t=list(np.array(t).transpose((1,0))) 第一步:torch.tensor转换成ndarray 第二步:ndarray前两维互换 第三步:转换成list ATTN: 如果使用list(array1)的形式,则只将最外层转换成list,里边还是array 如果使用array1.tolist()方法,则整个array的每一层都变成了list 补充3: 在第一个batch中,将t,v,a,y,...
features=c("CLEC4C","LILRA4"),sort=TRUE)+NoLegend()treg_markers<-FindMarkers(pbmc3k,ident.1="Treg",only.pos=TRUE,logfc.threshold=0.1)print(head(treg_markers))DefaultAssay(pbmc3k)<-'predicted_ADT'# see a list of proteins: rownames(pbmc3k)FeaturePlot(pbmc3k,features=c("CD3...
However, single-cell -omics data integration relies on correlation of features across modalities, which is limiting, whereas direct measurement of multiple modalities in the same cell can bring more meaningful insights into chromatin functions. Currently, multimodal profiling of chromatin and gene ...
Take a look at list of MMF features here. MMF also acts as starter codebase for challenges around vision and language datasets (The Hateful Memes, TextVQA, TextCaps and VQA challenges). MMF was formerly known as Pythia. The next video shows an overview of how datasets and models work ...
In addition to supervised fine-tuning (SFT), NeMo also supports the latest parameter efficient fine-tuning (PEFT) techniques such as LoRA, P-Tuning, Adapters, and IA3. Refer to theNeMo Framework User Guidefor the full list of supported models and techniques. ...
first analyzes the visual features of the painting, recognizing elements such as the sun sinking low over the horizon, casting a warm, golden light across the bustling street, the shadows of the cars across the street, and the silhouettes of buildings, evoking a sense of nostalgia and tranquili...
In the Street Name Fields list, click Primary to select it. Click in the Name column, and choose FULL NAME. Click OK to return to the New Network Dataset wizard. Click Next. Check Build Service Area Index. Building the network dataset in the next section of this tu...
Output (5 queries):"""prompt_decomposition=ChatPromptTemplate.from_template(template)generate_queries_decomposition=(prompt_decomposition|llm_bedrock|StrOutputParser()|(lambdax:x.split("\n")))questions=generate_queries_decomposition.invoke({"question":question})defreciprocal_rank_fusion(results:...
kinect_processed: Data from Kinect processed to obtain landmarks ofland_proc_<Repetitive_index>.csv, vocal features ofaudio_proc_<Repetitive_index>.wavand video frame features of boxes and landmarks of <frames_index>.npy, and mouth frame of <frames_index>.png. ...
Boost throughput with built-in features: Replicas for parallel processing Shards for data partitioning Dynamic batching for efficient model inference Example scaling a Stable Diffusion deployment: jtype:Deploymentwith:uses:TextToImagetimeout_ready:-1py_modules: -text_to_image.pyenv:CUDA_VISIBLE_DEVICES...