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deformable_conv_2d.py
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"""Deformable 2D convolution
Taken from https://github.com/oeway/pytorch-deform-conv/
"""
import torch
import torch.nn as nn
import numpy as np
from torch.autograd import Variable
def th_flatten(a):
return a.contiguous().view(a.nelement())
def th_repeat(a, repeats, axis=0):
assert len(a.size()) == 1
return th_flatten(torch.transpose(a.repeat(repeats, 1), 0, 1))
def np_repeat_2d(a, repeats):
assert len(a.shape) == 2
a = np.expand_dims(a, 0)
a = np.tile(a, [repeats, 1, 1])
return a
def th_batch_map_coordinates(input, coords, order=1):
batch_size = input.size(0)
input_height = input.size(1)
input_width = input.size(2)
n_coords = coords.size(1)
# understand what coords narrow is doing!
coords = torch.cat((torch.clamp(coords.narrow(2, 0, 1), 0, input_height - 1), torch.clamp(coords.narrow(2, 1, 1), 0, input_width - 1)), 2)
assert (coords.size(1) == n_coords)
coords_lt = coords.floor().long()
coords_rb = coords.ceil().long()
coords_rt = torch.stack([coords_lt[..., 0], coords_rb[..., 1]], 2)
coords_lb = torch.stack([coords_rb[..., 0], coords_lt[..., 1]], 2)
idx = th_repeat(torch.arange(0, batch_size), n_coords).long()
idx = Variable(idx, requires_grad=False)
if input.is_cuda:
idx = idx.cuda()
def _get_vals_by_coords(input, coords):
indices = torch.stack([
idx, th_flatten(coords[..., 0]), th_flatten(coords[..., 1])
], 1)
inds = indices[:, 0]*input.size(1)*input.size(2)+ indices[:, 1]*input.size(2) + indices[:, 2]
vals = th_flatten(input).index_select(0, inds)
vals = vals.view(batch_size, n_coords)
return vals
vals_lt = _get_vals_by_coords(input, coords_lt.detach())
vals_rb = _get_vals_by_coords(input, coords_rb.detach())
vals_lb = _get_vals_by_coords(input, coords_lb.detach())
vals_rt = _get_vals_by_coords(input, coords_rt.detach())
# mostly 0 coordinates
coords_offset_lt = coords - coords_lt.type(coords.data.type())
vals_t = coords_offset_lt[..., 0]*(vals_rt - vals_lt) + vals_lt
vals_b = coords_offset_lt[..., 0]*(vals_rb - vals_lb) + vals_lb
mapped_vals = coords_offset_lt[...,1]* (vals_b - vals_t) + vals_t
return mapped_vals
def th_generate_grid(batch_size, input_height, input_width, dtype, cuda):
grid = np.meshgrid(
range(input_height), range(input_width), indexing='ij'
)
grid = np.stack(grid, axis=-1)
grid = grid.reshape(-1, 2)
grid = np_repeat_2d(grid, batch_size)
grid = torch.from_numpy(grid).type(dtype)
if cuda:
grid = grid.cuda()
return Variable(grid, requires_grad=False)
def th_batch_map_offsets(input, offsets, grid=None, order=1):
batch_size = input.size(0)
input_height = input.size(1)
input_width = input.size(2)
offsets = offsets.view(batch_size, -1, 2)
if grid is None:
grid = th_generate_grid(batch_size, input_height, input_width, offsets.data.type(), offsets.data.is_cuda)
coords = offsets + grid
mapped_vals = th_batch_map_coordinates(input, coords)
return mapped_vals
class ConvOffset2D(nn.Conv2d):
def __init__(self, filters, init_normal_stddev=0.01, **kwargs):
self.filters = filters
self._grid_param = None
super(ConvOffset2D, self).__init__(self.filters, self.filters*2, 3, padding=1, bias=False, **kwargs)
self.weight.data.copy_(self._init_weights(self.weight, init_normal_stddev))
def forward(self, x):
x_shape = x.size()
offsets = super(ConvOffset2D, self).forward(x)
# offsets: (b*c, h, w, 2)
offsets = self._to_bc_h_w_2(offsets, x_shape)
# x: (b*c, h, w)
x = self._to_bc_h_w(x, x_shape)
# X_offset: (b*c, h, w)
x_offset = th_batch_map_offsets(x, offsets, grid=self._get_grid(self,x))
# x_offset: (b, h, w, c)
x_offset = self._to_b_c_h_w(x_offset, x_shape)
return x_offset
@staticmethod
def _get_grid(self, x):
batch_size, input_height, input_width = x.size(0), x.size(1), x.size(2)
dtype, cuda = x.data.type(), x.data.is_cuda
if self._grid_param == (batch_size, input_height, input_width, dtype, cuda):
return self._grid
self._grid_param = (batch_size, input_height, input_width, dtype, cuda)
self._grid = th_generate_grid(batch_size, input_height, input_width, dtype, cuda)
return self._grid
@staticmethod
def _init_weights(weights, std):
fan_out = weights.size(0)
fan_in = weights.size(1) * weights.size(2) * weights.size(3)
w = np.random.normal(0.0, std, (fan_out, fan_in))
return torch.from_numpy(w.reshape(weights.size()))
@staticmethod
def _to_bc_h_w_2(x, x_shape):
"""(b, 2c, h, w) -> (b*c, h, w, 2)"""
x = x.contiguous().view(-1, int(x_shape[2]), int(x_shape[3]), 2)
return x
@staticmethod
def _to_bc_h_w(x, x_shape):
"""(b, c, h, w) -> (b*c, h, w)"""
x = x.contiguous().view(-1, int(x_shape[2]), int(x_shape[3]))
return x
@staticmethod
def _to_b_c_h_w(x, x_shape):
"""(b*c, h, w) -> (b, c, h, w)"""
x = x.contiguous().view(-1, int(x_shape[1]), int(x_shape[2]), int(x_shape[3]))
return x