Revert ultralytics-thop to optional (#13290)

pyproject.toml

@@ -77,7 +77,7 @@ dependencies = [
     "py-cpuinfo", # display CPU info
     "pandas>=1.1.4",
     "seaborn>=0.11.0", # plotting
-    "ultralytics-thop>=0.2.4", # FLOPs computation https://github.com/ultralytics/thop
+    "ultralytics-thop>=0.2.5", # FLOPs computation https://github.com/ultralytics/thop
 ]
 
 # Optional dependencies ------------------------------------------------------------------------------------------------

ultralytics/nn/tasks.py

@@ -4,7 +4,6 @@ import contextlib
 from copy import deepcopy
 from pathlib import Path
 
-import thop
 import torch
 import torch.nn as nn
 
@@ -66,6 +65,11 @@ from ultralytics.utils.torch_utils import (
     time_sync,
 )
 
+try:
+    import thop
+except ImportError:
+    thop = None
+
 
 class BaseModel(nn.Module):
     """The BaseModel class serves as a base class for all the models in the Ultralytics YOLO family."""
@@ -153,7 +157,7 @@ class BaseModel(nn.Module):
             None
         """
         c = m == self.model[-1] and isinstance(x, list)  # is final layer list, copy input as inplace fix
-        flops = thop.profile(m, inputs=[x.copy() if c else x], verbose=False)[0] / 1e9 * 2  # GFLOPs
+        flops = thop.profile(m, inputs=[x.copy() if c else x], verbose=False)[0] / 1e9 * 2 if thop else 0  # GFLOPs
         t = time_sync()
         for _ in range(10):
             m(x.copy() if c else x)

ultralytics/utils/torch_utils.py

@@ -11,7 +11,6 @@ from pathlib import Path
 from typing import Union
 
 import numpy as np
-import thop
 import torch
 import torch.distributed as dist
 import torch.nn as nn
@@ -28,6 +27,11 @@ from ultralytics.utils import (
 )
 from ultralytics.utils.checks import check_version
 
+try:
+    import thop
+except ImportError:
+    thop = None
+
 # Version checks (all default to version>=min_version)
 TORCH_1_9 = check_version(torch.__version__, "1.9.0")
 TORCH_1_13 = check_version(torch.__version__, "1.13.0")
@@ -304,6 +308,9 @@ def model_info_for_loggers(trainer):
 
 def get_flops(model, imgsz=640):
     """Return a YOLO model's FLOPs."""
+    if not thop:
+        return 0.0  # if not installed return 0.0 GFLOPs
+
     try:
         model = de_parallel(model)
         p = next(model.parameters())
@@ -564,7 +571,7 @@ def profile(input, ops, n=10, device=None):
             m = m.half() if hasattr(m, "half") and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
             tf, tb, t = 0, 0, [0, 0, 0]  # dt forward, backward
             try:
-                flops = thop.profile(m, inputs=[x], verbose=False)[0] / 1e9 * 2  # GFLOPs
+                flops = thop.profile(m, inputs=[x], verbose=False)[0] / 1e9 * 2 if thop else 0  # GFLOPs
             except Exception:
                 flops = 0