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update backend for PyTorch Update (#130)
* update backend * version fixes #123
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‎LICENSE‎

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MIT License
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Copyright (c) 2017 Hang Zhang. All rights reserved.
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Copyright (c) 2018 Amazon.com, Inc. or its affiliates. All rights reserved.
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Copyright (c) 2017- Hang Zhang. All rights reserved.
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Copyright (c) 2018- Amazon.com, Inc. or its affiliates. All rights reserved.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal

‎docs/source/experiments/segmentation.rst‎

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<code xml:space="preserve" id="cmd_enc101_ade" style="display: none; text-align: left; white-space: pre-wrap">
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CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset ADE20K --model EncNet --aux --se-loss --backbone resnet101
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CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset ADE20K --model EncNet --aux --se-loss --backbone resnet101 --base-size 640 --crop-size 576
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</code>
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<code xml:space="preserve" id="cmd_enc101_voc" style="display: none; text-align: left; white-space: pre-wrap">
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# First finetuning COCO dataset pretrained model on augmented set
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# You can also train from scratch on COCO by yourself
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CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset Pascal_aug --model-zoo EncNet_Resnet101_COCO --aux --se-loss --lr 0.001 --syncbn --ngpus 4 --checkname res101
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CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset Pascal_aug --model-zoo EncNet_Resnet101_COCO --aux --se-loss --lr 0.001 --syncbn --ngpus 4 --checkname res101 --ft
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# Finetuning on original set
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CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset Pascal_voc --model encnet --aux --se-loss --backbone resnet101 --lr 0.0001 --syncbn --ngpus 4 --checkname res101 --resume runs/Pascal_aug/encnet/res101/checkpoint.params
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CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset Pascal_voc --model encnet --aux --se-loss --backbone resnet101 --lr 0.0001 --syncbn --ngpus 4 --checkname res101 --resume runs/Pascal_aug/encnet/res101/checkpoint.params --ft
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</code>
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Quick Demo

‎docs/source/experiments/texture.rst‎

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cd PyTorch-Encoding/
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python scripts/prepare_minc.py
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- Download pre-trained model (pre-trained on train-1 split using single training size of 224, with an error rate of :math:`19.70\%` using single crop on test-1 set)::
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- Download pre-trained model (pre-trained on train-1 split using single training size of 224, with an error rate of :math:`18.96\%` using single crop on test-1 set)::
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cd experiments/recognition
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python model/download_models.py

‎encoding/__init__.py‎

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"""An optimized PyTorch package with CUDA backend."""
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from .version import __version__
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from . import nn, functions, dilated, parallel, utils, models, datasets, optimizer
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from . import nn, functions, dilated, parallel, utils, models, datasets

‎encoding/datasets/cityscapes.py‎

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import os
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import sys
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import random
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import numpy as np
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from tqdm import tqdm, trange
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from PIL import Image, ImageOps, ImageFilter
@@ -93,7 +94,7 @@ def _sync_transform(self, img, mask):
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mask = mask.transpose(Image.FLIP_LEFT_RIGHT)
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crop_size = self.crop_size
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# random scale (short edge from 480 to 720)
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short_size = random.randint(int(self.base_size*0.5), int(self.base_size*2.5))
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short_size = random.randint(int(self.base_size*0.5), int(self.base_size*2.0))
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w, h = img.size
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if h > w:
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ow = short_size

‎encoding/lib/cpu/nms_cpu.cpp‎

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#include <torch/tensor.h>
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#include <ATen/ATen.h>
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#include <ATen/NativeFunctions.h>
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auto num_boxes = input.size(1);
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auto batch_size = input.size(0);
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auto mask = input.type().toScalarType(at::kByte).tensor({batch_size, num_boxes});
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auto mask = torch::zeros({batch_size, num_boxes}, input.type().toScalarType(at::kByte));
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//auto mask = input.type().toScalarType(at::kByte).tensor({batch_size, num_boxes});
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mask.fill_(1);
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auto *rawMask = mask.data<unsigned char>();
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auto *rawIdx = sorted_inds.data<int64_t>();

‎encoding/lib/cpu/roi_align_cpu.cpp‎

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#include <torch/tensor.h>
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#include <ATen/ATen.h>
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//#include <omp.h>
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AT_ASSERT(roi_cols == 4 || roi_cols == 5);
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// Output at::Tensor is (num_rois, C, pooled_height, pooled_width)
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auto output = input.type().tensor({num_rois, channels, pooled_height, pooled_width});
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auto output = torch::zeros({num_rois, channels, pooled_height, pooled_width}, input.options());
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AT_ASSERT(input.is_contiguous());
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AT_ASSERT(bottom_rois.is_contiguous());
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AT_ASSERT(roi_cols == 4 || roi_cols == 5);
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// Output at::Tensor is (num_rois, C, pooled_height, pooled_width)
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auto grad_in = bottom_rois.type().tensor({b_size, channels, height, width}).zero_();
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auto grad_in = torch::zeros({b_size, channels, height, width}, bottom_rois.options());
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AT_ASSERT(bottom_rois.is_contiguous());
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‎encoding/lib/cpu/syncbn_cpu.cpp‎

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#include <torch/tensor.h>
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#include <ATen/ATen.h>
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#include <vector>
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std::vector<at::Tensor> Sum_Square_Forward_CPU(
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const at::Tensor input) {
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/* outputs */
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at::Tensor sum = input.type().tensor({input.size(1)}).zero_();
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at::Tensor square = input.type().tensor({input.size(1)}).zero_();
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at::Tensor sum = torch::zeros({input.size(1)}, input.options());
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at::Tensor square = torch::zeros({input.size(1)}, input.options());
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return {sum, square};
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}
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‎encoding/lib/gpu/encoding_kernel.cu‎

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#include <vector>
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#include <torch/tensor.h>
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#include <ATen/ATen.h>
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#include <ATen/cuda/CUDAContext.h>
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const at::Tensor X_,
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const at::Tensor C_) {
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/* Device tensors */
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auto E_ = A_.type().tensor({A_.size(0), C_.size(0), C_.size(1)}).zero_();
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auto E_ = torch::zeros({A_.size(0), C_.size(0), C_.size(1)}, A_.options());
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cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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// B, K, D
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dim3 blocks(C_.size(1), C_.size(0), X_.size(0));
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const at::Tensor X_,
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const at::Tensor C_,
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const at::Tensor S_) {
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auto SL_ = X_.type().tensor({X_.size(0), X_.size(1), C_.size(0)}).zero_();
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auto SL_ = torch::zeros({X_.size(0), X_.size(1), C_.size(0)}, X_.options());
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cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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dim3 blocks(C_.size(0), X_.size(1), X_.size(0));
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dim3 threads(getNumThreads(C_.size(1)));

‎encoding/lib/gpu/encodingv2_kernel.cu‎

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#include <vector>
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#include <torch/tensor.h>
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#include <ATen/ATen.h>
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#include <ATen/Functions.h>
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#include <ATen/cuda/CUDAContext.h>
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const at::Tensor STD_) {
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// const at::Tensor S_,
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// X \in R^{B, N, D}, C \in R^{K, D}, S \in R^K
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auto KD_ = X_.type().tensor({X_.size(0), X_.size(1), C_.size(0)}).zero_();
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auto KD_ = torch::zeros({X_.size(0), X_.size(1), C_.size(0)}, X_.options());
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// E(x), E(x^2)
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int N = X_.size(0) * X_.size(1);
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cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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double eps) {
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// const at::Tensor S_,
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// X \in R^{B, N, D}, C \in R^{K, D}, S \in R^K
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auto KD_ = X_.type().tensor({X_.size(0), X_.size(1), C_.size(0)}).zero_();
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auto KD_ = torch::zeros({X_.size(0), X_.size(1), C_.size(0)}, X_.options());
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// E(x), E(x^2)
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int N = X_.size(0) * X_.size(1);
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auto SVar_ = (X_.pow(2).sum(0).sum(0).view({1, X_.size(2)}) -
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const at::Tensor C_,
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const at::Tensor STD_) {
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/* Device tensors */
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auto E_ = A_.type().tensor({A_.size(0), C_.size(0), C_.size(1)}).zero_();
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auto E_ = torch::zeros({A_.size(0), C_.size(0), C_.size(1)}, A_.options());
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// auto IS_ = 1.0f / (S_ + eps).sqrt();
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cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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// B, K, D

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