From d285712b1c0a3e0785a4e5b17debdde650a7c26a Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期三, 13 九月 2017 21:34:38 +0800
Subject: [PATCH] Removed hourglass
---
/dev/null | 194 ------------------------------------------------
practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb | 26 +++---
practice/smoothing_ypr.ipynb | 10 +-
3 files changed, 18 insertions(+), 212 deletions(-)
diff --git a/code/hg-hopenet.py b/code/hg-hopenet.py
deleted file mode 100644
index 12476cd..0000000
--- a/code/hg-hopenet.py
+++ /dev/null
@@ -1,194 +0,0 @@
-'''
-Modified from https://github.com/bearpaw/pytorch-pose
-'''
-
-import torch.nn as nn
-import torch.nn.functional as F
-import math
-# from .preresnet import BasicBlock, Bottleneck
-
-__all__ = ['HourglassNet', 'hg1', 'hg2', 'hg4', 'hg8']
-
-class Bottleneck(nn.Module):
- expansion = 2
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(Bottleneck, self).__init__()
- self.bn1 = nn.BatchNorm2d(inplanes)
- self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=True)
- self.bn2 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
- padding=1, bias=True)
- self.bn3 = nn.BatchNorm2d(planes)
- self.conv3 = nn.Conv2d(planes, planes * 2, kernel_size=1, bias=True)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.bn1(x)
- out = self.relu(out)
- out = self.conv1(out)
-
- out = self.bn2(out)
- out = self.relu(out)
- out = self.conv2(out)
-
- out = self.bn3(out)
- out = self.relu(out)
- out = self.conv3(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
-
- return out
-
-class Hourglass(nn.Module):
- def __init__(self, block, num_blocks, planes, depth):
- super(Hourglass, self).__init__()
- self.depth = depth
- self.block = block
- self.upsample = nn.UpsamplingNearest2d(scale_factor=2)
- self.hg = self._make_hour_glass(block, num_blocks, planes, depth)
-
- def _make_residual(self, block, num_blocks, planes):
- layers = []
- for i in range(0, num_blocks):
- layers.append(block(planes*block.expansion, planes))
- # the splat * operator gives: [[1,2], [2]] -> ([1,2], [2])
- return nn.Sequential(*layers)
-
- def _make_hour_glass(self, block, num_blocks, planes, depth):
- hg = []
- for i in range(depth):
- res = []
- for j in range(3):
- res.append(self._make_residual(block, num_blocks, planes))
- if i == 0:
- res.append(self._make_residual(block, num_blocks, planes))
- # ModuleList acts like a list
- hg.append(nn.ModuleList(res))
- return nn.ModuleList(hg)
-
-
- def _hour_glass_forward(self, n, x):
- up1 = self.hg[n-1][0](x)
- low1 = F.max_pool2d(x, 2, stride=2)
- low1 = self.hg[n-1][1](low1)
-
- if n > 1:
- low2 = self._hour_glass_forward(n-1, low1)
- else:
- low2 = self.hg[n-1][3](low1)
- low3 = self.hg[n-1][2](low2)
- up2 = self.upsample(low3)
- out = up1 + up2
- return out
-
- def forward(self, x):
- return self._hour_glass_forward(self.depth, x)
-
-
-class HourglassNet(nn.Module):
- '''Hourglass model from Newell et al ECCV 2016'''
- def __init__(self, block, num_stacks=2, num_blocks=4, num_classes=16):
- super(HourglassNet, self).__init__()
-
- self.inplanes = 64
- self.num_feats = 128
- self.num_stacks = num_stacks
- self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
- bias=True)
- self.bn1 = nn.BatchNorm2d(self.inplanes)
- self.relu = nn.ReLU(inplace=True)
- self.layer1 = self._make_residual(block, self.inplanes, 1)
- self.layer2 = self._make_residual(block, self.inplanes, 1)
- self.layer3 = self._make_residual(block, self.num_feats, 1)
- self.maxpool = nn.MaxPool2d(2, stride=2)
-
- # build hourglass modules
- ch = self.num_feats*block.expansion
- hg, res, fc, score, fc_, score_ = [], [], [], [], [], []
- for i in range(num_stacks):
- hg.append(Hourglass(block, num_blocks, self.num_feats, 4))
- res.append(self._make_residual(block, self.num_feats, num_blocks))
- fc.append(self._make_fc(ch, ch))
- score.append(nn.Conv2d(ch, num_classes, kernel_size=1, bias=True))
- if i < num_stacks-1:
- fc_.append(nn.Conv2d(ch, ch, kernel_size=1, bias=True))
- score_.append(nn.Conv2d(num_classes, ch, kernel_size=1, bias=True))
- self.hg = nn.ModuleList(hg)
- self.res = nn.ModuleList(res)
- self.fc = nn.ModuleList(fc)
- self.score = nn.ModuleList(score)
- self.fc_ = nn.ModuleList(fc_)
- self.score_ = nn.ModuleList(score_)
-
- def _make_residual(self, block, planes, blocks, stride=1):
- downsample = None
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- nn.Conv2d(self.inplanes, planes * block.expansion,
- kernel_size=1, stride=stride, bias=True),
- )
-
- layers = []
- layers.append(block(self.inplanes, planes, stride, downsample))
- self.inplanes = planes * block.expansion
- for i in range(1, blocks):
- layers.append(block(self.inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def _make_fc(self, inplanes, outplanes):
- bn = nn.BatchNorm2d(inplanes)
- conv = nn.Conv2d(inplanes, outplanes, kernel_size=1, bias=True)
- return nn.Sequential(
- conv,
- bn,
- self.relu,
- )
-
- def forward(self, x):
- out = []
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
-
- x = self.layer1(x)
- x = self.maxpool(x)
- x = self.layer2(x)
- x = self.layer3(x)
-
- for i in range(self.num_stacks):
- y = self.hg[i](x)
- y = self.res[i](y)
- y = self.fc[i](y)
- score = self.score[i](y)
- out.append(score)
- if i < self.num_stacks-1:
- fc_ = self.fc_[i](y)
- score_ = self.score_[i](score)
- x = x + fc_ + score_
-
- return out
-
-def hg1(**kwargs):
- model = HourglassNet(Bottleneck, num_stacks=1, num_blocks=8, **kwargs)
- return model
-
-def hg2(**kwargs):
- model = HourglassNet(Bottleneck, num_stacks=2, num_blocks=4, **kwargs)
- return model
-
-def hg4(**kwargs):
- model = HourglassNet(Bottleneck, num_stacks=4, num_blocks=2, **kwargs)
- return model
-
-def hg8(**kwargs):
- model = HourglassNet(Bottleneck, num_stacks=8, num_blocks=1, **kwargs)
- return model
diff --git a/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb b/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb
index 8102abf..cd62e38 100644
--- a/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb
+++ b/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 187,
+ "execution_count": 1,
"metadata": {
"collapsed": false
},
@@ -17,22 +17,22 @@
},
{
"cell_type": "code",
- "execution_count": 188,
+ "execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
- "video_path = '../data/video/SGT036_2016_07_25_pivothead_AVI.avi'\n",
- "bbox_path = '../data/video/annotations/SGT036_childface.txt'\n",
+ "video_path = '../data/video/SWC016_2016_04_08_pivothead_AVI.avi'\n",
+ "bbox_path = '../data/video/annotations/SWC016_childface.txt'\n",
"\n",
- "annot_path = '../output/video/output-SGT036_resnet50_lowlr_epoch_20.txt'\n",
- "output_string = 'SGT036_resnet50_lowlr_epoch_20_smoothed'"
+ "annot_path = '../output/video/output-SWC016_normal_1e-5_epoch_20.txt'\n",
+ "output_string = 'SWC016_normal_1e-5_epoch_20_smooth'"
]
},
{
"cell_type": "code",
- "execution_count": 189,
+ "execution_count": 3,
"metadata": {
"collapsed": false
},
@@ -41,9 +41,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[ 4.170376 0.790443 -0.178368 ..., -3.437805 0.396835 -1.276176]\n",
- "(8508,)\n",
- "(53464,)\n"
+ "[ -1.15884 -14.885593 -16.727406 ..., -57.590378 -56.963156 -58.641377]\n",
+ "(9322,)\n",
+ "(63691,)\n"
]
}
],
@@ -93,7 +93,7 @@
},
{
"cell_type": "code",
- "execution_count": 190,
+ "execution_count": 4,
"metadata": {
"collapsed": false
},
@@ -107,7 +107,7 @@
}
],
"source": [
- "window_len = 5\n",
+ "window_len = 7\n",
"pad = window_len / 2\n",
"window = 'flat'\n",
"window_2 = 'flat'\n",
@@ -159,7 +159,7 @@
},
{
"cell_type": "code",
- "execution_count": 191,
+ "execution_count": 7,
"metadata": {
"collapsed": false
},
diff --git a/practice/smoothing_ypr.ipynb b/practice/smoothing_ypr.ipynb
index 731b91f..cd62e38 100644
--- a/practice/smoothing_ypr.ipynb
+++ b/practice/smoothing_ypr.ipynb
@@ -26,8 +26,8 @@
"video_path = '../data/video/SWC016_2016_04_08_pivothead_AVI.avi'\n",
"bbox_path = '../data/video/annotations/SWC016_childface.txt'\n",
"\n",
- "annot_path = '../output/video/output-SWC016_resnet50_lowlr_epoch_20.txt'\n",
- "output_string = 'SWC016_resnet50_lowlr_epoch_20_smoothed'"
+ "annot_path = '../output/video/output-SWC016_normal_1e-5_epoch_20.txt'\n",
+ "output_string = 'SWC016_normal_1e-5_epoch_20_smooth'"
]
},
{
@@ -41,8 +41,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[ 4.870468 -2.072102 -6.589153 ..., -4.716797 -2.319111 -3.147715]\n",
- "(8967,)\n",
+ "[ -1.15884 -14.885593 -16.727406 ..., -57.590378 -56.963156 -58.641377]\n",
+ "(9322,)\n",
"(63691,)\n"
]
}
@@ -159,7 +159,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 7,
"metadata": {
"collapsed": false
},
--
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