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import torch
import torch.nn as nn
import torch.nn.functional as F
import pdb
import math
from maskrcnn_benchmark.modeling.utils import cat, concat_box_prediction_layers, permute_and_flatten
from timm.models.layers import DropPath
from transformers.activations import ACT2FN
class BertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
class BertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = BertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
class FeatureResizer(nn.Module):
"""
This class takes as input a set of embeddings of dimension C1 and outputs a set of
embedding of dimension C2, after a linear transformation, dropout and normalization (LN).
"""
def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True):
super().__init__()
self.do_ln = do_ln
# Object feature encoding
self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True)
self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12)
self.dropout = nn.Dropout(dropout)
def forward(self, encoder_features):
x = self.fc(encoder_features)
if self.do_ln:
x = self.layer_norm(x)
output = self.dropout(x)
return output
def _make_conv(input_dim, output_dim, k, stride=1):
pad = (k - 1) // 2
return nn.Sequential(
nn.Conv2d(input_dim, output_dim, (k, k), padding=(pad, pad), stride=(stride, stride)),
nn.BatchNorm2d(output_dim),
nn.ReLU(inplace=True),
)
def _make_mlp(input_dim, output_dim, drop):
return nn.Sequential(
nn.Linear(input_dim, output_dim),
nn.BatchNorm1d(output_dim),
nn.ReLU(inplace=True),
nn.Dropout(drop),
nn.Linear(output_dim, output_dim),
nn.BatchNorm1d(output_dim),
nn.ReLU(inplace=True),
)
def _make_coord(batch, height, width):
# relative position encoding
xv, yv = torch.meshgrid([torch.arange(0, height), torch.arange(0, width)])
xv_min = (xv.float() * 2 - width) / width
yv_min = (yv.float() * 2 - height) / height
xv_max = ((xv + 1).float() * 2 - width) / width
yv_max = ((yv + 1).float() * 2 - height) / height
xv_ctr = (xv_min + xv_max) / 2
yv_ctr = (yv_min + yv_max) / 2
hmap = torch.ones(height, width) * (1.0 / height)
wmap = torch.ones(height, width) * (1.0 / width)
coord = torch.autograd.Variable(
torch.cat(
[
xv_min.unsqueeze(0),
yv_min.unsqueeze(0),
xv_max.unsqueeze(0),
yv_max.unsqueeze(0),
xv_ctr.unsqueeze(0),
yv_ctr.unsqueeze(0),
hmap.unsqueeze(0),
wmap.unsqueeze(0),
],
dim=0,
)
)
coord = coord.unsqueeze(0).repeat(batch, 1, 1, 1)
return coord
def l1norm(X, dim, eps=1e-8):
"""L1-normalize columns of X"""
norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps
X = torch.div(X, norm)
return X
def l2norm(X, dim, eps=1e-8):
"""L2-normalize columns of X"""
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
X = torch.div(X, norm)
return X
def func_attention(query, context, smooth=1, raw_feature_norm="softmax", eps=1e-8):
"""
query: (n_context, queryL, d)
context: (n_context, sourceL, d)
"""
batch_size_q, queryL = query.size(0), query.size(1)
batch_size, sourceL = context.size(0), context.size(1)
# Get attention
# --> (batch, d, queryL)
queryT = torch.transpose(query, 1, 2)
# (batch, sourceL, d)(batch, d, queryL)
# --> (batch, sourceL, queryL)
attn = torch.bmm(context, queryT)
if raw_feature_norm == "softmax":
# --> (batch*sourceL, queryL)
attn = attn.view(batch_size * sourceL, queryL)
attn = nn.Softmax()(attn)
# --> (batch, sourceL, queryL)
attn = attn.view(batch_size, sourceL, queryL)
elif raw_feature_norm == "l2norm":
attn = l2norm(attn, 2)
elif raw_feature_norm == "clipped_l2norm":
attn = nn.LeakyReLU(0.1)(attn)
attn = l2norm(attn, 2)
else:
raise ValueError("unknown first norm type:", raw_feature_norm)
# --> (batch, queryL, sourceL)
attn = torch.transpose(attn, 1, 2).contiguous()
# --> (batch*queryL, sourceL)
attn = attn.view(batch_size * queryL, sourceL)
attn = nn.Softmax()(attn * smooth)
# --> (batch, queryL, sourceL)
attn = attn.view(batch_size, queryL, sourceL)
# --> (batch, sourceL, queryL)
attnT = torch.transpose(attn, 1, 2).contiguous()
# --> (batch, d, sourceL)
contextT = torch.transpose(context, 1, 2)
# (batch x d x sourceL)(batch x sourceL x queryL)
# --> (batch, d, queryL)
weightedContext = torch.bmm(contextT, attnT)
# --> (batch, queryL, d)
weightedContext = torch.transpose(weightedContext, 1, 2)
return weightedContext, attnT
class BiMultiHeadAttention(nn.Module):
def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None):
super(BiMultiHeadAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
self.v_dim = v_dim
self.l_dim = l_dim
assert (
self.head_dim * self.num_heads == self.embed_dim
), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
self.scale = self.head_dim ** (-0.5)
self.dropout = dropout
self.v_proj = nn.Linear(self.v_dim, self.embed_dim)
self.l_proj = nn.Linear(self.l_dim, self.embed_dim)
self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim)
self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim)
self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim)
self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim)
self.stable_softmax_2d = cfg.MODEL.DYHEAD.FUSE_CONFIG.STABLE_SOFTMAX_2D
self.clamp_min_for_underflow = cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MIN_FOR_UNDERFLOW
self.clamp_max_for_overflow = cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MAX_FOR_OVERFLOW
self._reset_parameters()
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def _reset_parameters(self):
nn.init.xavier_uniform_(self.v_proj.weight)
self.v_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(self.l_proj.weight)
self.l_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(self.values_v_proj.weight)
self.values_v_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(self.values_l_proj.weight)
self.values_l_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(self.out_v_proj.weight)
self.out_v_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(self.out_l_proj.weight)
self.out_l_proj.bias.data.fill_(0)
def forward(self, v, l, attention_mask_l=None):
bsz, tgt_len, embed_dim = v.size()
query_states = self.v_proj(v) * self.scale
key_states = self._shape(self.l_proj(l), -1, bsz)
value_v_states = self._shape(self.values_v_proj(v), -1, bsz)
value_l_states = self._shape(self.values_l_proj(l), -1, bsz)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_v_states = value_v_states.view(*proj_shape)
value_l_states = value_l_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
)
# attn_weights_l = nn.functional.softmax(attn_weights.transpose(1, 2), dim=-1)
if self.stable_softmax_2d:
attn_weights = attn_weights - attn_weights.max()
if self.clamp_min_for_underflow:
attn_weights = torch.clamp(
attn_weights, min=-50000
) # Do not increase -50000, data type half has quite limited range
if self.clamp_max_for_overflow:
attn_weights = torch.clamp(
attn_weights, max=50000
) # Do not increase 50000, data type half has quite limited range
attn_weights_T = attn_weights.transpose(1, 2)
attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0]
if self.clamp_min_for_underflow:
attn_weights_l = torch.clamp(
attn_weights_l, min=-50000
) # Do not increase -50000, data type half has quite limited range
if self.clamp_max_for_overflow:
attn_weights_l = torch.clamp(
attn_weights_l, max=50000
) # Do not increase 50000, data type half has quite limited range
attn_weights_l = attn_weights_l.softmax(dim=-1)
if attention_mask_l is not None:
assert attention_mask_l.dim() == 2
attention_mask = attention_mask_l.unsqueeze(1).unsqueeze(1)
attention_mask = attention_mask.expand(bsz, 1, tgt_len, src_len)
attention_mask = attention_mask.masked_fill(attention_mask == 0, -9e15)
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}")
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights_v = nn.functional.softmax(attn_weights, dim=-1)
attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training)
attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training)
attn_output_v = torch.bmm(attn_probs_v, value_l_states)
attn_output_l = torch.bmm(attn_probs_l, value_v_states)
if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}"
)
if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim):
raise ValueError(
f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}"
)
attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output_v = attn_output_v.transpose(1, 2)
attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim)
attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim)
attn_output_l = attn_output_l.transpose(1, 2)
attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim)
attn_output_v = self.out_v_proj(attn_output_v)
attn_output_l = self.out_l_proj(attn_output_l)
return attn_output_v, attn_output_l
# Bi-Direction MHA (text->image, image->text)
class BiAttentionBlock(nn.Module):
def __init__(
self,
v_dim,
l_dim,
embed_dim,
num_heads,
hidden_dim=None,
dropout=0.1,
drop_path=0.0,
init_values=1e-4,
cfg=None,
):
"""
Inputs:
embed_dim - Dimensionality of input and attention feature vectors
hidden_dim - Dimensionality of hidden layer in feed-forward network
(usually 2-4x larger than embed_dim)
num_heads - Number of heads to use in the Multi-Head Attention block
dropout - Amount of dropout to apply in the feed-forward network
"""
super(BiAttentionBlock, self).__init__()
# pre layer norm
self.layer_norm_v = nn.LayerNorm(v_dim)
self.layer_norm_l = nn.LayerNorm(l_dim)
self.attn = BiMultiHeadAttention(
v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout, cfg=cfg
)
# add layer scale for training stability
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True)
self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True)
def forward(self, v, l, attention_mask_l=None, dummy_tensor=None):
v = self.layer_norm_v(v)
l = self.layer_norm_l(l)
delta_v, delta_l = self.attn(v, l, attention_mask_l=attention_mask_l)
# v, l = v + delta_v, l + delta_l
v = v + self.drop_path(self.gamma_v * delta_v)
l = l + self.drop_path(self.gamma_l * delta_l)
return v, l
class BiAttentionBlockForCheckpoint(nn.Module):
def __init__(
self,
v_dim,
l_dim,
embed_dim,
num_heads,
hidden_dim=None,
dropout=0.1,
drop_path=0.0,
init_values=1e-4,
cfg=None,
):
"""
Inputs:
embed_dim - Dimensionality of input and attention feature vectors
hidden_dim - Dimensionality of hidden layer in feed-forward network
(usually 2-4x larger than embed_dim)
num_heads - Number of heads to use in the Multi-Head Attention block
dropout - Amount of dropout to apply in the feed-forward network
"""
super(BiAttentionBlockForCheckpoint, self).__init__()
# pre layer norm
self.layer_norm_v = nn.LayerNorm(v_dim)
self.layer_norm_l = nn.LayerNorm(l_dim)
self.attn = BiMultiHeadAttention(
v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout, cfg=cfg
)
# add layer scale for training stability
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True)
self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True)
self.cfg = cfg
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SEPARATE_BIDIRECTIONAL:
if not self.cfg.MODEL.DYHEAD.FUSE_CONFIG.DO_LANG_PROJ_OUTSIDE_CHECKPOINT:
self.shrink_lang = FeatureResizer(l_dim * 5, l_dim, 0.1)
def forward(self, q0, q1, q2, q3, q4, l, attention_mask_l=None, dummy_tensor=None):
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SEPARATE_BIDIRECTIONAL:
visu_feat = []
lang_feat = []
for ii, feat in enumerate([q0, q1, q2, q3, q4]):
bs, _, h, w = feat.shape
q = feat.flatten(2).transpose(1, 2)
new_v, new_l = self.single_attention_call(q, l, attention_mask_l=attention_mask_l)
new_v = new_v.transpose(1, 2).contiguous().view(bs, -1, h, w)
lang_feat.append(new_l)
visu_feat.append(new_v)
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.DO_LANG_PROJ_OUTSIDE_CHECKPOINT:
pass
else:
lang_feat = self.shrink_lang(torch.cat(lang_feat, dim=-1)) # From multiple dimensions
lang_feat = [lang_feat, None, None, None, None]
else:
visu_feat = []
size_per_level, visual_features_flatten = [], []
for ii, feat_per_level in enumerate([q0, q1, q2, q3, q4]):
bs, c, h, w = feat_per_level.shape
size_per_level.append([h, w])
feat = permute_and_flatten(feat_per_level, bs, 1, c, h, w)
visual_features_flatten.append(feat)
visual_features_flatten = cat(visual_features_flatten, dim=1)
new_v, new_l = self.single_attention_call(visual_features_flatten, l, attention_mask_l=attention_mask_l)
# [bs, N, C] -> [bs, C, N]
new_v = new_v.transpose(1, 2).contiguous()
start = 0
for (h, w) in size_per_level:
new_v_per_level = new_v[:, :, start : start + h * w].view(bs, -1, h, w).contiguous()
visu_feat.append(new_v_per_level)
start += h * w
lang_feat = [new_l, None, None, None, None]
return (
visu_feat[0],
visu_feat[1],
visu_feat[2],
visu_feat[3],
visu_feat[4],
lang_feat[0],
lang_feat[1],
lang_feat[2],
lang_feat[3],
lang_feat[4],
)
def single_attention_call(self, v, l, attention_mask_l=None, dummy_tensor=None):
v = self.layer_norm_v(v)
l = self.layer_norm_l(l)
delta_v, delta_l = self.attn(v, l, attention_mask_l=attention_mask_l)
# v, l = v + delta_v, l + delta_l
v = v + self.drop_path(self.gamma_v * delta_v)
l = l + self.drop_path(self.gamma_l * delta_l)
return v, l
# Single Direction MHA
class MultiHeadAttention(nn.Module):
"""
Multi-head attention module for both image and text
"""
def __init__(
self,
q_dim,
k_dim,
embed_dim,
num_heads,
dropout=0.1,
clamp_min_for_underflow=False,
clamp_max_for_overflow=False,
):
super(MultiHeadAttention, self).__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
self.q_dim = q_dim
self.k_dim = k_dim
assert (
self.head_dim * self.num_heads == self.embed_dim
), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
self.scale = self.head_dim ** (-0.5)
self.dropout = dropout
self.q_proj = nn.Linear(self.q_dim, self.embed_dim)
self.k_proj = nn.Linear(self.k_dim, self.embed_dim)
self.v_proj = nn.Linear(self.k_dim, self.embed_dim)
self.out_proj = nn.Linear(self.embed_dim, self.q_dim)
self.clamp_min_for_underflow = clamp_min_for_underflow
self.clamp_max_for_overflow = clamp_max_for_overflow
self._reset_parameters()
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def _reset_parameters(self):
nn.init.xavier_uniform_(self.q_proj.weight)
self.q_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(self.k_proj.weight)
self.k_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(self.v_proj.weight)
self.v_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(self.out_proj.weight)
self.out_proj.bias.data.fill_(0)
def forward(self, q, k, v, attention_mask=None, return_attention=False):
bsz, tgt_len, embed_dim = q.size()
query_states = self.q_proj(q) * self.scale
key_states = self._shape(self.k_proj(k), -1, bsz)
value_states = self._shape(self.v_proj(v), -1, bsz)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
)
if self.clamp_min_for_underflow:
attn_weights = torch.clamp(
attn_weights, min=-50000
) # Do not increase -50000, data type half has quite limited range
if self.clamp_max_for_overflow:
attn_weights = torch.clamp(
attn_weights, max=50000
) # Do not increase 50000, data type half has quite limited range
if attention_mask is not None:
# [bsz, src_len]
assert attention_mask.dim() == 2
attention_mask = attention_mask.unsqueeze(1).unsqueeze(1)
attention_mask = attention_mask.expand(bsz, 1, tgt_len, src_len)
attention_mask = attention_mask.masked_fill(attention_mask == 0, -9e15)
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}")
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if return_attention:
# this operation is a bit akward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = F.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights
class AttentionMLP(nn.Module):
def __init__(self, q_dim, hidden_dim, dropout=0.1):
super(AttentionMLP, self).__init__()
self.hidden_dim = hidden_dim
self.activation_fn = nn.GELU()
self.fc1 = nn.Linear(q_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, q_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, hidden_states):
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class AttentionT2I(nn.Module):
def __init__(
self,
q_dim,
k_dim,
embed_dim,
num_heads,
hidden_dim=None,
dropout=0.1,
drop_path=0.0,
init_values=1e-4,
mode="i2t",
use_layer_scale=False,
clamp_min_for_underflow=False,
clamp_max_for_overflow=False,
):
"""
Inputs:
embed_dim - Dimensionality of input and attention feature vectors
hidden_dim - Dimensionality of hidden layer in feed-forward network
(usually 2-4x larger than embed_dim)
num_heads - Number of heads to use in the Multi-Head Attention block
dropout - Amount of dropout to apply in the feed-forward network
"""
super(AttentionT2I, self).__init__()
# pre_layer norm
self.layer_norm_q_1 = nn.LayerNorm(q_dim)
self.layer_norm_k_1 = nn.LayerNorm(k_dim)
self.attn = MultiHeadAttention(
q_dim=q_dim,
k_dim=k_dim,
embed_dim=embed_dim,
num_heads=num_heads,
clamp_min_for_underflow=clamp_min_for_underflow,
clamp_max_for_overflow=clamp_max_for_overflow,
)
self.mode = mode
# add layer scale for training stability
self.use_layer_scale = use_layer_scale
if self.use_layer_scale:
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.gamma = nn.Parameter(init_values * torch.ones((q_dim)), requires_grad=True)
def forward(self, q0, q1, q2, q3, q4, k, v, attention_mask, dummy_arg=None):
qs = []
for q_index, q in enumerate([q0, q1, q2, q3, q4]):
bs, _, h, w = q.shape
# (batch, seq_len, embed_size)
q = q.flatten(2).transpose(1, 2)
q = self.layer_norm_q_1(q)
k, v = self.layer_norm_k_1(k), self.layer_norm_k_1(v)
delta_q = self.attn(q, k, v, attention_mask=attention_mask)[0]
if self.use_layer_scale:
q = q + self.drop_path(self.gamma * delta_q)
else:
q = q + delta_q
q = q.transpose(1, 2).contiguous().view(bs, -1, h, w)
qs.append(q)
return qs[0], qs[1], qs[2], qs[3], qs[4]
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