Cleaned up a bit

natanielruiz

2017-10-30 2f6778c2db9ce1a887f04fdc85ad0d5db4ba84b8

 code/train_alexnet.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
@@ -8,17 +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 = {
    'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
@@ -43,16 +39,12 @@
    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.features[0])
    b.append(model.features[1])
    b.append(model.features[2])
    b = [model.features[0], model.features[1], model.features[2]]
    for i in range(len(b)):
        for module_name, module in b[i].named_modules():
            if 'bn' in module_name:
@@ -75,10 +67,7 @@
                yield param

def get_fc_params(model):
    b = []
    b.append(model.fc_yaw)
    b.append(model.fc_pitch)
    b.append(model.fc_roll)
    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():
@@ -87,11 +76,8 @@
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__':
@@ -116,6 +102,8 @@

    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':
@@ -141,48 +129,38 @@
    # 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 '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 = 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)
@@ -195,17 +173,13 @@

            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: