model.py

import torch
import torch.nn as nn
from modelscope import snapshot_download
from torchvision.models import squeezenet1_1

MODEL_DIR = snapshot_download(
    "ccmusic-database/pianos",
    cache_dir="./__pycache__",
)


def Classifier(cls_num=8, output_size=512, linear_output=False):
    q = (1.0 * output_size / cls_num) ** 0.25
    l1 = int(q * cls_num)
    l2 = int(q * l1)
    l3 = int(q * l2)
    if linear_output:
        return torch.nn.Sequential(
            nn.Dropout(),
            nn.Linear(output_size, l3),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(l3, l2),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(l2, l1),
            nn.ReLU(inplace=True),
            nn.Linear(l1, cls_num),
        )

    else:
        return torch.nn.Sequential(
            nn.Dropout(),
            nn.Conv2d(output_size, l3, kernel_size=(1, 1), stride=(1, 1)),
            nn.ReLU(inplace=True),
            nn.AdaptiveAvgPool2d(output_size=(1, 1)),
            nn.Flatten(),
            nn.Linear(l3, l2),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(l2, l1),
            nn.ReLU(inplace=True),
            nn.Linear(l1, cls_num),
        )


def net(weights=f"{MODEL_DIR}/save.pt"):
    model = squeezenet1_1(pretrained=False)
    model.classifier = Classifier()
    model.load_state_dict(torch.load(weights, map_location=torch.device("cpu")))
    model.eval()
    return model