ip_adapter/style_encoder.py

import torch
import torch.nn as nn
import numpy as np
from collections import OrderedDict


def conv_nd(dims, *args, **kwargs):
 """
 Create a 1D, 2D, or 3D convolution module.
 """
 if dims == 1:
 return nn.Conv1d(*args, **kwargs)
 elif dims == 2:
 return nn.Conv2d(*args, **kwargs)
 elif dims == 3:
 return nn.Conv3d(*args, **kwargs)
 raise ValueError(f"unsupported dimensions: {dims}")


def avg_pool_nd(dims, *args, **kwargs):
 """
 Create a 1D, 2D, or 3D average pooling module.
 """
 if dims == 1:
 return nn.AvgPool1d(*args, **kwargs)
 elif dims == 2:
 return nn.AvgPool2d(*args, **kwargs)
 elif dims == 3:
 return nn.AvgPool3d(*args, **kwargs)
 raise ValueError(f"unsupported dimensions: {dims}")

def get_parameter_dtype(parameter: torch.nn.Module):
 try:
 params = tuple(parameter.parameters())
 if len(params) > 0:
 return params[0].dtype

 buffers = tuple(parameter.buffers())
 if len(buffers) > 0:
 return buffers[0].dtype

 except StopIteration:
 # For torch.nn.DataParallel compatibility in PyTorch 1.5

 def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, Tensor]]:
 tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
 return tuples

 gen = parameter._named_members(get_members_fn=find_tensor_attributes)
 first_tuple = next(gen)
 return first_tuple[1].dtype

class Downsample(nn.Module):
 """
 A downsampling layer with an optional convolution.
 :param channels: channels in the inputs and outputs.
 :param use_conv: a bool determining if a convolution is applied.
 :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
 downsampling occurs in the inner-two dimensions.
 """

 def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
 super().__init__()
 self.channels = channels
 self.out_channels = out_channels or channels
 self.use_conv = use_conv
 self.dims = dims
 stride = 2 if dims != 3 else (1, 2, 2)
 if use_conv:
 self.op = conv_nd(dims, self.channels, self.out_channels, 3, stride=stride, padding=padding)
 else:
 assert self.channels == self.out_channels
 self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)

 def forward(self, x):
 assert x.shape[1] == self.channels
 return self.op(x)


class ResnetBlock(nn.Module):

 def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):
 super().__init__()
 ps = ksize // 2
 if in_c != out_c or sk == False:
 self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps)
 else:
 self.in_conv = None
 self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1)
 self.act = nn.ReLU()
 self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps)
 if sk == False:
 self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps)
 else:
 self.skep = None

 self.down = down
 if self.down == True:
 self.down_opt = Downsample(in_c, use_conv=use_conv)

 def forward(self, x):
 if self.down == True:
 x = self.down_opt(x)
 if self.in_conv is not None: # edit
 x = self.in_conv(x)

 h = self.block1(x)
 h = self.act(h)
 h = self.block2(h)
 if self.skep is not None:
 return h + self.skep(x)
 else:
 return h + x

class Low_CNN(nn.Module):
 def __init__(self, cin=192, ksize=1, sk=False, use_conv=True):
 super(Low_CNN, self).__init__()
 self.unshuffle = nn.PixelUnshuffle(8)
 self.body = nn.Sequential(ResnetBlock(320, 320, down=False, ksize=ksize, sk=sk, use_conv=use_conv),
 ResnetBlock(320, 640, down=False, ksize=ksize, sk=sk, use_conv=use_conv),
 ResnetBlock(640, 1280, down=True, ksize=ksize, sk=sk, use_conv=use_conv),
 ResnetBlock(1280, 1280, down=False, ksize=ksize, sk=sk, use_conv=use_conv))
 self.conv_in = nn.Conv2d(cin, 320, 3, 1, 1)
 self.pool = nn.AvgPool2d(kernel_size=2, stride=2)
 self.adapter = nn.Linear(1280, 1280)

 @property
 def dtype(self) -> torch.dtype:
 """
 `torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
 """
 return get_parameter_dtype(self)

 def forward(self, x):
 x = self.unshuffle(x)
 x = self.conv_in(x)
 x = self.body(x)
 x = self.pool(x)
 x = x.flatten(start_dim=1, end_dim=-1)
 x = self.adapter(x)
 return x


class Middle_CNN(nn.Module):
 def __init__(self, cin=192, ksize=1, sk=False, use_conv=True):
 super(Middle_CNN, self).__init__()
 self.unshuffle = nn.PixelUnshuffle(8)
 self.body = nn.Sequential(ResnetBlock(320, 320, down=False, ksize=ksize, sk=sk, use_conv=use_conv),
 ResnetBlock(320, 640, down=False, ksize=ksize, sk=sk, use_conv=use_conv),
 ResnetBlock(640, 640, down=True, ksize=ksize, sk=sk, use_conv=use_conv),
 ResnetBlock(640, 1280, down=True, ksize=ksize, sk=sk, use_conv=use_conv),
 ResnetBlock(1280, 1280, down=False, ksize=ksize, sk=sk, use_conv=use_conv))
 self.conv_in = nn.Conv2d(cin, 320, 3, 1, 1)
 self.pool = nn.AvgPool2d(kernel_size=2, stride=2)
 self.adapter = nn.Linear(1280, 1280)

 @property
 def dtype(self) -> torch.dtype:
 """
 `torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
 """
 return get_parameter_dtype(self)

 def forward(self, x):
 x = self.unshuffle(x)
 x = self.conv_in(x)
 x = self.body(x)
 x = self.pool(x)
 x = x.flatten(start_dim=1, end_dim=-1)
 x = self.adapter(x)
 return x


class High_CNN(nn.Module):
 def __init__(self, cin=192, ksize=1, sk=False, use_conv=True):
 super(High_CNN, self).__init__()
 self.unshuffle = nn.PixelUnshuffle(8)
 self.body = nn.Sequential(ResnetBlock(320, 320, down=False, ksize=ksize, sk=sk, use_conv=use_conv),
 ResnetBlock(320, 640, down=False, ksize=ksize, sk=sk, use_conv=use_conv),
 ResnetBlock(640, 640, down=True, ksize=ksize, sk=sk, use_conv=use_conv),
 ResnetBlock(640, 640, down=True, ksize=ksize, sk=sk, use_conv=use_conv),
 ResnetBlock(640, 1280, down=True, ksize=ksize, sk=sk, use_conv=use_conv),
 ResnetBlock(1280, 1280, down=False, ksize=ksize, sk=sk, use_conv=use_conv))
 self.conv_in = nn.Conv2d(cin, 320, 3, 1, 1)
 self.pool = nn.AvgPool2d(kernel_size=2, stride=2)
 self.adapter = nn.Linear(1280, 1280)

 @property
 def dtype(self) -> torch.dtype:
 """
 `torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
 """
 return get_parameter_dtype(self)

 def forward(self, x):
 x = self.unshuffle(x)
 x = self.conv_in(x)
 x = self.body(x)
 x = self.pool(x)
 x = x.flatten(start_dim=1, end_dim=-1)
 x = self.adapter(x)
 return x


class Style_Aware_Encoder(torch.nn.Module):
 def __init__(self, image_encoder):
 super().__init__()
 self.image_encoder = image_encoder
 self.projection_dim = self.image_encoder.config.projection_dim
 self.num_positions = 59
 self.embed_dim = 1280
 self.cnn = nn.ModuleList(
 [High_CNN(sk=True, use_conv=False),
 Middle_CNN(sk=True, use_conv=False),
 Low_CNN(sk=True, use_conv=False)]
 )
 self.style_embeddings = nn.ParameterList(
 [nn.Parameter(torch.randn(self.embed_dim)),
 nn.Parameter(torch.randn(self.embed_dim)),
 nn.Parameter(torch.randn(self.embed_dim))]
 )
 self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
 self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)

 def forward(self, inputs, batch_size=1):
 embeddings = []
 for idx, x in enumerate(inputs):
 class_embed = self.style_embeddings[idx].expand(batch_size, 1, -1)
 patch_embed = self.cnn[idx](x)
 patch_embed = patch_embed.view(batch_size, -1, patch_embed.shape[1])
 embedding = torch.cat([class_embed, patch_embed], dim=1)
 embeddings.append(embedding)
 embeddings = torch.cat(embeddings, dim=1)
 embeddings = embeddings + self.position_embedding(self.position_ids) # [B, 256, 1280] - [B, P, 1280]
 embeddings = self.image_encoder.vision_model.pre_layrnorm(embeddings)
 encoder_outputs = self.image_encoder.vision_model.encoder(
 inputs_embeds=embeddings,
 output_attentions=None,
 output_hidden_states=None,
 return_dict=None,
 )
 last_hidden_state = encoder_outputs[0]
 pooled_output = last_hidden_state[:, [0, 9, 26], :]
 pooled_output = self.image_encoder.vision_model.post_layernorm(pooled_output)
 out = self.image_encoder.visual_projection(pooled_output)
 return out