# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # # 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. import os import sys from darknet import ConvBNLayer, DarkNet53_conv_body import paddle import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddle.fluid.regularizer import L2Decay from paddle.jit.api import declarative class AttrDict(dict): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def __getattr__(self, name): if name in self.__dict__: return self.__dict__[name] elif name in self: return self[name] else: raise AttributeError(name) def __setattr__(self, name, value): if name in self.__dict__: self.__dict__[name] = value else: self[name] = value # # Training options # cfg = AttrDict() # Snapshot period cfg.snapshot_iter = 2000 # min valid area for gt boxes cfg.gt_min_area = -1 # max target box number in an image cfg.max_box_num = 50 # valid score threshold to include boxes cfg.valid_thresh = 0.005 # threshold vale for box non-max suppression cfg.nms_thresh = 0.45 # the number of top k boxes to perform nms cfg.nms_topk = 400 # the number of output boxes after nms cfg.nms_posk = 100 # score threshold for draw box in debug mode cfg.draw_thresh = 0.5 # Use label smooth in class label cfg.label_smooth = True # # Model options # # input size cfg.input_size = 224 if sys.platform == 'darwin' else 608 # pixel mean values cfg.pixel_means = [0.485, 0.456, 0.406] # pixel std values cfg.pixel_stds = [0.229, 0.224, 0.225] # anchors box weight and height cfg.anchors = [ 10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326, ] # anchor mask of each yolo layer cfg.anchor_masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]] # IoU threshold to ignore objectness loss of pred box cfg.ignore_thresh = 0.7 # # SOLVER options # # batch size cfg.batch_size = 1 if sys.platform == 'darwin' or os.name == 'nt' else 4 # derived learning rate the to get the final learning rate. cfg.learning_rate = 0.001 # maximum number of iterations cfg.max_iter = 20 if fluid.is_compiled_with_cuda() else 1 # Disable mixup in last N iter cfg.no_mixup_iter = 10 if fluid.is_compiled_with_cuda() else 1 # warm up to learning rate cfg.warm_up_iter = 10 if fluid.is_compiled_with_cuda() else 1 cfg.warm_up_factor = 0.0 # lr steps_with_decay cfg.lr_steps = [400000, 450000] cfg.lr_gamma = 0.1 # L2 regularization hyperparameter cfg.weight_decay = 0.0005 # momentum with SGD cfg.momentum = 0.9 # # ENV options # # support both CPU and GPU cfg.use_gpu = fluid.is_compiled_with_cuda() # Class number cfg.class_num = 80 class YoloDetectionBlock(fluid.dygraph.Layer): def __init__(self, ch_in, channel, is_test=True): super().__init__() assert channel % 2 == 0, "channel {} cannot be divided by 2".format( channel ) self.conv0 = ConvBNLayer( ch_in=ch_in, ch_out=channel, filter_size=1, stride=1, padding=0, is_test=is_test, ) self.conv1 = ConvBNLayer( ch_in=channel, ch_out=channel * 2, filter_size=3, stride=1, padding=1, is_test=is_test, ) self.conv2 = ConvBNLayer( ch_in=channel * 2, ch_out=channel, filter_size=1, stride=1, padding=0, is_test=is_test, ) self.conv3 = ConvBNLayer( ch_in=channel, ch_out=channel * 2, filter_size=3, stride=1, padding=1, is_test=is_test, ) self.route = ConvBNLayer( ch_in=channel * 2, ch_out=channel, filter_size=1, stride=1, padding=0, is_test=is_test, ) self.tip = ConvBNLayer( ch_in=channel, ch_out=channel * 2, filter_size=3, stride=1, padding=1, is_test=is_test, ) def forward(self, inputs): out = self.conv0(inputs) out = self.conv1(out) out = self.conv2(out) out = self.conv3(out) route = self.route(out) tip = self.tip(route) return route, tip class Upsample(fluid.dygraph.Layer): def __init__(self, scale=2): super().__init__() self.scale = scale def forward(self, inputs): # get dynamic upsample output shape shape_nchw = fluid.layers.shape(inputs) shape_hw = paddle.slice(shape_nchw, axes=[0], starts=[2], ends=[4]) shape_hw.stop_gradient = True in_shape = fluid.layers.cast(shape_hw, dtype='int32') out_shape = in_shape * self.scale out_shape.stop_gradient = True # reisze by actual_shape out = fluid.layers.resize_nearest( input=inputs, scale=self.scale, actual_shape=out_shape ) return out class YOLOv3(fluid.dygraph.Layer): def __init__(self, ch_in, is_train=True, use_random=False): super().__init__() self.is_train = is_train self.use_random = use_random self.block = DarkNet53_conv_body(ch_in=ch_in, is_test=not self.is_train) self.block_outputs = [] self.yolo_blocks = [] self.route_blocks_2 = [] ch_in_list = [1024, 768, 384] for i in range(3): yolo_block = self.add_sublayer( "yolo_detecton_block_%d" % (i), YoloDetectionBlock( ch_in_list[i], channel=512 // (2**i), is_test=not self.is_train, ), ) self.yolo_blocks.append(yolo_block) num_filters = len(cfg.anchor_masks[i]) * (cfg.class_num + 5) block_out = self.add_sublayer( "block_out_%d" % (i), paddle.nn.Conv2D( in_channels=1024 // (2**i), out_channels=num_filters, kernel_size=1, stride=1, padding=0, weight_attr=ParamAttr( initializer=fluid.initializer.Normal(0.0, 0.02) ), bias_attr=ParamAttr( initializer=fluid.initializer.Constant(0.0), regularizer=L2Decay(0.0), ), ), ) self.block_outputs.append(block_out) if i < 2: route = self.add_sublayer( "route2_%d" % i, ConvBNLayer( ch_in=512 // (2**i), ch_out=256 // (2**i), filter_size=1, stride=1, padding=0, is_test=(not self.is_train), ), ) self.route_blocks_2.append(route) self.upsample = Upsample() @declarative def forward( self, inputs, gtbox=None, gtlabel=None, gtscore=None, im_id=None, im_shape=None, ): self.outputs = [] self.boxes = [] self.scores = [] self.losses = [] self.downsample = 32 blocks = self.block(inputs) for i, block in enumerate(blocks): if i > 0: block = fluid.layers.concat( input=[route, block], axis=1 # noqa: F821 ) route, tip = self.yolo_blocks[i](block) block_out = self.block_outputs[i](tip) self.outputs.append(block_out) if i < 2: route = self.route_blocks_2[i](route) route = self.upsample(route) self.gtbox = gtbox self.gtlabel = gtlabel self.gtscore = gtscore self.im_id = im_id self.im_shape = im_shape # cal loss for i, out in enumerate(self.outputs): anchor_mask = cfg.anchor_masks[i] if self.is_train: loss = fluid.layers.yolov3_loss( x=out, gt_box=self.gtbox, gt_label=self.gtlabel, gt_score=self.gtscore, anchors=cfg.anchors, anchor_mask=anchor_mask, class_num=cfg.class_num, ignore_thresh=cfg.ignore_thresh, downsample_ratio=self.downsample, use_label_smooth=cfg.label_smooth, ) self.losses.append(paddle.mean(loss)) else: mask_anchors = [] for m in anchor_mask: mask_anchors.append(cfg.anchors[2 * m]) mask_anchors.append(cfg.anchors[2 * m + 1]) boxes, scores = fluid.layers.yolo_box( x=out, img_size=self.im_shape, anchors=mask_anchors, class_num=cfg.class_num, conf_thresh=cfg.valid_thresh, downsample_ratio=self.downsample, name="yolo_box" + str(i), ) self.boxes.append(boxes) self.scores.append(paddle.transpose(scores, perm=[0, 2, 1])) self.downsample //= 2 if not self.is_train: # get pred yolo_boxes = fluid.layers.concat(self.boxes, axis=1) yolo_scores = fluid.layers.concat(self.scores, axis=2) pred = fluid.layers.multiclass_nms( bboxes=yolo_boxes, scores=yolo_scores, score_threshold=cfg.valid_thresh, nms_top_k=cfg.nms_topk, keep_top_k=cfg.nms_posk, nms_threshold=cfg.nms_thresh, background_label=-1, ) return pred else: return sum(self.losses)