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 | 26 +++++++++++++++-----------
1 files changed, 15 insertions(+), 11 deletions(-)
diff --git a/code/train_resnet_bins.py b/code/train_resnet_bins.py
index 6b07747..dab3800 100644
--- a/code/train_resnet_bins.py
+++ b/code/train_resnet_bins.py
@@ -91,9 +91,13 @@
if not os.path.exists('output/snapshots'):
os.makedirs('output/snapshots')
- # ResNet50 with 3 outputs.
- model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
- load_filtered_state_dict(model, model_zoo.load_url(model_urls['resnet50']))
+ # 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.'
@@ -109,15 +113,15 @@
model.cuda(gpu)
criterion = nn.CrossEntropyLoss()
- # 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.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)
+ # 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.'
@@ -147,7 +151,7 @@
if epoch % 1 == 0 and epoch < num_epochs - 1:
print 'Taking snapshot...'
torch.save(model.state_dict(),
- 'output/snapshots/resnet50_binned_RMSprop_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/resnet50_binned_RMSprop_epoch_' + str(epoch+1) + '.pkl')
+ torch.save(model.state_dict(), 'output/snapshots/resnet18_cr_epoch_' + str(epoch+1) + '.pkl')
--
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