Datasets:
jblitzar
/
github-python-corpus

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# When doing analysis of the websocket responses to try to identify exceptions
# and other errors, ignore these errors since they are common for the
# application under test
ignore_errors = []
#
# Do not touch these lines
#
send_payloads_in_websocket(ws_address,
messages,
session_active_message,
ignore_errors,
0,
log_path,
http_proxy_host,
http_proxy_port)
# <FILESEP>
from utils import create_dir, eval_inverse, viz_img, delete_all_but_N_files
from discretizations import get_discretization
from averagemeter import AverageMeter
from distances import get_distance
from solvers import get_solver
from networks import SongUNet
from data import Sampler
import torch.optim as optim
import numpy as np
import pprint
import argparse
import torch
import lpips
import copy
import math
import time
import os
def save_ckpt(X0_eval, X1_eval, net, net_ema, opt_DSM, opt_CTM, avgmeter, best_PSNR, ckpt_dir, idx, best=False):
ckpt = {
'X0_eval': X0_eval,
'X1_eval': X1_eval,
'net': net.state_dict(),
'net_ema' : net_ema.state_dict(),
'opt_DSM' : opt_DSM.state_dict(),
'opt_CTM' : opt_CTM.state_dict(),
'avgmeter': avgmeter.state_dict(),
'best_PSNR' : best_PSNR
}
if best:
torch.save(ckpt, os.path.join(ckpt_dir, 'idx_0_best.pt'))
else:
torch.save(ckpt, os.path.join(ckpt_dir, 'idx_{}_curr.pt'.format(idx)))
def train(datasets, data_roots, X1_eps_std, vars, coupling, lmda_CTM, solver, ctm_distance, compare_zero, size, rho, discretization, smin, smax, edm_rho,
t_sm_dists, disc_steps, init_steps, ODE_N, bs, coupling_bs, lr, use_pcgrad, ema_decay, n_grad_accum, offline, double_iter, t_ctm_dists,
nc, model_channels, num_blocks, dropout, param, v_iter, s_iter, b_iter, FID_iter, FID_bs, n_FID, n_viz, n_save, base_dir, ckpt_name):
size = max(size,32)
sampler = Sampler(datasets, data_roots, nc, size, X1_eps_std, coupling, coupling_bs, bs)
disc = get_discretization(discretization,disc_steps,smin=smin,smax=smax,rho=edm_rho,t_sm_dists=t_sm_dists,t_ctm_dists=t_ctm_dists)
ctm_dist, l2_loss = get_distance(ctm_distance), get_distance('l2')
solver = get_solver(solver,disc)
vars[1] += X1_eps_std**2
net = SongUNet(vars=vars, param=param, discretization=disc, img_resolution=size, in_channels=nc, out_channels=nc,
num_blocks=num_blocks, dropout=dropout, model_channels=model_channels).cuda()
opt_DSM = optim.Adam(net.parameters(), lr=lr/(lmda_CTM+1))
opt_CTM = optim.Adam(net.parameters(), lr=lr)
net_ema = copy.deepcopy(net)
avgmeter = AverageMeter(window=125,
loss_names=['DSM Loss', 'CTM Loss', 'PSNR G', 'PSNR g', 'SSIM G', 'SSIM g', 'LPIPS G', 'LPIPS g'],
yscales=['log','log','linear', 'linear','linear', 'linear','linear', 'linear'])
loss_dir = os.path.join(base_dir, 'losses')
sample_B_dir = os.path.join(base_dir, 'samples_B')
sample_F_dir = os.path.join(base_dir, 'samples_F')
ckpt_dir = os.path.join(base_dir, 'ckpts')
if ckpt_name:
print('\nLoading state from [{}]\n'.format(ckpt_name))
ckpt = torch.load(os.path.join(ckpt_dir, ckpt_name))
X0_eval = ckpt['X0_eval'].cuda()
X1_eval = ckpt['X1_eval'].cuda()
net.load_state_dict(ckpt['net'])
opt_DSM.load_state_dict(ckpt['opt_DSM'])
opt_CTM.load_state_dict(ckpt['opt_CTM'])
net_ema.load_state_dict(ckpt['net_ema'])
avgmeter.load_state_dict(ckpt['avgmeter'])
loss_DSM = avgmeter.losses['DSM Loss'][-1]
loss_CTM = avgmeter.losses['CTM Loss'][-1]
best_PSNR = ckpt['best_PSNR']
else:
X0_eval = torch.cat([sampler.sample_X0() for _ in range(math.ceil(n_viz/bs))], dim=0)[:n_viz]
X1_eval = torch.cat([sampler.sample_X1() for _ in range(math.ceil(n_viz/bs))], dim=0)[:n_viz]
best_PSNR = 0