Tag Archives: Medical image processing

[Solved] Visdom Error: raise ConnectionError

Use visdom to visualize loss in the network. First, enter in the terminal:

python -m visdom.server

Then run the code, the result is an error, and the following information appears:

 raise ConnectionError(e, request=request)
requests.exceptions.ConnectionError: 
HTTPConnectionPool(host='localhost', port=8097): 
Max retries exceeded with url: /events (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f6734182d50>: 
Failed to establish a new connection: 
[Errno 111] Connection refused'))

After Baidu, I found the solution:

Finished running the command: python -m visdom.serve, the following sentence will appear:

Setting up a new session...

To keep the terminal on this page, create a new terminal and run the network code in the new terminal.

Then open the link in the browser:

http://localhost:8097/

You can see the curve being drawn.

[ONNXRuntimeError] : 10 : INVALID_Graph loading model error

Project scenario:

The python model is converted to onnx model and can be exported successfully, but the following errors occur when loading the model using onnxruntime

InvalidGraph: [ONNXRuntimeError] : 10 : INVALID_ GRAPH : Load model from T.onnx failed:This is an invalid model. Type Error: Type ‘tensor(bool)’ of input parameter (8) of operator (ScatterND) in node (ScatterND_ 15) is invalid.

Problem Description:

import torch
import torch.nn as nn
import onnxruntime
from torch.onnx import export

class Preprocess(nn.Module):
    def __init__(self):
        super().__init__()
        self.max = 1000
        self.min = -44

    def forward(self, inputs):
        inputs[inputs>self.max] = self.max
        inputs[inputs<self.min] = self.min
        return inputs
        
x = torch.randint(-1024,3071,(1,1,28,28))
model = Preprocess()
model.eval()

export(
    model,
    x,
    "test.onnx",
    input_names=["input"],
    output_names=["output"],
    opset_version=11,
)

session = onnxruntime.InferenceSession("test.onnx")

Cause analysis:

The same problem can be found in GitHub of pytorch #34054

Solution:

The specific operations are as follows: Mr. Cheng mask , and then use torch.masked_ Fill() operation. Instead of using the index to directly assign the entered tensor 

class MaskHigh(nn.Module):
    def __init__(self, val):
        super().__init__()
        self.val = val

    def forward(self, inputs):
        x = inputs.clone()
        mask = x > self.val
        output = torch.masked_fill(inputs, mask, self.val)
        return output


class MaskLow(nn.Module):
    def __init__(self, val):
        super().__init__()
        self.val = val

    def forward(self, inputs):
        x = inputs.clone()
        mask = x < self.val
        output = torch.masked_fill(inputs, mask, self.val)
        return output


class Clip(nn.Module):
    def __init__(self):
        super().__init__()
        self.high = MaskHigh(1300)
        self.low = MaskLow(-44)

    def forward(self, inputs):
        output = self.high(inputs)
        output = self.low(output)
        return output

Netron can be used to visualize the calculation diagram generated by the front and rear methods

Index assignment