Issue Running a GRU/LSTM Model on STM32 with Neural-ART Hello everyone,I’m trying to run a model on an STM32 MCU with Neural-ART using the ST Edge AI Developer Cloud.The final model includes GRU layers, but when I attempt to quantize and then optimize it, I encounter issues. To debug this, I created a minimal test model, which is as follows:class GRUTEST(nn.Module):
def __init__(self, hidden_layer_size, n_layers, output_size, dropout, device):
super(GRUTEST, self).__init__()
self.device = device
self.hidden_layer_size = hidden_layer_size
self.n_layers = n_layers
self.rnn = nn.GRU(256, hidden_layer_size, n_layers, batch_first=True, dropout=dropout, bidirectional=False)
self.fc = nn.Linear(64, output_size)
def forward(self, x):
x, _ = self.rnn(x)
x = self.fc(x[:, -1, :])
return xI then convert this model to ONNX as follows:torch_model = GRUTEST(
hidden_layer_size=64,
n_layers=2,
output_size=1,
dropout=0.2,
device='cpu'
)
torch_model.eval()
torch.onnx.export(
torch_model,
torch.randn(1, 1, 256),
"modelGRU.onnx",
opset_ve
