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import os
from torchvision.models.inception import inception_v3
import torch.nn.init as init
import torch.nn as nn
import torch
class InceptionV3Classifier(nn.Module):
def __init__(self, num_classes: int = 14):
super(InceptionV3Classifier, self).__init__()
self.inception = inception_v3(
pretrained=False, num_classes=num_classes, aux_logits=False
)
# Initialize weights if not loading from a file
if not self._load():
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
if m.bias is not None:
init.zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2d):
init.ones_(m.weight)
init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
init.xavier_normal_(m.weight)
if m.bias is not None:
init.zeros_(m.bias)
def forward(self, x):
x = self.inception(x)
return x
def _load(self, filename: str = None) -> bool:
if filename is None:
current_work_dir = os.path.dirname(__file__)
filename = os.path.join(
current_work_dir, "best_pth", "InceptionV3Classifier.pth"
)
if not os.path.exists(filename):
print("Model file does not exist.")
return False
self.load_state_dict(torch.load(filename))
print("Model loaded successfully.")
return True
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