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import importlib
import os
import socket
import sys
import ipdb # noqa: F401
import torch
import torch.nn as nn
from omegaconf import OmegaConf
HOSTNAME = socket.gethostname()
if "trinity" in HOSTNAME:
# Might be outdated
config_path = "/home/amylin2/latent-diffusion/configs/autoencoder/autoencoder_kl_16x16x16.yaml"
weights_path = "/home/amylin2/latent-diffusion/model.ckpt"
elif "grogu" in HOSTNAME:
# Might be outdated
config_path = "/home/jasonzh2/code/latent-diffusion/configs/autoencoder/autoencoder_kl_16x16x16.yaml"
weights_path = "/home/jasonzh2/code/latent-diffusion/model.ckpt"
elif "ender" in HOSTNAME:
config_path = "/home/jason/ray_diffusion/external/latent-diffusion/configs/autoencoder/autoencoder_kl_16x16x16.yaml"
weights_path = "/home/jason/ray_diffusion/external/latent-diffusion/model.ckpt"
else:
config_path = None
weights_path = None
if weights_path is not None:
LDM_PATH = os.path.dirname(weights_path)
if LDM_PATH not in sys.path:
sys.path.append(LDM_PATH)
def resize(image, size=None, scale_factor=None):
return nn.functional.interpolate(
image,
size=size,
scale_factor=scale_factor,
mode="bilinear",
align_corners=False,
)
def instantiate_from_config(config):
if "target" not in config:
if config == "__is_first_stage__":
return None
elif config == "__is_unconditional__":
return None
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def get_obj_from_str(string, reload=False):
module, cls = string.rsplit(".", 1)
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
return getattr(importlib.import_module(module, package=None), cls)
class PretrainedVAE(nn.Module):
def __init__(self, freeze_weights=True, num_patches_x=16, num_patches_y=16):
super().__init__()
config = OmegaConf.load(config_path)
self.model = instantiate_from_config(config.model)
self.model.init_from_ckpt(weights_path)
self.model.eval()
self.feature_dim = 16
self.num_patches_x = num_patches_x
self.num_patches_y = num_patches_y
if freeze_weights:
for param in self.model.parameters():
param.requires_grad = False
def forward(self, x, autoresize=False):
"""
Spatial dimensions of output will be H // 16, W // 16. If autoresize is True,
then the input will be resized such that the output feature map is the correct
dimensions.
Args:
x (torch.Tensor): Images (B, C, H, W). Should be normalized to be [-1, 1].
autoresize (bool): Whether to resize the input to match the num_patch
dimensions.
Returns:
torch.Tensor: Latent sample (B, 16, h, w)
"""
*B, c, h, w = x.shape
x = x.reshape(-1, c, h, w)
if autoresize:
new_w = self.num_patches_x * 16
new_h = self.num_patches_y * 16
x = resize(x, size=(new_h, new_w))
decoded, latent = self.model(x)
# A little ambiguous bc it's all 16, but it is (c, h, w)
latent_sample = latent.sample().reshape(
*B, self.feature_dim, self.num_patches_y, self.num_patches_x
)
return latent_sample
activations = {}
def get_activation(name):
def hook(model, input, output):
activations[name] = output
return hook
class SpatialDino(nn.Module):
def __init__(
self,
freeze_weights=True,
model_type="dinov2_vits14",
num_patches_x=16,
num_patches_y=16,
activation_hooks=False,
):
super().__init__()
self.model = torch.hub.load("facebookresearch/dinov2", model_type)
self.feature_dim = self.model.embed_dim
self.num_patches_x = num_patches_x
self.num_patches_y = num_patches_y
if freeze_weights:
for param in self.model.parameters():
param.requires_grad = False
self.activation_hooks = activation_hooks
if self.activation_hooks:
self.model.blocks[5].register_forward_hook(get_activation("encoder1"))
self.model.blocks[11].register_forward_hook(get_activation("encoder2"))
self.activations = activations
def forward(self, x, autoresize=False):
"""
Spatial dimensions of output will be H // 14, W // 14. If autoresize is True,
then the output will be resized to the correct dimensions.
Args:
x (torch.Tensor): Images (B, C, H, W). Should be ImageNet normalized.
autoresize (bool): Whether to resize the input to match the num_patch
dimensions.
Returns:
feature_map (torch.tensor): (B, C, h, w)
"""
*B, c, h, w = x.shape
x = x.reshape(-1, c, h, w)
# if autoresize:
# new_w = self.num_patches_x * 14
# new_h = self.num_patches_y * 14
# x = resize(x, size=(new_h, new_w))
# Output will be (B, H * W, C)
features = self.model.forward_features(x)["x_norm_patchtokens"]
features = features.permute(0, 2, 1)
features = features.reshape( # (B, C, H, W)
-1, self.feature_dim, h // 14, w // 14
)
if autoresize:
features = resize(features, size=(self.num_patches_y, self.num_patches_x))
features = features.reshape(
*B, self.feature_dim, self.num_patches_y, self.num_patches_x
)
return features
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