The most surprising thing about using TensorRT for ResNet and EfficientNet is that it doesn’t just make them faster, it fundamentally changes how they operate to achieve that speed, often by eliminating entire layers or operations that were deemed redundant during its optimization process.
Let’s see what that looks like in practice. Imagine we have a trained ResNet-50 model. Normally, you’d load it with PyTorch or TensorFlow, do some preprocessing on your input image (resizing, normalization, converting to a tensor), and then feed it through the model.
import torch
import torchvision.models as models
from PIL import Image
import torchvision.transforms as transforms
import numpy as np
# Load a pre-trained ResNet-50
model = models.resnet50(pretrained=True)
model.eval()
# Sample input image
img_path = 'path/to/your/image.jpg'
img = Image.open(img_path).convert('RGB')
# Standard PyTorch preprocessing
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(img)
input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
# Inference
with torch.no_grad():
output = model(input_batch)
# Post-processing (e.g., getting top-5 predictions)
probabilities = torch.nn.functional.softmax(output[0], dim=0)
top5_prob, top5_catid = torch.topk(probabilities, 5)
Now, let’s bring TensorRT into the picture. The goal is to take this PyTorch model and convert it into an optimized TensorRT engine. This involves several steps:
-
Export to ONNX: TensorRT typically consumes models in the ONNX (Open Neural Network Exchange) format. You’d export your PyTorch model to ONNX.
# Assuming you have your PyTorch model 'model' defined as above torch.onnx.export(model, # model being run input_batch, # Model input (for shape inference) "resnet50.onnx", # where to save the model export_params=True, # store the trained parameter weights inside the model file opset_version=11, # the ONNX version to export the model to do_constant_folding=True, # whether to execute constant folding for optimization input_names = ['input'], # the model's input names output_names = ['output'], # the model's output names dynamic_axes={'input' : {0 : 'batch_size'}, # variable length axes 'output' : {0 : 'batch_size'}}) -
Build the TensorRT Engine: This is where the magic happens. You use the TensorRT Python API to build an optimized engine from the ONNX file. This process involves specifying the target GPU, precision (FP32, FP16, INT8), and batch size.
import tensorrt as trt TRT_LOGGER = trt.Logger(trt.Logger.WARNING) def build_engine(onnx_file_path, engine_file_path): with trt.Builder(TRT_LOGGER) as builder, builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) as network, trt.OnnxParser(network, TRT_LOGGER) as parser: # Load ONNX model with open(onnx_file_path, 'rb') as model: if not parser.parse(model.read()): print('ERROR: Failed to parse the ONNX file.') for error in range(parser.num_errors): print(parser.get_error(error)) return None # Build engine configuration config = builder.create_builder_config() # Enable FP16 if supported and desired for faster inference if builder.platform_has_fast_fp16: config.set_flag(trt.BuilderFlag.FP16) print("FP16 enabled.") else: print("FP16 not supported or not enabled.") # Set max batch size # builder.max_batch_size = 1 # Or set to a larger value if dynamic batching is used # Optimize the network print("Building TensorRT engine...") serialized_engine = builder.build_serialized_network(network, config) if serialized_engine is None: print("ERROR: Failed to build TensorRT engine.") return None # Save the engine with open(engine_file_path, 'wb') as f: f.write(serialized_engine) print(f"TensorRT engine saved to {engine_file_path}") return serialized_engine onnx_file = "resnet50.onnx" engine_file = "resnet50.plan" # Build the engine (this might take a few minutes) build_engine(onnx_file, engine_file) -
Inference with the TensorRT Engine: Once the engine (
.planfile) is built, you load it and perform inference.import tensorrt as trt import pycuda.driver as cuda import pycuda.autoinit # Initializes CUDA TRT_LOGGER = trt.Logger(trt.Logger.WARNING) def infer_with_engine(engine_file_path, input_data): # Load the engine with open(engine_file_path, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime: engine = runtime.deserialize_cuda_engine(f.read()) if engine is None: print("ERROR: Failed to load TensorRT engine.") return None # Create execution context context = engine.create_execution_context() # Allocate host and device buffers inputs, outputs, bindings, stream = allocate_buffers(engine) # Copy input data to the GPU np.copyto(inputs[0].host, input_data.flatten()) batch_size = input_data.shape[0] # Assuming input_data is already batched # Run inference with stream: trt.cuda.memcpy_htod_async(bindings[0], inputs[0].host) context.execute_async_v2(bindings=bindings, stream_handle=stream.handle) trt.cuda.memcpy_dtoh_async(outputs[0].host, bindings[outputs[0].binding_index]) # Synchronize and return results stream.synchronize() return outputs[0].host.reshape((batch_size, -1)) # Reshape to original output dimensions def allocate_buffers(engine): inputs = [] outputs = [] bindings = [] stream = cuda.Stream() for binding in engine: size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size dtype = trt.nptype(engine.get_binding_dtype(binding)) # Allocate host and device buffers host_mem = cuda.pagelocked_empty(size, dtype) device_mem = cuda.mem_alloc(host_mem.nbytes) # Append the input or output object to the appropriate list binding = {'binding': binding, 'host': host_mem, 'device': device_mem, 'size': size, 'dtype': dtype} if engine.binding_is_input(binding['binding']): inputs.append(binding) else: outputs.append(binding) # Create a list of bindings that will be passed to the engine - they are just # device pointers. bindings = [None] * engine.num_bindings for binding in inputs + outputs: bindings[engine.get_binding_index(binding['binding'])] = binding['device'] # Return the host and device buffers, and the stream return inputs, outputs, bindings, stream # Load preprocessed input data (assuming you have it as a numpy array) # For demonstration, let's use the 'input_batch' from earlier, converted to numpy input_np = input_batch.cpu().numpy() # Perform inference # Note: The input_np needs to match the input shape expected by the TensorRT engine # If you used dynamic_axes, you might need to set batch size explicitly or # ensure your input_np has the correct shape. # For a fixed batch size engine, input_np should be e.g., (1, 3, 224, 224) engine_file = "resnet50.plan" results = infer_with_engine(engine_file, input_np) if results is not None: print("Inference successful. Output shape:", results.shape) # Process results (e.g., softmax, top-k) as needed probabilities = np.exp(results) / np.sum(np.exp(results), axis=1, keepdims=True) top5_indices = np.argsort(probabilities[0])[-5:][::-1] print("Top 5 predicted class indices:", top5_indices)
The core problem TensorRT solves is latency and throughput for deep learning inference on NVIDIA GPUs. For ResNet and EfficientNet, which are computationally intensive, this means getting more inferences per second with less power consumption.
Internally, TensorRT performs several optimizations:
- Layer and Tensor Fusion: It merges multiple layers (like convolution, bias addition, and ReLU activation) into a single kernel. This drastically reduces kernel launch overhead and memory bandwidth requirements.
- Precision Calibration: TensorRT can run models in FP16 or INT8 precision. FP16 offers a significant speedup with minimal accuracy loss. INT8 can provide even greater speedups but requires a calibration step to determine the optimal quantization ranges for weights and activations.
- Kernel Auto-Tuning: It selects the best GPU kernels for each operation based on the target GPU architecture and the specific layer parameters.
- Constant Folding: Computations that involve only constants are performed at build time, not inference time.
- Elimination of Redundant Operations: This is where TensorRT can be surprising. It might identify that certain parts of the network are mathematically equivalent to simpler operations, or even that some layers are not contributing meaningfully to the output for specific input ranges and can be pruned. For example, it might fuse a convolution with a batch norm layer into a single, optimized convolution.
The one thing most people don’t realize is that TensorRT doesn’t just take your existing model and run it faster; it rebuilds the model. During the builder.build_serialized_network phase, TensorRT analyzes the ONNX graph and applies its optimizations. This means the resulting TensorRT engine might not look anything like the original PyTorch or TensorFlow graph. It’s a highly specialized, hardware-aware representation. You’re not just running a pre-trained model; you’re running an optimized inference kernel derived from it.
The next step after optimizing your image classification models with TensorRT is to explore its capabilities for object detection models like YOLO or SSD, which involve more complex graph structures and post-processing steps.