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| | | import numpy as np |
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| | | import torch |
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| | | import torch.nn as nn |
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| | | from torch.autograd import Variable |
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| | | from torch.utils.data import DataLoader |
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| | | from torchvision import transforms |
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| | | import torchvision |
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| | | import torch.backends.cudnn as cudnn |
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| | | import torch.nn.functional as F |
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| | | |
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| | | import cv2 |
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| | | import matplotlib.pyplot as plt |
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| | | import sys |
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| | | import os |
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| | | import argparse |
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| | | import datasets |
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| | | import hopenet |
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| | | import torch.utils.model_zoo as model_zoo |
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| | | model_urls = { |
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| | | 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', |
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| | | 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', |
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| | | 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', |
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| | | 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', |
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| | | 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', |
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| | | } |
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| | | |
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| | | def parse_args(): |
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| | | """Parse input arguments.""" |
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| | | parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.') |
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| | | parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]', |
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| | | default=0, type=int) |
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| | | parser.add_argument('--num_epochs', dest='num_epochs', help='Maximum number of training epochs.', |
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| | | default=5, type=int) |
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| | | parser.add_argument('--num_epochs_ft', dest='num_epochs_ft', help='Maximum number of finetuning epochs.', |
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| | | default=5, type=int) |
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| | | parser.add_argument('--batch_size', dest='batch_size', help='Batch size.', |
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| | | default=16, type=int) |
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| | | parser.add_argument('--lr', dest='lr', help='Base learning rate.', |
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| | | default=0.001, type=float) |
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| | | parser.add_argument('--data_dir', dest='data_dir', help='Directory path for data.', |
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| | | default='', type=str) |
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| | | parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.', |
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| | | default='', type=str) |
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| | | parser.add_argument('--output_string', dest='output_string', help='String appended to output snapshots.', default = '', type=str) |
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| | | parser.add_argument('--alpha', dest='alpha', help='Regression loss coefficient.', |
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| | | default=0.001, type=float) |
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| | | parser.add_argument('--iter_ref', dest='iter_ref', help='Number of iterative refinement passes.', |
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| | | default=1, type=int) |
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| | | args = parser.parse_args() |
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| | | return args |
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| | | |
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| | | def get_ignored_params(model): |
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| | | # Generator function that yields ignored params. |
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| | | b = [] |
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| | | b.append(model.conv1) |
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| | | b.append(model.bn1) |
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| | | for i in range(len(b)): |
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| | | for module_name, module in b[i].named_modules(): |
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| | | if 'bn' in module_name: |
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| | | module.eval() |
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| | | for name, param in module.named_parameters(): |
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| | | yield param |
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| | | |
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| | | def get_non_ignored_params(model): |
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| | | # Generator function that yields params that will be optimized. |
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| | | b = [] |
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| | | b.append(model.layer1) |
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| | | b.append(model.layer2) |
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| | | b.append(model.layer3) |
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| | | b.append(model.layer4) |
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| | | for i in range(len(b)): |
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| | | for module_name, module in b[i].named_modules(): |
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| | | if 'bn' in module_name: |
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| | | module.eval() |
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| | | for name, param in module.named_parameters(): |
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| | | yield param |
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| | | |
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| | | def get_fc_params(model): |
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| | | b = [] |
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| | | b.append(model.fc_yaw) |
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| | | b.append(model.fc_pitch) |
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| | | b.append(model.fc_roll) |
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| | | b.append(model.fc_finetune) |
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| | | for i in range(len(b)): |
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| | | for module_name, module in b[i].named_modules(): |
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| | | for name, param in module.named_parameters(): |
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| | | yield param |
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| | | |
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| | | def load_filtered_state_dict(model, snapshot): |
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| | | # By user apaszke from discuss.pytorch.org |
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| | | model_dict = model.state_dict() |
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| | | # 1. filter out unnecessary keys |
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| | | snapshot = {k: v for k, v in snapshot.items() if k in model_dict} |
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| | | # 2. overwrite entries in the existing state dict |
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| | | model_dict.update(snapshot) |
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| | | # 3. load the new state dict |
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| | | model.load_state_dict(model_dict) |
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| | | |
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| | | if __name__ == '__main__': |
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| | | args = parse_args() |
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| | | |
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| | | cudnn.enabled = True |
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| | | num_epochs = args.num_epochs |
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| | | num_epochs_ft = args.num_epochs_ft |
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| | | batch_size = args.batch_size |
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| | | gpu = args.gpu_id |
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| | | |
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| | | if not os.path.exists('output/snapshots'): |
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| | | os.makedirs('output/snapshots') |
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| | | |
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| | | # ResNet101 with 3 outputs |
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| | | # model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 23, 3], 66) |
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| | | # ResNet50 |
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| | | model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66, args.iter_ref) |
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| | | # ResNet18 |
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| | | # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66) |
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| | | load_filtered_state_dict(model, model_zoo.load_url(model_urls['resnet50'])) |
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| | | |
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| | | print 'Loading data.' |
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| | | |
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| | | transformations = transforms.Compose([transforms.Scale(240), |
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| | | transforms.RandomCrop(224), transforms.ToTensor(), |
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| | | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]) |
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| | | |
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| | | pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, |
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| | | transformations) |
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| | | train_loader = torch.utils.data.DataLoader(dataset=pose_dataset, |
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| | | batch_size=batch_size, |
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| | | shuffle=True, |
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| | | num_workers=2) |
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| | | |
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| | | model.cuda(gpu) |
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| | | softmax = nn.Softmax() |
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| | | criterion = nn.CrossEntropyLoss().cuda() |
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| | | reg_criterion = nn.MSELoss().cuda() |
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| | | # Regression loss coefficient |
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| | | alpha = args.alpha |
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| | | |
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| | | idx_tensor = [idx for idx in xrange(66)] |
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| | | idx_tensor = Variable(torch.FloatTensor(idx_tensor)).cuda(gpu) |
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| | | |
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| | | optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': 0}, |
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| | | {'params': get_non_ignored_params(model), 'lr': args.lr}, |
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| | | {'params': get_fc_params(model), 'lr': args.lr * 2}], |
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| | | lr = args.lr) |
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| | | |
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| | | print 'Ready to train network.' |
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| | | |
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| | | print 'First phase of training.' |
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| | | for epoch in range(num_epochs): |
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| | | for i, (images, labels, name) in enumerate(train_loader): |
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| | | images = Variable(images.cuda(gpu)) |
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| | | label_yaw = Variable(labels[:,0].cuda(gpu)) |
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| | | label_pitch = Variable(labels[:,1].cuda(gpu)) |
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| | | label_roll = Variable(labels[:,2].cuda(gpu)) |
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| | | |
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| | | optimizer.zero_grad() |
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| | | model.zero_grad() |
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| | | pre_yaw, pre_pitch, pre_roll, angles = model(images) |
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| | | |
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| | | # Cross entropy loss |
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| | | loss_yaw = criterion(pre_yaw, label_yaw) |
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| | | loss_pitch = criterion(pre_pitch, label_pitch) |
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| | | loss_roll = criterion(pre_roll, label_roll) |
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| | | |
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| | | # MSE loss |
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| | | yaw_predicted = softmax(pre_yaw) |
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| | | pitch_predicted = softmax(pre_pitch) |
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| | | roll_predicted = softmax(pre_roll) |
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| | | yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1) |
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| | | pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1) |
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| | | roll_predicted = torch.sum(roll_predicted * idx_tensor, 1) |
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| | | loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw.float()) |
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| | | loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float()) |
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| | | loss_reg_roll = reg_criterion(roll_predicted, label_roll.float()) |
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| | | |
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| | | # Total loss |
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| | | loss_yaw += alpha * loss_reg_yaw |
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| | | loss_pitch += alpha * loss_reg_pitch |
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| | | loss_roll += alpha * loss_reg_roll |
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| | | loss_seq = [loss_yaw, loss_pitch, loss_roll] |
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| | | grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))] |
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| | | torch.autograd.backward(loss_seq, grad_seq) |
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| | | optimizer.step() |
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| | | if (i+1) % 100 == 0: |
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| | | print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f' |
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| | | %(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0])) |
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| | | # if epoch == 0: |
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| | | # torch.save(model.state_dict(), |
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| | | # 'output/snapshots/' + args.output_string + '_iter_'+ str(i+1) + '.pkl') |
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| | | |
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| | | # Save models at numbered epochs. |
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| | | if epoch % 1 == 0 and epoch < num_epochs: |
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| | | print 'Taking snapshot...' |
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| | | torch.save(model.state_dict(), |
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| | | 'output/snapshots/' + args.output_string + '_epoch_'+ str(epoch+1) + '.pkl') |
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| | | |
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| | | print 'Second phase of training (finetuning layer).' |
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| | | for epoch in range(num_epochs_ft): |
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| | | for i, (images, labels, name) in enumerate(train_loader): |
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| | | images = Variable(images.cuda(gpu)) |
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| | | label_yaw = Variable(labels[:,0].cuda(gpu)) |
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| | | label_pitch = Variable(labels[:,1].cuda(gpu)) |
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| | | label_roll = Variable(labels[:,2].cuda(gpu)) |
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| | | label_angles = Variable(labels[:,:3].cuda(gpu)) |
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| | | |
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| | | optimizer.zero_grad() |
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| | | model.zero_grad() |
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| | | |
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| | | pre_yaw, pre_pitch, pre_roll, angles = model(images) |
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| | | |
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| | | # Cross entropy loss |
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| | | loss_yaw = criterion(pre_yaw, label_yaw) |
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| | | loss_pitch = criterion(pre_pitch, label_pitch) |
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| | | loss_roll = criterion(pre_roll, label_roll) |
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| | | |
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| | | # MSE loss |
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| | | yaw_predicted = softmax(pre_yaw) |
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| | | pitch_predicted = softmax(pre_pitch) |
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| | | roll_predicted = softmax(pre_roll) |
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| | | yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1) |
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| | | pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1) |
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| | | roll_predicted = torch.sum(roll_predicted * idx_tensor, 1) |
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| | | loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw.float()) |
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| | | loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float()) |
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| | | loss_reg_roll = reg_criterion(roll_predicted, label_roll.float()) |
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| | | |
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| | | # Total loss |
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| | | loss_yaw += alpha * loss_reg_yaw |
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| | | loss_pitch += alpha * loss_reg_pitch |
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| | | loss_roll += alpha * loss_reg_roll |
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| | | |
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| | | # Finetuning loss |
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| | | loss_seq = [loss_yaw, loss_pitch, loss_roll] |
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| | | for idx in xrange(args.iter_ref+1): |
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| | | loss_angles = reg_criterion(angles[idx], label_angles.float()) |
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| | | loss_seq.append(loss_angles) |
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| | | |
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| | | grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))] |
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| | | torch.autograd.backward(loss_seq, grad_seq) |
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| | | optimizer.step() |
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| | | |
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| | | if (i+1) % 100 == 0: |
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| | | print ('Epoch [%d/%d], Iter [%d/%d] Losses: pre-yaw %.4f, pre-pitch %.4f, pre-roll %.4f, finetuning %.4f' |
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| | | %(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])) |
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| | | # if epoch == 0: |
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| | | # torch.save(model.state_dict(), |
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| | | # 'output/snapshots/' + args.output_string + '_iter_'+ str(i+1) + '.pkl') |
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| | | |
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| | | # Save models at numbered epochs. |
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| | | if epoch % 1 == 0 and epoch < num_epochs_ft: |
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| | | print 'Taking snapshot...' |
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| | | torch.save(model.state_dict(), |
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| | | 'output/snapshots/' + args.output_string + '_epoch_'+ str(num_epochs+epoch+1) + '.pkl') |
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