def load_model(self, path):
# torch.load最后返回的是一个dict，里面包含了保存模型时的一些参数和模型
checkpoint = torch.load(path, map_location='cpu')
self.generator = self.get_generator(checkpoint)
# AttrDict是根据参数中的dict内容生成一个更加方便访问的dict实例
self.args = AttrDict(checkpoint['args'])
train_path = get_dset_path(self.args.dataset_name, "train")
test_path = get_dset_path(self.args.dataset_name, "test")
self.args.batch_size = 1
_, self.loader = data_loader(self.args, train_path)
_, self.test_loader = data_loader(self.args, test_path)
self.metrics_val = checkpoint['metrics_val']
self.metrics_train = checkpoint['metrics_train']
def main(args):
if os.path.isdir(args.model_path):
filenames = os.listdir(args.model_path)
filenames.sort()
paths = [
os.path.join(args.model_path, file_) for file_ in filenames
]
else:
paths = [args.model_path]
for path in paths:
checkpoint = torch.load(path)
generator = get_generator(checkpoint)
_args = AttrDict(checkpoint['args'])
path = get_dset_path(_args.dataset_name, args.dset_type)
_, loader = data_loader(_args, path)
ade, fde = evaluate(_args, loader, generator, args.num_samples)
print('Dataset: {}, Pred Len: {}, ADE: {:.2f}, FDE: {:.2f}'.format(
_args.dataset_name, _args.pred_len, ade, fde))
def main(args):
global global_step
global_step = 13086
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_num
print(args)
train_path = get_dset_path(args.dataset_name, 'train')
val_path = get_dset_path(args.dataset_name, 'val')
long_dtype, float_dtype = get_dtypes(args)
logger.info("Initializing train dataset")
train_dset, train_loader = data_loader(args, train_path)
logger.info("Initializing val dataset")
_, val_loader = data_loader(args, val_path)
iterations_per_epoch = len(train_dset) / args.batch_size
if args.num_epochs:
args.num_iterations = int(iterations_per_epoch * args.num_epochs)
logger.info(
'There are {} iterations per epoch'.format(iterations_per_epoch))
linear = LinearModel(seq_len=args.pred_len, use_cuda=args.use_gpu)
linear.apply(init_weights)
linear.type(float_dtype).train()
logger.info('Here is the linear:')
logger.info(linear)
optimizer = optim.Adam(linear.parameters(), lr=args.g_learning_rate)
# Maybe restore from checkpoint
restore_path = None
if args.checkpoint_start_from is not None:
restore_path = args.checkpoint_start_from
elif args.restore_from_checkpoint == 1:
restore_path = os.path.join(args.output_dir,
'%s_with_model.pt' % args.checkpoint_name)
if restore_path is not None and os.path.isfile(restore_path):
logger.info('Restoring from checkpoint {}'.format(restore_path))
checkpoint = torch.load(restore_path)
linear.load_state_dict(checkpoint['state'])
optimizer.load_state_dict(checkpoint['optim_state'])
t = checkpoint['counters']['t']
epoch = checkpoint['counters']['epoch']
checkpoint['restore_ts'].append(t)
else:
# Starting from scratch, so initialize checkpoint dataset structure
t, epoch = 0, 0
checkpoint = {
'args': args.__dict__,
'losses': defaultdict(list),
'losses_ts': [],
'metrics_val': defaultdict(list),
'metrics_train': defaultdict(list),
'sample_ts': [],
'restore_ts': [],
'norm': [],
'counters': {
't': None,
'epoch': None,
},
'state': None,
'optim_state': None,
'best_state': None,
'best_t': None,
'layer1.weight': None,
'layer2.weight': None,
"layer1.bias": None,
"layer2.bias": None
}
t0 = None
while t < args.num_iterations:
gc.collect()
epoch += 1
logger.info('Starting epoch {}'.format(epoch))
for batch in train_loader:
if args.timing == 1:
torch.cuda.synchronize()
t1 = time.time()
# Decide whether to use the batch for stepping on discriminator or
# generator; an iteration consists of args.d_steps steps on the
# discriminator followed by args.g_steps steps on the generator.
generator_step(args, batch, linear, optimizer)
# checkpoint['norm_g'].append(
# get_total_norm(lstm.parameters())
# )
if args.timing == 1:
if t0 is not None:
logger.info('Interation {} took {}'.format(
t - 1,
time.time() - t0))
t0 = time.time()
# Maybe save loss
if t % args.print_every == 0:
logger.info('t = {} / {}'.format(t + 1, args.num_iterations))
# for k, v in sorted(losses.items()):
# logger.info(' [D] {}: {:.7f}'.format(k, v))
# checkpoint['losses'][k].append(v)
# checkpoint['losses_ts'].append(t)
# Maybe save a checkpoint
if t > 0 and t % args.checkpoint_every == 0:
checkpoint['counters']['t'] = t
checkpoint['counters']['epoch'] = epoch
checkpoint['sample_ts'].append(t)
# Check stats on the validation set
logger.info('Checking stats on val ...')
metrics_val = check_accuracy(args,
val_loader,
linear,
is_train=False)
logger.info('Checking stats on train ...')
metrics_train = check_accuracy(args,
train_loader,
linear,
limit=True,
is_train=True)
for k, v in sorted(metrics_val.items()):
logger.info(' [val] {}: {:.7f}'.format(k, v))
checkpoint['metrics_val'][k].append(v)
for k, v in sorted(metrics_train.items()):
logger.info(' [train] {}: {:.7f}'.format(k, v))
checkpoint['metrics_train'][k].append(v)
min_ade = min(checkpoint['metrics_val']['ade'])
if metrics_val['ade'] == min_ade:
logger.info('New low for avg_disp_error')
checkpoint['best_t'] = t
checkpoint['best_state'] = linear.state_dict()
# Save another checkpoint with model weights and
# optimizer state
checkpoint['state'] = linear.state_dict()
checkpoint['optim_state'] = optimizer.state_dict()
checkpoint_path = os.path.join(
args.output_dir, '%s_with_model.pt' % args.checkpoint_name)
logger.info('Saving checkpoint to {}'.format(checkpoint_path))
torch.save(checkpoint, checkpoint_path)
logger.info('Done.')
t += 1
if t >= args.num_iterations:
break
def main(args):
global t
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_num
train_path = get_dset_path(args.dataset_name, 'train')
val_path = get_dset_path(args.dataset_name, 'val')
long_dtype, float_dtype = get_dtypes(args)
logger.info("Initializing train dataset")
train_dset, train_loader = data_loader(args, train_path)
logger.info("Initializing val dataset")
_, val_loader = data_loader(args, val_path)
iterations_per_epoch = len(train_dset) / args.batch_size / args.d_steps
if args.num_epochs:
args.num_iterations = int(iterations_per_epoch * args.num_epochs)
logger.info(
'There are {} iterations per epoch'.format(iterations_per_epoch))
generator = TrajectoryGenerator(
obs_len=args.obs_len,
pred_len=args.pred_len,
embedding_dim=args.embedding_dim,
encoder_h_dim=args.encoder_h_dim_g,
decoder_h_dim=args.decoder_h_dim_g,
mlp_dim=args.mlp_dim,
num_layers=args.num_layers,
noise_dim=args.noise_dim,
noise_type=args.noise_type,
noise_mix_type=args.noise_mix_type,
pooling_type=args.pooling_type,
pool_every_timestep=args.pool_every_timestep,
dropout=args.dropout,
bottleneck_dim=args.bottleneck_dim,
neighborhood_size=args.neighborhood_size,
grid_size=args.grid_size,
batch_norm=args.batch_norm,
use_cuda=args.use_gpu)
generator.apply(init_weights)
generator.type(float_dtype).train()
logger.info('Here is the generator:')
logger.info(generator)
discriminator = TrajectoryDiscriminator(obs_len=args.obs_len,
pred_len=args.pred_len,
embedding_dim=args.embedding_dim,
h_dim=args.encoder_h_dim_d,
mlp_dim=args.mlp_dim,
num_layers=args.num_layers,
dropout=args.dropout,
batch_norm=args.batch_norm,
d_type=args.d_type,
use_cuda=args.use_gpu)
discriminator.apply(init_weights)
discriminator.type(float_dtype).train()
logger.info('Here is the discriminator:')
logger.info(discriminator)
g_loss_fn = gan_g_loss
d_loss_fn = gan_d_loss
# optimizer_g = optim.Adam([{'params': generator.parameters(), 'initial_lr': args.g_learning_rate}], lr=args.g_learning_rate)
# optimizer_d = optim.Adam([{'params': discriminator.parameters(), 'initial_lr': args.d_learning_rate}], lr=args.d_learning_rate)
optimizer_g = optim.Adam(params=generator.parameters(),
lr=args.g_learning_rate)
optimizer_d = optim.Adam(params=discriminator.parameters(),
lr=args.d_learning_rate)
lr_scheduler_g = ReduceLROnPlateau(optimizer_g,
threshold=1e-4,
patience=100,
factor=8e-1,
min_lr=1e-5,
verbose=True)
lr_scheduler_d = ReduceLROnPlateau(optimizer_d,
threshold=1e-4,
patience=100,
factor=8e-1,
min_lr=1e-5,
verbose=True)
# Maybe restore from checkpoint
restore_path = None
if args.checkpoint_start_from is not None:
restore_path = args.checkpoint_start_from
elif args.restore_from_checkpoint == 1:
restore_path = os.path.join(args.output_dir,
'%s_with_model.pt' % args.checkpoint_name)
if restore_path is not None and os.path.isfile(restore_path):
logger.info('Restoring from checkpoint {}'.format(restore_path))
checkpoint = torch.load(restore_path)
generator.load_state_dict(checkpoint['g_state'])
discriminator.load_state_dict(checkpoint['d_state'])
optimizer_g.load_state_dict(checkpoint['g_optim_state'])
optimizer_d.load_state_dict(checkpoint['d_optim_state'])
lr_scheduler_g.load_state_dict(checkpoint['lr_scheduler_g_state'])
lr_scheduler_d.load_state_dict(checkpoint['lr_scheduler_d_state'])
# t = checkpoint['counters']['t']
# epoch = checkpoint['counters']['epoch']
t, epoch = 0, 0
checkpoint['restore_ts'].append(t)
else:
# Starting from scratch, so initialize checkpoint dataset structure
t, epoch = 0, 0
checkpoint = {
'args': args.__dict__,
'G_losses': defaultdict(list),
'D_losses': defaultdict(list),
'losses_ts': [],
'metrics_val': defaultdict(list),
'metrics_train': defaultdict(list),
'sample_ts': [],
'restore_ts': [],
'norm_g': [],
'norm_d': [],
'counters': {
't': None,
'epoch': None,
},
'g_state': None,
'g_optim_state': None,
'd_state': None,
'd_optim_state': None,
'g_best_state': None,
'd_best_state': None,
'lr_scheduler_g_state': None,
'lr_scheduler_d_state': None,
'best_t': None,
'g_best_nl_state': None,
'd_best_state_nl': None,
'best_t_nl': None,
}
# scheduler_g = optim.lr_scheduler.StepLR(optimizer_g, step_size=1000, gamma=0.5, last_epoch=(epoch if epoch != 0 else -1))
# scheduler_d = optim.lr_scheduler.StepLR(optimizer_d, step_size=1000, gamma=0.5, last_epoch=(epoch if epoch != 0 else -1))
t0 = None
while t < args.num_iterations:
gc.collect()
d_steps_left = args.d_steps if args.discriminator_weight > 0 else 0
g_steps_left = args.g_steps
epoch += 1
logger.info('Starting epoch {}'.format(epoch))
for batch in train_loader:
if args.timing == 1:
torch.cuda.synchronize()
t1 = time.time()
# Decide whether to use the batch for stepping on discriminator or
# generator; an iteration consists of args.d_steps steps on the
# discriminator followed by args.g_steps steps on the generator.
if d_steps_left > 0:
step_type = 'd'
losses_d = discriminator_step(args, batch, generator,
discriminator, d_loss_fn,
optimizer_d)
checkpoint['norm_d'].append(
get_total_norm(discriminator.parameters()))
d_steps_left -= 1
elif g_steps_left > 0:
step_type = 'g'
losses_g = generator_step(args, batch, generator,
discriminator, g_loss_fn,
optimizer_g)
checkpoint['norm_g'].append(
get_total_norm(generator.parameters()))
g_steps_left -= 1
if args.timing == 1:
torch.cuda.synchronize()
t2 = time.time()
logger.info('{} step took {}'.format(step_type, t2 - t1))
# Skip the rest if we are not at the end of an iteration
if d_steps_left > 0 or g_steps_left > 0:
continue
if args.timing == 1:
if t0 is not None:
logger.info('Interation {} took {}'.format(
t - 1,
time.time() - t0))
t0 = time.time()
# Maybe save loss
if t % args.print_every == 0:
logger.info('t = {} / {}'.format(t + 1, args.num_iterations))
if args.discriminator_weight > 0:
for k, v in sorted(losses_d.items()):
logger.info(' [D] {}: {:.7f}'.format(k, v))
checkpoint['D_losses'][k].append(v)
for k, v in sorted(losses_g.items()):
logger.info(' [G] {}: {:.7f}'.format(k, v))
checkpoint['G_losses'][k].append(v)
checkpoint['losses_ts'].append(t)
# Maybe save a checkpoint
if t > 0 and t % args.checkpoint_every == 0:
checkpoint['counters']['t'] = t
checkpoint['counters']['epoch'] = epoch
checkpoint['sample_ts'].append(t)
# Check stats on the validation set
logger.info('Checking stats on val ...')
metrics_val = check_accuracy(args,
val_loader,
generator,
discriminator,
d_loss_fn,
lr_scheduler_g,
lr_scheduler_d,
is_train=False)
logger.info('Checking stats on train ...')
metrics_train = check_accuracy(args,
train_loader,
generator,
discriminator,
d_loss_fn,
lr_scheduler_g,
lr_scheduler_d,
limit=True,
is_train=True)
for k, v in sorted(metrics_val.items()):
logger.info(' [val] {}: {:.7f}'.format(k, v))
checkpoint['metrics_val'][k].append(v)
for k, v in sorted(metrics_train.items()):
logger.info(' [train] {}: {:.7f}'.format(k, v))
checkpoint['metrics_train'][k].append(v)
min_ade = min(checkpoint['metrics_val']['ade'])
if metrics_val['ade'] == min_ade:
logger.info('New low for avg_disp_error')
checkpoint['best_t'] = t
checkpoint['g_best_state'] = generator.state_dict()
checkpoint['d_best_state'] = discriminator.state_dict()
# Save another checkpoint with model weights and
# optimizer state
checkpoint['g_state'] = generator.state_dict()
checkpoint['g_optim_state'] = optimizer_g.state_dict()
checkpoint['lr_scheduler_g_state'] = lr_scheduler_g.state_dict(
)
checkpoint['d_state'] = discriminator.state_dict()
checkpoint['d_optim_state'] = optimizer_d.state_dict()
checkpoint['lr_scheduler_d_state'] = lr_scheduler_d.state_dict(
)
checkpoint_path = os.path.join(
args.output_dir, '%s_with_model.pt' % args.checkpoint_name)
logger.info('Saving checkpoint to {}'.format(checkpoint_path))
torch.save(checkpoint, checkpoint_path)
logger.info('Done.')
# Save a checkpoint with no model weights by making a shallow
# copy of the checkpoint excluding some items
checkpoint_path = os.path.join(
args.output_dir, '%s_no_model.pt' % args.checkpoint_name)
logger.info('Saving checkpoint to {}'.format(checkpoint_path))
key_blacklist = [
'g_state', 'g_best_state', 'g_best_nl_state',
'g_optim_state'
]
small_checkpoint = {}
for k, v in checkpoint.items():
if k not in key_blacklist:
small_checkpoint[k] = v
torch.save(small_checkpoint, checkpoint_path)
logger.info('Done.')
t += 1
d_steps_left = args.d_steps
g_steps_left = args.g_steps
if t >= args.num_iterations:
break
