################################## ### load the pre-store normalization constant ## Load Prior and transitional Matrix dic = sio.loadmat('Transition_matrix.mat') Transition_matrix = dic['Transition_matrix'] Prior = dic['Prior'] outPred = './training/pred/' #################################### ##################load CNN here###### import getopt as opt from gpumodel import IGPUModel from options import * op = ShowConvNet.get_options_parser() op.options['load_file'].value = r'.\tmp\tmp\ConvNet__2014-05-14_21.42.41' op.options['feature_path'].value = r'.\prediction_feature' op.options['test_batch_range'].value = 1 op.options['write_features'].value = 'probs' load_dic = IGPUModel.load_checkpoint(op.options["load_file"].value) old_op = load_dic["op"] old_op.merge_from(op) op = old_op op.eval_expr_defaults() #op, load_dic = IGPUModel.parse_options(op) op.options['train_batch_range'].value = [1] op.options['test_batch_range'].value = [1] model = ShowConvNet(op, load_dic) meta = pickle.load(open(r'.\storage\batches.meta')) data_mean = meta['data_mean']
################################## ### load the pre-store normalization constant ## Load Prior and transitional Matrix dic=sio.loadmat('Transition_matrix.mat') Transition_matrix = dic['Transition_matrix'] Prior = dic['Prior'] outPred='./training/pred/' #################################### ##################load CNN here###### import getopt as opt from gpumodel import IGPUModel from options import * op = ShowConvNet.get_options_parser() op.options['load_file'].value=r'.\tmp\tmp\ConvNet__2014-05-14_21.42.41' op.options['feature_path'].value=r'.\prediction_feature' op.options['test_batch_range'].value=1 op.options['write_features'].value ='probs' load_dic = IGPUModel.load_checkpoint(op.options["load_file"].value) old_op = load_dic["op"] old_op.merge_from(op) op = old_op op.eval_expr_defaults() #op, load_dic = IGPUModel.parse_options(op) op.options['train_batch_range'].value=[1] op.options['test_batch_range'].value=[1] model = ShowConvNet(op, load_dic) meta = pickle.load(open(r'.\storage\batches.meta')) data_mean = meta['data_mean']
#################################### PER CNN customize Flag_multiview = 0 CNN_NAME = 'ConvNet__2014-05-26_03.40.18_155' outPred='./ConvNet_3DCNN/training/Test_3DCNN_' + CNN_NAME if not os.path.exists(outPred): os.makedirs(outPred) #################################### ##################load CNN here###### import getopt as opt from gpumodel import IGPUModel from options import * op = ShowConvNet.get_options_parser() #op.options['load_file'].value=r'.\ConvNet_3DCNN\tmp\ConvNet__2014-05-28_01.59.00' ### old op.options['load_file'].value=r'I:\Kaggle_multimodal\StartingKit_track3\Final_project\ConvNet_3DCNN\tmp\ConvNet__2014-05-26_03.40.18' op.options['write_features'].value ='probs' load_dic = IGPUModel.load_checkpoint(op.options["load_file"].value) old_op = load_dic["op"] old_op.merge_from(op) op = old_op op.eval_expr_defaults() op.options['train_batch_range'].value=[1] op.options['test_batch_range'].value=[1] op.options['data_path'].value=r'.\ConvNet_3DCNN\storage_sk_final' model = ShowConvNet(op, load_dic)
################################## ### load the pre-store normalization constant ## Load Prior and transitional Matrix dic=sio.loadmat('Transition_matrix.mat') Transition_matrix = dic['Transition_matrix'] Prior = dic['Prior'] outPred='./ConvNet_3DCNN/training/pred_sk_norm/' #################################### ##################load CNN here###### import getopt as opt from gpumodel import IGPUModel from options import * Flag_multiview = 0 op = ShowConvNet.get_options_parser() op.options['load_file'].value=r'.\ConvNet_3DCNN\tmp\ConvNet__2014-05-28_01.59.00' op.options['write_features'].value ='probs' load_dic = IGPUModel.load_checkpoint(op.options["load_file"].value) old_op = load_dic["op"] old_op.merge_from(op) op = old_op op.eval_expr_defaults() op.options['train_batch_range'].value=[1] op.options['test_batch_range'].value=[1] op.options['data_path'].value=r'.\ConvNet_3DCNN\storage_sk_final' model = ShowConvNet(op, load_dic) model.crop_border = 0