懒,就直接用Resnet50了 先写个残差块 class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None): super(Bottleneck, s
class FocalLoss(nn.Module): # 标签coco是xywh但是在加载的时候好像转化成了xyxy # anno torch.Size([1,9,5]) xyxy catagory defforward(self, classifications, regressions, anchors, annotations): alpha =0.25gamma =2.0batch_size = classifications.shape[0] classification_losses = [] regression_losses =...
self.bn1 = nn.BatchNorm2d(planes) #3*3 self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) # channels*4 self.conv3 = nn.Conv2d(planes, planes*4, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2...
Understanding PyTorch Buffers Chapter 4: Implementing a GPT model from scratch FLOPS Analysis Chapter 5: Pretraining on unlabeled data: Alternative Weight Loading from Hugging Face Model Hub using Transformers Pretraining GPT on the Project Gutenberg Dataset Adding Bells and Whistles to the Training...
This code is only mean't as a companion to the tutorial series and won't be updated. If you want to have a look at the ever updating YOLO v3 code, go to my other repo athttps://github.com/ayooshkathuria/pytorch-yolo-v3 Also, the other repo offers a lot of customisation options, ...
https://blog.paperspace.com/how-to-implement-a-yolo-v3-object-detector-from-scratch-in-pytorch-part-2/ *首先翻译遵循不删不改的原则有一说一,对容易起到歧义的中文采取保留英文的方式。其中对原文没有删减但是略有扩充,其中某些阐释是我一句话的总结,如有错误请大家在留言区指出扶正。
https://blog.paperspace.com/how-to-implement-a-yolo-object-detector-in-pytorch/ *首先翻译遵循不删不改的原则有一说一,对容易起到歧义的中文采取保留英文的方式。其中对原文没有删减但是略有扩充,其中某些阐释是我一句话的总结,如有错误请大家在留言区指出扶正。
from backend.patcher.lora import merge_lora_to_model_weight, LoraLoader from backend import memory_management, utils from backend.patcher.lora import LoraLoader def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None): @@ -229,7 +227,6 @@ ...
The project is mainly based onFasterTransformer, and on this basis, we have integrated some kernel implementations from TensorRT-LLM. FasterTransformer and TensorRT-LLM have provided us with reliable performance guarantees. Flash-Attention2 and cutlass have also provided a lot of help in our ...
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