def_detection_layer(inputs,num_classes,anchors,img_size,data_format):print(inputs.get_shape())"""得到通道数为num_anchors*(5+num_classes),大小为h*w的预测结果"""num_anchors=len(anchors)#候选框个数predictions=slim.conv2d(inputs,num_anchors*(5+num_classes),1,stride=1,normalizer_fn=None,...
h = w = input_size num_scale = len(strides) all_anchor_size = anchor_size anchor_number = len(all_anchor_size) // num_scale gt_tensor = [] for s in strides: gt_tensor.append(np.zeros([batch_size, h//s, w//s, anchor_number, 1+1+4+1+4])) # generate gt datas for batc...
grid_y= torch.linspace(0, input_height - 1, input_height).repeat(input_width, 1).t().repeat( batch_size* self.num_anchors, 1, 1).view(y.shape).type(FloatTensor)#按照网格格式生成先验框的宽高#batch_size,3,13,13anchor_w = FloatTensor(scaled_anchors).index_select(1, LongTensor([0]...
model = Model([model_body.input, *y_true], model_loss) return model def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes): n = len(annotation_lines) np.random.shuffle(annotation_lines) i = 0 while True: image_data = [] box_data = [] for b in range...
img = input('Input image filename:') try: image = Image.open(img) except: print('Open Error! Try again!') continue else: r_image = yolo.detect_image(image) r_image.show() yolo.close_session() if __name__ == '__main__': ...
读取图片,对图片前处理,把图片调整到模型的input size及输入顺序(rgb c x h x w). 加载模型,读取模型权重文件. 将第一步读到的矩阵送给模型.进行forward运算.得到prediction 后处理,我们得到的box坐标是相对于调整后的图片的.要处理成原图上的坐标.
Full size image Meanwhile, the input size of the dataset is normalized, and the input feature map is adjusted to 416 \(\times\) 416. Both ML-YOLOv3 and YOLOv3 use three feature maps of different scales for target detection. The paper uses the K-means method to generate 9 anchors with...
运行image_demo.py,文件头的pb_file路径就是刚生成的pb文件,image_path设置为想要测试的图片路径,需要核对input_size是否合适。 从代码学习模型 一、数据输入 1、数据预处理 从数据集中的图片开始,如上文所述,先运行了一个voc_annotation.py,这段程序对放置好的三个训练和测试集文件做了以下几个操作,以其中一个...
batch_size=32, shuffle=True) forepochinrange(2): fori, datainenumerate(train_loader2): # 将数据从 train_loader 中读出来,一次读取的样本数是32个 inputs, labels = data # 将这些数据转换成Variable类型 inputs, labels = Variable(inputs), Variable(labels) ...
ToTensor(), ]) # 转换为float格式,便于进行计算 image_tensor = img_transforms(img).float() image_tensor = image_tensor.unsqueeze_(0) input_img = Variable(image_tensor.type(Tensor)) # 进行目标检测 with torch.no_grad(): detections = model(input_img) detections = utils.non_max_suppression(...