimeo_triplet/sequences')
parser.add_argument('--file_list', type=str, default='tri_testlist.txt')
parser.add_argument('--model_type', type=str, default='FBPNSR_RBPN_V4_REF')
parser.add_argument('--residual', type=bool, default=False)
parser.add_argument('--output', default='Results_T_SR_HR/', help='Location to save checkpoint models')
parser.add_argument('--model', default='weights/FBPNSR_RBPN_V4_REF_Lf_STAR_T_HR.pth', help='sr pretrained base model')
opt = parser.parse_args()
gpus_list=range(opt.gpus)
print(opt)
cuda = opt.gpu_mode
if cuda and not torch.cuda.is_available():
raise Exception("No GPU found, please run without --cuda")
torch.manual_seed(opt.seed)
if cuda:
torch.cuda.manual_seed(opt.seed)
print('===> Loading datasets')
test_set = get_test_set_interp(opt.data_dir, opt.file_list)
testing_data_loader = DataLoader(dataset=test_set, num_workers=opt.threads, batch_size=opt.testBatchSize, shuffle=False)
print('===> Building model ', opt.model_type)
if opt.model_type == 'FBPNSR_RBPN_V1_REF':
model = FBPNSR_RBPN_V1_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor)
elif opt.model_type == 'FBPNSR_RBPN_V2_REF':
model = FBPNSR_RBPN_V2_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor)
elif opt.model_type == 'FBPNSR_RBPN_V3_REF':
model = FBPNSR_RBPN_V3_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor)
elif opt.model_type == 'FBPNSR_RBPN_V4_REF':
model = FBPNSR_RBPN_V4_REF(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor)
elif opt.model_type == 'FBPNSR_RBPN_V1':
model = FBPNSR_RBPN_V1(base_filter=256, feat = 64, num_stages=3, n_resblock=5, scale_factor=opt.upscale_factor)