blame | 历史 | 补丁 | 提交 | 提交对比 | ignore whitespace
natanielruiz
2017-10-30 2f6778c2db9ce1a887f04fdc85ad0d5db4ba84b8 
 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


@@ -8,25 +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




import time




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."""


@@ -53,10 +41,7 @@




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:


@@ -66,11 +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:


@@ -79,10 +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():


@@ -91,11 +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__':


@@ -109,13 +85,9 @@


    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


    model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66, 0)


    # 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']))


    # ResNet50 structure


    model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)


    load_filtered_state_dict(model, model_zoo.load_url('https://download.pytorch.org/models/resnet50-19c8e357.pth'))




    print 'Loading data.'




@@ -140,20 +112,17 @@


    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().cuda(gpu)


    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},


@@ -161,39 +130,32 @@


                                   lr = args.lr)




    print 'Ready to train network.'


    print 'First phase of training.'


    for epoch in range(num_epochs):


        # start = time.time()


        for i, (images, labels, cont_labels, name) in enumerate(train_loader):


            # print i


            # print 'start: ', time.time() - start


            images = Variable(images).cuda(gpu)




            # Binned labels


            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)


            # 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)




            optimizer.zero_grad()


            model.zero_grad()


            # Forward pass


            yaw, pitch, roll, angles = model(images)




            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)


            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 = torch.sum(yaw_predicted * idx_tensor, 1) * 3 - 99


            pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1) * 3 - 99


            roll_predicted = torch.sum(roll_predicted * idx_tensor, 1) * 3 - 99


            yaw_predicted = angles[:,0]


            pitch_predicted = angles[:,1]


            roll_predicted = angles[:,2]




            loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw_cont)


            loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch_cont)


@@ -206,17 +168,13 @@




            loss_seq = [loss_yaw, loss_pitch, loss_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()




            # print 'end: ', time.time() - start




            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: