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# Copyright 2020 Erik Härkönen. All rights reserved.
# This file is licensed to you under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. You may obtain a copy
# of the License at http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under
# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS
# OF ANY KIND, either express or implied. See the License for the specific language
# governing permissions and limitations under the License.
import torch
import numpy as np
import re
import os
import random
from pathlib import Path
from types import SimpleNamespace
from utils import download_ckpt
from config import Config
from netdissect import proggan, zdataset
from . import biggan
from . import stylegan
from . import stylegan2
from abc import abstractmethod, ABC as AbstractBaseClass
from functools import singledispatch
class BaseModel(AbstractBaseClass, torch.nn.Module):
# Set parameters for identifying model from instance
def __init__(self, model_name, class_name):
super(BaseModel, self).__init__()
self.model_name = model_name
self.outclass = class_name
# Stop model evaluation as soon as possible after
# given layer has been executed, used to speed up
# netdissect.InstrumentedModel::retain_layer().
# Validate with tests/partial_forward_test.py
# Can use forward() as fallback at the cost of performance.
@abstractmethod
def partial_forward(self, x, layer_name):
pass
# Generate batch of latent vectors
@abstractmethod
def sample_latent(self, n_samples=1, seed=None, truncation=None):
pass
# Maximum number of latents that can be provided
# Typically one for each layer
def get_max_latents(self):
return 1
# Name of primary latent space
# E.g. StyleGAN can alternatively use W
def latent_space_name(self):
return 'Z'
def get_latent_shape(self):
return tuple(self.sample_latent(1).shape)
def get_latent_dims(self):
return np.prod(self.get_latent_shape())
def set_output_class(self, new_class):
self.outclass = new_class
# Map from typical range [-1, 1] to [0, 1]
def forward(self, x):
out = self.model.forward(x)
return 0.5*(out+1)
# Generate images and convert to numpy
def sample_np(self, z=None, n_samples=1, seed=None):
if z is None:
z = self.sample_latent(n_samples, seed=seed)
elif isinstance(z, list):
z = [torch.tensor(l).to(self.device) if not torch.is_tensor(l) else l for l in z]
elif not torch.is_tensor(z):
z = torch.tensor(z).to(self.device)
img = self.forward(z)
img_np = img.permute(0, 2, 3, 1).cpu().detach().numpy()
return np.clip(img_np, 0.0, 1.0).squeeze()
# For models that use part of latent as conditioning
def get_conditional_state(self, z):
return None
# For models that use part of latent as conditioning
def set_conditional_state(self, z, c):
return z
def named_modules(self, *args, **kwargs):
return self.model.named_modules(*args, **kwargs)
# PyTorch port of StyleGAN 2
class StyleGAN2(BaseModel):
def __init__(self, device, class_name, truncation=1.0, use_w=False):
super(StyleGAN2, self).__init__('StyleGAN2', class_name or 'ffhq')
self.device = device
self.truncation = truncation
self.latent_avg = None
self.w_primary = use_w # use W as primary latent space?
# Image widths
configs = {
# Converted NVIDIA official
'ffhq': 1024,
'car': 512,
'cat': 256,
'church': 256,
'horse': 256,
# Tuomas
'bedrooms': 256,
'kitchen': 256,
'places': 256,
'lookbook': 512
}
assert self.outclass in configs, \
f'Invalid StyleGAN2 class {self.outclass}, should be one of [{", ".join(configs.keys())}]'
self.resolution = configs[self.outclass]
self.name = f'StyleGAN2-{self.outclass}'
self.has_latent_residual = True
self.load_model()
self.set_noise_seed(0)
def latent_space_name(self):
return 'W' if self.w_primary else 'Z'
def use_w(self):
self.w_primary = True
def use_z(self):
self.w_primary = False
# URLs created with https://sites.google.com/site/gdocs2direct/
def download_checkpoint(self, outfile):
checkpoints = {
'horse': 'https://drive.google.com/uc?export=download&id=18SkqWAkgt0fIwDEf2pqeaenNi4OoCo-0',
'ffhq': 'https://drive.google.com/uc?export=download&id=1FJRwzAkV-XWbxgTwxEmEACvuqF5DsBiV',
'church': 'https://drive.google.com/uc?export=download&id=1HFM694112b_im01JT7wop0faftw9ty5g',
'car': 'https://drive.google.com/uc?export=download&id=1iRoWclWVbDBAy5iXYZrQnKYSbZUqXI6y',
'cat': 'https://drive.google.com/uc?export=download&id=15vJP8GDr0FlRYpE8gD7CdeEz2mXrQMgN',
'places': 'https://drive.google.com/uc?export=download&id=1X8-wIH3aYKjgDZt4KMOtQzN1m4AlCVhm',
'bedrooms': 'https://drive.google.com/uc?export=download&id=1nZTW7mjazs-qPhkmbsOLLA_6qws-eNQu',
'kitchen': 'https://drive.google.com/uc?export=download&id=15dCpnZ1YLAnETAPB0FGmXwdBclbwMEkZ',
'lookbook': 'https://drive.google.com/uc?export=download&id=1-F-RMkbHUv_S_k-_olh43mu5rDUMGYKe'
}
url = checkpoints[self.outclass]
download_ckpt(url, outfile)
def load_model(self):
checkpoint_root = os.environ.get('GANCONTROL_CHECKPOINT_DIR', Path(__file__).parent / 'checkpoints')
checkpoint = Path(checkpoint_root) / f'stylegan2/stylegan2_{self.outclass}_{self.resolution}.pt'
print(checkpoint)
self.model = stylegan2.Generator(self.resolution, 512, 2).to(self.device)
if not checkpoint.is_file():
os.makedirs(checkpoint.parent, exist_ok=True)
self.download_checkpoint(checkpoint)
ckpt = torch.load(checkpoint)
self.model.load_state_dict(ckpt['g_ema'], strict=False)
self.latent_avg = 0
def sample_latent(self, n_samples=1, seed=None, truncation=None):
if seed is None:
seed = np.random.randint(np.iinfo(np.int32).max) # use (reproducible) global rand state
rng = np.random.RandomState(seed)
z = torch.from_numpy(
rng.standard_normal(512 * n_samples)
.reshape(n_samples, 512)).float().to(self.device) #[N, 512]
if self.w_primary:
z = self.model.style(z)
return z
def get_max_latents(self):
return self.model.n_latent
def set_output_class(self, new_class):
if self.outclass != new_class:
raise RuntimeError('StyleGAN2: cannot change output class without reloading')
def forward(self, x):
x = x if isinstance(x, list) else [x]
out, _ = self.model(x, noise=self.noise,
truncation=self.truncation, truncation_latent=self.latent_avg, input_is_w=self.w_primary)
return 0.5*(out+1)
def partial_forward(self, x, layer_name):
styles = x if isinstance(x, list) else [x]
inject_index = None
noise = self.noise
if not self.w_primary:
styles = [self.model.style(s) for s in styles]
if len(styles) == 1:
# One global latent
inject_index = self.model.n_latent
latent = self.model.strided_style(styles[0].unsqueeze(1).repeat(1, inject_index, 1)) # [N, 18, 512]
elif len(styles) == 2:
# Latent mixing with two latents
if inject_index is None:
inject_index = random.randint(1, self.model.n_latent - 1)
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
latent2 = styles[1].unsqueeze(1).repeat(1, self.model.n_latent - inject_index, 1)
latent = self.model.strided_style(torch.cat([latent, latent2], 1))
else:
# One latent per layer
assert len(styles) == self.model.n_latent, f'Expected {self.model.n_latents} latents, got {len(styles)}'
styles = torch.stack(styles, dim=1) # [N, 18, 512]
latent = self.model.strided_style(styles)
if 'style' in layer_name:
return
out = self.model.input(latent)
if 'input' == layer_name:
return
out = self.model.conv1(out, latent[:, 0], noise=noise[0])
if 'conv1' in layer_name:
return
skip = self.model.to_rgb1(out, latent[:, 1])
if 'to_rgb1' in layer_name:
return
i = 1
noise_i = 1
for conv1, conv2, to_rgb in zip(
self.model.convs[::2], self.model.convs[1::2], self.model.to_rgbs
):
out = conv1(out, latent[:, i], noise=noise[noise_i])
if f'convs.{i-1}' in layer_name:
return
out = conv2(out, latent[:, i + 1], noise=noise[noise_i + 1])
if f'convs.{i}' in layer_name:
return
skip = to_rgb(out, latent[:, i + 2], skip)
if f'to_rgbs.{i//2}' in layer_name:
return
i += 2
noise_i += 2
image = skip
raise RuntimeError(f'Layer {layer_name} not encountered in partial_forward')
def set_noise_seed(self, seed):
torch.manual_seed(seed)
self.noise = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=self.device)]
for i in range(3, self.model.log_size + 1):
for _ in range(2):
self.noise.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=self.device))
# PyTorch port of StyleGAN 1
class StyleGAN(BaseModel):
def __init__(self, device, class_name, truncation=1.0, use_w=False):
super(StyleGAN, self).__init__('StyleGAN', class_name or 'ffhq')
self.device = device
self.w_primary = use_w # is W primary latent space?
configs = {
# Official
'ffhq': 1024,
'celebahq': 1024,
'bedrooms': 256,
'cars': 512,
'cats': 256,
# From https://github.com/justinpinkney/awesome-pretrained-stylegan
'vases': 1024,
'wikiart': 512,
'fireworks': 512,
'abstract': 512,
'anime': 512,
'ukiyo-e': 512,
}
assert self.outclass in configs, \
f'Invalid StyleGAN class {self.outclass}, should be one of [{", ".join(configs.keys())}]'
self.resolution = configs[self.outclass]
self.name = f'StyleGAN-{self.outclass}'
self.has_latent_residual = True
self.load_model()
self.set_noise_seed(0)
def latent_space_name(self):
return 'W' if self.w_primary else 'Z'
def use_w(self):
self.w_primary = True
def use_z(self):
self.w_primary = False
def load_model(self):
checkpoint_root = os.environ.get('GANCONTROL_CHECKPOINT_DIR', Path(__file__).parent / 'checkpoints')
checkpoint = Path(checkpoint_root) / f'stylegan/stylegan_{self.outclass}_{self.resolution}.pt'
self.model = stylegan.StyleGAN_G(self.resolution).to(self.device)
urls_tf = {
'vases': 'https://thisvesseldoesnotexist.s3-us-west-2.amazonaws.com/public/network-snapshot-008980.pkl',
'fireworks': 'https://mega.nz/#!7uBHnACY!quIW-pjdDa7NqnZOYh1z5UemWwPOW6HkYSoJ4usCg9U',
'abstract': 'https://mega.nz/#!vCQyHQZT!zdeOg3VvT4922Z2UfxO51xgAfJD-NAK2nW7H_jMlilU',
'anime': 'https://mega.nz/#!vawjXISI!F7s13yRicxDA3QYqYDL2kjnc2K7Zk3DwCIYETREmBP4',
'ukiyo-e': 'https://drive.google.com/uc?id=1CHbJlci9NhVFifNQb3vCGu6zw4eqzvTd',
}
urls_torch = {
'celebahq': 'https://drive.google.com/uc?export=download&id=1lGcRwNoXy_uwXkD6sy43aAa-rMHRR7Ad',
'bedrooms': 'https://drive.google.com/uc?export=download&id=1r0_s83-XK2dKlyY3WjNYsfZ5-fnH8QgI',
'ffhq': 'https://drive.google.com/uc?export=download&id=1GcxTcLDPYxQqcQjeHpLUutGzwOlXXcks',
'cars': 'https://drive.google.com/uc?export=download&id=1aaUXHRHjQ9ww91x4mtPZD0w50fsIkXWt',
'cats': 'https://drive.google.com/uc?export=download&id=1JzA5iiS3qPrztVofQAjbb0N4xKdjOOyV',
'wikiart': 'https://drive.google.com/uc?export=download&id=1fN3noa7Rsl9slrDXsgZVDsYFxV0O08Vx',
}
if not checkpoint.is_file():
os.makedirs(checkpoint.parent, exist_ok=True)
if self.outclass in urls_torch:
download_ckpt(urls_torch[self.outclass], checkpoint)
else:
checkpoint_tf = checkpoint.with_suffix('.pkl')
if not checkpoint_tf.is_file():
download_ckpt(urls_tf[self.outclass], checkpoint_tf)
print('Converting TensorFlow checkpoint to PyTorch')
self.model.export_from_tf(checkpoint_tf)
self.model.load_weights(checkpoint)
def sample_latent(self, n_samples=1, seed=None, truncation=None):
if seed is None:
seed = np.random.randint(np.iinfo(np.int32).max) # use (reproducible) global rand state
rng = np.random.RandomState(seed)
noise = torch.from_numpy(
rng.standard_normal(512 * n_samples)
.reshape(n_samples, 512)).float().to(self.device) #[N, 512]
if self.w_primary:
noise = self.model._modules['g_mapping'].forward(noise)
return noise
def get_max_latents(self):
return 18
def set_output_class(self, new_class):
if self.outclass != new_class:
raise RuntimeError('StyleGAN: cannot change output class without reloading')
def forward(self, x):
out = self.model.forward(x, latent_is_w=self.w_primary)
return 0.5*(out+1)
# Run model only until given layer
def partial_forward(self, x, layer_name):
mapping = self.model._modules['g_mapping']
G = self.model._modules['g_synthesis']
trunc = self.model._modules.get('truncation', lambda x : x)
if not self.w_primary:
x = mapping.forward(x) # handles list inputs
if isinstance(x, list):
x = torch.stack(x, dim=1)
else:
x = x.unsqueeze(1).expand(-1, 18, -1)
# Whole mapping
if 'g_mapping' in layer_name:
return
x = trunc(x)
if layer_name == 'truncation':
return
# Get names of children
def iterate(m, name, seen):
children = getattr(m, '_modules', [])
if len(children) > 0:
for child_name, module in children.items():
seen += iterate(module, f'{name}.{child_name}', seen)
return seen
else:
return [name]
# Generator
batch_size = x.size(0)
for i, (n, m) in enumerate(G.blocks.items()): # InputBlock or GSynthesisBlock
if i == 0:
r = m(x[:, 2*i:2*i+2])
else:
r = m(r, x[:, 2*i:2*i+2])
children = iterate(m, f'g_synthesis.blocks.{n}', [])
for c in children:
if layer_name in c: # substring
return
raise RuntimeError(f'Layer {layer_name} not encountered in partial_forward')
def set_noise_seed(self, seed):
G = self.model._modules['g_synthesis']
def for_each_child(this, name, func):
children = getattr(this, '_modules', [])
for child_name, module in children.items():
for_each_child(module, f'{name}.{child_name}', func)
func(this, name)
def modify(m, name):
if isinstance(m, stylegan.NoiseLayer):
H, W = [int(s) for s in name.split('.')[2].split('x')]
torch.random.manual_seed(seed)
m.noise = torch.randn(1, 1, H, W, device=self.device, dtype=torch.float32)
#m.noise = 1.0 # should be [N, 1, H, W], but this also works
for_each_child(G, 'g_synthesis', modify)
class GANZooModel(BaseModel):
def __init__(self, device, model_name):
super(GANZooModel, self).__init__(model_name, 'default')
self.device = device
self.base_model = torch.hub.load('facebookresearch/pytorch_GAN_zoo:hub',
model_name, pretrained=True, useGPU=(device.type == 'cuda'))
self.model = self.base_model.netG.to(self.device)
self.name = model_name
self.has_latent_residual = False
def sample_latent(self, n_samples=1, seed=0, truncation=None):
# Uses torch.randn
noise, _ = self.base_model.buildNoiseData(n_samples)
return noise
# Don't bother for now
def partial_forward(self, x, layer_name):
return self.forward(x)
def get_conditional_state(self, z):
return z[:, -20:] # last 20 = conditioning
def set_conditional_state(self, z, c):
z[:, -20:] = c
return z
def forward(self, x):
out = self.base_model.test(x)
return 0.5*(out+1)
class ProGAN(BaseModel):
def __init__(self, device, lsun_class=None):
super(ProGAN, self).__init__('ProGAN', lsun_class)
self.device = device
# These are downloaded by GANDissect
valid_classes = [ 'bedroom', 'churchoutdoor', 'conferenceroom', 'diningroom', 'kitchen', 'livingroom', 'restaurant' ]
assert self.outclass in valid_classes, \
f'Invalid LSUN class {self.outclass}, should be one of {valid_classes}'
self.load_model()
self.name = f'ProGAN-{self.outclass}'
self.has_latent_residual = False
def load_model(self):
checkpoint_root = os.environ.get('GANCONTROL_CHECKPOINT_DIR', Path(__file__).parent / 'checkpoints')
checkpoint = Path(checkpoint_root) / f'progan/{self.outclass}_lsun.pth'
if not checkpoint.is_file():
os.makedirs(checkpoint.parent, exist_ok=True)
url = f'http://netdissect.csail.mit.edu/data/ganmodel/karras/{self.outclass}_lsun.pth'
download_ckpt(url, checkpoint)
self.model = proggan.from_pth_file(str(checkpoint.resolve())).to(self.device)
def sample_latent(self, n_samples=1, seed=None, truncation=None):
if seed is None:
seed = np.random.randint(np.iinfo(np.int32).max) # use (reproducible) global rand state
noise = zdataset.z_sample_for_model(self.model, n_samples, seed=seed)[...]
return noise.to(self.device)
def forward(self, x):
if isinstance(x, list):
assert len(x) == 1, "ProGAN only supports a single global latent"
x = x[0]
out = self.model.forward(x)
return 0.5*(out+1)
# Run model only until given layer
def partial_forward(self, x, layer_name):
assert isinstance(self.model, torch.nn.Sequential), 'Expected sequential model'
if isinstance(x, list):
assert len(x) == 1, "ProGAN only supports a single global latent"
x = x[0]
x = x.view(x.shape[0], x.shape[1], 1, 1)
for name, module in self.model._modules.items(): # ordered dict
x = module(x)
if name == layer_name:
return
raise RuntimeError(f'Layer {layer_name} not encountered in partial_forward')
class BigGAN(BaseModel):
def __init__(self, device, resolution, class_name, truncation=1.0):
super(BigGAN, self).__init__(f'BigGAN-{resolution}', class_name)
self.device = device
self.truncation = truncation
self.load_model(f'biggan-deep-{resolution}')
self.set_output_class(class_name or 'husky')
self.name = f'BigGAN-{resolution}-{self.outclass}-t{self.truncation}'
self.has_latent_residual = True
# Default implementaiton fails without an internet
# connection, even if the model has been cached
def load_model(self, name):
if name not in biggan.model.PRETRAINED_MODEL_ARCHIVE_MAP:
raise RuntimeError('Unknown BigGAN model name', name)
checkpoint_root = os.environ.get('GANCONTROL_CHECKPOINT_DIR', Path(__file__).parent / 'checkpoints')
model_path = Path(checkpoint_root) / name
os.makedirs(model_path, exist_ok=True)
model_file = model_path / biggan.model.WEIGHTS_NAME
config_file = model_path / biggan.model.CONFIG_NAME
model_url = biggan.model.PRETRAINED_MODEL_ARCHIVE_MAP[name]
config_url = biggan.model.PRETRAINED_CONFIG_ARCHIVE_MAP[name]
for filename, url in ((model_file, model_url), (config_file, config_url)):
if not filename.is_file():
print('Downloading', url)
with open(filename, 'wb') as f:
if url.startswith("s3://"):
biggan.s3_get(url, f)
else:
biggan.http_get(url, f)
self.model = biggan.BigGAN.from_pretrained(model_path).to(self.device)
def sample_latent(self, n_samples=1, truncation=None, seed=None):
if seed is None:
seed = np.random.randint(np.iinfo(np.int32).max) # use (reproducible) global rand state
noise_vector = biggan.truncated_noise_sample(truncation=truncation or self.truncation, batch_size=n_samples, seed=seed)
noise = torch.from_numpy(noise_vector) #[N, 128]
return noise.to(self.device)
# One extra for gen_z
def get_max_latents(self):
return len(self.model.config.layers) + 1
def get_conditional_state(self, z):
return self.v_class
def set_conditional_state(self, z, c):
self.v_class = c
def is_valid_class(self, class_id):
if isinstance(class_id, int):
return class_id < 1000
elif isinstance(class_id, str):
return biggan.one_hot_from_names([class_id.replace(' ', '_')]) is not None
else:
raise RuntimeError(f'Unknown class identifier {class_id}')
def set_output_class(self, class_id):
if isinstance(class_id, int):
self.v_class = torch.from_numpy(biggan.one_hot_from_int([class_id])).to(self.device)
self.outclass = f'class{class_id}'
elif isinstance(class_id, str):
self.outclass = class_id.replace(' ', '_')
self.v_class = torch.from_numpy(biggan.one_hot_from_names([class_id])).to(self.device)
else:
raise RuntimeError(f'Unknown class identifier {class_id}')
def forward(self, x):
# Duplicate along batch dimension
if isinstance(x, list):
c = self.v_class.repeat(x[0].shape[0], 1)
class_vector = len(x)*[c]
else:
class_vector = self.v_class.repeat(x.shape[0], 1)
out = self.model.forward(x, class_vector, self.truncation) # [N, 3, 128, 128], in [-1, 1]
return 0.5*(out+1)
# Run model only until given layer
# Used to speed up PCA sample collection
def partial_forward(self, x, layer_name):
if layer_name in ['embeddings', 'generator.gen_z']:
n_layers = 0
elif 'generator.layers' in layer_name:
layer_base = re.match('^generator\.layers\.[0-9]+', layer_name)[0]
n_layers = int(layer_base.split('.')[-1]) + 1
else:
n_layers = len(self.model.config.layers)
if not isinstance(x, list):
x = self.model.n_latents*[x]
if isinstance(self.v_class, list):
labels = [c.repeat(x[0].shape[0], 1) for c in class_label]
embed = [self.model.embeddings(l) for l in labels]
else:
class_label = self.v_class.repeat(x[0].shape[0], 1)
embed = len(x)*[self.model.embeddings(class_label)]
assert len(x) == self.model.n_latents, f'Expected {self.model.n_latents} latents, got {len(x)}'
assert len(embed) == self.model.n_latents, f'Expected {self.model.n_latents} class vectors, got {len(class_label)}'
cond_vectors = [torch.cat((z, e), dim=1) for (z, e) in zip(x, embed)]
# Generator forward
z = self.model.generator.gen_z(cond_vectors[0])
z = z.view(-1, 4, 4, 16 * self.model.generator.config.channel_width)
z = z.permute(0, 3, 1, 2).contiguous()
cond_idx = 1
for i, layer in enumerate(self.model.generator.layers[:n_layers]):
if isinstance(layer, biggan.GenBlock):
z = layer(z, cond_vectors[cond_idx], self.truncation)
cond_idx += 1
else:
z = layer(z)
return None
# Version 1: separate parameters
@singledispatch
def get_model(name, output_class, device, **kwargs):
# Check if optionally provided existing model can be reused
inst = kwargs.get('inst', None)
model = kwargs.get('model', None)
if inst or model:
cached = model or inst.model
network_same = (cached.model_name == name)
outclass_same = (cached.outclass == output_class)
can_change_class = ('BigGAN' in name)
if network_same and (outclass_same or can_change_class):
cached.set_output_class(output_class)
return cached
if name == 'DCGAN':
import warnings
warnings.filterwarnings("ignore", message="nn.functional.tanh is deprecated")
model = GANZooModel(device, 'DCGAN')
elif name == 'ProGAN':
model = ProGAN(device, output_class)
elif 'BigGAN' in name:
assert '-' in name, 'Please specify BigGAN resolution, e.g. BigGAN-512'
model = BigGAN(device, name.split('-')[-1], class_name=output_class)
elif name == 'StyleGAN':
model = StyleGAN(device, class_name=output_class)
elif name == 'StyleGAN2':
model = StyleGAN2(device, class_name=output_class)
else:
raise RuntimeError(f'Unknown model {name}')
return model
# Version 2: Config object
@get_model.register(Config)
def _(cfg, device, **kwargs):
kwargs['use_w'] = kwargs.get('use_w', cfg.use_w) # explicit arg can override cfg
return get_model(cfg.model, cfg.output_class, device, **kwargs)
# Version 1: separate parameters
@singledispatch
def get_instrumented_model(name, output_class, layers, device, **kwargs):
model = get_model(name, output_class, device, **kwargs)
model.eval()
inst = kwargs.get('inst', None)
if inst:
inst.close()
if not isinstance(layers, list):
layers = [layers]
# Verify given layer names
module_names = [name for (name, _) in model.named_modules()]
for layer_name in layers:
if not layer_name in module_names:
print(f"Layer '{layer_name}' not found in model!")
print("Available layers:", '\n'.join(module_names))
raise RuntimeError(f"Unknown layer '{layer_name}''")
# Reset StyleGANs to z mode for shape annotation
if hasattr(model, 'use_z'):
model.use_z()
from netdissect.modelconfig import create_instrumented_model
inst = create_instrumented_model(SimpleNamespace(
model = model,
layers = layers,
cuda = device.type == 'cuda',
gen = True,
latent_shape = model.get_latent_shape()
))
if kwargs.get('use_w', False):
model.use_w()
return inst
# Version 2: Config object
@get_instrumented_model.register(Config)
def _(cfg, device, **kwargs):
kwargs['use_w'] = kwargs.get('use_w', cfg.use_w) # explicit arg can override cfg
return get_instrumented_model(cfg.model, cfg.output_class, cfg.layer, device, **kwargs)
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