From af51d0ecb51ad4d6c8ed086855bd3c411ebc4aa0 Mon Sep 17 00:00:00 2001
From: natanielruiz <nruiz9@gatech.edu>
Date: 星期一, 30 十月 2017 06:29:51 +0800
Subject: [PATCH] Fixed stuff
---
code/train_preangles.py | 198 ++++++++++++++-----------------------------------
1 files changed, 58 insertions(+), 140 deletions(-)
diff --git a/code/train_preangles.py b/code/train_preangles.py
index 3179c24..1fe626c 100644
--- a/code/train_preangles.py
+++ b/code/train_preangles.py
@@ -1,4 +1,9 @@
+import sys, os, argparse, time
+
import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
import torch
import torch.nn as nn
from torch.autograd import Variable
@@ -8,23 +13,8 @@
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 datasets, 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."""
@@ -32,8 +22,6 @@
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)
@@ -46,15 +34,14 @@
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('--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)
- b.append(model.fc_finetune)
+ b = [model.conv1, model.bn1, model.fc_finetune]
for i in range(len(b)):
for module_name, module in b[i].named_modules():
if 'bn' in module_name:
@@ -64,11 +51,7 @@
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)
+ b = [model.layer1, model.layer2, model.layer3, model.layer4]
for i in range(len(b)):
for module_name, module in b[i].named_modules():
if 'bn' in module_name:
@@ -77,10 +60,8 @@
yield param
def get_fc_params(model):
- b = []
- b.append(model.fc_yaw)
- b.append(model.fc_pitch)
- b.append(model.fc_roll)
+ # Generator function that yields fc layer params.
+ b = [model.fc_yaw, model.fc_pitch, model.fc_roll]
for i in range(len(b)):
for module_name, module in b[i].named_modules():
for name, param in module.named_parameters():
@@ -89,11 +70,8 @@
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__':
@@ -101,20 +79,15 @@
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')
- # ResNet101 with 3 outputs
- # model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 23, 3], 66)
- # ResNet50
+ # ResNet50 structure
model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
- # ResNet18
- # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
- load_filtered_state_dict(model, model_zoo.load_url(model_urls['resnet50']))
+ load_filtered_state_dict(model, model_zoo.load_url('https://download.pytorch.org/models/resnet50-19c8e357.pth'))
print 'Loading data.'
@@ -122,144 +95,89 @@
transforms.RandomCrop(224), transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
- pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list,
- transformations)
+ if args.dataset == 'Pose_300W_LP':
+ pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
+ elif args.dataset == 'Pose_300W_LP_random_ds':
+ pose_dataset = datasets.Pose_300W_LP_random_ds(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()
+ criterion = nn.CrossEntropyLoss().cuda(gpu)
+ reg_criterion = nn.MSELoss().cuda(gpu)
# 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 * 2}],
+ {'params': get_fc_params(model), 'lr': args.lr * 5}],
lr = args.lr)
print 'Ready to train network.'
-
- print 'First phase of training.'
for epoch in range(num_epochs):
- for i, (images, 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))
+ for i, (images, labels, cont_labels, name) in enumerate(train_loader):
+ images = Variable(images).cuda(gpu)
- optimizer.zero_grad()
- model.zero_grad()
+ # Binned labels
+ label_yaw = Variable(labels[:,0]).cuda(gpu)
+ label_pitch = Variable(labels[:,1]).cuda(gpu)
+ label_roll = Variable(labels[:,2]).cuda(gpu)
- pre_yaw, pre_pitch, pre_roll, angles = model(images)
+ # Continuous labels
+ 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)
+
+ # Forward pass
+ yaw, pitch, roll, 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)
+ loss_yaw = criterion(yaw, label_yaw)
+ loss_pitch = criterion(pitch, label_pitch)
+ loss_roll = criterion(roll, label_roll)
# MSE loss
- yaw_predicted = softmax(pre_yaw)
- pitch_predicted = softmax(pre_pitch)
- roll_predicted = softmax(pre_roll)
+ yaw_predicted = angles[:,0]
+ pitch_predicted = angles[:,1]
+ roll_predicted = angles[:,2]
- yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1)
- pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1)
- roll_predicted = torch.sum(roll_predicted * idx_tensor, 1)
+ 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)
- loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw.float())
- loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float())
- loss_reg_roll = reg_criterion(roll_predicted, label_roll.float())
-
- # print yaw_predicted, label_yaw.float(), loss_reg_yaw
# Total loss
loss_yaw += alpha * loss_reg_yaw
loss_pitch += alpha * loss_reg_pitch
loss_roll += alpha * loss_reg_roll
loss_seq = [loss_yaw, loss_pitch, loss_roll]
- # loss_seq = [loss_reg_yaw, loss_reg_pitch, loss_reg_roll]
grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]
+ optimizer.zero_grad()
torch.autograd.backward(loss_seq, grad_seq)
optimizer.step()
if (i+1) % 100 == 0:
print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f'
%(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.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:
print 'Taking snapshot...'
torch.save(model.state_dict(),
'output/snapshots/' + args.output_string + '_epoch_'+ str(epoch+1) + '.pkl')
-
- print 'Second phase of training (finetuning layer).'
- for epoch in range(num_epochs_ft):
- for i, (images, 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(labels[:,:3].cuda(gpu))
-
- optimizer.zero_grad()
- model.zero_grad()
-
- pre_yaw, pre_pitch, pre_roll, 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 = softmax(pre_yaw)
- pitch_predicted = softmax(pre_pitch)
- roll_predicted = softmax(pre_roll)
-
- yaw_predicted = torch.sum(yaw_predicted.data * idx_tensor, 1)
- pitch_predicted = torch.sum(pitch_predicted.data * idx_tensor, 1)
- roll_predicted = torch.sum(roll_predicted.data * idx_tensor, 1)
-
- loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw.float())
- loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float())
- loss_reg_roll = reg_criterion(roll_predicted, label_roll.float())
-
- # Total loss
- loss_yaw += alpha * loss_reg_yaw
- loss_pitch += alpha * loss_reg_pitch
- loss_roll += alpha * loss_reg_roll
-
- # Finetuning loss
- loss_angles = reg_criterion(angles[0], label_angles.float())
-
- loss_seq = [loss_yaw, loss_pitch, loss_roll, loss_angles]
- 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 - 1:
- print 'Taking snapshot...'
- torch.save(model.state_dict(),
- 'output/snapshots/' + args.output_string + '_epoch_'+ str(num_epochs+epoch+1) + '.pkl')
-
-
- # Save the final Trained Model
- torch.save(model.state_dict(), 'output/snapshots/' + args.output_string + '_epoch_' + str(num_epochs+epoch+1) + '.pkl')
--
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