conf = ModelConfig() train, val, test = VG.splits(num_val_im=conf.val_size, filter_non_overlap=False) set_name = '' if conf.test: val = test set_name = 'test' elif conf.val: val = val set_name = 'val' else: val = train set_name = 'train' train_loader, val_loader = VGDataLoader.splits(train, val, mode='rel', batch_size=conf.batch_size, num_workers=conf.num_workers, num_gpus=conf.num_gpus) detector = RelModel(classes=train.ind_to_classes, rel_classes=train.ind_to_predicates, num_gpus=conf.num_gpus, mode=conf.mode, require_overlap_det=False, use_resnet=conf.use_resnet, order=conf.order, nl_edge=conf.nl_edge, nl_obj=conf.nl_obj, hidden_dim=conf.hidden_dim, use_proposals=conf.use_proposals, pass_in_obj_feats_to_decoder=conf.pass_in_obj_feats_to_decoder, pass_in_obj_feats_to_edge=conf.pass_in_obj_feats_to_edge, pooling_dim=conf.pooling_dim, rec_dropout=conf.rec_dropout, use_bias=conf.use_bias, use_tanh=conf.use_tanh, limit_vision=conf.limit_vision )
val.ids = val.ids[:conf.val_size]
train.ids = train.ids
train_loader, val_loader = CocoDataLoader.splits(
train,
val,
batch_size=conf.batch_size,
num_workers=conf.num_workers,
num_gpus=detector_num_gpus)
else:
train, val, _ = VG.splits(num_val_im=conf.val_size,
filter_non_overlap=False,
filter_empty_rels=False,
use_proposals=conf.use_proposals)
train_loader, val_loader = VGDataLoader.splits(
train,
val,
batch_size=conf.batch_size,
num_workers=conf.num_workers,
num_gpus=detector_num_gpus)
detector = ObjectDetector(
classes=train.ind_to_classes,
num_gpus=detector_num_gpus,
mode='refinerels' if not conf.use_proposals else 'proposals',
use_resnet=conf.use_resnet)
# print(detector)
# os._exit(0)
detector.cuda()
# Note: if you're doing the stanford setup, you'll need to change this to freeze the lower layers
if conf.use_proposals:
for n, param in detector.named_parameters():
def main():
fname = os.path.join(conf.save_dir, 'train_losses.csv')
train_f = open(fname, 'w')
train_f.write(
'iter,class_loss,rel_loss,total,recall20,recall50,recall100,recall20_con,recall50_con,recall100_con\n'
)
train_f.flush()
fname = os.path.join(conf.save_dir, 'val_losses.csv')
val_f = open(fname, 'w')
val_f.write(
'recall20,recall50,recall100,recall20_con,recall50_con,recall100_con\n'
)
val_f.flush()
train, val, _ = VG.splits(num_val_im=conf.val_size,
filter_duplicate_rels=True,
use_proposals=conf.use_proposals,
filter_non_overlap=conf.mode == 'sgdet')
train_loader, val_loader = VGDataLoader.splits(
train,
val,
mode='rel',
batch_size=conf.batch_size,
num_workers=conf.num_workers,
num_gpus=conf.num_gpus)
detector = RelModel(
classes=train.ind_to_classes,
rel_classes=train.ind_to_predicates,
num_gpus=conf.num_gpus,
mode=conf.mode,
require_overlap_det=True,
use_resnet=conf.use_resnet,
order=conf.order,
nl_edge=conf.nl_edge,
nl_obj=conf.nl_obj,
hidden_dim=conf.hidden_dim,
use_proposals=conf.use_proposals,
pass_in_obj_feats_to_decoder=conf.pass_in_obj_feats_to_decoder,
pass_in_obj_feats_to_edge=conf.pass_in_obj_feats_to_edge,
pooling_dim=conf.pooling_dim,
rec_dropout=conf.rec_dropout,
use_bias=conf.use_bias,
use_tanh=conf.use_tanh,
limit_vision=conf.limit_vision,
lml_topk=conf.lml_topk,
lml_softmax=conf.lml_softmax,
entr_topk=conf.entr_topk,
ml_loss=conf.ml_loss)
# Freeze the detector
for n, param in detector.detector.named_parameters():
param.requires_grad = False
print(print_para(detector), flush=True)
ckpt = torch.load(conf.ckpt)
if conf.ckpt.split('-')[-2].split('/')[-1] == 'vgrel':
print("Loading EVERYTHING")
start_epoch = ckpt['epoch']
if not optimistic_restore(detector, ckpt['state_dict']):
start_epoch = -1
# optimistic_restore(
# detector.detector,
# torch.load('checkpoints/vgdet/vg-28.tar')['state_dict']
# )
else:
start_epoch = -1
optimistic_restore(detector.detector, ckpt['state_dict'])
detector.roi_fmap[1][0].weight.data.copy_(
ckpt['state_dict']['roi_fmap.0.weight'])
detector.roi_fmap[1][3].weight.data.copy_(
ckpt['state_dict']['roi_fmap.3.weight'])
detector.roi_fmap[1][0].bias.data.copy_(
ckpt['state_dict']['roi_fmap.0.bias'])
detector.roi_fmap[1][3].bias.data.copy_(
ckpt['state_dict']['roi_fmap.3.bias'])
detector.roi_fmap_obj[0].weight.data.copy_(
ckpt['state_dict']['roi_fmap.0.weight'])
detector.roi_fmap_obj[3].weight.data.copy_(
ckpt['state_dict']['roi_fmap.3.weight'])
detector.roi_fmap_obj[0].bias.data.copy_(
ckpt['state_dict']['roi_fmap.0.bias'])
detector.roi_fmap_obj[3].bias.data.copy_(
ckpt['state_dict']['roi_fmap.3.bias'])
detector.cuda()
print("Training starts now!")
optimizer, scheduler = get_optim(detector,
conf.lr * conf.num_gpus * conf.batch_size)
best_eval = None
for epoch in range(start_epoch + 1, start_epoch + 1 + conf.num_epochs):
rez = train_epoch(epoch, detector, train, train_loader, optimizer,
conf, train_f)
print("overall{:2d}: ({:.3f})\n{}".format(epoch,
rez.mean(1)['total'],
rez.mean(1)),
flush=True)
mAp = val_epoch(detector, val, val_loader, val_f)
scheduler.step(mAp)
if conf.save_dir is not None:
if best_eval is None or mAp > best_eval:
torch.save(
{
'epoch': epoch,
'state_dict': detector.state_dict(),
# 'optimizer': optimizer.state_dict(),
},
os.path.join(conf.save_dir, 'best-val.tar'))
best_eval = mAp
def main(args):
print(args)
check_args(args)
if not exists(args.output_dir):
os.makedirs(args.output_dir)
summary_writer = SummaryWriter(args.output_dir)
if args.coco:
train, val = CocoDetection.splits()
val.ids = val.ids[:args.val_size]
train.ids = train.ids
train_loader, val_loader = CocoDataLoader.splits(train, val, batch_size=args.batch_size,
num_workers=args.num_workers,
num_gpus=args.num_gpus)
else:
train, val, _ = VG.splits(num_val_im=args.val_size, filter_non_overlap=False,
filter_empty_rels=False, use_proposals=args.use_proposals)
train_loader, val_loader = VGDataLoader.splits(train, val, batch_size=args.batch_size,
num_workers=args.num_workers,
num_gpus=args.num_gpus)
print(train.ind_to_classes)
os._exit(0)
all_in_one_model = neural_motifs_sg2im_model(args, train.ind_to_classes)
# Freeze the detector
for n, param in all_in_one_model.detector.named_parameters():
param.requires_grad = False
all_in_one_model.cuda()
gan_g_loss, gan_d_loss = get_gan_losses(args.gan_loss_type)
t, epoch, checkpoint = all_in_one_model.t, all_in_one_model.epoch, all_in_one_model.checkpoint
while True:
if t >= args.num_iterations:
break
epoch += 1
print('Starting epoch %d' % epoch)
for step, batch in enumerate(tqdm(train_loader, desc='Training Epoch %d' % epoch, total=len(train_loader))):
if t == args.eval_mode_after:
print('switching to eval mode')
all_in_one_model.model.eval()
all_in_one_model.optimizer = optim.Adam(all_in_one_model.parameters(), lr=args.learning_rate)
t += 1
with timeit('forward', args.timing):
result = all_in_one_model[batch]
imgs, imgs_pred, objs, g_scores_fake_crop, g_obj_scores_fake_crop, g_scores_fake_img, \
d_scores_fake_crop, d_obj_scores_fake_crop, d_scores_real_crop, d_obj_scores_real_crop, \
d_scores_fake_img, d_scores_real_img = result.imgs, result.imgs_pred, result.objs, \
result.g_scores_fake_crop, result.g_obj_scores_fake_crop, result.g_scores_fake_img, \
result.d_scores_fake_crop, result.d_obj_scores_fake_crop, result.d_scores_real_crop, \
result.d_obj_scores_real_crop, result.d_scores_fake_img, result.d_scores_real_img
with timeit('loss', args.timing):
total_loss, losses = calculate_model_losses(
args, imgs, imgs_pred)
if all_in_one_model.obj_discriminator is not None:
total_loss = add_loss(total_loss, F.cross_entropy(g_obj_scores_fake_crop, objs), losses, 'ac_loss',
args.ac_loss_weight)
weight = args.discriminator_loss_weight * args.d_obj_weight
total_loss = add_loss(total_loss, gan_g_loss(g_scores_fake_crop), losses,
'g_gan_obj_loss', weight)
if all_in_one_model.img_discriminator is not None:
weight = args.discriminator_loss_weight * args.d_img_weight
total_loss = add_loss(total_loss, gan_g_loss(g_scores_fake_img), losses,
'g_gan_img_loss', weight)
losses['total_loss'] = total_loss.item()
if not math.isfinite(losses['total_loss']):
print('WARNING: Got loss = NaN, not backpropping')
continue
with timeit('backward', args.timing):
all_in_one_model.optimizer.zero_grad()
total_loss.backward()
all_in_one_model.optimizer.step()
if all_in_one_model.obj_discriminator is not None:
with timeit('d_obj loss', args.timing):
d_obj_losses = LossManager()
d_obj_gan_loss = gan_d_loss(d_scores_real_crop, d_scores_fake_crop)
d_obj_losses.add_loss(d_obj_gan_loss, 'd_obj_gan_loss')
d_obj_losses.add_loss(F.cross_entropy(d_obj_scores_real_crop, objs), 'd_ac_loss_real')
d_obj_losses.add_loss(F.cross_entropy(d_obj_scores_fake_crop, objs), 'd_ac_loss_fake')
with timeit('d_obj backward', args.timing):
all_in_one_model.optimizer_d_obj.zero_grad()
d_obj_losses.total_loss.backward()
all_in_one_model.optimizer_d_obj.step()
if all_in_one_model.img_discriminator is not None:
with timeit('d_img loss', args.timing):
d_img_losses = LossManager()
d_img_gan_loss = gan_d_loss(d_scores_real_img, d_scores_fake_img)
d_img_losses.add_loss(d_img_gan_loss, 'd_img_gan_loss')
with timeit('d_img backward', args.timing):
all_in_one_model.optimizer_d_img.zero_grad()
d_img_losses.total_loss.backward()
all_in_one_model.optimizer_d_img.step()
if t % args.print_every == 0:
print('t = %d / %d' % (t, args.num_iterations))
G_loss_list = []
for name, val in losses.items():
G_loss_list.append('[%s]: %.4f' % (name, val))
checkpoint['losses'][name].append(val)
summary_writer.add_scalar("G_%s" % name, val, t)
print("G: %s" % ", ".join(G_loss_list))
checkpoint['losses_ts'].append(t)
if all_in_one_model.obj_discriminator is not None:
D_obj_loss_list = []
for name, val in d_obj_losses.items():
D_obj_loss_list.append('[%s]: %.4f' % (name, val))
checkpoint['d_losses'][name].append(val)
summary_writer.add_scalar("D_obj_%s" % name, val, t)
print("D_obj: %s" % ", ".join(D_obj_loss_list))
if all_in_one_model.img_discriminator is not None:
D_img_loss_list = []
for name, val in d_img_losses.items():
D_img_loss_list.append('[%s]: %.4f' % (name, val))
checkpoint['d_losses'][name].append(val)
summary_writer.add_scalar("D_img_%s" % name, val, t)
print("D_img: %s" % ", ".join(D_img_loss_list))
if t % args.checkpoint_every == 0:
print('checking on train')
train_results = check_model(args, train_loader, all_in_one_model)
t_losses, t_samples = train_results
checkpoint['train_samples'].append(t_samples)
checkpoint['checkpoint_ts'].append(t)
for name, images in t_samples.items():
summary_writer.add_image("train_%s" % name, images, t)
print('checking on val')
val_results = check_model(args, val_loader, all_in_one_model)
val_losses, val_samples = val_results
checkpoint['val_samples'].append(val_samples)
for name, images in val_samples.items():
summary_writer.add_image("val_%s" % name, images, t)
for k, v in val_losses.items():
checkpoint['val_losses'][k].append(v)
summary_writer.add_scalar("val_%s" % k, v, t)
checkpoint['model_state'] = all_in_one_model.model.state_dict()
if all_in_one_model.obj_discriminator is not None:
checkpoint['d_obj_state'] = all_in_one_model.obj_discriminator.state_dict()
checkpoint['d_obj_optim_state'] = all_in_one_model.optimizer_d_obj.state_dict()
if all_in_one_model.img_discriminator is not None:
checkpoint['d_img_state'] = all_in_one_model.img_discriminator.state_dict()
checkpoint['d_img_optim_state'] = all_in_one_model.optimizer_d_img.state_dict()
checkpoint['optim_state'] = all_in_one_model.optimizer.state_dict()
checkpoint['counters']['t'] = t
checkpoint['counters']['epoch'] = epoch
checkpoint_path = os.path.join(args.output_dir,
'%s_with_model.pt' % args.checkpoint_name)
print('Saving checkpoint to ', checkpoint_path)
torch.save(checkpoint, checkpoint_path)
# Save another checkpoint without any model or optim state
checkpoint_path = os.path.join(args.output_dir,
'%s_no_model.pt' % args.checkpoint_name)
key_blacklist = ['model_state', 'optim_state', 'model_best_state',
'd_obj_state', 'd_obj_optim_state', 'd_obj_best_state',
'd_img_state', 'd_img_optim_state', 'd_img_best_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)
from config import ModelConfig, FG_FRACTION, RPN_FG_FRACTION, IM_SCALE, BOX_SCALE
from torch.nn import functional as F
from lib.fpn.box_utils import bbox_loss
import torch.backends.cudnn as cudnn
from lib.pytorch_misc import optimistic_restore, clip_grad_norm
from torch.optim.lr_scheduler import ReduceLROnPlateau
os.environ['CUDA_VISIBLE_DEVICES'] = '3'
torch.cuda.set_device(0)
cudnn.benchmark = True
conf = ModelConfig()
train, val, _ = VG.splits(num_val_im=conf.val_size,
filter_non_overlap=False,
filter_empty_rels=False,
use_proposals=False)
train_loader, val_loader = VGDataLoader.splits(train,
val,
batch_size=1,
num_workers=1,
num_gpus=1)
detector = ObjectDetector(classes=train.ind_to_classes,
num_gpus=1,
mode='refinerels',
use_resnet=False)
detector.cuda()
ckpt = torch.load(conf.ckpt)
optimistic_restore(detector, ckpt['state_dict'])
detector.eval()
for batch in train_loader:
results = detector[batch]
conf = ModelConfig()
if conf.model == 'motifnet':
from lib.rel_model import RelModel
elif conf.model == 'stanford':
from lib.rel_model_stanford import RelModelStanford as RelModel
else:
raise ValueError()
train, val, _ = VG.splits(num_val_im=conf.val_size,
filter_duplicate_rels=True,
use_proposals=conf.use_proposals,
filter_non_overlap=conf.mode == 'sgdet')
train_loader, val_loader = VGDataLoader.splits(
train,
val,
mode='rel', #这个mode的作用是标识需不需要加载关系数据
batch_size=conf.batch_size,
num_workers=conf.num_workers,
num_gpus=conf.num_gpus)
detector = RelModel(
classes=train.ind_to_classes,
rel_classes=train.ind_to_predicates,
num_gpus=conf.num_gpus,
mode=conf.mode,
require_overlap_det=True, #这个conf.mode用于区分三种实验：predcls,sgcls,sgdet
use_resnet=conf.use_resnet,
order=conf.order,
nl_edge=conf.nl_edge,
nl_obj=conf.nl_obj,
hidden_dim=conf.hidden_dim,
from config import *
from dataloaders.visual_genome import VGDataLoader, VG
conf = ModelConfig()
VG.split = conf.split # set VG, GQA or VTE split here to use as a global variable
from sgg_models.rel_model_stanford import RelModelStanford
from lib.pytorch_misc import *
# Load VG data
train_loader, eval_loaders = VGDataLoader.splits(
data_dir=conf.data,
batch_size=conf.batch_size,
num_workers=conf.num_workers,
num_gpus=conf.num_gpus,
is_cuda=conf.device == 'cuda',
backbone=conf.backbone,
square_pad=True,
num_val_im=conf.val_size,
filter_non_overlap=conf.mode == 'sgdet',
exclude_left_right=conf.exclude_left_right,
min_graph_size=conf.min_graph_size,
max_graph_size=conf.max_graph_size)
# Define SGG model
sgg_model = RelModelStanford(train_data=train_loader.dataset,
mode=conf.mode,
use_bias=conf.use_bias,
test_bias=conf.test_bias,
backbone=conf.backbone,
RELS_PER_IMG=conf.rels_per_img,
edge_model=conf.edge_model)
raise ValueError()
# -- Create Tensorboard summary writer
writer = SummaryWriter(comment='_run#'+ conf.save_dir.split('/')[-1])
# -- Create dataset splits and dataset loader
train, val, _ = VG.splits(num_val_im=conf.val_size, filter_duplicate_rels=True,
use_proposals=conf.use_proposals,
filter_non_overlap=conf.mode == 'sgdet',
# -- Depth dataset parameters
use_depth=conf.load_depth,
three_channels_depth=conf.pretrained_depth)
train_loader, val_loader = VGDataLoader.splits(train, val, mode='rel',
batch_size=conf.batch_size,
num_workers=conf.num_workers,
num_gpus=conf.num_gpus,
# -- Depth dataset parameters
use_depth=conf.load_depth)
# -- Create the specified Relation-Detection model
detector = RelModel(classes=train.ind_to_classes, rel_classes=train.ind_to_predicates,
num_gpus=conf.num_gpus, mode=conf.mode, require_overlap_det=True,
use_resnet=conf.use_resnet, order=conf.order,
nl_edge=conf.nl_edge, nl_obj=conf.nl_obj, hidden_dim=conf.hidden_dim,
use_proposals=conf.use_proposals,
pass_in_obj_feats_to_decoder=conf.pass_in_obj_feats_to_decoder,
pass_in_obj_feats_to_edge=conf.pass_in_obj_feats_to_edge,
pooling_dim=conf.pooling_dim,
rec_dropout=conf.rec_dropout,
use_bias=conf.use_bias,
use_tanh=conf.use_tanh,
conf = ModelConfig()
# -- Create Tensorboard summary writer
writer = SummaryWriter(comment='_run#' + conf.save_dir.split('/')[-1])
# -- Create dataset splits and dataset loader
train, val, _ = VG.splits(num_val_im=conf.val_size, filter_duplicate_rels=True,
use_proposals=False,
filter_non_overlap=False,
# -- (ADDED) add depth related parameters
use_depth=True,
three_channels_depth=False)
train_loader, val_loader = VGDataLoader.splits(train, val, mode='det',
batch_size=conf.batch_size,
num_workers=conf.num_workers,
num_gpus=conf.num_gpus,
use_depth=True)
# -- Create Auto-Encoder model
detector = AEModel(num_gpus=conf.num_gpus, depth_model=conf.depth_model)
# -- Print model parameters
print(print_para(detector), flush=True)
# -- Load the specified checkpoint
if conf.ckpt is not None:
print("Loading the checkpoint: ", conf.ckpt)
ckpt = torch.load(conf.ckpt)
start_epoch = ckpt['epoch']
if not optimistic_restore(detector, ckpt['state_dict']):
def main():
args = 'X -m predcls -model motifnet -order leftright -nl_obj 2 -nl_edge 4 -b 6 -clip 5 -p 100 -hidden_dim 512 -pooling_dim 4096 -lr 1e-3 -ngpu 1 -test -ckpt checkpoints/vgrel-motifnet-sgcls.tar -nepoch 50 -use_bias -multipred -cache motifnet_predcls1'
sys.argv = args.split(' ')
conf = ModelConfig()
if conf.model == 'motifnet':
from lib.rel_model import RelModel
elif conf.model == 'stanford':
from lib.rel_model_stanford import RelModelStanford as RelModel
else:
raise ValueError()
train, val, test = VG.splits(
num_val_im=conf.val_size, filter_duplicate_rels=True,
use_proposals=conf.use_proposals,
filter_non_overlap=conf.mode == 'sgdet',
)
if conf.test:
val = test
train_loader, val_loader = VGDataLoader.splits(
train, val, mode='rel', batch_size=conf.batch_size,
num_workers=conf.num_workers, num_gpus=conf.num_gpus
)
detector = RelModel(
classes=train.ind_to_classes, rel_classes=train.ind_to_predicates,
num_gpus=conf.num_gpus, mode=conf.mode, require_overlap_det=True,
use_resnet=conf.use_resnet, order=conf.order,
nl_edge=conf.nl_edge, nl_obj=conf.nl_obj, hidden_dim=conf.hidden_dim,
use_proposals=conf.use_proposals,
pass_in_obj_feats_to_decoder=conf.pass_in_obj_feats_to_decoder,
pass_in_obj_feats_to_edge=conf.pass_in_obj_feats_to_edge,
pooling_dim=conf.pooling_dim,
rec_dropout=conf.rec_dropout,
use_bias=conf.use_bias,
use_tanh=conf.use_tanh,
limit_vision=conf.limit_vision
)
detector.cuda()
ckpt = torch.load(conf.ckpt)
optimistic_restore(detector, ckpt['state_dict'])
evaluator = BasicSceneGraphEvaluator.all_modes(
multiple_preds=conf.multi_pred)
mode, N = 'test.multi_pred', 20
recs = pkl.load(open('{}.{}.pkl'.format(mode, N), 'rb'))
np.random.seed(0)
# sorted_idxs = np.argsort(recs)
selected_idxs = np.random.choice(range(len(recs)), size=100, replace=False)
sorted_idxs = selected_idxs[np.argsort(np.array(recs)[selected_idxs])]
print('Sorted idxs: {}'.format(sorted_idxs.tolist()))
save_dir = '/nethome/bamos/2018-intel/data/2018-07-31/sgs.multi'
for idx in selected_idxs:
gt_entry = {
'gt_classes': val.gt_classes[idx].copy(),
'gt_relations': val.relationships[idx].copy(),
'gt_boxes': val.gt_boxes[idx].copy(),
}
detector.eval()
det_res = detector[vg_collate([test[idx]], num_gpus=1)]
boxes_i, objs_i, obj_scores_i, rels_i, pred_scores_i = det_res
pred_entry = {
'pred_boxes': boxes_i * BOX_SCALE/IM_SCALE,
'pred_classes': objs_i,
'pred_rel_inds': rels_i,
'obj_scores': obj_scores_i,
'rel_scores': pred_scores_i,
}
unique_cnames = get_unique_cnames(gt_entry, test)
save_img(idx, recs, test, gt_entry, det_res, unique_cnames, save_dir)
save_gt_graph(idx, test, gt_entry, det_res, unique_cnames, save_dir)
save_pred_graph(idx, test, pred_entry, det_res,
unique_cnames, save_dir,
multi_pred=conf.multi_pred, n_pred=20)