def setup_model_for_training(model, weights_file, output_dir):
"""Loaded saved weights and create the network in the C2 workspace."""
logger = logging.getLogger(__name__)
# 添加网络输入
add_model_training_inputs(model)
if weights_file:
# Override random weight initialization with weights from a saved model
# 加载预训练模型参数
nu.initialize_gpu_from_weights_file(model, weights_file, gpu_id=0)
# Even if we're randomly initializing we still need to synchronize
# parameters across GPUs
nu.broadcast_parameters(model)
# 创建网络
workspace.CreateNet(model.net)
logger.info('Outputs saved to: {:s}'.format(os.path.abspath(output_dir)))
dump_proto_files(model, output_dir)
# Start loading mini-batches and enqueuing blobs
# 开始加载数据
model.roi_data_loader.register_sigint_handler()
model.roi_data_loader.start(prefill=True)
return output_dir
def main(args):
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
for i, weights_file in enumerate(args.weights_list):
args.weights_list[i] = cache_url(weights_file, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
preffix_list = args.preffix_list if len(args.preffix_list) \
else [""] * len(args.weights_list)
model = model_builder.create(cfg.MODEL.TYPE, train=False)
# Initialize GPU from weights files
for i, weights_file in enumerate(args.weights_list):
nu.initialize_gpu_from_weights_file(model,
weights_file,
gpu_id=0,
preffix=preffix_list[i])
nu.broadcast_parameters(model)
blobs = {}
# Save all parameters
for param in model.params:
scoped_name = str(param)
unscoped_name = c2_utils.UnscopeName(scoped_name)
if unscoped_name not in blobs:
if workspace.HasBlob(scoped_name):
blobs[unscoped_name] = workspace.FetchBlob(scoped_name)
# Save merged weights file
save_object(dict(blobs=blobs), args.output_wts)
def test_restore_checkpoint():
# Create Model
model = model_builder.create(cfg.MODEL.TYPE, train=True)
add_momentum_init_ops(model)
init_weights(model)
# Fill input blobs
roidb = combined_roidb_for_training(cfg.TRAIN.DATASETS,
cfg.TRAIN.PROPOSAL_FILES)
model_builder.add_training_inputs(model, roidb=roidb)
workspace.CreateNet(model.net)
# Bookkeeping for checkpoint creation
iter_num = 0
checkpoints = {}
output_dir = get_output_dir(cfg.TRAIN.DATASETS, training=True)
chk_file_path = os.path.join(output_dir,
'model_iter{}.pkl'.format(iter_num))
checkpoints[iter_num] = chk_file_path
# Save model weights
nu.save_model_to_weights_file(checkpoints[iter_num], model)
orig_gpu_0_params, orig_all_params = get_params(model)
# Change the model weights
init_weights(model)
# Reload the weights in the model
nu.initialize_gpu_from_weights_file(model, chk_file_path, gpu_id=0)
nu.broadcast_parameters(model)
shutil.rmtree(cfg.OUTPUT_DIR)
_, restored_all_params = get_params(model)
# Check if all params are loaded correctly
for scoped_name, blob in orig_all_params.items():
np.testing.assert_array_equal(blob, restored_all_params[scoped_name])
# Check if broadcast_parameters works
for scoped_name, blob in restored_all_params.items():
unscoped_name = c2_utils.UnscopeName(scoped_name)
np.testing.assert_array_equal(blob, orig_gpu_0_params[unscoped_name])
def test_restore_checkpoint():
# Create Model
model = model_builder.create(cfg.MODEL.TYPE, train=True)
add_momentum_init_ops(model)
init_weights(model)
# Fill input blobs
roidb = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES
)
model_builder.add_training_inputs(model, roidb=roidb)
workspace.CreateNet(model.net)
# Bookkeeping for checkpoint creation
iter_num = 0
checkpoints = {}
output_dir = get_output_dir(cfg.TRAIN.DATASETS, training=True)
chk_file_path = os.path.join(output_dir, 'model_iter{}.pkl'.format(iter_num))
checkpoints[iter_num] = chk_file_path
# Save model weights
nu.save_model_to_weights_file(checkpoints[iter_num], model)
orig_gpu_0_params, orig_all_params = get_params(model)
# Change the model weights
init_weights(model)
# Reload the weights in the model
nu.initialize_gpu_from_weights_file(model, chk_file_path, gpu_id=0)
nu.broadcast_parameters(model)
shutil.rmtree(cfg.OUTPUT_DIR)
_, restored_all_params = get_params(model)
# Check if all params are loaded correctly
for scoped_name, blob in orig_all_params.items():
np.testing.assert_array_equal(blob, restored_all_params[scoped_name])
# Check if broadcast_parameters works
for scoped_name, blob in restored_all_params.items():
unscoped_name = c2_utils.UnscopeName(scoped_name)
np.testing.assert_array_equal(blob, orig_gpu_0_params[unscoped_name])
def initialize_model_from_cfg(weights_file, gpu_id=0):
"""Initialize a model from the global cfg. Loads test-time weights and
creates the networks in the Caffe2 workspace.
"""
model = model_builder.create(cfg.MODEL.TYPE, train=False, gpu_id=gpu_id)
net_utils.initialize_gpu_from_weights_file(
model, weights_file, gpu_id=gpu_id,
)
model_builder.add_inference_inputs(model)
workspace.CreateNet(model.net)
workspace.CreateNet(model.conv_body_net)
if cfg.MODEL.MASK_ON:
workspace.CreateNet(model.mask_net)
if cfg.MODEL.KEYPOINTS_ON:
workspace.CreateNet(model.keypoint_net)
return model
def initialize_model_from_cfg(weights_file, gpu_id=0):
"""Initialize a model from the global cfg. Loads test-time weights and
creates the networks in the Caffe2 workspace.
"""
model = model_builder.create(cfg.MODEL.TYPE, train=False, gpu_id=gpu_id)
net_utils.initialize_gpu_from_weights_file(
model, weights_file, gpu_id=gpu_id,
)
model_builder.add_inference_inputs(model)
workspace.CreateNet(model.net)
workspace.CreateNet(model.conv_body_net)
if cfg.MODEL.MASK_ON:
workspace.CreateNet(model.mask_net)
if cfg.MODEL.KEYPOINTS_ON:
workspace.CreateNet(model.keypoint_net)
return model
def generate_rpn_on_range(
weights_file,
dataset_name,
_proposal_file_ignored,
output_dir,
ind_range=None,
gpu_id=0
):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert cfg.MODEL.RPN_ONLY or cfg.MODEL.FASTER_RCNN
roidb, start_ind, end_ind, total_num_images = get_roidb(
dataset_name, ind_range
)
logger.info(
'Output will be saved to: {:s}'.format(os.path.abspath(output_dir))
)
model = model_builder.create(cfg.MODEL.TYPE, train=False, gpu_id=gpu_id)
nu.initialize_gpu_from_weights_file(
model, weights_file, gpu_id=gpu_id,
)
model_builder.add_inference_inputs(model)
workspace.CreateNet(model.net)
boxes, scores, ids = generate_proposals_on_roidb(
model,
roidb,
start_ind=start_ind,
end_ind=end_ind,
total_num_images=total_num_images,
gpu_id=gpu_id,
)
cfg_yaml = envu.yaml_dump(cfg)
if ind_range is not None:
rpn_name = 'rpn_proposals_range_%s_%s.pkl' % tuple(ind_range)
else:
rpn_name = 'rpn_proposals.pkl'
rpn_file = os.path.join(output_dir, rpn_name)
save_object(
dict(boxes=boxes, scores=scores, ids=ids, cfg=cfg_yaml), rpn_file
)
logger.info('Wrote RPN proposals to {}'.format(os.path.abspath(rpn_file)))
return boxes, scores, ids, rpn_file
def generate_rpn_on_range(
weights_file,
dataset_name,
_proposal_file_ignored,
output_dir,
ind_range=None,
gpu_id=0
):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert cfg.MODEL.RPN_ONLY or cfg.MODEL.FASTER_RCNN
roidb, start_ind, end_ind, total_num_images = get_roidb(
dataset_name, ind_range
)
logger.info(
'Output will be saved to: {:s}'.format(os.path.abspath(output_dir))
)
model = model_builder.create(cfg.MODEL.TYPE, train=False, gpu_id=gpu_id)
nu.initialize_gpu_from_weights_file(
model, weights_file, gpu_id=gpu_id,
)
model_builder.add_inference_inputs(model)
workspace.CreateNet(model.net)
boxes, scores, ids = generate_proposals_on_roidb(
model,
roidb,
start_ind=start_ind,
end_ind=end_ind,
total_num_images=total_num_images,
gpu_id=gpu_id,
)
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
rpn_name = 'rpn_proposals_range_%s_%s.pkl' % tuple(ind_range)
else:
rpn_name = 'rpn_proposals.pkl'
rpn_file = os.path.join(output_dir, rpn_name)
save_object(
dict(boxes=boxes, scores=scores, ids=ids, cfg=cfg_yaml), rpn_file
)
logger.info('Wrote RPN proposals to {}'.format(os.path.abspath(rpn_file)))
return boxes, scores, ids, rpn_file
def create_model(weights_file):
"""adapted from utils.train.setup_model_for_training
"""
model = model_builder.create(cfg.MODEL.TYPE, train=True)
if cfg.MEMONGER:
optimize_memory(model)
# Performs random weight initialization as defined by the model
workspace.RunNetOnce(model.param_init_net)
roidb = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES
)
# To make debugging easier you can set cfg.DATA_LOADER.NUM_THREADS = 1
model.roi_data_loader = RoIDataLoaderSimple(
roidb,
num_loaders=cfg.DATA_LOADER.NUM_THREADS,
minibatch_queue_size=cfg.DATA_LOADER.MINIBATCH_QUEUE_SIZE,
blobs_queue_capacity=cfg.DATA_LOADER.BLOBS_QUEUE_CAPACITY
)
orig_num_op = len(model.net._net.op)
blob_names = roi_data_minibatch.get_minibatch_blob_names(is_training=True)
with c2_utils.NamedCudaScope(0):
for blob_name in blob_names:
workspace.CreateBlob(core.ScopedName(blob_name))
model.net.DequeueBlobs(
model.roi_data_loader._blobs_queue_name, blob_names
)
# A little op surgery to move input ops to the start of the net
diff = len(model.net._net.op) - orig_num_op
new_op = model.net._net.op[-diff:] + model.net._net.op[:-diff]
del model.net._net.op[:]
model.net._net.op.extend(new_op)
nu.initialize_gpu_from_weights_file(model, weights_file, gpu_id=0)
nu.broadcast_parameters(model)
workspace.CreateBlob("gpu_0/track_n_rois_two")
workspace.CreateNet(model.net)
# Start loading mini-batches and enqueuing blobs
model.roi_data_loader.register_sigint_handler()
model.roi_data_loader.start(prefill=True)
return model
def setup_model_for_training(model, weights_file, output_dir):
"""Loaded saved weights and create the network in the C2 workspace."""
logger = logging.getLogger(__name__)
add_model_training_inputs(model)
if weights_file:
# Override random weight initialization with weights from a saved model
nu.initialize_gpu_from_weights_file(model, weights_file, gpu_id=0)
# Even if we're randomly initializing we still need to synchronize
# parameters across GPUs
nu.broadcast_parameters(model)
workspace.CreateNet(model.net)
logger.info('Outputs saved to: {:s}'.format(os.path.abspath(output_dir)))
dump_proto_files(model, output_dir)
# Start loading mini-batches and enqueuing blobs
model.roi_data_loader.register_sigint_handler()
model.roi_data_loader.start(prefill=True)
return output_dir
def setup_model_for_training(model, weights_file, output_dir):
"""Loaded saved weights and create the network in the C2 workspace."""
logger = logging.getLogger(__name__)
if cfg.TRAIN.DOMAIN_ADAPTATION:
add_model_da_training_inputs(model)
else:
add_model_training_inputs(model)
if weights_file:
# Override random weight initialization with weights from a saved model
nu.initialize_gpu_from_weights_file(model, weights_file, gpu_id=0)
# Even if we're randomly initializing we still need to synchronize
# parameters across GPUs
nu.broadcast_parameters(model)
workspace.CreateNet(model.net)
logger.info('Outputs saved to: {:s}'.format(os.path.abspath(output_dir)))
dump_proto_files(model, output_dir)
# from IPython import display
# graph = net_drawer.GetPydotGraphMinimal(model.net.Proto().op,"da-frcnn",rankdir='LR')
# png = graph.create(format='png')
# with open('graph.png','w') as f:
# f.write(png)
# f.flush()
# print(graph)
# import pydot
# print(pydot.graph_from_dot_data(graph))
# (graph2,) = pydot.graph_from_dot_data(str(graph))
# png = graph2.create_png()
# png = graph.create_png()
# import matplotlib.pyplot as plt
# plt.imshow('graph.png')
# plt.show()
# Start loading mini-batches and enqueuing blobs
model.roi_data_loader.register_sigint_handler()
# Jerome: TODO: set back to True:
model.roi_data_loader.start(prefill=False)
return output_dir
def setup_model_for_training(model, weights_file, output_dir):
"""Loaded saved weights and create the network in the C2 workspace."""
logger = logging.getLogger(__name__)
add_model_training_inputs(model)
if weights_file:
# Override random weight initialization with weights from a saved model
nu.initialize_gpu_from_weights_file(model, weights_file, gpu_id=0)
logger.info("{}".format(cfg.TRAIN.Load_SqueezeNetWeights))
logger.info("{}".format(cfg.TRAIN.SqueezeNetWeightsFile))
# print (cfg.TRAIN.SqueezeNetWeightsFile)
if cfg.TRAIN.Load_SqueezeNetWeights:
prefix = "gpu_0/"
logger.info(
'\n\n\n\n========> Loading Weights For SqueezeNet<======================\n\n\n\n'
)
pickle_file = cfg.TRAIN.SqueezeNetWeightsFile
with open(pickle_file, 'rb') as file:
weights = pickle.load(file)
dev = c2_utils.CudaDevice(0)
for i in weights.keys():
# workspace.FetchBlob(prefix+i)
workspace.FeedBlob(prefix + i, weights[i], device_option=dev)
# Even if we're randomly initializing we still need to synchronize
# parameters across GPUs
nu.broadcast_parameters(model)
workspace.CreateNet(model.net)
logger.info('Outputs saved to: {:s}'.format(os.path.abspath(output_dir)))
dump_proto_files(model, output_dir)
# Start loading mini-batches and enqueuing blobs
model.roi_data_loader.register_sigint_handler()
model.roi_data_loader.start(prefill=True)
return output_dir
def initialize_mixed_model_from_cfg(weights_list, preffix_list, gpu_id=0):
"""Initialize a model from the global cfg. Loads and combinds multiple test-time weights
and creates the networks in the Caffe2 workspace.
"""
model = model_builder.create(cfg.MODEL.TYPE, train=False, gpu_id=gpu_id)
assert len(weights_list) == len(preffix_list)
for i, weights_file in enumerate(weights_list):
net_utils.initialize_gpu_from_weights_file(model,
weights_file,
gpu_id=gpu_id,
preffix=preffix_list[i])
model_builder.add_inference_inputs(model)
workspace.CreateNet(model.net)
workspace.CreateNet(model.conv_body_net)
if cfg.MODEL.MASK_ON:
workspace.CreateNet(model.mask_net)
if cfg.MODEL.KEYPOINTS_ON:
workspace.CreateNet(model.keypoint_net)
if cfg.MODEL.TRACKING_ON:
workspace.CreateNet(model.track_net)
return model
def main():
# Initialize C2
workspace.GlobalInit(
['caffe2', '--caffe2_log_level=0', '--caffe2_gpu_memory_tracking=1'])
# Set up logging and load config options
logger = setup_logging(__name__)
logging.getLogger('detectron.roi_data.loader').setLevel(logging.INFO)
args = parse_args()
logger.info('Called with args:')
logger.info(args)
if args.cfg_file is not None:
merge_cfg_from_file(args.cfg_file)
if args.opts is not None:
merge_cfg_from_list(args.opts)
assert_and_infer_cfg()
smi_output, cuda_ver, cudnn_ver = c2_utils.get_nvidia_info()
logger.info("cuda version : {}".format(cuda_ver))
logger.info("cudnn version: {}".format(cudnn_ver))
logger.info("nvidia-smi output:\n{}".format(smi_output))
logger.info('Training with config:')
logger.info(pprint.pformat(cfg))
# Note that while we set the numpy random seed network training will not be
# deterministic in general. There are sources of non-determinism that cannot
# be removed with a reasonble execution-speed tradeoff (such as certain
# non-deterministic cudnn functions).
np.random.seed(cfg.RNG_SEED)
# test model
logger.info("creat test model ...")
test_model = test_engine.initialize_model_from_cfg(cfg.TEST.WEIGHTS,
gpu_id=0)
logger.info("created test model ...")
train_data = DataLoader(root,
"train_id.txt",
cfg,
test_model,
is_train=True)
# creat mode
model, weights_file, start_iter, checkpoints = create_model(
True, cfg, output_dir)
# test blob
print(workspace.Blobs())
# create input blob
blob_names = ['data_stage2', 'gt_label_stage2']
for gpu_id in range(cfg.NUM_GPUS):
with c2_utils.NamedCudaScope(gpu_id):
for blob_name in blob_names:
workspace.CreateBlob(core.ScopedName(blob_name))
# Override random weight initialization with weights from a saved model
if weights_file:
nu.initialize_gpu_from_weights_file(model, weights_file, gpu_id=0)
# Even if we're randomly initializing we still need to synchronize
# parameters across GPUs
nu.broadcast_parameters(model)
workspace.CreateNet(model.net)
logger.info('Outputs saved to: {:s}'.format(os.path.abspath(output_dir)))
dump_proto_files(model, output_dir)
writer = SummaryWriter(log_dir=output_dir)
training_stats = TrainingStats(model, writer)
CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS)
logger.info("start train ...")
for cur_iter in range(start_iter, cfg.SOLVER.MAX_ITER):
# feed data
# print("{} iter starting feed data...".format(cur_iter))
data_stage2, gt_label = train_data.next_batch()
with c2_utils.NamedCudaScope(gpu_id):
workspace.FeedBlob(core.ScopedName('data_stage2'), data_stage2)
workspace.FeedBlob(core.ScopedName('gt_label_stage2'), gt_label)
# print("workspace.RunNet(model.net.Proto().name)")
training_stats.IterTic()
lr = model.UpdateWorkspaceLr(cur_iter,
lr_policy.get_lr_at_iter(cur_iter))
workspace.RunNet(model.net.Proto().name)
if cur_iter == start_iter:
nu.print_net(model)
training_stats.IterToc()
training_stats.UpdateIterStats(cur_iter)
training_stats.LogIterStats(cur_iter, lr)
writer.add_scalar('learning_rate', lr, cur_iter)
# print("end of RunNet")
if (cur_iter + 1) % CHECKPOINT_PERIOD == 0 and cur_iter > start_iter:
checkpoints[cur_iter] = os.path.join(
output_dir, 'model_iter{}.pkl'.format(cur_iter))
nu.save_model_to_weights_file(checkpoints[cur_iter], model)
if cur_iter == start_iter + training_stats.LOG_PERIOD:
# Reset the iteration timer to remove outliers from the first few
# SGD iterations
training_stats.ResetIterTimer()
if np.isnan(training_stats.iter_total_loss):
handle_critical_error(model, 'Loss is NaN')
# Save the final model
checkpoints['final'] = os.path.join(output_dir, 'model_final.pkl')
nu.save_model_to_weights_file(checkpoints['final'], model)
# save train loss and metric
state_file = os.path.join(output_dir, 'training_state.json')
training_stats.SaveTrainingStates(state_file)
# Execute the training run
checkpoints = detectron.utils.train.train_model()
# Test the trained model
if not args.skip_test:
test_model(checkpoints['final'], args.multi_gpu_testing, args.opts)
def initialize_model_from_cfg(weights_file, gpu_id=0, int8=True):
"""Initialize a model from the global cfg. Loads test-time weights and
creates the networks in the Caffe2 workspace.
"""
ob = None
ob_mask = None
ob_keypoint = None
model = model_builder.create(cfg.MODEL.TYPE, train=False, gpu_id=gpu_id)
net_utils.initialize_gpu_from_weights_file(
model,
weights_file,
gpu_id=gpu_id,
)
model_builder.add_inference_inputs(model)
int8_path = os.environ.get('INT8PATH')
def LoadModuleFile(fname):
with open(fname) as f:
from caffe2.proto import caffe2_pb2
net_def = caffe2_pb2.NetDef()
if os.environ.get('INT8PTXT') == "1":
import google.protobuf.text_format as ptxt
net_def = ptxt.Parse(f.read(), caffe2_pb2.NetDef())
else:
net_def.ParseFromString(f.read())
if gpu_id == -2:
device_opts = caffe2_pb2.DeviceOption()
device_opts.device_type = caffe2_pb2.IDEEP
for op in net_def.op:
op.device_option.CopyFrom(device_opts)
return net_def
return None
def CreateNet(net):
int8_file_path = int8_path if int8_path else ''
if os.environ.get('INT8PTXT') == "1":
int8_predict_file = int8_file_path + '/' + net.Proto(
).name + '_predict_int8.pbtxt'
int8_init_file = int8_file_path + '/' + net.Proto(
).name + '_init_int8.pbtxt'
else:
int8_predict_file = int8_file_path + '/' + net.Proto(
).name + '_predict_int8.pb'
int8_init_file = int8_file_path + '/' + net.Proto(
).name + '_init_int8.pb'
if os.path.isfile(int8_init_file):
logging.warning('Loading Int8 init file for module {}'.format(
net.Proto().name))
workspace.RunNetOnce(LoadModuleFile(int8_init_file))
if os.path.isfile(int8_predict_file):
logging.warning('Loading Int8 predict file for module {}'.format(
net.Proto().name))
net.Proto().CopyFrom(LoadModuleFile(int8_predict_file))
if os.environ.get('DEBUGMODE') == "1":
for i, op in enumerate(net.Proto().op):
if len(op.name) == 0:
op.name = op.type.lower() + str(i)
if gpu_id == -2 and os.environ.get('DNOOPT') != "1":
logging.warning('Optimize module {}....................'.format(
net.Proto().name))
tf.optimizeForIDEEP(net)
if os.environ.get('DEBUGMODE') == "1":
with open("{}_opt_predict_net.pb".format(net.Proto().name),
"w") as fid:
fid.write(net.Proto().SerializeToString())
with open("{}_opt_predict_net.pbtxt".format(net.Proto().name),
"w") as fid:
fid.write(str(net.Proto()))
workspace.CreateNet(net)
if os.environ.get('COSIM') and int8 == False:
int8_path = None
CreateNet(model.net)
if os.environ.get('DPROFILE') == "1":
logging.warning('need profile, add observer....................')
ob = model.net.AddObserver("TimeObserver")
workspace.CreateNet(model.conv_body_net)
if cfg.MODEL.MASK_ON:
CreateNet(model.mask_net)
if os.environ.get('DPROFILE') == "1":
ob_mask = model.mask_net.AddObserver("TimeObserver")
if cfg.MODEL.KEYPOINTS_ON:
CreateNet(model.keypoint_net)
if os.environ.get('DPROFILE') == "1":
ob_keypoint = model.keypoint_net.AddObserver("TimeObserver")
return model, ob, ob_mask, ob_keypoint
def main():
# Initialize C2
workspace.GlobalInit(
['caffe2', '--caffe2_log_level=0', '--caffe2_gpu_memory_tracking=1']
)
# Set up logging and load config options
logger = setup_logging(__name__)
logging.getLogger('detectron.roi_data.loader').setLevel(logging.INFO)
args = parse_args()
logger.info('Called with args:')
logger.info(args)
if args.cfg_file is not None:
merge_cfg_from_file(args.cfg_file)
if args.opts is not None:
merge_cfg_from_list(args.opts)
assert_and_infer_cfg()
smi_output, cuda_ver, cudnn_ver = c2_utils.get_nvidia_info()
logger.info("cuda version : {}".format(cuda_ver))
logger.info("cudnn version: {}".format(cudnn_ver))
logger.info("nvidia-smi output:\n{}".format(smi_output))
logger.info('Training with config:')
logger.info(pprint.pformat(cfg))
# Note that while we set the numpy random seed network training will not be
# deterministic in general. There are sources of non-determinism that cannot
# be removed with a reasonble execution-speed tradeoff (such as certain
# non-deterministic cudnn functions).
np.random.seed(cfg.RNG_SEED)
# test model
logger.info("creat test model ...")
test_model = test_engine.initialize_model_from_cfg(cfg.TEST.WEIGHTS, gpu_id=0)
logger.info("created test model ...")
#cfg.TRAIN.IMS_PER_BATCH = 1
train_data = DataLoader(root, "val_id.txt", cfg, test_model, is_train=False)
# creat mode
model, weights_file, start_iter, checkpoints = create_model(False, cfg, output_dir)
# test blob
print(workspace.Blobs())
# create input blob
blob_names = ['data_stage2']
for gpu_id in range(cfg.NUM_GPUS):
with c2_utils.NamedCudaScope(gpu_id):
for blob_name in blob_names:
workspace.CreateBlob(core.ScopedName(blob_name))
# Override random weight initialization with weights from a saved model
if weights_file:
nu.initialize_gpu_from_weights_file(model, weights_file, gpu_id=0)
# Even if we're randomly initializing we still need to synchronize
# parameters across GPUs
nu.broadcast_parameters(model)
workspace.CreateNet(model.net)
logger.info('Outputs saved to: {:s}'.format(os.path.abspath(output_dir)))
logger.info("start test ...")
save_root = os.path.join(output_dir, 'fusion')
if not os.path.exists(save_root):
os.makedirs(save_root)
for cur_iter in range(10000):
# feed data
# print("{} iter starting feed data...".format(cur_iter))
data_stage2, gt_label, meta = train_data.next_batch()
'''#
print('input0-20 sungalsses max score:', np.max(data_stage2[0, 4, :, :]))
print('input20-40 sungalsses max score:', np.max(data_stage2[0, 24, :, :]))
print('input0-20 glovess max score:', np.max(data_stage2[0, 3, :, :]))
print('input20-40 glovess max score:', np.max(data_stage2[0, 23, :, :]))
#'''
with c2_utils.NamedCudaScope(gpu_id):
workspace.FeedBlob(core.ScopedName('data_stage2'), data_stage2)
# print("workspace.RunNet(model.net.Proto().name)")
with c2_utils.NamedCudaScope(gpu_id):
workspace.RunNet(model.net.Proto().name)
batch_probs = workspace.FetchBlob(core.ScopedName('probs_human_NCHW_stage2'))
batch_probs = batch_probs.transpose((0, 2, 3, 1))
assert len(meta) == batch_probs.shape[0]
#print('batch_probs shape:', batch_probs.shape)
for i in range(len(meta)):
probs = cv2.resize(batch_probs[i], (meta[i]['width'], meta[i]['height']), interpolation=cv2.INTER_LINEAR)
probs = probs.transpose((2,0,1))
print('sungalsses max score:', np.max(probs[4, :, :]))
print('glovess max score:', np.max(probs[3, :, :]))
#print('probs shape:', probs.shape)
cv2.imwrite(os.path.join(save_root, meta[i]['id']+'.png'), probs.argmax(0))
print("prossed ", cur_iter)
