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import tensorflow as tf
from tensorflow.keras.layers import Dense,LayerNormalization,Dropout,Identity,Activation
from tensorflow.keras import Model
def pair(t):
return t if isinstance(t, tuple) else (t, t)
class FeedForward:
def __init__(self, dim, hidden_dim, drop_rate = 0.):
self.net = tf.keras.Sequential()
self.net.add(LayerNormalization())
self.net.add(Dense(hidden_dim))
self.net.add(Activation('gelu'))
self.net.add(Dropout(drop_rate))
self.net.add(Dense(dim))
self.net.add(Dropout(drop_rate))
def __call__(self, x):
return self.net(x)
class Attention:
def __init__(self, dim, heads = 8, dim_head = 64, drop_rate = 0.):
inner_dim = dim_head * heads
project_out = not (heads == 1 and dim_head == dim)
self.heads = heads
self.scale = dim_head ** -0.5
self.norm = LayerNormalization()
self.attend = tf.nn.softmax
self.dropout = Dropout(drop_rate)
self.to_qkv = Dense(inner_dim * 3, use_bias = False)
if project_out:
self.to_out = tf.keras.Sequential()
self.to_out.add(Dense(dim))
self.to_out.add(Dropout(drop_rate))
else:
self.to_out = Identity()
def __call__(self, x):
x = self.norm(x)
qkv = self.to_qkv(x)
q, k, v = tf.split(qkv, 3, axis=-1)
b = q.shape[0]
h = self.heads
n = q.shape[1]
d = q.shape[2] // self.heads
q = tf.reshape(q, (b, h, n, d))
k = tf.reshape(k, (b, h, n, d))
v = tf.reshape(v, (b, h, n, d))
dots = tf.matmul(q, tf.transpose(k, [0, 1, 3, 2])) * self.scale
attn = self.attend(dots)
attn = self.dropout(attn)
out = tf.matmul(attn, v)
out = tf.transpose(out, [0, 1, 3, 2])
out = tf.reshape(out, shape=[-1, n, h*d])
return self.to_out(out)
class Transformer:
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
self.norm = LayerNormalization()
self.layers = []
for _ in range(depth):
self.layers.append([Attention(dim, heads = heads, dim_head = dim_head, drop_rate = dropout),
FeedForward(dim, mlp_dim, drop_rate = dropout)])
def __call__(self, x):
for attn, ff in self.layers:
x = attn(x) + x
x = ff(x) + x
return self.norm(x)
class ViT(Model):
def __init__(self, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, drop_rate = 0., emb_dropout = 0.):
super(ViT, self).__init__()
image_height, image_width = pair(image_size)
patch_height, patch_width = pair(patch_size)
self.p1, self.p2 = patch_height, patch_width
self.dim = dim
assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
num_patches = (image_height // patch_height) * (image_width // patch_width)
assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
self.to_patch_embedding = tf.keras.Sequential()
self.to_patch_embedding.add(LayerNormalization())
self.to_patch_embedding.add(Dense(dim))
self.to_patch_embedding.add(LayerNormalization())
self.pos_embedding = tf.Variable(tf.random.normal((1, num_patches + 1, dim)))
self.cls_token = tf.Variable(tf.random.normal(((1, 1, dim))))
self.dropout = Dropout(emb_dropout)
self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, drop_rate)
self.pool = pool
self.to_latent = Identity()
self.mlp_head = Dense(num_classes)
def __call__(self, data):
b = data.shape[0]
h = data.shape[1] // self.p1
w = data.shape[2] // self.p2
c = data.shape[3]
data = tf.reshape(data, (b, h * w, self.p1 * self.p2 * c))
x = self.to_patch_embedding(data)
b, n, _ = x.shape
cls_tokens = tf.tile(self.cls_token, multiples=[b, 1, 1])
x = tf.concat([cls_tokens, x], axis=1)
x += self.pos_embedding[:, :(n + 1)]
x = self.dropout(x)
x = self.transformer(x)
x = tf.reduce_mean(x, axis = 1) if self.pool == 'mean' else x[:, 0]
x = self.to_latent(x)
return tf.nn.softmax(self.mlp_head(x)) |