Algorithm/deepHeadPose.git

			@@ -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(1717512, 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(
			@@ -234,199 +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

			class Hopenet_new(nn.Module):
			# This is just Hopenet with 3 output layers for yaw, pitch and roll.
			def __init__(self, block, layers, num_bins):
			self.inplanes = 64
			super(Hopenet_new, self).__init__()
			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.fc_finetune_new = nn.Linear(512 * block.expansion + 256 * block.expansion + 3, 3)
			self.conv1x1 = nn.Conv2d(1024, 64, kernel_size = 1, stride = 1, bias=False)
			self.maxpool_interm = nn.MaxPool2d(kernel_size=5, stride=3, padding=1)

			self.idx_tensor = Variable(torch.FloatTensor(range(66))).cuda()

			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):
			x = self.conv1(x)
			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_interm = self.conv1x1(x)
			x_interm = self.relu(x_interm)
			x_interm = self.maxpool_interm(x_interm)
			x_interm = x_interm.view(x_interm.size(0), -1)

			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) * 3 - 99
			pitch = self.softmax(pre_pitch)
			pitch = Variable(torch.sum(pitch.data * self.idx_tensor.data, 1), requires_grad=True) * 3 - 99
			roll = self.softmax(pre_roll)
			roll = Variable(torch.sum(roll.data * self.idx_tensor.data, 1), requires_grad=True) * 3 - 99
			yaw = yaw.view(yaw.size(0), 1)
			pitch = pitch.view(pitch.size(0), 1)
			roll = roll.view(roll.size(0), 1)
			preangles = torch.cat([yaw, pitch, roll], 1)

			# angles predicts the residual
			residuals = self.fc_finetune_new(torch.cat((preangles, x_interm, x), 1))
			final_angles = preangles + residuals

			return pre_yaw, pre_pitch, pre_roll, preangles, final_angles