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docs/_cpp_api/program_listing_file_cpp_include_trtorch_trtorch.h.html

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<span class="n">DataType</span> <span class="n">dtype</span><span class="p">,</span>
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<span class="n">TensorFormat</span> <span class="n">format</span> <span class="o">=</span> <span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
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<span class="n">Input</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">tensor</span><span class="p">);</span>
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<span class="kt">bool</span> <span class="nf">get_explicit_set_dtype</span><span class="p">()</span> <span class="p">{</span>
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<span class="k">return</span> <span class="n">explicit_set_dtype</span><span class="p">;</span>
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docs/_cpp_api/structtrtorch_1_1CompileSpec.html

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Range object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8)
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spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8)
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spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8)
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spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes.
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Construct a new
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spec object using a torch tensor as an example The tensor’s shape, type and layout inform the spec’s values.
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Note: You cannot set dynamic shape through this method, you must use an alternative constructor
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spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8)
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spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8)
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spec object using a torch tensor as an example The tensor’s shape, type and layout inform the spec’s values.
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docs/_notebooks/Resnet50-example.html

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docs/_notebooks/vgg-qat.html

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## 2. VGG16 Overview ### Very Deep Convolutional Networks for Large-Scale Image Recognition VGG is one of the earliest family of image classification networks that first used small (3x3) convolution filters and achieved significant improvements on ImageNet recognition challenge. The network architecture looks as follows
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## 3. Training a baseline VGG16 model We train VGG16 on CIFAR10 dataset. Define training and testing datasets and dataloaders. This will download the CIFAR 10 data in your

docs/_sources/_cpp_api/program_listing_file_cpp_include_trtorch_trtorch.h.rst.txt

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