spice-mnist / modeling_spice_cnn.py
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import torch.nn as nn
# from torchsummary import summary
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, 16, kernel_size=config.kernel_size, padding=1
),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.MaxPool2d(kernel_size=config.pooling_size),
nn.Conv2d(16, 32, kernel_size=config.kernel_size, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=config.pooling_size),
nn.Conv2d(32, 64, kernel_size=config.kernel_size, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(kernel_size=config.pooling_size),
nn.Flatten(),
nn.Linear(64 * 3 * 3, 128),
nn.ReLU(),
nn.Dropout(0.5),
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}
# config = SpiceCNNConfig(in_channels=1)
# cnn = SpiceCNNModelForImageClassification(config)
# summary(cnn, (1,28,28))