Use new ultralytics-thop package (#13282)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

pyproject.toml

@@ -75,9 +75,9 @@ dependencies = [
     "tqdm>=4.64.0", # progress bars
     "psutil", # system utilization
     "py-cpuinfo", # display CPU info
-    "thop>=0.1.1", # FLOPs computation
     "pandas>=1.1.4",
     "seaborn>=0.11.0", # plotting
+    "ultralytics-thop>=0.2.4", # FLOPs computation https://github.com/ultralytics/thop
 ]
 
 # Optional dependencies ------------------------------------------------------------------------------------------------
@@ -94,7 +94,7 @@ dev = [
     "mkdocstrings[python]",
     "mkdocs-jupyter", # for notebooks
     "mkdocs-redirects", # for 301 redirects
-    "mkdocs-ultralytics-plugin>=0.0.44", # for meta descriptions and images, dates and authors
+    "mkdocs-ultralytics-plugin>=0.0.45", # for meta descriptions and images, dates and authors
 ]
 export = [
     "onnx>=1.12.0", # ONNX export

ultralytics/nn/tasks.py

@@ -4,6 +4,7 @@ import contextlib
 from copy import deepcopy
 from pathlib import Path
 
+import thop
 import torch
 import torch.nn as nn
 
@@ -65,11 +66,6 @@ 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."""
@@ -157,7 +153,7 @@ 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 if thop else 0  # FLOPs
+        flops = thop.profile(m, inputs=[x.copy() if c else x], verbose=False)[0] / 1e9 * 2  # GFLOPs
         t = time_sync()
         for _ in range(10):
             m(x.copy() if c else x)

ultralytics/utils/torch_utils.py

@@ -11,6 +11,7 @@ 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
@@ -27,11 +28,6 @@ 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")
@@ -308,9 +304,6 @@ 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())
@@ -571,7 +564,7 @@ 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 if thop else 0  # GFLOPs
+                flops = thop.profile(m, inputs=[x], verbose=False)[0] / 1e9 * 2  # GFLOPs
             except Exception:
                 flops = 0