mindvision.utils
Init charts tools
- mindvision.utils.accuracy_model_flops_chart(accuracy_data: Dict, save_path: str = './', ylim: Optional[List] = None, figsize: Optional[Tuple] = None, title: Optional[str] = None, xlabel: Optional[str] = None, ylabel: Optional[str] = None)[source]
The function is used to plot the accuracy comparison between architectures on different pre-trained compute.
- Parameters
accuracy_data (dict) – The accuracy of model on different pre-trained compute.
save_path (str) – Path to save the chart. Default: ‘./’.
ylim (list, optional) – The range of y coordinate. Default: None.
figsize (tuple, optional) – The size of figure. Default: None.
title (str, optional) – The title of chart. Default:None.
xlabel (str, optional) – The Label of x coordinate. Default: None.
ylabel (str, optional) – The Label of y coordinate. Default: None.
Examples
>>> accuracy_data = { ... 'Transform(ViT)': { ... "vit-B_32_7": {55: 80.73}, ... "ViT-B_16_7": {224: 84.15}, ... "ViT-L_32_7": {196: 84.37}, ... "ViT-L_16_7": {783: 86.30} ... } ... } >>> accuracy_model_flops_chart(accuracy_data=accuracy_data, ylim=[75, 90])
- mindvision.utils.accuracy_model_size_chart(accuracy_data: Dict, size_unit: str, save_path: str = './', ylim: Optional[List] = None, figsize: Optional[Tuple] = None, title: Optional[str] = None, xlabel: Optional[str] = None, ylabel: Optional[str] = None)[source]
The function is used to plot the accuracy comparison between models on different pre-trained dataset size.
- Parameters
accuracy_data (dict) – The accuracy of model on different pre-trained dataset size.
size_unit – Units for dataset size.
save_path (str) – Path to save the chart. Default: ‘./’.
ylim (list, optional) – The range of y coordinate. Default: None.
figsize (tuple, optional) – The size of figure. Default: None.
title (str, optional) – The title of chart. Default: None.
xlabel (str, optional) – The Label of x coordinate. Default: None.
ylabel (str, optional) – The Label of y coordinate. Default: None.
Examples
>>> accuracy_data = { ... 'ViT-b_32': { ... 10: 37, ... 30: 41, ... 100: 41.5 ... }, ... 'ViT-B_32': { ... 10: 38, ... 30: 53, ... 100: 54 ... } ... } >>> accuracy_model_size_chart(accuracy_data=accuracy_data, size_unit='M')
- mindvision.utils.accuracy_on_dataset_chart_v1(accuracy_data: Dict, save_path: str = './', ylim: Optional[List] = None, figsize: Optional[Tuple] = None, title: Optional[str] = None, xlabel: Optional[str] = None, ylabel: Optional[str] = None)[source]
The function is used to plot the accuracy range between models on different dataset.
- Parameters
accuracy_data (dict) – The data of line chart models.
save_path (str) – Path to save the chart. Default: ‘./’.
ylim (list, optional) – The range of y coordinate. Default: None.
figsize (tuple, optional) – The size of figure. Default: None.
title (str, optional) – The title of chart. Default: None.
xlabel (str, optional) – The Label of x coordinate. Default: None.
ylabel (str, optional) – The Label of y coordinate. Default: None.
Examples
>>> accuracy_data = { ... 'ResNet50': { ... 'accuracy': { ... 'ImageNet': 76.8, ... 'ImageNet21K': 80.2, ... 'JFT-300M': 79.2, ... }, ... 'marker_size': 4 ... }, ... 'ResNet152': { ... 'accuracy': { ... 'ImageNet': 81.2, ... 'ImageNet21K': 85.5, ... 'JFT-300M': 87.8, ... }, ... 'marker_size': 6 ... } ... } >>> accuracy_on_dataset_chart_v1(accuracy_data=accuracy_data)
- mindvision.utils.accuracy_on_dataset_chart_v2(accuracy_data: Dict, save_path: str = './', ylim: Optional[List] = None, figsize: Optional[Tuple] = None, title: Optional[str] = None, xlabel: Optional[str] = None, ylabel: Optional[str] = None)[source]
The function is used to plot the accuracy comparison between models on different dataset.
- Parameters
accuracy_data (dict) – The data of scatter models.
save_path (str) – Path to save the chart. Default: ‘./’.
ylim (list, optional) – The range of y coordinate. Default: None.
figsize (tuple, optional) – The size of figure. Default: None.
title (str, optional) – The title of chart. Default: None.
xlabel (str, optional) – The Label of x coordinate. Default: None.
ylabel (str, optional) – The Label of y coordinate. Default: None.
Examples
>>> accuracy_data = { ... 'ViT-B_32': { ... 'accuracy': { ... 'ImageNet': 73.38, ... 'ImageNet21K': 81.28, ... 'JFT-300M': 80.73, ... }, ... 'marker_size': 45 ... } ... } >>> accuracy_on_dataset_chart_v2(accuracy_data=accuracy_data)
- mindvision.utils.accuracy_on_dataset_chart_v3(line_models_data: Dict, scatter_models_data: Dict, save_path: str = './', ylim: Optional[List] = None, figsize: Optional[Tuple] = None, title: Optional[str] = None, xlabel: Optional[str] = None, ylabel: Optional[str] = None)[source]
The function is used to plot the accuracy comparison between architectures on different dataset.
- Parameters
line_models_data (dict) – The data of line chart models.
scatter_models_data (dict) – The data of scatter models.
save_path (str) – Path to save the chart. Default: ‘./’.
ylim (list, optional) – The range of y coordinate. Default: None.
figsize (tuple, optional) – The size of figure. Default: None.
title (str, optional) – The title of chart. Default: None.
xlabel (str, optional) – The Label of x coordinate. Default: None.
ylabel (str, optional) – The Label of y coordinate. Default: None.
Examples
>>> line_models_data = { ... 'ResNet50': { ... 'accuracy': { ... 'ImageNet': 76.8, ... 'ImageNet21K': 80.2, ... 'JFT-300M': 79.2, ... }, ... 'marker_size': 4 ... }, ... 'ResNet152': { ... 'accuracy': { ... 'ImageNet': 81.2, ... 'ImageNet21K': 85.5, ... 'JFT-300M': 87.8, ... }, ... 'marker_size': 6 ... } ... } >>> scatter_models_data = { ... 'ViT-B_32': { ... 'accuracy': { ... 'ImageNet': 73.38, ... 'ImageNet21K': 81.28, ... 'JFT-300M': 80.73, ... }, ... 'marker_size': 45 ... } ... } >>> accuracy_on_dataset_chart_v3(line_models_data=line_models_data, ... scatter_models_data=scatter_models_data)
- mindvision.utils.pos_embedding_cosine_chart(pos_embedding: np.ndarray, save_path: str = './', title: Optional[str] = None, xlabel: Optional[str] = None, ylabel: Optional[str] = None, colorbar_label: Optional[str] = None)[source]
The function is used to plot the cosine similarity of position embedding.
- Parameters
pos_embedding (ndarray) – The data of position embedding.
save_path (str) – Path to save the chart. Default: ‘./’.
title (str, optional) – The title of chart. Default: None.
xlabel (str, optional) – The Label of x coordinate. Default: None.
ylabel (str, optional) – The Label of y coordinate. Default: None.
colorbar_label (str, optional) – The Label of colorbar. Default: None.
Examples
>>> pos_embedding = np.random.randn(1, 50, 768) >>> pos_embedding_cosine_chart(pos_embedding=pos_embedding)
- mindvision.utils.topn_accuracy_chart(accuracy_data: Dict, save_path: str = './', ylim: Optional[List] = None, figsize: Optional[Tuple] = None, title: Optional[str] = None, xlabel: Optional[str] = None, ylabel: Optional[str] = None)[source]
Accuracy charts, xlabel can be network models or iteration numbers, ylabel is the accuracy.
- Parameters
accuracy_data (dict) – The accuracy data of models on different AI frame.
save_path (str) – The save path of line chart. Default: ‘./’.
ylim (list, optional) – The range of y coordinate. Default: None.
figsize (tuple, optional) – The size of figure. Default: None.
title (str, optional) – The title of graph. Default: None.
xlabel (str, optional) – The Label of x coordinate. Default: None.
ylabel (str, optional) – The Label of y coordinate. Default: None.
Examples
>>> accuracy_data = {'MindSpore': {'Resnet18': 70.078, 'Resnet34': 73.72, 'Resnet50': 76.6}, ... 'Pytorch': {'Resnet18': 69.758, 'Resnet34': 73.31, 'Resnet50': 76.13}} >>> topn_accuracy_chart(accuracy_data=accuracy_data)