毕业设计-基于paddle使用tinyyolo开发的人脸检测+人脸表情识别项目-项目实战-项目源码-优质项目.zip

import paddle import time from paddle import fluid as fluid from paddle.fluid import layers as pfl from yolo3tiny.imageSolver import * import numpy as np def sigmoid(x): return 1./(1+np.exp(-x)) class YOLOv3_tiny: def __init__(self, use_cuda=False): self._USE_CUDA = use_cuda def build(self, boxNum=64, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, regularization=None, lazy_mode=False): dataInput = pfl.data(name='data_input', shape=[ 3, 416, 416], dtype='float32') gtbox = pfl.data(name='data_gtbox', shape=[boxNum, 4], dtype='float32') gtlabel = pfl.data(name='data_gtlabel', shape=[boxNum], dtype='int32') anchors = [10, 14, 23, 27, 37, 58, 81, 82, 135, 169, 344, 319] layer0_output = _DBL(input=dataInput, num_filters=16, filter_size=3, name='layer0') layer1_output = pfl.pool2d( input=layer0_output, pool_size=2, pool_type='max', pool_stride=2, name='layer1_max') layer2_output = _DBL(input=layer1_output, num_filters=32, filter_size=3, name='layer2') layer3_output = pfl.pool2d( input=layer2_output, pool_size=2, pool_type='max', pool_stride=2, name='layer3_max') layer4_output = _DBL(input=layer3_output, num_filters=64, filter_size=3, name='layer4') layer5_output = pfl.pool2d( input=layer4_output, pool_size=2, pool_type='max', pool_stride=2, name='layer5_max') layer6_output = _DBL(input=layer5_output, num_filters=128, filter_size=3, name='layer6') layer7_output = pfl.pool2d( input=layer6_output, pool_size=2, pool_type='max', pool_stride=2, name='layer7_max') layer8_output = _DBL(input=layer7_output, num_filters=256, filter_size=3, name='layer8') layer9_output = pfl.pool2d( input=layer8_output, pool_size=2, pool_type='max', pool_stride=2, name='layer9_max') layer10_output = _DBL(input=layer9_output, num_filters=512, filter_size=3, name='layer10') layer11_output = pfl.pool2d(input=pfl.pad(layer10_output, paddings=[ 0, 0, 0, 0, 0, 1, 0, 1]), pool_size=2, pool_type='max', pool_stride=1, name='layer11_max') layer12_output = _DBL(input=layer11_output, num_filters=1024, filter_size=3, name='layer12') layer13_output = _DBL( input=layer12_output, num_filters=256, filter_size=1, padding=0, name='layer13') layer14_output = _DBL(input=layer13_output, num_filters=512, filter_size=3, name='layer14') layer15_output = pfl.conv2d( input=layer14_output, num_filters=18, filter_size=1, name='layer15_conv') # layer16_yolo -> -1 x 18 x 13 x 13 yolo1_loss = pfl.yolov3_loss(name='yolo1_loss', x=layer15_output, gtbox=gtbox, gtlabel=gtlabel, anchors=anchors, anchor_mask=[3, 4, 5], class_num=1, ignore_thresh=0.5, downsample_ratio=32) # layer17_route_13 layer18_output = _DBL( input=layer13_output, num_filters=128, filter_size=1, padding=0, name='layer18') layer19_output = pfl.expand(layer18_output, expand_times=[ 1, 1, 2, 2], name='layer19_upsample') # layer20_route_19_8 layer20_output = pfl.concat( [layer19_output, layer8_output], axis=1, name='layer20_concat') layer21_output = _DBL(layer20_output, num_filters=256, filter_size=3, name='layer21') layer22_output = pfl.conv2d( input=layer21_output, num_filters=18, filter_size=1, name='layer22_conv') # layer23_yolo -> -1 x 18 x 26 x 26 yolo2_loss = pfl.yolov3_loss(name='yolo2_loss', x=layer22_output, gtbox=gtbox, gtlabel=gtlabel, anchors=anchors, anchor_mask=[0, 1, 2], class_num=1, ignore_thresh=0.5, downsample_ratio=16) loss = pfl.reduce_mean(pfl.elementwise_add( yolo1_loss, yolo2_loss), name="loss_output") optimizer = fluid.optimizer.AdamOptimizer(learning_rate=learning_rate, beta1=beta1, beta2=beta2, epsilon=epsilon, regularization=regularization, lazy_mode=lazy_mode) optimizer.minimize(loss) self._netOutput1, self._netOutput2 = layer15_output, layer22_output self._loss = loss self._trainExe = fluid.Executor(fluid.CUDAPlace( 0)) if self._USE_CUDA else fluid.Executor(fluid.CPUPlace()) def train(self, imgClass, epoches=10, batchSize=64, stopRounds=10, initialize=True, confidence_threshold=0.5, nms_threshold=0.3): if initialize: self._trainExe.run(fluid.default_startup_program()) imgStream = imgClass.random_batch_img_stream(batchSize=batchSize) dataGtlabel = np.zeros([batchSize, imgClass.maxBoxNum], dtype='int32') itersPerEpoch = imgClass.imgNum//batchSize st = time.time() for i in range(1, epoches+1): for j in range(1, itersPerEpoch+1): dataInput, dataGtbox = next(imgStream) outs = self._trainExe.run(feed={'data_input': dataInput, 'data_gtbox': dataGtbox, 'data_gtlabel': dataGtlabel}, fetch_list=[self._loss, self._netOutput1, self._netOutput2]) if ((i-1)*itersPerEpoch+j) % stopRounds == 0: restNum = ((itersPerEpoch-j)+(epoches-i) * itersPerEpoch)*batchSize trainSpeed = stopRounds*batchSize/(time.time()-st) print('After iterations %d: loss = %.3lf mAP: %.3lf %.3lf images//s Estimated remaining time: %.3lfs' % ((i-1)*itersPerEpoch + j, outs[0][0], _mAP(outs[1], outs[2], dataGtbox, confidence_threshold, nms_threshold), trainSpeed, restNum/trainSpeed)) st = time.time() def save(self, modelSavePath='./infer_model'): fluid.io.save_inference_model(dirname=modelSavePath, feeded_var_names=['data_input'], target_vars=[self._netOutput1, self._netOutput2], executor=self._trainExe) class Detector: def __init__(self, modelPath='./infer_model', USE_CUDA=False): self._exe = fluid.Executor(fluid.CUDAPlace( 0)) if USE_CUDA else fluid.Executor(fluid.CPUPlace()) self._inferenceModel, self._feedTargetNames, self._fetchTargets = fluid.io.load_inference_model( dirname=modelPath, executor=self._exe) def __call__(self, imgList, confidence_threshold=0.5, nms_threshold=0.3): if len(imgList) == 0: return [], [] imgArrs = list(map(scale_img, imgList)) netOutput1, netOutput2 = self._exe.run(program=self._inferenceModel, feed={self._feedTargetNames[0]: np.array( imgArrs, dtype='float32')}, fetch_list=self._fetchTargets) # netOutput1 -1 x 18 x 13 x 13 # netOutput2 -1 x 18 x 26 x 26 yoloBboxes = _get_bboxes([netOutput1, netOutput2]) selectedBboxes = _NMS(yoloBboxes, confidence_threshold, nms_threshold) selectedBboxes = list(map(toBoundingBox, selectedBboxes)) return imgArrs, selectedBboxes def detect(self, imgArrs, confidence_threshold=0.5, nms_threshold=0.3): im = np.expand_dims(imgAr