def _is_in(self, param_group, param_group_list):
assert is_list_of(param_group_list, dict)
param = set(param_group['params'])
param_set = set()
for group in param_group_list:
param_set.update(set(group['params']))
return (not param.isdisjoint(param_set))
def __init__(self,
output_scale,
num_classes=0,
base_channels=64,
out_channels=3,
input_scale=4,
noise_size=128,
attention_cfg=dict(type='SelfAttentionBlock'),
attention_after_nth_block=0,
channels_cfg=None,
blocks_cfg=dict(type='SNGANGenResBlock'),
act_cfg=dict(type='ReLU'),
use_cbn=True,
auto_sync_bn=True,
with_spectral_norm=False,
with_embedding_spectral_norm=None,
norm_eps=1e-4,
sn_eps=1e-12,
init_cfg=dict(type='BigGAN'),
pretrained=None):
super().__init__()
self.input_scale = input_scale
self.output_scale = output_scale
self.noise_size = noise_size
self.num_classes = num_classes
self.init_type = init_cfg.get('type', None)
self.blocks_cfg = deepcopy(blocks_cfg)
self.blocks_cfg.setdefault('num_classes', num_classes)
self.blocks_cfg.setdefault('act_cfg', act_cfg)
self.blocks_cfg.setdefault('use_cbn', use_cbn)
self.blocks_cfg.setdefault('auto_sync_bn', auto_sync_bn)
self.blocks_cfg.setdefault('with_spectral_norm', with_spectral_norm)
# set `norm_spectral_norm` as `with_spectral_norm` if not defined
with_embedding_spectral_norm = with_embedding_spectral_norm \
if with_embedding_spectral_norm is not None else with_spectral_norm
self.blocks_cfg.setdefault('with_embedding_spectral_norm',
with_embedding_spectral_norm)
self.blocks_cfg.setdefault('init_cfg', init_cfg)
self.blocks_cfg.setdefault('norm_eps', norm_eps)
self.blocks_cfg.setdefault('sn_eps', sn_eps)
channels_cfg = deepcopy(self._default_channels_cfg) \
if channels_cfg is None else deepcopy(channels_cfg)
if isinstance(channels_cfg, dict):
if output_scale not in channels_cfg:
raise KeyError(f'`output_scale={output_scale} is not found in '
'`channel_cfg`, only support configs for '
f'{[chn for chn in channels_cfg.keys()]}')
self.channel_factor_list = channels_cfg[output_scale]
elif isinstance(channels_cfg, list):
self.channel_factor_list = channels_cfg
else:
raise ValueError('Only support list or dict for `channel_cfg`, '
f'receive {type(channels_cfg)}')
self.noise2feat = nn.Linear(
noise_size,
input_scale**2 * base_channels * self.channel_factor_list[0])
if with_spectral_norm:
self.noise2feat = spectral_norm(self.noise2feat)
# check `attention_after_nth_block`
if not isinstance(attention_after_nth_block, list):
attention_after_nth_block = [attention_after_nth_block]
if not is_list_of(attention_after_nth_block, int):
raise ValueError('`attention_after_nth_block` only support int or '
'a list of int. Please check your input type.')
self.conv_blocks = nn.ModuleList()
self.attention_block_idx = []
for idx in range(len(self.channel_factor_list)):
factor_input = self.channel_factor_list[idx]
factor_output = self.channel_factor_list[idx+1] \
if idx < len(self.channel_factor_list)-1 else 1
# get block-specific config
block_cfg_ = deepcopy(self.blocks_cfg)
block_cfg_['in_channels'] = factor_input * base_channels
block_cfg_['out_channels'] = factor_output * base_channels
self.conv_blocks.append(build_module(block_cfg_))
# build self-attention block
# `idx` is start from 0, add 1 to get the index
if idx + 1 in attention_after_nth_block:
self.attention_block_idx.append(len(self.conv_blocks))
attn_cfg_ = deepcopy(attention_cfg)
attn_cfg_['in_channels'] = factor_output * base_channels
self.conv_blocks.append(build_module(attn_cfg_))
to_rgb_norm_cfg = dict(type='BN', eps=norm_eps)
if check_dist_init() and auto_sync_bn:
to_rgb_norm_cfg['type'] = 'SyncBN'
self.to_rgb = ConvModule(factor_output * base_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1,
bias=True,
norm_cfg=to_rgb_norm_cfg,
act_cfg=act_cfg,
order=('norm', 'act', 'conv'),
with_spectral_norm=with_spectral_norm)
self.final_act = build_activation_layer(dict(type='Tanh'))
self.init_weights(pretrained)
def sample_conditional_model(model,
num_samples=16,
num_batches=4,
sample_model='ema',
label=None,
**kwargs):
"""Sampling from conditional models.
Args:
model (nn.Module): Conditional models in MMGeneration.
num_samples (int, optional): The total number of samples.
Defaults to 16.
num_batches (int, optional): The number of batch size for inference.
Defaults to 4.
sample_model (str, optional): Which model you want to use. ['ema',
'orig']. Defaults to 'ema'.
label (int | torch.Tensor | list[int], optional): Labels used to
generate images. Default to None.,
Returns:
Tensor: Generated image tensor.
"""
# set eval mode
model.eval()
# construct sampling list for batches
n_repeat = num_samples // num_batches
batches_list = [num_batches] * n_repeat
# check and convert the input labels
if isinstance(label, int):
label = torch.LongTensor([label] * num_samples)
elif isinstance(label, torch.Tensor):
label = label.type(torch.int64)
if label.numel() == 1:
# repeat single tensor
# call view(-1) to avoid nested tensor like [[[1]]]
label = label.view(-1).repeat(num_samples)
else:
# flatten multi tensors
label = label.view(-1)
elif isinstance(label, list):
if is_list_of(label, int):
label = torch.LongTensor(label)
# `nargs='+'` parse single integer as list
if label.numel() == 1:
# repeat single tensor
label = label.repeat(num_samples)
else:
raise TypeError('Only support `int` for label list elements, '
f'but receive {type(label[0])}')
elif label is None:
pass
else:
raise TypeError('Only support `int`, `torch.Tensor`, `list[int]` or '
f'None as label, but receive {type(label)}.')
# check the length of the (converted) label
if label is not None and label.size(0) != num_samples:
raise ValueError('Number of elements in the label list should be ONE '
'or the length of `num_samples`. Requires '
f'{num_samples}, but receive {label.size(0)}.')
# make label list
label_list = []
for n in range(n_repeat):
if label is None:
label_list.append(None)
else:
label_list.append(label[n * num_batches:(n + 1) * num_batches])
if num_samples % num_batches > 0:
batches_list.append(num_samples % num_batches)
if label is None:
label_list.append(None)
else:
label_list.append(label[(n + 1) * num_batches:])
res_list = []
# inference
for batches, labels in zip(batches_list, label_list):
res = model.sample_from_noise(None,
num_batches=batches,
label=labels,
sample_model=sample_model,
**kwargs)
res_list.append(res.cpu())
results = torch.cat(res_list, dim=0)
return results
