| import mxnet as mx |
| import numpy as np |
| |
| def conv_act_layer(from_layer, name, num_filter, kernel=(1,1), pad=(0,0), \ |
| stride=(1,1), act_type="relu", use_batchnorm=False): |
| """ |
| wrapper for a small Convolution group |
| |
| Parameters: |
| ---------- |
| from_layer : mx.symbol |
| continue on which layer |
| name : str |
| base name of the new layers |
| num_filter : int |
| how many filters to use in Convolution layer |
| kernel : tuple (int, int) |
| kernel size (h, w) |
| pad : tuple (int, int) |
| padding size (h, w) |
| stride : tuple (int, int) |
| stride size (h, w) |
| act_type : str |
| activation type, can be relu... |
| use_batchnorm : bool |
| whether to use batch normalization |
| |
| Returns: |
| ---------- |
| (conv, relu) mx.Symbols |
| """ |
| assert not use_batchnorm, "batchnorm not yet supported" |
| conv = mx.symbol.Convolution(data=from_layer, kernel=kernel, pad=pad, \ |
| stride=stride, num_filter=num_filter, name="conv{}".format(name)) |
| relu = mx.symbol.Activation(data=conv, act_type=act_type, \ |
| name="{}{}".format(act_type, name)) |
| if use_batchnorm: |
| relu = mx.symbol.BatchNorm(data=relu, name="bn{}".format(name)) |
| return conv, relu |
| |
| def multibox_layer(from_layers, num_classes, sizes=[.2, .95], |
| ratios=[1], normalization=-1, num_channels=[], |
| clip=True, interm_layer=0): |
| """ |
| the basic aggregation module for SSD detection. Takes in multiple layers, |
| generate multiple object detection targets by customized layers |
| |
| Parameters: |
| ---------- |
| from_layers : list of mx.symbol |
| generate multibox detection from layers |
| num_classes : int |
| number of classes excluding background, will automatically handle |
| background in this function |
| sizes : list or list of list |
| [min_size, max_size] for all layers or [[], [], []...] for specific layers |
| ratios : list or list of list |
| [ratio1, ratio2...] for all layers or [[], [], ...] for specific layers |
| normalizations : int or list of int |
| use normalizations value for all layers or [...] for specific layers, |
| -1 indicate no normalizations and scales |
| num_channels : list of int |
| number of input layer channels, used when normalization is enabled, the |
| length of list should equals to number of normalization layers |
| clip : bool |
| whether to clip out-of-image boxes |
| interm_layer : int |
| if > 0, will add a intermediate Convolution layer |
| |
| Returns: |
| ---------- |
| list of outputs, as [loc_preds, cls_preds, anchor_boxes] |
| loc_preds : localization regression prediction |
| cls_preds : classification prediction |
| anchor_boxes : generated anchor boxes |
| """ |
| assert len(from_layers) > 0, "from_layers must not be empty list" |
| assert num_classes > 0, \ |
| "num_classes {} must be larger than 0".format(num_classes) |
| |
| assert len(ratios) > 0, "aspect ratios must not be empty list" |
| if not isinstance(ratios[0], list): |
| # provided only one ratio list, broadcast to all from_layers |
| ratios = [ratios] * len(from_layers) |
| assert len(ratios) == len(from_layers), \ |
| "ratios and from_layers must have same length" |
| |
| assert len(sizes) > 0, "sizes must not be empty list" |
| if len(sizes) == 2 and not isinstance(sizes[0], list): |
| # provided size range, we need to compute the sizes for each layer |
| assert sizes[0] > 0 and sizes[0] < 1 |
| assert sizes[1] > 0 and sizes[1] < 1 and sizes[1] > sizes[0] |
| tmp = np.linspace(sizes[0], sizes[1], num=(len(from_layers)-1)) |
| min_sizes = [start_offset] + tmp.tolist() |
| max_sizes = tmp.tolist() + [tmp[-1]+start_offset] |
| sizes = zip(min_sizes, max_sizes) |
| assert len(sizes) == len(from_layers), \ |
| "sizes and from_layers must have same length" |
| |
| if not isinstance(normalization, list): |
| normalization = [normalization] * len(from_layers) |
| assert len(normalization) == len(from_layers) |
| |
| assert sum(x > 0 for x in normalization) == len(num_channels), \ |
| "must provide number of channels for each normalized layer" |
| |
| loc_pred_layers = [] |
| cls_pred_layers = [] |
| anchor_layers = [] |
| num_classes += 1 # always use background as label 0 |
| |
| for k, from_layer in enumerate(from_layers): |
| from_name = from_layer.name |
| # normalize |
| if normalization[k] > 0: |
| from_layer = mx.symbol.L2Normalization(data=from_layer, \ |
| mode="channel", name="{}_norm".format(from_name)) |
| # from_layer = mx.symbol.NaiveScale(data=from_layer, mode="spatial", \ |
| # name="{}_scale_{}".format(from_name, normalization[k])) |
| # scale = mx.symbol.InferedVariable(data=from_layer, shape=(1, 0, 1, 1), |
| # suffix="{}_scale".format(normalization[k]), |
| # name="{}_scale".format(from_name)) |
| scale = mx.symbol.Variable(name="{}_scale".format(from_name), |
| shape=(1, num_channels.pop(0), 1, 1)) |
| # scale = mx.symbol.Reshape(data=scale, shape=(1, 512, 1, 1)) |
| from_layer = normalization[k] * mx.symbol.broadcast_mul(lhs=scale, rhs=from_layer) |
| if interm_layer > 0: |
| from_layer = mx.symbol.Convolution(data=from_layer, kernel=(3,3), \ |
| stride=(1,1), pad=(1,1), num_filter=interm_layer, \ |
| name="{}_inter_conv".format(from_name)) |
| from_layer = mx.symbol.Activation(data=from_layer, act_type="relu", \ |
| name="{}_inter_relu".format(from_name)) |
| |
| # estimate number of anchors per location |
| # here I follow the original version in caffe |
| # TODO: better way to shape the anchors?? |
| size = sizes[k] |
| assert len(size) > 0, "must provide at least one size" |
| size_str = "(" + ",".join([str(x) for x in size]) + ")" |
| ratio = ratios[k] |
| assert len(ratio) > 0, "must provide at least one ratio" |
| ratio_str = "(" + ",".join([str(x) for x in ratio]) + ")" |
| num_anchors = len(size) -1 + len(ratio) |
| |
| # create location prediction layer |
| num_loc_pred = num_anchors * 4 |
| loc_pred = mx.symbol.Convolution(data=from_layer, kernel=(3,3), \ |
| stride=(1,1), pad=(1,1), num_filter=num_loc_pred, \ |
| name="{}_loc_pred_conv".format(from_name)) |
| loc_pred = mx.symbol.transpose(loc_pred, axes=(0,2,3,1)) |
| loc_pred = mx.symbol.Flatten(data=loc_pred) |
| loc_pred_layers.append(loc_pred) |
| |
| # create class prediction layer |
| num_cls_pred = num_anchors * num_classes |
| cls_pred = mx.symbol.Convolution(data=from_layer, kernel=(3,3), \ |
| stride=(1,1), pad=(1,1), num_filter=num_cls_pred, \ |
| name="{}_cls_pred_conv".format(from_name)) |
| cls_pred = mx.symbol.transpose(cls_pred, axes=(0,2,3,1)) |
| cls_pred = mx.symbol.Flatten(data=cls_pred) |
| cls_pred_layers.append(cls_pred) |
| |
| # create anchor generation layer |
| anchors = mx.contrib.symbol.MultiBoxPrior(from_layer, sizes=size_str, ratios=ratio_str, \ |
| clip=clip, name="{}_anchors".format(from_name)) |
| anchors = mx.symbol.Flatten(data=anchors) |
| anchor_layers.append(anchors) |
| |
| loc_preds = mx.symbol.Concat(*loc_pred_layers, num_args=len(loc_pred_layers), \ |
| dim=1, name="multibox_loc_pred") |
| cls_preds = mx.symbol.Concat(*cls_pred_layers, num_args=len(cls_pred_layers), \ |
| dim=1) |
| cls_preds = mx.symbol.Reshape(data=cls_preds, shape=(0, -1, num_classes)) |
| cls_preds = mx.symbol.transpose(cls_preds, axes=(0, 2, 1), name="multibox_cls_pred") |
| anchor_boxes = mx.symbol.Concat(*anchor_layers, \ |
| num_args=len(anchor_layers), dim=1) |
| anchor_boxes = mx.symbol.Reshape(data=anchor_boxes, shape=(0, -1, 4), name="multibox_anchors") |
| return [loc_preds, cls_preds, anchor_boxes] |
