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import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.modeling_outputs import ImageClassifierOutput
from .configuration_nula import NulaConfig
class BlurPool2d(nn.Module):
def __init__(self, channels, stride=2):
super().__init__()
if stride not in (1, 2):
raise ValueError
self.channels = channels
self.stride = stride
kernel_1d = pt.tensor([1.0, 2.0, 1.0], dtype=pt.float32)
kernel_2d = kernel_1d[:, None] * kernel_1d[None, :]
kernel_2d = kernel_2d / kernel_2d.sum()
filt = kernel_2d[None, None, :, :].repeat(channels, 1, 1, 1)
self.register_buffer("filt", filt)
self.pad = nn.ReflectionPad2d(1)
def forward(self, x):
if x.ndim != 4:
raise ValueError("x needs shape (B, C, H, W)")
if x.shape[1] != self.channels:
raise ValueError(f"expected : {self.channels} channels\ngot : {x.shape[1]}")
x = self.pad(x)
x = F.conv2d(
x,
self.filt,
stride=self.stride,
padding=0,
groups=self.channels
)
return x
class SEBlock(nn.Module):
def __init__(self, channels, se_reduction=16):
super().__init__()
hidden = max(channels // se_reduction, 1)
self.pool = nn.AdaptiveAvgPool2d((1,1))
self.fc1 = nn.Linear(channels, hidden)
self.act = nn.ReLU(inplace=True)
self.fc2 = nn.Linear(hidden, channels)
self.gate = nn.Sigmoid()
def forward(self, x):
b, c, h, w = x.shape
z = self.pool(x)
z = z.view(b, c)
z = self.act(self.fc1(z))
z = self.gate(self.fc2(z))
z = z.view(b, c, 1, 1)
return x * z
class NulaConvBlock(nn.Module):
def __init__(self, in_ch, out_ch, downsample=False, use_se=False, se_reduction=16):
super().__init__()
stride = 1
self.conv1 = nn.Conv2d(
in_ch,
out_ch,
kernel_size=3,
stride=stride,
padding=1,
bias=False
)
self.bn1 = nn.BatchNorm2d(out_ch)
self.act1 = nn.SiLU(inplace=True)
self.conv2 = nn.Conv2d(
out_ch, out_ch, kernel_size=3, stride=1, padding=1, bias=False
)
self.bn2 = nn.BatchNorm2d(out_ch)
self.se = SEBlock(out_ch, se_reduction=se_reduction) if use_se else nn.Identity()
self.blurpool = BlurPool2d(out_ch, stride=2) if downsample else nn.Identity()
if downsample or in_ch != out_ch:
layers = []
layers.append(nn.Conv2d(in_ch, out_ch, kernel_size=1, stride=1, bias=False))
layers.append(nn.BatchNorm2d(out_ch))
if downsample:
layers.append(BlurPool2d(out_ch, stride=2))
self.shortcut = nn.Sequential(*layers)
else:
self.shortcut = nn.Identity()
self.out_act = nn.SiLU(inplace=True)
def forward(self, x):
identity = self.shortcut(x)
out = self.act1(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out = self.se(out)
out = self.blurpool(out)
out = out + identity
out = self.out_act(out)
return out
class NulaForImageClassification(PreTrainedModel):
config_class = NulaConfig
base_model_prefix = "nula"
def __init__(self, config):
super().__init__(config)
self.config = config
dims = list(config.block_channels)
if len(dims) != 3:
raise ValueError("For v0.1, block_channels must exactly have 3 entries :>")
c1, c2, c3 = dims
self.stem_conv = nn.Conv2d(
in_channels=config.in_channels,
out_channels=c1,
kernel_size=3,
stride=1,
padding=1,
bias=False
)
self.stem_bn = nn.BatchNorm2d(c1)
self.stem_act = nn.SiLU(inplace=True)
self.s1 = NulaConvBlock(c1, c1, False, config.use_se, config.se_reduction)
self.s2 = NulaConvBlock(c1, c2, True, config.use_se, config.se_reduction)
self.s3 = NulaConvBlock(c2, c3, True, config.use_se, config.se_reduction)
self.global_pool = nn.AdaptiveAvgPool2d((1,1))
self.head = nn.Sequential(
nn.Linear(c3, config.classifier_hidden_dim),
nn.SiLU(inplace=True),
nn.Dropout(p=0.30),
nn.Linear(config.classifier_hidden_dim, config.num_classes)
)
self.criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
self.post_init()
def _init_weights(self, module):
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(module, nn.BatchNorm2d):
nn.init.ones_(module.weight)
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Linear):
nn.init.xavier_uniform_(module.weight)
nn.init.zeros_(module.bias)
if isinstance(module, NulaConvBlock) and hasattr(module, "bn2"):
nn.init.zeros_(module.bn2.weight)
def forward(self, pixel_values=None, labels=None, return_dict=None):
self._val_invars(pixel_values)
return_dict = return_dict if return_dict is not None else self.config.return_dict
x = pixel_values
x = self.stem_act(self.stem_bn(self.stem_conv(x)))
x = self.s1(x)
x = self.s2(x)
x = self.s3(x)
x = self.global_pool(x)
x = pt.flatten(x, 1)
logits = self.head(x)
loss = None
if labels is not None:
loss = self.criterion(logits, labels)
if not return_dict:
output = (logits,)
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutput(
loss=loss,
logits=logits
)
def _val_invars(self, pixel_values):
if pixel_values is None:
raise ValueError("pixel_values must be provided")
if pixel_values.ndim != 4:
raise ValueError(f"Expected (B, C, H, W), got {pixel_values.shape}")
B, C, H, W = pixel_values.shape
if C != self.config.in_channels:
raise ValueError(f"Channel mismatch:\nexpected : {self.config.in_channels},\ngot : {C}") |