train_MPER.py

import torch, numpy, argparse, pdb, os, time, math, random, re
import utils
from dataloader import DataLoader
from torch.autograd import Variable
import torch.nn.functional as F
import torch.optim as optim
import models, planning
import importlib

torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

#################################################
# Train an action-conditional forward model
#################################################

parser = argparse.ArgumentParser()
# data params
parser.add_argument('-seed', type=int, default=1)
parser.add_argument('-dataset', type=str, default='i80')
parser.add_argument('-v', type=int, default=4)
parser.add_argument('-model', type=str, default='fwd-cnn')
parser.add_argument('-policy', type=str, default='policy-gauss')
parser.add_argument('-data_dir', type=str, default='/misc/vlgscratch4/LecunGroup/nvidia-collab/data/')
parser.add_argument('-model_dir', type=str, default='/misc/vlgscratch4/LecunGroup/nvidia-collab/models_v11/')
parser.add_argument('-ncond', type=int, default=20)
parser.add_argument('-npred', type=int, default=16)
parser.add_argument('-batch_size', type=int, default=8)
parser.add_argument('-layers', type=int, default=3)
parser.add_argument('-nfeature', type=int, default=256)
parser.add_argument('-n_hidden', type=int, default=256)
parser.add_argument('-beta', type=float, default=0.0, help='weight coefficient of prior loss')
parser.add_argument('-p_dropout', type=float, default=0.0, help='set z=0 with this probability')
parser.add_argument('-dropout', type=float, default=0.0, help='regular dropout')
parser.add_argument('-nz', type=int, default=2)
parser.add_argument('-n_mixture', type=int, default=10)
parser.add_argument('-context_dim', type=int, default=2)
parser.add_argument('-actions_subsample', type=int, default=4)
parser.add_argument('-lrt', type=float, default=0.0001)
parser.add_argument('-grad_clip', type=float, default=1.0)
parser.add_argument('-epoch_size', type=int, default=500)
parser.add_argument('-curriculum_length', type=int, default=16)
parser.add_argument('-zeroact', type=int, default=0)
parser.add_argument('-warmstart', type=int, default=0)
parser.add_argument('-targetprop', type=int, default=0)
parser.add_argument('-loss_c', type=int, default=0)
parser.add_argument('-lambda_c', type=float, default=0.0)
parser.add_argument('-lambda_h', type=float, default=0.0)
parser.add_argument('-lambda_lane', type=float, default=0.1)
parser.add_argument('-lrt_traj', type=float, default=0.5)
parser.add_argument('-niter_traj', type=int, default=20)
parser.add_argument('-gamma', type=float, default=1.0)
#parser.add_argument('-mfile', type=str, default='model=fwd-cnn-vae-fp-layers=3-bsize=64-ncond=20-npred=20-lrt=0.0001-nfeature=256-dropout=0.1-nz=32-beta=1e-06-zdropout=0.5-gclip=5.0-warmstart=1-seed=1.step200000.model')
parser.add_argument('-mfile', type=str, default='model=fwd-cnn-layers=3-bsize=64-ncond=20-npred=20-lrt=0.0001-nfeature=256-dropout=0.1-gclip=5.0-warmstart=0-seed=1.step200000.model')
parser.add_argument('-load_model_file', type=str, default='')
parser.add_argument('-combine', type=str, default='add')
parser.add_argument('-debug', action='store_true')
parser.add_argument('-test_only', type=int, default=0)
opt = parser.parse_args()

opt.n_inputs = 4
opt.n_actions = 2
opt.height = 117
opt.width = 24
opt.h_height = 14
opt.h_width = 3
opt.hidden_size = opt.nfeature*opt.h_height*opt.h_width


os.system('mkdir -p ' + opt.model_dir + '/policy_networks/')

random.seed(opt.seed)
numpy.random.seed(opt.seed)
torch.manual_seed(opt.seed)
torch.cuda.manual_seed(opt.seed)



opt.model_file = f'{opt.model_dir}/policy_networks/'

opt.model_file += f'mbil-{opt.policy}-nfeature={opt.nfeature}-npred={opt.npred}-lambdac={opt.lambda_c}-gamma={opt.gamma}-seed={opt.seed}'
if 'vae' in opt.mfile:
    opt.model_file += f'-model=vae'
    model_type = 'vae'
elif 'ten' in opt.mfile:
    opt.model_file += f'-model=ten'
    model_type = 'ten'
elif 'model=fwd-cnn-layers' in opt.mfile:
    model_type = 'det'
    opt.model_file += '-deterministic'
if 'zdropout=0.5' in opt.mfile:
    opt.model_file += '-zdropout=0.5'
elif 'zdropout=0.0' in opt.mfile:
    opt.model_file += '-zdropout=0.0'


print(f'[will save as: {opt.model_file}]')

if os.path.isfile(opt.model_file + '.model') and False:
    print('[found previous checkpoint, loading]')
    checkpoint = torch.load(opt.model_file + '.model')
    model = checkpoint['model']
    optimizer = optim.Adam(model.policy_net.parameters(), opt.lrt)
    optimizer.load_state_dict(checkpoint['optimizer'])
    n_iter = checkpoint['n_iter']
    if opt.test_only == 0:
        utils.log(opt.model_file + '.log', '[resuming from checkpoint]')
else:
    # load the model
    model = torch.load(opt.model_dir + opt.mfile)
    if type(model) is dict: model = model['model']
    model.create_policy_net(opt)
    model.opt.actions_subsample = opt.actions_subsample
    optimizer = optim.Adam(model.policy_net.parameters(), opt.lrt)
    n_iter = 0
#    stats = torch.load('/misc/vlgscratch4/LecunGroup/nvidia-collab/traffic-data-atcold/data_i80_v0/data_stats.pth')
#    model.stats=stats
    if 'ten' in opt.mfile:
        pzfile = opt.model_dir + opt.mfile + '.pz'
        p_z = torch.load(pzfile)
        model.p_z = p_z


if opt.actions_subsample == -1:
    opt.context_dim = 0

model.intype('gpu')
model.cuda()
model.disable_unet=False


print('[loading data]')
dataloader = DataLoader(None, opt, opt.dataset)


# training and testing functions. We will compute several losses:
# loss_i: images
# loss_s: states
# loss_c: costs
# loss_p: prior (optional)

def compute_loss(targets, predictions, gamma=1.0, r=True):
    target_images, target_states, target_costs = targets
    pred_images, pred_states, pred_costs, loss_p = predictions
    loss_i = F.mse_loss(pred_images, target_images, reduce=False).mean(4).mean(3).mean(2)
    loss_s = F.mse_loss(pred_states, target_states, reduce=False).mean(2)
#    loss_c = F.mse_loss(pred_costs, target_costs, reduce=False).mean(2)
    if gamma < 1.0:
        loss_i *= gamma_mask
        loss_s *= gamma_mask
        loss_c *= gamma_mask
    return loss_i.mean(), loss_s.mean(), Variable(torch.zeros(1)), loss_p.mean()

def train(nbatches, npred):
    gamma_mask = torch.Tensor([opt.gamma**t for t in range(npred)]).view(1, -1).cuda()
    model.eval()
    model.policy_net.train()
    total_loss_i, total_loss_s, total_loss_c, total_loss_policy, total_loss_p, n_updates = 0, 0, 0, 0, 0, 0
    for i in range(nbatches):
        optimizer.zero_grad()
        inputs, actions, targets, _, _ = dataloader.get_batch_fm('train', npred)
        inputs = utils.make_variables(inputs)
        targets = utils.make_variables(targets)
        actions = Variable(actions)
        pred, _ = planning.train_policy_net_mper(model, inputs, targets, dropout=opt.p_dropout, model_type=model_type)
        loss_i, loss_s, loss_c_, loss_p = compute_loss(targets, pred)
#        proximity_cost, lane_cost = pred[2][:, :, 0], pred[2][:, :, 1]
#        proximity_cost = proximity_cost * Variable(gamma_mask)
#        lane_cost = lane_cost * Variable(gamma_mask)
#        loss_c = proximity_cost.mean() + opt.lambda_lane * lane_cost.mean()
        loss_policy = loss_i + loss_s + opt.lambda_h*loss_p
        if opt.loss_c == 1:
            loss_policy += loss_c_
        if not math.isnan(loss_policy.item()):
            loss_policy.backward()
            torch.nn.utils.clip_grad_norm(model.policy_net.parameters(), opt.grad_clip)
            optimizer.step()
            total_loss_i += loss_i.item()
            total_loss_s += loss_s.item()
            total_loss_p += loss_p.item()
            total_loss_policy += loss_policy.item()
            n_updates += 1
        else:
            print('warning, NaN')

        del inputs, actions, targets, pred

    total_loss_i /= n_updates
    total_loss_s /= n_updates
    total_loss_c /= n_updates
    total_loss_policy /= n_updates
    total_loss_p /= n_updates
    return total_loss_i, total_loss_s, total_loss_c, total_loss_policy, total_loss_p


def test(nbatches, npred):
    gamma_mask = torch.Tensor([opt.gamma**t for t in range(npred)]).view(1, -1).cuda()
    model.eval()
    total_loss_i, total_loss_s, total_loss_c, total_loss_policy, total_loss_p, n_updates = 0, 0, 0, 0, 0, 0
    for i in range(nbatches):
        inputs, actions, targets, _, _ = dataloader.get_batch_fm('test', npred)
        inputs = utils.make_variables(inputs)
        targets = utils.make_variables(targets)
        actions = Variable(actions)
        pred, pred_actions = planning.train_policy_net_mper(model, inputs, targets, targetprop = opt.targetprop, dropout=0.0, model_type = model_type)
        loss_i, loss_s, loss_c_, loss_p = compute_loss(targets, pred)
        loss_policy = loss_i + loss_s
        if opt.loss_c == 1:
            loss_policy += loss_c_
        if not math.isnan(loss_policy.item()):
            total_loss_i += loss_i.item()
            total_loss_s += loss_s.item()
            total_loss_p += loss_p.item()
            total_loss_policy += loss_policy.item()
            n_updates += 1
        del inputs, actions, targets, pred

    total_loss_i /= n_updates
    total_loss_s /= n_updates
    total_loss_c /= n_updates
    total_loss_policy /= n_updates
    total_loss_p /= n_updates
    return total_loss_i, total_loss_s, total_loss_c, total_loss_policy, total_loss_p


# set by hand to fit on 12gb GPU
def get_batch_size(npred):
    if npred <= 15:
        return 64
    elif npred <= 50:
        return 32
    elif npred <= 100:
        return 16
    elif npred <= 200:
        return 8
    elif npred <= 400:
        return 4
    elif npred <= 800:
        return 2
    else:
        return 1


if opt.test_only == 1:
    print('[testing]')
    valid_losses = test(10, 200)
else:
    print('[training]')
    utils.log(opt.model_file + '.log', f'[job name: {opt.model_file}]')
    npred = opt.npred if opt.npred != -1 else 16

    for i in range(500):
        bsize = get_batch_size(npred)
        dataloader.opt.batch_size = bsize
        train_losses = train(opt.epoch_size, npred)
        valid_losses = test(int(opt.epoch_size / 2), npred)
        n_iter += opt.epoch_size
        model.intype('cpu')
        torch.save({'model': model,
                    'optimizer': optimizer.state_dict(),
                    'opt': opt,
                    'npred': npred,
                    'n_iter': n_iter},
                   opt.model_file + '.model')
        model.intype('gpu')
        log_string = f'step {n_iter} | npred {npred} | bsize {bsize} | esize {opt.epoch_size} | '
        log_string += utils.format_losses(train_losses[0], train_losses[1], split='train')
        log_string += utils.format_losses(valid_losses[0], valid_losses[1], split='valid')
        print(log_string)
        utils.log(opt.model_file + '.log', log_string)
        if i > 0 and(i % opt.curriculum_length == 0) and (opt.npred == -1) and npred < 400:
            npred += 8