feat: Improves latency script by adding ONNX

.github/workflows/scripts.yml

@@ -30,6 +30,7 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install -e . --upgrade
+          pip install onnx onnxruntime
 
       - name: Run analysis script
         run: python scripts/eval_latency.py rexnet1_0x

scripts/eval_latency.py

@@ -11,47 +11,94 @@
 import time
 
 import numpy as np
+import onnxruntime
 import torch
 
 from holocron import models
 
 
 @torch.inference_mode()
-def main(args):
-    if args.device is None:
-        args.device = "cuda:0" if torch.cuda.is_available() else "cpu"
-
-    device = torch.device(args.device)
+def run_evaluation(
+    model: torch.nn.Module, img_tensor: torch.Tensor, num_it: int = 100, warmup_it: int = 10
+) -> np.array:
+    # Warmup
+    for _ in range(warmup_it):
+        _ = model(img_tensor)
 
-    # Pretrained imagenet model
-    model = models.__dict__[args.arch](pretrained=args.pretrained).eval().to(device=device)
+    timings = []
 
-    # RepVGG
-    if args.arch.startswith("repvgg") or args.arch.startswith("mobileone"):
-        model.reparametrize()
+    # Evaluation runs
+    for _ in range(num_it):
+        start_ts = time.perf_counter()
+        _ = model(img_tensor)
+        timings.append(time.perf_counter() - start_ts)
 
-    # Compile (using tensor cores)
-    torch.set_float32_matmul_precision("high")
-    model = torch.compile(model)
+    return np.array(timings)
 
-    # Input
-    img_tensor = torch.rand((1, 3, args.size, args.size)).to(device=device)
 
+def run_onnx_evaluation(
+    model: onnxruntime.InferenceSession, img_tensor: np.array, num_it: int = 100, warmup_it: int = 10
+) -> np.array:
+    # Set input
+    ort_input = {model.get_inputs()[0].name: img_tensor}
     # Warmup
-    for _ in range(10):
-        _ = model(img_tensor)
+    for _ in range(warmup_it):
+        _ = model.run(None, ort_input)
 
     timings = []
 
     # Evaluation runs
-    for _ in range(args.it):
+    for _ in range(num_it):
         start_ts = time.perf_counter()
-        _ = model(img_tensor)
+        _ = model.run(None, ort_input)
         timings.append(time.perf_counter() - start_ts)
 
-    _timings = np.array(timings)
+    return np.array(timings)
+
+
+@torch.inference_mode()
+def main(args):
+    # Pretrained imagenet model
+    model = models.__dict__[args.arch](pretrained=args.pretrained).eval()
+    # Reparametrizable models
+    if args.arch.startswith("repvgg") or args.arch.startswith("mobileone"):
+        model.reparametrize()
+
+    # Input
+    img_tensor = torch.rand((1, 3, args.size, args.size))
+
+    _timings = run_evaluation(model, img_tensor, args.it)
+    cpu_str = f"mean {1000 * _timings.mean():.2f}ms, std {1000 * _timings.std():.2f}ms"
+
+    # ONNX
+    torch.onnx.export(
+        model,
+        img_tensor,
+        "tmp.onnx",
+        export_params=True,
+        opset_version=14,
+    )
+    onnx_session = onnxruntime.InferenceSession("tmp.onnx")
+    npy_tensor = img_tensor.numpy()
+    _timings = run_onnx_evaluation(onnx_session, npy_tensor, args.it)
+    onnx_str = f"mean {1000 * _timings.mean():.2f}ms, std {1000 * _timings.std():.2f}ms"
+
+    # GPU
+    if args.device is None:
+        args.device = "cuda:0" if torch.cuda.is_available() else "cpu"
+    if args.device == "cpu":
+        gpu_str = "N/A"
+    else:
+        device = torch.device(args.device)
+        model = model.to(device=device)
+
+        # Input
+        img_tensor = img_tensor.to(device=device)
+        _timings = run_evaluation(model, img_tensor, args.it)
+        gpu_str = f"mean {1000 * _timings.mean():.2f}ms, std {1000 * _timings.std():.2f}ms"
+
     print(f"{args.arch} ({args.it} runs on ({args.size}, {args.size}) inputs)")
-    print(f"mean {1000 * _timings.mean():.2f}ms, std {1000 * _timings.std():.2f}ms")
+    print(f"CPU - {cpu_str}\nONNX - {onnx_str}\nGPU - {gpu_str}")
 
 
 if __name__ == "__main__":
@@ -62,6 +109,7 @@ def main(args):
     parser.add_argument("--size", type=int, default=224, help="The image input size")
     parser.add_argument("--device", type=str, default=None, help="Default device to perform computation on")
     parser.add_argument("--it", type=int, default=100, help="Number of iterations to run")
+    parser.add_argument("--warmup", type=int, default=10, help="Number of iterations for warmup")
     parser.add_argument(
         "--pretrained", dest="pretrained", help="Use pre-trained models from the modelzoo", action="store_true"
     )