Pixel-Adaptive Convolutional Neural Networks
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Pixel-Adaptive Convolutional Neural Networks
Hang Su, Varun Jampani, Deqing Sun, Orazio Gallo, Erik Learned-Miller, and Jan Kautz.
CVPR 2019.
License
Copyright (C) 2019 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
Installation
- Make sure you have Python>=3.5 (we recommend using a Conda environment).
- Add the project directory to your Python paths.
- Install dependencies:
- PyTorch>=0.4 (incl. torchvision) with CUDA: see PyTorch instructions.
- Additional libraries:
pip install -r requirements.txt
- (Optional) Verify installation:
python -m unittest
Layer Catalog
We implemented 5 types of PAC layers (as PyTorch Module):
PacConv2d: the standard variantPacConvTranspose2d: the transposed (fractionally-strided) variant for upsamplingPacPool2d: the pooling variantPacCRF: Mean-Field (MF) inference of a CRFPacCRFLoose: MF inference of a CRF where the MF steps do not share weights
More details regarding each layer is provided below.
PacConv2d
PacConv2d is the PAC counterpart of nn.Conv2d. It accepts most standard nn.Conv2d arguments (including in_channels, out_channels, kernel_size, bias, stride, padding, dilation, but not groups and padding_mode),
and we make sure that when the same arguments are used, PacConv2d and nn.Conv2d have the exact same output sizes.
A few additional optional arguments are available:
Args (in addition to those of Conv2d):
kernel_type (str): 'gaussian' | 'inv_{alpha}_{lambda}[_asym][_fixed]'. Default: 'gaussian'
smooth_kernel_type (str): 'none' | 'gaussian' | 'average_{sz}' | 'full_{sz}'. Default: 'none'
normalize_kernel (bool): Default: False
shared_filters (bool): Default: False
filler (str): 'uniform'. Default: 'uniform'
Note:
- kernel_size only accepts odd numbers
- padding should not be larger than :math:`dilation * (kernel_size - 1) / 2`
When used to build computation graphs, this layer takes two input tensors and generates one output tensor:
in_ch, out_ch, g_ch = 16, 32, 8 # channel sizes of input, output and guidance
f, b, h, w = 5, 2, 64, 64 # filter size, batch size, input height and width
input = torch.rand(b, in_ch, h, w)
guide = torch.rand(b, g_ch, h, w) # guidance feature ('f' in Eq.3 of paper)
conv = nn.Conv2d(in_ch, out_ch, f)
out_conv = conv(input) # standard spatial convolution
pacconv = PacConv2d(in_ch, out_ch, f)
out_pac = pacconv(input, guide) # PAC
out_pac = pacconv(input, None, guide_k) # alternative interface
# guide_k is pre-computed 'K' (see Eq.3 of paper)
# of shape [b, g_ch, f, f, h, w]. packernel2d can be
# used for its creation. Use pacconv2d (in conjunction with packernel2d) for its functional interface.
PacConvTranspose2d
PacConvTranspose2d is the PAC counterpart of nn.ConvTranspose2d. It accepts most standard nn.ConvTranspose2d
arguments (including in_channels, out_channels, kernel_size, bias, stride, padding, output_padding, dilation, but not groups and padding_mode), and we make sure that when the same arguments are used,
PacConvTranspose2d and nn.ConvTranspose2d have the exact same output sizes.
A few additional optional arguments are available: , and also a few additional ones:
Args (in addition to those of ConvTranspose2d):
kernel_type (str): 'gaussian' | 'inv_{alpha}_{lambda}[_asym][_fixed]'. Default: 'gaussian'
smooth_kernel_type (str): 'none' | 'gaussian' | 'average_{sz}' | 'full_{sz}'. Default: 'none'
normalize_kernel (bool): Default: False
shared_filters (bool): Default: False
filler (str): 'uniform' | 'linear'. Default: 'uniform'
Note:
- kernel_size only accepts odd numbers
- padding should not be larger than :math:`dilation * (kernel_size - 1) / 2`
Similar to PacConv2d, PacConvTranspose2d also offers two ways of usage:
in_ch, out_ch, g_ch = 16, 32, 8 # channel sizes of input, output and guidance
f, b, h, w, oh, ow = 5, 2, 8, 8, 16, 16 # filter size, batch size, input height and width
input = torch.rand(b, in_ch, h, w)
guide = torch.rand(b, g_ch, oh, ow) # guidance feature, note that it needs to match
# the spatial sizes of the output
convt = nn.ConvTranspose2d(in_ch, out_ch, f, stride=2, padding=2, output_padding=1)
out_convt = convt(input) # standard transposed convolution
pacconvt = PacConvTranspose2d(in_ch, out_ch, f, stride=2, padding=2, output_padding=1)
out_pact = pacconvt(input, guide) # PAC
out_pact = pacconvt(input, None, guide_k) # alternative interface
# guide_k is pre-computed 'K'
# of shape [b, g_ch, f, f, oh, ow].
# packernel2d can be used for its creation. Use pacconv_transpose2d (in conjunction with packernel2d) for its functional interface.
PacPool2d
PacPool2d is the PAC counterpart of nn.AvgPool2d. It accepts most standard nn.AvgPool2d
arguments (including kernel_size, stride, padding, dilation, but not ceil_mode and count_include_pad), and we make sure that when the same arguments are used,
PacPool2d and nn.AvgPool2d have the exact same output sizes.
A few additional optional arguments are available: , and also a few additional ones:
Args:
kernel_size, stride, padding, dilation
kernel_type (str): 'gaussian' | 'inv_{alpha}_{lambda}[_asym][_fixed]'. Default: 'gaussian'
smooth_kernel_type (str): 'none' | 'gaussian' | 'average_{sz}' | 'full_{sz}'. Default: 'none'
channel_wise (bool): Default: False
normalize_kernel (bool): Default: False
out_channels (int): needs to be specified for channel_wise 'inv_*' (non-fixed) kernels. Default: -1
Note:
- kernel_size only accepts odd numbers
- padding should not be larger than :math:`dilation * (kernel_size - 1) / 2`
Similar to PacConv2d, PacPool2d also offers two ways of usage:
in_ch, g_ch = 16, 8 # channel sizes of input and guidance
stride, f, b, h, w = 5, 2, 64, 64 # stride, filter size, batch size, input height and width
input = torch.rand(b, in_ch, h, w)
guide = torch.rand(b, g_ch, h, w) # guidance feature
pool = nn.AvgPool2d(f, stride)
out_pool = pool(input) # standard spatial convolution
pacpool = PacPool2d(f, stride)
out_pac = pacpool(input, guide) # PAC
out_pac = pacpool(input, None, guide_k) # alternative interface
# guide_k is pre-computed 'K'
# of shape [b, g_ch, f, f, h, w]. packernel2d can be
# used for its creation. Use pacpool2d (in conjunction with packernel2d) for its functional interface.
PacCRF and PacCRFLoose
These layers offer a convenient way to add a CRF component at the end of a dense prediction network. They performs approximate mean-field inference under the hood. Available arguments include:
Args:
channels (int): number of categories.
num_steps (int): number of mean-field update steps.
final_output (str): 'log_softmax' | 'softmax' | 'log_Q'. Default: 'log_Q'
perturbed_init (bool): whether to perturb initialization. Default: True
native_impl (bool): Default: False
fixed_weighting (bool): whether to use fixed weighting for unary/pairwise terms. Default: False
unary_weight (float): Default: 1.0
pairwise_kernels (dict or list): pairwise kernels, see add_pairwise_kernel() for details. Default: NoneUsage example:
# create a CRF layer for 21 classes using 5 mean-field steps
crf = PacCRF(21, num_steps=5, unary_weight=1.0)
# add a pariwise term with equal weight with the unary term
crf.add_pairwise_kernel(kernel_size=5, dilation=1, blur=1, compat_type='4d', pairwise_weight=1.0)
# a convenient function is provided for creating pairwise features based on pixel color and positions
edge_features = [paccrf.create_YXRGB(im, yx_scale=100.0, rgb_scale=30.0)]
output = crf(unary, edge_features)
# Note that we use constant values for unary_weight, pairwise_weight, yx_scale, rgb_scale, but they can
# also take tensors and be learned through backprop.Experiments
Joint upsampling
Joint depth upsampling on NYU Depth V2
-
Train/test split is provided by Li et al.
-
Test with one of our pre-trained models:
python -m task_jointUpsampling.main --load-weights weights_depth/x8_pac_weights_epoch_5000.pth \ --download \ --factor 8 \ --model PacJointUpsample \ --dataset NYUDepthV2 \ --data-root data/nyu4x 8x 16x BilinearRMSE: 5.43 RMSE: 8.36 RMSE: 12.90 PacJointUpsampleRMSE: 2.39 | download RMSE: 4.59 | download RMSE: 8.09 | download PacJointUpsampleLiteRMSE: 2.55 | download RMSE: 4.82 | download RMSE: 8.52 | download DJIFRMSE: 2.64 | download RMSE: 5.15 | download RMSE: 9.39 | download -
Train from scratch:
python -m task_jointUpsampling.main --factor 8 \ --data-root data/nyu \ --exp-root exp/nyu \ --download \ --dataset NYUDepthV2 \ --epochs 5000 \ --lr-steps 3500 4500See
python -m task_jointUpsampling.main -hfor the complete list of command line options.
Joint optical flow upsampling on Sintel
-
Train/val split (
1- train,2- val) is provided in meta/Sintel_train_val.txt (original source):- Validation: 133 pairs
ambush_6(all 19)bamboo_2(last 25)cave_4(last 25)market_6(all 39)temple_2(last 25)
- Training: remaining 908 pairs
- Validation: 133 pairs
-
Test with one of our pre-trained models:
python -m task_jointUpsampling.main --load-weights weights_flow/x8_pac_weights_epoch_5000.pth \ --download \ --factor 8 \ --model PacJointUpsample \ --dataset Sintel \ --data-root data/sintel4x 8x 16x BilinearEPE: 0.4650 EPE: 0.9011 EPE: 1.6281 PacJointUpsampleEPE: 0.1042 | download EPE: 0.2558 | download EPE: 0.5921 | download DJIFEPE: 0.1760 | download EPE: 0.4382 | download EPE: 1.0422 | download -
Train from scratch:
python -m task_jointUpsampling.main --factor 8 \ --data-root data/sintel \ --exp-root exp/sintel \ --download \ --dataset Sintel \ --epochs 5000 \ --lr-steps 3500 4500See
python -m task_jointUpsampling.main -hfor the complete list of command line options.
Semantic segmentation
-
Test with one of the pre-trained models:
python -m task_semanticSegmentation.main --data-root data/voc \ --exp-root exp/voc \ --download \ --load-weights fcn8s_from_caffe.pth \ --model fcn8s \ --test-split val11_sbd \ --test-crop -1miou (val / test) model name weights Backbone (FCN8s) 65.51% / 67.20% fcn8sdownload PacCRF 68.90% / 69.82% fcn8s_crfi5p4d5641p4d5161download PacCRF-32 68.52% / 69.41% fcn8s_crfi5p4d5321download PacFCN (hot-swapping) 67.44% / 69.18% fcn8spacdownload PacFCN+PacCRF 69.87% / 71.34% fcn8spac_crfi5p4d5641p4d5161download Note that the last two models requires argument
--test-crop 512. -
Generate predictions
Use the
--eval predmode to save predictions instead of reporting scores. Predictions will be saved underexp-root/outputs_*_pred, and can be used for VOC evaluation server:python -m task_semanticSegmentation.main \ --data-root data/voc \ --exp-root exp/voc \ --load-weights fcn8s_paccrf_epoch_30.pth \ --test-crop -1 \ --test-split test \ --eval pred \ --model fcn8s_crfi5p4d5641p4d5161 cd exp/voc mkdir -p results/VOC2012/Segmentation mv outputs_test_pred results/VOC2012/Segmentation/comp6_test_cls tar zcf results_fcn8s_crf.tgz results
Note that since there is no publicly available URL for the test split of VOC, when using the test split, the data files need to be downloaded from the official website manually. Simply place the downloaded VOC2012test.tar under the data root and untar.
-
Train models
As an example, here shows the commands for the two-stage training of PacCRF:
# stage 1: train CRF only with frozen backbone python -m task_semanticSegmentation.main \ --data-root data/voc \ --exp-root exp/voc/crf_only \ --load-weights-backbone fcn8s_from_caffe.pth \ --train-split train11 \ --test-split val11_sbd \ --train-crop 449 \ --test-crop -1 \ --model fcn8sfrozen_crfi5p4d5641p4d5161 \ --epochs 40 \ --lr 0.001 \ --lr-steps 20 # stage 2: train CRF and backbone jointly python -m task_semanticSegmentation.main \ --data-root data/voc \ --exp-root exp/voc/joint \ --load-weights-backbone fcn8s_from_caffe.pth \ --load-weights exp/voc/crf_only/weights_epoch_40.pth \ --train-split train11 \ --test-split val11_sbd \ --train-crop 449 \ --test-crop -1 \ --model fcn8s_crfi5lp4d5641p4d5161 \ --epochs 30 \ --lr 0.0000001 \ --lr-steps 20
See python -m task_semanticSegmentation.main -h for the complete list of command line options.
Citation
If you use this code for your research, please consider citing our paper:
@inproceedings{su2019pixel,
author = {Hang Su and
Varun Jampani and
Deqing Sun and
Orazio Gallo and
Erik Learned-Miller and
Jan Kautz},
title = {Pixel-Adaptive Convolutional Neural Networks},
booktitle = {Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR)},
year = {2019}
}