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test_modulated.py
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test_modulated.py
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#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import time
import torch
import torch.nn as nn
from torch.autograd import gradcheck
from modules.modulated_dcn import ModulatedDeformConvPack
from modules.modulated_dcn import DeformRoIPooling
from modules.modulated_dcn import ModulatedDeformRoIPoolingPack
deformable_groups = 1
N, inC, inH, inW = 2, 2, 4, 4
outC = 2
kH, kW = 3, 3
def example_dconv():
from modules.modulated_dcn import ModulatedDeformConv
input = torch.randn(2, 64, 128, 128).cuda()
# wrap all things (offset and mask) in DCN
dcn = ModulatedDeformConvPack(64, 64, kernel_size=(3,3), stride=1, padding=1, deformable_groups=2, no_bias=True).cuda()
output = dcn(input)
targert = output.new(*output.size())
targert.data.uniform_(-0.01, 0.01)
error = (targert - output).mean()
error.backward()
print(output.shape)
def example_dpooling():
from modules.modulated_dcn import ModulatedDeformRoIPoolingPack
input = torch.randn(2, 32, 64, 64).cuda()
batch_inds = torch.randint(2, (20, 1)).cuda().float()
x = torch.randint(256, (20, 1)).cuda().float()
y = torch.randint(256, (20, 1)).cuda().float()
w = torch.randint(64, (20, 1)).cuda().float()
h = torch.randint(64, (20, 1)).cuda().float()
rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1)
offset = torch.randn(20, 2, 7, 7).cuda()
input.requires_grad = True
offset.requires_grad = True
# normal roi_align
pooling = DeformRoIPooling(spatial_scale=1.0 / 4,
pooled_size=7,
output_dim=32,
no_trans=True,
group_size=1,
trans_std=0.1).cuda()
# deformable pooling
dpooling = DeformRoIPooling(spatial_scale=1.0 / 4,
pooled_size=7,
output_dim=32,
no_trans=False,
group_size=1,
trans_std=0.1).cuda()
out = pooling(input, rois, offset)
dout = dpooling(input, rois, offset)
print(out.shape)
print(dout.shape)
target_out = out.new(*out.size())
target_out.data.uniform_(-0.01, 0.01)
target_dout = dout.new(*dout.size())
target_dout.data.uniform_(-0.01, 0.01)
e = (target_out - out).mean()
e.backward()
e = (target_dout - dout).mean()
e.backward()
def example_mdpooling():
from modules.modulated_dcn import ModulatedDeformRoIPoolingPack
input = torch.randn(2, 32, 64, 64).cuda()
input.requires_grad = True
batch_inds = torch.randint(2, (20, 1)).cuda().float()
x = torch.randint(256, (20, 1)).cuda().float()
y = torch.randint(256, (20, 1)).cuda().float()
w = torch.randint(64, (20, 1)).cuda().float()
h = torch.randint(64, (20, 1)).cuda().float()
rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1)
# mdformable pooling (V2)
dpooling = ModulatedDeformRoIPoolingPack(spatial_scale=1.0 / 4,
pooled_size=7,
output_dim=32,
no_trans=False,
group_size=1,
trans_std=0.1).cuda()
for i in range(2):
dout = dpooling(input, rois)
target = dout.new(*dout.size())
target.data.uniform_(-0.1, 0.1)
error = (target - dout).mean()
error.backward()
print(dout.shape)
if __name__ == '__main__':
example_dconv()
example_dpooling()
example_mdpooling()