def Train(args):
if args.gpus is not None:
gpus = [int(x) for x in args.gpus.split(',')]
num_gpus = len(gpus)
else:
gpus = range(args.num_gpus)
num_gpus = args.num_gpus
log.info("Running on GPUs: {}".format(gpus))
# Modify to make it consistent with the distributed trainer
total_batch_size = args.batch_size * num_gpus
batch_per_device = args.batch_size
# Round down epoch size to closest multiple of batch size across machines
epoch_iters = int(args.epoch_size / total_batch_size)
args.epoch_size = epoch_iters * total_batch_size
log.info("Using epoch size: {}".format(args.epoch_size))
# Create CNNModeLhelper object
train_model = cnn.CNNModelHelper(
order="NCHW",
name='{}_train'.format(args.model_name),
use_cudnn=(True if args.use_cudnn == 1 else False),
cudnn_exhaustive_search=True,
ws_nbytes_limit=(args.cudnn_workspace_limit_mb * 1024 * 1024),
)
# Model building functions
def create_model_ops(model, loss_scale):
return model_builder.build_model(
model=model,
model_name=args.model_name,
model_depth=args.model_depth,
num_labels=args.num_labels,
num_channels=args.num_channels,
crop_size=args.crop_size,
clip_length=(args.clip_length_of
if args.input_type else args.clip_length_rgb),
loss_scale=loss_scale,
pred_layer_name=args.pred_layer_name,
)
# SGD
def add_parameter_update_ops(model):
model.AddWeightDecay(args.weight_decay)
ITER = model.Iter("ITER")
stepsz = args.step_epoch * args.epoch_size / args.batch_size / num_gpus
LR = model.net.LearningRate(
[ITER],
"LR",
base_lr=args.base_learning_rate * num_gpus,
policy="step",
stepsize=int(stepsz),
gamma=args.gamma,
)
AddMomentumParameterUpdate(model, LR)
# Input. Note that the reader must be shared with all GPUS.
train_reader, train_examples = model_builder.create_data_reader(
train_model,
name="train_reader",
input_data=args.train_data,
)
log.info("train set has {} examples".format(train_examples))
def add_video_input(model):
model_helper.AddVideoInput(
model,
train_reader,
batch_size=batch_per_device,
length_rgb=args.clip_length_rgb,
clip_per_video=1,
random_mirror=True,
decode_type=0,
sampling_rate_rgb=args.sampling_rate_rgb,
scale_h=args.scale_h,
scale_w=args.scale_w,
crop_size=args.crop_size,
num_decode_threads=args.num_decode_threads,
num_of_class=args.num_labels,
random_crop=True,
input_type=args.input_type,
length_of=args.clip_length_of,
sampling_rate_of=args.sampling_rate_of,
frame_gap_of=args.frame_gap_of,
do_flow_aggregation=args.do_flow_aggregation,
flow_data_type=args.flow_data_type,
get_rgb=(args.input_type == 0),
get_optical_flow=(args.input_type == 1),
get_video_id=args.get_video_id,
use_local_file=args.use_local_file,
)
# Create parallelized model
data_parallel_model.Parallelize_GPU(
train_model,
input_builder_fun=add_video_input,
forward_pass_builder_fun=create_model_ops,
param_update_builder_fun=add_parameter_update_ops,
devices=gpus,
rendezvous=None,
optimize_gradient_memory=True,
net_type=('prof_dag' if args.profiling == 1 else 'dag'),
)
# Add test model, if specified
test_model = None
if args.test_data is not None:
log.info("----- Create test net ----")
test_model = cnn.CNNModelHelper(
order="NCHW",
name='{}_test'.format(args.model_name),
use_cudnn=(True if args.use_cudnn == 1 else False),
cudnn_exhaustive_search=True)
test_reader, test_examples = model_builder.create_data_reader(
test_model,
name="test_reader",
input_data=args.test_data,
)
log.info('test set has {} examples'.format(test_examples))
def test_input_fn(model):
model_helper.AddVideoInput(
model,
test_reader,
batch_size=batch_per_device,
length_rgb=args.clip_length_rgb,
clip_per_video=1,
decode_type=0,
random_mirror=False,
random_crop=False,
sampling_rate_rgb=args.sampling_rate_rgb,
scale_h=args.scale_h,
scale_w=args.scale_w,
crop_size=args.crop_size,
num_decode_threads=args.num_decode_threads,
num_of_class=args.num_labels,
input_type=args.input_type,
length_of=args.clip_length_of,
sampling_rate_of=args.sampling_rate_of,
frame_gap_of=args.frame_gap_of,
do_flow_aggregation=args.do_flow_aggregation,
flow_data_type=args.flow_data_type,
get_rgb=(args.input_type == 0),
get_optical_flow=(args.input_type == 1),
get_video_id=args.get_video_id,
use_local_file=args.use_local_file,
)
data_parallel_model.Parallelize_GPU(
test_model,
input_builder_fun=test_input_fn,
forward_pass_builder_fun=create_model_ops,
param_update_builder_fun=None,
devices=gpus,
)
workspace.RunNetOnce(test_model.param_init_net)
workspace.CreateNet(test_model.net)
workspace.RunNetOnce(train_model.param_init_net)
workspace.CreateNet(train_model.net)
epoch = 0
# load the pre-trained model and reset epoch
if args.pretrained_model is not None:
if args.db_type == 'minidb':
model_helper.LoadModel(args.pretrained_model, args.db_type)
elif args.db_type == 'pickle':
model_loader.LoadModelFromPickleFile(train_model,
args.pretrained_model,
root_gpu_id=gpus[0])
data_parallel_model.FinalizeAfterCheckpoint(
train_model,
GetCheckpointParams(train_model),
)
if args.is_checkpoint:
# reset epoch. load_model_path should end with *_X.mdl,
# where X is the epoch number
last_str = args.pretrained_model.split('_')[-1]
if last_str.endswith('.mdl'):
epoch = int(last_str[:-4])
log.info("Reset epoch to {}".format(epoch))
else:
log.warning("The format of load_model_path doesn't match!")
expname = "%s_gpu%d_b%d_L%d_lr%.2f" % (
args.model_name,
args.num_gpus,
total_batch_size,
args.num_labels,
args.base_learning_rate,
)
explog = experiment_util.ModelTrainerLog(
"%s/%s" % (args.file_store_path, expname), args)
# Run the training one epoch a time
while epoch < args.num_epochs:
epoch = RunEpoch(args, epoch, train_model, test_model,
total_batch_size, 1, expname, explog)
# Save the model for each epoch
SaveModel(args, train_model, epoch)
def Test(args):
if args.gpus is not None:
gpus = [int(x) for x in args.gpus.split(',')]
num_gpus = len(gpus)
else:
gpus = range(args.num_gpus)
num_gpus = args.num_gpus
if num_gpus > 0:
total_batch_size = args.batch_size * num_gpus
log.info("Running on GPUs: {}".format(gpus))
log.info("total_batch_size: {}".format(total_batch_size))
else:
total_batch_size = args.batch_size
log.info("Running on CPU")
log.info("total_batch_size: {}".format(total_batch_size))
# Model building functions
def create_model_ops(model, loss_scale):
return model_builder.build_model(
model=model,
model_name=args.model_name,
model_depth=args.model_depth,
num_labels=args.num_labels,
num_channels=args.num_channels,
crop_size=args.crop_size,
clip_length=(
args.clip_length_of if args.input_type == 1
else args.clip_length_rgb
),
loss_scale=loss_scale,
is_test=1,
pred_layer_name=args.pred_layer_name,
)
test_model = cnn.CNNModelHelper(
order="NCHW",
name="video_model_test",
use_cudnn=(True if args.use_cudnn == 1 else False),
cudnn_exhaustive_search=True,
)
test_reader, number_of_examples = model_builder.create_data_reader(
test_model,
name="test_reader",
input_data=args.test_data,
)
if args.num_iter <= 0:
num_iter = int(number_of_examples / total_batch_size)
else:
num_iter = args.num_iter
def test_input_fn(model):
model_helper.AddVideoInput(
test_model,
test_reader,
batch_size=args.batch_size,
clip_per_video=args.clip_per_video,
decode_type=1,
length_rgb=args.clip_length_rgb,
sampling_rate_rgb=args.sampling_rate_rgb,
scale_h=args.scale_h,
scale_w=args.scale_w,
crop_size=args.crop_size,
num_decode_threads=4,
num_of_class=args.num_labels,
random_mirror=False,
random_crop=False,
input_type=args.input_type,
length_of=args.clip_length_of,
sampling_rate_of=args.sampling_rate_of,
frame_gap_of=args.frame_gap_of,
do_flow_aggregation=args.do_flow_aggregation,
flow_data_type=args.flow_data_type,
get_rgb=(args.input_type == 0),
get_optical_flow=(args.input_type == 1),
get_video_id=args.get_video_id,
use_local_file=args.use_local_file,
)
if num_gpus > 0:
data_parallel_model.Parallelize_GPU(
test_model,
input_builder_fun=test_input_fn,
forward_pass_builder_fun=create_model_ops,
param_update_builder_fun=None,
devices=gpus
)
else:
test_model._device_type = caffe2_pb2.CPU
test_model._devices = [0]
device_opt = core.DeviceOption(test_model._device_type, 0)
with core.DeviceScope(device_opt):
# Because our loaded models are named with "gpu_x", keep the naming for now.
# TODO: Save model using `data_parallel_model.ExtractPredictorNet`
# to extract the model for "gpu_0". It also renames
# the input and output blobs by stripping the "gpu_x/" prefix
with core.NameScope("{}_{}".format("gpu", 0)):
test_input_fn(test_model)
create_model_ops(test_model, 1.0)
workspace.RunNetOnce(test_model.param_init_net)
workspace.CreateNet(test_model.net)
if args.db_type == 'minidb':
if num_gpus > 0:
model_helper.LoadModel(args.load_model_path, args.db_type)
data_parallel_model.FinalizeAfterCheckpoint(test_model)
else:
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU, 0)):
model_helper.LoadModel(args.load_model_path, args.db_type)
elif args.db_type == 'pickle':
if num_gpus > 0:
model_loader.LoadModelFromPickleFile(
test_model,
args.load_model_path,
use_gpu=True,
root_gpu_id=gpus[0]
)
data_parallel_model.FinalizeAfterCheckpoint(test_model)
else:
model_loader.LoadModelFromPickleFile(
test_model,
args.load_model_path,
use_gpu=False
)
else:
log.warning("Unsupported db_type: {}".format(args.db_type))
# metric counters for classification
clip_acc = 0
video_top1 = 0
video_topk = 0
video_count = 0
clip_count = 0
for i in range(num_iter):
workspace.RunNet(test_model.net.Proto().name)
num_devices = 1 # default for cpu
if args.num_gpus > 0:
num_devices = args.num_gpus
for g in range(num_devices):
# get labels
label = workspace.FetchBlob(
"gpu_{}".format(g) + '/label'
)
# get predictions
predicts = workspace.FetchBlob("gpu_{}".format(g) + '/softmax')
assert predicts.shape[0] == args.batch_size * args.clip_per_video
for j in range(args.batch_size):
# get label for one video
sample_label = label[j * args.clip_per_video]
# get clip accuracy
for k in range(args.clip_per_video):
c1, _ = metric.accuracy_metric(
predicts[j * args.clip_per_video + k, :],
label[j * args.clip_per_video + k])
clip_acc = clip_acc + c1
# get all clip predictions for one video
all_clips = predicts[
j * args.clip_per_video:(j + 1) * args.clip_per_video, :]
# aggregate predictions into one
video_pred = PredictionAggregation(all_clips, args.aggregation)
c1, ck = metric.accuracy_metric(
video_pred, sample_label, args.top_k)
video_top1 = video_top1 + c1
video_topk = video_topk + ck
video_count = video_count + args.batch_size
clip_count = clip_count + label.shape[0]
if i > 0 and i % args.display_iter == 0:
log.info('Iter {}/{}: clip: {}, top1: {}, top 5: {}'.format(
i,
num_iter,
clip_acc / clip_count,
video_top1 / video_count,
video_topk / video_count))
log.info("Test accuracy: clip: {}, top 1: {}, top{}: {}".format(
clip_acc / clip_count,
video_top1 / video_count,
args.top_k,
video_topk / video_count
))
if num_gpus > 0:
flops, params = model_helper.GetFlopsAndParams(test_model, gpus[0])
else:
flops, params = model_helper.GetFlopsAndParams(test_model)
log.info('FLOPs: {}, params: {}'.format(flops, params))
def ExtractFeatures(args):
if args.gpus is not None:
gpus = [int(x) for x in args.gpus.split(',')]
num_gpus = len(gpus)
else:
gpus = range(args.num_gpus)
num_gpus = args.num_gpus
if num_gpus > 0:
log.info("Running on GPUs: {}".format(gpus))
else:
log.info("Running on CPU")
log.info("Running on GPUs: {}".format(gpus))
my_arg_scope = {
'order': 'NCHW',
'use_cudnn': True,
'cudnn_exhaustive_search': True
}
model = cnn.CNNModelHelper(name="Extract Features", **my_arg_scope)
reader, num_examples = model_builder.create_data_reader(
model,
name="reader",
input_data=args.test_data,
)
def input_fn(model):
model_helper.AddVideoInput(
model,
reader,
batch_size=args.batch_size,
clip_per_video=args.clip_per_video,
decode_type=args.decode_type,
length_rgb=args.clip_length_rgb,
sampling_rate_rgb=args.sampling_rate_rgb,
scale_h=args.scale_h,
scale_w=args.scale_w,
crop_size=args.crop_size,
num_decode_threads=args.num_decode_threads,
num_of_class=args.num_labels,
random_mirror=False,
random_crop=False,
input_type=args.input_type,
length_of=args.clip_length_of,
sampling_rate_of=args.sampling_rate_of,
frame_gap_of=args.frame_gap_of,
do_flow_aggregation=args.do_flow_aggregation,
flow_data_type=args.flow_data_type,
get_rgb=(args.input_type == 0),
get_optical_flow=(args.input_type == 1),
get_video_id=args.get_video_id,
use_local_file=args.use_local_file,
)
def create_model_ops(model, loss_scale):
return model_builder.build_model(
model=model,
model_name=args.model_name,
model_depth=args.model_depth,
num_labels=args.num_labels,
num_channels=args.num_channels,
crop_size=args.crop_size,
clip_length=(args.clip_length_of
if args.input_type == 1 else args.clip_length_rgb),
loss_scale=loss_scale,
is_test=1,
)
if num_gpus > 0:
data_parallel_model.Parallelize_GPU(
model,
input_builder_fun=input_fn,
forward_pass_builder_fun=create_model_ops,
param_update_builder_fun=None, # 'None' since we aren't training
devices=gpus,
)
else:
model._device_type = caffe2_pb2.CPU
model._devices = [0]
device_opt = core.DeviceOption(model._device_type, 0)
with core.DeviceScope(device_opt):
with core.NameScope("{}_{}".format("gpu", 0)):
input_fn(model)
create_model_ops(model, 1.0)
workspace.RunNetOnce(model.param_init_net)
workspace.CreateNet(model.net)
if args.db_type == 'minidb':
if num_gpus > 0:
model_helper.LoadModel(args.load_model_path, args.db_type)
data_parallel_model.FinalizeAfterCheckpoint(model)
else:
with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU, 0)):
model_helper.LoadModel(args.load_model_path, args.db_type)
elif args.db_type == 'pickle':
if num_gpus > 0:
model_loader.LoadModelFromPickleFile(model,
args.load_model_path,
use_gpu=True,
root_gpu_id=gpus[0])
else:
model_loader.LoadModelFromPickleFile(
model,
args.load_model_path,
use_gpu=False,
)
else:
log.warning("Unsupported db_type: {}".format(args.db_type))
def fetchActivations(model, outputs, num_iterations):
all_activations = {}
for counter in range(num_iterations):
workspace.RunNet(model.net.Proto().name)
num_devices = 1 # default for cpu
for g in gpus if num_gpus > 0 else range(num_devices):
for output_name in outputs:
blob_name = 'gpu_{}/'.format(g) + output_name
activations = workspace.FetchBlob(blob_name)
if output_name not in all_activations:
all_activations[output_name] = []
all_activations[output_name].append(activations)
if counter % 20 == 0:
log.info('{}/{} iterations'.format(counter, num_iterations))
# each key holds a list of activations obtained from each minibatch.
# we now concatenate these lists to get the final arrays.
# concatenating during the loop requires a realloc and can get slow.
for key in all_activations:
all_activations[key] = np.concatenate(all_activations[key])
return all_activations
outputs = [name.strip() for name in args.features.split(',')]
assert len(outputs) > 0
if args.num_iterations > 0:
num_iterations = args.num_iterations
else:
if num_gpus > 0:
examples_per_iteration = args.batch_size * num_gpus
else:
examples_per_iteration = args.batch_size
num_iterations = int(num_examples / examples_per_iteration)
activations = fetchActivations(model, outputs, num_iterations)
# saving extracted features
for index in range(len(outputs)):
log.info("Read '{}' with shape {}".format(
outputs[index], activations[outputs[index]].shape))
if args.output_path:
output_path = args.output_path
else:
output_path = os.path.dirname(args.test_data) + '/features.pickle'
log.info('Writing to {}'.format(output_path))
with open(output_path, 'wb') as handle:
pickle.dump(activations, handle)
# perform sanity check
if args.sanity_check == 1: # check clip accuracy
clip_acc = 0
softmax = activations['softmax']
label = activations['label']
for i in range(len(softmax)):
sorted_preds = \
np.argsort(softmax[i])
sorted_preds[:] = sorted_preds[::-1]
if sorted_preds[0] == label[i]:
clip_acc += 1
log.info('Sanity check --- clip accuracy: {}'.format(clip_acc /
len(softmax)))