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import torch.nn as nn
from transformers import PreTrainedModel
from .configuration_spice_cnn import SpiceCNNConfig
class SpiceCNNModelForImageClassification(PreTrainedModel):
config_class = SpiceCNNConfig
def __init__(self, config: SpiceCNNConfig):
super().__init__(config)
layers = [
nn.Conv2d(
config.in_channels,
32,
kernel_size=3,
),
nn.ReLU(),
nn.BatchNorm2d(32),
nn.Conv2d(32, 32, kernel_size=3),
nn.ReLU(),
nn.BatchNorm2d(32),
nn.Conv2d(32, 32, kernel_size=5, stride=2),
nn.ReLU(),
nn.Dropout(0.4),
nn.Conv2d(32, 64, kernel_size=3),
nn.ReLU(),
nn.BatchNorm2d(64),
nn.Conv2d(64, 64, kernel_size=3),
nn.ReLU(),
nn.BatchNorm2d(64),
nn.Conv2d(64, 64, kernel_size=5, stride=2),
nn.ReLU(),
nn.BatchNorm2d(64),
nn.Dropout(0.4),
nn.Flatten(),
nn.Linear(64 * 28 * 28, 128),
nn.BatchNorm1d(128),
nn.Dropout(0.4),
nn.Linear(128, config.num_classes),
]
self.model = nn.Sequential(*layers)
def forward(self, tensor, labels=None):
logits = self.model(tensor)
if labels is not None:
loss_fnc = nn.CrossEntropyLoss()
loss = loss_fnc(logits, labels)
return {"loss": loss, "logits": logits}
return {"logits": logits}
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