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ultralytics 8.0.104 bug fixes and thop dependency removal (#2665)

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Co-authored-by: Kevin Abraham <5976139+abraha2d@users.noreply.github.com>
Co-authored-by: Kevin Abraham <abraha2d@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-05-17 02:26:01 +02:00
committed by GitHub
parent 7884098857
commit b1119d512e
13 changed files with 132 additions and 27 deletions
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10
ultralytics/yolo/utils/torch_utils.py
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@ -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
@ -21,6 +20,11 @@ import torchvision
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, __version__
from ultralytics.yolo.utils.checks import check_version
try:
import thop
except ImportError:
thop = None
TORCHVISION_0_10 = check_version(torchvision.__version__, '0.10.0')
TORCH_1_9 = check_version(torch.__version__, '1.9.0')
TORCH_1_11 = check_version(torch.__version__, '1.11.0')
@ -193,7 +197,7 @@ def get_flops(model, imgsz=640):
p = next(model.parameters())
stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 # max stride
im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format
flops = thop.profile(deepcopy(model), inputs=[im], verbose=False)[0] / 1E9 * 2 # stride GFLOPs
flops = thop.profile(deepcopy(model), inputs=[im], verbose=False)[0] / 1E9 * 2 if thop else 0 # stride GFLOPs
imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float
flops = flops * imgsz[0] / stride * imgsz[1] / stride # 640x640 GFLOPs
return flops
@ -378,7 +382,7 @@ 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