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import torch
from torch import nn
from transformers import PreTrainedModel
from .configuration import CustomModelConfig
from torchvision import transforms
from PIL import Image
import sys
class CustomModel(nn.Module):
def __init__(self, input_shape, num_classes):
super(CustomModel, self).__init__()
self.conv_layers = nn.Sequential(
nn.Conv2d(in_channels=input_shape[0], out_channels=32, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(32),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(128),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(128),
nn.MaxPool2d(kernel_size=2)
)
self.fc_layers = nn.Sequential(
nn.Flatten(),
nn.Dropout(0.5),
nn.Linear(128 * (input_shape[1] // 16) * (input_shape[2] // 16), 512),
nn.ReLU(),
nn.BatchNorm1d(512),
nn.Dropout(0.5),
nn.Linear(512, num_classes)
)
def forward(self, x):
x = self.conv_layers(x)
x = self.fc_layers(x)
return x
class CustomClassifier(PreTrainedModel):
config_class = CustomModelConfig
def __init__(self, config):
super().__init__(config)
self.model = CustomModel(config.input_size, config.num_classes)
self.preprocess = transforms.Compose([
transforms.Resize((128, 128)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
self.classes = ['cat', 'dog']
def forward(self, x):
try:
x = Image.open(x).convert("RGB")
except Exception as e:
raise Exception(f"Error: Unable to load image file {x}. Check if the file exists or is in the right format. Details: {e}")
x = self.preprocess(x).unsqueeze(0)
return self.model(x)
def predict(self, x, get_class=False):
self.eval()
with torch.no_grad():
outputs = self.forward(x)
probabilities = torch.nn.functional.softmax(outputs, dim=1)
if not get_class:
return {
"cat": round(probabilities[0][0].item(), 3),
"dog": round(probabilities[0][1].item(), 3)
}
else:
return self.classes[probabilities.argmax(dim=1).item()]
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