我们从Python开源项目中，提取了以下15个代码示例，用于说明如何使用fast_rcnn.config.cfg.MODELS_DIR。
def get_solvers(net_name): # Faster R-CNN Alternating Optimization n = 'faster_rcnn_alt_opt' # Solver for each training stage solvers = [[net_name, n, 'stage1_rpn_solver60k80k.pt'], [net_name, n, 'stage1_fast_rcnn_solver30k40k.pt'], [net_name, n, 'stage2_rpn_solver60k80k.pt'], [net_name, n, 'stage2_fast_rcnn_solver30k40k.pt']] solvers = [os.path.join(cfg.MODELS_DIR, *s) for s in solvers] # Iterations for each training stage max_iters = [80000, 40000, 80000, 40000] # max_iters = [100, 100, 100, 100] # Test prototxt for the RPN rpn_test_prototxt = os.path.join( cfg.MODELS_DIR, net_name, n, 'rpn_test.pt') return solvers, max_iters, rpn_test_prototxt # ------------------------------------------------------------------------------ # Pycaffe doesn't reliably free GPU memory when instantiated nets are discarded # (e.g. "del net" in Python code). To work around this issue, each training # stage is executed in a separate process using multiprocessing.Process. # ------------------------------------------------------------------------------
def get_solvers(net_name): # Faster R-CNN Alternating Optimization n = 'faster_rcnn_alt_opt' # Solver for each training stage solvers = [[net_name, n, 'stage1_rpn_solver60k80k.pt'], [net_name, n, 'stage1_fast_rcnn_solver30k40k.pt'], [net_name, n, 'stage2_rpn_solver60k80k.pt'], [net_name, n, 'stage2_fast_rcnn_solver30k40k.pt']] solvers = [os.path.join(cfg.MODELS_DIR, *s) for s in solvers] # Iterations for each training stage max_iters = [320000, 320000, 320000, 320000] # max_iters = [100, 100, 100, 100] # Test prototxt for the RPN rpn_test_prototxt = os.path.join( cfg.MODELS_DIR, net_name, n, 'rpn_test.pt') return solvers, max_iters, rpn_test_prototxt # ------------------------------------------------------------------------------ # Pycaffe doesn't reliably free GPU memory when instantiated nets are discarded # (e.g. "del net" in Python code). To work around this issue, each training # stage is executed in a separate process using multiprocessing.Process. # ------------------------------------------------------------------------------
def get_solvers(imdb_name, net_name, model_name): # R-FCN Alternating Optimization # Solver for each training stage if imdb_name.startswith('coco'): solvers = [[net_name, model_name, 'stage1_rpn_solver360k480k.pt'], [net_name, model_name, 'stage1_rfcn_ohem_solver360k480k.pt'], [net_name, model_name, 'stage2_rpn_solver360k480k.pt'], [net_name, model_name, 'stage2_rfcn_ohem_solver360k480k.pt'], [net_name, model_name, 'stage3_rpn_solver360k480k.pt']] solvers = [os.path.join('.', 'models', 'coco', *s) for s in solvers] # Iterations for each training stage max_iters = [480000, 480000, 480000, 480000, 480000] # Test prototxt for the RPN rpn_test_prototxt = os.path.join( '.', 'models', 'coco', net_name, model_name, 'rpn_test.pt') else: solvers = [[net_name, model_name, 'stage1_rpn_solver60k80k.pt'], [net_name, model_name, 'stage1_rfcn_ohem_solver80k120k.pt'], [net_name, model_name, 'stage2_rpn_solver60k80k.pt'], [net_name, model_name, 'stage2_rfcn_ohem_solver80k120k.pt'], [net_name, model_name, 'stage3_rpn_solver60k80k.pt']] solvers = [os.path.join(cfg.MODELS_DIR, *s) for s in solvers] # Iterations for each training stage max_iters = [80000, 120000, 80000, 120000, 80000] # Test prototxt for the RPN rpn_test_prototxt = os.path.join( cfg.MODELS_DIR, net_name, model_name, 'rpn_test.pt') return solvers, max_iters, rpn_test_prototxt
