我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用mxnet.ctx()。
def __init__(self, symbol, model_prefix, epoch, data_hw, mean_pixels, img_stride=32, th_nms=0.3333, ctx=None): ''' ''' self.ctx = mx.cpu() if not ctx else ctx if isinstance(data_hw, int): data_hw = (data_hw, data_hw) assert data_hw[0] % img_stride == 0 and data_hw[1] % img_stride == 0 self.data_hw = data_hw _, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, epoch) self.mod = mx.mod.Module(symbol, label_names=None, context=ctx) self.mod.bind(data_shapes=[('data', (1, 3, data_hw[0], data_hw[1]))]) self.mod.set_params(arg_params, aux_params) self.mean_pixels = mean_pixels self.img_stride = img_stride self.th_nms = th_nms
def __init__(self, symbol, model_prefix, epoch, data_shape, mean_pixels, \ batch_size=1, ctx=None): self.ctx = ctx if self.ctx is None: self.ctx = mx.cpu() load_symbol, args, auxs = mx.model.load_checkpoint(model_prefix, epoch) if symbol is None: symbol = load_symbol self.mod = mx.mod.Module(symbol, label_names=None, context=ctx) self.data_shape = data_shape self.mod.bind(data_shapes=[('data', (batch_size, 3, data_shape, data_shape))]) self.mod.set_params(args, auxs) self.data_shape = data_shape self.mean_pixels = mean_pixels
def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, num_class, nms_thresh=0.5, force_nms=True, nms_topk=400): """ wrapper for initialize a detector Parameters: ---------- net : str test network name prefix : str load model prefix epoch : int load model epoch data_shape : int resize image shape mean_pixels : tuple (float, float, float) mean pixel values (R, G, B) ctx : mx.ctx running context, mx.cpu() or mx.gpu(?) num_class : int number of classes nms_thresh : float non-maximum suppression threshold force_nms : bool force suppress different categories """ if net is not None: net = get_symbol(net, data_shape, num_classes=num_class, nms_thresh=nms_thresh, force_nms=force_nms, nms_topk=nms_topk) detector = Detector(net, prefix, epoch, data_shape, mean_pixels, ctx=ctx) return detector
def __init__(self, symbol, model_prefix, epoch, data_shape, mean_pixels, \ batch_size=1, ctx=None): self.ctx = ctx if self.ctx is None: self.ctx = mx.cpu() _, args, auxs = mx.model.load_checkpoint(model_prefix, epoch) self.mod = mx.mod.Module(symbol, context=ctx) self.data_shape = data_shape self.mod.bind(data_shapes=[('data', (batch_size, 3, data_shape, data_shape))]) self.mod.set_params(args, auxs) self.data_shape = data_shape self.mean_pixels = mean_pixels
def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, nms_thresh=0.5, force_nms=True): """ wrapper for initialize a detector Parameters: ---------- net : str test network name prefix : str load model prefix epoch : int load model epoch data_shape : int resize image shape mean_pixels : tuple (float, float, float) mean pixel values (R, G, B) ctx : mx.ctx running context, mx.cpu() or mx.gpu(?) force_nms : bool force suppress different categories """ sys.path.append(os.path.join(os.getcwd(), 'symbol')) net = importlib.import_module("symbol_" + net) \ .get_symbol(len(CLASSES), nms_thresh, force_nms) detector = Detector(net, prefix + "_" + str(data_shape), epoch, \ data_shape, mean_pixels, ctx=ctx) return detector
def __init__(self, symbol, model_prefix, epoch, data_shape, mean_pixels, \ batch_size=1, ctx=None): self.ctx = ctx if self.ctx is None: self.ctx = mx.cpu() load_symbol, args, auxs = mx.model.load_checkpoint(model_prefix, epoch) if symbol is None: symbol = load_symbol self.mod = mx.mod.Module(symbol, label_names=None, context=ctx) self.data_shape = data_shape self.mod.bind(data_shapes=[('data', (batch_size, 3, data_shape, data_shape))]) self.mod.set_params(args, auxs) self.data_shape = data_shape self.mean_pixels = mean_pixels self.th_nms = cfg.valid['th_nms']
def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, num_class, nms_thresh=0.5, force_nms=True, nms_topk=400): """ wrapper for initialize a detector Parameters: ---------- net : str test network name prefix : str load model prefix epoch : int load model epoch data_shape : int resize image shape mean_pixels : tuple (float, float, float) mean pixel values (R, G, B) ctx : mx.ctx running context, mx.cpu() or mx.gpu(?) num_class : int number of classes nms_thresh : float non-maximum suppression threshold force_nms : bool force suppress different categories """ if net is not None: net = get_symbol(net, data_shape, num_classes=num_class, nms_thresh=nms_thresh, force_nms=force_nms, nms_topk=nms_topk) detector = FaceDetector(net, prefix, epoch, data_shape, mean_pixels, ctx=ctx) return detector
def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, num_class, nms_thresh=0.5, force_nms=True, nms_topk=400): """ wrapper for initialize a detector Parameters: ---------- net : str test network name prefix : str load model prefix epoch : int load model epoch data_shape : int resize image shape mean_pixels : tuple (float, float, float) mean pixel values (R, G, B) ctx : mx.ctx running context, mx.cpu() or mx.gpu(?) num_class : int number of classes nms_thresh : float non-maximum suppression threshold force_nms : bool force suppress different categories """ if net is not None: net = get_symbol(net, data_shape, num_classes=num_class, nms_thresh=nms_thresh, force_nms=force_nms, nms_topk=nms_topk) _, _ = estimate_mac(net, data_shape=(1, 3, data_shape, data_shape)) detector = Detector(net, prefix, epoch, data_shape, mean_pixels, ctx=ctx) return detector
def __init__(self, symbol, model_prefix, epoch, data_shape, mean_pixels, \ batch_size=1, ctx=None): self.ctx = ctx if self.ctx is None: self.ctx = mx.cpu() load_symbol, args, auxs = mx.model.load_checkpoint(model_prefix, epoch) if symbol is None: symbol = load_symbol self.mod = mx.mod.Module(symbol, label_names=("yolo_output_label",), context=ctx) self.data_shape = data_shape self.mod.bind(data_shapes=[('data', (batch_size, 3, data_shape, data_shape))], label_shapes=[('yolo_output_label', (batch_size, 2, 5))]) self.mod.set_params(args, auxs) self.data_shape = data_shape self.mean_pixels = mean_pixels
def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, nms_thresh=0.5, force_nms=True): """ wrapper for initialize a detector Parameters: ---------- net : str test network name prefix : str load model prefix epoch : int load model epoch data_shape : int resize image shape mean_pixels : tuple (float, float, float) mean pixel values (R, G, B) ctx : mx.ctx running context, mx.cpu() or mx.gpu(?) force_nms : bool force suppress different categories """ sys.path.append(os.path.join(os.getcwd(), 'symbol')) if net is not None: prefix = prefix + "_" + net.strip('_yolo') + '_' + str(416) net = importlib.import_module("symbol_" + net) \ .get_symbol(len(CLASSES), nms_thresh, force_nms) detector = Detector(net, prefix, epoch, \ data_shape, mean_pixels, ctx=ctx) return detector
