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ConvGRUCell.py
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ConvGRUCell.py
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#!/usr/bin/evn python
# -*- coding: utf-8 -*-
# Copyright (c) 2017 - zihao.chen <zihao.chen@moji.com>
'''
Author: zihao.chen
Create Date: 2018-03-29
Modify Date: 2018-03-29
descirption: ""
'''
import torch
from torch import nn
import torch.nn.functional as f
import numpy as np
from torch.autograd import Variable
class ConvGRUCell(nn.Module):
#input_size=(8, 16), hidden_size=512, kernel_size=(3,3)
def __init__(self, input_size, hidden_size, kernel_size):
super(ConvGRUCell, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.kernel_size = kernel_size
self.height, self.width = 8, 16
self.dropout = nn.Dropout(p=0.5)
# print('kernal-----', kernel_size[0], kernel_size[1])
self.padding = kernel_size[0] // 2, kernel_size[1] // 2
self.ConvGates = nn.Conv2d(self.input_size + self.hidden_size,
2 * self.hidden_size,
self.kernel_size,
padding=self.padding
)
self.Conv_ct = nn.Conv2d(self.input_size + self.hidden_size, self.hidden_size, self.kernel_size,
padding=self.padding)
# dtype = torch.FloatTensor
#input = [5, 3, 512, 8, 16], hidden=
def forward(self, input, hidden):
if hidden is None:
# print (input.data.size()[0])
# print (self.hidden_size)
# print (list(input.data.size()[2:]))
size_h = [input.data.size()[0], self.hidden_size] + list(input.data.size()[2:])
# print size_h
hidden = Variable(torch.zeros(size_h).cuda())
if input is None:
# print (input.data.size()[0])
# print (self.hidden_size)
# print (list(input.data.size()[2:]))
size_h = [hidden.data.size()[0], self.input_size] + list(hidden.data.size()[2:])
# print size_h
input = Variable(torch.zeros(size_h).cuda())
# print input.size()
# print hidden.size()
c1 = self.ConvGates(torch.cat((input, hidden), 1))
# print('c1-----', c1.shape)
(rt, ut) = c1.chunk(2, 1)
# print('rt-----', rt.shape)
# print('ut-----', ut.shape)
# reset_gate = self.dropout(f.sigmoid(rt))
# update_gate = self.dropout(f.sigmoid(ut))
reset_gate = self.dropout(torch.sigmoid(rt))
update_gate = self.dropout(torch.sigmoid(ut))
# print('reset_gate-----', reset_gate.shape)
# print('update_gate-----', update_gate.shape)
# print('hidden----', hidden.shape)
gated_hidden = torch.mul(reset_gate, hidden)
p1 = self.Conv_ct(torch.cat((input, gated_hidden), 1))
# ct = f.tanh(p1)
#ct = h(t-1)
ct = torch.tanh(p1)
#ht
next_h = torch.mul(update_gate, hidden) + (1 - update_gate) * ct
# print('next_h----', next_h.shape)
return next_h, 0
def init_hidden(self, batch_size):
return (torch.zeros(batch_size, self.hidden_size, self.height, self.width).cuda(),
torch.zeros(batch_size, self.hidden_size, self.height, self.width).cuda())
class ConvGRU(nn.Module):
# input_size=(8,16), input_dim=512, hidden_dim=[512, 512], kernel_size=(3,3), num_layers=2
def __init__(self, input_size, input_dim, hidden_dim, kernel_size, num_layers,
batch_first=False, bias=True, return_all_layers=False):
super(ConvGRU, self).__init__()
self._check_kernel_size_consistency(kernel_size)
# Make sure that both `kernel_size` and `hidden_dim` are lists having len == num_layers
kernel_size = self._extend_for_multilayer(kernel_size, num_layers)
hidden_dim = self._extend_for_multilayer(hidden_dim, num_layers)
if not len(kernel_size) == len(hidden_dim) == num_layers:
raise ValueError('Inconsistent list length.')
# input_size = (8, 16) is a tuple, notate the useful method to sign values for height, and width
self.height, self.width = input_size
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.kernel_size = kernel_size
self.num_layers = num_layers
self.batch_first = batch_first
self.bias = bias
self.return_all_layers = return_all_layers
cell_list = []
for i in range(0, self.num_layers):
cur_input_dim = self.input_dim if i == 0 else self.hidden_dim[i - 1]
# to prepare all parameters for every layer, and then construct a object to be called when needed/required
cell_list.append(ConvGRUCell(input_size=self.input_dim,
#input_dim=cur_input_dim,
hidden_size= self.hidden_dim[i],
#hidden_dim=self.hidden_dim[i],
kernel_size=self.kernel_size[i])
#bias=self.bias)
)
self.cell_list = nn.ModuleList(cell_list)
def forward(self, input_tensor, hidden_state=None):
"""
Parameters
----------
input_tensor: todo
5-D Tensor either of shape (t, b, c, h, w) or (b, t, c, h, w)
hidden_state: todo
None. todo implement stateful
Returns
-------
last_state_list, layer_output
-------
here data = [5, 1, 512, 8, 16]= input_tensor is passed into ConvLSTM
5 is number of times, each time is one batch, as 1,
and each batch_size size has 512 feature maps, each feature map size is 8x16
"""
# print('input_tensor---', input_tensor.shape)
# input_tensor = [5, 3, 512, 8, 16]
if not self.batch_first:
# (t, b, c, h, w) -> (b, t, c, h, w)
input_tensor = input_tensor.permute(1, 0, 2, 3, 4)
# input_tensor.shape = [3, 5, 512, 8, 16]
# Implement stateful ConvLSTM
if hidden_state is not None:
raise NotImplementedError()
else:
# input_tensor.size(0)=1
# print('input_tensor.size(0)--', input_tensor.size(0))
hidden_state = self._init_hidden(batch_size=input_tensor.size(0))
layer_output_list = []
last_state_list = []
# seq_len = 5, actually is 5 feature map, each size =
seq_len = input_tensor.size(1)
cur_layer_input = input_tensor
#initilize hidden_state only once
h, c = hidden_state[layer_idx]
for layer_idx in range(self.num_layers):
# initilize hidden_state
h, c = hidden_state[layer_idx]
# h = c size = [3, 512, 8, 16]
# print('h, c,---', h.shape, c.shape)
output_inner = []
output_inner1 = []
final_out = []
for t in range(seq_len):
# change to run forward of ConvLSTMCell, after input_tensor=cur_layer_input[:, t, :, :, :]
# input_tensor shape is [3, 512, 8, 16]
# h, c = self.cell_list[layer_idx](input_tensor=cur_layer_input[:, t, :, :, :],
# cur_state=[h, c])
# hl = torch.cat((h, c), dim=1)
h, c = self.cell_list[layer_idx](input=cur_layer_input[:, t, :, :, :],
hidden=h)
# h = h
# h1, c1 = self.cell_list[layer_idx](input=cur_layer_input[:, t, :, :, :],
# hidden=h)
# aa = torch.sum(h1)
# h = h - h1 / abs(aa)
# print('h------', aa)
# print('h, c----', h.shape, c.shape)
output_inner.append(h)
layer_output = torch.stack(output_inner, dim=1)
# print('1--------', layer_output.shape)
cur_layer_input = layer_output
# print('2--------', cur_layer_input.shape)
layer_output = layer_output.permute(1, 0, 2, 3, 4)
# print('3-------', layer_output.shape)
layer_output_list.append(layer_output)
# print('4-------', layer_output_list)
last_state_list.append([h, c])
# print('5-------', last_state_list)
if not self.return_all_layers:
layer_output_list = layer_output_list[-1:]
# print('6-------', layer_output_list)
last_state_list = last_state_list[-1:]
# print('7-------', last_state_list)
return layer_output_list, last_state_list
def _init_hidden(self, batch_size):
init_states = []
for i in range(self.num_layers):
init_states.append(self.cell_list[i].init_hidden(batch_size))
return init_states
"""
check the type of kernel_size to be the same
"""
@staticmethod
def _check_kernel_size_consistency(kernel_size):
if not (isinstance(kernel_size, tuple) or
(isinstance(kernel_size, list) and all([isinstance(elem, tuple) for elem in kernel_size]))):
raise ValueError('`kernel_size` must be tuple or list of tuples')
"""
if kerner_size = (3,3), num_layers = 2, then _extend_for_multilayer() will make
kernal_size = [(3,3), (3,3)] for each layer
"""
@staticmethod
def _extend_for_multilayer(param, num_layers):
if not isinstance(param, list):
param = [param] * num_layers
return param