How can I convert numpy.ndarray having type object to torch.tensor?
I'm trying to work on lstm in pytorch. It takes only tensors as the input. The data that I have is in the form of a numpy.object_ and if I convert this to a numpy.float, then it can be converted to tensor.

I checked the data type using print(type(array)) it gives class 'numpy.ndarray' as output and print(arr.dtype.type) gives class 'numpy.object_' as output.

Or is there any way to convert tuple directly to torch.tensor?

The pytorch LSTM returns a tuple. So you get this error as your second LSTM layer self.seq2 can not handle this tuple. So, 
change 

prefix1=self.seq1(input1) 
suffix1=self.seq1(input2)


to something like this:

prefix1_out, prefix1_states = self.seq1(input1) 
suffix1_out, suffix1_states = self.seq1(input2) 


and then pass prefix1_out and suffix1_out tensors to the next LSTM layers as

prefix2_out, prefix2_states = self.seq2(prefix1_out) 
suffix2_out, suffix2_states = self.seq2(suffix1_out)


And, concat prefix1_out and suffix1_out tensors like this

result = torch.cat([out1,out2],1) 


Also, change 

r1=F.sigmoid(self.fc1(result)) 
r2=self.fc2(r1)


to something like this:

out_ll = self.fc1(result)
r1 = nn.Sigmoid() 
r2 = self.fc2(r1(out_ll))

