botnet.py

import functools
import numpy as np
import tensorflow.compat.v1 as tf
from tensorflow.python.tpu import tpu_function


BATCH_NORM_DECAY = 0.9
BATCH_NORM_EPSILON = 1e-5


def Activation(inputs, activation='relu'):
  """Only supports ReLU and SiLU/Swish."""
  assert activation in ['relu', 'silu']
  if activation == 'relu':
    return tf.nn.relu(inputs)
  else:
    return tf.nn.swish(inputs)


def BNReLU(
    inputs, is_training, nonlinearity=True,
    init_zero=False, activation='relu'):
  """Performs a batch normalization followed by a ReLU."""
  if init_zero:
    gamma_initializer = tf.zeros_initializer()
  else:
    gamma_initializer = tf.ones_initializer()

  inputs = tf.layers.batch_normalization(
        inputs=inputs,
        axis=3,
        momentum=BATCH_NORM_DECAY,
        epsilon=BATCH_NORM_EPSILON,
        center=True,
        scale=True,
        training=is_training,
        fused=True,
        gamma_initializer=gamma_initializer)

  if nonlinearity:
    inputs = Activation(inputs, activation=activation)

  return inputs


def fixed_padding(inputs, kernel_size):
  """Pads the input along the spatial dimensions independently of input size."""
  pad_total = kernel_size - 1
  pad_beg = pad_total // 2
  pad_end = pad_total - pad_beg
  padded_inputs = tf.pad(
      inputs, [[0, 0], [pad_beg, pad_end], pad_beg, pad_end], [0, 0]])
  return padded_inputs


def Conv2D(inputs, *, filters, kernel_size, strides=1):
  """Strided 2-D convolution with explicit padding."""
  if strides > 1:
    inputs = fixed_padding(inputs, kernel_size)

  return tf.layers.conv2d(
      inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides,
      padding=('SAME' if strides == 1 else 'VALID'), use_bias=False,
      kernel_initializer=tf.variance_scaling_initializer(
          scale=2., mode='fan_in', distribution='untruncated_normal'))


# Functions `rel_to_abs`, `relative_logits_1d`, `relative_logits`
# and `relpos_self_attention` are fully based on
# https://github.com/tensorflow/tensor2tensor/blob/21dba2c1bdcc7ab582a2bfd8c0885c217963bb4f/tensor2tensor/layers/common_attention.py#L2225.


def rel_to_abs(x):
  """
  Converts relative indexing to absolute.
  Input: [bs, heads, length, 2*length - 1]
  Output: [bs, heads, length, length]
  """
  bs, heads, length, _ = x.shape
  col_pad = tf.zeros((bs, heads, length, 1), dtype=x.dtype)
  x = tf.concat([x, col_pad], axis=3)
  flat_x = tf.reshape(x, [bs, heads, -1])
  flat_pad = tf.zeros((bs, heads, length-1), dtype=x.dtype)
  flat_x_padded = tf.concat([flat_x, flat_pad], axis=2)
  final_x = tf.reshape(
    flat_x_padded, [bs, heads, length+1, 2*length-1])
  final_x = final_x[:, :, :length, length-1:]
  return final_x


def relative_logits_1d(*, q, rel_k, transpose_mask):
  """
  Compute relative logits along one dimenion.
  `q`: [bs, heads, height, width, dim]
  `rel_k`: [2*width - 1, dim]
  """
  bs, heads, h, w, dim = q.shape
  rel_logits = tf.einsum('bhxyd,md->bhxym', q, rel_k)
  rel_logits = tf.reshape(rel_logits, [-1, heads * h, w, 2*w-1])
  rel_logits = rel_to_abs(rel_logits)
  rel_logits = tf.reshape(rel_logits, [-1, heads, h, w, w])
  rel_logits = tf.expand_dims(rel_logits, axis=3)
  rel_logits = tf.tile(rel_logits, [1, 1, 1, h, 1, 1])
  rel_logits = tf.transpose(rel_logits, transpose_mask)
  return rel_logits


def relative_logits(q):
  """Compute relative position enc logits."""
  with tf.variable_scope('relative', reuse=tf.AUTO_REUSE):
    bs, heads, h, w, dim = q.shape
    int_dim = dim.value
    # Note: below, we passed stddev arg as mean for the initializer.
    # Providing code as is, with this small error.
    # right way: normal_initializer(stddev=int_dim**-0.5)

    # Relative logits in width dimension.
    rel_emb_w = tf.get_variable(
        'r_width', shape=(2*w - 1, dim),
        dtype=q.dtype,
        initializer=tf.random_normal_initializer(int_dim**-0.5))
    rel_logits_w = relative_logits_1d(
      q=q, rel_k=rel_emb_w,
      transpose_mask=[0, 1, 2, 4, 3, 5])

    # Relative logits in height dimension.
    rel_emb_h = tf.get_variable(
        'r_height', shape=(2*h - 1, dim),
        dtype=q.dtype,
        initializer=tf.random_normal_initializer(int_dim**-0.5))
    rel_logits_h = relative_logits_1d(
        q=tf.transpose(q, [0, 1, 3, 2, 4]),
        rel_k=rel_emb_h,
        transpose_mask=[0, 1, 4, 2, 5, 3])
    return rel_logits_h + rel_logits_w


def relpos_self_attention(
  *, q, k, v, relative=True, fold_heads=False):
  """2D self-attention with rel-pos. Add option to fold heads."""
  bs, heads, h, w, dim = q.shape
  int_dim = dim.value
  q = q * (dim ** -0.5) # scaled dot-product
  logits = tf.einsum('bhHWd,bhPQd->bhHWPQ', q, k)
  if relative:
    logits += relative_logits(q)
  weights = tf.reshape(logits, [-1, heads, h, w, h * w])
  weights = tf.nn.softmax(weights)
  weights = tf.reshape(weights, [-1, heads, h, w, h, w])
  attn_out = tf.einsum('bhHWPQ,bhPQd->bHWhd', weights, v)
  if fold_heads:
    attn_out = tf.reshape(attn_out, [-1, h, w, heads * dim])
  return attn_out

def absolute_logits(q):
  """Compute absolute position enc logits."""
  with tf.variable_scope('absolute', reuse=tf.AUTO_REUSE):
    emb_w = tf.get_variable(
        'r_width', shape=(W, dkh),
        dtype=q.dtype,
        initializer=tf.random_normal_initializer(dkh**-0.5))
    emb_h = tf.get_variable(
        'r_height', shape=(H, dkh),
        dtype=q.dtype,
        initializer=tf.random_normal_initializer(dkh**-0.5))
    emb_h = emb_h[:, None, :]
    emb_w = emb_w[None, :, :]
    emb = emb_h + emb_w
    abs_logits = tf.einsum('bhxyd,pqd->bhxypq', q, emb)
    return abs_logits


def abspos_self_attention(*, q, k, v, absolue=True, fold_heads=False):
  """2D self-attention with abs-pos. Add option to fold heads."""
  bs, heads, h, w, dim = q.shape
  int_dim = dim.value
  q = q * (dim ** -0.5) # scaled dot-product
  logits = tf.einsum('bhHWd,bhPQd->bhHWPQ', q, k)
  abs_logits = absolute_logits(q)
  if absolute:
    logits += abs_logits
  weights = tf.reshape(logits, [-1, heads, h, w, h * w])
  weights = tf.nn.softmax(weights)
  weights = tf.reshape(weights, [-1, heads, h, w, h, w])
  attn_out = tf.einsum('bhHWPQ,bhPQd->bHWhd', weights, v)
  if fold_heads:
    attn_out = tf.reshape(attn_out, [-1, h, w, heads * dim])
  return attn_out


def group_pointwise(
  featuremap, proj_factor=1, name='grouppoint',
  heads=4, target_dimension=None):
  """1x1 conv with heads."""
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    in_channels = featuremap.shape[-1]
    if target_dimension is not None:
      proj_channels = target_dimension // proj_factor
    else:
      proj_channels = in_channels // proj_factor
    w = tf.get_variable(
        'w',
        [in_channels, heads, proj_channels // heads],
        dtype=featuremap.dtype,
        initializer=tf.random_normal_initializer(stddev=0.01))
    out = tf.einsum('bHWD,Dhd->bhHWd', featuremap, w)
    return out


def MHSA(featuremap, pos_enc_type='relative', use_pos=True):
  """Multi-Head Self-Attention."""
  q = group_pointwise(
      featuremap, proj_factor=1, name='q_proj', heads=heads,
      target_dimension=bottleneck_dimension)
  k = group_pointwise(
      featuremap, proj_factor=1, name='k_proj', heads=heads,
      target_dimension=bottleneck_dimension)
  v = group_pointwise(
      featuremap, proj_factor=1, name='v_proj', heads=heads,
      target_dimension=bottleneck_dimension)
  assert pos_enc_type in ['relative', 'absolute']
  if pos_enc_type == 'relative':
    o = relpos_self_attention(
        q=q, k=k, v=v, relative=use_pos, fold_heads=True)
  else:
    o = abspos_self_attention(
        q=q, k=k, v=v, absolute=use_pos, fold_heads=True)
  return o


def BoT_Block(
    featuremap, is_training=False,
    heads=4, proj_factor=4,
    activation='relu',
    pos_enc_type='relative',
    name='all2all', strides=1,
    target_dimension=2048):
  """Bottleneck Transformer (BoT) Block."""
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    shortcut = featuremap
    in_dimension = featuremap.shape[-1]
    if strides != 1 or in_dimension != target_dimension:
      shortcut = Conv2D(
        shortcut, filters=target_dimension, kernel_size=1, strides=strides)
      shortcut = BNReLU(
          shortcut, is_training, activation=activation, nonlinearity=True)

    bottleneck_dimension = target_dimension // proj_factor
    featuremap = Conv2D(
        featuremap, filters=bottleneck_dimension, kernel_size=1, strides=1)
    featuremap = BNReLU(
        featuremap, is_training, activation=activation, nonlinearity=True)

    featuremap = MHSA(featuremap, pos_enc_type=pos_enc_type)
    if strides != 1:
      assert strides == 2
      featuremap = tf.keras.layers.AveragePooling2D(
          pool_size=(2, 2), strides=(2, 2), padding='same')(featuremap)
    featuremap = BNReLU(
        featuremap, is_training, activation=activation, nonlinearity=True)

    featuremap= Conv2D(
        featuremap, filters=target_dimension,
        kernel_size=1, strides=1)
    featuremap = BNReLU(
        featuremap, is_training, nonlinearity=False, init_zero=True)

    return Activation(shortcut + featuremap, activation=activation)


def BoT_Stack(
    featuremap, *,
    blocks_so_far,
    total_blocks,
    is_training=False,
    heads=4, proj_factor=4,
    activation='relu',
    pos_enc_type='relative',
    name='all2all_stack',
    strides=2, num_layers=3,
    target_dimension=2048):
  """c5 Blockgroup of BoT Blocks."""
  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
    for i in range(num_layers):
      featuremap = BoT_Block(
          featuremap,
          is_training=is_training,
          heads=heads,
          proj_factor=proj_factor,
          activation=activation,
          pos_enc_type=pos_enc_type,
          strides=strides if i == 0 else 1,
          target_dimension=target_dimension,
          name='all2all_layer_{}'.format(i))
    return featuremap

When will you release the pretained model?

Looks like the multi-head self-attention positional encoding implementation only supports inputs of static constant shapes. However, in the paper, the authors described that they used multi-scale image inputs for training. So I wonder whether this released code is the one that is used for the authors' experiments.

Aravind Srinivas, congrats on your PhD. Do you think you have more time now to publish the pretrained BotNet model? If so, do you have an indication as from when?

This is a very solid work.
But I am very confused about the way the relative position enc logits is used here, why should it be set like this?
Can you give a more detailed explanation?

+1

This is a very solid work.
But I am very confused about the way the relative position enc logits is used here, why should it be set like this?
Can you give a more detailed explanation?

+1

Hi, I found a good explanation of relative position embedding:
https://theaisummer.com/positional-embeddings/

And here is a Chinese version of the explanation I wrote:
https://www.yuque.com/lart/ugkv9f/oazsec

Understanding the calculating process of rel_to_abs gave me an idea of simplify. I think here the zeropaddings can be removed, but maybe some scenarios I missed:

def rel_to_abs_2(rel_pos):
    _, heads, hh, ww, dim = rel_pos.shape # [bs, heads, height, width, 2 * width - 1]
    # [bs, heads, height, width * (2 * width - 1)] --> [bs, heads, height, width * (2 * width - 1) - width]
    flat_x = tf.reshape(rel_pos, [-1, heads, hh, ww * (ww * 2 - 1)])[:, :, :, ww - 1:-1]
    # [bs, heads, height, width, 2 * (width - 1)] --> [bs, heads, height, width, width]
    return tf.reshape(flat_x, [-1, heads, hh, ww, 2 * (ww - 1)])[:, :, :, :, :ww]

Test

rel_pos = tf.random.uniform([12, 6, 14, 16, 2 * 16 - 1])
orignal_rel_to_abs = tf.reshape(rel_to_abs(tf.reshape(rel_pos, [-1, 6 * 14, 16, 2 * 16 - 1])), [-1, 6, 14, 16, 16])
print(np.allclose(orignal_rel_to_abs, rel_to_abs_2(rel_pos)))
# True

• Add here keras_cv_attention_models/botnet is my botnet with weights loaded from timm
• I think this relative positional embedding still makes sense in some future works...

@BIGBALLON the Drive link you provided for the .pth weights is not in the right format it seems:

Could you clarify a bit?

@leondgarse

I agree with you that the zeros paddings can be omitted, and your implementation seems more concise and easy-to-understand.

Would you care to push your version to Pytorch Image Models (also known as the timm package), to see if the author agree with you to replace the current version with yours (no padding)?

And also, could the Relative Positional Embedding in HaloNet also be replaced with no padding?

@bsun0802 I have been using this implementation for a long time. Here my keras_cv_attention_models/botnet and also keras_cv_attention_models/halonet both sharing this no-padding version. Those model weights all ported from timm and kept close outputs. I may discuss this with rwightman.

@leondgarse
Thanks for your reply. I just verified that your idea without padding works for HaloNet as well with a slight different.

The code need to be changed to:

b = 6 # block size
h = 1 # halo size
w = b + 2 * h # window size

To visualize, the index 1 to 8 are the indices we wanted.

x = torch.tensor([[0] * (w-1-i) + list(range(1,1+w)) + [0] * i for i in range(b)])
assert x.shape == (b, 2*w-1)
x
tensor([[0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8],
        [0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0],
        [0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0],
        [0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0],
        [0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0],
        [0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0, 0]])

For halo attention, we need to add a single 0 at the end of flatten tensor. (If im not wrong, this implementation should work for any block size b and window size w, maybe need to be adjusted if halo size h != 1. )

x = F.pad(x.flatten(), [0, 1])[w-1:].reshape(b, -1)  # rel_to_abs
x
tensor([[1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0, 0, 0],
        [1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0, 0, 0],
        [1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0, 0, 0],
        [1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0, 0, 0],
        [1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0, 0, 0],
        [1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0, 0, 0]])

Then simply slice out the intended positions.

out = x[:, :w]
out
tensor([[1, 2, 3, 4, 5, 6, 7, 8],
        [1, 2, 3, 4, 5, 6, 7, 8],
        [1, 2, 3, 4, 5, 6, 7, 8],
        [1, 2, 3, 4, 5, 6, 7, 8],
        [1, 2, 3, 4, 5, 6, 7, 8],
        [1, 2, 3, 4, 5, 6, 7, 8]])

You may refer to my implementation rel_to_abs, that padding is also not necessary. I'm calling it a full_rank_gap for this scenario, just need to clip them:

hh = 1
ww, dim = x.shape

pos_dim = (dim + 1) // 2
full_rank_gap = pos_dim - ww
print(f"{pos_dim = }, {full_rank_gap = }")
# pos_dim = 8, full_rank_gap = 2

flat_x = x.reshape([-1, hh, ww * dim])[:, :, ww - 1 : -1]
out = flat_x.reshape([-1, hh, ww, 2 * (pos_dim - 1)])
out
# tensor([[[[0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0],
#           [0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0],
#           [0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0],
#           [0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0],
#           [0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0],
#           [0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 0, 0]]]])

out[:, :, :, full_rank_gap : pos_dim + full_rank_gap]
# tensor([[1, 2, 3, 4, 5, 6, 7, 8],
#         [1, 2, 3, 4, 5, 6, 7, 8],
#         [1, 2, 3, 4, 5, 6, 7, 8],
#         [1, 2, 3, 4, 5, 6, 7, 8],
#         [1, 2, 3, 4, 5, 6, 7, 8],
#         [1, 2, 3, 4, 5, 6, 7, 8]])

@bsun0802 I created a PR in timm #1061, but then closed it... It's not the logic, but torch.fx currently not supporting dynamic control flow depending on inputs, and I have no idea how to fix it... We can discuss it there if you still want it. Anyway, it's just a tiny change.