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import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import sys
from ldm.util import instantiate_from_config
from transformers.models.clip.modeling_clip import CLIPTextModel
from omegaconf import OmegaConf
from lib.mask_predictor import SimpleDecoding
from evp.models import UNetWrapper, TextAdapterRefer
def icnr(x, scale=2, init=nn.init.kaiming_normal_):
"""
Checkerboard artifact free sub-pixel convolution
https://arxiv.org/abs/1707.02937
"""
ni,nf,h,w = x.shape
ni2 = int(ni/(scale**2))
k = init(torch.zeros([ni2,nf,h,w])).transpose(0, 1)
k = k.contiguous().view(ni2, nf, -1)
k = k.repeat(1, 1, scale**2)
k = k.contiguous().view([nf,ni,h,w]).transpose(0, 1)
x.data.copy_(k)
class PixelShuffle(nn.Module):
"""
Real-Time Single Image and Video Super-Resolution
https://arxiv.org/abs/1609.05158
"""
def __init__(self, n_channels, scale):
super(PixelShuffle, self).__init__()
self.conv = nn.Conv2d(n_channels, n_channels*(scale**2), kernel_size=1)
icnr(self.conv.weight)
self.shuf = nn.PixelShuffle(scale)
self.relu = nn.ReLU()
def forward(self,x):
x = self.shuf(self.relu(self.conv(x)))
return x
class AttentionModule(nn.Module):
def __init__(self, in_channels, out_channels):
super(AttentionModule, self).__init__()
# Convolutional Layers
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
# Group Normalization
self.group_norm = nn.GroupNorm(20, out_channels)
# ReLU Activation
self.relu = nn.ReLU()
# Spatial Attention
self.spatial_attention = nn.Sequential(
nn.Conv2d(in_channels, 1, kernel_size=1),
nn.Sigmoid()
)
def forward(self, x):
# Apply spatial attention
spatial_attention = self.spatial_attention(x)
x = x * spatial_attention
# Apply convolutional layer
x = self.conv1(x)
x = self.group_norm(x)
x = self.relu(x)
return x
class AttentionDownsamplingModule(nn.Module):
def __init__(self, in_channels, out_channels, scale_factor=2):
super(AttentionDownsamplingModule, self).__init__()
# Spatial Attention
self.spatial_attention = nn.Sequential(
nn.Conv2d(in_channels, 1, kernel_size=1),
nn.Sigmoid()
)
# Channel Attention
self.channel_attention = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels, in_channels // 8, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels // 8, in_channels, kernel_size=1),
nn.Sigmoid()
)
# Convolutional Layers
if scale_factor == 2:
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
elif scale_factor == 4:
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=2, padding=1)
# Group Normalization
self.group_norm = nn.GroupNorm(20, out_channels)
# ReLU Activation
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
# Apply spatial attention
spatial_attention = self.spatial_attention(x)
x = x * spatial_attention
# Apply channel attention
channel_attention = self.channel_attention(x)
x = x * channel_attention
# Apply convolutional layers
x = self.conv1(x)
x = self.group_norm(x)
x = self.relu(x)
x = self.conv2(x)
x = self.group_norm(x)
x = self.relu(x)
return x
class AttentionUpsamplingModule(nn.Module):
def __init__(self, in_channels, out_channels):
super(AttentionUpsamplingModule, self).__init__()
# Spatial Attention for outs[2]
self.spatial_attention = nn.Sequential(
nn.Conv2d(in_channels, 1, kernel_size=1),
nn.Sigmoid()
)
# Channel Attention for outs[2]
self.channel_attention = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels, in_channels // 8, kernel_size=1),
nn.ReLU(),
nn.Conv2d(in_channels // 8, in_channels, kernel_size=1),
nn.Sigmoid()
)
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
# Group Normalization
self.group_norm = nn.GroupNorm(20, out_channels)
# ReLU Activation
self.relu = nn.ReLU()
self.upscale = PixelShuffle(in_channels, 2)
def forward(self, x):
# Apply spatial attention
spatial_attention = self.spatial_attention(x)
x = x * spatial_attention
# Apply channel attention
channel_attention = self.channel_attention(x)
x = x * channel_attention
# Apply convolutional layers
x = self.conv1(x)
x = self.group_norm(x)
x = self.relu(x)
x = self.conv2(x)
x = self.group_norm(x)
x = self.relu(x)
# Upsample
x = self.upscale(x)
return x
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConvLayer, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 1),
nn.GroupNorm(20, out_channels),
nn.ReLU(),
)
def forward(self, x):
x = self.conv1(x)
return x
class InverseMultiAttentiveFeatureRefinement(nn.Module):
def __init__(self, in_channels_list):
super(InverseMultiAttentiveFeatureRefinement, self).__init__()
self.layer1 = AttentionModule(in_channels_list[0], in_channels_list[0])
self.layer2 = AttentionDownsamplingModule(in_channels_list[0], in_channels_list[0]//2, scale_factor = 2)
self.layer3 = ConvLayer(in_channels_list[0]//2 + in_channels_list[1], in_channels_list[1])
self.layer4 = AttentionDownsamplingModule(in_channels_list[1], in_channels_list[1]//2, scale_factor = 2)
self.layer5 = ConvLayer(in_channels_list[1]//2 + in_channels_list[2], in_channels_list[2])
self.layer6 = AttentionDownsamplingModule(in_channels_list[2], in_channels_list[2]//2, scale_factor = 2)
self.layer7 = ConvLayer(in_channels_list[2]//2 + in_channels_list[3], in_channels_list[3])
'''
self.layer8 = AttentionUpsamplingModule(in_channels_list[3], in_channels_list[3])
self.layer9 = ConvLayer(in_channels_list[2] + in_channels_list[3], in_channels_list[2])
self.layer10 = AttentionUpsamplingModule(in_channels_list[2], in_channels_list[2])
self.layer11 = ConvLayer(in_channels_list[1] + in_channels_list[2], in_channels_list[1])
self.layer12 = AttentionUpsamplingModule(in_channels_list[1], in_channels_list[1])
self.layer13 = ConvLayer(in_channels_list[0] + in_channels_list[1], in_channels_list[0])
'''
def forward(self, inputs):
x_c4, x_c3, x_c2, x_c1 = inputs
x_c4 = self.layer1(x_c4)
x_c4_3 = self.layer2(x_c4)
x_c3 = torch.cat([x_c4_3, x_c3], dim=1)
x_c3 = self.layer3(x_c3)
x_c3_2 = self.layer4(x_c3)
x_c2 = torch.cat([x_c3_2, x_c2], dim=1)
x_c2 = self.layer5(x_c2)
x_c2_1 = self.layer6(x_c2)
x_c1 = torch.cat([x_c2_1, x_c1], dim=1)
x_c1 = self.layer7(x_c1)
'''
x_c1_2 = self.layer8(x_c1)
x_c2 = torch.cat([x_c1_2, x_c2], dim=1)
x_c2 = self.layer9(x_c2)
x_c2_3 = self.layer10(x_c2)
x_c3 = torch.cat([x_c2_3, x_c3], dim=1)
x_c3 = self.layer11(x_c3)
x_c3_4 = self.layer12(x_c3)
x_c4 = torch.cat([x_c3_4, x_c4], dim=1)
x_c4 = self.layer13(x_c4)
'''
return [x_c4, x_c3, x_c2, x_c1]
class EVPRefer(nn.Module):
"""Encoder Decoder segmentors.
EncoderDecoder typically consists of backbone, decode_head, auxiliary_head.
Note that auxiliary_head is only used for deep supervision during training,
which could be dumped during inference.
"""
def __init__(self,
sd_path=None,
base_size=512,
token_embed_dim=768,
neck_dim=[320,680,1320,1280],
**args):
super().__init__()
config = OmegaConf.load('./v1-inference.yaml')
if os.path.exists(f'{sd_path}'):
config.model.params.ckpt_path = f'{sd_path}'
else:
config.model.params.ckpt_path = None
sd_model = instantiate_from_config(config.model)
self.encoder_vq = sd_model.first_stage_model
self.unet = UNetWrapper(sd_model.model, base_size=base_size)
del sd_model.cond_stage_model
del self.encoder_vq.decoder
for param in self.encoder_vq.parameters():
param.requires_grad = True
self.text_adapter = TextAdapterRefer(text_dim=token_embed_dim)
self.classifier = SimpleDecoding(dims=neck_dim)
self.gamma = nn.Parameter(torch.ones(token_embed_dim) * 1e-4)
self.aggregation = InverseMultiAttentiveFeatureRefinement([320,680,1320,1280])
self.clip_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
for param in self.clip_model.parameters():
param.requires_grad = True
def forward(self, img, sentences):
input_shape = img.shape[-2:]
latents = self.encoder_vq.encode(img).mode()
latents = latents / 4.7164
l_feats = self.clip_model(input_ids=sentences).last_hidden_state
c_crossattn = self.text_adapter(latents, l_feats, self.gamma) # NOTE: here the c_crossattn should be expand_dim as latents
t = torch.ones((img.shape[0],), device=img.device).long()
outs = self.unet(latents, t, c_crossattn=[c_crossattn])
outs = self.aggregation(outs)
x_c1, x_c2, x_c3, x_c4 = outs
x = self.classifier(x_c4, x_c3, x_c2, x_c1)
x = F.interpolate(x, size=input_shape, mode='bilinear', align_corners=True)
return x
def get_latent(self, x):
return self.encoder_vq.encode(x).mode()
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