Python FineTune the Pixel2Style2Pixel model with my custom data set

[btb4198]

I want to fine-tune the Pixel2Style2Pixel model with my custom data set, but I keep getting an error when I'm trying to load in the pre-train weights. Here is my code :

[CODE lang="python" title="fine-tune the Pixel2Style2Pixel model"]# Load the pre-trained model
os.chdir("/content/pixel2style2pixel")
from models.psp import pSp

config = {
"lr": 0.0001,
"betas": (0.9, 0.999),
"weight_decay": 1e-6,
"stylegan_size": 1024,
"checkpoint_path": MODEL_PATH,
"device": DEVICE,
"input_nc": 3, # Assuming 3 input channels
"output_size": 256, # Add the missing attribute
"encoder_type": 'GradualStyleEncoder', # Add the missing attribute
}

config["checkpoint_path"] = "/content/pixel2style2pixel/pretrained_models/psp_ffhq_encode.pt"
updated_config = config.copy()
updated_config['n_styles'] = 16
config_object = SimpleNamespace(**updated_config)
from pixel2style2pixel.models.psp import pSp

def get_keys(d, name):
if 'state_dict' in d:
d = d['state_dict']
d_filt = {k[len(name) + 1:]: v for k, v in d.items() if k.startswith(name)}
return d_filt

class CustompSp(pSp):
def __init__(self, opts):
super().__init__(opts)
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

def load_weights(self):
ckpt = torch.load(self.opts.checkpoint_path, map_location=self.device)

filtered_state_dict = {k: v for k, v in ckpt['state_dict'].items() if 'encoder' in k}
self.encoder.load_state_dict(filtered_state_dict, strict=False)

decoder_state_dict = {k.replace('module.', ''): v for k, v in ckpt['state_dict'].items() if 'decoder' in k}
self.decoder.load_state_dict(decoder_state_dict, strict=False)

self.load_latent_avg(ckpt)

def load_latent_avg(self, ckpt):
if 'latent_avg' in ckpt:
self.latent_avg = ckpt['latent_avg'].to(self.device)
else:
self.latent_avg = None

model = CustompSp(config_object)
model.train()
model.to(DEVICE)[/CODE]I am getting this error: AttributeError: 'CustompSp' object has no attribute 'device'
[CODE title="Full error message"]---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-71-36ef46aa58b7> in <cell line: 1>()
----> 1 model = CustompSp(config_object)
2 model.train()
3 model.to(DEVICE)

3 frames
/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py in __getattr__(self, name)
1612 if name in modules:
1613 return modules[name]
-> 1614 raise AttributeError("'{}' object has no attribute '{}'".format(
1615 type(self).__name__, name))
1616

AttributeError: 'CustompSp' object has no attribute 'device'[/CODE]

 

[Mark44]

Your custompSp class inherits from the pSp class. Does the pSp class (which isn't shown in the code you provided) have a device attribute?

 

[btb4198]

Sorry, I am posting it now:
[CODE title="psp.py"]"""
This file defines the core research contribution
"""
import matplotlib
matplotlib.use('Agg')
import math

import torch
from torch import nn
from models.encoders import psp_encoders
from models.stylegan2.model import Generator
from configs.paths_config import model_pathsdef get_keys(d, name):
if 'state_dict' in d:
d = d['state_dict']
d_filt = {k[len(name) + 1:]: v for k, v in d.items() if k[:len(name)] == name}
return d_filtclass pSp(nn.Module):

def __init__(self, opts):
super(pSp, self).__init__()
self.set_opts(opts)
# compute number of style inputs based on the output resolution
self.opts.n_styles = int(math.log(self.opts.output_size, 2)) * 2 - 2
# Define architecture
self.encoder = self.set_encoder()
self.decoder = Generator(self.opts.output_size, 512, 8)
self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256))
# Load weights if needed
self.load_weights()

def set_encoder(self):
if self.opts.encoder_type == 'GradualStyleEncoder':
encoder = psp_encoders.GradualStyleEncoder(50, 'ir_se', self.opts)
elif self.opts.encoder_type == 'BackboneEncoderUsingLastLayerIntoW':
encoder = psp_encoders.BackboneEncoderUsingLastLayerIntoW(50, 'ir_se', self.opts)
elif self.opts.encoder_type == 'BackboneEncoderUsingLastLayerIntoWPlus':
encoder = psp_encoders.BackboneEncoderUsingLastLayerIntoWPlus(50, 'ir_se', self.opts)
else:
raise Exception('{} is not a valid encoders'.format(self.opts.encoder_type))
return encoder

def load_weights(self):
if self.opts.checkpoint_path is not None:
print('Loading pSp from checkpoint: {}'.format(self.opts.checkpoint_path))
ckpt = torch.load(self.opts.checkpoint_path, map_location='cpu')
self.encoder.load_state_dict(get_keys(ckpt, 'encoder'), strict=True)
self.decoder.load_state_dict(get_keys(ckpt, 'decoder'), strict=True)
self.__load_latent_avg(ckpt)
else:
print('Loading encoders weights from irse50!')
encoder_ckpt = torch.load(model_paths['ir_se50'])
# if input to encoder is not an RGB image, do not load the input layer weights
if self.opts.label_nc != 0:
encoder_ckpt = {k: v for k, v in encoder_ckpt.items() if "input_layer" not in k}
self.encoder.load_state_dict(encoder_ckpt, strict=False)
print('Loading decoder weights from pretrained!')
ckpt = torch.load(self.opts.stylegan_weights)
self.decoder.load_state_dict(ckpt['g_ema'], strict=False)
if self.opts.learn_in_w:
self.__load_latent_avg(ckpt, repeat=1)
else:
self.__load_latent_avg(ckpt, repeat=self.opts.n_styles)

def forward(self, x, resize=True, latent_mask=None, input_code=False, randomize_noise=True,
inject_latent=None, return_latents=False, alpha=None):
if input_code:
codes = x
else:
codes = self.encoder(x)
# normalize with respect to the center of an average face
if self.opts.start_from_latent_avg:
if self.opts.learn_in_w:
codes = codes + self.latent_avg.repeat(codes.shape[0], 1)
else:
codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1) if latent_mask is not None:
for i in latent_mask:
if inject_latent is not None:
if alpha is not None:
codes[:, i] = alpha * inject_latent[:, i] + (1 - alpha) * codes[:, i]
else:
codes[:, i] = inject_latent[:, i]
else:
codes[:, i] = 0

input_is_latent = not input_code
images, result_latent = self.decoder([codes],
input_is_latent=input_is_latent,
randomize_noise=randomize_noise,
return_latents=return_latents)

if resize:
images = self.face_pool(images)

if return_latents:
return images, result_latent
else:
return images

def set_opts(self, opts):
self.opts = opts

def __load_latent_avg(self, ckpt, repeat=None):
if 'latent_avg' in ckpt:
self.latent_avg = ckpt['latent_avg'].to(self.opts.device)
if repeat is not None:
self.latent_avg = self.latent_avg.repeat(repeat, 1)
else:
self.latent_avg = None[/CODE]

You are correct that the pSp class does not have a device attribute. So I modify the CustompSp class by adding a device attribute as follows:

[CODE title="updated CustompSp class"]class CustompSp(pSp):
def __init__(self, opts):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
super().__init__(opts)

def load_weights(self):
ckpt = torch.load(self.opts.checkpoint_path, map_location=self.device)

filtered_state_dict = {k: v for k, v in ckpt['state_dict'].items() if 'encoder' in k}
self.encoder.load_state_dict(filtered_state_dict, strict=False)

decoder_state_dict = {k.replace('module.', ''): v for k, v in ckpt['state_dict'].items() if 'decoder' in k}
self.decoder.load_state_dict(decoder_state_dict, strict=False)

self.load_latent_avg(ckpt)

def load_latent_avg(self, ckpt):
if 'latent_avg' in ckpt:
self.latent_avg = ckpt['latent_avg'].to(self.device)
else:
self.latent_avg = None
[/CODE]Anyhow it seemed to work

 

[Mark44]

You are correct that the pSp class does not have a device attribute.

I didn't say that the pSp class was missing a device attribute. I just asked whether the pSp class had such an attribute. If you get an error that such-and-such attribute is missing, an obvious thing to do is to see if that attribute is present in the class or any super class.

So I modify the CustompSp class by adding a device attribute as follows:

Anyhow it seemed to work

What a surprise...