Hello,
I am trying to implement the methodology proposed in this paper here 13 as the authors have not released the code yet.
The network architecture is a combination of a BaseCNN and a LSTM layer. The image below depicts the network architecture used by the authors taken from the paper mentioned above.
image1600×392 73.6 KB
My questions are as follows:


Considering my input raster size to be [B,3,350,350] with batch_size ‘B’ = 8 and my BaseCNN as mobilenet_v2, the output feature dimension is condensed to [B, 4096] feature vector after the dense layer as per the architecture shown in the above image (Fig 2)


The output of first FC layer is further reduced to [B,128] vector and then given as an input to a LSTM (Fig 3). The initial cell state is initialized to zero. Here is where I am confused on how can I give a 1D vector as an input to LSTM which expects input as (seq_len, batch, input_size) and  hidden state  of shape (num_layers * num_directions, batch, hidden_size)


PS: Section 3.3 in the paper explains the network architecture in detail.
How can I implement this ?
Thanks