From 18a21d4b07c581a8954b08518115fb035c712b28 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期二, 08 八月 2017 07:34:09 +0800
Subject: [PATCH] Added new correct cropping for training and smoothing for video.
---
code/train_resnet_bins.py | 24 +++++++++++++++++-------
1 files changed, 17 insertions(+), 7 deletions(-)
diff --git a/code/train_resnet_bins.py b/code/train_resnet_bins.py
index 1bbf5be..dab3800 100644
--- a/code/train_resnet_bins.py
+++ b/code/train_resnet_bins.py
@@ -91,10 +91,14 @@
if not os.path.exists('output/snapshots'):
os.makedirs('output/snapshots')
- # ResNet18 with 3 outputs.
+ # 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['resnet18']))
-
+
print 'Loading data.'
transformations = transforms.Compose([transforms.Scale(224),transforms.RandomCrop(224),
@@ -109,9 +113,15 @@
model.cuda(gpu)
criterion = nn.CrossEntropyLoss()
- optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': .0},
- {'params': get_non_ignored_params(model), 'lr': args.lr}],
+ 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)
+ # 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.'
@@ -137,11 +147,11 @@
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]))
- # Save models at even numbered epochs.
+ # 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_binned_epoch_' + str(epoch+1) + '.pkl')
+ 'output/snapshots/resnet18_cr_epoch_'+ str(epoch+1) + '.pkl')
# Save the final Trained Model
- torch.save(model.state_dict(), 'output/snapshots/resnet18_binned_epoch_' + str(epoch+1) + '.pkl')
+ torch.save(model.state_dict(), 'output/snapshots/resnet18_cr_epoch_' + str(epoch+1) + '.pkl')
--
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