ByteTensor not working with F.conv2d?
print(model(Variable(obs, volatile=True))) I encounter when executing this line obs is a size(1, 4, 84, 84) ByteTensor corresponding to (input_size, channels, height, width) Here is my model definition: <SCODE>class DQN(nn.Module):
    def __init__(self, in_channels=4, num_actions=18):
        super(DQN, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, 32, kernel_size=8, stride=4)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)
        self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1)
        self.fc4 = nn.Linear(7 * 7 * 64, 512)
        self.fc5 = nn.Linear(512, num_actions)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = F.relu(self.fc4(x.view(x.size(0), -1)))
        return self.fc5(x)
<ECODE> Below are errors: <SCODE>/Users/mac/anaconda/envs/py35/lib/python3.5/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    208 
    209     def __call__(self, *input, **kwargs):
--> 210         result = self.forward(*input, **kwargs)
    211         for hook in self._forward_hooks.values():
    212             hook_result = hook(self, input, result)

<ipython-input-25-3131e91fac28> in forward(self, x)
     17 
     18     def forward(self, x):
---> 19         x = F.relu(self.conv1(x))
     20         x = F.relu(self.conv2(x))
     21         x = F.relu(self.conv3(x))

/Users/mac/anaconda/envs/py35/lib/python3.5/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    208 
    209     def __call__(self, *input, **kwargs):
--> 210         result = self.forward(*input, **kwargs)
    211         for hook in self._forward_hooks.values():
    212             hook_result = hook(self, input, result)

/Users/mac/anaconda/envs/py35/lib/python3.5/site-packages/torch/nn/modules/conv.py in forward(self, input)
    235     def forward(self, input):
    236         return F.conv2d(input, self.weight, self.bias, self.stride,
--> 237                         self.padding, self.dilation, self.groups)
    238 
    239 

/Users/mac/anaconda/envs/py35/lib/python3.5/site-packages/torch/nn/functional.py in conv2d(input, weight, bias, stride, padding, dilation, groups)
     35     f = ConvNd(_pair(stride), _pair(padding), _pair(dilation), False,
     36                _pair(0), groups)
---> 37     return f(input, weight, bias) if bias is not None else f(input, weight)
     38 
     39 

/Users/mac/anaconda/envs/py35/lib/python3.5/site-packages/torch/nn/_functions/conv.py in forward(self, input, weight, bias)
     32         if k == 3:
     33             input, weight = _view4d(input, weight)
---> 34         output = self._update_output(input, weight, bias)
     35         if k == 3:
     36             output, = _view3d(output)

/Users/mac/anaconda/envs/py35/lib/python3.5/site-packages/torch/nn/_functions/conv.py in _update_output(self, input, weight, bias)
     89 
     90         self._bufs = [[] for g in range(self.groups)]
---> 91         return self._thnn('update_output', input, weight, bias)
     92 
     93     def _grad_input(self, input, weight, grad_output):

/Users/mac/anaconda/envs/py35/lib/python3.5/site-packages/torch/nn/_functions/conv.py in _thnn(self, fn_name, input, weight, *args)
    148         impl = _thnn_convs[self.thnn_class_name(input)]
    149         if self.groups == 1:
--> 150             return impl[fn_name](self, self._bufs[0], input, weight, *args)
    151         else:
    152             res = []

/Users/mac/anaconda/envs/py35/lib/python3.5/site-packages/torch/nn/_functions/conv.py in call_update_output(self, bufs, input, weight, bias)
    220 def make_update_output(fn):
    221     def call_update_output(self, bufs, input, weight, bias):
--> 222         backend = type2backend[type(input)]
    223         bufs.extend([input.new(), input.new()])
    224         output = input.new(*self._output_size(input, weight))

/Users/mac/anaconda/envs/py35/lib/python3.5/site-packages/torch/_thnn/__init__.py in __getitem__(self, name)
     13 
     14     def __getitem__(self, name):
---> 15         return self.backends[name].load()
     16 
     17 

KeyError: <class 'torch.ByteTensor'>
<ECODE>
transedward
I am not very sure but the model weights must be stored as the “FloatTensor” and pytorch raises an error when the types mismatch. Running same thing with x as “FloatTensor” runs on my machine
gsp-27
smth
Is there plans to change this? To support fine-tuning quantized models in 8 bit or similar?
luke
we cannot compute gradients for discrete operations. Hence we dont expect this to change (atleast we dont plan to change this).
smth