modified file paths

Author: Nikil Ravi

10_FAIR_AI/trt_and_containerization/build_engine.py

@@ -4,8 +4,8 @@
 import tensorrt as trt 
 
 name = 'cifar_net_10_epochs' 
-engine_name = '{}_4.plan'.format(name)
-onnx_path = '/home/nravi/ai-science-training-series/{}.onnx'.format(name)
+engine_name = '{}_4_trial.plan'.format(name)
+onnx_path = '/home/nravi/ai-science-training-series/10_FAIR_AI/trt_and_containerization/saved_models/{}.onnx'.format(name)
 batch_size = 4
 
 model = ModelProto()

10_FAIR_AI/trt_and_containerization/cifar-model-for-trt.ipynb

@@ -28,7 +28,7 @@ def forward(self, x):
         return x
 
 
-PATH = './cifar_net_10_epochs.pth'
+PATH = './saved_models/cifar_net_10_epochs.pth'
 net = Net()
 net.load_state_dict(torch.load(PATH))
 
@@ -37,13 +37,13 @@ def forward(self, x):
      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
 
 batch_size = 250
-
-trainset = torchvision.datasets.CIFAR10(root='./', train=True,
+data_dir = '../../../ai-science-training-series-old/'
+trainset = torchvision.datasets.CIFAR10(root=data_dir, train=True,
                                         download=False, transform=transform)
 trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                           shuffle=True, num_workers=2)
 
-testset = torchvision.datasets.CIFAR10(root='./', train=False,
+testset = torchvision.datasets.CIFAR10(root=data_dir, train=False,
                                        download=False, transform=transform)
 testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                          shuffle=False, num_workers=2)

10_FAIR_AI/trt_and_containerization/cifar-script-torch.py

@@ -95,19 +95,20 @@ def do_inference(engine, pics_1, h_input_1, d_input_1, h_output, d_output, strea
 
 TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
 trt_runtime = trt.Runtime(TRT_LOGGER)
-engine = load_engine(trt_runtime, '/home/nravi/ai-science-training-series/cifar_net_10_epochs_250.plan')
+engine = load_engine(trt_runtime,'./saved_models/cifar_net_10_epochs_250.plan')
 print("Loaded engine")
 
 transform = transforms.Compose(
     [transforms.ToTensor(),
      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
 
 batch_size = 250
-trainset = torchvision.datasets.CIFAR10(root='./', train=True,
+data_dir = '../../../ai-science-training-series-old/'
+trainset = torchvision.datasets.CIFAR10(root=data_dir, train=True,
                                         download=False, transform=transform)
 trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                           shuffle=True, num_workers=2)
-testset = torchvision.datasets.CIFAR10(root='./', train=False,
+testset = torchvision.datasets.CIFAR10(root=data_dir, train=False,
                                        download=False, transform=transform)
 testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                          shuffle=False, num_workers=2)