KeyError: 'dropout.weight'

[lith0613]

I have encounterd a new problem when I tried to run the code as follows:

import torch.nn as nn
from pytorch2keras.converter import pytorch_to_keras
import numpy as np
import torch
from torch.autograd import Variable

class CifarNet(nn.Module):
    def __init__(self):
        super(CifarNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3)
        self.conv4 = nn.Conv2d(128, 128, kernel_size=3)

        self.pool = nn.MaxPool2d(2, 2)

        self.relu = nn.ReLU(inplace=True)
        self.fc1 = nn.Linear(3200, 256)

        self.dropout = nn.Dropout(0.5)

        self.fc2 = nn.Linear(256, 256)
        self.fc3 = nn.Linear(256, 10)

    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.relu(self.conv2(x))
        x = self.pool(x)

        x = self.relu(self.conv3(x))
        x = self.relu(self.conv4(x))
        x = self.pool(x)

        x = x.view(-1, 3200)
        x = self.relu(self.fc1(x))
        x = self.dropout(x)

        x = self.relu(self.fc2(x))
        x = self.fc3(x)

        return x

# Load the trained model from file
trained_model = CifarNet()
trained_model.load_state_dict(torch.load('results/cifar_best.pth'))

input_np = np.random.uniform(0, 1, (1, 3, 32, 32))
input_var = Variable(torch.FloatTensor(input_np))
k_model = pytorch_to_keras(trained_model, input_var, [(3, 32, 32,)], verbose=True)

which reported as follows:

Traceback (most recent call last):
  File "pytorch4keras.py", line 40, in <module>
    k_model = pytorch_to_keras(trained_model, input_var, [(3, 32, 32,)], verbose=True)
  File "/root/anaconda3/envs/python3_lth2/lib/python3.6/site-packages/pytorch2keras/converter.py", line 332, in pytorch_to_keras
    names
  File "/root/anaconda3/envs/python3_lth2/lib/python3.6/site-packages/pytorch2keras/linear_layers.py", line 34, in convert_gemm
    W = weights[weights_name].numpy().transpose()
KeyError: 'dropout.weight'

Is the dropout layer not include in the converter framework ?

[lith0613]

I have encounterd a new problem when I tried to run the code as follows:

import torch.nn as nn
from pytorch2keras.converter import pytorch_to_keras
import numpy as np
import torch
from torch.autograd import Variable

class CifarNet(nn.Module):
    def __init__(self):
        super(CifarNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3)
        self.conv4 = nn.Conv2d(128, 128, kernel_size=3)

        self.pool = nn.MaxPool2d(2, 2)

        self.relu = nn.ReLU(inplace=True)
        self.fc1 = nn.Linear(3200, 256)

        self.dropout = nn.Dropout(0.5)

        self.fc2 = nn.Linear(256, 256)
        self.fc3 = nn.Linear(256, 10)

    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.relu(self.conv2(x))
        x = self.pool(x)

        x = self.relu(self.conv3(x))
        x = self.relu(self.conv4(x))
        x = self.pool(x)

        x = x.view(-1, 3200)
        x = self.relu(self.fc1(x))
        x = self.dropout(x)

        x = self.relu(self.fc2(x))
        x = self.fc3(x)

        return x

# Load the trained model from file
trained_model = CifarNet()
trained_model.load_state_dict(torch.load('results/cifar_best.pth'))

input_np = np.random.uniform(0, 1, (1, 3, 32, 32))
input_var = Variable(torch.FloatTensor(input_np))
k_model = pytorch_to_keras(trained_model, input_var, [(3, 32, 32,)], verbose=True)

which reported as follows:

Traceback (most recent call last):
  File "pytorch4keras.py", line 40, in <module>
    k_model = pytorch_to_keras(trained_model, input_var, [(3, 32, 32,)], verbose=True)
  File "/root/anaconda3/envs/python3_lth2/lib/python3.6/site-packages/pytorch2keras/converter.py", line 332, in pytorch_to_keras
    names
  File "/root/anaconda3/envs/python3_lth2/lib/python3.6/site-packages/pytorch2keras/linear_layers.py", line 34, in convert_gemm
    W = weights[weights_name].numpy().transpose()
KeyError: 'dropout.weight'

Is the dropout layer not include in the converter framework ?

I change the pytorch from 1.0 into 0.4.0, then it works, I don't know why that happens