print('Creating Iterator with {} Images'.format(len(roidb)))
train_iter = MNIteratorE2E(roidb=roidb, config=config, batch_size=batch_size, nGPUs=nGPUs,
threads=config.TRAIN.NUM_THREAD, pad_rois_to=400) #, crop_size=(config.TRAIN.SCALES[-1],config.TRAIN.SCALES[-1]))
print('The Iterator has {} samples!'.format(len(train_iter)))
# Creating the Logger
logger, output_path = create_logger(config.output_path, args.cfg, config.dataset.image_set)
# get list of fixed parameters
print('Initializing the model...')
sym_inst = eval('{}.{}'.format(config.symbol, config.symbol))(n_proposals=400, momentum=args.momentum)
sym = sym_inst.get_symbol_rcnn(config)
fixed_param_names = get_fixed_param_names(config.network.FIXED_PARAMS, sym)
# Creating the module
mod = mx.mod.Module(symbol=sym,
context=context,
data_names=[k[0] for k in train_iter.provide_data_single],
label_names=[k[0] for k in train_iter.provide_label_single],
fixed_param_names=fixed_param_names)
shape_dict = dict(train_iter.provide_data_single + train_iter.provide_label_single)
sym_inst.infer_shape(shape_dict)
arg_params, aux_params = load_param(config.network.pretrained, config.network.pretrained_epoch, convert=True)
sym_inst.init_weight_rcnn(config, arg_params, aux_params)
# Creating the metrics
print('Creating Iterator with {} Images'.format(len(roidb)))
train_iter = MNIteratorE2E(roidb=roidb, config=config, batch_size=batch_size, nGPUs=nGPUs,
threads=config.TRAIN.NUM_THREAD, pad_rois_to=400)
print('The Iterator has {} samples!'.format(len(train_iter)))
# Creating the Logger
logger, output_path = create_logger(config.output_path, args.cfg, config.dataset.image_set)
# get list of fixed parameters
print('Initializing the model...')
sym_inst = eval('{}.{}'.format(config.symbol, config.symbol))(n_proposals=400, momentum=args.momentum)
sym = sym_inst.get_symbol_rcnn(config)
fixed_param_names = get_fixed_param_names(config.network.FIXED_PARAMS, sym)
# Creating the module
mod = mx.mod.Module(symbol=sym,
context=context,
data_names=[k[0] for k in train_iter.provide_data_single],
label_names=[k[0] for k in train_iter.provide_label_single],
fixed_param_names=fixed_param_names)
shape_dict = dict(train_iter.provide_data_single + train_iter.provide_label_single)
sym_inst.infer_shape(shape_dict)
arg_params, aux_params = load_param(config.network.pretrained, config.network.pretrained_epoch, convert=True)
sym_inst.init_weight_rcnn(config, arg_params, aux_params)
# Creating the metrics