init
This commit is contained in:
logtd
@@ -0,0 +1,3 @@
|
||||
**/__pycache__
|
||||
**/*.pyc
|
||||
.DS_Store
|
||||
+16
@@ -0,0 +1,16 @@
|
||||
from .nodes.load_seecoder_node import LoadSeeCoderNode
|
||||
from .nodes.seecoder_encode_node import SeecoderEncodeNode
|
||||
from .nodes.load_seecoder_uncond_node import LoadSeeCoderUncondNode
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
'LoadSeeCoder': LoadSeeCoderNode,
|
||||
'SeecoderEncode': SeecoderEncodeNode,
|
||||
'LoadSeeCoderUncond': LoadSeeCoderUncondNode,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
'LoadSeeCoder': 'Seecoder Load Model',
|
||||
'SeecoderEncode': 'Seecoder Encode Latent',
|
||||
'LoadSeeCoderUncond': 'Seecoder Load Uncond',
|
||||
}
|
||||
@@ -0,0 +1,56 @@
|
||||
|
||||
|
||||
|
||||
seecoder_decoder_config = {
|
||||
'inchannels': {
|
||||
'res3': 384,
|
||||
'res4': 768,
|
||||
'res5': 1536
|
||||
},
|
||||
'trans_input_tags': ['res3', 'res4', 'res5'],
|
||||
'trans_dim': 768,
|
||||
'trans_dropout': 0.1,
|
||||
'trans_nheads': 8,
|
||||
'trans_feedforward_dim': 1024,
|
||||
'trans_num_layers': 6
|
||||
}
|
||||
|
||||
seecoder_query_config = {
|
||||
'in_channels' : 768,
|
||||
'hidden_dim': 768,
|
||||
'num_queries': [4, 144],
|
||||
'nheads': 8,
|
||||
'num_layers': 9,
|
||||
'feedforward_dim': 2048,
|
||||
'pre_norm': False,
|
||||
'num_feature_levels': 3,
|
||||
'enforce_input_project': False,
|
||||
'with_fea2d_pos': False
|
||||
}
|
||||
|
||||
# Not used
|
||||
seecoder_query_pa_config = {
|
||||
**seecoder_query_config,
|
||||
'with_fea2d_pos': True
|
||||
}
|
||||
|
||||
swin_config = {
|
||||
'embed_dim': 128,
|
||||
'depths': [ 2, 2, 18, 2 ],
|
||||
'num_heads': [ 4, 8, 16, 32 ],
|
||||
'window_size': 7,
|
||||
'ape': False,
|
||||
'drop_path_rate': 0.3,
|
||||
'patch_norm': True,
|
||||
}
|
||||
|
||||
swin_large_config = {
|
||||
**swin_config,
|
||||
'embed_dim': 192,
|
||||
'depths': [ 2, 2, 18, 2 ],
|
||||
'num_heads': [ 6, 12, 24, 48 ],
|
||||
'window_size': 12,
|
||||
'ape': False,
|
||||
'drop_path_rate': 0.3,
|
||||
'patch_norm': True,
|
||||
}
|
||||
@@ -0,0 +1,520 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import copy
|
||||
|
||||
|
||||
from .configs import swin_large_config, seecoder_decoder_config, seecoder_query_config, seecoder_query_pa_config
|
||||
from .swin import SwinTransformer
|
||||
|
||||
###########
|
||||
# helpers #
|
||||
###########
|
||||
|
||||
def with_pos_embed(x, pos):
|
||||
return x if pos is None else x + pos
|
||||
|
||||
def _get_clones(module, N):
|
||||
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
|
||||
|
||||
def _get_activation_fn(activation):
|
||||
"""Return an activation function given a string"""
|
||||
if activation == "relu":
|
||||
return F.relu
|
||||
if activation == "gelu":
|
||||
return F.gelu
|
||||
if activation == "glu":
|
||||
return F.glu
|
||||
raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
|
||||
|
||||
def c2_xavier_fill(module):
|
||||
# Caffe2 implementation of XavierFill in fact
|
||||
nn.init.kaiming_uniform_(module.weight, a=1)
|
||||
if module.bias is not None:
|
||||
nn.init.constant_(module.bias, 0)
|
||||
|
||||
def with_pos_embed(x, pos):
|
||||
return x if pos is None else x + pos
|
||||
|
||||
###########
|
||||
# Modules #
|
||||
###########
|
||||
|
||||
class Conv2d_Convenience(nn.Conv2d):
|
||||
def __init__(self, *args, **kwargs):
|
||||
norm = kwargs.pop("norm", None)
|
||||
activation = kwargs.pop("activation", None)
|
||||
super().__init__(*args, **kwargs)
|
||||
self.norm = norm
|
||||
self.activation = activation
|
||||
|
||||
def forward(self, x):
|
||||
x = F.conv2d(
|
||||
x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
|
||||
if self.norm is not None:
|
||||
x = self.norm(x)
|
||||
if self.activation is not None:
|
||||
x = self.activation(x)
|
||||
return x
|
||||
|
||||
|
||||
class DecoderLayer(nn.Module):
|
||||
def __init__(self,
|
||||
dim=256,
|
||||
feedforward_dim=1024,
|
||||
dropout=0.1,
|
||||
activation="relu",
|
||||
n_heads=8,):
|
||||
|
||||
super().__init__()
|
||||
|
||||
self.self_attn = nn.MultiheadAttention(dim, n_heads, dropout=dropout)
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
self.norm1 = nn.LayerNorm(dim)
|
||||
|
||||
self.linear1 = nn.Linear(dim, feedforward_dim)
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.dropout2 = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(feedforward_dim, dim)
|
||||
self.dropout3 = nn.Dropout(dropout)
|
||||
self.norm2 = nn.LayerNorm(dim)
|
||||
|
||||
def forward(self, x):
|
||||
h = x
|
||||
h1 = self.self_attn(x, x, x, attn_mask=None)[0]
|
||||
h = h + self.dropout1(h1)
|
||||
h = self.norm1(h)
|
||||
|
||||
h2 = self.linear2(self.dropout2(self.activation(self.linear1(h))))
|
||||
h = h + self.dropout3(h2)
|
||||
h = self.norm2(h)
|
||||
return h
|
||||
|
||||
class DecoderLayerStacked(nn.Module):
|
||||
def __init__(self, layer, num_layers, norm=None):
|
||||
super().__init__()
|
||||
self.layers = _get_clones(layer, num_layers)
|
||||
self.num_layers = num_layers
|
||||
self.norm = norm
|
||||
|
||||
def forward(self, x):
|
||||
h = x
|
||||
for _, layer in enumerate(self.layers):
|
||||
h = layer(h)
|
||||
if self.norm is not None:
|
||||
h = self.norm(h)
|
||||
return h
|
||||
|
||||
class SelfAttentionLayer(nn.Module):
|
||||
def __init__(self, channels, nhead, dropout=0.0,
|
||||
activation="relu", normalize_before=False):
|
||||
super().__init__()
|
||||
self.self_attn = nn.MultiheadAttention(channels, nhead, dropout=dropout)
|
||||
|
||||
self.norm = nn.LayerNorm(channels)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.normalize_before = normalize_before
|
||||
|
||||
def forward(self,
|
||||
qkv,
|
||||
qk_pos = None,
|
||||
mask = None,):
|
||||
h = qkv
|
||||
qk = with_pos_embed(qkv, qk_pos).transpose(0, 1)
|
||||
v = qkv.transpose(0, 1)
|
||||
h1 = self.self_attn(qk, qk, v, attn_mask=mask)[0]
|
||||
h1 = h1.transpose(0, 1)
|
||||
h = h + self.dropout(h1)
|
||||
h = self.norm(h)
|
||||
return h
|
||||
|
||||
|
||||
class CrossAttentionLayer(nn.Module):
|
||||
def __init__(self, channels, nhead, dropout=0.0,
|
||||
activation="relu", normalize_before=False):
|
||||
super().__init__()
|
||||
self.multihead_attn = nn.MultiheadAttention(channels, nhead, dropout=dropout)
|
||||
|
||||
self.norm = nn.LayerNorm(channels)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.normalize_before = normalize_before
|
||||
|
||||
def forward(self,
|
||||
q,
|
||||
kv,
|
||||
q_pos = None,
|
||||
k_pos = None,
|
||||
mask = None,):
|
||||
h = q
|
||||
q = with_pos_embed(q, q_pos).transpose(0, 1)
|
||||
k = with_pos_embed(kv, k_pos).transpose(0, 1)
|
||||
v = kv.transpose(0, 1)
|
||||
h1 = self.multihead_attn(q, k, v, attn_mask=mask)[0]
|
||||
h1 = h1.transpose(0, 1)
|
||||
h = h + self.dropout(h1)
|
||||
h = self.norm(h)
|
||||
return h
|
||||
|
||||
|
||||
class FeedForwardLayer(nn.Module):
|
||||
def __init__(self, channels, hidden_channels=2048, dropout=0.0,
|
||||
activation="relu", normalize_before=False):
|
||||
super().__init__()
|
||||
self.linear1 = nn.Linear(channels, hidden_channels)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(hidden_channels, channels)
|
||||
self.norm = nn.LayerNorm(channels)
|
||||
self.activation = _get_activation_fn(activation)
|
||||
self.normalize_before = normalize_before
|
||||
|
||||
def forward_post(self, x):
|
||||
h = x
|
||||
h1 = self.linear2(self.dropout(self.activation(self.linear1(h))))
|
||||
h = h + self.dropout(h1)
|
||||
h = self.norm(h)
|
||||
return h
|
||||
|
||||
def forward_pre(self, x):
|
||||
xn = self.norm(x)
|
||||
h = x
|
||||
h1 = self.linear2(self.dropout(self.activation(self.linear1(xn))))
|
||||
h = h + self.dropout(h1)
|
||||
return h
|
||||
|
||||
def forward(self, *args, **kwargs):
|
||||
if self.normalize_before:
|
||||
return self.forward_pre(*args, **kwargs)
|
||||
return self.forward_post(*args, **kwargs)
|
||||
|
||||
|
||||
class MLP(nn.Module):
|
||||
def __init__(self, in_channels, channels, out_channels, num_layers):
|
||||
super().__init__()
|
||||
self.num_layers = num_layers
|
||||
h = [channels] * (num_layers - 1)
|
||||
self.layers = nn.ModuleList(
|
||||
nn.Linear(n, k)
|
||||
for n, k in zip([in_channels]+h, h+[out_channels]))
|
||||
|
||||
def forward(self, x):
|
||||
for i, layer in enumerate(self.layers):
|
||||
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
|
||||
return x
|
||||
|
||||
|
||||
class PPE_MLP(nn.Module):
|
||||
def __init__(self, freq_num=20, freq_max=None, out_channel=768, mlp_layer=3):
|
||||
import math
|
||||
super().__init__()
|
||||
self.freq_num = freq_num
|
||||
self.freq_max = freq_max
|
||||
self.out_channel = out_channel
|
||||
self.mlp_layer = mlp_layer
|
||||
self.twopi = 2 * math.pi
|
||||
|
||||
mlp = []
|
||||
in_channel = freq_num*4
|
||||
for idx in range(mlp_layer):
|
||||
linear = nn.Linear(in_channel, out_channel, bias=True)
|
||||
nn.init.xavier_normal_(linear.weight)
|
||||
nn.init.constant_(linear.bias, 0)
|
||||
mlp.append(linear)
|
||||
if idx != mlp_layer-1:
|
||||
mlp.append(nn.SiLU())
|
||||
in_channel = out_channel
|
||||
self.mlp = nn.Sequential(*mlp)
|
||||
nn.init.constant_(self.mlp[-1].weight, 0)
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
assert mask is None, "Mask not implemented"
|
||||
h, w = x.shape[-2:]
|
||||
minlen = min(h, w)
|
||||
|
||||
h_embed, w_embed = torch.meshgrid(torch.arange(h), torch.arange(w), indexing='ij')
|
||||
if self.training:
|
||||
import numpy.random as npr
|
||||
pertube_h, pertube_w = npr.uniform(-0.5, 0.5), npr.uniform(-0.5, 0.5)
|
||||
else:
|
||||
pertube_h, pertube_w = 0, 0
|
||||
|
||||
h_embed = (h_embed+0.5 - h/2 + pertube_h) / (minlen) * self.twopi
|
||||
w_embed = (w_embed+0.5 - w/2 + pertube_w) / (minlen) * self.twopi
|
||||
h_embed, w_embed = h_embed.to(x.device).to(x.dtype), w_embed.to(x.device).to(x.dtype)
|
||||
|
||||
dim_t = torch.linspace(0, 1, self.freq_num, dtype=torch.float32, device=x.device)
|
||||
freq_max = self.freq_max if self.freq_max is not None else minlen/2
|
||||
dim_t = freq_max ** dim_t.to(x.dtype)
|
||||
|
||||
pos_h = h_embed[:, :, None] * dim_t
|
||||
pos_w = w_embed[:, :, None] * dim_t
|
||||
pos = torch.cat((pos_h.sin(), pos_h.cos(), pos_w.sin(), pos_w.cos()), dim=-1)
|
||||
pos = self.mlp(pos)
|
||||
pos = pos.permute(2, 0, 1)[None]
|
||||
return pos
|
||||
|
||||
def __repr__(self, _repr_indent=4):
|
||||
head = "Positional encoding " + self.__class__.__name__
|
||||
body = [
|
||||
"num_pos_feats: {}".format(self.num_pos_feats),
|
||||
"temperature: {}".format(self.temperature),
|
||||
"normalize: {}".format(self.normalize),
|
||||
"scale: {}".format(self.scale),
|
||||
]
|
||||
# _repr_indent = 4
|
||||
lines = [head] + [" " * _repr_indent + line for line in body]
|
||||
return "\n".join(lines)
|
||||
|
||||
###########
|
||||
# Decoder #
|
||||
###########
|
||||
|
||||
class Decoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
inchannels,
|
||||
trans_input_tags,
|
||||
trans_num_layers,
|
||||
trans_dim,
|
||||
trans_nheads,
|
||||
trans_dropout,
|
||||
trans_feedforward_dim,):
|
||||
|
||||
super().__init__()
|
||||
trans_inchannels = {
|
||||
k: v for k, v in inchannels.items() if k in trans_input_tags}
|
||||
fpn_inchannels = {
|
||||
k: v for k, v in inchannels.items() if k not in trans_input_tags}
|
||||
|
||||
self.trans_tags = sorted(list(trans_inchannels.keys()))
|
||||
self.fpn_tags = sorted(list(fpn_inchannels.keys()))
|
||||
self.all_tags = sorted(list(inchannels.keys()))
|
||||
|
||||
if len(self.trans_tags)==0:
|
||||
assert False # Not allowed
|
||||
|
||||
self.num_trans_lvls = len(self.trans_tags)
|
||||
|
||||
self.inproj_layers = nn.ModuleDict()
|
||||
for tagi in self.trans_tags:
|
||||
layeri = nn.Sequential(
|
||||
nn.Conv2d(trans_inchannels[tagi], trans_dim, kernel_size=1),
|
||||
nn.GroupNorm(32, trans_dim),)
|
||||
nn.init.xavier_uniform_(layeri[0].weight, gain=1)
|
||||
nn.init.constant_(layeri[0].bias, 0)
|
||||
self.inproj_layers[tagi] = layeri
|
||||
|
||||
tlayer = DecoderLayer(
|
||||
dim = trans_dim,
|
||||
n_heads = trans_nheads,
|
||||
dropout = trans_dropout,
|
||||
feedforward_dim = trans_feedforward_dim,
|
||||
activation = 'relu',)
|
||||
|
||||
self.transformer = DecoderLayerStacked(tlayer, trans_num_layers)
|
||||
for p in self.transformer.parameters():
|
||||
if p.dim() > 1:
|
||||
nn.init.xavier_uniform_(p)
|
||||
self.level_embed = nn.Parameter(torch.Tensor(len(self.trans_tags), trans_dim))
|
||||
nn.init.normal_(self.level_embed)
|
||||
|
||||
self.lateral_layers = nn.ModuleDict()
|
||||
self.output_layers = nn.ModuleDict()
|
||||
for tagi in self.all_tags:
|
||||
lateral_conv = Conv2d_Convenience(
|
||||
inchannels[tagi], trans_dim, kernel_size=1,
|
||||
bias=False, norm=nn.GroupNorm(32, trans_dim))
|
||||
c2_xavier_fill(lateral_conv)
|
||||
self.lateral_layers[tagi] = lateral_conv
|
||||
|
||||
for tagi in self.fpn_tags:
|
||||
output_conv = Conv2d_Convenience(
|
||||
trans_dim, trans_dim, kernel_size=3, stride=1, padding=1,
|
||||
bias=False, norm=nn.GroupNorm(32, trans_dim), activation=F.relu,)
|
||||
c2_xavier_fill(output_conv)
|
||||
self.output_layers[tagi] = output_conv
|
||||
|
||||
def forward(self, features):
|
||||
x = []
|
||||
spatial_shapes = {}
|
||||
for idx, tagi in enumerate(self.trans_tags[::-1]):
|
||||
xi = features[tagi]
|
||||
xi = self.inproj_layers[tagi](xi)
|
||||
bs, _, h, w = xi.shape
|
||||
spatial_shapes[tagi] = (h, w)
|
||||
xi = xi.flatten(2).transpose(1, 2) + self.level_embed[idx].view(1, 1, -1)
|
||||
x.append(xi)
|
||||
|
||||
x_length = [xi.shape[1] for xi in x]
|
||||
x_concat = torch.cat(x, 1)
|
||||
y_concat = self.transformer(x_concat)
|
||||
y = torch.split(y_concat, x_length, dim=1)
|
||||
|
||||
out = {}
|
||||
for idx, tagi in enumerate(self.trans_tags[::-1]):
|
||||
h, w = spatial_shapes[tagi]
|
||||
yi = y[idx].transpose(1, 2).view(bs, -1, h, w)
|
||||
out[tagi] = yi
|
||||
|
||||
for idx, tagi in enumerate(self.all_tags[::-1]):
|
||||
lconv = self.lateral_layers[tagi]
|
||||
if tagi in self.trans_tags:
|
||||
out[tagi] = out[tagi] + lconv(features[tagi])
|
||||
tag_save = tagi
|
||||
else:
|
||||
oconv = self.output_layers[tagi]
|
||||
h = lconv(features[tagi])
|
||||
oprev = out[tag_save]
|
||||
h = h + F.interpolate(oconv(oprev), size=h.shape[-2:], mode="bilinear", align_corners=False)
|
||||
out[tagi] = h
|
||||
|
||||
return out
|
||||
|
||||
#####################
|
||||
# Query Transformer #
|
||||
#####################
|
||||
|
||||
class QueryTransformer(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
hidden_dim,
|
||||
num_queries = [8, 144],
|
||||
nheads = 8,
|
||||
num_layers = 9,
|
||||
feedforward_dim = 2048,
|
||||
mask_dim = 256,
|
||||
pre_norm = False,
|
||||
num_feature_levels = 3,
|
||||
enforce_input_project = False,
|
||||
with_fea2d_pos = True):
|
||||
|
||||
super().__init__()
|
||||
|
||||
if with_fea2d_pos:
|
||||
self.pe_layer = PPE_MLP(freq_num=20, freq_max=None, out_channel=hidden_dim, mlp_layer=3)
|
||||
else:
|
||||
self.pe_layer = None
|
||||
|
||||
if in_channels!=hidden_dim or enforce_input_project:
|
||||
self.input_proj = nn.ModuleList()
|
||||
for _ in range(num_feature_levels):
|
||||
self.input_proj.append(nn.Conv2d(in_channels, hidden_dim, kernel_size=1))
|
||||
c2_xavier_fill(self.input_proj[-1])
|
||||
else:
|
||||
self.input_proj = None
|
||||
|
||||
self.num_heads = nheads
|
||||
self.num_layers = num_layers
|
||||
self.transformer_selfatt_layers = nn.ModuleList()
|
||||
self.transformer_crossatt_layers = nn.ModuleList()
|
||||
self.transformer_feedforward_layers = nn.ModuleList()
|
||||
|
||||
for _ in range(self.num_layers):
|
||||
self.transformer_selfatt_layers.append(
|
||||
SelfAttentionLayer(
|
||||
channels=hidden_dim,
|
||||
nhead=nheads,
|
||||
dropout=0.0,
|
||||
normalize_before=pre_norm, ))
|
||||
|
||||
self.transformer_crossatt_layers.append(
|
||||
CrossAttentionLayer(
|
||||
channels=hidden_dim,
|
||||
nhead=nheads,
|
||||
dropout=0.0,
|
||||
normalize_before=pre_norm, ))
|
||||
|
||||
self.transformer_feedforward_layers.append(
|
||||
FeedForwardLayer(
|
||||
channels=hidden_dim,
|
||||
hidden_channels=feedforward_dim,
|
||||
dropout=0.0,
|
||||
normalize_before=pre_norm, ))
|
||||
|
||||
self.num_queries = num_queries
|
||||
num_gq, num_lq = self.num_queries
|
||||
self.init_query = nn.Embedding(num_gq+num_lq, hidden_dim)
|
||||
self.query_pos_embedding = nn.Embedding(num_gq+num_lq, hidden_dim)
|
||||
|
||||
self.num_feature_levels = num_feature_levels
|
||||
self.level_embed = nn.Embedding(num_feature_levels, hidden_dim)
|
||||
|
||||
def forward(self, x):
|
||||
# x is a list of multi-scale feature
|
||||
assert len(x) == self.num_feature_levels
|
||||
fea2d = []
|
||||
fea2d_pos = []
|
||||
size_list = []
|
||||
|
||||
for i in range(self.num_feature_levels):
|
||||
size_list.append(x[i].shape[-2:])
|
||||
if self.pe_layer is not None:
|
||||
pi = self.pe_layer(x[i], None).flatten(2)
|
||||
pi = pi.transpose(1, 2)
|
||||
else:
|
||||
pi = None
|
||||
xi = self.input_proj[i](x[i]) if self.input_proj is not None else x[i]
|
||||
xi = xi.flatten(2) + self.level_embed.weight[i][None, :, None]
|
||||
xi = xi.transpose(1, 2)
|
||||
fea2d.append(xi)
|
||||
fea2d_pos.append(pi)
|
||||
|
||||
bs, _, _ = fea2d[0].shape
|
||||
num_gq, num_lq = self.num_queries
|
||||
gquery = self.init_query.weight[:num_gq].unsqueeze(0).repeat(bs, 1, 1)
|
||||
lquery = self.init_query.weight[num_gq:].unsqueeze(0).repeat(bs, 1, 1)
|
||||
|
||||
gquery_pos = self.query_pos_embedding.weight[:num_gq].unsqueeze(0).repeat(bs, 1, 1)
|
||||
lquery_pos = self.query_pos_embedding.weight[num_gq:].unsqueeze(0).repeat(bs, 1, 1)
|
||||
|
||||
for i in range(self.num_layers):
|
||||
level_index = i % self.num_feature_levels
|
||||
|
||||
qout = self.transformer_crossatt_layers[i](
|
||||
q = lquery,
|
||||
kv = fea2d[level_index],
|
||||
q_pos = lquery_pos,
|
||||
k_pos = fea2d_pos[level_index],
|
||||
mask = None,)
|
||||
lquery = qout
|
||||
|
||||
qout = self.transformer_selfatt_layers[i](
|
||||
qkv = torch.cat([gquery, lquery], dim=1),
|
||||
qk_pos = torch.cat([gquery_pos, lquery_pos], dim=1),)
|
||||
|
||||
qout = self.transformer_feedforward_layers[i](qout)
|
||||
|
||||
gquery = qout[:, :num_gq]
|
||||
lquery = qout[:, num_gq:]
|
||||
|
||||
output = torch.cat([gquery, lquery], dim=1)
|
||||
|
||||
return output
|
||||
|
||||
##################
|
||||
# Main structure #
|
||||
##################
|
||||
|
||||
class SemanticContextEncoder(nn.Module):
|
||||
def __init__(self, has_pe=False):
|
||||
super().__init__()
|
||||
query_config = seecoder_query_config if not has_pe else seecoder_query_pa_config
|
||||
self.imencoder = SwinTransformer(**swin_large_config)
|
||||
self.imdecoder = Decoder(**seecoder_decoder_config)
|
||||
self.qtransformer = QueryTransformer(**query_config)
|
||||
|
||||
def forward(self, x):
|
||||
fea = self.imencoder(x)
|
||||
hs = {'res3' : fea['res3'],
|
||||
'res4' : fea['res4'],
|
||||
'res5' : fea['res5'], }
|
||||
hs = self.imdecoder(hs)
|
||||
hs = [hs['res3'], hs['res4'], hs['res5']]
|
||||
q = self.qtransformer(hs)
|
||||
return q
|
||||
|
||||
def encode(self, x):
|
||||
return self(x)
|
||||
+657
@@ -0,0 +1,657 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.checkpoint as checkpoint
|
||||
import numpy as np
|
||||
|
||||
|
||||
##############################
|
||||
# timm.models.layers helpers #
|
||||
##############################
|
||||
|
||||
def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
|
||||
if drop_prob == 0. or not training:
|
||||
return x
|
||||
keep_prob = 1 - drop_prob
|
||||
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
|
||||
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
|
||||
if keep_prob > 0.0 and scale_by_keep:
|
||||
random_tensor.div_(keep_prob)
|
||||
return x * random_tensor
|
||||
|
||||
class DropPath(nn.Module):
|
||||
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
||||
"""
|
||||
def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):
|
||||
super(DropPath, self).__init__()
|
||||
self.drop_prob = drop_prob
|
||||
self.scale_by_keep = scale_by_keep
|
||||
|
||||
def forward(self, x):
|
||||
return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
|
||||
|
||||
def extra_repr(self):
|
||||
return f'drop_prob={round(self.drop_prob,3):0.3f}'
|
||||
|
||||
def _ntuple(n):
|
||||
def parse(x):
|
||||
from itertools import repeat
|
||||
import collections.abc
|
||||
if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
|
||||
return tuple(x)
|
||||
return tuple(repeat(x, n))
|
||||
return parse
|
||||
|
||||
to_1tuple = _ntuple(1)
|
||||
to_2tuple = _ntuple(2)
|
||||
to_3tuple = _ntuple(3)
|
||||
to_4tuple = _ntuple(4)
|
||||
to_ntuple = _ntuple
|
||||
|
||||
def _trunc_normal_(tensor, mean, std, a, b):
|
||||
import warnings
|
||||
import math
|
||||
|
||||
def norm_cdf(x):
|
||||
return (1. + math.erf(x / math.sqrt(2.))) / 2.
|
||||
|
||||
if (mean < a - 2 * std) or (mean > b + 2 * std):
|
||||
warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
|
||||
"The distribution of values may be incorrect.",
|
||||
stacklevel=2)
|
||||
|
||||
l = norm_cdf((a - mean) / std)
|
||||
u = norm_cdf((b - mean) / std)
|
||||
tensor.uniform_(2 * l - 1, 2 * u - 1)
|
||||
tensor.erfinv_()
|
||||
tensor.mul_(std * math.sqrt(2.))
|
||||
tensor.add_(mean)
|
||||
tensor.clamp_(min=a, max=b)
|
||||
return tensor
|
||||
|
||||
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
|
||||
with torch.no_grad():
|
||||
return _trunc_normal_(tensor, mean, std, a, b)
|
||||
|
||||
#############
|
||||
# main swin #
|
||||
#############
|
||||
|
||||
class Mlp(nn.Module):
|
||||
""" Multilayer perceptron."""
|
||||
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
|
||||
def window_partition(x, window_size):
|
||||
"""
|
||||
Args:
|
||||
x: (B, H, W, C)
|
||||
window_size (int): window size
|
||||
Returns:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
"""
|
||||
B, H, W, C = x.shape
|
||||
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
|
||||
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
|
||||
return windows
|
||||
|
||||
|
||||
def window_reverse(windows, window_size, H, W):
|
||||
"""
|
||||
Args:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
window_size (int): Window size
|
||||
H (int): Height of image
|
||||
W (int): Width of image
|
||||
Returns:
|
||||
x: (B, H, W, C)
|
||||
"""
|
||||
B = int(windows.shape[0] / (H * W / window_size / window_size))
|
||||
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
|
||||
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
|
||||
return x
|
||||
|
||||
|
||||
class WindowAttention(nn.Module):
|
||||
""" Window based multi-head self attention (W-MSA) module with relative position bias.
|
||||
It supports both of shifted and non-shifted window.
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
window_size (tuple[int]): The height and width of the window.
|
||||
num_heads (int): Number of attention heads.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
|
||||
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
|
||||
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
|
||||
"""
|
||||
|
||||
def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
|
||||
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.window_size = window_size # Wh, Ww
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
|
||||
# define a parameter table of relative position bias
|
||||
self.relative_position_bias_table = nn.Parameter(
|
||||
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
|
||||
|
||||
# get pair-wise relative position index for each token inside the window
|
||||
coords_h = torch.arange(self.window_size[0])
|
||||
coords_w = torch.arange(self.window_size[1])
|
||||
coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij')) # 2, Wh, Ww
|
||||
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
|
||||
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
|
||||
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
||||
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
|
||||
relative_coords[:, :, 1] += self.window_size[1] - 1
|
||||
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
|
||||
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
||||
self.register_buffer("relative_position_index", relative_position_index)
|
||||
|
||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
trunc_normal_(self.relative_position_bias_table, std=.02)
|
||||
self.softmax = nn.Softmax(dim=-1)
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
""" Forward function.
|
||||
Args:
|
||||
x: input features with shape of (num_windows*B, N, C)
|
||||
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
|
||||
"""
|
||||
B_, N, C = x.shape
|
||||
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
|
||||
|
||||
q = q * self.scale
|
||||
attn = (q @ k.transpose(-2, -1))
|
||||
|
||||
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
|
||||
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
|
||||
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
|
||||
attn = attn + relative_position_bias.unsqueeze(0)
|
||||
|
||||
if mask is not None:
|
||||
nW = mask.shape[0]
|
||||
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
|
||||
attn = attn.view(-1, self.num_heads, N, N)
|
||||
attn = self.softmax(attn)
|
||||
else:
|
||||
attn = self.softmax(attn)
|
||||
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class SwinTransformerBlock(nn.Module):
|
||||
""" Swin Transformer Block.
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
num_heads (int): Number of attention heads.
|
||||
window_size (int): Window size.
|
||||
shift_size (int): Shift size for SW-MSA.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float, optional): Stochastic depth rate. Default: 0.0
|
||||
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
|
||||
def __init__(self, dim, num_heads, window_size=7, shift_size=0,
|
||||
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
|
||||
act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.window_size = window_size
|
||||
self.shift_size = shift_size
|
||||
self.mlp_ratio = mlp_ratio
|
||||
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
|
||||
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = WindowAttention(
|
||||
dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
|
||||
qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
|
||||
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
||||
|
||||
self.H = None
|
||||
self.W = None
|
||||
|
||||
def forward(self, x, mask_matrix):
|
||||
""" Forward function.
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
mask_matrix: Attention mask for cyclic shift.
|
||||
"""
|
||||
B, L, C = x.shape
|
||||
H, W = self.H, self.W
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
|
||||
shortcut = x
|
||||
x = self.norm1(x)
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
# pad feature maps to multiples of window size
|
||||
pad_l = pad_t = 0
|
||||
pad_r = (self.window_size - W % self.window_size) % self.window_size
|
||||
pad_b = (self.window_size - H % self.window_size) % self.window_size
|
||||
x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
|
||||
_, Hp, Wp, _ = x.shape
|
||||
|
||||
# cyclic shift
|
||||
if self.shift_size > 0:
|
||||
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
|
||||
attn_mask = mask_matrix
|
||||
else:
|
||||
shifted_x = x
|
||||
attn_mask = None
|
||||
|
||||
# partition windows
|
||||
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
|
||||
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
|
||||
|
||||
# W-MSA/SW-MSA
|
||||
attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
|
||||
|
||||
# merge windows
|
||||
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
|
||||
shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
|
||||
|
||||
# reverse cyclic shift
|
||||
if self.shift_size > 0:
|
||||
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
|
||||
else:
|
||||
x = shifted_x
|
||||
|
||||
if pad_r > 0 or pad_b > 0:
|
||||
x = x[:, :H, :W, :].contiguous()
|
||||
|
||||
x = x.view(B, H * W, C)
|
||||
|
||||
# FFN
|
||||
x = shortcut + self.drop_path(x)
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class PatchMerging(nn.Module):
|
||||
""" Patch Merging Layer
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
def __init__(self, dim, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
|
||||
self.norm = norm_layer(4 * dim)
|
||||
|
||||
def forward(self, x, H, W):
|
||||
""" Forward function.
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
"""
|
||||
B, L, C = x.shape
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
# padding
|
||||
pad_input = (H % 2 == 1) or (W % 2 == 1)
|
||||
if pad_input:
|
||||
x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
|
||||
|
||||
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
|
||||
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
|
||||
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
|
||||
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
|
||||
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
|
||||
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
|
||||
|
||||
x = self.norm(x)
|
||||
x = self.reduction(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class BasicLayer(nn.Module):
|
||||
""" A basic Swin Transformer layer for one stage.
|
||||
Args:
|
||||
dim (int): Number of feature channels
|
||||
depth (int): Depths of this stage.
|
||||
num_heads (int): Number of attention head.
|
||||
window_size (int): Local window size. Default: 7.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
dim,
|
||||
depth,
|
||||
num_heads,
|
||||
window_size=7,
|
||||
mlp_ratio=4.,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop=0.,
|
||||
attn_drop=0.,
|
||||
drop_path=0.,
|
||||
norm_layer=nn.LayerNorm,
|
||||
downsample=None,
|
||||
use_checkpoint=False):
|
||||
super().__init__()
|
||||
self.window_size = window_size
|
||||
self.shift_size = window_size // 2
|
||||
self.depth = depth
|
||||
self.use_checkpoint = use_checkpoint
|
||||
|
||||
# build blocks
|
||||
self.blocks = nn.ModuleList([
|
||||
SwinTransformerBlock(
|
||||
dim=dim,
|
||||
num_heads=num_heads,
|
||||
window_size=window_size,
|
||||
shift_size=0 if (i % 2 == 0) else window_size // 2,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop,
|
||||
attn_drop=attn_drop,
|
||||
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
|
||||
norm_layer=norm_layer)
|
||||
for i in range(depth)])
|
||||
|
||||
# patch merging layer
|
||||
if downsample is not None:
|
||||
self.downsample = downsample(dim=dim, norm_layer=norm_layer)
|
||||
else:
|
||||
self.downsample = None
|
||||
|
||||
def forward(self, x, H, W):
|
||||
""" Forward function.
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
"""
|
||||
|
||||
# calculate attention mask for SW-MSA
|
||||
Hp = int(np.ceil(H / self.window_size)) * self.window_size
|
||||
Wp = int(np.ceil(W / self.window_size)) * self.window_size
|
||||
img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device, dtype=x.dtype) # 1 Hp Wp 1
|
||||
h_slices = (slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None))
|
||||
w_slices = (slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None))
|
||||
cnt = 0
|
||||
for h in h_slices:
|
||||
for w in w_slices:
|
||||
img_mask[:, h, w, :] = cnt
|
||||
cnt += 1
|
||||
|
||||
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
|
||||
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
|
||||
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
||||
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
|
||||
|
||||
for blk in self.blocks:
|
||||
blk.H, blk.W = H, W
|
||||
if self.use_checkpoint:
|
||||
x = checkpoint.checkpoint(blk, x, attn_mask)
|
||||
else:
|
||||
x = blk(x, attn_mask)
|
||||
if self.downsample is not None:
|
||||
x_down = self.downsample(x, H, W)
|
||||
Wh, Ww = (H + 1) // 2, (W + 1) // 2
|
||||
return x, H, W, x_down, Wh, Ww
|
||||
else:
|
||||
return x, H, W, x, H, W
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
""" Image to Patch Embedding
|
||||
Args:
|
||||
patch_size (int): Patch token size. Default: 4.
|
||||
in_chans (int): Number of input image channels. Default: 3.
|
||||
embed_dim (int): Number of linear projection output channels. Default: 96.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: None
|
||||
"""
|
||||
|
||||
def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
|
||||
super().__init__()
|
||||
patch_size = to_2tuple(patch_size)
|
||||
self.patch_size = patch_size
|
||||
|
||||
self.in_chans = in_chans
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
|
||||
if norm_layer is not None:
|
||||
self.norm = norm_layer(embed_dim)
|
||||
else:
|
||||
self.norm = None
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward function."""
|
||||
# padding
|
||||
_, _, H, W = x.size()
|
||||
if W % self.patch_size[1] != 0:
|
||||
x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
|
||||
if H % self.patch_size[0] != 0:
|
||||
x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
|
||||
|
||||
x = self.proj(x) # B C Wh Ww
|
||||
if self.norm is not None:
|
||||
Wh, Ww = x.size(2), x.size(3)
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.norm(x)
|
||||
x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class SwinTransformer(nn.Module):
|
||||
""" Swin Transformer backbone.
|
||||
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
|
||||
https://arxiv.org/pdf/2103.14030
|
||||
Args:
|
||||
pretrain_img_size (int): Input image size for training the pretrained model,
|
||||
used in absolute postion embedding. Default 224.
|
||||
patch_size (int | tuple(int)): Patch size. Default: 4.
|
||||
in_chans (int): Number of input image channels. Default: 3.
|
||||
embed_dim (int): Number of linear projection output channels. Default: 96.
|
||||
depths (tuple[int]): Depths of each Swin Transformer stage.
|
||||
num_heads (tuple[int]): Number of attention head of each stage.
|
||||
window_size (int): Window size. Default: 7.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
||||
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop_rate (float): Dropout rate.
|
||||
attn_drop_rate (float): Attention dropout rate. Default: 0.
|
||||
drop_path_rate (float): Stochastic depth rate. Default: 0.2.
|
||||
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
|
||||
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
|
||||
patch_norm (bool): If True, add normalization after patch embedding. Default: True.
|
||||
out_indices (Sequence[int]): Output from which stages.
|
||||
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
|
||||
-1 means not freezing any parameters.
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
pretrain_img_size=224,
|
||||
patch_size=4,
|
||||
in_chans=3,
|
||||
embed_dim=96,
|
||||
depths=[2, 2, 6, 2],
|
||||
num_heads=[3, 6, 12, 24],
|
||||
window_size=7,
|
||||
mlp_ratio=4.,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop_rate=0.,
|
||||
attn_drop_rate=0.,
|
||||
drop_path_rate=0.2,
|
||||
norm_layer=nn.LayerNorm,
|
||||
ape=False,
|
||||
patch_norm=True,
|
||||
out_indices=(0, 1, 2, 3),
|
||||
frozen_stages=-1,
|
||||
use_checkpoint=False):
|
||||
super().__init__()
|
||||
|
||||
self.pretrain_img_size = pretrain_img_size
|
||||
self.num_layers = len(depths)
|
||||
self.embed_dim = embed_dim
|
||||
self.ape = ape
|
||||
self.patch_norm = patch_norm
|
||||
self.out_indices = out_indices
|
||||
self.frozen_stages = frozen_stages
|
||||
|
||||
# split image into non-overlapping patches
|
||||
self.patch_embed = PatchEmbed(
|
||||
patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
|
||||
norm_layer=norm_layer if self.patch_norm else None)
|
||||
|
||||
# absolute position embedding
|
||||
if self.ape:
|
||||
pretrain_img_size = to_2tuple(pretrain_img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
|
||||
|
||||
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
|
||||
trunc_normal_(self.absolute_pos_embed, std=.02)
|
||||
|
||||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||||
|
||||
# stochastic depth
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
||||
|
||||
# build layers
|
||||
self.layers = nn.ModuleList()
|
||||
for i_layer in range(self.num_layers):
|
||||
layer = BasicLayer(
|
||||
dim=int(embed_dim * 2 ** i_layer),
|
||||
depth=depths[i_layer],
|
||||
num_heads=num_heads[i_layer],
|
||||
window_size=window_size,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop_rate,
|
||||
attn_drop=attn_drop_rate,
|
||||
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
|
||||
norm_layer=norm_layer,
|
||||
downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
|
||||
use_checkpoint=use_checkpoint)
|
||||
self.layers.append(layer)
|
||||
|
||||
num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
|
||||
self.num_features = num_features
|
||||
|
||||
# add a norm layer for each output
|
||||
for i_layer in out_indices:
|
||||
layer = norm_layer(num_features[i_layer])
|
||||
layer_name = f'norm{i_layer}'
|
||||
self.add_module(layer_name, layer)
|
||||
|
||||
self._freeze_stages()
|
||||
|
||||
def _freeze_stages(self):
|
||||
if self.frozen_stages >= 0:
|
||||
self.patch_embed.eval()
|
||||
for param in self.patch_embed.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
if self.frozen_stages >= 1 and self.ape:
|
||||
self.absolute_pos_embed.requires_grad = False
|
||||
|
||||
if self.frozen_stages >= 2:
|
||||
self.pos_drop.eval()
|
||||
for i in range(0, self.frozen_stages - 1):
|
||||
m = self.layers[i]
|
||||
m.eval()
|
||||
for param in m.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward function."""
|
||||
x = self.patch_embed(x)
|
||||
|
||||
Wh, Ww = x.size(2), x.size(3)
|
||||
if self.ape:
|
||||
# interpolate the position embedding to the corresponding size
|
||||
absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
|
||||
x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C
|
||||
else:
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.pos_drop(x)
|
||||
|
||||
outs = []
|
||||
for i in range(self.num_layers):
|
||||
layer = self.layers[i]
|
||||
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
|
||||
|
||||
if i in self.out_indices:
|
||||
norm_layer = getattr(self, f'norm{i}')
|
||||
x_out = norm_layer(x_out)
|
||||
|
||||
out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(out)
|
||||
|
||||
outputs = {
|
||||
'res2' : outs[0],
|
||||
'res3' : outs[1],
|
||||
'res4' : outs[2],
|
||||
'res5' : outs[3],}
|
||||
return outputs
|
||||
|
||||
def train(self, mode=True):
|
||||
"""Convert the model into training mode while keep layers freezed."""
|
||||
super(SwinTransformer, self).train(mode)
|
||||
self._freeze_stages()
|
||||
return self
|
||||
@@ -0,0 +1,36 @@
|
||||
import os
|
||||
|
||||
from safetensors.torch import load_file
|
||||
|
||||
from folder_paths import models_dir
|
||||
|
||||
from ..modules.seecoder import SemanticContextEncoder
|
||||
|
||||
|
||||
SEECODER_PATH = os.path.join(models_dir, 'seecoder')
|
||||
os.makedirs(SEECODER_PATH, exist_ok=True)
|
||||
|
||||
|
||||
class LoadSeeCoderNode:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "checkpoint": (os.listdir(SEECODER_PATH), )}}
|
||||
|
||||
RETURN_TYPES = ("SEECODER",)
|
||||
FUNCTION = "load"
|
||||
|
||||
CATEGORY = "seecoder"
|
||||
|
||||
def load(self, checkpoint):
|
||||
checkpoint_path = os.path.join(SEECODER_PATH, checkpoint)
|
||||
state_dict = load_file(checkpoint_path)
|
||||
cleaned_state_dict = {}
|
||||
has_pe = False
|
||||
for key in state_dict.keys():
|
||||
cleaned_key = key.replace('ctx.image.', '')
|
||||
if 'pe_layer' in cleaned_key:
|
||||
has_pe = True
|
||||
cleaned_state_dict[cleaned_key] = state_dict[key]
|
||||
seecoder = SemanticContextEncoder(has_pe)
|
||||
seecoder.load_state_dict(cleaned_state_dict)
|
||||
return (seecoder,)
|
||||
@@ -0,0 +1,25 @@
|
||||
import os
|
||||
|
||||
import torch
|
||||
|
||||
from folder_paths import models_dir
|
||||
|
||||
|
||||
SEECODER_PATH = os.path.join(models_dir, 'seecoder')
|
||||
os.makedirs(SEECODER_PATH, exist_ok=True)
|
||||
|
||||
|
||||
class LoadSeeCoderUncondNode:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "pth": (os.listdir(SEECODER_PATH), )}}
|
||||
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "load"
|
||||
|
||||
CATEGORY = "seecoder"
|
||||
|
||||
def load(self, pth):
|
||||
checkpoint_path = os.path.join(SEECODER_PATH, pth)
|
||||
conditioning = torch.load(checkpoint_path).unsqueeze(0)
|
||||
return ([[conditioning, {}]],)
|
||||
@@ -0,0 +1,29 @@
|
||||
import comfy.model_management
|
||||
|
||||
|
||||
class SeecoderEncodeNode:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"seecoder": ("SEECODER",),
|
||||
"image": ("IMAGE",),
|
||||
},
|
||||
}
|
||||
RETURN_TYPES = ("CONDITIONING",)
|
||||
FUNCTION = "encode"
|
||||
CATEGORY = "seecoder"
|
||||
|
||||
def encode(self,
|
||||
seecoder,
|
||||
image):
|
||||
|
||||
intermediate_device = comfy.model_management.intermediate_device()
|
||||
torch_device = comfy.model_management.get_torch_device()
|
||||
|
||||
seecoder.to(torch_device)
|
||||
# latent = latent_image['samples'].to(torch_device)
|
||||
embed = seecoder(image.permute(0,3,1,2).to(torch_device)).to(intermediate_device)
|
||||
seecoder.to(intermediate_device)
|
||||
|
||||
return ([[embed, {}]], )
|
||||
Reference in New Issue
Block a user