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deform_conv.py
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deform_conv.py
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#author:github:https://github.com/oeway/pytorch-deform-conv.git
from __future__ import absolute_import, division
import torch
from torch.autograd import Variable
import numpy as np
from scipy.ndimage.interpolation import map_coordinates as sp_map_coordinates
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_gather_2d(input, coords):
inds = coords[:, 0]*input.size(1) + coords[:, 1]
x = torch.index_select(th_flatten(input), 0, inds)
return x.view(coords.size(0))
def th_map_coordinates(input, coords, order=1):
assert order == 1
input_size = input.size(0)
coords = torch.clamp(coords, 0, input_size - 1)
coords_lt = coords.floor().long()
coords_rb = coords.ceil().long()
coords_lb = torch.stack([coords_lt[:, 0], coords_rb[:, 1]], 1)
coords_rt = torch.stack([coords_rb[:, 0], coords_lt[:, 1]], 1)
vals_lt = th_gather_2d(input, coords_lt.detach())
vals_rb = th_gather_2d(input, coords_rb.detach())
vals_lb = th_gather_2d(input, coords_lb.detach())
vals_rt = th_gather_2d(input, coords_rt.detach())
coords_offset_lt = coords - coords_lt.type(coords.data.type())
vals_t = vals_lt + (vals_rt - vals_lt) * coords_offset_lt[:, 0]
vals_b = vals_lb + (vals_rb - vals_lb) * coords_offset_lt[:, 0]
mapped_vals = vals_t + (vals_b - vals_t) * coords_offset_lt[:, 1]
return mapped_vals
def sp_batch_map_coordinates(inputs, coords):
# coords = coords.clip(0, inputs.shape[1] - 1)
assert (coords.shape[2] == 2)
height = coords[:,:,0].clip(0, inputs.shape[1] - 1)
width = coords[:,:,1].clip(0, inputs.shape[2] - 1)
np.concatenate((np.expand_dims(height, axis=2), np.expand_dims(width, axis=2)), 2)
mapped_vals = np.array([
sp_map_coordinates(input, coord.T, mode='nearest', order=1)
for input, coord in zip(inputs, coords)
])
return mapped_vals
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)
# coords = torch.clamp(coords, 0, input_size - 1)
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_lb = torch.stack([coords_lt[..., 0], coords_rb[..., 1]], 2)
coords_rt = 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())
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 sp_batch_map_offsets(input, offsets):
batch_size = input.shape[0]
input_height = input.shape[1]
input_width = input.shape[2]
offsets = offsets.reshape(batch_size, -1, 2)
grid = np.stack(np.mgrid[:input_height, :input_width], -1).reshape(-1, 2)
grid = np.repeat([grid], batch_size, axis=0)
coords = offsets + grid
# coords = coords.clip(0, input_size - 1)
mapped_vals = sp_batch_map_coordinates(input, coords)
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