# attention-visual

**Repository Path**: lddsdu/attention-visual

## Basic Information

- **Project Name**: attention-visual
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-07-12
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


#### 实验结果展示



**Attention**

```python
import numpy as np
import matplotlib.pyplot as plt


def plot_attention_weight(x_labels, y_labels, color, font_size, values):
    """

    :param x_labels: ["Q", "bird", "head", "legs", "slides", "wood"]
    :param y_labels: ["wood /r/DistinctFrom carpet", "word /r/AtLocation a fire", "split /r/DistinctFrom like"]
    :param color:
    :param font_size:
    :return:
    """
    x_len = len(x_labels) + 2
    y_len = len(y_labels)
    plt.figure(figsize=(x_len + 1, y_len + 1))
    # plt.plot(np.linspace(0, x_len, 100), np.linspace(0, y_len, 100))
    x_unit = 1.0 / (x_len + 1)
    y_unit = 1.0 / (y_len + 1)
    x_half_unit = x_unit / 2
    y_half_unit = y_unit / 2

    # assert values.shape[0] == x_len
    assert values.shape[1] == y_len

    ax = plt.gca()
    # p = plt.Rectangle((0, 0), 1, 1, fill=False)
    # p.set_transform(ax.transAxes)
    # p.set_clip_on(False)
    # ax.add_patch(p)

    plt.axis("off")

    for x_index, x_label in enumerate(x_labels):
        ax.text((x_index + 0.5) * x_unit + x_half_unit, 0.0, x_label,
                horizontalalignment='center',
                verticalalignment='top',
                rotation=90,
                transform=ax.transAxes,
                fontsize=font_size)

    for y_index, y_label in enumerate(y_labels):
        ax.text(0.0, (y_index + 0.5) * y_unit + y_half_unit, y_label,
                horizontalalignment='right',
                verticalalignment='center',
                transform=ax.transAxes,
                fontsize=font_size)

    # x_index = 1
    # y_index = 1
    # x = np.linspace(x_index - 0.5, x_index + 1 - 0.5, 100)
    # y1 = np.linspace(y_index + 1 - 0.5, y_index + 1 - 0.5, 100)
    # y2 = np.linspace(y_index - 0.5, y_index - 0.5, 100)
    # plt.fill_between(x, y1, y2, where=y1 > y2, facecolor="red")

    for x_index, v_value in enumerate(values, start=0):
        for y_index, v in enumerate(v_value, start=0):
            x = np.linspace(x_index - 0.5, x_index + 1 - 0.5, 100)
            y1 = np.linspace(y_index + 1 - 0.5, y_index + 1 - 0.5, 100)
            y2 = np.linspace(y_index - 0.5, y_index - 0.5, 100)
            plt.fill_between(x, y1, y2, where=y1 > y2, facecolor=color, alpha=v)

    x = np.linspace(x_index + 1 - 0.5, x_index + 2 - 0.5, 1000)
    y1 = np.linspace(y_len - 0.5, y_len - 0.5, 1000)
    y2 = np.linspace(- 0.5, - 0.5, 1000)
    plt.fill_between(x, y1, y2, where=y1>y2, facecolor="white")

    grain_num = 1000
    grain = 1.0 / grain_num
    for i in np.linspace(0, 1.0 - grain, grain_num - 1):
        x = np.linspace(x_index + 2 - 0.5, x_index + 3 - 0.5, 1000)
        y1 = np.linspace(i * y_len - 0.5 + grain * y_len + grain, i * y_len - 0.5 + grain * y_len + grain, 1000)
        y2 = np.linspace(i * y_len - 0.5, i * y_len - 0.5, 1000)
        plt.fill_between(x, y1, y2, where=y1 > y2, facecolor=color, alpha=i)

    plt.text(x_unit * (x_len + 1), y_half_unit, "0.0",
             horizontalalignment='center',
             verticalalignment='top',
             transform=ax.transAxes,
             fontsize=font_size)

    plt.text(x_unit * (x_len + 1), 1. - y_half_unit / 2.0, "1.0",
             horizontalalignment='center',
             verticalalignment='top',
             transform=ax.transAxes,
             fontsize=font_size)

    plt.show()


if __name__ == '__main__':
    values = np.random.random((12, 6))
    values_sum = np.tile(np.sum(values, axis=1).reshape(-1, 1), (1, 6))
    values = values / values_sum
    plot_attention_weight(
        ["Q", "bird", "head", "legs", "slides", "wood", "Q", "bird", "head", "legs", "slides", "wood"],
        ["wood /r/DistinctFrom carpet", "word /r/AtLocation a fire", "split /r/DistinctFrom like"] * 2,
        color="green",
        font_size=20,
        values=values)

    values = np.asarray([[0.1, 0.3, 0.5],
                         [0.9, 0.7, 0.5]], dtype=np.float32)
    values = np.transpose(values)
    print(values)
    print(values.shape)
    x = ["a", "b", "c"]
    y = ["d", "e"]
    plot_attention_weight(x, y, color="red", font_size=18, values=values)
```



**效果分别如下所示**

![attention_weight](assets/attention_weight.png)

![attention_weight2](assets/attention_weight2.png)

![attention_weight2](assets/attention_weight3.png)