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| import tensorflow as tf | |
| from tensorflow.keras import layers | |
| class WindowAttention(layers.Layer): | |
| def __init__( | |
| self, | |
| dim, | |
| window_size, | |
| num_heads, | |
| qkv_bias=True, | |
| dropout_rate=0.0, | |
| return_attention_scores=False, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.dim = dim | |
| self.window_size = window_size | |
| self.num_heads = num_heads | |
| self.scale = (dim // num_heads) ** -0.5 | |
| self.return_attention_scores = return_attention_scores | |
| self.qkv = layers.Dense(dim * 3, use_bias=qkv_bias) | |
| self.dropout = layers.Dropout(dropout_rate) | |
| self.proj = layers.Dense(dim) | |
| def build(self, input_shape): | |
| self.relative_position_bias_table = self.add_weight( | |
| shape=( | |
| (2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1), | |
| self.num_heads, | |
| ), | |
| initializer="zeros", | |
| trainable=True, | |
| name="relative_position_bias_table", | |
| ) | |
| self.relative_position_index = self.get_relative_position_index( | |
| self.window_size[0], self.window_size[1] | |
| ) | |
| super().build(input_shape) | |
| def get_relative_position_index(self, window_height, window_width): | |
| x_x, y_y = tf.meshgrid(range(window_height), range(window_width)) | |
| coords = tf.stack([y_y, x_x], axis=0) | |
| coords_flatten = tf.reshape(coords, [2, -1]) | |
| relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] | |
| relative_coords = tf.transpose(relative_coords, perm=[1, 2, 0]) | |
| x_x = (relative_coords[:, :, 0] + window_height - 1) * (2 * window_width - 1) | |
| y_y = relative_coords[:, :, 1] + window_width - 1 | |
| relative_coords = tf.stack([x_x, y_y], axis=-1) | |
| return tf.reduce_sum(relative_coords, axis=-1) | |
| def call(self, x, mask=None): | |
| _, size, channels = x.shape | |
| head_dim = channels // self.num_heads | |
| x_qkv = self.qkv(x) | |
| x_qkv = tf.reshape(x_qkv, shape=(-1, size, 3, self.num_heads, head_dim)) | |
| x_qkv = tf.transpose(x_qkv, perm=(2, 0, 3, 1, 4)) | |
| q, k, v = x_qkv[0], x_qkv[1], x_qkv[2] | |
| q = q * self.scale | |
| k = tf.transpose(k, perm=(0, 1, 3, 2)) | |
| attn = q @ k | |
| relative_position_bias = tf.gather( | |
| self.relative_position_bias_table, | |
| self.relative_position_index, | |
| axis=0, | |
| ) | |
| relative_position_bias = tf.transpose(relative_position_bias, [2, 0, 1]) | |
| attn = attn + tf.expand_dims(relative_position_bias, axis=0) | |
| if mask is not None: | |
| nW = mask.get_shape()[0] | |
| mask_float = tf.cast( | |
| tf.expand_dims(tf.expand_dims(mask, axis=1), axis=0), tf.float32 | |
| ) | |
| attn = ( | |
| tf.reshape(attn, shape=(-1, nW, self.num_heads, size, size)) | |
| + mask_float | |
| ) | |
| attn = tf.reshape(attn, shape=(-1, self.num_heads, size, size)) | |
| attn = tf.nn.softmax(attn, axis=-1) | |
| else: | |
| attn = tf.nn.softmax(attn, axis=-1) | |
| attn = self.dropout(attn) | |
| x_qkv = attn @ v | |
| x_qkv = tf.transpose(x_qkv, perm=(0, 2, 1, 3)) | |
| x_qkv = tf.reshape(x_qkv, shape=(-1, size, channels)) | |
| x_qkv = self.proj(x_qkv) | |
| x_qkv = self.dropout(x_qkv) | |
| if self.return_attention_scores: | |
| return x_qkv, attn | |
| else: | |
| return x_qkv | |
| def get_config(self): | |
| config = super().get_config() | |
| config.update( | |
| { | |
| "dim": self.dim, | |
| "window_size": self.window_size, | |
| "num_heads": self.num_heads, | |
| "scale": self.scale, | |
| } | |
| ) | |
| return config | |