Convert old model to new convention

dattran2346 · May 2, 2018 · 100795d · 100795d
1 parent ec3bc5d
commit 100795d
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Showing 10 changed files with 124 additions and 147 deletions.
diff --git a/src/LearningCurve.ipynb b/src/LearningCurve.ipynb
diff --git a/src/constant.py b/src/constant.py
@@ -1,6 +1,6 @@
 import os
 os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"   # see issue #152
-os.environ["CUDA_VISIBLE_DEVICES"] = "2"
+os.environ["CUDA_VISIBLE_DEVICES"] = "3"
 current_dir = os.path.dirname(os.path.abspath(__file__))
 ROOT = os.path.abspath(os.path.join(current_dir, os.path.pardir))
 

diff --git a/src/models/densenet161.py b/src/models/densenet161.py
diff --git a/src/models/densenet201.py b/src/models/densenet201.py
diff --git a/src/models/resnet101.py b/src/models/resnet101.py
diff --git a/src/models/resnet152.py b/src/models/resnet152.py
diff --git a/src/models/resnet50.py b/src/models/resnet50.py
diff --git a/src/test.py b/src/test.py
@@ -7,6 +7,7 @@
 import argparse
 import sys
 import importlib
+from models.densenet121 import DenseNet121
 
 
 def main(args):
@@ -15,7 +16,7 @@ def main(args):
     architect = network.architect
     model_name = '%s/%s/%s/%s/model.path.tar' % (args.models_base_dir, architect, args.model_variant, args.model_name)
     net = network.build(args.model_variant)
-    parallel_net = torch.nn.DataParallel(net, device_ids=[0, 1, 2, 3]).cuda()
+    parallel_net = torch.nn.DataParallel(net, device_ids=[0]).cuda()
 
     checkpoint = torch.load(model_name)
     print('Best loss', checkpoint['best_loss'])

diff --git a/src/utils.py b/src/utils.py
@@ -27,6 +27,7 @@ def test_dataloader(image_list_file='test_list.csv', percentage=PERCENTAGE, agum
         # base on https://github.com/arnoweng/CheXNet/blob/master/model.py
         transform = transforms.Compose([
             transforms.Resize(256),
+            # transforms.Resize(586),
             transforms.TenCrop(WIDTH),
             transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])),
             transforms.Lambda(lambda crops: torch.stack([normalize(crop) for crop in crops]))

diff --git a/stats/test.csv b/stats/test.csv
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