From f111cb002b9c6065fdf6bb274ce5857a9e875e8c Mon Sep 17 00:00:00 2001
From: chenshijun <csj_sky@126.com>
Date: 星期三, 05 六月 2019 15:38:49 +0800
Subject: [PATCH] face rectangle
---
code/hopenet.py | 187 ++--------------------------------------------
1 files changed, 8 insertions(+), 179 deletions(-)
diff --git a/code/hopenet.py b/code/hopenet.py
index c6bf0db..c9e0b74 100644
--- a/code/hopenet.py
+++ b/code/hopenet.py
@@ -4,54 +4,10 @@
import math
import torch.nn.functional as F
-def ycbcr_to_rgb(input):
- # input is mini-batch N x 3 x H x W of an YCbCr image
- output = Variable(input.data.new(*input.size()))
- output[:, 0, :, :] = input[:, 0, :, :] + (input[:, 2, :, :] - 0.502) * 1.4
- output[:, 1, :, :] = input[:, 0, :, :] - (input[:, 1, :, :] - 0.502) * 0.343 - (input[:, 2, :, :] - 0.502) * 0.711
- output[:, 2, :, :] = input[:, 0, :, :] + (input[:, 1, :, :] - 0.502) * 1.765
- # output[output <= 0] = 0.
- # output[output > 1] = 1.
- return output
-
-# CNN Model (2 conv layer)
-class Simple_CNN(nn.Module):
- def __init__(self):
- super(Simple_CNN, self).__init__()
- self.layer1 = nn.Sequential(
- nn.Conv2d(3, 64, kernel_size=3, padding=0),
- nn.BatchNorm2d(64),
- nn.ReLU(),
- nn.MaxPool2d(2))
- self.layer2 = nn.Sequential(
- nn.Conv2d(64, 128, kernel_size=3, padding=0),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.MaxPool2d(2))
- self.layer3 = nn.Sequential(
- nn.Conv2d(128, 256, kernel_size=3, padding=0),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.MaxPool2d(2))
- self.layer4 = nn.Sequential(
- nn.Conv2d(256, 512, kernel_size=3, padding=0),
- nn.BatchNorm2d(512),
- nn.ReLU(),
- nn.MaxPool2d(2))
- self.fc = nn.Linear(17*17*512, 3)
-
- def forward(self, x):
- out = self.layer1(x)
- out = self.layer2(out)
- out = self.layer3(out)
- out = self.layer4(out)
- out = out.view(out.size(0), -1)
- out = self.fc(out)
- return out
-
class Hopenet(nn.Module):
- # This is just Hopenet with 3 output layers for yaw, pitch and roll.
- def __init__(self, block, layers, num_bins, iter_ref):
+ # Hopenet with 3 output layers for yaw, pitch and roll
+ # Predicts Euler angles by binning and regression with the expected value
+ def __init__(self, block, layers, num_bins):
self.inplanes = 64
super(Hopenet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
@@ -68,12 +24,8 @@
self.fc_pitch = nn.Linear(512 * block.expansion, num_bins)
self.fc_roll = nn.Linear(512 * block.expansion, num_bins)
- self.softmax = nn.Softmax()
+ # Vestigial layer from previous experiments
self.fc_finetune = nn.Linear(512 * block.expansion + 3, 3)
-
- self.idx_tensor = Variable(torch.FloatTensor(range(66))).cuda()
-
- self.iter_ref = iter_ref
for m in self.modules():
if isinstance(m, nn.Conv2d):
@@ -117,27 +69,10 @@
pre_pitch = self.fc_pitch(x)
pre_roll = self.fc_roll(x)
- yaw = self.softmax(pre_yaw)
- yaw = Variable(torch.sum(yaw.data * self.idx_tensor.data, 1), requires_grad=True)
- pitch = self.softmax(pre_pitch)
- pitch = Variable(torch.sum(pitch.data * self.idx_tensor.data, 1), requires_grad=True)
- roll = self.softmax(pre_roll)
- roll = Variable(torch.sum(roll.data * self.idx_tensor.data, 1), requires_grad=True)
- yaw = yaw.view(yaw.size(0), 1)
- pitch = pitch.view(pitch.size(0), 1)
- roll = roll.view(roll.size(0), 1)
- angles = []
- preangles = torch.cat([yaw, pitch, roll], 1)
- angles.append(preangles)
-
- # angles predicts the residual
- for idx in xrange(self.iter_ref):
- angles.append(self.fc_finetune(torch.cat((angles[idx], x), 1)))
-
- return pre_yaw, pre_pitch, pre_roll, angles
+ return pre_yaw, pre_pitch, pre_roll
class ResNet(nn.Module):
-
+ # ResNet for regression of 3 Euler angles.
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
@@ -192,11 +127,11 @@
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc_angles(x)
-
return x
class AlexNet(nn.Module):
-
+ # AlexNet laid out as a Hopenet - classify Euler angles in bins and
+ # regress the expected value.
def __init__(self, num_bins):
super(AlexNet, self).__init__()
self.features = nn.Sequential(
@@ -234,109 +169,3 @@
pitch = self.fc_pitch(x)
roll = self.fc_roll(x)
return yaw, pitch, roll
-
-class Hopenet_SR(nn.Module):
- # This is just Hopenet with 3 output layers for yaw, pitch and roll.
- def __init__(self, block, layers, num_bins, upscale_factor):
- self.inplanes = 64
- super(Hopenet, self).__init__()
- # Super resolution sub-network
- self.sr_relu = nn.ReLU()
- self.sr_conv1 = nn.Conv2d(1, 64, (5, 5), (1, 1), (2, 2))
- self.sr_conv2 = nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1))
- self.sr_conv3 = nn.Conv2d(64, 32, (3, 3), (1, 1), (1, 1))
- self.sr_conv4 = nn.Conv2d(32, upscale_factor ** 2, (3, 3), (1, 1), (1, 1))
- self.sr_pixel_shuffle = nn.PixelShuffle(upscale_factor)
-
- # Pose estimation sub-network
- self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- self.bn1 = nn.BatchNorm2d(64)
- self.relu = nn.ReLU(inplace=True)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- self.layer1 = self._make_layer(block, 64, layers[0])
- self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
- self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
- self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
- self.avgpool = nn.AvgPool2d(7)
- self.fc_yaw = nn.Linear(512 * block.expansion, num_bins)
- self.fc_pitch = nn.Linear(512 * block.expansion, num_bins)
- self.fc_roll = nn.Linear(512 * block.expansion, num_bins)
-
- self.softmax = nn.Softmax()
- self.idx_tensor = Variable(torch.FloatTensor(range(66))).cuda()
-
- self.upscale_factor = upscale_factor
-
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- m.weight.data.normal_(0, math.sqrt(2. / n))
- elif isinstance(m, nn.BatchNorm2d):
- m.weight.data.fill_(1)
- m.bias.data.zero_()
-
- def _make_layer(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=False),
- nn.BatchNorm2d(planes * block.expansion),
- )
-
- 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 forward(self, x):
- # Super-resolution sub-network
- y_channel = x[:,0,:,:]
-
- sr_y = self.sr_relu(self.sr_conv1(y_channel))
- sr_y = self.sr_relu(self.sr_conv2(sr_y))
- sr_y = self.sr_relu(self.sr_conv3(sr_y))
- sr_y = self.sr_pixel_shuffle(self.sr_conv4(sr_y))
-
- x[:,0,:,:] = sr_y
- x_rgb = ycbcr_to_rgb(x)
-
- out_img_cb = cb.resize(out_img_y.size, Image.BICUBIC)
- out_img_cr = cr.resize(out_img_y.size, Image.BICUBIC)
- out_img = Image.merge('YCbCr', [out_img_y, out_img_cb, out_img_cr]).convert('RGB')
-
- # Pose estimation sub-network
- x = self.conv1(sr_output)
- x = self.bn1(x)
- x = self.relu(x)
- x = self.maxpool(x)
-
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
-
- x = self.avgpool(x)
- x = x.view(x.size(0), -1)
- pre_yaw = self.fc_yaw(x)
- pre_pitch = self.fc_pitch(x)
- pre_roll = self.fc_roll(x)
-
- yaw = self.softmax(pre_yaw)
- yaw = Variable(torch.sum(yaw.data * self.idx_tensor.data, 1), requires_grad=True)
- pitch = self.softmax(pre_pitch)
- pitch = Variable(torch.sum(pitch.data * self.idx_tensor.data, 1), requires_grad=True)
- roll = self.softmax(pre_roll)
- roll = Variable(torch.sum(roll.data * self.idx_tensor.data, 1), requires_grad=True)
- yaw = yaw.view(yaw.size(0), 1)
- pitch = pitch.view(pitch.size(0), 1)
- roll = roll.view(roll.size(0), 1)
- angles = []
- preangles = torch.cat([yaw, pitch, roll], 1)
- angles.append(preangles)
-
- return pre_yaw, pre_pitch, pre_roll, angles, sr_output
--
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