Simplify thop imports (#18717)

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

ultralytics/data/utils.py

@@ -567,7 +567,7 @@ class HUBDatasetStats:
 
             # Get dataset statistics
             if self.task == "classify":
-                from torchvision.datasets import ImageFolder
+                from torchvision.datasets import ImageFolder  # scope for faster 'import ultralytics'
 
                 dataset = ImageFolder(self.data[split])
 

ultralytics/engine/results.py

@@ -839,7 +839,7 @@ class Results(SimpleClass):
             >>> df_result = results[0].to_df()
             >>> print(df_result)
         """
-        import pandas as pd
+        import pandas as pd  # scope for faster 'import ultralytics'
 
         return pd.DataFrame(self.summary(normalize=normalize, decimals=decimals))
 

ultralytics/nn/modules/block.py

@@ -1131,7 +1131,7 @@ class TorchVision(nn.Module):
 
     def __init__(self, c1, c2, model, weights="DEFAULT", unwrap=True, truncate=2, split=False):
         """Load the model and weights from torchvision."""
-        import torchvision
+        import torchvision  # scope for faster 'import ultralytics'
 
         super().__init__()
         if hasattr(torchvision.models, "get_model"):

ultralytics/nn/tasks.py

@@ -7,6 +7,7 @@ import types
 from copy import deepcopy
 from pathlib import Path
 
+import thop
 import torch
 import torch.nn as nn
 
@@ -86,11 +87,6 @@ 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."""

ultralytics/utils/torch_utils.py

@@ -12,6 +12,7 @@ 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
@@ -30,11 +31,6 @@ 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")
@@ -367,9 +363,6 @@ 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())
@@ -674,7 +667,7 @@ def profile(input, ops, n=10, device=None, max_num_obj=0):
             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