Datasets:
HenryAI
/
KerasCodeExamples.txt

Dataset card Data Studio Files
xet
 Community
text
stringlengths
0
4.99k
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
num_window_elements = self.window_size[0] * self.window_size[1]
relative_position_index_flat = tf.reshape(
self.relative_position_index, shape=(-1,)
)
relative_position_bias = tf.gather(
self.relative_position_bias_table, relative_position_index_flat
)
relative_position_bias = tf.reshape(
relative_position_bias, shape=(num_window_elements, num_window_elements, -1)
)
relative_position_bias = tf.transpose(relative_position_bias, perm=(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 = keras.activations.softmax(attn, axis=-1)
else:
attn = keras.activations.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)
return x_qkv
The complete Swin Transformer model
Finally, we put together the complete Swin Transformer by replacing the standard multi-head attention (MHA) with shifted windows attention. As suggested in the original paper, we create a model comprising of a shifted window-based MHA layer, followed by a 2-layer MLP with GELU nonlinearity in between, applying LayerNor...
Notice that we only create a simple MLP with 2 Dense and 2 Dropout layers. Often you will see models using ResNet-50 as the MLP which is quite standard in the literature. However in this paper the authors use a 2-layer MLP with GELU nonlinearity in between.
class SwinTransformer(layers.Layer):
def __init__(
self,
dim,
num_patch,
num_heads,
window_size=7,
shift_size=0,
num_mlp=1024,
qkv_bias=True,
dropout_rate=0.0,
**kwargs,
):
super(SwinTransformer, self).__init__(**kwargs)
self.dim = dim # number of input dimensions
self.num_patch = num_patch # number of embedded patches
self.num_heads = num_heads # number of attention heads
self.window_size = window_size # size of window
self.shift_size = shift_size # size of window shift
self.num_mlp = num_mlp # number of MLP nodes
self.norm1 = layers.LayerNormalization(epsilon=1e-5)
self.attn = WindowAttention(
dim,
window_size=(self.window_size, self.window_size),
num_heads=num_heads,
qkv_bias=qkv_bias,
dropout_rate=dropout_rate,
)
self.drop_path = DropPath(dropout_rate)
self.norm2 = layers.LayerNormalization(epsilon=1e-5)
self.mlp = keras.Sequential(
[
layers.Dense(num_mlp),
layers.Activation(keras.activations.gelu),
layers.Dropout(dropout_rate),
layers.Dense(dim),
layers.Dropout(dropout_rate),
]
)
if min(self.num_patch) < self.window_size:
self.shift_size = 0
self.window_size = min(self.num_patch)
def build(self, input_shape):
if self.shift_size == 0:
self.attn_mask = None
else:
height, width = self.num_patch