x_tensor = torch.from_numpy(image).to(DEVICE).unsqueeze(0) pr_mask = best_model.predict(x_tensor) pr_mask = (pr_mask.squeeze().cpu().numpy()) pr_mask = (np.argmax(pr_mask, axis=0) * 255 / (pr_mask.shape[0])).astype(np.uint8) # 恢复图片原来的分辨率 gt_mask = cv2.res...
output = F.sigmoid(output) predict=torch.where(output>0.5,torch.ones_like(output),torch.zeros_like(output)) 第二种输出是多通道,即网络的输出output为 [batch_size, num_class, height, width] 形状。其中batch_szie为批量大小,num_class表示输出的通道数与分类数量一致,height和width与输入图像的高和宽...
predict_y = torch.max(outputs, dim=1)[1] # 以output中值最大位置对应的索引(标签)作为预测输出 accuracy = (predict_y == test_label).sum().item() / test_label.size(0) print('[%d, %5d] train_loss: %.3f test_accuracy: %.3f' % # 打印epoch,step,loss,accuracy (epoch + 1, step ...
model.forward(x)- sequentially passxthrough model`s encoder and decoder (return logits!) model.predict(x)- inference method, switch model to.eval()mode, call.forward(x)and apply activation function withtorch.no_grad() Installation PyPI version: $ pip install segmentation-models-pytorch Latest v...
output=F.sigmoid(output)predict=torch.where(output>0.5,torch.ones_like(output),torch.zeros_like(output)) 第二种输出是多通道,即网络的输出output为 [batch_size, num_class, height, width] 形状。其中batch_szie为批量大小,num_class表示输出的通道数与分类数量一致,height和width与输入图像的高和宽保持...
we propose a Detail Aggregation module by integrating the learning of spatial information into low-level layers in single-stream manner. Finally, the low-level features and deep features are fused to predict the final segmentation results. Extensive experiments on Cityscapes and CamVid dataset demonstra...
python predict.py --help Evaluating For Cityscapes cd tools python trainID2labelID.py Contact If you think this work useful, please give me a star! And if you find any errors or have any suggestions, please contact me. GitHub:xiaoyufenfeiEmail:wangy314159@163.com ...
core Switch model to eval mode in predict function Mar 13, 2024 datasets add PP-LiteSeg Jul 31, 2023 models add FANet Apr 16, 2024 tools add STDC Jan 22, 2024 utils add auxiliary heads Jun 12, 2023 LICENSE Create LICENSE Mar 11, 2024 README.md add FANet Apr 16, 2024 ...
we propose a Detail Aggregation module by integrating the learning of spatial information into low-level layers in single-stream manner. Finally, the low-level features and deep features are fused to predict the final segmentation results. Extensive experiments on Cityscapes and CamVid dataset demonstra...