# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from paddle import fluid from paddle.fluid.param_attr import ParamAttr from paddle.fluid.regularizer import L2Decay from ppdet.modeling.ops import MultiClassNMS, MultiClassSoftNMS, MatrixNMS from ppdet.modeling.losses.yolo_loss import YOLOv3Loss from ppdet.core.workspace import register from ppdet.modeling.ops import DropBlock from .iou_aware import get_iou_aware_score try: from collections.abc import Sequence except Exception: from collections import Sequence from ppdet.utils.check import check_version __all__ = ['YOLOv3Head', 'YOLOv4Head'] @register class YOLOv3Head(object): """ Head block for YOLOv3 network Args: conv_block_num (int): number of conv block in each detection block norm_decay (float): weight decay for normalization layer weights num_classes (int): number of output classes anchors (list): anchors anchor_masks (list): anchor masks nms (object): an instance of `MultiClassNMS` """ __inject__ = ['yolo_loss', 'nms'] __shared__ = ['num_classes', 'weight_prefix_name'] def __init__(self, conv_block_num=2, norm_decay=0., num_classes=80, anchors=[[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]], anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]], drop_block=False, coord_conv=False, iou_aware=False, iou_aware_factor=0.4, block_size=3, keep_prob=0.9, yolo_loss="YOLOv3Loss", spp=False, nms=MultiClassNMS( score_threshold=0.01, nms_top_k=1000, keep_top_k=100, nms_threshold=0.45, background_label=-1).__dict__, weight_prefix_name='', downsample=[32, 16, 8], scale_x_y=1.0, clip_bbox=True): check_version("1.8.4") self.conv_block_num = conv_block_num self.norm_decay = norm_decay self.num_classes = num_classes self.anchor_masks = anchor_masks self._parse_anchors(anchors) self.yolo_loss = yolo_loss self.nms = nms self.prefix_name = weight_prefix_name self.drop_block = drop_block self.iou_aware = iou_aware self.coord_conv = coord_conv self.iou_aware_factor = iou_aware_factor self.block_size = block_size self.keep_prob = keep_prob self.use_spp = spp if isinstance(nms, dict): self.nms = MultiClassNMS(**nms) self.downsample = downsample self.scale_x_y = scale_x_y self.clip_bbox = clip_bbox def _create_tensor_from_numpy(self, numpy_array): paddle_array = fluid.layers.create_global_var( shape=numpy_array.shape, value=0., dtype=numpy_array.dtype) fluid.layers.assign(numpy_array, paddle_array) return paddle_array def _add_coord(self, input, is_test=True): if not self.coord_conv: return input # NOTE: here is used for exporting model for TensorRT inference, # only support batch_size=1 for input shape should be fixed, # and we create tensor with fixed shape from numpy array if is_test and input.shape[2] > 0 and input.shape[3] > 0: batch_size = 1 grid_x = int(input.shape[3]) grid_y = int(input.shape[2]) idx_i = np.array( [[i / (grid_x - 1) * 2.0 - 1 for i in range(grid_x)]], dtype='float32') gi_np = np.repeat(idx_i, grid_y, axis=0) gi_np = np.reshape(gi_np, newshape=[1, 1, grid_y, grid_x]) gi_np = np.tile(gi_np, reps=[batch_size, 1, 1, 1]) x_range = self._create_tensor_from_numpy(gi_np.astype(np.float32)) x_range.stop_gradient = True y_range = self._create_tensor_from_numpy( gi_np.transpose([0, 1, 3, 2]).astype(np.float32)) y_range.stop_gradient = True # NOTE: in training mode, H and W is variable for random shape, # implement add_coord with shape as Variable else: input_shape = fluid.layers.shape(input) b = input_shape[0] h = input_shape[2] w = input_shape[3] x_range = fluid.layers.range(0, w, 1, 'float32') / ((w - 1.) / 2.) x_range = x_range - 1. x_range = fluid.layers.unsqueeze(x_range, [0, 1, 2]) x_range = fluid.layers.expand(x_range, [b, 1, h, 1]) x_range.stop_gradient = True y_range = fluid.layers.transpose(x_range, [0, 1, 3, 2]) y_range.stop_gradient = True return fluid.layers.concat([input, x_range, y_range], axis=1) def _conv_bn(self, input, ch_out, filter_size, stride, padding, act='leaky', is_test=True, name=None): conv = fluid.layers.conv2d( input=input, num_filters=ch_out, filter_size=filter_size, stride=stride, padding=padding, act=None, param_attr=ParamAttr(name=name + ".conv.weights"), bias_attr=False) bn_name = name + ".bn" bn_param_attr = ParamAttr( regularizer=L2Decay(self.norm_decay), name=bn_name + '.scale') bn_bias_attr = ParamAttr( regularizer=L2Decay(self.norm_decay), name=bn_name + '.offset') out = fluid.layers.batch_norm( input=conv, act=None, is_test=is_test, param_attr=bn_param_attr, bias_attr=bn_bias_attr, moving_mean_name=bn_name + '.mean', moving_variance_name=bn_name + '.var') if act == 'leaky': out = fluid.layers.leaky_relu(x=out, alpha=0.1) return out def _spp_module(self, input, is_test=True, name=""): output1 = input output2 = fluid.layers.pool2d( input=output1, pool_size=5, pool_stride=1, pool_padding=2, ceil_mode=False, pool_type='max') output3 = fluid.layers.pool2d( input=output1, pool_size=9, pool_stride=1, pool_padding=4, ceil_mode=False, pool_type='max') output4 = fluid.layers.pool2d( input=output1, pool_size=13, pool_stride=1, pool_padding=6, ceil_mode=False, pool_type='max') output = fluid.layers.concat( input=[output1, output2, output3, output4], axis=1) return output def _detection_block(self, input, channel, conv_block_num=2, is_first=False, is_test=True, name=None): assert channel % 2 == 0, \ "channel {} cannot be divided by 2 in detection block {}" \ .format(channel, name) conv = input for j in range(conv_block_num): conv = self._add_coord(conv, is_test=is_test) conv = self._conv_bn( conv, channel, filter_size=1, stride=1, padding=0, is_test=is_test, name='{}.{}.0'.format(name, j)) if self.use_spp and is_first and j == 1: conv = self._spp_module(conv, is_test=is_test, name="spp") conv = self._conv_bn( conv, 512, filter_size=1, stride=1, padding=0, is_test=is_test, name='{}.{}.spp.conv'.format(name, j)) conv = self._conv_bn( conv, channel * 2, filter_size=3, stride=1, padding=1, is_test=is_test, name='{}.{}.1'.format(name, j)) if self.drop_block and j == 0 and not is_first: conv = DropBlock( conv, block_size=self.block_size, keep_prob=self.keep_prob, is_test=is_test) if self.drop_block and is_first: conv = DropBlock( conv, block_size=self.block_size, keep_prob=self.keep_prob, is_test=is_test) conv = self._add_coord(conv, is_test=is_test) route = self._conv_bn( conv, channel, filter_size=1, stride=1, padding=0, is_test=is_test, name='{}.2'.format(name)) new_route = self._add_coord(route, is_test=is_test) tip = self._conv_bn( new_route, channel * 2, filter_size=3, stride=1, padding=1, is_test=is_test, name='{}.tip'.format(name)) return route, tip def _upsample(self, input, scale=2, name=None): out = fluid.layers.resize_nearest( input=input, scale=float(scale), name=name) return out def _parse_anchors(self, anchors): """ Check ANCHORS/ANCHOR_MASKS in config and parse mask_anchors """ self.anchors = [] self.mask_anchors = [] assert len(anchors) > 0, "ANCHORS not set." assert len(self.anchor_masks) > 0, "ANCHOR_MASKS not set." for anchor in anchors: assert len(anchor) == 2, "anchor {} len should be 2".format(anchor) self.anchors.extend(anchor) anchor_num = len(anchors) for masks in self.anchor_masks: self.mask_anchors.append([]) for mask in masks: assert mask < anchor_num, "anchor mask index overflow" self.mask_anchors[-1].extend(anchors[mask]) def _get_outputs(self, input, is_train=True): """ Get YOLOv3 head output Args: input (list): List of Variables, output of backbone stages is_train (bool): whether in train or test mode Returns: outputs (list): Variables of each output layer """ outputs = [] # get last out_layer_num blocks in reverse order out_layer_num = len(self.anchor_masks) blocks = input[-1:-out_layer_num - 1:-1] route = None for i, block in enumerate(blocks): if i > 0: # perform concat in first 2 detection_block block = fluid.layers.concat(input=[route, block], axis=1) route, tip = self._detection_block( block, channel=64 * (2**out_layer_num) // (2**i), is_first=i == 0, is_test=(not is_train), conv_block_num=self.conv_block_num, name=self.prefix_name + "yolo_block.{}".format(i)) # out channel number = mask_num * (5 + class_num) if self.iou_aware: num_filters = len(self.anchor_masks[i]) * (self.num_classes + 6) else: num_filters = len(self.anchor_masks[i]) * (self.num_classes + 5) with fluid.name_scope('yolo_output'): block_out = fluid.layers.conv2d( input=tip, num_filters=num_filters, filter_size=1, stride=1, padding=0, act=None, param_attr=ParamAttr( name=self.prefix_name + "yolo_output.{}.conv.weights".format(i)), bias_attr=ParamAttr( regularizer=L2Decay(0.), name=self.prefix_name + "yolo_output.{}.conv.bias".format(i))) outputs.append(block_out) if i < len(blocks) - 1: # do not perform upsample in the last detection_block route = self._conv_bn( input=route, ch_out=256 // (2**i), filter_size=1, stride=1, padding=0, is_test=(not is_train), name=self.prefix_name + "yolo_transition.{}".format(i)) # upsample route = self._upsample(route) return outputs def get_loss(self, input, gt_box, gt_label, gt_score, targets): """ Get final loss of network of YOLOv3. Args: input (list): List of Variables, output of backbone stages gt_box (Variable): The ground-truth boudding boxes. gt_label (Variable): The ground-truth class labels. gt_score (Variable): The ground-truth boudding boxes mixup scores. targets ([Variables]): List of Variables, the targets for yolo loss calculatation. Returns: loss (Variable): The loss Variable of YOLOv3 network. """ outputs = self._get_outputs(input, is_train=True) return self.yolo_loss(outputs, gt_box, gt_label, gt_score, targets, self.anchors, self.anchor_masks, self.mask_anchors, self.num_classes, self.prefix_name) def get_prediction(self, input, im_size, exclude_nms=False): """ Get prediction result of YOLOv3 network Args: input (list): List of Variables, output of backbone stages im_size (Variable): Variable of size([h, w]) of each image Returns: pred (Variable): The prediction result after non-max suppress. """ outputs = self._get_outputs(input, is_train=False) boxes = [] scores = [] for i, output in enumerate(outputs): if self.iou_aware: output = get_iou_aware_score(output, len(self.anchor_masks[i]), self.num_classes, self.iou_aware_factor) scale_x_y = self.scale_x_y if not isinstance( self.scale_x_y, Sequence) else self.scale_x_y[i] box, score = fluid.layers.yolo_box( x=output, img_size=im_size, anchors=self.mask_anchors[i], class_num=self.num_classes, conf_thresh=self.nms.score_threshold, downsample_ratio=self.downsample[i], name=self.prefix_name + "yolo_box" + str(i), clip_bbox=self.clip_bbox, scale_x_y=scale_x_y) boxes.append(box) scores.append(fluid.layers.transpose(score, perm=[0, 2, 1])) yolo_boxes = fluid.layers.concat(boxes, axis=1) yolo_scores = fluid.layers.concat(scores, axis=2) # Only for benchmark, postprocess(NMS) is not needed if exclude_nms: return {'bbox': yolo_scores} if type(self.nms) is MultiClassSoftNMS: yolo_scores = fluid.layers.transpose(yolo_scores, perm=[0, 2, 1]) pred = self.nms(bboxes=yolo_boxes, scores=yolo_scores) return {'bbox': pred} @register class YOLOv4Head(YOLOv3Head): """ Head block for YOLOv4 network Args: anchors (list): anchors anchor_masks (list): anchor masks nms (object): an instance of `MultiClassNMS` spp_stage (int): apply spp on which stage. num_classes (int): number of output classes downsample (list): downsample ratio for each yolo_head scale_x_y (list): scale the center point of bbox at each stage """ __inject__ = ['nms', 'yolo_loss'] __shared__ = ['num_classes', 'weight_prefix_name'] def __init__(self, anchors=[[12, 16], [19, 36], [40, 28], [36, 75], [76, 55], [72, 146], [142, 110], [192, 243], [459, 401]], anchor_masks=[[0, 1, 2], [3, 4, 5], [6, 7, 8]], nms=MultiClassNMS( score_threshold=0.01, nms_top_k=-1, keep_top_k=-1, nms_threshold=0.45, background_label=-1).__dict__, spp_stage=5, num_classes=80, weight_prefix_name='', downsample=[8, 16, 32], scale_x_y=1.0, yolo_loss="YOLOv3Loss", iou_aware=False, iou_aware_factor=0.4, clip_bbox=False): super(YOLOv4Head, self).__init__( anchors=anchors, anchor_masks=anchor_masks, nms=nms, num_classes=num_classes, weight_prefix_name=weight_prefix_name, downsample=downsample, scale_x_y=scale_x_y, yolo_loss=yolo_loss, iou_aware=iou_aware, iou_aware_factor=iou_aware_factor, clip_bbox=clip_bbox) self.spp_stage = spp_stage def _upsample(self, input, scale=2, name=None): out = fluid.layers.resize_nearest( input=input, scale=float(scale), name=name) return out def max_pool(self, input, size): pad = [(size - 1) // 2] * 2 return fluid.layers.pool2d(input, size, 'max', pool_padding=pad) def spp(self, input): branch_a = self.max_pool(input, 13) branch_b = self.max_pool(input, 9) branch_c = self.max_pool(input, 5) out = fluid.layers.concat([branch_a, branch_b, branch_c, input], axis=1) return out def stack_conv(self, input, ch_list=[512, 1024, 512], filter_list=[1, 3, 1], stride=1, name=None): conv = input for i, (ch_out, f_size) in enumerate(zip(ch_list, filter_list)): padding = 1 if f_size == 3 else 0 conv = self._conv_bn( conv, ch_out=ch_out, filter_size=f_size, stride=stride, padding=padding, name='{}.{}'.format(name, i)) return conv def spp_module(self, input, name=None): conv = self.stack_conv(input, name=name + '.stack_conv.0') spp_out = self.spp(conv) conv = self.stack_conv(spp_out, name=name + '.stack_conv.1') return conv def pan_module(self, input, filter_list, name=None): for i in range(1, len(input)): ch_out = input[i].shape[1] // 2 conv_left = self._conv_bn( input[i], ch_out=ch_out, filter_size=1, stride=1, padding=0, name=name + '.{}.left'.format(i)) ch_out = input[i - 1].shape[1] // 2 conv_right = self._conv_bn( input[i - 1], ch_out=ch_out, filter_size=1, stride=1, padding=0, name=name + '.{}.right'.format(i)) conv_right = self._upsample(conv_right) pan_out = fluid.layers.concat([conv_left, conv_right], axis=1) ch_list = [pan_out.shape[1] // 2 * k for k in [1, 2, 1, 2, 1]] input[i] = self.stack_conv( pan_out, ch_list=ch_list, filter_list=filter_list, name=name + '.stack_conv.{}'.format(i)) return input def _get_outputs(self, input, is_train=True): outputs = [] filter_list = [1, 3, 1, 3, 1] spp_stage = len(input) - self.spp_stage # get last out_layer_num blocks in reverse order out_layer_num = len(self.anchor_masks) blocks = input[-1:-out_layer_num - 1:-1] blocks[spp_stage] = self.spp_module( blocks[spp_stage], name=self.prefix_name + "spp_module") blocks = self.pan_module( blocks, filter_list=filter_list, name=self.prefix_name + 'pan_module') # reverse order back to input blocks = blocks[::-1] route = None for i, block in enumerate(blocks): if i > 0: # perform concat in first 2 detection_block route = self._conv_bn( route, ch_out=route.shape[1] * 2, filter_size=3, stride=2, padding=1, name=self.prefix_name + 'yolo_block.route.{}'.format(i)) block = fluid.layers.concat(input=[route, block], axis=1) ch_list = [block.shape[1] // 2 * k for k in [1, 2, 1, 2, 1]] block = self.stack_conv( block, ch_list=ch_list, filter_list=filter_list, name=self.prefix_name + 'yolo_block.stack_conv.{}'.format(i)) route = block block_out = self._conv_bn( block, ch_out=block.shape[1] * 2, filter_size=3, stride=1, padding=1, name=self.prefix_name + 'yolo_output.{}.conv.0'.format(i)) if self.iou_aware: num_filters = len(self.anchor_masks[i]) * (self.num_classes + 6) else: num_filters = len(self.anchor_masks[i]) * (self.num_classes + 5) block_out = fluid.layers.conv2d( input=block_out, num_filters=num_filters, filter_size=1, stride=1, padding=0, act=None, param_attr=ParamAttr(name=self.prefix_name + "yolo_output.{}.conv.1.weights".format(i)), bias_attr=ParamAttr( regularizer=L2Decay(0.), name=self.prefix_name + "yolo_output.{}.conv.1.bias".format(i))) outputs.append(block_out) return outputs