def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr=0.001, lr_step='5'): # set up logger logging.basicConfig() logger = logging.getLogger() logger.setLevel(logging.INFO) # setup config config.TRAIN.BATCH_IMAGES = 1 config.TRAIN.BATCH_ROIS = 128 config.TRAIN.END2END = True config.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED = True # load symbol sym = eval('get_' + args.network + '_train')() feat_sym = sym.get_internals()['rpn_cls_score_output'] # setup multi-gpu batch_size = len(ctx) input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size # print config pprint.pprint(config) # load dataset and prepare imdb for training imdb = eval(args.dataset)(args.image_set, args.root_path, args.dataset_path) roidb = imdb.gt_roidb() if args.flip: roidb = imdb.append_flipped_images(roidb) # load training data train_data = AnchorLoader(feat_sym, roidb, batch_size=input_batch_size, shuffle=True, ctx=ctx, work_load_list=args.work_load_list, feat_stride=config.RPN_FEAT_STRIDE, anchor_scales=config.ANCHOR_SCALES, anchor_ratios=config.ANCHOR_RATIOS, aspect_grouping=config.TRAIN.ASPECR_GROUPING) # infer max shape max_data_shape = [('data', (input_batch_size, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))] max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape) max_data_shape.append(('gt_boxes', (input_batch_size, 100, 5))) print 'providing maximum shape', max_data_shape, max_label_shape # load pretrained arg_params, aux_params = load_param(pretrained, epoch, convert=True) # infer shape data_shape_dict = dict(train_data.provide_data + train_data.provide_label) arg_shape, out_shape, aux_shape = sym.infer_shape(**data_shape_dict) arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape)) out_shape_dict = dict(zip(sym.list_outputs(), out_shape)) aux_shape_dict = dict(zip(sym.list_auxiliary_states(), aux_shape)) print 'output shape' pprint.pprint(out_shape_dict) # initialize params if not args.resume: arg_params['rpn_conv_3x3_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['rpn_conv_3x3_weight']) arg_params['rpn_conv_3x3_bias'] = mx.nd.zeros( shape=arg_shape_dict['rpn_conv_3x3_bias']) arg_params['rpn_cls_score_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['rpn_cls_score_weight']) arg_params['rpn_cls_score_bias'] = mx.nd.zeros( shape=arg_shape_dict['rpn_cls_score_bias']) arg_params['rpn_bbox_pred_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['rpn_bbox_pred_weight']) arg_params['rpn_bbox_pred_bias'] = mx.nd.zeros( shape=arg_shape_dict['rpn_bbox_pred_bias']) arg_params['cls_score_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['cls_score_weight']) arg_params['cls_score_bias'] = mx.nd.zeros( shape=arg_shape_dict['cls_score_bias']) arg_params['bbox_pred_weight'] = mx.random.normal( 0, 0.001, shape=arg_shape_dict['bbox_pred_weight']) arg_params['bbox_pred_bias'] = mx.nd.zeros( shape=arg_shape_dict['bbox_pred_bias']) # check parameter shapes for k in sym.list_arguments(): if k in data_shape_dict: continue assert k in arg_params, k + ' not initialized' assert arg_params[k].shape == arg_shape_dict[k], \ 'shape inconsistent for ' + k + ' inferred ' + str(arg_shape_dict[k]) + ' provided ' + str(arg_params[k].shape) for k in sym.list_auxiliary_states(): assert k in aux_params, k + ' not initialized' assert aux_params[k].shape == aux_shape_dict[k], \ 'shape inconsistent for ' + k + ' inferred ' + str(aux_shape_dict[k]) + ' provided ' + str(aux_params[k].shape) # create solver fixed_param_prefix = config.FIXED_PARAMS data_names = [k[0] for k in train_data.provide_data] label_names = [k[0] for k in train_data.provide_label] mod = MutableModule(sym, data_names=data_names, label_names=label_names, logger=logger, context=ctx, work_load_list=args.work_load_list, max_data_shapes=max_data_shape, max_label_shapes=max_label_shape, fixed_param_prefix=fixed_param_prefix) # decide training params # metric rpn_eval_metric = metric.RPNAccMetric() rpn_cls_metric = metric.RPNLogLossMetric() rpn_bbox_metric = metric.RPNL1LossMetric() eval_metric = metric.RCNNAccMetric() cls_metric = metric.RCNNLogLossMetric() bbox_metric = metric.RCNNL1LossMetric() eval_metrics = mx.metric.CompositeEvalMetric() for child_metric in [ rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric ]: eval_metrics.add(child_metric) # callback batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=args.frequent) means = np.tile(np.array(config.TRAIN.BBOX_MEANS), imdb.num_classes) stds = np.tile(np.array(config.TRAIN.BBOX_STDS), imdb.num_classes) epoch_end_callback = callback.do_checkpoint(prefix, means, stds) # decide learning rate base_lr = lr lr_factor = 0.1 lr_epoch = [int(epoch) for epoch in lr_step.split(',')] lr_epoch_diff = [ epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch ] lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff))) lr_iters = [ int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff ] print 'lr', lr, 'lr_epoch', lr_epoch, 'lr_epoch_diff', lr_epoch_diff lr_scheduler = mx.lr_scheduler.MultiFactorScheduler(lr_iters, lr_factor) # optimizer optimizer_params = { 'momentum': 0.9, 'wd': 0.0005, 'learning_rate': lr, 'lr_scheduler': lr_scheduler, 'rescale_grad': (1.0 / batch_size), 'clip_gradient': 5 } # train mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback, batch_end_callback=batch_end_callback, kvstore=args.kvstore, optimizer='sgd', optimizer_params=optimizer_params, arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch, num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr=0.001, lr_step='5'): # set up logger logging.basicConfig() logger = logging.getLogger() logger.setLevel(logging.INFO) # setup config config.TRAIN.BATCH_IMAGES = 1 config.TRAIN.BATCH_ROIS = 128 config.TRAIN.END2END = True config.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED = True # load symbol sym_instance = eval('symbol_' + args.network)() sym_gen = sym_instance.get_symbol sym = sym_gen(46,config,is_train=True) feat_sym = sym.get_internals()['rpn_cls_score_output'] # setup multi-gpu batch_size = len(ctx) input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size # print config pprint.pprint(config) # load dataset and prepare imdb for training dataset = Dataset(args.root_path,args.dataset,args.subset,split = args.split) roidb = dataset.gt_roidb() W = dataset.W # load training data train_data = AnchorLoader(feat_sym, roidb, batch_size=input_batch_size, shuffle=not args.no_shuffle, ctx=ctx, work_load_list=args.work_load_list, feat_stride=config.RPN_FEAT_STRIDE, anchor_scales=config.ANCHOR_SCALES, anchor_ratios=config.ANCHOR_RATIOS) # infer max shape max_data_shape = [('data', (input_batch_size, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))] max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape) max_data_shape.append(('gt_boxes', (input_batch_size, 100, 5))) print('providing maximum shape', max_data_shape, max_label_shape) # infer shape #get a new symbol bucket_key = train_data.bucket_key print(train_data.provide_data) data_shape_dict = dict(train_data.provide_data + train_data.provide_label) sym_instance.infer_shape(data_shape_dict) #arg_shape, out_shape, aux_shape = curr_sym.infer_shape(**data_shape_dict) #arg_shape_dict = dict(zip(curr_sym.list_arguments(), arg_shape)) #out_shape_dict = dict(zip(curr_sym.list_outputs(), out_shape)) #aux_shape_dict = dict(zip(curr_sym.list_auxiliary_states(), aux_shape)) #del arg_shape_dict['lstm_parameters'] #print(curr_sym.list_arguments()) #print(aux_shape_dict) # load and initialize params if args.resume: print("continue training from epoch {}".format(begin_epoch)) arg_params, aux_params = load_param(prefix, begin_epoch, convert=True) else: arg_params, aux_params = load_param(pretrained, epoch, convert=True) if config.RNN.USE_W2V: arg_params['embed_weight'] = mx.nd.array(W) else: arg_params['embed_weight'] = mx.random.uniform(0,0.01,shape=arg_shape_dict['embed_weight']) sym_instance.init_weight(config,arg_params,aux_params) #no checking #for k in arg_shape_dict.iterkeys(): # if k in data_shape_dict: # continue # assert k in arg_params, k + ' not initialized' #assert arg_params[k].shape == arg_shape_dict[k], \ # 'shape inconsistent for ' + k + ' inferred ' + str(arg_shape_dict[k]) + ' provided ' + str(arg_params[k].shape) #for k in sym.list_auxiliary_states(): # assert k in aux_params, k + ' not initialized' # assert aux_params[k].shape == aux_shape_dict[k], \ # 'shape inconsistent for ' + k + ' inferred ' + str(aux_shape_dict[k]) + ' provided ' + str(aux_params[k].shape) # create solver fixed_param_prefix = config.FIXED_PARAMS data_names = [k[0] for k in train_data.provide_data] label_names = [k[0] for k in train_data.provide_label] mod = MutableModule(sym_gen,config, data_names=data_names, label_names=label_names, logger=logger, context=ctx, work_load_list=args.work_load_list, max_data_shapes=max_data_shape, max_label_shapes=max_label_shape, fixed_param_prefix=fixed_param_prefix) # decide training params # metric rpn_eval_metric = metric.RPNAccMetric() rpn_cls_metric = metric.RPNLogLossMetric() rpn_bbox_metric = metric.RPNL1LossMetric() eval_metric = metric.RCNNAccMetric() cls_metric = metric.RCNNLogLossMetric() bbox_metric = metric.RCNNL1LossMetric() eval_metrics = mx.metric.CompositeEvalMetric() for child_metric in [rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric]: eval_metrics.add(child_metric) # callback batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=args.frequent) means = np.tile(np.array(config.TRAIN.BBOX_MEANS), config.NUM_CLASSES) stds = np.tile(np.array(config.TRAIN.BBOX_STDS), config.NUM_CLASSES) epoch_end_callback = callback.do_checkpoint(config.ENCODER_CELL,prefix, means, stds) # decide learning rate base_lr = lr lr_factor = 0.1 lr_epoch = [int(epoch) for epoch in lr_step.split(',')] lr_epoch_diff = [epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch] lr = base_lr * (lr_factor ** (len(lr_epoch) - len(lr_epoch_diff))) lr_iters = [int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff] print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters) lr_scheduler = mx.lr_scheduler.MultiFactorScheduler(lr_iters, lr_factor) # optimizer optimizer_params = {'momentum': 0.9, 'wd': 0.0005, 'learning_rate': lr, 'lr_scheduler': lr_scheduler, 'rescale_grad': (1.0 / batch_size), 'clip_gradient': 5} #initializer for fused RNN #TODO:not successfully added,try ask it on github issues. initializer = mx.initializer.FusedRNN(init=mx.init.Xavier(factor_type='in', magnitude=2.34), num_hidden = 1024,num_layers=2,mode='lstm') # train mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback, batch_end_callback=batch_end_callback, kvstore=args.kvstore, optimizer='sgd', optimizer_params=optimizer_params,allow_missing=True,initializer=mx.init.Xavier(factor_type='in', magnitude=2.34), arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch, num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr=0.001, lr_step='5'): # set up logger logging.basicConfig() logger = logging.getLogger() logger.setLevel(logging.INFO) # setup config config.TRAIN.BATCH_IMAGES = 1 config.TRAIN.BATCH_ROIS = 128 config.TRAIN.END2END = True config.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED = True # load symbol if args.use_global_context or args.use_roi_align: sym = eval('get_' + args.network + '_train')( num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHORS, use_global_context=args.use_global_context, use_roi_align=args.use_roi_align) else: sym = eval('get_' + args.network + '_train')( num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHORS) feat_sym = sym.get_internals()['rpn_cls_score_output'] # setup multi-gpu batch_size = len(ctx) input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size # print config pprint.pprint(config) if not args.use_ava_recordio: # load dataset and prepare imdb for training image_sets = [iset for iset in args.image_set.split('+')] roidbs = [ load_gt_roidb(args.dataset, image_set, args.root_path, args.dataset_path, flip=not args.no_flip) for image_set in image_sets ] roidb = merge_roidb(roidbs) roidb = filter_roidb(roidb) # load training data train_data = AnchorLoader( feat_sym, roidb, batch_size=input_batch_size, shuffle=not args.no_shuffle, ctx=ctx, work_load_list=args.work_load_list, feat_stride=config.RPN_FEAT_STRIDE, anchor_scales=config.ANCHOR_SCALES, anchor_ratios=config.ANCHOR_RATIOS, aspect_grouping=config.TRAIN.ASPECT_GROUPING, use_data_augmentation=args.use_data_augmentation) else: f = open(args.classes_names) classes = ['__background__'] for line in f.readlines(): classes.append(line.strip().split(' ')[0]) path_imgidx = args.ava_recordio_name + '.idx' path_imgrec = args.ava_recordio_name + '.rec' record = mx.recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r') # pylint: disable=redefined-variable-type train_data = AnchorLoaderAvaRecordIO( feat_sym, record, classes, batch_size=input_batch_size, shuffle=not args.no_shuffle, ctx=ctx, work_load_list=args.work_load_list, feat_stride=config.RPN_FEAT_STRIDE, anchor_scales=config.ANCHOR_SCALES, anchor_ratios=config.ANCHOR_RATIOS, aspect_grouping=config.TRAIN.ASPECT_GROUPING, use_data_augmentation=args.use_data_augmentation) # infer max shape max_data_shape = [('data', (input_batch_size, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))] max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape) max_data_shape.append(('gt_boxes', (input_batch_size, 100, 5))) print('providing maximum shape', max_data_shape, max_label_shape) # infer shape data_shape_dict = dict(train_data.provide_data + train_data.provide_label) arg_shape, out_shape, aux_shape = sym.infer_shape(**data_shape_dict) arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape)) out_shape_dict = dict(zip(sym.list_outputs(), out_shape)) aux_shape_dict = dict(zip(sym.list_auxiliary_states(), aux_shape)) print('output shape') pprint.pprint(out_shape_dict) print('arg shape') # pprint.pprint(arg_shape_dict) # load and initialize params if args.resume: arg_params, aux_params = load_param(prefix, begin_epoch, convert=True) else: arg_params, aux_params = load_param(pretrained, epoch, convert=True) arg_params['rpn_conv_3x3_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['rpn_conv_3x3_weight']) arg_params['rpn_conv_3x3_bias'] = mx.nd.zeros( shape=arg_shape_dict['rpn_conv_3x3_bias']) arg_params['rpn_cls_score_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['rpn_cls_score_weight']) arg_params['rpn_cls_score_bias'] = mx.nd.zeros( shape=arg_shape_dict['rpn_cls_score_bias']) arg_params['rpn_bbox_pred_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['rpn_bbox_pred_weight']) arg_params['rpn_bbox_pred_bias'] = mx.nd.zeros( shape=arg_shape_dict['rpn_bbox_pred_bias']) arg_params['cls_score_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['cls_score_weight']) arg_params['cls_score_bias'] = mx.nd.zeros( shape=arg_shape_dict['cls_score_bias']) arg_params['bbox_pred_weight'] = mx.random.normal( 0, 0.001, shape=arg_shape_dict['bbox_pred_weight']) arg_params['bbox_pred_bias'] = mx.nd.zeros( shape=arg_shape_dict['bbox_pred_bias']) if args.use_global_context: # additional params for using global context """ for arg_param_name in sym.list_arguments(): if 'stage5' in arg_param_name: # print(arg_param_name, arg_param_name.replace('stage5', 'stage4')) arg_params[arg_param_name] = arg_params[arg_param_name.replace('stage5', 'stage4')].copy() # params of stage5 is initialized from stage4 arg_params['bn2_gamma'] = arg_params['bn1_gamma'].copy() arg_params['bn2_beta'] = arg_params['bn1_beta'].copy() """ for aux_param_name in sym.list_auxiliary_states(): if 'stage5' in aux_param_name: # print(aux_param_name, aux_param_name.replace('stage5', 'stage4')) aux_params[aux_param_name] = aux_params[aux_param_name.replace( 'stage5', 'stage4')].copy( ) # params of stage5 is initialized from stage4 aux_params['bn2_moving_mean'] = aux_params['bn1_moving_mean'].copy() aux_params['bn2_moving_var'] = aux_params['bn1_moving_var'].copy() # check parameter shapes for k in sym.list_arguments(): if k in data_shape_dict: continue assert k in arg_params, k + ' not initialized' assert arg_params[k].shape == arg_shape_dict[k], \ 'shape inconsistent for ' + k + ' inferred ' + str(arg_shape_dict[k]) + ' provided ' + str(arg_params[k].shape) for k in sym.list_auxiliary_states(): assert k in aux_params, k + ' not initialized' assert aux_params[k].shape == aux_shape_dict[k], \ 'shape inconsistent for ' + k + ' inferred ' + str(aux_shape_dict[k]) + ' provided ' + str(aux_params[k].shape) # create solver fixed_param_prefix = config.FIXED_PARAMS data_names = [k[0] for k in train_data.provide_data] label_names = [k[0] for k in train_data.provide_label] mod = MutableModule(sym, data_names=data_names, label_names=label_names, logger=logger, context=ctx, work_load_list=args.work_load_list, max_data_shapes=max_data_shape, max_label_shapes=max_label_shape, fixed_param_prefix=fixed_param_prefix) # decide training params # metric rpn_eval_metric = metric.RPNAccMetric() rpn_cls_metric = metric.RPNLogLossMetric() rpn_bbox_metric = metric.RPNL1LossMetric() eval_metric = metric.RCNNAccMetric() cls_metric = metric.RCNNLogLossMetric() bbox_metric = metric.RCNNL1LossMetric() eval_metrics = mx.metric.CompositeEvalMetric() for child_metric in [ rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric ]: eval_metrics.add(child_metric) # callback batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=args.frequent) means = np.tile(np.array(config.TRAIN.BBOX_MEANS), config.NUM_CLASSES) stds = np.tile(np.array(config.TRAIN.BBOX_STDS), config.NUM_CLASSES) epoch_end_callback = callback.do_checkpoint(prefix, means, stds) # decide learning rate base_lr = lr lr_factor = 0.1 lr_epoch = [int(epoch) for epoch in lr_step.split(',')] lr_epoch_diff = [ epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch ] lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff))) if not args.use_ava_recordio: lr_iters = [ int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff ] else: lr_iters = [ int(epoch * train_data.provide_size() / batch_size) for epoch in lr_epoch_diff ] print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters) lr_scheduler = mx.lr_scheduler.MultiFactorScheduler(lr_iters, lr_factor) # optimizer optimizer_params = { 'momentum': 0.9, 'wd': 0.0005, 'learning_rate': lr, 'lr_scheduler': lr_scheduler, 'rescale_grad': (1.0 / batch_size), 'clip_gradient': 5 } # train mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback, batch_end_callback=batch_end_callback, kvstore=args.kvstore, optimizer='sgd', optimizer_params=optimizer_params, arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch, num_epoch=end_epoch)
def train_net(args): if args.rand_seed > 0: np.random.seed(args.rand_seed) mx.random.seed(args.rand_seed) random.seed(args.rand_seed) # print config logger.info(pprint.pformat(config)) logger.info(pprint.pformat(args)) # load dataset and prepare imdb for training image_sets = [iset for iset in args.image_set.split('+')] roidbs = [load_gt_roidb(args.dataset, image_set, args.dataset_path, flip=args.flip) for image_set in image_sets] roidb = merge_roidb(roidbs) roidb = filter_roidb(roidb) samplepcnt = args.begin_sample if samplepcnt == 100: sroidb = roidb else: sroidb = sample_roidb(roidb, samplepcnt) # Sample by percentage of all images logger.info('Sampling %d pcnt : %d training slices' % (samplepcnt, len(sroidb))) # Debug to see if we can concatenate ROIDB's #print(sroidb) #dir(sroidb) #newroidb = sroidb + roidb #newroidb = append_roidb(sroidb, roidb) #print( "--Append test: " + str(len(sroidb)) +" " + str(len(roidb)) + " = " + str(len(newroidb)) ) # load symbol sym = eval('get_' + args.network)(is_train=True, num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHORS) feat_sym = sym.get_internals()['rpn_cls_score_output'] # setup multi-gpu ctx = [mx.gpu(int(i)) for i in args.gpus.split(',')] batch_size = len(ctx) input_batch_size = config.TRAIN.SAMPLES_PER_BATCH * batch_size # load training data train_data = AnchorLoader(feat_sym, sroidb, batch_size=input_batch_size, shuffle=args.shuffle, ctx=ctx, work_load_list=args.work_load_list, feat_stride=config.RPN_FEAT_STRIDE, anchor_scales=config.ANCHOR_SCALES, anchor_ratios=config.ANCHOR_RATIOS, aspect_grouping=config.TRAIN.ASPECT_GROUPING, nThreads=default.prefetch_thread_num) # infer max shape max_data_shape = [('data', (input_batch_size*config.NUM_IMAGES_3DCE, config.NUM_SLICES, config.MAX_SIZE, config.MAX_SIZE))] max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape) max_data_shape.append(('gt_boxes', (input_batch_size*config.NUM_IMAGES_3DCE, 5, 5))) logger.info('providing maximum shape %s %s' % (max_data_shape, max_label_shape)) # load and initialize and check params arg_params, aux_params = init_params(args, sym, train_data) # create solver fixed_param_prefix = config.FIXED_PARAMS data_names = [k[0] for k in train_data.provide_data] label_names = [k[0] for k in train_data.provide_label] mod = MutableModule(sym, data_names=data_names, label_names=label_names, logger=logger, context=ctx, work_load_list=args.work_load_list, max_data_shapes=max_data_shape, max_label_shapes=max_label_shape, fixed_param_prefix=fixed_param_prefix) # decide training params # metric # rpn_eval_metric = metric.RPNAccMetric() rpn_cls_metric = metric.RPNLogLossMetric() rpn_bbox_metric = metric.RPNL1LossMetric() # eval_metric = metric.RCNNAccMetric() cls_metric = metric.RCNNLogLossMetric() bbox_metric = metric.RCNNL1LossMetric() eval_metrics = mx.metric.CompositeEvalMetric() for child_metric in [rpn_cls_metric, rpn_bbox_metric, cls_metric, bbox_metric]: eval_metrics.add(child_metric) # callback batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=args.frequent) means = np.tile(np.array(config.TRAIN.BBOX_MEANS), config.NUM_CLASSES) stds = np.tile(np.array(config.TRAIN.BBOX_STDS), config.NUM_CLASSES) epoch_end_callback = (callback.do_checkpoint(args.e2e_prefix, means, stds), callback.do_validate(args.e2e_prefix)) arg_names = [x for x in sym.list_arguments() if x not in data_names+label_names] opt = get_optimizer(args, arg_names, len(sroidb) / input_batch_size, args.iter_size) # train default.testing = False mod.fit(train_data, roidb, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback, batch_end_callback=batch_end_callback, kvstore=args.kvstore, optimizer=opt, iter_size=args.iter_size, arg_params=arg_params, aux_params=aux_params, begin_epoch=args.begin_epoch, num_epoch=args.e2e_epoch)
def train_net(network, dataset, image_set, root_path, dataset_path, frequent, kvstore, work_load_list, no_flip, no_shuffle, resume, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, train_shared, lr, lr_step, proposal, maskrcnn_stage=None): # set up logger logging.basicConfig() logger = logging.getLogger() logger.setLevel(logging.INFO) # setup config config.TRAIN.BATCH_IMAGES = 1 config.TRAIN.BATCH_ROIS = 128 config.TRAIN.END2END = True config.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED = True # load symbol sym = eval('get_' + network + '_train')(num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHORS) feat_sym = [] for stride in config.RPN_FEAT_STRIDE: feat_sym.append(sym.get_internals()['rpn_cls_score_stride%s_output' % stride]) # setup multi-gpu batch_size = len(ctx) input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size # print config logger.info(pprint.pformat(config)) roidb_file = root_path + '/cache/' + dataset + '_roidb_with_mask.pkl' mean_file = root_path + '/cache/' + dataset + '_roidb_mean.pkl' std_file = root_path + '/cache/' + dataset + '_roidb_std.pkl' if maskrcnn_stage is not None: roidb_file = root_path + '/cache/' + dataset + '_roidb_with_mask_' + maskrcnn_stage + '.pkl' mean_file = root_path + '/cache/' + dataset + '_roidb_mean_' + maskrcnn_stage + '.pkl' std_file = root_path + '/cache/' + dataset + '_roidb_std_' + maskrcnn_stage + '.pkl' if osp.exists(roidb_file) and osp.exists(mean_file) and osp.exists( std_file): print('Load ' + roidb_file) with open(roidb_file, 'r') as f: roidb = pkl.load(f) print('Load ' + mean_file) with open(mean_file, 'r') as f: means = pkl.load(f) print('Load ' + std_file) with open(std_file, 'r') as f: stds = pkl.load(f) else: # load dataset and prepare imdb for training image_sets = [iset for iset in image_set.split('+')] roidbs = [ load_proposal_roidb(dataset, image_set, root_path, dataset_path, proposal=proposal, append_gt=True, flip=not no_flip) for image_set in image_sets ] roidb = merge_roidb(roidbs) def filter_roidb(roidb): """ remove roidb entries without usable rois """ def is_valid(entry): """ valid images have at least 1 fg or bg roi """ overlaps = entry['max_overlaps'] fg_inds = np.where(overlaps >= config.TRAIN.FG_THRESH)[0] bg_inds = np.where((overlaps < config.TRAIN.BG_THRESH_HI) & (overlaps >= config.TRAIN.BG_THRESH_LO))[0] valid = len(fg_inds) > 0 and len(bg_inds) > 0 return valid num = len(roidb) filtered_roidb = [entry for entry in roidb if is_valid(entry)] num_after = len(filtered_roidb) print('filtered %d roidb entries: %d -> %d' % (num - num_after, num, num_after)) return filtered_roidb roidb = filter_roidb(roidb) means, stds = add_bbox_regression_targets(roidb) add_assign_targets(roidb) add_mask_targets(roidb) for file, obj in zip([roidb_file, mean_file, std_file], [roidb, means, stds]): with open(file, 'w') as f: pkl.dump(obj, f, -1) # load training data train_data = MaskROIIter(roidb, batch_size=input_batch_size, shuffle=not no_shuffle, ctx=ctx, work_load_list=work_load_list, aspect_grouping=config.TRAIN.ASPECT_GROUPING) # infer max shape max_data_shape = [('data', (input_batch_size, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))] max_label_shape = [] for s in config.RCNN_FEAT_STRIDE: max_data_shape.append(('rois_stride%s' % s, (input_batch_size, config.TRAIN.BATCH_ROIS, 5))) max_label_shape.append(('label_stride%s' % s, (input_batch_size, config.TRAIN.BATCH_ROIS))) max_label_shape.append( ('bbox_target_stride%s' % s, (input_batch_size, config.TRAIN.BATCH_ROIS * config.NUM_CLASSES * 4))) max_label_shape.append( ('bbox_weight_stride%s' % s, (input_batch_size, config.TRAIN.BATCH_ROIS * config.NUM_CLASSES * 4))) max_label_shape.append(('mask_target_stride%s' % s, (input_batch_size, config.TRAIN.BATCH_ROIS, config.NUM_CLASSES, 28, 28))) max_label_shape.append(('mask_weight_stride%s' % s, (input_batch_size, config.TRAIN.BATCH_ROIS, config.NUM_CLASSES, 1, 1))) # infer shape data_shape_dict = dict(train_data.provide_data + train_data.provide_label) arg_shape, out_shape, aux_shape = sym.infer_shape(**data_shape_dict) arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape)) out_shape_dict = zip(sym.list_outputs(), out_shape) aux_shape_dict = dict(zip(sym.list_auxiliary_states(), aux_shape)) print('output shape:') pprint.pprint(out_shape_dict) # load and initialize params if resume: arg_params, aux_params = load_param(prefix, begin_epoch, convert=True) else: arg_params, aux_params = load_param(pretrained, epoch, convert=True) init_bbox_pred = mx.init.Normal(sigma=0.001) init_internal = mx.init.Normal(sigma=0.01) init = mx.init.Xavier(factor_type="in", rnd_type='gaussian', magnitude=2) for k in sym.list_arguments(): if k in data_shape_dict: continue if k not in arg_params: print('init', k) arg_params[k] = mx.nd.zeros(shape=arg_shape_dict[k]) init_internal(k, arg_params[k]) if k in ['rcnn_fc_bbox_weight', 'bbox_pred_weight']: init_bbox_pred(k, arg_params[k]) if k.endswith('bias'): arg_params[k] = mx.nd.zeros(shape=arg_shape_dict[k]) if 'ctx_red_weight' in k: ctx_shape = np.array(arg_shape_dict[k]) ctx_shape[1] /= 2 arg_params[k][:] = np.concatenate((np.eye( ctx_shape[1]).reshape(ctx_shape), np.zeros(ctx_shape)), axis=1) for k in sym.list_auxiliary_states(): if k not in aux_params: print('init', k) aux_params[k] = mx.nd.zeros(shape=aux_shape_dict[k]) init(k, aux_params[k]) # check parameter shapes for k in sym.list_arguments(): if k in data_shape_dict: continue assert k in arg_params, k + ' not initialized' assert arg_params[k].shape == arg_shape_dict[k], \ 'shape inconsistent for ' + k + ' inferred ' + str(arg_shape_dict[k]) + ' provided ' + str(arg_params[k].shape) for k in sym.list_auxiliary_states(): assert k in aux_params, k + ' not initialized' assert aux_params[k].shape == aux_shape_dict[k], \ 'shape inconsistent for ' + k + ' inferred ' + str(aux_shape_dict[k]) + ' provided ' + str(aux_params[k].shape) # create solver data_names = [k[0] for k in train_data.provide_data] label_names = [k[0] for k in train_data.provide_label] if train_shared: fixed_param_prefix = config.FIXED_PARAMS_SHARED else: fixed_param_prefix = config.FIXED_PARAMS mod = MutableModule(sym, data_names=data_names, label_names=label_names, logger=logger, context=ctx, work_load_list=work_load_list, max_data_shapes=max_data_shape, max_label_shapes=max_label_shape, fixed_param_prefix=fixed_param_prefix) # decide training params # metric eval_metric = metric.RCNNAccMetric() cls_metric = metric.RCNNLogLossMetric() bbox_metric = metric.RCNNRegLossMetric() mask_acc_metric = metric.MaskAccMetric() mask_log_metric = metric.MaskLogLossMetric() eval_metrics = mx.metric.CompositeEvalMetric() for child_metric in [ eval_metric, cls_metric, bbox_metric, mask_acc_metric, mask_log_metric ]: eval_metrics.add(child_metric) # callback batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=frequent) epoch_end_callback = callback.do_checkpoint(prefix, means, stds) # decide learning rate base_lr = lr lr_factor = 0.1 lr_epoch = [int(epoch) for epoch in lr_step.split(',')] lr_epoch_diff = [ epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch ] lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff))) lr_iters = [ int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff ] print('lr', lr, 'lr_epoch_diff', lr_epoch_diff, 'lr_iters', lr_iters) lr_scheduler = mx.lr_scheduler.MultiFactorScheduler(lr_iters, lr_factor) # optimizer optimizer_params = { 'momentum': 0.9, 'wd': 0.0001, 'learning_rate': lr, 'lr_scheduler': lr_scheduler, 'rescale_grad': (1.0 / batch_size), 'clip_gradient': 5 } # train mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback, batch_end_callback=batch_end_callback, kvstore=kvstore, optimizer='sgd', optimizer_params=optimizer_params, arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch, num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr=0.001, lr_step=50000): # set up logger logging.basicConfig() logger = logging.getLogger() logger.setLevel(logging.INFO) # setup config config.TRAIN.HAS_RPN = True config.TRAIN.BATCH_SIZE = 1 config.TRAIN.BATCH_IMAGES = 1 config.TRAIN.BATCH_ROIS = 128 config.TRAIN.END2END = True config.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED = True config.TRAIN.BG_THRESH_LO = 0.0 # load symbol sym = eval('get_' + args.network + '_train')() feat_sym = sym.get_internals()['rpn_cls_score_output'] # setup multi-gpu config.TRAIN.BATCH_IMAGES *= len(ctx) config.TRAIN.BATCH_SIZE *= len(ctx) # print config pprint.pprint(config) # load dataset and prepare imdb for training imdb = eval(args.dataset)(args.image_set, args.root_path, args.dataset_path) roidb = imdb.gt_roidb() if args.flip: roidb = imdb.append_flipped_images(roidb) # load training data train_data = AnchorLoader(feat_sym, roidb, batch_size=config.TRAIN.BATCH_SIZE, shuffle=True, ctx=ctx, work_load_list=args.work_load_list) # infer max shape max_data_shape = [('data', (config.TRAIN.BATCH_SIZE, 3, 1000, 1000))] max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape) max_data_shape.append(('gt_boxes', (config.TRAIN.BATCH_SIZE, 100, 5))) print 'providing maximum shape', max_data_shape, max_label_shape # load pretrained arg_params, aux_params = load_param(pretrained, epoch, convert=True) # infer shape data_shape_dict = dict(train_data.provide_data + train_data.provide_label) arg_shape, out_shape, aux_shape = sym.infer_shape(**data_shape_dict) arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape)) out_shape_dict = dict(zip(sym.list_outputs(), out_shape)) aux_shape_dict = dict(zip(sym.list_auxiliary_states(), aux_shape)) print 'output shape' pprint.pprint(out_shape_dict) # initialize params if not args.resume: arg_params['rpn_conv_3x3_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['rpn_conv_3x3_weight']) arg_params['rpn_conv_3x3_bias'] = mx.nd.zeros( shape=arg_shape_dict['rpn_conv_3x3_bias']) arg_params['rpn_cls_score_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['rpn_cls_score_weight']) arg_params['rpn_cls_score_bias'] = mx.nd.zeros( shape=arg_shape_dict['rpn_cls_score_bias']) arg_params['rpn_bbox_pred_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['rpn_bbox_pred_weight']) arg_params['rpn_bbox_pred_bias'] = mx.nd.zeros( shape=arg_shape_dict['rpn_bbox_pred_bias']) arg_params['cls_score_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['cls_score_weight']) arg_params['cls_score_bias'] = mx.nd.zeros( shape=arg_shape_dict['cls_score_bias']) arg_params['bbox_pred_weight'] = mx.random.normal( 0, 0.001, shape=arg_shape_dict['bbox_pred_weight']) arg_params['bbox_pred_bias'] = mx.nd.zeros( shape=arg_shape_dict['bbox_pred_bias']) # check parameter shapes for k in sym.list_arguments(): if k in data_shape_dict: continue assert arg_params[k].shape == arg_shape_dict[k], \ 'shape inconsistent for ' + k + ' inferred ' + str(arg_shape_dict[k]) + ' provided ' + str(arg_params[k].shape) for k in sym.list_auxiliary_states(): assert aux_params[k].shape == aux_shape_dict[k], \ 'shape inconsistent for ' + k + ' inferred ' + str(aux_shape_dict[k]) + ' provided ' + str(aux_params[k].shape) # create solver fixed_param_prefix = ['conv1', 'conv2'] data_names = [k[0] for k in train_data.provide_data] label_names = [k[0] for k in train_data.provide_label] mod = MutableModule(sym, data_names=data_names, label_names=label_names, logger=logger, context=ctx, work_load_list=args.work_load_list, max_data_shapes=max_data_shape, max_label_shapes=max_label_shape, fixed_param_prefix=fixed_param_prefix) # decide training params # metric rpn_eval_metric = metric.RPNAccMetric() rpn_cls_metric = metric.RPNLogLossMetric() rpn_bbox_metric = metric.RPNL1LossMetric() eval_metric = metric.RCNNAccMetric() cls_metric = metric.RCNNLogLossMetric() bbox_metric = metric.RCNNL1LossMetric() eval_metrics = mx.metric.CompositeEvalMetric() for child_metric in [ rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric ]: eval_metrics.add(child_metric) # callback batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=args.frequent) means = np.tile(np.array(config.TRAIN.BBOX_MEANS), imdb.num_classes) stds = np.tile(np.array(config.TRAIN.BBOX_STDS), imdb.num_classes) epoch_end_callback = callback.do_checkpoint(prefix, means, stds) # optimizer optimizer_params = { 'momentum': 0.9, 'wd': 0.0005, 'learning_rate': lr, 'lr_scheduler': mx.lr_scheduler.FactorScheduler(lr_step, 0.1), 'rescale_grad': (1.0 / config.TRAIN.BATCH_SIZE) } # train mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback, batch_end_callback=batch_end_callback, kvstore=args.kvstore, optimizer='sgd', optimizer_params=optimizer_params, arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch, num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr=0.001, lr_step='5'): # setup config config.TRAIN.BATCH_IMAGES = 1 config.TRAIN.BATCH_ROIS = 128 config.TRAIN.END2END = True config.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED = True # load symbol sym = eval('get_' + args.network + '_train')( num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHORS) if 'fpn' not in args.network: feat_sym = sym.get_internals()['rpn_cls_score_output'] else: feat_sym = [] for stride in config.RPN_FEAT_STRIDE: feat_sym.append( sym.get_internals()['rpn_cls_score_stride%s_output' % stride]) # setup multi-gpu batch_size = len(ctx) input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size # print config logger.info(pprint.pformat(config)) # load dataset and prepare imdb for training image_sets = [iset for iset in args.image_set.split('+')] roidbs = [ load_gt_roidb(args.dataset, image_set, args.root_path, args.dataset_path, flip=not args.no_flip) for image_set in image_sets ] roidb = merge_roidb(roidbs) roidb = filter_roidb(roidb) # load training data if 'fpn' not in args.network: train_data = AnchorLoader(feat_sym, roidb, batch_size=input_batch_size, shuffle=not args.no_shuffle, ctx=ctx, work_load_list=args.work_load_list, feat_stride=config.RPN_FEAT_STRIDE, anchor_scales=config.ANCHOR_SCALES, anchor_ratios=config.ANCHOR_RATIOS, aspect_grouping=config.TRAIN.ASPECT_GROUPING) else: train_data = AnchorLoaderFPN( feat_sym, roidb, batch_size=input_batch_size, shuffle=not args.no_shuffle, ctx=ctx, work_load_list=args.work_load_list, feat_stride=config.RPN_FEAT_STRIDE, anchor_scales=config.ANCHOR_SCALES, anchor_ratios=config.ANCHOR_RATIOS, aspect_grouping=config.TRAIN.ASPECT_GROUPING) # infer max shape max_data_shape = [('data', (input_batch_size, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))] max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape) max_data_shape.append(('gt_boxes', (input_batch_size, 100, 5))) max_data_shape.append(('gt_keypoints', (input_batch_size, 100, 42))) logger.info('providing maximum shape %s %s' % (max_data_shape, max_label_shape)) # infer shape data_shape_dict = dict(train_data.provide_data + train_data.provide_label) arg_shape, out_shape, aux_shape = sym.infer_shape(**data_shape_dict) arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape)) out_shape_dict = dict(zip(sym.list_outputs(), out_shape)) aux_shape_dict = dict(zip(sym.list_auxiliary_states(), aux_shape)) logger.info('output shape %s' % pprint.pformat(out_shape_dict)) # load and initialize params if args.resume: arg_params, aux_params = load_param(prefix, begin_epoch, convert=True) else: arg_params, aux_params = load_param(pretrained, epoch, convert=True) for k in sym.list_arguments(): if k in data_shape_dict: continue if k not in arg_params: print 'init', k arg_params[k] = mx.nd.zeros(shape=arg_shape_dict[k]) if not k.endswith('bias'): arg_params[k] = mx.random.normal(0, 0.01, shape=arg_params[k].shape) if 'upsampling' in k: v = arg_params[k].shape filt = upsample_filt(v[3]) initw = np.zeros(v) initw[:, :] = filt # becareful here is the slice assing arg_params[k] = mx.nd.array(initw) # # fix overflow in exp # if 'rpn_' in k: # arg_params[k] = mx.random.normal(0, 0.0001, shape=arg_params[k].shape) # # fix overflow in exp # if 'rcnn_' in k: # arg_params[k] = mx.random.normal(0, 0.0001, shape=arg_params[k].shape) for k in sym.list_auxiliary_states(): if k not in aux_params: print 'init', k aux_params[k] = mx.nd.zeros(shape=aux_shape_dict[k]) aux_params[k] = mx.random.normal(0, 0.01, shape=aux_params[k].shape) # check parameter shapes for k in sym.list_arguments(): if k in data_shape_dict: continue assert k in arg_params, k + ' not initialized' assert arg_params[k].shape == arg_shape_dict[k], \ 'shape inconsistent for ' + k + ' inferred ' + str(arg_shape_dict[k]) + ' provided ' + str(arg_params[k].shape) for k in sym.list_auxiliary_states(): assert k in aux_params, k + ' not initialized' assert aux_params[k].shape == aux_shape_dict[k], \ 'shape inconsistent for ' + k + ' inferred ' + str(aux_shape_dict[k]) + ' provided ' + str(aux_params[k].shape) # create solver fixed_param_prefix = config.FIXED_PARAMS data_names = [k[0] for k in train_data.provide_data] label_names = [k[0] for k in train_data.provide_label] mod = MutableModule(sym, data_names=data_names, label_names=label_names, logger=logger, context=ctx, work_load_list=args.work_load_list, max_data_shapes=max_data_shape, max_label_shapes=max_label_shape, fixed_param_prefix=fixed_param_prefix) # decide training params # metric rpn_eval_metric = metric.RPNAccMetric() rpn_cls_metric = metric.RPNLogLossMetric() rpn_bbox_metric = metric.RPNL1LossMetric() eval_metric = metric.RCNNAccMetric() cls_metric = metric.RCNNLogLossMetric() bbox_metric = metric.RCNNL1LossMetric() key_metric = metric.RCNNKeyAccMetric() eval_metrics = mx.metric.CompositeEvalMetric() for child_metric in [ rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric, key_metric ]: eval_metrics.add(child_metric) # callback batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=args.frequent) means = np.tile(np.array(config.TRAIN.BBOX_MEANS), config.NUM_CLASSES) stds = np.tile(np.array(config.TRAIN.BBOX_STDS), config.NUM_CLASSES) epoch_end_callback = callback.do_checkpoint(prefix, means, stds) # decide learning rate base_lr = lr lr_factor = 0.1 lr_epoch = [int(epoch) for epoch in lr_step.split(',')] lr_epoch_diff = [ epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch ] lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff))) lr_iters = [ int(epoch * len(roidb) / batch_size) for epoch in lr_epoch_diff ] logger.info('lr %f lr_epoch_diff %s lr_iters %s' % (lr, lr_epoch_diff, lr_iters)) lr_scheduler = mx.lr_scheduler.MultiFactorScheduler(lr_iters, lr_factor) # optimizer optimizer_params = { 'momentum': 0.9, 'wd': 0.0005, 'learning_rate': lr, 'lr_scheduler': lr_scheduler, 'rescale_grad': (1.0 / batch_size), 'clip_gradient': 5 } # optimizer_params = {'learning_rate': lr, 'clip_gradient': 1} # train mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback, batch_end_callback=batch_end_callback, kvstore=args.kvstore, optimizer='sgd', optimizer_params=optimizer_params, arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch, num_epoch=end_epoch)
def train_net(args, ctx, pretrained, epoch, prefix, begin_epoch, end_epoch, lr=0.001, lr_step='5'): # setup config config.TRAIN.BATCH_IMAGES = 1 config.TRAIN.BATCH_ROIS = 128 config.TRAIN.END2END = True config.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED = True # load symbol print "loading symbol..............." sym = eval('get_' + args.network + '_train')( num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHORS) feat_sym = sym.get_internals()['rpn_cls_score_output'] # setup multi-gpu batch_size = len(ctx) input_batch_size = config.TRAIN.BATCH_IMAGES * batch_size # print config logger.info(pprint.pformat(config)) # load dataset and prepare imdb for training image_sets = [iset for iset in args.image_set.split('+')] sdsdbs = [ load_gt_sdsdb( args.dataset, image_set, args.root_path, args.dataset_path, mask_size=config.MASK_SIZE, binary_thresh=config.BINARY_THRESH) #, flip=not args.no_flip) for image_set in image_sets ] sdsdb = merge_roidb(sdsdbs) sdsdb = filter_roidb(sdsdb) # load training data train_data = AnchorLoader(feat_sym, sdsdb, batch_size=input_batch_size, shuffle=not args.no_shuffle, ctx=ctx, work_load_list=args.work_load_list, feat_stride=config.RPN_FEAT_STRIDE, anchor_scales=config.ANCHOR_SCALES, anchor_ratios=config.ANCHOR_RATIOS, aspect_grouping=config.TRAIN.ASPECT_GROUPING) # print out training data shapes pprint.pprint(train_data.provide_data_single) pprint.pprint(train_data.provide_label_single) # infer max shape max_data_shape = [('data', (input_batch_size, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))] max_data_shape, max_label_shape = train_data.infer_shape(max_data_shape) max_data_shape.append(('gt_boxes', (input_batch_size, 100, 5))) max_data_shape.append(('gt_masks', (input_batch_size, 100, max([v[0] for v in config.SCALES]), max(v[1] for v in config.SCALES)))) logger.info('providing maximum shape %s %s' % (max_data_shape, max_label_shape)) # infer shape data_shape_dict = dict(train_data.provide_data_single + train_data.provide_label_single) pprint.pprint(data_shape_dict) arg_shape, out_shape, aux_shape = sym.infer_shape(**data_shape_dict) arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape)) out_shape_dict = dict(zip(sym.list_outputs(), out_shape)) aux_shape_dict = dict(zip(sym.list_auxiliary_states(), aux_shape)) logger.info('output shape %s' % pprint.pformat(out_shape_dict)) # visualize symbol internals = sym.get_internals() _, out_shapes, _ = internals.infer_shape(**data_shape_dict) shape_dict = dict(zip(internals.list_outputs(), out_shapes)) pprint.pprint(shape_dict) a = mx.viz.plot_network(sym) #a.view('faster-rcnn') # .................................................. # load and initialize params if args.resume: arg_params, aux_params = load_param(prefix, begin_epoch, convert=True) else: arg_params, aux_params = load_param(pretrained, epoch, convert=True) arg_params['rpn_conv_3x3_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['rpn_conv_3x3_weight']) arg_params['rpn_conv_3x3_bias'] = mx.nd.zeros( shape=arg_shape_dict['rpn_conv_3x3_bias']) arg_params['rpn_cls_score_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['rpn_cls_score_weight']) arg_params['rpn_cls_score_bias'] = mx.nd.zeros( shape=arg_shape_dict['rpn_cls_score_bias']) arg_params['rpn_bbox_pred_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['rpn_bbox_pred_weight']) arg_params['rpn_bbox_pred_bias'] = mx.nd.zeros( shape=arg_shape_dict['rpn_bbox_pred_bias']) arg_params['cls_score_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['cls_score_weight']) arg_params['cls_score_bias'] = mx.nd.zeros( shape=arg_shape_dict['cls_score_bias']) arg_params['bbox_pred_weight'] = mx.random.normal( 0, 0.001, shape=arg_shape_dict['bbox_pred_weight']) arg_params['bbox_pred_bias'] = mx.nd.zeros( shape=arg_shape_dict['bbox_pred_bias']) arg_params['mask_pred1_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['mask_pred1_weight']) arg_params['mask_pred2_weight'] = mx.random.normal( 0, 0.01, shape=arg_shape_dict['mask_pred2_weight']) arg_params['mask_pred2_bias'] = mx.nd.zeros( shape=arg_shape_dict['mask_pred2_bias']) # check parameter shapes for k in sym.list_arguments(): if k in data_shape_dict: continue assert k in arg_params, k + ' not initialized' assert arg_params[k].shape == arg_shape_dict[k], \ 'shape inconsistent for ' + k + ' inferred ' + str(arg_shape_dict[k]) + ' provided ' + str(arg_params[k].shape) for k in sym.list_auxiliary_states(): assert k in aux_params, k + ' not initialized' assert aux_params[k].shape == aux_shape_dict[k], \ 'shape inconsistent for ' + k + ' inferred ' + str(aux_shape_dict[k]) + ' provided ' + str(aux_params[k].shape) # create solver fixed_param_prefix = config.FIXED_PARAMS data_names = [k[0] for k in train_data.provide_data_single] label_names = [k[0] for k in train_data.provide_label_single] mod = MutableModule(sym, data_names=data_names, label_names=label_names, logger=logger, context=ctx, work_load_list=args.work_load_list, max_data_shapes=max_data_shape, max_label_shapes=max_label_shape, fixed_param_prefix=fixed_param_prefix) # decide training params # metric rpn_eval_metric = metric.RPNAccMetric() rpn_cls_metric = metric.RPNLogLossMetric() rpn_bbox_metric = metric.RPNL1LossMetric() eval_metric = metric.RCNNAccMetric() cls_metric = metric.RCNNLogLossMetric() bbox_metric = metric.RCNNL1LossMetric() mask_metric = metric.MaskLossMetric() eval_metrics = mx.metric.CompositeEvalMetric() for child_metric in [ rpn_eval_metric, rpn_cls_metric, rpn_bbox_metric, eval_metric, cls_metric, bbox_metric, mask_metric ]: eval_metrics.add(child_metric) # callback batch_end_callback = callback.Speedometer(train_data.batch_size, frequent=args.frequent) means = np.tile(np.array(config.TRAIN.BBOX_MEANS), config.NUM_CLASSES) stds = np.tile(np.array(config.TRAIN.BBOX_STDS), config.NUM_CLASSES) epoch_end_callback = callback.do_checkpoint(prefix, means, stds) # decide learning rate base_lr = lr lr_factor = 0.1 lr_epoch = [int(epoch) for epoch in lr_step.split(',')] print lr_epoch lr_epoch_diff = [ epoch - begin_epoch for epoch in lr_epoch if epoch > begin_epoch ] lr = base_lr * (lr_factor**(len(lr_epoch) - len(lr_epoch_diff))) lr_iters = [ int(epoch * len(sdsdb) / batch_size) for epoch in lr_epoch_diff ] logger.info('lr %f lr_epoch_diff %s lr_iters %s' % (lr, lr_epoch_diff, lr_iters)) lr_scheduler = mx.lr_scheduler.MultiFactorScheduler(lr_iters, lr_factor) # optimizer optimizer_params = { 'momentum': 0.9, 'wd': 0.0005, 'learning_rate': lr, 'lr_scheduler': lr_scheduler, 'rescale_grad': (1.0 / batch_size), 'clip_gradient': 5 } # train mod.fit(train_data, eval_metric=eval_metrics, epoch_end_callback=epoch_end_callback, batch_end_callback=batch_end_callback, kvstore=args.kvstore, optimizer='sgd', optimizer_params=optimizer_params, arg_params=arg_params, aux_params=aux_params, begin_epoch=begin_epoch, num_epoch=end_epoch)