From ccc7a25994f660b1eab138907f4fcc035469441e Mon Sep 17 00:00:00 2001
From: osmr <osemery@gmail.com>
Date: Fri, 17 Aug 2018 01:34:42 +0300
Subject: [PATCH] Remove an unused script

---
 pytorch/models/others/shiftresnet.py | 89 ----------------------------
 1 file changed, 89 deletions(-)
 delete mode 100644 pytorch/models/others/shiftresnet.py

diff --git a/pytorch/models/others/shiftresnet.py b/pytorch/models/others/shiftresnet.py
deleted file mode 100644
index 615298af7..000000000
--- a/pytorch/models/others/shiftresnet.py
+++ /dev/null
@@ -1,89 +0,0 @@
-"""
-    ShiftResNet.
-    Original paper: 'Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions'
-    Source repo: https://github.com/alvinwan/shiftresnet-cifar
-"""
-
-"""PyTorch implementation of ShiftResNet
-
-ShiftResNet modifications written by Bichen Wu and Alvin Wan. Efficient CUDA
-implementation of shift written by Peter Jin.
-
-Reference:
-[1] Bichen Wu, Alvin Wan, Xiangyu Yue, Peter Jin, Sicheng Zhao, Noah Golmant,
-    Amir Gholaminejad, Joseph Gonzalez, Kurt Keutzer
-    Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions.
-    arXiv:1711.08141
-"""
-
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .resnet import ResNet
-from models.shiftnet_cuda_v2.nn import GenericShift_cuda
-
-
-class ShiftConv(nn.Module):
-
-    def __init__(self, in_planes, out_planes, stride=1, expansion=1):
-        super(ShiftConv, self).__init__()
-        self.expansion = expansion
-        self.in_planes = in_planes
-        self.out_planes = out_planes
-        self.mid_planes = mid_planes = int(out_planes * self.expansion)
-
-        self.conv1 = nn.Conv2d(
-            in_planes, mid_planes, kernel_size=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(mid_planes)
-
-        self.shift2 = GenericShift_cuda(kernel_size=3, dilate_factor=1)
-        self.conv2 = nn.Conv2d(
-            mid_planes, out_planes, kernel_size=1, bias=False, stride=stride)
-        self.bn2 = nn.BatchNorm2d(out_planes)
-
-        self.shortcut = nn.Sequential()
-        if stride != 1 or in_planes != out_planes:
-            self.shortcut = nn.Sequential(
-                nn.Conv2d(
-                  in_planes, out_planes, kernel_size=1, stride=stride,
-                  bias=False),
-                nn.BatchNorm2d(out_planes)
-            )
-
-    def flops(self):
-        if not hasattr(self, 'int_nchw'):
-            raise UserWarning('Must run forward at least once')
-        (_, _, int_h, int_w), (_, _, out_h, out_w) = self.int_nchw, self.out_nchw
-        flops = int_h * int_w * self.in_planes * self.mid_planes + \
-                out_h * out_w * self.mid_planes * self.out_planes
-        if len(self.shortcut) > 0:
-            flops += self.in_planes * self.out_planes * out_h * out_w
-        return flops
-
-    def forward(self, x):
-        shortcut = self.shortcut(x)
-        x = F.relu(self.bn1(self.conv1(x)))
-        self.int_nchw = x.size()
-        x = F.relu(self.bn2(self.conv2(self.shift2(x))))
-        self.out_nchw = x.size()
-        x += shortcut
-        return x
-
-
-def ShiftResNet20(expansion=1, num_classes=10):
-    block = lambda in_planes, out_planes, stride: \
-        ShiftConv(in_planes, out_planes, stride, expansion=expansion)
-    return ResNet(block, [3, 3, 3], num_classes=num_classes)
-
-
-def ShiftResNet56(expansion=1, num_classes=10):
-    block = lambda in_planes, out_planes, stride: \
-        ShiftConv(in_planes, out_planes, stride, expansion=expansion)
-    return ResNet(block, [9, 9, 9], num_classes=num_classes)
-
-
-def ShiftResNet110(expansion=1, num_classes=10):
-    block = lambda in_planes, out_planes, stride: \
-        ShiftConv(in_planes, out_planes, stride, expansion=expansion)
-    return ResNet(block, [18, 18, 18], num_classes=num_classes)
-