File size: 3,088 Bytes
f5cf82d f116465 f5cf82d c01459e f5cf82d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | from transformers import PreTrainedConfig
class NulaConfig(PreTrainedConfig):
model_type="nula"
def __init__(
self, num_classes=10, in_channels=3, input_size=(3, 32, 32), block_channels=(128, 256, 512),
use_residual=True, use_se=True, use_spatial_attention=False, se_reduction=16, classifier_hidden_dim=256,
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), id2label=None, label2id=None, **kwargs
):
super().__init__(**kwargs)
self.num_classes = num_classes
self.in_channels = in_channels
self.input_size = input_size
self.block_channels = list(block_channels)
self.use_residual = use_residual
self.use_se = use_se
self.use_spatial_attention = use_spatial_attention
self.se_reduction = se_reduction
self.classifier_hidden_dim = classifier_hidden_dim
self.mean = list(mean)
self.std = list(std)
if id2label is None or label2id is None:
labels = [
"airplane", "automobile", "bird", "cat", "deer",
"dog", "frog", "horse", "ship", "truck"
]
self.id2label = {i: label for i, label in enumerate(labels)}
self.label2id = {label: i for i, label in enumerate(labels)}
else:
self.id2label = id2label
self.label2id = label2id
self.id2label = {int(k): v for k, v in self.id2label.items()}
self._val_invars()
def _val_invars(self):
if self.num_classes <= 0:
raise ValueError(f"num_classes must be positive, got {self.num_classes}")
if self.classifier_hidden_dim <= 0:
raise ValueError(
f"classifier_hidden_dim must be positive, got {self.classifier_hidden_dim}"
)
if len(self.input_size) != 3:
raise ValueError(f"input_size must be (C, H, W), got {self.input_size}")
C, H, W = self.input_size
if C != self.in_channels:
raise ValueError(f"channel mismatch: input_size[0]={C}, in_channels={self.in_channels}")
if H <= 0 or W <= 0:
raise ValueError(f"spatial dimensions must be positive, got H={H}, W={W}")
if len(self.block_channels) == 0:
raise ValueError("the model needs at least one layer!")
if any(c <= 0 for c in self.block_channels):
raise ValueError(f"invalid block_channels: {self.block_channels}")
if self.use_se and self.se_reduction <= 0:
raise ValueError(f"se_reduction must be positive, got {self.se_reduction}")
if len(self.mean) != self.in_channels or len(self.std) != self.in_channels:
raise ValueError("mean/std length must match in_channels")
if self.num_classes != len(self.id2label):
raise ValueError(f"num_classes ({self.num_classes}) != label count ({len(self.id2label)})")
if set(self.label2id.values()) != set(range(self.num_classes)):
raise ValueError("label2id values must cover [0, ..., num_classes-1]") |