Issue Converting Network to Hardware Layers with FINN #1301

PedroAntunes178 · 2025-03-18T16:31:43Z

PedroAntunes178
Mar 18, 2025

Hi everyone,

I'm encountering an issue (cycle-free graph violated: partition depends on itself) while converting the following network to hardware layers using the FINN dataflow compiler:

import torch
import torch.nn as nn
import torch.nn.functional as F

import brevitas.nn as qnn

class CNetPlusScalar(nn.Module):
    def __init__(self):
        super().__init__()
        self.quant_inp_img = qnn.QuantIdentity(return_quant_tensor=True)
        self.quant_inp_scalar = qnn.QuantIdentity(return_quant_tensor=True)
        self.conv1 = qnn.QuantConv2d(1, 6, 5, bias=True, return_quant_tensor=True) 
        self.conv2 = qnn.QuantConv2d(6, 16, 5, bias=True, return_quant_tensor=True) 
        self.conv3 = qnn.QuantConv2d(16, 32, 5, bias=True, return_quant_tensor=True) 
        self.conv4 = qnn.QuantConv2d(32, 32, 5, bias=True, return_quant_tensor=True)
        self.fc1 = qnn.QuantLinear(32 * 28 * 28, 120, bias=True, return_quant_tensor=True)
        self.fc2 = qnn.QuantLinear(120, 84, bias=True, return_quant_tensor=True)
        self.fc3 = qnn.QuantLinear(84+1, 1, bias=True, return_quant_tensor=False)
        # Separate pooling instances for each stage
        self.pool1 = nn.MaxPool2d(2, 2)
        self.pool2 = nn.MaxPool2d(2, 2)
        self.pool3 = nn.MaxPool2d(2, 2)
        self.pool4 = nn.MaxPool2d(2, 2)
        # Instantiate separate activation layers
        self.act_conv1 = qnn.QuantReLU(return_quant_tensor=True)
        self.act_conv2 = qnn.QuantReLU(return_quant_tensor=True)
        self.act_conv3 = qnn.QuantReLU(return_quant_tensor=True)
        self.act_conv4 = qnn.QuantReLU(return_quant_tensor=True)
        self.act_fc1   = qnn.QuantReLU(return_quant_tensor=True)
        self.act_fc2   = qnn.QuantReLU(return_quant_tensor=True)

    def forward(self, image, background_scalar):
        image = self.quant_inp_img(image)
        x = self.pool1(self.act_conv1(self.conv1(image))) 
        x = self.pool2(self.act_conv2(self.conv2(x))) 
        x = self.pool3(self.act_conv3(self.conv3(x))) 
        x = self.pool4(self.act_conv4(self.conv4(x))) 
        x = torch.flatten(x, 1) 
        x = self.act_fc1(self.fc1(x))
        x = self.act_fc2(self.fc2(x))
        x = self.quant_inp_scalar(x)
        background_scalar = self.quant_inp_scalar(background_scalar)
        x = torch.cat((x, background_scalar), dim=1)
        x = self.fc3(x)
        return x

model = CNetPlusScalar()

I’ve attached part of the step_convert_to_hw.onnx graph. As you can see, the last pooling layer does not have the fpgadataflow modifier.

Does anyone know what custom steps might be required or what I might be doing wrong? Any guidance would be greatly appreciated!

Thanks in advance!

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Uh oh!

Issue Converting Network to Hardware Layers with FINN #1301

Uh oh!

{{title}}

Uh oh!

Uh oh!

{{editor}}'s edit

{{editor}}'s edit

Uh oh!

Replies: 0 comments

Select a reply

Uh oh!

Issue Converting Network to Hardware Layers with FINN #1301

Uh oh!

Uh oh!

PedroAntunes178 Mar 18, 2025

Replies: 0 comments

PedroAntunes178
Mar 18, 2025