| | """ |
| | File: model.py |
| | Author: Elena Ryumina and Dmitry Ryumin |
| | Description: This module provides functions for loading and processing a pre-trained deep learning model |
| | for facial expression recognition. |
| | License: MIT License |
| | """ |
| |
|
| | import torch |
| | import requests |
| | from PIL import Image |
| | from torchvision import transforms |
| | from pytorch_grad_cam import GradCAM |
| |
|
| | |
| | from app.config import config_data |
| | from app.model_architectures import ResNet50, LSTMPyTorch |
| |
|
| |
|
| | def load_model(model_url, model_path): |
| | try: |
| | with requests.get(model_url, stream=True) as response: |
| | with open(model_path, "wb") as file: |
| | for chunk in response.iter_content(chunk_size=8192): |
| | file.write(chunk) |
| | return model_path |
| | except Exception as e: |
| | print(f"Error loading model: {e}") |
| | return None |
| |
|
| | path_static = load_model(config_data.model_static_url, config_data.model_static_path) |
| | pth_model_static = ResNet50(7, channels=3) |
| | pth_model_static.load_state_dict(torch.load(path_static)) |
| | pth_model_static.eval() |
| |
|
| | path_dynamic = load_model(config_data.model_dynamic_url, config_data.model_dynamic_path) |
| | pth_model_dynamic = LSTMPyTorch() |
| | pth_model_dynamic.load_state_dict(torch.load(path_dynamic)) |
| | pth_model_dynamic.eval() |
| |
|
| | target_layers = [pth_model_static.layer4] |
| | cam = GradCAM(model=pth_model_static, target_layers=target_layers) |
| |
|
| | def pth_processing(fp): |
| | class PreprocessInput(torch.nn.Module): |
| | def init(self): |
| | super(PreprocessInput, self).init() |
| |
|
| | def forward(self, x): |
| | x = x.to(torch.float32) |
| | x = torch.flip(x, dims=(0,)) |
| | x[0, :, :] -= 91.4953 |
| | x[1, :, :] -= 103.8827 |
| | x[2, :, :] -= 131.0912 |
| | return x |
| |
|
| | def get_img_torch(img, target_size=(224, 224)): |
| | transform = transforms.Compose([transforms.PILToTensor(), PreprocessInput()]) |
| | img = img.resize(target_size, Image.Resampling.NEAREST) |
| | img = transform(img) |
| | img = torch.unsqueeze(img, 0) |
| | return img |
| |
|
| | return get_img_torch(fp) |
| |
|
