From 54818d253649ff588ed0054d10dabb2a3a170309 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期四, 10 八月 2017 04:08:12 +0800
Subject: [PATCH] Doing pretty well now with resnet50 and adam with low learning rate. Also fixed test script to use large batches.

---
 code/test_on_video.py                                      |    4 
 code/train_resnet_bins_comb.py                             |  198 +++++++++++++++++++
 practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb |   42 ++-
 code/test_resnet_bins.py                                   |   34 ++-
 code/train_resnet_bins.py                                  |   47 ++++
 code/train_resnet_bins_comb_dup.py                         |  198 +++++++++++++++++++
 practice/smoothing_ypr.ipynb                               |   42 ++-
 7 files changed, 512 insertions(+), 53 deletions(-)

diff --git a/code/test_on_video.py b/code/test_on_video.py
index 247c2db..20dfaac 100644
--- a/code/test_on_video.py
+++ b/code/test_on_video.py
@@ -48,9 +48,9 @@
     # 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)
+    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)
+    # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
 
     print 'Loading snapshot.'
     # Load snapshot
diff --git a/code/test_resnet_bins.py b/code/test_resnet_bins.py
index 00d2109..699c9c9 100644
--- a/code/test_resnet_bins.py
+++ b/code/test_resnet_bins.py
@@ -42,16 +42,15 @@
     args = parse_args()
 
     cudnn.enabled = True
-    batch_size = 1
     gpu = args.gpu_id
     snapshot_path = os.path.join('output/snapshots', args.snapshot + '.pkl')
 
     # 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)
+    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)
+    # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
 
     print 'Loading snapshot.'
     # Load snapshot
@@ -66,7 +65,7 @@
     pose_dataset = datasets.AFLW2000_binned(args.data_dir, args.filename_list,
                                 transformations)
     test_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
-                                               batch_size=batch_size,
+                                               batch_size=args.batch_size,
                                                num_workers=2)
 
     model.cuda(gpu)
@@ -88,12 +87,14 @@
     pitch_error = .0
     roll_error = .0
 
+    l1loss = torch.nn.L1Loss(size_average=False)
+
     for i, (images, labels, name) in enumerate(test_loader):
         images = Variable(images).cuda(gpu)
         total += labels.size(0)
-        label_yaw = labels[:,0]
-        label_pitch = labels[:,1]
-        label_roll = labels[:,2]
+        label_yaw = labels[:,0].float()
+        label_pitch = labels[:,1].float()
+        label_roll = labels[:,2].float()
 
         yaw, pitch, roll = model(images)
 
@@ -107,14 +108,18 @@
         roll_predicted = F.softmax(roll)
 
         # Continuous predictions
-        yaw_predicted = torch.sum(yaw_predicted.data[0] * idx_tensor)
-        pitch_predicted = torch.sum(pitch_predicted.data[0] * idx_tensor)
-        roll_predicted = torch.sum(roll_predicted.data[0] * idx_tensor)
+        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)
+
+        yaw_predicted = yaw_predicted.cpu()
+        pitch_predicted = pitch_predicted.cpu()
+        roll_predicted = roll_predicted.cpu()
 
         # Mean absolute error
-        yaw_error += abs(yaw_predicted - label_yaw[0]) * 3
-        pitch_error += abs(pitch_predicted - label_pitch[0]) * 3
-        roll_error += abs(roll_predicted - label_roll[0]) * 3
+        yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw) * 3)
+        pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch) * 3)
+        roll_error += torch.sum(torch.abs(roll_predicted - label_roll) * 3)
 
         # Binned Accuracy
         # for er in xrange(n_margins):
@@ -125,13 +130,14 @@
         # print label_yaw[0], yaw_bpred[0,0]
 
         # Save images with pose cube.
+        # TODO: fix for larger batch size
         if args.save_viz:
             name = name[0]
             cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg'))
             #print os.path.join('output/images', name + '.jpg')
             #print label_yaw[0] * 3 - 99, label_pitch[0] * 3 - 99, label_roll[0] * 3 - 99
             #print yaw_predicted * 3 - 99, pitch_predicted * 3 - 99, roll_predicted * 3 - 99
-            utils.plot_pose_cube(cv2_img, yaw_predicted * 3 - 99, pitch_predicted * 3 - 99, roll_predicted * 3 - 99)
+            utils.plot_pose_cube(cv2_img, yaw_predicted[0] * 3 - 99, pitch_predicted[0] * 3 - 99, roll_predicted[0] * 3 - 99)
             cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img)
 
     print('Test error in degrees of the model on the ' + str(total) +
diff --git a/code/train_resnet_bins.py b/code/train_resnet_bins.py
index dab3800..f98bbc3 100644
--- a/code/train_resnet_bins.py
+++ b/code/train_resnet_bins.py
@@ -6,6 +6,7 @@
 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
@@ -113,11 +114,19 @@
 
     model.cuda(gpu)
     criterion = nn.CrossEntropyLoss()
+    reg_criterion = nn.MSELoss()
+    # Regression loss coefficient
+    alpha = 0.01
+    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}],
+    #                              {'params': get_non_ignored_params(model), 'lr': args.lr}],
     #                               lr = args.lr, momentum=0.9)
     # optimizer = torch.optim.RMSprop([{'params': get_ignored_params(model), 'lr': args.lr},
     #                               {'params': get_non_ignored_params(model), 'lr': args.lr * 10}],
@@ -134,24 +143,56 @@
 
             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/resnet18_sgd_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/resnet18_cr_epoch_'+ str(epoch+1) + '.pkl')
+            'output/snapshots/resnet18_sgd_epoch_'+ str(epoch+1) + '.pkl')
 
     # Save the final Trained Model
-    torch.save(model.state_dict(), 'output/snapshots/resnet18_cr_epoch_' + str(epoch+1) + '.pkl')
+    torch.save(model.state_dict(), 'output/snapshots/resnet18_sgd_epoch_' + str(epoch+1) + '.pkl')
diff --git a/code/train_resnet_bins_comb.py b/code/train_resnet_bins_comb.py
new file mode 100644
index 0000000..eb23590
--- /dev/null
+++ b/code/train_resnet_bins_comb.py
@@ -0,0 +1,198 @@
+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_bins_comb_dup.py b/code/train_resnet_bins_comb_dup.py
new file mode 100644
index 0000000..b435b89
--- /dev/null
+++ b/code/train_resnet_bins_comb_dup.py
@@ -0,0 +1,198 @@
+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_lowlr_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_lowlr_epoch_'+ str(epoch+1) + '.pkl')
+
+    # Save the final Trained Model
+    torch.save(model.state_dict(), 'output/snapshots/resnet50_lowlr_epoch_' + str(epoch+1) + '.pkl')
diff --git a/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb b/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb
index a411c30..8102abf 100644
--- a/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb
+++ b/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 187,
    "metadata": {
     "collapsed": false
    },
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 188,
    "metadata": {
     "collapsed": false
    },
@@ -26,13 +26,13 @@
     "video_path = '../data/video/SGT036_2016_07_25_pivothead_AVI.avi'\n",
     "bbox_path = '../data/video/annotations/SGT036_childface.txt'\n",
     "\n",
-    "annot_path = '../output/video/output-SGT036_resnet18_cr_epoch_1.txt'\n",
-    "output_string = 'SGT036_resnet18_cr_epoch_1_flat_smoothed'"
+    "annot_path = '../output/video/output-SGT036_resnet50_lowlr_epoch_20.txt'\n",
+    "output_string = 'SGT036_resnet50_lowlr_epoch_20_smoothed'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 189,
    "metadata": {
     "collapsed": false
    },
@@ -41,7 +41,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-6.069214 -0.831665  0.53318  ..., -3.836042 -3.868275 -2.377155]\n",
+      "[ 4.170376  0.790443 -0.178368 ..., -3.437805  0.396835 -1.276176]\n",
       "(8508,)\n",
       "(53464,)\n"
      ]
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 190,
    "metadata": {
     "collapsed": false
    },
@@ -107,31 +107,39 @@
     }
    ],
    "source": [
-    "window_len = 6\n",
+    "window_len = 5\n",
     "pad = window_len / 2\n",
     "window = 'flat'\n",
+    "window_2 = 'flat'\n",
+    "window_len_2 = 7\n",
+    "pad_2 = window_len_2 / 2\n",
     "\n",
     "s = np.r_[y[window_len-1:0:-1],y,y[-2:-window_len-1:-1]]\n",
     "t = np.r_[p[window_len-1:0:-1],p,p[-2:-window_len-1:-1]]\n",
     "u = np.r_[r[window_len-1:0:-1],r,r[-2:-window_len-1:-1]]\n",
     "\n",
-    "xa = np.r_[x_min_arr[window_len-1:0:-1],x_min_arr,x_min_arr[-2:-window_len-1:-1]]\n",
-    "xb = np.r_[x_max_arr[window_len-1:0:-1],x_max_arr,x_max_arr[-2:-window_len-1:-1]]\n",
-    "ya = np.r_[y_min_arr[window_len-1:0:-1],y_min_arr,y_min_arr[-2:-window_len-1:-1]]\n",
-    "yb = np.r_[y_max_arr[window_len-1:0:-1],y_max_arr,y_max_arr[-2:-window_len-1:-1]]\n",
+    "xa = np.r_[x_min_arr[window_len_2-1:0:-1],x_min_arr,x_min_arr[-2:-window_len_2-1:-1]]\n",
+    "xb = np.r_[x_max_arr[window_len_2-1:0:-1],x_max_arr,x_max_arr[-2:-window_len_2-1:-1]]\n",
+    "ya = np.r_[y_min_arr[window_len_2-1:0:-1],y_min_arr,y_min_arr[-2:-window_len_2-1:-1]]\n",
+    "yb = np.r_[y_max_arr[window_len_2-1:0:-1],y_max_arr,y_max_arr[-2:-window_len_2-1:-1]]\n",
     "\n",
     "if window == 'flat':\n",
     "    w=np.ones(window_len, 'd')\n",
     "else:\n",
     "    w=eval('np.' + window + '(window_len)')\n",
+    "    \n",
+    "if window_2 == 'flat':\n",
+    "    w_2=np.ones(window_len_2, 'd')\n",
+    "else:\n",
+    "    w_2=eval('np.' + window_2 + '(window_len_2)')    \n",
     "\n",
     "y = np.convolve(w / w.sum(), s, mode='valid')[pad:-pad]\n",
     "p = np.convolve(w / w.sum(), t, mode='valid')[pad:-pad]\n",
     "r = np.convolve(w / w.sum(), u, mode='valid')[pad:-pad]\n",
-    "x_min_arr = np.convolve(w / w.sum(), xa, mode='valid')[pad:-pad]\n",
-    "x_max_arr = np.convolve(w / w.sum(), xb, mode='valid')[pad:-pad]\n",
-    "y_min_arr = np.convolve(w / w.sum(), ya, mode='valid')[pad:-pad]\n",
-    "y_max_arr = np.convolve(w / w.sum(), yb, mode='valid')[pad:-pad]\n",
+    "x_min_arr = np.convolve(w_2 / w_2.sum(), xa, mode='valid')[pad_2:-pad_2]\n",
+    "x_max_arr = np.convolve(w_2 / w_2.sum(), xb, mode='valid')[pad_2:-pad_2]\n",
+    "y_min_arr = np.convolve(w_2 / w_2.sum(), ya, mode='valid')[pad_2:-pad_2]\n",
+    "y_max_arr = np.convolve(w_2 / w_2.sum(), yb, mode='valid')[pad_2:-pad_2]\n",
     "\n",
     "pose_dict = {}\n",
     "bbox_dict = {}\n",
@@ -151,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 191,
    "metadata": {
     "collapsed": false
    },
diff --git a/practice/smoothing_ypr.ipynb b/practice/smoothing_ypr.ipynb
index a411c30..96dc33f 100644
--- a/practice/smoothing_ypr.ipynb
+++ b/practice/smoothing_ypr.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 197,
    "metadata": {
     "collapsed": false
    },
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 198,
    "metadata": {
     "collapsed": false
    },
@@ -26,13 +26,13 @@
     "video_path = '../data/video/SGT036_2016_07_25_pivothead_AVI.avi'\n",
     "bbox_path = '../data/video/annotations/SGT036_childface.txt'\n",
     "\n",
-    "annot_path = '../output/video/output-SGT036_resnet18_cr_epoch_1.txt'\n",
-    "output_string = 'SGT036_resnet18_cr_epoch_1_flat_smoothed'"
+    "annot_path = '../output/video/output-SGT036_resnet50_lowlr_epoch_20.txt'\n",
+    "output_string = 'SGT036_resnet50_lowlr_epoch_20_smoothed'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 199,
    "metadata": {
     "collapsed": false
    },
@@ -41,7 +41,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-6.069214 -0.831665  0.53318  ..., -3.836042 -3.868275 -2.377155]\n",
+      "[ 4.170376  0.790443 -0.178368 ..., -3.437805  0.396835 -1.276176]\n",
       "(8508,)\n",
       "(53464,)\n"
      ]
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 200,
    "metadata": {
     "collapsed": false
    },
@@ -107,31 +107,39 @@
     }
    ],
    "source": [
-    "window_len = 6\n",
+    "window_len = 7\n",
     "pad = window_len / 2\n",
     "window = 'flat'\n",
+    "window_2 = 'flat'\n",
+    "window_len_2 = 7\n",
+    "pad_2 = window_len_2 / 2\n",
     "\n",
     "s = np.r_[y[window_len-1:0:-1],y,y[-2:-window_len-1:-1]]\n",
     "t = np.r_[p[window_len-1:0:-1],p,p[-2:-window_len-1:-1]]\n",
     "u = np.r_[r[window_len-1:0:-1],r,r[-2:-window_len-1:-1]]\n",
     "\n",
-    "xa = np.r_[x_min_arr[window_len-1:0:-1],x_min_arr,x_min_arr[-2:-window_len-1:-1]]\n",
-    "xb = np.r_[x_max_arr[window_len-1:0:-1],x_max_arr,x_max_arr[-2:-window_len-1:-1]]\n",
-    "ya = np.r_[y_min_arr[window_len-1:0:-1],y_min_arr,y_min_arr[-2:-window_len-1:-1]]\n",
-    "yb = np.r_[y_max_arr[window_len-1:0:-1],y_max_arr,y_max_arr[-2:-window_len-1:-1]]\n",
+    "xa = np.r_[x_min_arr[window_len_2-1:0:-1],x_min_arr,x_min_arr[-2:-window_len_2-1:-1]]\n",
+    "xb = np.r_[x_max_arr[window_len_2-1:0:-1],x_max_arr,x_max_arr[-2:-window_len_2-1:-1]]\n",
+    "ya = np.r_[y_min_arr[window_len_2-1:0:-1],y_min_arr,y_min_arr[-2:-window_len_2-1:-1]]\n",
+    "yb = np.r_[y_max_arr[window_len_2-1:0:-1],y_max_arr,y_max_arr[-2:-window_len_2-1:-1]]\n",
     "\n",
     "if window == 'flat':\n",
     "    w=np.ones(window_len, 'd')\n",
     "else:\n",
     "    w=eval('np.' + window + '(window_len)')\n",
+    "    \n",
+    "if window_2 == 'flat':\n",
+    "    w_2=np.ones(window_len_2, 'd')\n",
+    "else:\n",
+    "    w_2=eval('np.' + window_2 + '(window_len_2)')    \n",
     "\n",
     "y = np.convolve(w / w.sum(), s, mode='valid')[pad:-pad]\n",
     "p = np.convolve(w / w.sum(), t, mode='valid')[pad:-pad]\n",
     "r = np.convolve(w / w.sum(), u, mode='valid')[pad:-pad]\n",
-    "x_min_arr = np.convolve(w / w.sum(), xa, mode='valid')[pad:-pad]\n",
-    "x_max_arr = np.convolve(w / w.sum(), xb, mode='valid')[pad:-pad]\n",
-    "y_min_arr = np.convolve(w / w.sum(), ya, mode='valid')[pad:-pad]\n",
-    "y_max_arr = np.convolve(w / w.sum(), yb, mode='valid')[pad:-pad]\n",
+    "x_min_arr = np.convolve(w_2 / w_2.sum(), xa, mode='valid')[pad_2:-pad_2]\n",
+    "x_max_arr = np.convolve(w_2 / w_2.sum(), xb, mode='valid')[pad_2:-pad_2]\n",
+    "y_min_arr = np.convolve(w_2 / w_2.sum(), ya, mode='valid')[pad_2:-pad_2]\n",
+    "y_max_arr = np.convolve(w_2 / w_2.sum(), yb, mode='valid')[pad_2:-pad_2]\n",
     "\n",
     "pose_dict = {}\n",
     "bbox_dict = {}\n",
@@ -151,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 201,
    "metadata": {
     "collapsed": false
    },

--
Gitblit v1.8.0