From c13dba86b2dbe581353b72602d7fa6e40991964c Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期三, 27 九月 2017 04:11:23 +0800
Subject: [PATCH] next
---
/dev/null | 221 ------------------------------------
.gitignore | 3
code/hopenet.py | 90 ---------------
3 files changed, 2 insertions(+), 312 deletions(-)
diff --git a/.gitignore b/.gitignore
index 8fe1257..ab3731e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,4 +2,5 @@
*.npy
data/*
output/*
-*.jpg
\ No newline at end of file
+*.jpg
+*.png
\ No newline at end of file
diff --git a/code/hopenet.py b/code/hopenet.py
index b2dd097..c6bf0db 100644
--- a/code/hopenet.py
+++ b/code/hopenet.py
@@ -340,93 +340,3 @@
angles.append(preangles)
return pre_yaw, pre_pitch, pre_roll, angles, sr_output
-
-class Hopenet_LSTM(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_LSTM, 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 = nn.Linear(512 * block.expansion + 3, 3)
-
- self.idx_tensor = Variable(torch.FloatTensor(range(66))).cuda()
-
- self.lstm = nn.LSTM(512 * block.expansion + 3, 256 * block.expansion, 2, batch_first=True)
- self.fc_lstm = nn.Linear(256 * block.expansion, 3)
-
- self.block_expansion = block.expansion
-
- 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 = 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, pitch, roll
- 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)
-
- residuals, _ = self.lstm(torch.cat((preangles, x), 1), (h0, c0))
- residuals = self.fc_lstm(residuals[:, -1, :])
- final_angles = preangles + residuals
-
- return pre_yaw, pre_pitch, pre_roll, preangles, final_angles
diff --git a/code/train_hopenet_lstm.py b/code/train_hopenet_lstm.py
deleted file mode 100644
index 7df4708..0000000
--- a/code/train_hopenet_lstm.py
+++ /dev/null
@@ -1,221 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-from torch.autograd import Variable
-from torch.utils.data import DataLoader
-from torchvision import transforms
-import torchvision
-import torch.backends.cudnn as cudnn
-import torch.nn.functional as F
-
-import cv2
-import matplotlib.pyplot as plt
-import sys
-import os
-import argparse
-
-import datasets
-import hopenet
-import torch.utils.model_zoo as model_zoo
-
-model_urls = {
- 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
- 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
- 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
- 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
- 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
-}
-
-def parse_args():
- """Parse input arguments."""
- parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.')
- parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
- default=0, type=int)
- parser.add_argument('--num_epochs', dest='num_epochs', help='Maximum number of training epochs.',
- default=5, type=int)
- parser.add_argument('--num_epochs_ft', dest='num_epochs_ft', help='Maximum number of finetuning epochs.',
- default=5, type=int)
- parser.add_argument('--batch_size', dest='batch_size', help='Batch size.',
- default=16, type=int)
- parser.add_argument('--lr', dest='lr', help='Base learning rate.',
- default=0.001, type=float)
- parser.add_argument('--data_dir', dest='data_dir', help='Directory path for data.',
- default='', type=str)
- parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.',
- default='', type=str)
- parser.add_argument('--output_string', dest='output_string', help='String appended to output snapshots.', default = '', type=str)
- parser.add_argument('--alpha', dest='alpha', help='Regression loss coefficient.',
- default=0.001, type=float)
- parser.add_argument('--iter_ref', dest='iter_ref', help='Number of iterative refinement passes.',
- default=1, type=int)
- parser.add_argument('--dataset', dest='dataset', help='Dataset type.', default='Pose_300W_LP', type=str)
- args = parser.parse_args()
- return args
-
-def get_ignored_params(model):
- # Generator function that yields ignored params.
- b = []
- b.append(model.conv1)
- b.append(model.bn1)
- for i in range(len(b)):
- for module_name, module in b[i].named_modules():
- if 'bn' in module_name:
- module.eval()
- for name, param in module.named_parameters():
- yield param
-
-def get_non_ignored_params(model):
- # Generator function that yields params that will be optimized.
- b = []
- b.append(model.layer1)
- b.append(model.layer2)
- b.append(model.layer3)
- b.append(model.layer4)
-
- for i in range(len(b)):
- for module_name, module in b[i].named_modules():
- if 'bn' in module_name:
- module.eval()
- for name, param in module.named_parameters():
- yield param
-
-def get_fc_params(model):
- b = []
- b.append(model.fc_yaw)
- b.append(model.fc_pitch)
- b.append(model.fc_roll)
- b.append(model.lstm)
- b.append(model.fc_lstm)
- for i in range(len(b)):
- for module_name, module in b[i].named_modules():
- for name, param in module.named_parameters():
- yield param
-
-def load_filtered_state_dict(model, snapshot):
- # By user apaszke from discuss.pytorch.org
- model_dict = model.state_dict()
- # 1. filter out unnecessary keys
- snapshot = {k: v for k, v in snapshot.items() if k in model_dict}
- # 2. overwrite entries in the existing state dict
- model_dict.update(snapshot)
- # 3. load the new state dict
- model.load_state_dict(model_dict)
-
-if __name__ == '__main__':
- args = parse_args()
-
- cudnn.enabled = True
- num_epochs = args.num_epochs
- num_epochs_ft = args.num_epochs_ft
- batch_size = args.batch_size
- gpu = args.gpu_id
-
- if not os.path.exists('output/snapshots'):
- os.makedirs('output/snapshots')
-
- model = hopenet.Hopenet_LSTM(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
-
- load_filtered_state_dict(model, model_zoo.load_url(model_urls['resnet50']))
-
- print 'Loading data.'
-
- transformations = transforms.Compose([transforms.Scale(240),
- transforms.RandomCrop(224), transforms.ToTensor(),
- transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
-
- if args.dataset == 'Pose_300W_LP':
- pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
- elif args.dataset == 'AFLW2000':
- pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations)
- elif args.dataset == 'BIWI':
- pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations)
- elif args.dataset == 'AFLW':
- pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations)
- elif args.dataset == 'AFLW_aug':
- pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list, transformations)
- elif args.dataset == 'AFW':
- pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
- else:
- print 'Error: not a valid dataset name'
- sys.exit()
- train_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
- batch_size=batch_size,
- shuffle=True,
- num_workers=2)
-
- model.cuda(gpu)
- softmax = nn.Softmax()
- criterion = nn.CrossEntropyLoss().cuda()
- reg_criterion = nn.MSELoss().cuda()
- smooth_l1_loss = nn.SmoothL1Loss().cuda()
- # Regression loss coefficient
- alpha = args.alpha
-
- idx_tensor = [idx for idx in xrange(66)]
- idx_tensor = Variable(torch.FloatTensor(idx_tensor)).cuda(gpu)
-
- optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': 0},
- {'params': get_non_ignored_params(model), 'lr': args.lr},
- {'params': get_fc_params(model), 'lr': args.lr * 5}],
- lr = args.lr)
-
- print 'Ready to train network.'
-
- print 'Second phase of training (finetuning layer).'
- for epoch in range(num_epochs_ft):
- for i, (images, labels, cont_labels, name) in enumerate(train_loader):
- images = Variable(images.cuda(gpu))
- label_yaw = Variable(labels[:,0].cuda(gpu))
- label_pitch = Variable(labels[:,1].cuda(gpu))
- label_roll = Variable(labels[:,2].cuda(gpu))
-
- label_angles = Variable(cont_labels[:,:3].cuda(gpu))
- label_yaw_cont = Variable(cont_labels[:,0].cuda(gpu))
- label_pitch_cont = Variable(cont_labels[:,1].cuda(gpu))
- label_roll_cont = Variable(cont_labels[:,2].cuda(gpu))
-
- optimizer.zero_grad()
- model.zero_grad()
-
- pre_yaw, pre_pitch, pre_roll, preangles, final_angles = model(images)
-
- # Cross entropy loss
- loss_yaw = criterion(pre_yaw, label_yaw)
- loss_pitch = criterion(pre_pitch, label_pitch)
- loss_roll = criterion(pre_roll, label_roll)
-
- # MSE loss
- yaw_predicted = preangles[0]
- pitch_predicted = preangles[1]
- roll_predicted = preangles[2]
-
- loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw_cont)
- loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch_cont)
- loss_reg_roll = reg_criterion(roll_predicted, label_roll_cont)
-
- # Total loss
- loss_yaw += alpha * loss_reg_yaw
- loss_pitch += alpha * loss_reg_pitch
- loss_roll += alpha * loss_reg_roll
-
- # LSTM loss
- loss_seq = [loss_yaw, loss_pitch, loss_rol]
- loss_lstm = reg_criterion(final_angles, label_angles)
- loss_seq.append(loss_lstm)
-
- grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]
- torch.autograd.backward(loss_seq, grad_seq)
- optimizer.step()
-
- if (i+1) % 100 == 0:
- print ('Epoch [%d/%d], Iter [%d/%d] Losses: pre-yaw %.4f, pre-pitch %.4f, pre-roll %.4f, finetuning %.4f'
- %(epoch+1, num_epochs_ft, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0], loss_angles.data[0]))
- # if epoch == 0:
- # torch.save(model.state_dict(),
- # 'output/snapshots/' + args.output_string + '_iter_'+ str(i+1) + '.pkl')
-
- # Save models at numbered epochs.
- if epoch % 1 == 0 and epoch < num_epochs_ft:
- print 'Taking snapshot...'
- torch.save(model.state_dict(),
- 'output/snapshots/' + args.output_string + '_epoch_'+ str(num_epochs+epoch+1) + '.pkl')
--
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