Fix tensor device mismatch and improve dataset module usability

.gitignore

@@ -10,4 +10,7 @@ site
 .coverage
 runs
 dist
+build/
+.venv/
+*.pyc
 *.egg-info

docs/basic_faultinjection.md

@@ -4,7 +4,7 @@
 For example, the following code perform a quantized random integer bit flip injection on LeNet.
 
 ```python title="Setup LeNet default fault injection"
-from dataset.lenet_cifar import make_testloader, LeNet
+from mrfi_dataset.lenet_cifar import make_testloader, LeNet
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import Acc_experiment, Acc_golden, BER_Acc_experiment
 

docs/example_layerwise.md

@@ -9,7 +9,7 @@ Additionally, if the GPU is available, the following code automatically uses CUD
 
 ```python
 import torch
-from dataset.lenet_cifar import testset, Net
+from mrfi_dataset.lenet_cifar import testset, Net
 from mrfi import MRFI, EasyConfig
 import sys
 

experiments/lenet_FI_observe.py

@@ -1,4 +1,4 @@
-from dataset.lenet_cifar import make_testloader, LeNet
+from mrfi_dataset.lenet_cifar import make_testloader, LeNet
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import observeFI_experiment, observeFI_experiment_plus
 import matplotlib.pyplot as plt

experiments/lenet_basic_observe.ipynb

@@ -20,7 +20,7 @@
     "import matplotlib.pyplot as plt\n",
     "import torch\n",
     "\n",
-    "from dataset.lenet_cifar import LeNet, make_testloader\n",
+    "from mrfi_dataset.lenet_cifar import LeNet, make_testloader\n",
     "from mrfi import MRFI, EasyConfig\n",
     "from mrfi.experiment import get_activation_info, get_weight_info"
    ]

experiments/lenet_basic_observe.py

@@ -3,7 +3,7 @@
 import matplotlib.pyplot as plt
 import torch
 
-from dataset.lenet_cifar import LeNet, make_testloader
+from mrfi_dataset.lenet_cifar import LeNet, make_testloader
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import get_activation_info, get_weight_info
 

experiments/lenet_float.py

@@ -1,4 +1,4 @@
-from dataset.lenet_cifar import make_testloader, Net
+from mrfi_dataset.lenet_cifar import make_testloader, Net
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import BER_Acc_experiment, logspace_density
 import matplotlib.pyplot as plt

experiments/lenet_layerwise.py

@@ -1,5 +1,5 @@
 import torch
-from dataset.lenet_cifar import testset, Net
+from mrfi_dataset.lenet_cifar import testset, Net
 from mrfi import MRFI, EasyConfig
 import sys
 

experiments/lenet_scale_BER_Acc.py

@@ -1,4 +1,4 @@
-from dataset.lenet_cifar import make_testloader, Net
+from mrfi_dataset.lenet_cifar import make_testloader, Net
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import BER_Acc_experiment, logspace_density
 import matplotlib.pyplot as plt

experiments/lenet_visualize.py

@@ -1,4 +1,4 @@
-from dataset.lenet_cifar import make_testloader, LeNet, testset
+from mrfi_dataset.lenet_cifar import make_testloader, LeNet, testset
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import get_activation_info, observeFI_experiment_plus, Acc_golden, Acc_experiment
 import matplotlib.pyplot as plt

experiments/poisson_sample.py

@@ -1,5 +1,5 @@
 '''Compare speed of poisson_sample'''
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import BER_Acc_experiment, logspace_density, Acc_golden
 from torchvision.models import resnet18

experiments/resnet18_BER_Acc.py

@@ -1,5 +1,5 @@
 
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import BER_Acc_experiment, logspace_density, Acc_golden, benchmark_range
 import matplotlib.pyplot as plt

experiments/resnet18_activation.py

@@ -1,7 +1,7 @@
 '''
 Conduct BER_Acc experiemnt on resnet18 with different datatype.
 '''
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import BER_Acc_experiment, logspace_density, Acc_golden, get_activation_info
 from torchvision.models import resnet18

experiments/resnet18_floatbit.py

@@ -1,5 +1,5 @@
 
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import BER_Acc_experiment, logspace_density, Acc_golden
 import matplotlib.pyplot as plt

experiments/resnet18_pixelwise.py

@@ -1,5 +1,5 @@
 
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import get_activation_info, observeFI_experiment_plus, Acc_experiment
 from torchvision.models import resnet18

experiments/resnet18_qbit.py

@@ -1,5 +1,5 @@
 
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import BER_Acc_experiment, logspace_density, Acc_golden
 import matplotlib.pyplot as plt

experiments/resnet18_weight.py

@@ -1,5 +1,5 @@
 
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import BER_Acc_experiment, logspace_density, Acc_golden, get_weight_info
 from torchvision.models import resnet18

experiments/torchvision_classify_activation.py

@@ -1,5 +1,5 @@
 
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import Acc_golden, Acc_experiment, get_activation_info
 from torchvision.models import alexnet

experiments/vgg11_channelwise.py

@@ -1,5 +1,5 @@
 
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import observeFI_experiment_plus, get_activation_info
 from torchvision.models import vgg11

experiments/vgg11_floatbit_cnt.py

@@ -1,5 +1,5 @@
 
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from torchvision.models import vgg11
 import torch

experiments/vgg11_pixelwise.py

@@ -1,5 +1,5 @@
 
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import get_activation_info, observeFI_experiment_plus, Acc_experiment
 from torchvision.models import vgg11

experiments/vgg11_speedtest.py

@@ -1,5 +1,5 @@
 
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import Acc_experiment, Acc_golden
 from torchvision.models import vgg11

experiments/vgg11_spread.py

@@ -1,5 +1,5 @@
 
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import get_activation_info, observeFI_experiment_plus, Acc_experiment
 from torchvision.models import vgg11

experiments/vgg16_activation.py

@@ -1,5 +1,5 @@
 
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import BER_Acc_experiment, logspace_density, Acc_golden, get_activation_info
 from torchvision.models import vgg16

experiments/vgg16_fixnum.py

@@ -1,5 +1,5 @@
 
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import Acc_golden, Acc_experiment
 from torchvision.models import vgg16

experiments/vgg16_floatbit_cnt.py

@@ -1,5 +1,5 @@
 
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from torchvision.models import vgg11
 import torch

experiments/vgg16_weight.py

@@ -1,5 +1,5 @@
 
-from dataset.imagenet import make_testloader
+from mrfi_dataset.imagenet import make_testloader
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import BER_Acc_experiment, logspace_density, Acc_golden, get_weight_info
 from torchvision.models import vgg16

mrfi/mrfi.py

@@ -335,7 +335,9 @@ def _FI_activation(config, act):
         modifier = named_functions[config.error_mode.method]
         modifier_args = config.error_mode.args.raw_dict
 
-        values = act.view(-1)[error_list]
+        error_idx = error_list.to(act.device) if isinstance(error_list, torch.Tensor) else error_list
+
+        values = act.view(-1)[error_idx]
 
         if fi_quantization: 
             quantization_method.quantize(values, **quantization_args)
@@ -345,7 +347,7 @@ def _FI_activation(config, act):
         if fi_quantization: 
             quantization_method.dequantize(fi_value, **quantization_args)
 
-        act.view(-1)[error_list] = fi_value
+        act.view(-1)[error_idx] = fi_value
 
     if layerwise_quantization:
         quantization_method.dequantize(act, **quantization_args)
@@ -405,7 +407,9 @@ def _FI_weight(config, weight):
         modifier = named_functions[config.error_mode.method]
         modifier_args = config.error_mode.args.raw_dict
 
-        values = weight.view(-1)[error_list]
+        error_idx = error_list.to(weight.device) if isinstance(error_list, torch.Tensor) else error_list
+
+        values = weight.view(-1)[error_idx]
 
         if fi_quantization: 
             quantization_method.quantize(values, **quantization_args)
@@ -415,7 +419,7 @@ def _FI_weight(config, weight):
         if fi_quantization: 
             quantization_method.dequantize(fi_value, **quantization_args)
 
-        weight.view(-1)[error_list] = fi_value
+        weight.view(-1)[error_idx] = fi_value
 
     if layerwise_quantization:
         quantization_method.dequantize(weight, **quantization_args)
@@ -518,7 +522,19 @@ def __init__(self, model: nn.Module, config: Union[str, EasyConfig]) -> None:
         if isinstance(config, EasyConfig):
             self.config = self.__expand_config(config)
         elif isinstance(config, str):
-            self.config = ConfigTree(_read_config(config), self)
+            treedict = self.__empty_configtree(self.model)
+            user_config = _read_config(config)
+
+            def update_dict(d, u):
+                for k, v in u.items():
+                    if isinstance(v, dict) and isinstance(d.get(k, None), dict):
+                        update_dict(d[k], v)
+                    else:
+                        d[k] = v
+
+            update_dict(treedict, user_config)
+            self.config = ConfigTree(treedict, self)
+            self.__add_moduleconfig(self.config, self.model)
         else:
             raise TypeError(config)
 

dataset/cnn_exp.py → mrfi_dataset/cnn_exp.py

@@ -9,9 +9,16 @@
     [transforms.ToTensor(),
      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
 
-trainset = torchvision.datasets.CIFAR10(root='./_data', train=True,
+try:
+    import os
+    datapath = os.environ['DATASETS'] + '/cifar10'
+except Exception:
+    import os
+    datapath = os.path.join(os.path.dirname(os.path.dirname(os.path.realpath(__file__))), '_data')
+
+trainset = torchvision.datasets.CIFAR10(root=datapath, train=True,
                                         download=False, transform=transform)
-testset = torchvision.datasets.CIFAR10(root='./_data', train=False,
+testset = torchvision.datasets.CIFAR10(root=datapath, train=False,
                                        download=False, transform=transform)
 classes = ('plane', 'car', 'bird', 'cat',
            'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
@@ -101,14 +108,18 @@ def train():
 
     print('Finished Training')
 
-    torch.save(net.state_dict(), 'cifar_vgg.pth')
+    import os
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    torch.save(net.state_dict(), os.path.join(dir_path, 'cifar_vgg.pth'))
 
 def test():
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     net = Net()
     testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                             shuffle=False)
-    net.load_state_dict(torch.load('cifar_vgg.pth'))
+    import os
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    net.load_state_dict(torch.load(os.path.join(dir_path, 'cifar_vgg.pth')))
     net.eval()
 
     correct = 0

dataset/imagenet.py → mrfi_dataset/imagenet.py

@@ -17,6 +17,8 @@
 testset = None
 
 def get_testset(folder = '~/dataset/val'):
+    import os
+    folder = os.path.expanduser(folder)
     global testset
     if testset is None:
         testset = datasets.ImageFolder(folder, tf)

dataset/lenet_cifar.py → mrfi_dataset/lenet_cifar.py

@@ -14,7 +14,8 @@
     import os
     datapath = os.environ['DATASETS'] + '/cifar10'
 except Exception:
-    datapath = './_data'
+    import os
+    datapath = os.path.join(os.path.dirname(os.path.dirname(os.path.realpath(__file__))), '_data')
 
 trainset = torchvision.datasets.CIFAR10(root=datapath, train=True,
                                         download=True, transform=transform)
@@ -43,7 +44,9 @@ def __init__(self, trained = False):
         self.fc2 = nn.Linear(120,84)
         self.fc3 = nn.Linear(84,10)
         if trained:
-            self.load_state_dict(torch.load('./dataset/lenet_cifar10.pth'))
+            import os
+            dir_path = os.path.dirname(os.path.realpath(__file__))
+            self.load_state_dict(torch.load(os.path.join(dir_path, 'lenet_cifar10.pth')))
 
     def forward(self,x):
         x = F.max_pool2d(F.relu(self.conv1(x)),(2,2))
@@ -90,13 +93,17 @@ def train():
 
     print('Finished Training')
 
-    torch.save(net.state_dict(), 'cifar10.pth')
+    import os
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    torch.save(net.state_dict(), os.path.join(dir_path, 'cifar10.pth'))
 
 def test():
     net=Net()
     testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                             shuffle=False)
-    net.load_state_dict(torch.load('cifar10.pth'))
+    import os
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    net.load_state_dict(torch.load(os.path.join(dir_path, 'cifar10.pth')))
     net.eval()
 
     correct = 0

pyproject.toml

@@ -22,10 +22,10 @@ dependencies = [
     "scipy",
     "torch>=1.9",
 ]
-version = "2.0.0"
+version = "2.1.0"
 
 [tool.setuptools]
-packages = ["mrfi"]
+packages = ["mrfi", "mrfi_dataset"]
 
 [project.urls]
 Homepage = "https://github.com/fffasttime/MRFI"

readme.md

@@ -47,7 +47,7 @@ The following code perform a quantized random integer bit flip injection on LeNe
 and find the relation between bit error rate (BER) and classification accuracy.
 
 ```python title="LeNet default fault injection"
-from dataset.lenet_cifar import make_testloader, LeNet
+from mrfi_dataset.lenet_cifar import make_testloader, LeNet
 from mrfi import MRFI, EasyConfig
 from mrfi.experiment import Acc_experiment, Acc_golden, BER_Acc_experiment
 

test/test_addfunc.py

@@ -3,7 +3,7 @@
 sys.path.append('.')
 from mrfi import experiment
 import torch
-from dataset.lenet_cifar import make_testloader, Net
+from mrfi_dataset.lenet_cifar import make_testloader, Net
 from mrfi import MRFI, EasyConfig, load_package_functions, add_function
 
 class NoQuantization:

test/test_experiment.py

@@ -3,7 +3,7 @@
 sys.path.append('.')
 from mrfi import experiment
 import torch
-from dataset.lenet_cifar import make_testloader, Net
+from mrfi_dataset.lenet_cifar import make_testloader, Net
 from mrfi import MRFI, EasyConfig
 
 def test_00():