def eval_ssd300_mlperf_coco(args):
from coco import COCO
# Check that GPUs are actually available
use_cuda = not args.no_cuda and torch.cuda.is_available()
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
val_trans = SSDTransformer(dboxes, (300, 300), val=True)
val_annotate = os.path.join(args.data, "annotations/instances_val2017.json")
val_coco_root = os.path.join(args.data, "val2017")
cocoGt = COCO(annotation_file=val_annotate)
val_coco = COCODetection(val_coco_root, val_annotate, val_trans)
inv_map = {v:k for k,v in val_coco.label_map.items()}
ssd300 = SSD300(val_coco.labelnum)
print("loading model checkpoint", args.checkpoint)
od = torch.load(args.checkpoint, map_location=lambda storage, loc: storage)
ssd300.load_state_dict(od["model"])
if use_cuda:
ssd300.cuda(args.device)
loss_func = Loss(dboxes)
if use_cuda:
loss_func.cuda(args.device)
coco_eval(ssd300, val_coco, cocoGt, encoder, inv_map, args.threshold,args.device)
def test300():
figsize = 300
feat_size = [38, 19, 10, 5, 3, 1]
steps = [8, 16, 32, 64, 100, 300]
scales = [30, 60, 111, 162, 213, 264, 315]
aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]
dboxes = DefaultBoxes(figsize, feat_size, steps, scales, aspect_ratios)
print(dboxes().shape)
img = torch.randn(1, 3, 300, 300)
model = SSD300(21)
loc, conf = model(img)
print(loc.shape, conf.shape)
def main():
# Parse arguments
args = parse_args()
# Get categories names
with open(args.annotations, 'r') as anno:
js = json.loads(anno.read())
coco_names = js['categories']
# Prepare map of COCO labels to COCO names
name_map = {}
for name in coco_names:
name_map[name['id']] = name['name']
# Prepare map of SSD to COCO labels
deleted = [12, 26, 29, 30, 45, 66, 68, 69, 71, 83]
inv_map = {}
cnt = 0
for i in range(1, 81):
while i + cnt in deleted:
cnt += 1
inv_map[i] = i + cnt
# Prepare colors for categories
category_id_to_color = dict([
(cat_id,
[random.uniform(0, 1),
random.uniform(0, 1),
random.uniform(0, 1)]) for cat_id in range(1, 91)
])
# Set math plot lib size
plt.rcParams["figure.figsize"] = (12, 8)
# Build and load SSD model
ssd300 = SSD300(81, backbone="resnet34", model_path=None, dilation=None)
load_checkpoint(ssd300, args.model)
ssd300.eval()
# Prepare encoder
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
# Print images
for image in args.images:
print_image(image, ssd300, encoder, inv_map, name_map,
category_id_to_color, args.threshold)
def run_eval(args):
args = setup_distributed(args)
from pycocotools.coco import COCO
local_seed = args.local_seed
encoder = build_ssd300_coder()
val_annotate = os.path.join(args.data,
"annotations/instances_val2017.json")
val_coco_root = os.path.join(args.data, "val2017")
cocoGt = COCO(annotation_file=val_annotate)
val_loader, inv_map = build_pipeline(args, training=False)
model_options = {
'use_nhwc': args.nhwc,
'pad_input': args.pad_input,
'bn_group': args.bn_group,
'pretrained': False,
}
ssd300_eval = SSD300(args, args.num_classes, **model_options).cuda()
if args.use_fp16:
convert_network(ssd300_eval, torch.half)
ssd300_eval.eval()
if args.checkpoint is not None:
load_checkpoint(ssd300_eval, args.checkpoint)
evaluator = AsyncEvaluator(num_threads=1)
coco_eval(
args,
ssd300_eval,
val_loader,
cocoGt,
encoder,
inv_map,
0, # epoch
0, # iter_num
evaluator=evaluator)
res = evaluator.task_result(0)
def train300_mlperf_coco(args):
global torch
from coco import COCO
# Check that GPUs are actually available
use_cuda = not args.no_cuda and torch.cuda.is_available()
args.distributed = False
if use_cuda:
try:
from apex.parallel import DistributedDataParallel as DDP
if 'WORLD_SIZE' in os.environ:
args.distributed = int(os.environ['WORLD_SIZE']) > 1
except:
raise ImportError(
"Please install APEX from https://github.com/nvidia/apex")
local_seed = args.seed
os.environ['USE_CUDA'] = str(use_cuda)
if args.world_size > 1:
args.distributed = True
if args.distributed:
# necessary pytorch imports
import torch.utils.data.distributed
import torch.distributed as dist
print('Distributed training with DDP')
if args.no_cuda:
device = torch.device('cpu')
os.environ['RANK'] = str(os.environ.get('PMI_RANK', args.rank))
os.environ['WORLD_SIZE'] = str(
os.environ.get('PMI_SIZE', args.world_size))
os.environ['MASTER_ADDR'] = args.master_addr
os.environ['MASTER_PORT'] = args.port
# Initialize the process group with ccl backend
if args.backend == 'ccl':
import torch_ccl
dist.init_process_group(backend=args.backend)
else:
torch.cuda.set_device(args.local_rank)
device = torch.device('cuda')
dist.init_process_group(backend='nccl', init_method='env://')
# set seeds properly
args.seed = broadcast_seeds(args.seed, device)
local_seed = (args.seed + dist.get_rank()) % 2**32
mllogger.event(key=mllog_const.SEED, value=local_seed)
# Refer to https://pytorch.org/docs/stable/notes/randomness.html#dataloader
torch.manual_seed(local_seed) # Set PyTorch seed
np.random.seed(seed=local_seed) # Set Numpy seed
random.seed(local_seed) # Set the Python seed
args.rank = dist.get_rank() if args.distributed else args.local_rank
print("args.rank = {}".format(args.rank))
print("local rank = {}".format(args.local_rank))
print("distributed={}".format(args.distributed))
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
input_size = 300
train_trans = SSDTransformer(
dboxes, (input_size, input_size),
val=False,
num_cropping_iterations=args.num_cropping_iterations)
val_trans = SSDTransformer(dboxes, (input_size, input_size), val=True)
val_annotate = os.path.join(args.data,
"annotations/instances_val2017.json")
val_coco_root = os.path.join(args.data, "val2017")
train_annotate = os.path.join(args.data,
"annotations/instances_train2017.json")
train_coco_root = os.path.join(args.data, "train2017")
cocoGt = COCO(annotation_file=val_annotate)
train_coco = COCODetection(train_coco_root, train_annotate, train_trans)
val_coco = COCODetection(val_coco_root, val_annotate, val_trans)
mllogger.event(key=mllog_const.TRAIN_SAMPLES, value=len(train_coco))
mllogger.event(key=mllog_const.EVAL_SAMPLES, value=len(val_coco))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_coco)
else:
train_sampler = None
train_dataloader = DataLoader(train_coco,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
sampler=train_sampler,
num_workers=0)
# set shuffle=True in DataLoader
# Leslie: here is the workaround: dist.broadcast will fail on other rank. we will run evalution on all the ranks
val_dataloader = DataLoader(val_coco,
batch_size=args.val_batch_size
or args.batch_size,
shuffle=False,
sampler=None,
num_workers=0)
ssd300 = SSD300(train_coco.labelnum, model_path=args.pretrained_backbone)
ssd300.train()
if use_cuda:
ssd300.cuda()
loss_func = Loss(dboxes)
if use_cuda:
loss_func.cuda()
if args.distributed:
N_gpu = torch.distributed.get_world_size()
else:
N_gpu = 1
global_batch_size = N_gpu * args.batch_size
mllogger.event(key=mllog_const.GLOBAL_BATCH_SIZE, value=global_batch_size)
# Reference doesn't support group batch norm, so bn_span==local_batch_size
mllogger.event(key=mllog_const.MODEL_BN_SPAN, value=args.batch_size)
current_lr = args.lr * (global_batch_size / 32)
assert args.batch_size % args.batch_splits == 0, "--batch-size must be divisible by --batch-splits"
fragment_size = args.batch_size // args.batch_splits
if args.batch_splits != 1:
print("using gradient accumulation with fragments of size {}".format(
fragment_size))
# Model to NHWC
ssd300 = ssd300.to(memory_format=torch.channels_last)
current_momentum = 0.9
optim = torch.optim.SGD(ssd300.parameters(),
lr=current_lr,
momentum=current_momentum,
weight_decay=args.weight_decay)
ssd_print(key=mllog_const.OPT_BASE_LR, value=current_lr)
ssd_print(key=mllog_const.OPT_WEIGHT_DECAY, value=args.weight_decay)
iter_num = args.iteration
avg_loss = 0.0
inv_map = {v: k for k, v in val_coco.label_map.items()}
success = torch.zeros(1)
if use_cuda:
success = success.cuda()
if args.warmup:
nonempty_imgs = len(train_coco)
wb = int(args.warmup * nonempty_imgs / (N_gpu * args.batch_size))
ssd_print(key=mllog_const.OPT_LR_WARMUP_STEPS, value=wb)
warmup_step = lambda iter_num, current_lr: lr_warmup(
optim, wb, iter_num, current_lr, args)
else:
warmup_step = lambda iter_num, current_lr: None
ssd_print(key=mllog_const.OPT_LR_WARMUP_FACTOR, value=args.warmup_factor)
ssd_print(key=mllog_const.OPT_LR_DECAY_BOUNDARY_EPOCHS,
value=args.lr_decay_schedule)
mllogger.start(key=mllog_const.BLOCK_START,
metadata={
mllog_const.FIRST_EPOCH_NUM: 1,
mllog_const.EPOCH_COUNT: args.epochs
})
if args.performance_only:
train_time = AverageMeter('TrainTime', ':6.3f')
progress = ProgressMeter(args.train_iteration, [train_time],
prefix='Train: ')
# Restore the model and optim from checkpoint
if args.checkpoint is not None:
print("loading model checkpoint", args.checkpoint)
od = torch.load(args.checkpoint)
ssd300.load_state_dict(od["model"])
optim.load_state_dict(od['optim'])
# Model Prepack
if use_ipex:
if args.autocast:
ssd300, optim = ipex.optimize(ssd300,
dtype=torch.bfloat16,
optimizer=optim)
else:
ssd300, optim = ipex.optimize(ssd300,
dtype=torch.float32,
optimizer=optim)
# parallelize
if args.distributed:
device_ids = None
ssd300 = torch.nn.parallel.DistributedDataParallel(
ssd300, device_ids=device_ids)
optim.zero_grad(set_to_none=True)
for epoch in range(args.epochs):
mllogger.start(key=mllog_const.EPOCH_START,
metadata={mllog_const.EPOCH_NUM: epoch})
# set the epoch for the sampler
if args.distributed:
train_sampler.set_epoch(epoch)
if epoch in args.lr_decay_schedule:
current_lr *= 0.1
print("")
print("lr decay step #{num}".format(
num=args.lr_decay_schedule.index(epoch) + 1))
for param_group in optim.param_groups:
param_group['lr'] = current_lr
for nbatch, (img, img_id, img_size, bbox,
label) in enumerate(train_dataloader):
naive_train_case = True # img.shape[0] == fragment_size
if naive_train_case:
# Naive train case
fimg, gloc, glabel, mask, pos_num, neg_num, num_mask = data_preprocess(
img, bbox, label, loss_func, args.autocast)
if args.performance_only and iter_num >= args.warmup_iterations:
start_time = time.time()
if args.profile and args.performance_only and iter_num == 30:
# Profile Mode
with torch.profiler.profile(
on_trace_ready=trace_handler) as prof:
with torch.cpu.amp.autocast(enabled=args.autocast):
ploc, plabel = ssd300(fimg)
loss = loss_func(ploc, plabel, gloc, glabel, mask,
pos_num, neg_num, num_mask,
args.autocast)
loss.backward()
warmup_step(iter_num, current_lr)
optim.step()
optim.zero_grad(set_to_none=True)
else:
# Non Profile Mode
with torch.cpu.amp.autocast(enabled=args.autocast):
ploc, plabel = ssd300(fimg)
loss = loss_func(ploc, plabel, gloc, glabel, mask,
pos_num, neg_num, num_mask,
args.autocast)
loss.backward()
warmup_step(iter_num, current_lr)
optim.step()
optim.zero_grad(set_to_none=True)
else:
# Train case: when split input to several fragment size
print("Not support input with several fragment size yet.")
exit(-1)
# current_batch_size = img.shape[0]
# # Split batch for gradient accumulation
# img = torch.split(img, fragment_size)
# bbox = torch.split(bbox, fragment_size)
# label = torch.split(label, fragment_size)
# if args.performance_only and iter_num >= args.warmup_iterations:
# start_time=time.time()
# for (fimg, fbbox, flabel) in zip(img, bbox, label):
# current_fragment_size = fimg.shape[0]
# trans_bbox = fbbox.transpose(1,2).contiguous()
# if use_cuda:
# fimg = fimg.cuda()
# trans_bbox = trans_bbox.cuda()
# flabel = flabel.cuda()
# fimg = Variable(fimg, requires_grad=True)
# gloc, glabel = Variable(trans_bbox, requires_grad=False), \
# Variable(flabel, requires_grad=False)
# gloc = loss_func._loc_vec(gloc)
# mask = glabel > 0
# pos_num = mask.sum(dim=1)
# neg_num = torch.clamp(3*pos_num, max=mask.size(1)).unsqueeze(-1)
# num_mask = (pos_num > 0).float()
# # image to NHWC
# fimg = fimg.contiguous(memory_format=torch.channels_last)
# if use_ipex:
# with ipex.amp.autocast(enabled=args.autocast, configure=ipex.conf.AmpConf(torch.bfloat16)):
# ploc, plabel = ssd300(fimg)
# loss = loss_func(ploc, plabel, gloc, glabel, mask, pos_num, neg_num, num_mask)
# else:
# ploc, plabel = ssd300(fimg)
# loss = loss_func(ploc, plabel, gloc, glabel, mask, pos_num, neg_num, num_mask)
# loss = loss * (current_fragment_size / current_batch_size) # weighted mean
# loss.backward()
# warmup_step(iter_num, current_lr)
# optim.step()
# optim.zero_grad(set_to_none=True)
if args.performance_only and iter_num >= args.warmup_iterations:
train_time.update(time.time() - start_time)
if args.performance_only and iter_num % args.print_freq == 0:
progress.display(iter_num)
if not np.isinf(loss.item()):
avg_loss = 0.999 * avg_loss + 0.001 * loss.item()
if args.log_interval and not iter_num % args.log_interval:
print("Iteration: {:6d}, Loss function: {:5.8f}, Average Loss: {:.8f}"\
.format(iter_num, loss.item(), avg_loss))
iter_num += 1
if args.performance_only and iter_num >= args.train_iteration:
break
if args.performance_only and iter_num >= args.train_iteration:
break
if (args.val_epochs and (epoch+1) in args.val_epochs) or \
(args.val_interval and not (epoch+1) % args.val_interval):
if args.distributed:
world_size = float(dist.get_world_size())
for bn_name, bn_buf in ssd300.module.named_buffers(
recurse=True):
if ('running_mean' in bn_name) or ('running_var'
in bn_name):
dist.all_reduce(bn_buf, op=dist.ReduceOp.SUM)
bn_buf /= world_size
ssd_print(key=mllog_const.MODEL_BN_SPAN,
value=bn_buf.cpu().detach().numpy())
if args.rank == 0 or True: # Leslie: here is the workaround: dist.broadcast will fail on other rank. we will run evalution on all the ranks
if not args.no_save:
print("")
print("saving model...")
torch.save(
{
"model": ssd300.state_dict(),
"label_map": train_coco.label_info,
"optim": optim.state_dict()
}, "./models/iter_{}.pt".format(iter_num))
if coco_eval(ssd300,
val_dataloader,
cocoGt,
encoder,
inv_map,
args.threshold,
epoch + 1,
iter_num,
log_interval=args.log_interval,
nms_valid_thresh=args.nms_valid_thresh,
use_autocast=args.autocast):
success = torch.ones(1)
if use_cuda:
success = success.cuda()
# Leslie: same Workaround: since we run evalution on all ranks, we don't need to broadcast the evalutation result
# if args.distributed:
# dist.broadcast(success, 0)
if success[0]:
return True
mllogger.end(key=mllog_const.EPOCH_STOP,
metadata={mllog_const.EPOCH_NUM: epoch})
mllogger.end(key=mllog_const.BLOCK_STOP,
metadata={
mllog_const.FIRST_EPOCH_NUM: 1,
mllog_const.EPOCH_COUNT: args.epochs
})
if args.performance_only:
batch_size = args.batch_size
latency = train_time.avg / batch_size * 1000
perf = batch_size / train_time.avg
print('train latency %.2f ms' % latency)
print('train performance %.2f fps' % perf)
print("Throughput: {:.3f} fps".format(perf))
return False
def train300_mlperf_coco(args):
from coco import COCO
# Check that GPUs are actually available
use_cuda = not args.no_cuda and torch.cuda.is_available()
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
input_size = 300
train_trans = SSDTransformer(dboxes, (input_size, input_size), val=False)
val_trans = SSDTransformer(dboxes, (input_size, input_size), val=True)
mlperf_log.ssd_print(key=mlperf_log.INPUT_SIZE, value=input_size)
val_annotate = os.path.join(args.data,
"annotations/instances_val2017.json")
val_coco_root = os.path.join(args.data, "val2017")
train_annotate = os.path.join(args.data,
"annotations/instances_train2017.json")
train_coco_root = os.path.join(args.data, "train2017")
cocoGt = COCO(annotation_file=val_annotate)
val_coco = COCODetection(val_coco_root, val_annotate, val_trans)
train_coco = COCODetection(train_coco_root, train_annotate, train_trans)
#print("Number of labels: {}".format(train_coco.labelnum))
train_dataloader = DataLoader(train_coco,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
# set shuffle=True in DataLoader
mlperf_log.ssd_print(key=mlperf_log.INPUT_SHARD, value=None)
mlperf_log.ssd_print(key=mlperf_log.INPUT_ORDER)
mlperf_log.ssd_print(key=mlperf_log.INPUT_BATCH_SIZE,
value=args.batch_size)
ssd300 = SSD300(train_coco.labelnum)
if args.checkpoint is not None:
print("loading model checkpoint", args.checkpoint)
od = torch.load(args.checkpoint)
ssd300.load_state_dict(od["model"])
ssd300.train()
if use_cuda:
ssd300.cuda()
loss_func = Loss(dboxes)
if use_cuda:
loss_func.cuda()
current_lr = 1e-3
current_momentum = 0.9
current_weight_decay = 5e-4
optim = torch.optim.SGD(ssd300.parameters(),
lr=current_lr,
momentum=current_momentum,
weight_decay=current_weight_decay)
mlperf_log.ssd_print(key=mlperf_log.OPT_NAME, value="SGD")
mlperf_log.ssd_print(key=mlperf_log.OPT_LR, value=current_lr)
mlperf_log.ssd_print(key=mlperf_log.OPT_MOMENTUM, value=current_momentum)
mlperf_log.ssd_print(key=mlperf_log.OPT_WEIGHT_DECAY,
value=current_weight_decay)
print("epoch", "nbatch", "loss")
iter_num = args.iteration
avg_loss = 0.0
inv_map = {v: k for k, v in val_coco.label_map.items()}
mlperf_log.ssd_print(key=mlperf_log.TRAIN_LOOP)
for epoch in range(args.epochs):
mlperf_log.ssd_print(key=mlperf_log.TRAIN_EPOCH, value=epoch)
for nbatch, (img, img_size, bbox,
label) in enumerate(train_dataloader):
if iter_num == 160000:
current_lr = 1e-4
print("")
print("lr decay step #1")
for param_group in optim.param_groups:
param_group['lr'] = current_lr
mlperf_log.ssd_print(key=mlperf_log.OPT_LR, value=current_lr)
if iter_num == 200000:
current_lr = 1e-5
print("")
print("lr decay step #2")
for param_group in optim.param_groups:
param_group['lr'] = current_lr
mlperf_log.ssd_print(key=mlperf_log.OPT_LR, value=current_lr)
if use_cuda:
img = img.cuda()
img = Variable(img, requires_grad=True)
ploc, plabel = ssd300(img)
trans_bbox = bbox.transpose(1, 2).contiguous()
if use_cuda:
trans_bbox = trans_bbox.cuda()
label = label.cuda()
gloc, glabel = Variable(trans_bbox, requires_grad=False), \
Variable(label, requires_grad=False)
loss = loss_func(ploc, plabel, gloc, glabel)
if not np.isinf(loss.item()):
avg_loss = 0.999 * avg_loss + 0.001 * loss.item()
print("Iteration: {:6d}, Loss function: {:5.3f}, Average Loss: {:.3f}"\
.format(iter_num, loss.item(), avg_loss), end="\r")
optim.zero_grad()
loss.backward()
optim.step()
if iter_num in args.evaluation:
if not args.no_save:
print("")
print("saving model...")
torch.save(
{
"model": ssd300.state_dict(),
"label_map": train_coco.label_info
}, "./models/iter_{}.pt".format(iter_num))
if coco_eval(ssd300, val_coco, cocoGt, encoder, inv_map,
args.threshold, epoch, iter_num):
return True
iter_num += 1
return False
def train300_mlperf_coco(args):
global torch
from coco import COCO
# Check that GPUs are actually available
use_cuda = not args.no_cuda and torch.cuda.is_available()
args.distributed = False
if use_cuda:
try:
from apex.parallel import DistributedDataParallel as DDP
if 'WORLD_SIZE' in os.environ:
args.distributed = int(os.environ['WORLD_SIZE']) > 1
except:
raise ImportError(
"Please install APEX from https://github.com/nvidia/apex")
if args.distributed:
# necessary pytorch imports
import torch.utils.data.distributed
import torch.distributed as dist
# ssd_print(key=mlperf_log.RUN_SET_RANDOM_SEED)
if args.no_cuda:
device = torch.device('cpu')
else:
torch.cuda.set_device(args.local_rank)
device = torch.device('cuda')
dist.init_process_group(backend='nccl', init_method='env://')
# set seeds properly
args.seed = broadcast_seeds(args.seed, device)
local_seed = (args.seed + dist.get_rank()) % 2**32
print(dist.get_rank(), "Using seed = {}".format(local_seed))
torch.manual_seed(local_seed)
np.random.seed(seed=local_seed)
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
input_size = 300
train_trans = SSDTransformer(dboxes, (input_size, input_size), val=False)
val_trans = SSDTransformer(dboxes, (input_size, input_size), val=True)
ssd_print(key=mlperf_log.INPUT_SIZE, value=input_size)
val_annotate = os.path.join(args.data,
"annotations/instances_val2017.json")
val_coco_root = os.path.join(args.data, "val2017")
train_annotate = os.path.join(args.data,
"annotations/instances_train2017.json")
train_coco_root = os.path.join(args.data, "train2017")
cocoGt = COCO(annotation_file=val_annotate)
val_coco = COCODetection(val_coco_root, val_annotate, val_trans)
train_coco = COCODetection(train_coco_root, train_annotate, train_trans)
#print("Number of labels: {}".format(train_coco.labelnum))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_coco)
else:
train_sampler = None
train_dataloader = DataLoader(train_coco,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
sampler=train_sampler,
num_workers=4)
# set shuffle=True in DataLoader
ssd_print(key=mlperf_log.INPUT_SHARD, value=None)
ssd_print(key=mlperf_log.INPUT_ORDER)
ssd_print(key=mlperf_log.INPUT_BATCH_SIZE, value=args.batch_size)
ssd300 = SSD300(train_coco.labelnum)
if args.checkpoint is not None:
print("loading model checkpoint", args.checkpoint)
od = torch.load(args.checkpoint)
ssd300.load_state_dict(od["model"])
ssd300.train()
if use_cuda:
ssd300.cuda()
loss_func = Loss(dboxes)
if use_cuda:
loss_func.cuda()
if args.distributed:
N_gpu = torch.distributed.get_world_size()
else:
N_gpu = 1
# parallelize
if args.distributed:
ssd300 = DDP(ssd300)
global_batch_size = N_gpu * args.batch_size
current_lr = args.lr * (global_batch_size / 32)
current_momentum = 0.9
current_weight_decay = 5e-4
optim = torch.optim.SGD(ssd300.parameters(),
lr=current_lr,
momentum=current_momentum,
weight_decay=current_weight_decay)
ssd_print(key=mlperf_log.OPT_NAME, value="SGD")
ssd_print(key=mlperf_log.OPT_LR, value=current_lr)
ssd_print(key=mlperf_log.OPT_MOMENTUM, value=current_momentum)
ssd_print(key=mlperf_log.OPT_WEIGHT_DECAY, value=current_weight_decay)
eval_points = args.evaluation
print("epoch", "nbatch", "loss")
iter_num = args.iteration
avg_loss = 0.0
inv_map = {v: k for k, v in val_coco.label_map.items()}
success = torch.zeros(1)
if use_cuda:
success = success.cuda()
if args.warmup:
nonempty_imgs = len(train_coco)
wb = int(args.warmup * nonempty_imgs / (N_gpu * args.batch_size))
warmup_step = lambda iter_num, current_lr: lr_warmup(
optim, wb, iter_num, current_lr, args)
else:
warmup_step = lambda iter_num, current_lr: None
for epoch in range(args.epochs):
ssd_print(key=mlperf_log.TRAIN_EPOCH, value=epoch)
# set the epoch for the sampler
if args.distributed:
train_sampler.set_epoch(epoch)
if epoch in args.lr_decay_schedule:
current_lr *= 0.1
print("")
print("lr decay step #{num}".format(
num=args.lr_decay_schedule.index(epoch) + 1))
for param_group in optim.param_groups:
param_group['lr'] = current_lr
ssd_print(key=mlperf_log.OPT_LR, value=current_lr)
for nbatch, (img, img_size, bbox,
label) in enumerate(train_dataloader):
if use_cuda:
img = img.cuda()
img = Variable(img, requires_grad=True)
ploc, plabel = ssd300(img)
trans_bbox = bbox.transpose(1, 2).contiguous()
if use_cuda:
trans_bbox = trans_bbox.cuda()
label = label.cuda()
gloc, glabel = Variable(trans_bbox, requires_grad=False), \
Variable(label, requires_grad=False)
loss = loss_func(ploc, plabel, gloc, glabel)
if not np.isinf(loss.item()):
avg_loss = 0.999 * avg_loss + 0.001 * loss.item()
print("Iteration: {:6d}, Loss function: {:5.3f}, Average Loss: {:.3f}"\
.format(iter_num, loss.item(), avg_loss), end="\r")
optim.zero_grad()
loss.backward()
warmup_step(iter_num, current_lr)
optim.step()
iter_num += 1
if epoch + 1 in eval_points:
rank = dist.get_rank() if args.distributed else args.local_rank
if args.distributed:
world_size = float(dist.get_world_size())
for bn_name, bn_buf in ssd300.module.named_buffers(
recurse=True):
if ('running_mean' in bn_name) or ('running_var'
in bn_name):
dist.all_reduce(bn_buf, op=dist.ReduceOp.SUM)
bn_buf /= world_size
if rank == 0:
if not args.no_save:
print("")
print("saving model...")
torch.save(
{
"model": ssd300.state_dict(),
"label_map": train_coco.label_info
}, "./models/iter_{}.pt".format(iter_num))
if coco_eval(ssd300, val_coco, cocoGt, encoder, inv_map,
args.threshold, epoch + 1, iter_num):
success = torch.ones(1)
if use_cuda:
success = success.cuda()
if args.distributed:
dist.broadcast(success, 0)
if success[0]:
return True
return False
def val300(path):
ssd300 = SSD300(21)
dboxes = dboxes300()
encoder = Encoder(dboxes)
trans = SSDTransformer(dboxes, (300, 300), val=True)
valmodel(ssd300, path, dboxes, trans, encoder)
def train300_coco():
dboxes = dboxes300_coco()
trans = SSDTransformer(dboxes, (300, 300), val=False)
#annotate = "../../coco_ssd/instances_valminusminival2014.json"
#coco_root = "../../coco_data/val2014"
#annotate = "../../coco_ssd/train.json"
#coco_root = "../../coco_data/train2014"
annotate = "../../coco_ssd/instances_train2017.json"
coco_root = "../../coco_data/train2017"
coco = COCODetection(coco_root, annotate, trans)
print("Number of labels: {}".format(coco.labelnum))
print("Number of images: {}".format(len(coco)))
#train_sampler = torch.utils.data.distributed.DistributedSampler(coco)
dataloader = DataLoader(coco, batch_size=32, shuffle=True, num_workers=4)
#dataloader = DataLoader(coco, batch_size=8, shuffle=True, num_workers=4, sampler=train_sampler, shuffle=(train_sampler is None))
nepochs = 800
ssd300 = SSD300(coco.labelnum)
#ssd300 = DDP(ssd300)
ssd300.train()
ssd300.cuda()
loss_func = Loss(dboxes)
loss_func.cuda()
optim = torch.optim.SGD(ssd300.parameters(),
lr=1e-3,
momentum=0.9,
weight_decay=5e-4)
print("epoch", "nbatch", "loss")
iter_num = 0
avg_loss = 0.0
#od = torch.load("./models/larger_iter_210000.pt")
#ssd300.load_state_dict(od["model"])
#iter_num = 210000
#optim = torch.optim.SGD(ssd300.parameters(), lr=1e-5, momentum=0.9, weight_decay=5e-4)
for epoch in range(nepochs):
#train_sampler.set_epoch(epoch)
if iter_num >= 240000:
break
for nbatch, (img, img_size, bbox, label) in enumerate(dataloader):
iter_num += 1
if iter_num == 160000:
print("")
print("lr decay step #1")
for param_group in optim.param_groups:
param_group['lr'] = 1e-4
if iter_num == 200000:
print("")
print("lr decay step #2")
for param_group in optim.param_groups:
param_group['lr'] = 1e-5
img = Variable(img.cuda(), requires_grad=True)
ploc, plabel = ssd300(img)
gloc, glabel = Variable(bbox.transpose(1,2).contiguous().cuda(),
requires_grad=False), \
Variable(label.cuda(), requires_grad=False)
loss = loss_func(ploc, plabel, gloc, glabel)
if not np.isinf(loss.item()):
avg_loss = 0.999 * avg_loss + 0.001 * loss.item()
print("Iteration: {:6d}, Loss function: {:5.3f}, Average Loss: {:.3f}"\
.format(iter_num, loss.item(), avg_loss), end="\r")
optim.zero_grad()
loss.backward()
optim.step()
if iter_num % 5000 == 0:
print("")
print("saving model...")
torch.save(
{
"model": ssd300.state_dict(),
"label_map": coco.label_info
}, "./models/crowd_iter_{}.pt".format(iter_num))
def train300_mlperf_coco(args):
global torch
from coco import COCO
# Check that GPUs are actually available
use_cuda = not args.no_cuda and torch.cuda.is_available()
args.distributed = False
if use_cuda:
try:
from apex.parallel import DistributedDataParallel as DDP
if 'WORLD_SIZE' in os.environ:
args.distributed = int(os.environ['WORLD_SIZE']) > 1
except:
raise ImportError("Please install APEX from https://github.com/nvidia/apex")
local_seed = args.seed
if args.distributed:
# necessary pytorch imports
import torch.utils.data.distributed
import torch.distributed as dist
if args.no_cuda:
device = torch.device('cpu')
else:
torch.cuda.set_device(args.local_rank)
device = torch.device('cuda')
dist.init_process_group(backend='nccl',
init_method='env://')
# set seeds properly
args.seed = broadcast_seeds(args.seed, device)
local_seed = (args.seed + dist.get_rank()) % 2**32
mllogger.event(key=mllog_const.SEED, value=local_seed)
torch.manual_seed(local_seed)
np.random.seed(seed=local_seed)
args.rank = dist.get_rank() if args.distributed else args.local_rank
print("args.rank = {}".format(args.rank))
print("local rank = {}".format(args.local_rank))
print("distributed={}".format(args.distributed))
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
input_size = 300
train_trans = SSDTransformer(dboxes, (input_size, input_size), val=False,
num_cropping_iterations=args.num_cropping_iterations)
val_trans = SSDTransformer(dboxes, (input_size, input_size), val=True)
val_annotate = os.path.join(args.data, "annotations/instances_val2017.json")
val_coco_root = os.path.join(args.data, "val2017")
train_annotate = os.path.join(args.data, "annotations/instances_train2017.json")
train_coco_root = os.path.join(args.data, "train2017")
cocoGt = COCO(annotation_file=val_annotate)
train_coco = COCODetection(train_coco_root, train_annotate, train_trans)
val_coco = COCODetection(val_coco_root, val_annotate, val_trans)
mllogger.event(key=mllog_const.TRAIN_SAMPLES, value=len(train_coco))
mllogger.event(key=mllog_const.EVAL_SAMPLES, value=len(val_coco))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_coco)
else:
train_sampler = None
train_dataloader = DataLoader(train_coco,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
sampler=train_sampler,
num_workers=4)
# set shuffle=True in DataLoader
if args.rank==0:
val_dataloader = DataLoader(val_coco,
batch_size=args.val_batch_size or args.batch_size,
shuffle=False,
sampler=None,
num_workers=4)
else:
val_dataloader = None
ssd300 = SSD300(train_coco.labelnum, model_path=args.pretrained_backbone)
if args.checkpoint is not None:
print("loading model checkpoint", args.checkpoint)
od = torch.load(args.checkpoint)
ssd300.load_state_dict(od["model"])
ssd300.train()
if use_cuda:
ssd300.cuda()
loss_func = Loss(dboxes)
if use_cuda:
loss_func.cuda()
if args.distributed:
N_gpu = torch.distributed.get_world_size()
else:
N_gpu = 1
# parallelize
if args.distributed:
ssd300 = DDP(ssd300)
global_batch_size = N_gpu * args.batch_size
mllogger.event(key=mllog_const.GLOBAL_BATCH_SIZE, value=global_batch_size)
# Reference doesn't support group batch norm, so bn_span==local_batch_size
mllogger.event(key=mllog_const.MODEL_BN_SPAN, value=args.batch_size)
current_lr = args.lr * (global_batch_size / 32)
assert args.batch_size % args.batch_splits == 0, "--batch-size must be divisible by --batch-splits"
fragment_size = args.batch_size // args.batch_splits
if args.batch_splits != 1:
print("using gradient accumulation with fragments of size {}".format(fragment_size))
current_momentum = 0.9
optim = torch.optim.SGD(ssd300.parameters(), lr=current_lr,
momentum=current_momentum,
weight_decay=args.weight_decay)
ssd_print(key=mllog_const.OPT_BASE_LR, value=current_lr)
ssd_print(key=mllog_const.OPT_WEIGHT_DECAY, value=args.weight_decay)
iter_num = args.iteration
avg_loss = 0.0
inv_map = {v:k for k,v in val_coco.label_map.items()}
success = torch.zeros(1)
if use_cuda:
success = success.cuda()
if args.warmup:
nonempty_imgs = len(train_coco)
wb = int(args.warmup * nonempty_imgs / (N_gpu*args.batch_size))
ssd_print(key=mllog_const.OPT_LR_WARMUP_STEPS, value=wb)
warmup_step = lambda iter_num, current_lr: lr_warmup(optim, wb, iter_num, current_lr, args)
else:
warmup_step = lambda iter_num, current_lr: None
ssd_print(key=mllog_const.OPT_LR_WARMUP_FACTOR, value=args.warmup_factor)
ssd_print(key=mllog_const.OPT_LR_DECAY_BOUNDARY_EPOCHS, value=args.lr_decay_schedule)
mllogger.start(
key=mllog_const.BLOCK_START,
metadata={mllog_const.FIRST_EPOCH_NUM: 1,
mllog_const.EPOCH_COUNT: args.epochs})
optim.zero_grad()
for epoch in range(args.epochs):
mllogger.start(
key=mllog_const.EPOCH_START,
metadata={mllog_const.EPOCH_NUM: epoch})
# set the epoch for the sampler
if args.distributed:
train_sampler.set_epoch(epoch)
if epoch in args.lr_decay_schedule:
current_lr *= 0.1
print("")
print("lr decay step #{num}".format(num=args.lr_decay_schedule.index(epoch) + 1))
for param_group in optim.param_groups:
param_group['lr'] = current_lr
for nbatch, (img, img_id, img_size, bbox, label) in enumerate(train_dataloader):
current_batch_size = img.shape[0]
# Split batch for gradient accumulation
img = torch.split(img, fragment_size)
bbox = torch.split(bbox, fragment_size)
label = torch.split(label, fragment_size)
for (fimg, fbbox, flabel) in zip(img, bbox, label):
current_fragment_size = fimg.shape[0]
trans_bbox = fbbox.transpose(1,2).contiguous()
if use_cuda:
fimg = fimg.cuda()
trans_bbox = trans_bbox.cuda()
flabel = flabel.cuda()
fimg = Variable(fimg, requires_grad=True)
ploc, plabel = ssd300(fimg)
gloc, glabel = Variable(trans_bbox, requires_grad=False), \
Variable(flabel, requires_grad=False)
loss = loss_func(ploc, plabel, gloc, glabel)
loss = loss * (current_fragment_size / current_batch_size) # weighted mean
loss.backward()
warmup_step(iter_num, current_lr)
optim.step()
optim.zero_grad()
if not np.isinf(loss.item()): avg_loss = 0.999*avg_loss + 0.001*loss.item()
if args.rank == 0 and args.log_interval and not iter_num % args.log_interval:
print("Iteration: {:6d}, Loss function: {:5.3f}, Average Loss: {:.3f}"\
.format(iter_num, loss.item(), avg_loss))
iter_num += 1
if (args.val_epochs and (epoch+1) in args.val_epochs) or \
(args.val_interval and not (epoch+1) % args.val_interval):
if args.distributed:
world_size = float(dist.get_world_size())
for bn_name, bn_buf in ssd300.module.named_buffers(recurse=True):
if ('running_mean' in bn_name) or ('running_var' in bn_name):
dist.all_reduce(bn_buf, op=dist.ReduceOp.SUM)
bn_buf /= world_size
ssd_print(key=mllog_const.MODEL_BN_SPAN,
value=bn_buf)
if args.rank == 0:
if not args.no_save:
print("")
print("saving model...")
torch.save({"model" : ssd300.state_dict(), "label_map": train_coco.label_info},
"./models/iter_{}.pt".format(iter_num))
if coco_eval(ssd300, val_dataloader, cocoGt, encoder, inv_map,
args.threshold, epoch + 1, iter_num,
log_interval=args.log_interval,
nms_valid_thresh=args.nms_valid_thresh):
success = torch.ones(1)
if use_cuda:
success = success.cuda()
if args.distributed:
dist.broadcast(success, 0)
if success[0]:
return True
mllogger.end(
key=mllog_const.EPOCH_STOP,
metadata={mllog_const.EPOCH_NUM: epoch})
mllogger.end(
key=mllog_const.BLOCK_STOP,
metadata={mllog_const.FIRST_EPOCH_NUM: 1,
mllog_const.EPOCH_COUNT: args.epochs})
return False
def run_eval(args):
from coco import COCO
# Setup multi-GPU if necessary
args.distributed = False
if 'WORLD_SIZE' in os.environ:
args.distributed = int(os.environ['WORLD_SIZE']) > 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
local_seed = set_seeds(args)
# start timing here
if args.distributed:
N_gpu = torch.distributed.get_world_size()
else:
N_gpu = 1
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
input_size = 300
val_trans = SSDTransformer(dboxes, (input_size, input_size), val=True)
val_annotate = os.path.join(args.data, "annotations/instances_val2017.json")
val_coco_root = os.path.join(args.data, "val2017")
cocoGt = COCO(annotation_file=val_annotate)
val_coco = COCODetection(val_coco_root, val_annotate, val_trans)
inv_map = {v:k for k,v in val_coco.label_map.items()}
if args.distributed:
val_sampler = GeneralDistributedSampler(val_coco, pad=False)
else:
val_sampler = None
val_dataloader = DataLoader(val_coco,
batch_size=args.eval_batch_size,
shuffle=False, # Note: distributed sampler is shuffled :(
sampler=val_sampler,
num_workers=args.num_workers)
ssd300_eval = SSD300(val_coco.labelnum, backbone=args.backbone, use_nhwc=args.nhwc, pad_input=args.pad_input).cuda()
if args.use_fp16:
ssd300_eval = network_to_half(ssd300_eval)
ssd300_eval.eval()
if args.checkpoint is not None:
load_checkpoint(ssd300_eval, args.checkpoint)
coco_eval(ssd300_eval,
val_dataloader,
cocoGt,
encoder,
inv_map,
args.threshold,
0, # epoch
0, # iter_num
args.eval_batch_size,
use_fp16=args.use_fp16,
local_rank=args.local_rank if args.distributed else -1,
N_gpu=N_gpu,
use_nhwc=args.nhwc,
pad_input=args.pad_input)
from keras.optimizers import Adam, SGD
from keras import backend as K
from ssd300 import SSD300
from keras_loss_function.keras_ssd_loss import SSDLoss
img_height = 300
img_width = 300
img_channels = 3
n_classes = 20
K.clear_session()
# 1: 创建模型
model = SSD300(image_size=(img_height, img_width, img_channels),
n_classes=n_classes,
mode='training')
# 2: 加载VGG16模型权重
weights_path = 'vgg/VGG_ILSVRC_16_layers_fc_reduced.h5'
model.load_weights(weights_path, by_name=True)
# 3: 优化器及loss
#adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
sgd = SGD(lr=0.001, momentum=0.9, decay=0.0, nesterov=False)
ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)
model.compile(optimizer=sgd, loss=ssd_loss.compute_loss)
def train300_mlperf_coco(args):
from coco import COCO
# Check that GPUs are actually available
use_cuda = not args.no_cuda
# Setup multi-GPU if necessary
args.distributed = False
if 'WORLD_SIZE' in os.environ:
args.distributed = int(os.environ['WORLD_SIZE']) > 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
local_seed = set_seeds(args)
# start timing here
ssd_print(key=mlperf_log.RUN_START)
if args.distributed:
N_gpu = torch.distributed.get_world_size()
else:
N_gpu = 1
# Setup data, defaults
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
input_size = 300
val_trans = SSDTransformer(dboxes, (input_size, input_size), val=True)
ssd_print(key=mlperf_log.INPUT_SIZE, value=input_size)
val_annotate = os.path.join(args.data,
"annotations/instances_val2017.json")
val_coco_root = os.path.join(args.data, "val2017")
train_annotate = os.path.join(args.data,
"annotations/instances_train2017.json")
train_coco_root = os.path.join(args.data, "train2017")
cocoGt = COCO(annotation_file=val_annotate)
val_coco = COCODetection(val_coco_root, val_annotate, val_trans)
if args.distributed:
val_sampler = GeneralDistributedSampler(val_coco, pad=False)
else:
val_sampler = None
train_pipe = COCOPipeline(args.batch_size,
args.local_rank,
train_coco_root,
train_annotate,
N_gpu,
num_threads=args.num_workers,
output_fp16=args.use_fp16,
output_nhwc=args.nhwc,
pad_output=args.pad_input,
seed=local_seed - 2**31)
print_message(args.local_rank,
"time_check a: {secs:.9f}".format(secs=time.time()))
train_pipe.build()
print_message(args.local_rank,
"time_check b: {secs:.9f}".format(secs=time.time()))
test_run = train_pipe.run()
train_loader = DALICOCOIterator(train_pipe, 118287 / N_gpu)
val_dataloader = DataLoader(
val_coco,
batch_size=args.eval_batch_size,
shuffle=False, # Note: distributed sampler is shuffled :(
sampler=val_sampler,
num_workers=args.num_workers)
ssd_print(key=mlperf_log.INPUT_ORDER)
ssd_print(key=mlperf_log.INPUT_BATCH_SIZE, value=args.batch_size)
# Build the model
ssd300 = SSD300(val_coco.labelnum,
backbone=args.backbone,
use_nhwc=args.nhwc,
pad_input=args.pad_input)
if args.checkpoint is not None:
load_checkpoint(ssd300, args.checkpoint)
ssd300.train()
ssd300.cuda()
loss_func = Loss(dboxes)
loss_func.cuda()
if args.distributed:
N_gpu = torch.distributed.get_world_size()
else:
N_gpu = 1
if args.use_fp16:
ssd300 = network_to_half(ssd300)
# Parallelize. Need to do this after network_to_half.
if args.distributed:
if args.delay_allreduce:
print_message(args.local_rank,
"Delaying allreduces to the end of backward()")
ssd300 = DDP(ssd300,
delay_allreduce=args.delay_allreduce,
retain_allreduce_buffers=args.use_fp16)
# Create optimizer. This must also be done after network_to_half.
global_batch_size = (N_gpu * args.batch_size)
# mlperf only allows base_lr scaled by an integer
base_lr = 1e-3
requested_lr_multiplier = args.lr / base_lr
adjusted_multiplier = max(
1, round(requested_lr_multiplier * global_batch_size / 32))
current_lr = base_lr * adjusted_multiplier
current_momentum = 0.9
current_weight_decay = 5e-4
static_loss_scale = 128.
if args.use_fp16:
if args.distributed and not args.delay_allreduce:
# We can't create the flat master params yet, because we need to
# imitate the flattened bucket structure that DDP produces.
optimizer_created = False
else:
model_buckets = [
[
p for p in ssd300.parameters()
if p.requires_grad and p.type() == "torch.cuda.HalfTensor"
],
[
p for p in ssd300.parameters()
if p.requires_grad and p.type() == "torch.cuda.FloatTensor"
]
]
flat_master_buckets = create_flat_master(model_buckets)
optim = torch.optim.SGD(flat_master_buckets,
lr=current_lr,
momentum=current_momentum,
weight_decay=current_weight_decay)
optimizer_created = True
else:
optim = torch.optim.SGD(ssd300.parameters(),
lr=current_lr,
momentum=current_momentum,
weight_decay=current_weight_decay)
optimizer_created = True
# Add LARC if desired
if args.use_larc:
optim = LARC(optim)
ssd_print(key=mlperf_log.OPT_NAME, value="SGD")
ssd_print(key=mlperf_log.OPT_LR, value=current_lr)
ssd_print(key=mlperf_log.OPT_MOMENTUM, value=current_momentum)
ssd_print(key=mlperf_log.OPT_WEIGHT_DECAY, value=current_weight_decay)
if args.warmup is not None:
ssd_print(key=mlperf_log.OPT_LR_WARMUP_STEPS, value=args.warmup)
# Model is completely finished -- need to create separate copies, preserve parameters across
# them, and jit
ssd300_eval = SSD300(val_coco.labelnum,
backbone=args.backbone,
use_nhwc=args.nhwc,
pad_input=args.pad_input).cuda()
if args.use_fp16:
ssd300_eval = network_to_half(ssd300_eval)
# Get the existant state from the train model
# * if we use distributed, then we want .module
train_model = ssd300.module if args.distributed else ssd300
ssd300_eval.load_state_dict(train_model.state_dict())
ssd300_eval.eval()
if args.jit:
input_c = 4 if args.pad_input else 3
example_shape = [
args.batch_size, 300, 300, input_c
] if args.nhwc else [args.batch_size, input_c, 300, 300]
example_input = torch.randn(*example_shape).cuda()
if args.use_fp16:
example_input = example_input.half()
# DDP has some Python-side control flow. If we JIT the entire DDP-wrapped module,
# the resulting ScriptModule will elide this control flow, resulting in allreduce
# hooks not being called. If we're running distributed, we need to extract and JIT
# the wrapped .module.
# Replacing a DDP-ed ssd300 with a script_module might also cause the AccumulateGrad hooks
# to go out of scope, and therefore silently disappear.
module_to_jit = ssd300.module if args.distributed else ssd300
if args.distributed:
ssd300.module = torch.jit.trace(module_to_jit, example_input)
else:
ssd300 = torch.jit.trace(module_to_jit, example_input)
print_message(args.local_rank, "epoch", "nbatch", "loss")
eval_points = np.array(args.evaluation) * 32 / global_batch_size
eval_points = list(map(int, list(eval_points)))
iter_num = args.iteration
avg_loss = 0.0
inv_map = {v: k for k, v in val_coco.label_map.items()}
start_elapsed_time = time.time()
last_printed_iter = args.iteration
num_elapsed_samples = 0
# Generate normalization tensors
mean, std = generate_mean_std(args)
def step_maybe_fp16_maybe_distributed(optim):
if args.use_fp16:
if args.distributed:
for flat_master, allreduce_buffer in zip(
flat_master_buckets, ssd300.allreduce_buffers):
if allreduce_buffer is None:
raise RuntimeError("allreduce_buffer is None")
flat_master.grad = allreduce_buffer.float()
flat_master.grad.data.mul_(1. / static_loss_scale)
else:
for flat_master, model_bucket in zip(flat_master_buckets,
model_buckets):
flat_grad = apex_C.flatten(
[m.grad.data for m in model_bucket])
flat_master.grad = flat_grad.float()
flat_master.grad.data.mul_(1. / static_loss_scale)
optim.step()
if args.use_fp16:
for model_bucket, flat_master in zip(model_buckets,
flat_master_buckets):
for model, master in zip(
model_bucket,
apex_C.unflatten(flat_master.data, model_bucket)):
model.data.copy_(master.data)
ssd_print(key=mlperf_log.TRAIN_LOOP)
for epoch in range(args.epochs):
ssd_print(key=mlperf_log.TRAIN_EPOCH, value=epoch)
for p in ssd300.parameters():
p.grad = None
for i, data in enumerate(train_loader):
img = data[0][0][0]
bbox = data[0][1][0]
label = data[0][2][0]
label = label.type(torch.cuda.LongTensor)
bbox_offsets = data[0][3][0]
# handle random flipping outside of DALI for now
bbox_offsets = bbox_offsets.cuda()
img, bbox = C.random_horiz_flip(img, bbox, bbox_offsets, 0.5,
args.nhwc)
img.sub_(mean).div_(std)
if args.profile is not None and iter_num == args.profile:
return
if args.warmup is not None and optimizer_created:
lr_warmup(optim, args.warmup, iter_num, epoch, current_lr,
args)
if iter_num == ((args.decay1 * 1000 * 32) // global_batch_size):
print_message(args.local_rank, "lr decay step #1")
current_lr *= 0.1
for param_group in optim.param_groups:
param_group['lr'] = current_lr
ssd_print(key=mlperf_log.OPT_LR, value=current_lr)
if iter_num == ((args.decay2 * 1000 * 32) // global_batch_size):
print_message(args.local_rank, "lr decay step #2")
current_lr *= 0.1
for param_group in optim.param_groups:
param_group['lr'] = current_lr
ssd_print(key=mlperf_log.OPT_LR, value=current_lr)
if use_cuda:
img = img.cuda()
# NHWC direct from DALI now if necessary
bbox = bbox.cuda()
label = label.cuda()
bbox_offsets = bbox_offsets.cuda()
# Now run the batched box encoder
N = img.shape[0]
if bbox_offsets[-1].item() == 0:
print("No labels in batch")
continue
bbox, label = C.box_encoder(N, bbox, bbox_offsets, label,
encoder.dboxes.cuda(), 0.5)
# output is ([N*8732, 4], [N*8732], need [N, 8732, 4], [N, 8732] respectively
M = bbox.shape[0] // N
bbox = bbox.view(N, M, 4)
label = label.view(N, M)
# print(img.shape, bbox.shape, label.shape)
ploc, plabel = ssd300(img)
ploc, plabel = ploc.float(), plabel.float()
trans_bbox = bbox.transpose(1, 2).contiguous().cuda()
label = label.cuda()
gloc, glabel = Variable(trans_bbox, requires_grad=False), \
Variable(label, requires_grad=False)
loss = loss_func(ploc, plabel, gloc, glabel)
if not np.isinf(loss.item()):
avg_loss = 0.999 * avg_loss + 0.001 * loss.item()
num_elapsed_samples += N
if args.local_rank == 0 and iter_num % args.print_interval == 0:
end_elapsed_time = time.time()
elapsed_time = end_elapsed_time - start_elapsed_time
avg_samples_per_sec = num_elapsed_samples * N_gpu / elapsed_time
print("Iteration: {:6d}, Loss function: {:5.3f}, Average Loss: {:.3f}, avg. samples / sec: {:.2f}"\
.format(iter_num, loss.item(), avg_loss, avg_samples_per_sec), end="\n")
last_printed_iter = iter_num
start_elapsed_time = time.time()
num_elapsed_samples = 0
# loss scaling
if args.use_fp16:
loss = loss * static_loss_scale
loss.backward()
if not optimizer_created:
# Imitate the model bucket structure created by DDP.
# These will already be split by type (float or half).
model_buckets = []
for bucket in ssd300.active_i_buckets:
model_buckets.append([])
for active_i in bucket:
model_buckets[-1].append(
ssd300.active_params[active_i])
flat_master_buckets = create_flat_master(model_buckets)
optim = torch.optim.SGD(flat_master_buckets,
lr=current_lr,
momentum=current_momentum,
weight_decay=current_weight_decay)
optimizer_created = True
# Skip this first iteration because flattened allreduce buffers are not yet created.
# step_maybe_fp16_maybe_distributed(optim)
else:
step_maybe_fp16_maybe_distributed(optim)
# Likely a decent skew here, let's take this opportunity to set the gradients to None.
# After DALI integration, playing with the placement of this is worth trying.
for p in ssd300.parameters():
p.grad = None
if iter_num in eval_points:
if args.local_rank == 0:
if not args.no_save:
print("saving model...")
torch.save(
{
"model": ssd300.state_dict(),
"label_map": val_coco.label_info
}, "./models/iter_{}.pt".format(iter_num))
# Get the existant state from the train model
# * if we use distributed, then we want .module
train_model = ssd300.module if args.distributed else ssd300
ssd300_eval.load_state_dict(train_model.state_dict())
if coco_eval(
ssd300_eval,
val_dataloader,
cocoGt,
encoder,
inv_map,
args.threshold,
epoch,
iter_num,
args.eval_batch_size,
use_fp16=args.use_fp16,
local_rank=args.local_rank if args.distributed else -1,
N_gpu=N_gpu,
use_nhwc=args.nhwc,
pad_input=args.pad_input):
return True
iter_num += 1
train_loader.reset()
return False
import tools
# 设置默认的tensor类型
torch.set_default_tensor_type('torch.cuda.FloatTensor')
test_img_index = 113
conf_threshold = 0.2
nms_threshold = 0.45
test_img, norm_img, label = test_ds.__getitem__(test_img_index)
width, height = test_img.size
# 实例化一个网络模型
net = torch.nn.DataParallel(SSD300())
# 加载参数
net.load_state_dict(torch.load('./net179-0.645.pth'))
# pred_conf: 预测得到的分类概率向量---1, 8732, 21
# pred_loc: 预测得到的偏移向量---1, 8732, 4
norm_img = norm_img.cuda()
pred_conf, pred_loc = net(norm_img)
pred_loc = torch.squeeze(pred_loc)
pred_conf = torch.squeeze(pred_conf)
# 将pred_conf转换为概率向量
pred_conf = F.softmax(pred_conf, dim=1)
def train300():
label_map = {}
dboxes = dboxes300()
trans = SSDTransformer(dboxes, (300, 300), val=False)
img_folder = "../../VOCdevkit/VOC2007/JPEGImages"
ann_folder = "../../VOCdevkit/VOC2007/Annotations"
tgt_folder = "../../VOCdevkit/VOC2007/ImageSets/Main/trainval.txt"
vd = VOCDetection(img_folder, ann_folder, tgt_folder, label_map=label_map, \
transform = trans)
dataloader = DataLoader(vd, batch_size=32, shuffle=True, num_workers=8)
nepochs = 800
ssd300 = SSD300(21)
ssd300.train()
ssd300.cuda()
loss_func = Loss(dboxes)
loss_func.cuda()
optim = torch.optim.SGD(ssd300.parameters(),
lr=1e-3,
momentum=0.9,
weight_decay=5e-4)
print("epoch", "nbatch", "loss")
iter_num = 0
avg_loss = 0.0
for epoch in range(nepochs):
if iter_num >= 60000:
break
for nbatch, (img, img_size, bbox, label) in enumerate(dataloader):
#gc.collect()
iter_num += 1
if iter_num == 40000:
print("")
print("lr decay step #1")
for param_group in optim.param_groups:
param_group['lr'] = 1e-4
if iter_num == 50000:
print("")
print("lr decay step #2")
for param_group in optim.param_groups:
param_group['lr'] = 1e-5
img = Variable(img.cuda(), requires_grad=True)
ploc, plabel = ssd300(img)
#torch.cuda.synchronize()
#show_memusage()
gloc, glabel = Variable(bbox.transpose(1,2).contiguous().cuda(),
requires_grad=False), \
Variable(label.cuda(), requires_grad=False)
loss = loss_func(ploc, plabel, gloc, glabel)
#torch.cuda.synchronize()
#show_memusage()
avg_loss = 0.999 * avg_loss + 0.001 * loss.item()
print("Iteration: {:6d}, Loss function: {:5.3f}, Average Loss: {:.3f}"\
.format(iter_num, loss.item(), avg_loss), end="\r")
optim.zero_grad()
loss.backward()
#torch.cuda.synchronize()
#show_memusage()
optim.step()
if iter_num % 5000 == 0:
print("")
print("saving model...")
torch.save(
{
"model": ssd300.state_dict(),
"label_map": vd.label_map
}, "./models/iter_{}.pt".format(iter_num))
def test_coco(args):
# For testing purposes we have to use CUDA
use_cuda = True
# Setup multi-GPU if necessary
args.distributed = False
if 'WORLD_SIZE' in os.environ:
args.distributed = int(os.environ['WORLD_SIZE']) > 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
if args.distributed:
N_gpu = torch.distributed.get_world_size()
else:
N_gpu = 1
# Setup data, defaults
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
if args.use_train_dataset:
annotate = os.path.join(args.data,
"annotations/instances_train2017.json")
coco_root = os.path.join(args.data, "train2017")
img_number = 118287
else:
annotate = os.path.join(args.data,
"annotations/instances_val2017.json")
coco_root = os.path.join(args.data, "val2017")
img_number = 5000
pipe = COCOPipeline(args.batch_size,
args.local_rank,
coco_root,
annotate,
N_gpu,
num_threads=args.num_workers)
pipe.build()
test_run = pipe.run()
dataloader = DALICOCOIterator(pipe, img_number / N_gpu)
# Build the model
ssd300 = SSD300(81, backbone=args.backbone, model_path='', dilation=False)
"""
# Note: args.checkpoint is required, so this can never be false
if args.checkpoint is not None:
print("loading model checkpoint", args.checkpoint)
od = torch.load(args.checkpoint)
# remove proceeding 'module' from checkpoint
model = od["model"]
for k in list(model.keys()):
if k.startswith('module.'):
model[k[7:]] = model.pop(k)
ssd300.load_state_dict(model)
"""
ssd300.cuda()
ssd300.eval()
loss_func = Loss(dboxes)
loss_func.cuda()
# parallelize
if args.distributed:
ssd300 = DDP(ssd300)
if args.use_fp16:
ssd300 = network_to_half(ssd300)
if args.use_train_dataset and args.local_rank == 0:
print(
'Image 000000320612.jpg is in fact PNG and it will cause fail if '
+ 'used with nvJPEGDecoder in coco_pipeline')
for epoch in range(2):
if epoch == 1 and args.local_rank == 0:
print("Performance computation starts")
s = time.time()
for i, data in enumerate(dataloader):
with torch.no_grad():
# Get data from pipeline
img = data[0][0][0]
bbox = data[0][1][0]
label = data[0][2][0]
label = label.type(torch.cuda.LongTensor)
bbox_offsets = data[0][3][0]
bbox_offsets = bbox_offsets.cuda()
# Encode labels
N = img.shape[0]
if bbox_offsets[-1].item() == 0:
print("No labels in batch")
continue
bbox, label = C.box_encoder(N, bbox, bbox_offsets, label,
encoder.dboxes.cuda(), 0.5)
# Prepare tensors for computing loss
M = bbox.shape[0] // N
bbox = bbox.view(N, M, 4)
label = label.view(N, M)
trans_bbox = bbox.transpose(1, 2).contiguous()
gloc, glabel = Variable(trans_bbox, requires_grad=False), \
Variable(label, requires_grad=False)
if args.use_fp16:
img = img.half()
for _ in range(args.fbu):
ploc, plabel = ssd300(img)
ploc, plabel = ploc.float(), plabel.float()
loss = loss_func(ploc, plabel, gloc, glabel)
if epoch == 1 and args.local_rank == 0:
e = time.time()
print("Performance achieved: {:.2f} img/sec".format(img_number /
(e - s)))
dataloader.reset()
def train300_mlperf_coco(args):
from coco import COCO
# Check that GPUs are actually available
if not torch.cuda.is_available():
print("Error. No GPU available.")
return False
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
input_size = 300
train_trans = SSDTransformer(dboxes, (input_size, input_size), val=False)
val_trans = SSDTransformer(dboxes, (input_size, input_size), val=True)
mlperf_log.ssd_print(key=mlperf_log.INPUT_SIZE, value=input_size)
val_annotate = os.path.join(args.data,
"annotations/instances_val2017.json")
val_coco_root = os.path.join(args.data, "val2017")
train_annotate = os.path.join(args.data,
"annotations/instances_train2017.json")
train_coco_root = os.path.join(args.data, "train2017")
cocoGt = COCO(annotation_file=val_annotate)
val_coco = COCODetection(val_coco_root, val_annotate, val_trans)
train_coco = COCODetection(train_coco_root, train_annotate, train_trans)
train_pipe = COCOPipeline(args.batch_size, train_coco_root, train_annotate,
dboxes, args.seed)
train_pipe.build()
train_loader = DALIGenericIterator(train_pipe,
["images", "boxes", "labels"],
train_pipe.epoch_size("Reader"))
mlperf_log.ssd_print(key=mlperf_log.INPUT_SHARD, value=None)
mlperf_log.ssd_print(key=mlperf_log.INPUT_ORDER)
mlperf_log.ssd_print(key=mlperf_log.INPUT_BATCH_SIZE,
value=args.batch_size)
ssd300 = SSD300(train_coco.labelnum)
if args.checkpoint is not None:
print("loading model checkpoint", args.checkpoint)
od = torch.load(args.checkpoint)
ssd300.load_state_dict(od["model"])
ssd300.train()
ssd300.cuda()
loss_func = Loss(dboxes)
loss_func.cuda()
current_lr = 1e-3
current_momentum = 0.9
current_weight_decay = 5e-4
optim = torch.optim.SGD(ssd300.parameters(),
lr=current_lr,
momentum=current_momentum,
weight_decay=current_weight_decay)
mlperf_log.ssd_print(key=mlperf_log.OPT_NAME, value="SGD")
mlperf_log.ssd_print(key=mlperf_log.OPT_LR, value=current_lr)
mlperf_log.ssd_print(key=mlperf_log.OPT_MOMENTUM, value=current_momentum)
mlperf_log.ssd_print(key=mlperf_log.OPT_WEIGHT_DECAY,
value=current_weight_decay)
print("epoch", "nbatch", "loss")
iter_num = args.iteration
avg_loss = 0.0
inv_map = {v: k for k, v in val_coco.label_map.items()}
mean, std = generate_mean_std()
data_perf = AverageMeter()
batch_perf = AverageMeter()
end = time.time()
train_start = end
mlperf_log.ssd_print(key=mlperf_log.TRAIN_LOOP)
for epoch in range(args.epochs):
mlperf_log.ssd_print(key=mlperf_log.TRAIN_EPOCH, value=epoch)
for nbatch, data in enumerate(train_loader):
img = data[0]["images"]
bbox = data[0]["boxes"]
label = data[0]["labels"]
boxes_in_batch = len(label.nonzero())
if boxes_in_batch == 0:
print("No labels in batch")
continue
label = label.type(torch.cuda.LongTensor)
img = Variable(img, requires_grad=True)
trans_bbox = bbox.transpose(1, 2).contiguous()
gloc, glabel = Variable(trans_bbox, requires_grad=False), \
Variable(label, requires_grad=False)
data_perf.update(time.time() - end)
if iter_num == 160000:
current_lr = 1e-4
print("")
print("lr decay step #1")
for param_group in optim.param_groups:
param_group['lr'] = current_lr
mlperf_log.ssd_print(key=mlperf_log.OPT_LR, value=current_lr)
if iter_num == 200000:
current_lr = 1e-5
print("")
print("lr decay step #2")
for param_group in optim.param_groups:
param_group['lr'] = current_lr
mlperf_log.ssd_print(key=mlperf_log.OPT_LR, value=current_lr)
ploc, plabel = ssd300(img)
loss = loss_func(ploc, plabel, gloc, glabel)
if not np.isinf(loss.item()):
avg_loss = 0.999 * avg_loss + 0.001 * loss.item()
optim.zero_grad()
loss.backward()
optim.step()
batch_perf.update(time.time() - end)
if iter_num in args.evaluation:
if not args.no_save:
print("")
print("saving model...")
torch.save(
{
"model": ssd300.state_dict(),
"label_map": train_coco.label_info
}, "./models/iter_{}.pt".format(iter_num))
try:
if coco_eval(ssd300, val_coco, cocoGt, encoder, inv_map,
args.threshold, epoch, iter_num):
return True
except:
print("Eval error on iteration {0}".format(iter_num))
print("Iteration: {:6d}, Loss function: {:5.3f}, Average Loss: {:.3f}, Data perf: {:3f} img/sec, Batch perf: {:3f} img/sec, Avg Data perf: {:3f} img/sec, Avg Batch perf: {:3f} img/sec"\
.format(iter_num, loss.item(), avg_loss, args.batch_size / data_perf.val, args.batch_size / batch_perf.val, args.batch_size / data_perf.avg, args.batch_size / batch_perf.avg), end="\r")
end = time.time()
iter_num += 1
if iter_num == 10 and epoch == 0:
data_perf.reset()
batch_perf.reset()
train_loader.reset()
print("\n\n")
print("Training end: Data perf: {:3f} img/sec, Batch perf: {:3f} img/sec, Total time: {:3f} sec"\
.format(args.batch_size / data_perf.avg, args.batch_size / batch_perf.avg, time.time() - train_start))
return False
def val300_coco(model_path):
print("loading model at {}".format(model_path))
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
trans = SSDTransformer(dboxes, (300, 300), val=True)
#annotate = "../../coco_ssd/instances_minival2014.json"
#coco_root = "../../coco_data/val2014"
#annotate = "../../coco_ssd/image_info_test-dev2015.json"
#coco_root = "../../coco_data/test2015"
annotate = "../../coco_ssd/instances_val2017.json"
coco_root = "../../coco_data/val2017"
cocoGt = COCO(annotation_file=annotate)
coco = COCODetection(coco_root, annotate, trans)
model = SSD300(coco.labelnum)
od = torch.load(model_path)
model.load_state_dict(od["model"])
model.eval()
model.cuda()
ret = []
inv_map = {v: k for k, v in coco.label_map.items()}
start = time.time()
for idx, image_id in enumerate(coco.img_keys):
img, (htot, wtot), _, _ = coco[idx]
with torch.no_grad():
print("Parsing image: {}/{}".format(idx + 1, len(coco)), end="\r")
ploc, plabel = model(img.unsqueeze(0).cuda())
try:
result = encoder.decode_batch(ploc, plabel, 0.50, 200)[0]
except:
#raise
print("")
print("No object detected in idx: {}".format(idx), end="\r")
continue
loc, label, prob = [r.cpu().numpy() for r in result]
for loc_, label_, prob_ in zip(loc, label, prob):
ret.append([image_id, loc_[0]*wtot, \
loc_[1]*htot,
(loc_[2] - loc_[0])*wtot,
(loc_[3] - loc_[1])*htot,
prob_,
inv_map[label_]])
print("")
print("Predicting Ended, totoal time: {:.2f} s".format(time.time() -
start))
cocoDt = cocoGt.loadRes(np.array(ret))
E = COCOeval(cocoGt, cocoDt, iouType='bbox')
#E.params.useSegm = 0
#E.params.recThrs = [0.5]
#E.params.maxDets = [10, 100, 200]
E.evaluate()
E.accumulate()
E.summarize()
def train300_mlperf_coco(args):
args = setup_distributed(args)
# Build the model
model_options = {
'use_nhwc': args.nhwc,
'pad_input': args.pad_input,
'bn_group': args.bn_group,
}
ssd300 = SSD300(args, args.num_classes, **model_options)
if args.checkpoint is not None:
load_checkpoint(ssd300, args.checkpoint)
ssd300.train()
ssd300.cuda()
dboxes = dboxes300_coco()
# Note: No reason not to use optimised loss
loss_func = OptLoss()
loss_func.cuda()
# Create optimizer. This must also be done after network_to_half.
global_batch_size = (args.N_gpu * args.batch_size)
log_event(key=constants.MODEL_BN_SPAN,
value=args.bn_group * args.batch_size)
log_event(key=constants.GLOBAL_BATCH_SIZE, value=global_batch_size)
# mlperf only allows base_lr scaled by an integer
base_lr = 2.5e-3
requested_lr_multiplier = args.lr / base_lr
adjusted_multiplier = max(
1, round(requested_lr_multiplier * global_batch_size / 32))
current_lr = base_lr * adjusted_multiplier
current_momentum = 0.9
current_weight_decay = args.wd
static_loss_scale = 128.
optim = apex.optimizers.FusedSGD(ssd300.parameters(),
lr=current_lr,
momentum=current_momentum,
weight_decay=current_weight_decay)
ssd300, optim = apex.amp.initialize(ssd300,
optim,
opt_level='O2',
loss_scale=static_loss_scale)
# Parallelize. Need to do this after network_to_half.
if args.distributed:
if args.delay_allreduce:
print_message(args.local_rank,
"Delaying allreduces to the end of backward()")
ssd300 = DDP(ssd300,
gradient_predivide_factor=args.N_gpu / 8.0,
delay_allreduce=args.delay_allreduce,
retain_allreduce_buffers=args.use_fp16)
log_event(key=constants.OPT_BASE_LR, value=current_lr)
log_event(key=constants.OPT_LR_DECAY_BOUNDARY_EPOCHS,
value=args.lr_decay_epochs)
log_event(key=constants.OPT_LR_DECAY_STEPS, value=args.lr_decay_epochs)
log_event(key=constants.OPT_WEIGHT_DECAY, value=current_weight_decay)
if args.warmup is not None:
log_event(key=constants.OPT_LR_WARMUP_STEPS, value=args.warmup)
log_event(key=constants.OPT_LR_WARMUP_FACTOR, value=args.warmup_factor)
# Model is completely finished -- need to create separate copies, preserve parameters across
# them, and jit
ssd300_eval = SSD300(args, args.num_classes, **model_options).cuda()
if args.use_fp16:
convert_network(ssd300_eval, torch.half)
# Get the existant state from the train model
# * if we use distributed, then we want .module
train_model = ssd300.module if args.distributed else ssd300
ssd300_eval.load_state_dict(train_model.state_dict())
ssd300_eval.eval()
print_message(args.local_rank, "epoch", "nbatch", "loss")
iter_num = args.iteration
avg_loss = 0.0
start_elapsed_time = time.time()
last_printed_iter = args.iteration
num_elapsed_samples = 0
input_c = 4 if args.pad_input else 3
example_shape = [args.batch_size, 300, 300, input_c
] if args.nhwc else [args.batch_size, input_c, 300, 300]
example_input = torch.randn(*example_shape).cuda()
if args.use_fp16:
example_input = example_input.half()
if args.jit:
# DDP has some Python-side control flow. If we JIT the entire DDP-wrapped module,
# the resulting ScriptModule will elide this control flow, resulting in allreduce
# hooks not being called. If we're running distributed, we need to extract and JIT
# the wrapped .module.
# Replacing a DDP-ed ssd300 with a script_module might also cause the AccumulateGrad hooks
# to go out of scope, and therefore silently disappear.
module_to_jit = ssd300.module if args.distributed else ssd300
if args.distributed:
ssd300.module = torch.jit.trace(module_to_jit,
example_input,
check_trace=False)
else:
ssd300 = torch.jit.trace(module_to_jit,
example_input,
check_trace=False)
# JIT the eval model too
ssd300_eval = torch.jit.trace(ssd300_eval,
example_input,
check_trace=False)
# do a dummy fprop & bprop to make sure cudnnFind etc. are timed here
ploc, plabel = ssd300(example_input)
# produce a single dummy "loss" to make things easier
loss = ploc[0, 0, 0] + plabel[0, 0, 0]
dloss = torch.randn_like(loss)
# Cause cudnnFind for dgrad, wgrad to run
loss.backward(dloss)
# Necessary import in init
from pycocotools.coco import COCO
encoder = build_ssd300_coder()
evaluator = AsyncEvaluator(num_threads=1)
log_end(key=constants.INIT_STOP)
##### END INIT
# This is the first place we touch anything related to data
##### START DATA TOUCHING
barrier()
log_start(key=constants.RUN_START)
barrier()
train_pipe = prebuild_pipeline(args)
train_loader, epoch_size = build_pipeline(args,
training=True,
pipe=train_pipe)
if args.rank == 0:
print("epoch size is: ", epoch_size, " images")
val_loader, inv_map, cocoGt = build_pipeline(args, training=False)
if args.profile_gc_off:
gc.disable()
gc.collect()
##### END DATA TOUCHING
i_eval = 0
block_start_epoch = 1
log_start(key=constants.BLOCK_START,
metadata={
'first_epoch_num': block_start_epoch,
'epoch_count': args.evaluation[i_eval]
})
for epoch in range(args.epochs):
for p in ssd300.parameters():
p.grad = None
if epoch in args.evaluation:
# Get the existant state from the train model
# * if we use distributed, then we want .module
train_model = ssd300.module if args.distributed else ssd300
if args.distributed and args.allreduce_running_stats:
if args.rank == 0: print("averaging bn running means and vars")
# make sure every node has the same running bn stats before
# using them to evaluate, or saving the model for inference
world_size = float(torch.distributed.get_world_size())
for bn_name, bn_buf in train_model.named_buffers(recurse=True):
if ('running_mean' in bn_name) or ('running_var'
in bn_name):
torch.distributed.all_reduce(bn_buf,
op=dist.ReduceOp.SUM)
bn_buf /= world_size
if args.rank == 0:
if args.save:
print("saving model...")
if not os.path.isdir('./models'):
os.mkdir('./models')
torch.save({"model": ssd300.state_dict()},
"./models/iter_{}.pt".format(iter_num))
ssd300_eval.load_state_dict(train_model.state_dict())
# Note: No longer returns, evaluation is abstracted away inside evaluator
coco_eval(args,
ssd300_eval,
val_loader,
cocoGt,
encoder,
inv_map,
epoch,
iter_num,
evaluator=evaluator)
log_end(key=constants.BLOCK_STOP,
metadata={'first_epoch_num': block_start_epoch})
if epoch != max(args.evaluation):
i_eval += 1
block_start_epoch = epoch + 1
log_start(key=constants.BLOCK_START,
metadata={
'first_epoch_num':
block_start_epoch,
'epoch_count': (args.evaluation[i_eval] -
args.evaluation[i_eval - 1])
})
if epoch in args.lr_decay_epochs:
current_lr *= args.lr_decay_factor
print_message(
args.rank,
"lr decay step #" + str(bisect(args.lr_decay_epochs, epoch)))
for param_group in optim.param_groups:
param_group['lr'] = current_lr
log_start(key=constants.EPOCH_START,
metadata={
'epoch_num': epoch + 1,
'current_iter_num': iter_num
})
for i, (img, bbox, label) in enumerate(train_loader):
if args.profile_start is not None and iter_num == args.profile_start:
torch.cuda.profiler.start()
torch.cuda.synchronize()
if args.profile_nvtx:
torch.autograd._enable_profiler(
torch.autograd.ProfilerState.NVTX)
if args.profile is not None and iter_num == args.profile:
if args.profile_start is not None and iter_num >= args.profile_start:
# we turned cuda and nvtx profiling on, better turn it off too
if args.profile_nvtx:
torch.autograd._disable_profiler()
torch.cuda.profiler.stop()
return
if args.warmup is not None:
lr_warmup(optim, args.warmup, iter_num, epoch, current_lr,
args)
if (img is None) or (bbox is None) or (label is None):
print("No labels in batch")
continue
ploc, plabel = ssd300(img)
ploc, plabel = ploc.float(), plabel.float()
N = img.shape[0]
bbox.requires_grad = False
label.requires_grad = False
# reshape (N*8732X4 -> Nx8732x4) and transpose (Nx8732x4 -> Nx4x8732)
bbox = bbox.view(N, -1, 4).transpose(1, 2).contiguous()
# reshape (N*8732 -> Nx8732) and cast to Long
label = label.view(N, -1).long()
loss = loss_func(ploc, plabel, bbox, label)
if np.isfinite(loss.item()):
avg_loss = 0.999 * avg_loss + 0.001 * loss.item()
else:
print("model exploded (corrupted by Inf or Nan)")
sys.exit()
num_elapsed_samples += N
if args.rank == 0 and iter_num % args.print_interval == 0:
end_elapsed_time = time.time()
elapsed_time = end_elapsed_time - start_elapsed_time
avg_samples_per_sec = num_elapsed_samples * args.N_gpu / elapsed_time
print("Iteration: {:6d}, Loss function: {:5.3f}, Average Loss: {:.3f}, avg. samples / sec: {:.2f}"\
.format(iter_num, loss.item(), avg_loss, avg_samples_per_sec), end="\n")
last_printed_iter = iter_num
start_elapsed_time = time.time()
num_elapsed_samples = 0
with apex.amp.scale_loss(loss, optim) as scaled_loss:
scaled_loss.backward()
if not args.profile_fake_optim:
optim.step()
# Likely a decent skew here, let's take this opportunity to set the
# gradients to None. After DALI integration, playing with the
# placement of this is worth trying.
for p in ssd300.parameters():
p.grad = None
# Don't check every iteration due to cost of broadcast
if iter_num % 20 == 0:
finished = check_async_evals(args, evaluator, args.threshold)
if finished:
return True
iter_num += 1
train_loader.reset()
log_end(key=constants.EPOCH_STOP, metadata={'epoch_num': epoch + 1})
return False
def train300_mlperf_coco(args):
args.distributed = args.world_size > 1
from coco import COCO
# Check that GPUs are actually available
use_cuda = not args.no_cuda and torch.cuda.is_available()
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
train_trans = SSDTransformer(dboxes, (300, 300), val=False)
val_trans = SSDTransformer(dboxes, (300, 300), val=True)
val_annotate = os.path.join(args.data,
"annotations/instances_val2017.json")
val_coco_root = os.path.join(args.data, "val2017")
train_annotate = os.path.join(args.data,
"annotations/instances_train2017.json")
train_coco_root = os.path.join(args.data, "train2017")
cocoGt = COCO(annotation_file=val_annotate)
val_coco = COCODetection(val_coco_root, val_annotate, val_trans)
train_coco = COCODetection(train_coco_root, train_annotate, train_trans)
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_coco)
else:
train_sampler = None
train_dataloader = DataLoader(train_coco,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
sampler=train_sampler)
ssd300 = SSD300(train_coco.labelnum)
if args.checkpoint is not None:
print("loading model checkpoint", args.checkpoint)
od = torch.load(args.checkpoint)
ssd300.load_state_dict(od["model"])
ssd300.train()
if use_cuda:
ssd300.cuda()
loss_func = Loss(dboxes)
if use_cuda:
loss_func.cuda()
if args.distributed:
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size)
ssd300 = DistributedDataParallel(ssd300)
else:
ssd300 = torch.nn.DataParallel(ssd300)
optim = torch.optim.SGD(ssd300.parameters(),
lr=1e-3,
momentum=0.9,
weight_decay=5e-4)
print("epoch", "nbatch", "loss")
iter_num = args.iteration
avg_loss = 0.0
inv_map = {v: k for k, v in val_coco.label_map.items()}
for epoch in range(args.epochs):
for nbatch, (img, img_size, bbox,
label) in enumerate(train_dataloader):
start = time.time()
if iter_num == 160000:
print("")
print("lr decay step #1")
for param_group in optim.param_groups:
param_group['lr'] = 1e-4
if iter_num == 200000:
print("")
print("lr decay step #2")
for param_group in optim.param_groups:
param_group['lr'] = 1e-5
if use_cuda:
img = img.cuda()
img = Variable(img, requires_grad=True)
ploc, plabel = ssd300(img)
trans_bbox = bbox.transpose(1, 2).contiguous()
if use_cuda:
trans_bbox = trans_bbox.cuda()
label = label.cuda()
gloc, glabel = Variable(trans_bbox, requires_grad=False), \
Variable(label, requires_grad=False)
loss = loss_func(ploc, plabel, gloc, glabel)
if not np.isinf(loss.item()):
avg_loss = 0.999 * avg_loss + 0.001 * loss.item()
optim.zero_grad()
loss.backward()
optim.step()
end = time.time()
if nbatch % 10 == 0:
print("Iteration: {:6d}, Loss function: {:5.3f}, Average Loss: {:.3f}, Average time: {:.3f} secs"\
.format(iter_num, loss.item(), avg_loss, end - start))
if iter_num in args.evaluation:
if not args.no_save:
print("")
print("saving model...")
torch.save(
{
"model": ssd300.state_dict(),
"label_map": train_coco.label_info
}, "./models/iter_{}.pt".format(iter_num))
if coco_eval(ssd300, val_coco, cocoGt, encoder, inv_map,
args.threshold):
return
iter_num += 1
def train300_mlperf_coco(args):
from pycocotools.coco import COCO
# Check that GPUs are actually available
use_cuda = not args.no_cuda
# Setup multi-GPU if necessary
args.distributed = False
if 'WORLD_SIZE' in os.environ:
args.distributed = int(os.environ['WORLD_SIZE']) > 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
local_seed = set_seeds(args)
# start timing here
if args.distributed:
N_gpu = torch.distributed.get_world_size()
else:
N_gpu = 1
validate_group_bn(args.bn_group)
# Setup data, defaults
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
input_size = 300
val_trans = SSDTransformer(dboxes, (input_size, input_size), val=True)
val_annotate = os.path.join(args.data,
"annotations/instances_val2017.json")
val_coco_root = os.path.join(args.data, "val2017")
train_annotate = os.path.join(args.data,
"annotations/instances_train2017.json")
train_coco_root = os.path.join(args.data, "train2017")
# Build the model
model_options = {
'backbone': args.backbone,
'use_nhwc': args.nhwc,
'pad_input': args.pad_input,
'bn_group': args.bn_group,
}
ssd300 = SSD300(args.num_classes, **model_options)
if args.checkpoint is not None:
load_checkpoint(ssd300, args.checkpoint)
ssd300.train()
ssd300.cuda()
if args.opt_loss:
loss_func = OptLoss(dboxes)
else:
loss_func = Loss(dboxes)
loss_func.cuda()
if args.distributed:
N_gpu = torch.distributed.get_world_size()
else:
N_gpu = 1
if args.use_fp16:
ssd300 = network_to_half(ssd300)
# Parallelize. Need to do this after network_to_half.
if args.distributed:
if args.delay_allreduce:
print_message(args.local_rank,
"Delaying allreduces to the end of backward()")
ssd300 = DDP(ssd300,
gradient_predivide_factor=N_gpu / 8.0,
delay_allreduce=args.delay_allreduce,
retain_allreduce_buffers=args.use_fp16)
# Create optimizer. This must also be done after network_to_half.
global_batch_size = (N_gpu * args.batch_size)
mlperf_print(key=mlperf_compliance.constants.MODEL_BN_SPAN,
value=args.bn_group * args.batch_size)
mlperf_print(key=mlperf_compliance.constants.GLOBAL_BATCH_SIZE,
value=global_batch_size)
# mlperf only allows base_lr scaled by an integer
base_lr = 2.5e-3
requested_lr_multiplier = args.lr / base_lr
adjusted_multiplier = max(
1, round(requested_lr_multiplier * global_batch_size / 32))
current_lr = base_lr * adjusted_multiplier
current_momentum = 0.9
current_weight_decay = args.wd
static_loss_scale = 128.
if args.use_fp16:
if args.distributed and not args.delay_allreduce:
# We can't create the flat master params yet, because we need to
# imitate the flattened bucket structure that DDP produces.
optimizer_created = False
else:
model_buckets = [
[
p for p in ssd300.parameters()
if p.requires_grad and p.type() == "torch.cuda.HalfTensor"
],
[
p for p in ssd300.parameters()
if p.requires_grad and p.type() == "torch.cuda.FloatTensor"
]
]
flat_master_buckets = create_flat_master(model_buckets)
optim = torch.optim.SGD(flat_master_buckets,
lr=current_lr,
momentum=current_momentum,
weight_decay=current_weight_decay)
optimizer_created = True
else:
optim = torch.optim.SGD(ssd300.parameters(),
lr=current_lr,
momentum=current_momentum,
weight_decay=current_weight_decay)
optimizer_created = True
mlperf_print(key=mlperf_compliance.constants.OPT_BASE_LR, value=current_lr)
mlperf_print(key=mlperf_compliance.constants.OPT_WEIGHT_DECAY,
value=current_weight_decay)
if args.warmup is not None:
mlperf_print(key=mlperf_compliance.constants.OPT_LR_WARMUP_STEPS,
value=args.warmup)
mlperf_print(key=mlperf_compliance.constants.OPT_LR_WARMUP_FACTOR,
value=args.warmup_factor)
# Model is completely finished -- need to create separate copies, preserve parameters across
# them, and jit
ssd300_eval = SSD300(args.num_classes,
backbone=args.backbone,
use_nhwc=args.nhwc,
pad_input=args.pad_input).cuda()
if args.use_fp16:
ssd300_eval = network_to_half(ssd300_eval)
# Get the existant state from the train model
# * if we use distributed, then we want .module
train_model = ssd300.module if args.distributed else ssd300
ssd300_eval.load_state_dict(train_model.state_dict())
ssd300_eval.eval()
print_message(args.local_rank, "epoch", "nbatch", "loss")
eval_points = np.array(args.evaluation) * 32 / global_batch_size
eval_points = list(map(int, list(eval_points)))
iter_num = args.iteration
avg_loss = 0.0
start_elapsed_time = time.time()
last_printed_iter = args.iteration
num_elapsed_samples = 0
# Generate normalization tensors
mean, std = generate_mean_std(args)
dummy_overflow_buf = torch.cuda.IntTensor([0])
def step_maybe_fp16_maybe_distributed(optim):
if args.use_fp16:
if args.distributed:
for flat_master, allreduce_buffer in zip(
flat_master_buckets, ssd300.allreduce_buffers):
if allreduce_buffer is None:
raise RuntimeError("allreduce_buffer is None")
flat_master.grad = allreduce_buffer.float()
flat_master.grad.data.mul_(1. / static_loss_scale)
else:
for flat_master, model_bucket in zip(flat_master_buckets,
model_buckets):
flat_grad = apex_C.flatten(
[m.grad.data for m in model_bucket])
flat_master.grad = flat_grad.float()
flat_master.grad.data.mul_(1. / static_loss_scale)
optim.step()
if args.use_fp16:
# Use multi-tensor scale instead of loop & individual parameter copies
for model_bucket, flat_master in zip(model_buckets,
flat_master_buckets):
multi_tensor_applier(
amp_C.multi_tensor_scale, dummy_overflow_buf, [
apex_C.unflatten(flat_master.data, model_bucket),
model_bucket
], 1.0)
input_c = 4 if args.pad_input else 3
example_shape = [args.batch_size, 300, 300, input_c
] if args.nhwc else [args.batch_size, input_c, 300, 300]
example_input = torch.randn(*example_shape).cuda()
if args.use_fp16:
example_input = example_input.half()
if args.jit:
# DDP has some Python-side control flow. If we JIT the entire DDP-wrapped module,
# the resulting ScriptModule will elide this control flow, resulting in allreduce
# hooks not being called. If we're running distributed, we need to extract and JIT
# the wrapped .module.
# Replacing a DDP-ed ssd300 with a script_module might also cause the AccumulateGrad hooks
# to go out of scope, and therefore silently disappear.
module_to_jit = ssd300.module if args.distributed else ssd300
if args.distributed:
ssd300.module = torch.jit.trace(module_to_jit, example_input)
else:
ssd300 = torch.jit.trace(module_to_jit, example_input)
# JIT the eval model too
ssd300_eval = torch.jit.trace(ssd300_eval, example_input)
# do a dummy fprop & bprop to make sure cudnnFind etc. are timed here
ploc, plabel = ssd300(example_input)
# produce a single dummy "loss" to make things easier
loss = ploc[0, 0, 0] + plabel[0, 0, 0]
dloss = torch.randn_like(loss)
# Cause cudnnFind for dgrad, wgrad to run
loss.backward(dloss)
mlperf_print(key=mlperf_compliance.constants.INIT_STOP, sync=True)
##### END INIT
# This is the first place we touch anything related to data
##### START DATA TOUCHING
mlperf_print(key=mlperf_compliance.constants.RUN_START, sync=True)
barrier()
cocoGt = COCO(annotation_file=val_annotate, use_ext=True)
val_coco = COCODetection(val_coco_root, val_annotate, val_trans)
if args.distributed:
val_sampler = GeneralDistributedSampler(val_coco, pad=False)
else:
val_sampler = None
if args.no_dali:
train_trans = SSDTransformer(dboxes, (input_size, input_size),
val=False)
train_coco = COCODetection(train_coco_root, train_annotate,
train_trans)
if args.distributed:
train_sampler = GeneralDistributedSampler(train_coco, pad=False)
else:
train_sampler = None
train_loader = DataLoader(train_coco,
batch_size=args.batch_size *
args.input_batch_multiplier,
shuffle=(train_sampler is None),
sampler=train_sampler,
num_workers=args.num_workers,
collate_fn=partial(my_collate,
is_training=True))
else:
train_pipe = COCOPipeline(args.batch_size *
args.input_batch_multiplier,
args.local_rank,
train_coco_root,
train_annotate,
N_gpu,
num_threads=args.num_workers,
output_fp16=args.use_fp16,
output_nhwc=args.nhwc,
pad_output=args.pad_input,
seed=local_seed - 2**31,
use_nvjpeg=args.use_nvjpeg,
use_roi=args.use_roi_decode,
dali_cache=args.dali_cache,
dali_async=(not args.dali_sync))
print_message(args.local_rank,
"time_check a: {secs:.9f}".format(secs=time.time()))
train_pipe.build()
print_message(args.local_rank,
"time_check b: {secs:.9f}".format(secs=time.time()))
test_run = train_pipe.run()
train_loader = SingleDaliIterator(
train_pipe, [
'images',
DALIOutput('bboxes', False, True),
DALIOutput('labels', True, True)
],
train_pipe.epoch_size()['train_reader'],
ngpu=N_gpu)
train_loader = EncodingInputIterator(train_loader,
dboxes=encoder.dboxes.cuda(),
nhwc=args.nhwc,
fake_input=args.fake_input,
no_dali=args.no_dali)
if args.input_batch_multiplier > 1:
train_loader = RateMatcher(input_it=train_loader,
output_size=args.batch_size)
val_dataloader = DataLoader(
val_coco,
batch_size=args.eval_batch_size,
shuffle=False, # Note: distributed sampler is shuffled :(
sampler=val_sampler,
num_workers=args.num_workers)
inv_map = {v: k for k, v in val_coco.label_map.items()}
##### END DATA TOUCHING
i_eval = 0
first_epoch = 1
mlperf_print(key=mlperf_compliance.constants.BLOCK_START,
metadata={
'first_epoch_num':
first_epoch,
'epoch_count':
args.evaluation[i_eval] * 32 /
train_pipe.epoch_size()['train_reader']
},
sync=True)
for epoch in range(args.epochs):
mlperf_print(key=mlperf_compliance.constants.EPOCH_START,
metadata={'epoch_num': epoch + 1},
sync=True)
for p in ssd300.parameters():
p.grad = None
for i, (img, bbox, label) in enumerate(train_loader):
if args.profile_start is not None and iter_num == args.profile_start:
torch.cuda.profiler.start()
torch.cuda.synchronize()
if args.profile_nvtx:
torch.autograd._enable_profiler(
torch.autograd.ProfilerState.NVTX)
if args.profile is not None and iter_num == args.profile:
if args.profile_start is not None and iter_num >= args.profile_start:
# we turned cuda and nvtx profiling on, better turn it off too
if args.profile_nvtx:
torch.autograd._disable_profiler()
torch.cuda.profiler.stop()
return
if args.warmup is not None and optimizer_created:
lr_warmup(optim, args.warmup, iter_num, epoch, current_lr,
args)
if iter_num == ((args.decay1 * 1000 * 32) // global_batch_size):
print_message(args.local_rank, "lr decay step #1")
current_lr *= 0.1
for param_group in optim.param_groups:
param_group['lr'] = current_lr
if iter_num == ((args.decay2 * 1000 * 32) // global_batch_size):
print_message(args.local_rank, "lr decay step #2")
current_lr *= 0.1
for param_group in optim.param_groups:
param_group['lr'] = current_lr
if (img is None) or (bbox is None) or (label is None):
print("No labels in batch")
continue
ploc, plabel = ssd300(img)
ploc, plabel = ploc.float(), plabel.float()
N = img.shape[0]
gloc, glabel = Variable(bbox, requires_grad=False), \
Variable(label, requires_grad=False)
loss = loss_func(ploc, plabel, gloc, glabel)
if np.isfinite(loss.item()):
avg_loss = 0.999 * avg_loss + 0.001 * loss.item()
else:
print("model exploded (corrupted by Inf or Nan)")
sys.exit()
num_elapsed_samples += N
if args.local_rank == 0 and iter_num % args.print_interval == 0:
end_elapsed_time = time.time()
elapsed_time = end_elapsed_time - start_elapsed_time
avg_samples_per_sec = num_elapsed_samples * N_gpu / elapsed_time
print("Iteration: {:6d}, Loss function: {:5.3f}, Average Loss: {:.3f}, avg. samples / sec: {:.2f}"\
.format(iter_num, loss.item(), avg_loss, avg_samples_per_sec), end="\n")
last_printed_iter = iter_num
start_elapsed_time = time.time()
num_elapsed_samples = 0
# loss scaling
if args.use_fp16:
loss = loss * static_loss_scale
loss.backward()
if not optimizer_created:
# Imitate the model bucket structure created by DDP.
# These will already be split by type (float or half).
model_buckets = []
for bucket in ssd300.active_i_buckets:
model_buckets.append([])
for active_i in bucket:
model_buckets[-1].append(
ssd300.active_params[active_i])
flat_master_buckets = create_flat_master(model_buckets)
optim = torch.optim.SGD(flat_master_buckets,
lr=current_lr,
momentum=current_momentum,
weight_decay=current_weight_decay)
optimizer_created = True
# Skip this first iteration because flattened allreduce buffers are not yet created.
# step_maybe_fp16_maybe_distributed(optim)
else:
step_maybe_fp16_maybe_distributed(optim)
# Likely a decent skew here, let's take this opportunity to set the gradients to None.
# After DALI integration, playing with the placement of this is worth trying.
for p in ssd300.parameters():
p.grad = None
if iter_num in eval_points:
# Get the existant state from the train model
# * if we use distributed, then we want .module
train_model = ssd300.module if args.distributed else ssd300
if args.distributed and args.allreduce_running_stats:
if get_rank() == 0:
print("averaging bn running means and vars")
# make sure every node has the same running bn stats before
# using them to evaluate, or saving the model for inference
world_size = float(torch.distributed.get_world_size())
for bn_name, bn_buf in train_model.named_buffers(
recurse=True):
if ('running_mean' in bn_name) or ('running_var'
in bn_name):
torch.distributed.all_reduce(bn_buf,
op=dist.ReduceOp.SUM)
bn_buf /= world_size
if get_rank() == 0:
if not args.no_save:
print("saving model...")
torch.save(
{
"model": ssd300.state_dict(),
"label_map": val_coco.label_info
}, "./models/iter_{}.pt".format(iter_num))
ssd300_eval.load_state_dict(train_model.state_dict())
succ = coco_eval(
ssd300_eval,
val_dataloader,
cocoGt,
encoder,
inv_map,
args.threshold,
epoch,
iter_num,
args.eval_batch_size,
use_fp16=args.use_fp16,
local_rank=args.local_rank if args.distributed else -1,
N_gpu=N_gpu,
use_nhwc=args.nhwc,
pad_input=args.pad_input)
mlperf_print(key=mlperf_compliance.constants.BLOCK_STOP,
metadata={'first_epoch_num': first_epoch},
sync=True)
if succ:
return True
if iter_num != max(eval_points):
i_eval += 1
first_epoch = epoch + 1
mlperf_print(key=mlperf_compliance.constants.BLOCK_START,
metadata={
'first_epoch_num':
first_epoch,
'epoch_count':
(args.evaluation[i_eval] -
args.evaluation[i_eval - 1]) * 32 /
train_pipe.epoch_size()['train_reader']
},
sync=True)
iter_num += 1
if args.max_iter > 0:
if iter_num > args.max_iter:
break
train_loader.reset()
mlperf_print(key=mlperf_compliance.constants.EPOCH_STOP,
metadata={'epoch_num': epoch + 1},
sync=True)
return False
def train300_mlperf_coco(exp, args):
from coco import COCO
device = exp.get_device()
chrono = exp.chrono()
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
input_size = 300
train_trans = SSDTransformer(dboxes, (input_size, input_size), val=False)
val_trans = SSDTransformer(dboxes, (input_size, input_size), val=True)
# mlperf_log.ssd_print(key=# mlperf_log.INPUT_SIZE, value=input_size)
val_annotate = os.path.join(args.data,
"annotations/instances_val2017.json")
val_coco_root = os.path.join(args.data, "val2017")
train_annotate = os.path.join(args.data,
"annotations/instances_train2017.json")
train_coco_root = os.path.join(args.data, "train2017")
cocoGt = COCO(annotation_file=val_annotate)
val_coco = COCODetection(val_coco_root, val_annotate, val_trans)
train_coco = COCODetection(train_coco_root, train_annotate, train_trans)
#print("Number of labels: {}".format(train_coco.labelnum))
train_dataloader = DataLoader(train_coco,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
# set shuffle=True in DataLoader
# mlperf_log.ssd_print(key=# mlperf_log.INPUT_SHARD, value=None)
# mlperf_log.ssd_print(key=# mlperf_log.INPUT_ORDER)
# mlperf_log.ssd_print(key=# mlperf_log.INPUT_BATCH_SIZE, value=args.batch_size)
ssd300 = SSD300(train_coco.labelnum)
if args.checkpoint is not None:
print("loading model checkpoint", args.checkpoint)
od = torch.load(args.checkpoint)
ssd300.load_state_dict(od["model"])
ssd300.train()
ssd300 = ssd300.to(device)
loss_func = Loss(dboxes).to(device)
current_lr = 1e-3
current_momentum = 0.9
current_weight_decay = 5e-4
optim = torch.optim.SGD(ssd300.parameters(),
lr=current_lr,
momentum=current_momentum,
weight_decay=current_weight_decay)
# mlperf_log.ssd_print(key=# mlperf_log.OPT_NAME, value="SGD")
# mlperf_log.ssd_print(key=# mlperf_log.OPT_LR, value=current_lr)
# mlperf_log.ssd_print(key=# mlperf_log.OPT_MOMENTUM, value=current_momentum)
# mlperf_log.ssd_print(key=# mlperf_log.OPT_WEIGHT_DECAY, value=current_weight_decay)
print("epoch", "nbatch", "loss")
iter_num = args.iteration
avg_loss = 0.0
inv_map = {v: k for k, v in val_coco.label_map.items()}
# mlperf_log.ssd_print(key=# mlperf_log.TRAIN_LOOP)
for epoch in range(args.repeat):
# mlperf_log.ssd_print(key=# mlperf_log.TRAIN_EPOCH, value=epoch)
with chrono.time('train') as t:
for nbatch, (img, img_size, bbox,
label) in enumerate(train_dataloader):
if nbatch > args.number:
break
img = Variable(img.to(device), requires_grad=True)
ploc, plabel = ssd300(img)
trans_bbox = bbox.transpose(1, 2).contiguous()
trans_bbox = trans_bbox.to(device)
label = label.to(device)
gloc = Variable(trans_bbox, requires_grad=False)
glabel = Variable(label, requires_grad=False)
loss = loss_func(ploc, plabel, gloc, glabel)
if not np.isinf(loss.item()):
avg_loss = 0.999 * avg_loss + 0.001 * loss.item()
exp.log_batch_loss(loss)
optim.zero_grad()
loss.backward()
optim.step()
iter_num += 1
exp.show_eta(epoch, t)
exp.report()
return False
def train300_mlperf_coco(args):
global torch
from coco import COCO
# Check that GPUs are actually available
use_cuda = not args.no_cuda and torch.cuda.is_available()
args.distributed = False
if args.use_hpu:
if 'WORLD_SIZE' in os.environ:
args.distributed = int(os.environ['WORLD_SIZE']) > 1
args.world_size = int(os.environ['WORLD_SIZE'])
print("world_size = {}".format(args.world_size))
print("distributed={}".format(args.distributed))
if use_cuda:
try:
from apex.parallel import DistributedDataParallel as DDP
if 'WORLD_SIZE' in os.environ:
args.distributed = int(os.environ['WORLD_SIZE']) > 1
except:
raise ImportError(
"Please install APEX from https://github.com/nvidia/apex")
use_hpu = args.use_hpu
hpu_channels_last = args.hpu_channels_last
hpu_lazy_mode = args.hpu_lazy_mode
is_hmp = args.is_hmp
device = torch.device('cpu')
data_loader_type = DataLoader
if use_hpu:
device = torch.device('hpu')
if args.distributed:
os.environ["MAX_WAIT_ATTEMPTS"] = "90"
if hpu_lazy_mode:
os.environ["PT_HPU_LAZY_MODE"] = "1"
else:
os.environ["PT_HPU_LAZY_MODE"] = "2"
if is_hmp:
if not args.hmp_bf16:
raise IOError("Please provide list of BF16 ops")
if not args.hmp_fp32:
raise IOError("Please provide list of FP32 ops")
from habana_frameworks.torch.hpex import hmp
hmp.convert(opt_level=args.hmp_opt_level,
bf16_file_path=args.hmp_bf16,
fp32_file_path=args.hmp_fp32,
isVerbose=args.hmp_verbose)
from habana_frameworks.torch.utils.library_loader import load_habana_module
load_habana_module()
# TODO - add dataloader
local_seed = args.seed
if args.distributed:
# necessary pytorch imports
import torch.utils.data.distributed
import torch.distributed as dist
if use_hpu:
args.dist_backend = 'hccl'
import habana_frameworks.torch.core.hccl
os.environ["ID"] = os.environ["RANK"]
dist.init_process_group(args.dist_backend, init_method='env://')
# set seeds properly
args.seed = broadcast_seeds(args.seed, device, use_hpu=True)
local_seed = (args.seed + dist.get_rank()) % 2**32
elif args.no_cuda:
device = torch.device('cpu')
else:
torch.cuda.set_device(args.local_rank)
device = torch.device('cuda')
dist.init_process_group(backend='nccl', init_method='env://')
# set seeds properly
args.seed = broadcast_seeds(args.seed, device)
local_seed = (args.seed + dist.get_rank()) % 2**32
mllogger.event(key=mllog_const.SEED, value=local_seed)
torch.manual_seed(local_seed)
np.random.seed(seed=local_seed)
random.seed(local_seed) # amorgenstern
torch.cuda.manual_seed(local_seed) # amorgenstern
args.rank = dist.get_rank() if args.distributed else args.local_rank
print("args.rank = {}".format(args.rank))
print("local rank = {}".format(args.local_rank))
print("distributed={}".format(args.distributed))
if use_hpu and is_hmp:
with hmp.disable_casts():
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
else:
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
input_size = 300
if use_hpu and is_hmp:
with hmp.disable_casts():
train_trans = SSDTransformer(
dboxes, (input_size, input_size),
val=False,
num_cropping_iterations=args.num_cropping_iterations)
val_trans = SSDTransformer(dboxes, (input_size, input_size),
val=True)
else:
train_trans = SSDTransformer(
dboxes, (input_size, input_size),
val=False,
num_cropping_iterations=args.num_cropping_iterations)
val_trans = SSDTransformer(dboxes, (input_size, input_size), val=True)
val_annotate = os.path.join(args.data,
"annotations/instances_val2017.json")
val_coco_root = os.path.join(args.data, "val2017")
train_annotate = os.path.join(args.data,
"annotations/instances_train2017.json")
train_coco_root = os.path.join(args.data, "train2017")
if use_hpu and is_hmp:
with hmp.disable_casts():
cocoGt = COCO(annotation_file=val_annotate)
train_coco = COCODetection(train_coco_root, train_annotate,
train_trans)
val_coco = COCODetection(val_coco_root, val_annotate, val_trans)
else:
cocoGt = COCO(annotation_file=val_annotate)
train_coco = COCODetection(train_coco_root, train_annotate,
train_trans)
val_coco = COCODetection(val_coco_root, val_annotate, val_trans)
mllogger.event(key=mllog_const.TRAIN_SAMPLES, value=len(train_coco))
mllogger.event(key=mllog_const.EVAL_SAMPLES, value=len(val_coco))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_coco)
else:
train_sampler = None
if use_hpu:
# patch torch cuda functions that are being unconditionally invoked
# in the multiprocessing data loader
torch.cuda.current_device = lambda: None
torch.cuda.set_device = lambda x: None
train_dataloader = data_loader_type(train_coco,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
sampler=train_sampler,
num_workers=args.num_workers)
# set shuffle=True in DataLoader
if args.rank == 0:
val_dataloader = data_loader_type(val_coco,
batch_size=args.val_batch_size
or args.batch_size,
shuffle=False,
sampler=None,
num_workers=args.num_workers)
else:
val_dataloader = None
ssd300 = SSD300(train_coco.labelnum, model_path=args.pretrained_backbone)
if args.checkpoint is not None:
print("loading model checkpoint", args.checkpoint)
od = torch.load(args.checkpoint, map_location=torch.device('cpu'))
ssd300.load_state_dict(od["model"])
ssd300.train()
if use_cuda:
ssd300.cuda()
if use_hpu and is_hmp:
with hmp.disable_casts():
loss_func = Loss(dboxes, use_hpu=use_hpu, hpu_device=device)
else:
loss_func = Loss(dboxes, use_hpu=use_hpu, hpu_device=device)
if use_cuda:
loss_func.cuda()
if use_hpu:
ssd300.to(device)
loss_func.to(device)
if args.distributed:
N_gpu = torch.distributed.get_world_size()
else:
N_gpu = 1
global_batch_size = N_gpu * args.batch_size
mllogger.event(key=mllog_const.GLOBAL_BATCH_SIZE, value=global_batch_size)
# Reference doesn't support group batch norm, so bn_span==local_batch_size
mllogger.event(key=mllog_const.MODEL_BN_SPAN, value=args.batch_size)
current_lr = args.lr * (global_batch_size / 32)
assert args.batch_size % args.batch_splits == 0, "--batch-size must be divisible by --batch-splits"
fragment_size = args.batch_size // args.batch_splits
if args.batch_splits != 1:
print("using gradient accumulation with fragments of size {}".format(
fragment_size))
current_momentum = 0.9
sgd_optimizer = torch.optim.SGD
if use_hpu and hpu_lazy_mode:
from habana_frameworks.torch.hpex.optimizers import FusedSGD
sgd_optimizer = FusedSGD
optim = sgd_optimizer(ssd300.parameters(),
lr=current_lr,
momentum=current_momentum,
weight_decay=args.weight_decay)
if use_hpu:
permute_params(model=ssd300,
to_filters_last=True,
lazy_mode=hpu_lazy_mode)
permute_momentum(optimizer=optim,
to_filters_last=True,
lazy_mode=hpu_lazy_mode)
ssd_print(device=device,
use_hpu=use_hpu,
key=mllog_const.OPT_BASE_LR,
value=current_lr)
ssd_print(device=device,
use_hpu=use_hpu,
key=mllog_const.OPT_WEIGHT_DECAY,
value=args.weight_decay)
# parallelize
if args.distributed:
if use_hpu:
ssd300 = torch.nn.parallel.DistributedDataParallel(
ssd300,
bucket_cap_mb=100,
broadcast_buffers=False,
gradient_as_bucket_view=True)
else:
ssd300 = DDP(ssd300)
iter_num = args.iteration
end_iter_num = args.end_iteration
if end_iter_num:
print("--end-iteration set to: {}".format(end_iter_num))
assert end_iter_num > iter_num, "--end-iteration must have a value > --iteration"
avg_loss = 0.0
if use_hpu:
loss_iter = list()
inv_map = {v: k for k, v in val_coco.label_map.items()}
success = torch.zeros(1)
if use_cuda:
success = success.cuda()
if use_hpu:
success = success.to(device)
if args.warmup:
nonempty_imgs = len(train_coco)
wb = int(args.warmup * nonempty_imgs / (N_gpu * args.batch_size))
ssd_print(device=device,
use_hpu=use_hpu,
key=mllog_const.OPT_LR_WARMUP_STEPS,
value=wb)
warmup_step = lambda iter_num, current_lr: lr_warmup(
optim, wb, iter_num, current_lr, args)
else:
warmup_step = lambda iter_num, current_lr: None
ssd_print(device=device,
use_hpu=use_hpu,
key=mllog_const.OPT_LR_WARMUP_FACTOR,
value=args.warmup_factor)
ssd_print(device=device,
use_hpu=use_hpu,
key=mllog_const.OPT_LR_DECAY_BOUNDARY_EPOCHS,
value=args.lr_decay_schedule)
mllogger.start(key=mllog_const.BLOCK_START,
metadata={
mllog_const.FIRST_EPOCH_NUM: 1,
mllog_const.EPOCH_COUNT: args.epochs
})
optim.zero_grad(set_to_none=True)
if use_hpu:
start = time.time()
for epoch in range(args.epochs):
mllogger.start(key=mllog_const.EPOCH_START,
metadata={mllog_const.EPOCH_NUM: epoch})
# set the epoch for the sampler
if args.distributed:
train_sampler.set_epoch(epoch)
if epoch in args.lr_decay_schedule:
current_lr *= 0.1
print("")
print("lr decay step #{num}".format(
num=args.lr_decay_schedule.index(epoch) + 1))
for param_group in optim.param_groups:
param_group['lr'] = current_lr
for nbatch, (img, img_id, img_size, bbox,
label) in enumerate(train_dataloader):
current_batch_size = img.shape[0]
# Split batch for gradient accumulation
img = torch.split(img, fragment_size)
bbox = torch.split(bbox, fragment_size)
label = torch.split(label, fragment_size)
for (fimg, fbbox, flabel) in zip(img, bbox, label):
current_fragment_size = fimg.shape[0]
if not use_hpu:
trans_bbox = fbbox.transpose(1, 2).contiguous()
if use_cuda:
fimg = fimg.cuda()
trans_bbox = trans_bbox.cuda()
flabel = flabel.cuda()
if use_hpu:
fimg = fimg.to(device)
if hpu_channels_last:
fimg = fimg.contiguous(
memory_format=torch.channels_last)
if hpu_lazy_mode:
mark_step()
if is_hmp:
with hmp.disable_casts():
#TODO revert after SW-58188 is fixed
trans_bbox = fbbox.to(device).transpose(
1, 2).contiguous()
flabel = flabel.to(device)
else:
#TODO revert after SW-58188 is fixed
trans_bbox = fbbox.to(device).transpose(
1, 2).contiguous()
flabel = flabel.to(device)
fimg = Variable(fimg, requires_grad=True)
if args.lowp: # amorgenstern
import lowp
with lowp.Lowp(mode='BF16',
warn_patched=True,
warn_not_patched=True):
ploc, plabel = ssd300(fimg)
gloc, glabel = Variable(trans_bbox, requires_grad=False), \
Variable(flabel, requires_grad=False)
loss = loss_func(ploc, plabel, gloc, glabel)
else:
ploc, plabel = ssd300(fimg)
if use_hpu and is_hmp:
with hmp.disable_casts():
gloc, glabel = Variable(trans_bbox, requires_grad=False), \
Variable(flabel, requires_grad=False)
loss = loss_func(ploc.float(), plabel.float(),
gloc, glabel)
else:
gloc, glabel = Variable(trans_bbox, requires_grad=False), \
Variable(flabel, requires_grad=False)
loss = loss_func(ploc, plabel, gloc, glabel)
loss = loss * (current_fragment_size / current_batch_size
) # weighted mean
if use_hpu and hpu_lazy_mode and args.distributed:
mark_step()
loss.backward()
if use_hpu and hpu_lazy_mode:
mark_step()
warmup_step(iter_num, current_lr)
if use_hpu and is_hmp:
with hmp.disable_casts():
optim.step()
else:
optim.step()
optim.zero_grad(set_to_none=True)
if use_hpu:
loss_iter.append(loss.clone().detach())
else:
if not np.isinf(loss.item()):
avg_loss = 0.999 * avg_loss + 0.001 * loss.item()
if use_hpu and hpu_lazy_mode:
mark_step()
if use_hpu:
if args.log_interval and not iter_num % args.log_interval:
cur_loss = 0.0
for i, x in enumerate(loss_iter):
cur_loss = x.cpu().item()
if not np.isinf(cur_loss):
avg_loss = 0.999 * avg_loss + 0.001 * cur_loss
if args.rank == 0:
print("Rank: {:6d}, Iteration: {:6d}, Loss function: {:5.3f}, Average Loss: {:.3f}"\
.format(args.rank, iter_num, cur_loss, avg_loss))
loss_iter = list()
else:
if args.rank == 0 and args.log_interval and not iter_num % args.log_interval:
print("Iteration: {:6d}, Loss function: {:5.3f}, Average Loss: {:.3f}"\
.format(iter_num, loss.item(), avg_loss))
iter_num += 1
if use_hpu and iter_num == 50:
start = time.time()
if end_iter_num and iter_num >= end_iter_num:
if use_hpu:
print("Training Ended, total time: {:.2f} s".format(
time.time() - start))
break
if (args.val_epochs and (epoch+1) in args.val_epochs) or \
(args.val_interval and not (epoch+1) % args.val_interval):
if args.distributed:
world_size = float(dist.get_world_size())
for bn_name, bn_buf in ssd300.module.named_buffers(
recurse=True):
if ('running_mean' in bn_name) or ('running_var'
in bn_name):
dist.all_reduce(bn_buf, op=dist.ReduceOp.SUM)
bn_buf /= world_size
ssd_print(device=device,
use_hpu=use_hpu,
key=mllog_const.MODEL_BN_SPAN,
value=bn_buf)
if args.rank == 0:
if use_hpu:
print("Training Ended, total time: {:.2f} s".format(
time.time() - start))
if not args.no_save:
print("")
print("saving model...")
if use_hpu:
permute_params(model=ssd300,
to_filters_last=False,
lazy_mode=hpu_lazy_mode)
ssd300_copy = SSD300(
train_coco.labelnum,
model_path=args.pretrained_backbone)
if args.distributed:
ssd300_copy.load_state_dict(
ssd300.module.state_dict())
else:
ssd300_copy.load_state_dict(ssd300.state_dict())
torch.save(
{
"model": ssd300_copy.state_dict(),
"label_map": train_coco.label_info
}, "./models/iter_{}.pt".format(iter_num))
permute_params(model=ssd300,
to_filters_last=True,
lazy_mode=hpu_lazy_mode)
else:
torch.save(
{
"model": ssd300.state_dict(),
"label_map": train_coco.label_info
}, "./models/iter_{}.pt".format(iter_num))
if coco_eval(ssd300,
val_dataloader,
cocoGt,
encoder,
inv_map,
args.threshold,
epoch + 1,
iter_num,
log_interval=args.log_interval,
use_cuda=use_cuda,
use_hpu=use_hpu,
hpu_device=device,
is_hmp=is_hmp,
hpu_channels_last=hpu_channels_last,
hpu_lazy_mode=hpu_lazy_mode,
nms_valid_thresh=args.nms_valid_thresh):
success = torch.ones(1)
if use_cuda:
success = success.cuda()
if use_hpu:
success = success.to(device)
if args.distributed:
dist.broadcast(success, 0)
if success[0]:
return True
mllogger.end(key=mllog_const.EPOCH_STOP,
metadata={mllog_const.EPOCH_NUM: epoch})
mllogger.end(key=mllog_const.BLOCK_STOP,
metadata={
mllog_const.FIRST_EPOCH_NUM: 1,
mllog_const.EPOCH_COUNT: args.epochs
})
return False