HongChuang/ultralytics-ascend
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Use new ultralytics-thop package (#13282)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-06-01 01:51:39 +02:00 • committed by GitHub
parent 8fb140688a
commit 7453753544
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3 changed files with 6 additions and 17 deletions
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11
ultralytics/utils/torch_utils.py
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@ -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