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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=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
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