kit/metrics/aesthetics_scorer/model.py

import torch
import torch.nn as nn
import json
import os
import inspect


class AestheticScorer(nn.Module):
    def __init__(
        self,
        input_size=0,
        use_activation=False,
        dropout=0.2,
        config=None,
        hidden_dim=1024,
        reduce_dims=False,
        output_activation=None,
    ):
        super().__init__()
        self.config = {
            "input_size": input_size,
            "use_activation": use_activation,
            "dropout": dropout,
            "hidden_dim": hidden_dim,
            "reduce_dims": reduce_dims,
            "output_activation": output_activation,
        }
        if config != None:
            self.config.update(config)

        layers = [
            nn.Linear(self.config["input_size"], self.config["hidden_dim"]),
            nn.ReLU() if self.config["use_activation"] else None,
            nn.Dropout(self.config["dropout"]),
            nn.Linear(
                self.config["hidden_dim"],
                round(self.config["hidden_dim"] / (2 if reduce_dims else 1)),
            ),
            nn.ReLU() if self.config["use_activation"] else None,
            nn.Dropout(self.config["dropout"]),
            nn.Linear(
                round(self.config["hidden_dim"] / (2 if reduce_dims else 1)),
                round(self.config["hidden_dim"] / (4 if reduce_dims else 1)),
            ),
            nn.ReLU() if self.config["use_activation"] else None,
            nn.Dropout(self.config["dropout"]),
            nn.Linear(
                round(self.config["hidden_dim"] / (4 if reduce_dims else 1)),
                round(self.config["hidden_dim"] / (8 if reduce_dims else 1)),
            ),
            nn.ReLU() if self.config["use_activation"] else None,
            nn.Linear(round(self.config["hidden_dim"] / (8 if reduce_dims else 1)), 1),
        ]
        if self.config["output_activation"] == "sigmoid":
            layers.append(nn.Sigmoid())
        layers = [x for x in layers if x is not None]
        self.layers = nn.Sequential(*layers)

    def forward(self, x):
        if self.config["output_activation"] == "sigmoid":
            upper, lower = 10, 1
            scale = upper - lower
            return (self.layers(x) * scale) + lower
        else:
            return self.layers(x)

    def save(self, save_name):
        split_name = os.path.splitext(save_name)
        with open(f"{split_name[0]}.config", "w") as outfile:
            outfile.write(json.dumps(self.config, indent=4))

        for i in range(
            6
        ):  # saving sometiles fails, so retry 5 times, might be windows issue
            try:
                torch.save(self.state_dict(), save_name)
                break
            except RuntimeError as e:
                # check if error contains string "File"
                if "cannot be opened" in str(e) and i < 5:
                    print("Model save failed, retrying...")
                else:
                    raise e


def preprocess(embeddings):
    return embeddings / embeddings.norm(p=2, dim=-1, keepdim=True)


def load_model(weight_name, device="cuda" if torch.cuda.is_available() else "cpu"):
    weight_folder = os.path.abspath(
        os.path.join(
            inspect.getfile(load_model),
            "../weights",
        )
    )
    weight_path = os.path.join(weight_folder, f"{weight_name}.pth")
    config_path = os.path.join(weight_folder, f"{weight_name}.config")
    with open(config_path, "r") as config_file:
        config = json.load(config_file)
    model = AestheticScorer(config=config)
    model.load_state_dict(torch.load(weight_path, map_location=device))
    model.eval()
    return model