From 45efc9f70f87072eb9c0b81f1771fbb99f4a1073 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期六, 12 八月 2017 10:52:48 +0800
Subject: [PATCH] Cleanup

---
 code/test_shape.py  |    0 
 code/train.py       |    0 
 code/test_AFLW.py   |    0 
 /dev/null           |  198 -------------------------------------------------
 code/train_shape.py |    0 
 code/test.py        |    0 
 6 files changed, 0 insertions(+), 198 deletions(-)

diff --git a/code/test_resnet_bins.py b/code/test.py
similarity index 100%
rename from code/test_resnet_bins.py
rename to code/test.py
diff --git a/code/test_resnet_AFLW.py b/code/test_AFLW.py
similarity index 100%
rename from code/test_resnet_AFLW.py
rename to code/test_AFLW.py
diff --git a/code/test_resnet_shape.py b/code/test_shape.py
similarity index 100%
rename from code/test_resnet_shape.py
rename to code/test_shape.py
diff --git a/code/train_resnet_bins.py b/code/train.py
similarity index 100%
rename from code/train_resnet_bins.py
rename to code/train.py
diff --git a/code/train_resnet_bins_comb.py b/code/train_resnet_bins_comb.py
deleted file mode 100644
index eb23590..0000000
--- a/code/train_resnet_bins_comb.py
+++ /dev/null
@@ -1,198 +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('--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)
-
-    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.layer1)
-    b.append(model.layer2)
-    b.append(model.layer3)
-    b.append(model.layer4)
-    for i in range(len(b)):
-        for j in b[i].modules():
-            for k in j.parameters():
-                yield k
-
-def get_non_ignored_params(model):
-    # Generator function that yields params that will be optimized.
-    b = []
-    b.append(model.fc_yaw)
-    b.append(model.fc_pitch)
-    b.append(model.fc_roll)
-    for i in range(len(b)):
-        for j in b[i].modules():
-            for k in j.parameters():
-                    yield k
-
-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
-    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
-    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']))
-
-    print 'Loading data.'
-
-    transformations = transforms.Compose([transforms.Scale(224),transforms.RandomCrop(224),
-                                          transforms.ToTensor()])
-
-    pose_dataset = datasets.Pose_300W_LP_binned(args.data_dir, args.filename_list,
-                                transformations)
-    train_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
-                                               batch_size=batch_size,
-                                               shuffle=True,
-                                               num_workers=2)
-
-    model.cuda(gpu)
-    criterion = nn.CrossEntropyLoss()
-    reg_criterion = nn.MSELoss()
-    # Regression loss coefficient
-    alpha = 0.1
-    lsm = nn.Softmax()
-
-    idx_tensor = [idx for idx in xrange(66)]
-    idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
-
-    optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': args.lr},
-                                  {'params': get_non_ignored_params(model), 'lr': args.lr * 10}],
-                                  lr = args.lr)
-    # optimizer = torch.optim.SGD([{'params': get_ignored_params(model), 'lr': args.lr},
-    #                              {'params': get_non_ignored_params(model), 'lr': args.lr}],
-    #                               lr = args.lr, momentum=0.9, weight_decay=5e-4)
-    # optimizer = torch.optim.RMSprop([{'params': get_ignored_params(model), 'lr': args.lr},
-    #                               {'params': get_non_ignored_params(model), 'lr': args.lr * 10}],
-    #                               lr = args.lr)
-
-    print 'Ready to train network.'
-
-    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)
-
-            optimizer.zero_grad()
-            yaw, pitch, roll = model(images)
-
-            loss_yaw = criterion(yaw, label_yaw)
-            loss_pitch = criterion(pitch, label_pitch)
-            loss_roll = criterion(roll, label_roll)
-
-            # loss_seq = [loss_yaw, loss_pitch, loss_roll]
-            # grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]
-            # torch.autograd.backward(loss_seq, grad_seq)
-            # optimizer.step()
-
-            # MSE loss
-            yaw_predicted = F.softmax(yaw)
-            pitch_predicted = F.softmax(pitch)
-            roll_predicted = F.softmax(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())
-
-            # print yaw_predicted[0], label_yaw.data[0]
-
-            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]
-            grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]
-            model.zero_grad()
-            torch.autograd.backward(loss_seq, grad_seq)
-            optimizer.step()
-
-            # 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 (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/resnet50_iter_'+ str(i+1) + '.pkl')
-
-        # Save models at numbered epochs.
-        if epoch % 1 == 0 and epoch < num_epochs - 1:
-            print 'Taking snapshot...'
-            torch.save(model.state_dict(),
-            'output/snapshots/resnet50_epoch_'+ str(epoch+1) + '.pkl')
-
-    # Save the final Trained Model
-    torch.save(model.state_dict(), 'output/snapshots/resnet50_epoch_' + str(epoch+1) + '.pkl')
diff --git a/code/train_resnet_shape.py b/code/train_shape.py
similarity index 100%
rename from code/train_resnet_shape.py
rename to code/train_shape.py

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