2025-08-13 17:02:10,708 [WARNING] The first epoch will be compiled for the graph, which may take a long time; You can come back later :).
[ERROR] ANALYZER(819407,ffffb78d8020,python):2025-08-13-17:02:11.942.296 [mindspore/ccsrc/pipeline/jit/ps/static_analysis/evaluator.cc:717] Run] Primitive: <StandardPrimEvaluator_Conv2D> infer failed, failed info: For primitive[Conv2D], the input type must be same.
name:[w]:Ref[Tensor[Float32]].
name:[x]:Tensor[UInt8].
----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
mindspore/core/utils/check_convert_utils.cc:1137 CheckTypeSame
----------------------------------------------------
- The Traceback of Net Construct Code:
----------------------------------------------------
# 0 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:115, 19~41
return train_step_func(*args)
^~~~~~~~~~~~~~~~~~~~~~
# 1 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:97~98, 12~99
if clip_grad:
# 2 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:92, 40~62
(loss, loss_items), grads = grad_fn(x, label, seg)
^~~~~~~~~~~~~~~~~~~~~~
# 3 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/ops/composite/base.py:597, 31~56
return grad_(fn, weights)(*args)
^~~~~~~~~~~~~~~~~~~~~~~~~
# 4 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:84, 19~29
pred = network(x)
^~~~~~~~~~
# 5 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~28
return self.model(x)
^~~~~~~~~~~~~
# 6 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~25
return self.model(x)
^~~~~~~~~~
# 7 In file /opt/mindyolo/mindyolo/models/model_factory.py:67~85, 8~51
for i in range(len(self.model)):
# 8 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~25
return self.model(x)
^~~~~~~~~~
# 9 In file /opt/mindyolo/mindyolo/models/model_factory.py:83, 16~20
x = m(x) # run
^~~~
# 10 In file /opt/mindyolo/mindyolo/models/layers/conv.py:59, 32~44
return self.act(self.bn(self.conv(x)))
^~~~~~~~~~~~
# 11 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/nn/layer/conv.py:367~368, 8~53
if self.has_bias:
# 12 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/nn/layer/conv.py:366, 17~44
output = self.conv2d(x, self.weight)
^~~~~~~~~~~~~~~~~~~~~~~~~~~
(See file '/opt/mindyolo/rank_0/om/analyze_fail.ir' for more details. Get instructions about `analyze_fail.ir` at https://www.mindspore.cn/search?inputValue=analyze_fail.ir)
Traceback (most recent call last):
File "/opt/mindyolo/train.py", line 336, in <module>
train(args)
File "/opt/mindyolo/train.py", line 291, in train
trainer.train(
File "/opt/mindyolo/mindyolo/utils/trainer_factory.py", line 170, in train
run_context.loss, run_context.lr = self.train_step(imgs, labels, segments,
File "/opt/mindyolo/mindyolo/utils/trainer_factory.py", line 368, in train_step
loss, loss_item, _, grads_finite = self.train_step_fn(imgs, labels, segments, True)
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 1224, in staging_specialize
out = ms_function_executor(*args, **kwargs)
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 180, in wrapper
results = fn(*arg, **kwargs)
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 669, in __call__
raise err
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 666, in __call__
phase = self.compile(self.fn.__name__, *args_list, **kwargs)
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 772, in compile
is_compile = self._graph_executor.compile(self.fn, compile_args, kwargs, phase)
TypeError: For primitive[Conv2D], the input type must be same.
name:[w]:Ref[Tensor[Float32]].
name:[x]:Tensor[UInt8].
----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
mindspore/core/utils/check_convert_utils.cc:1137 CheckTypeSame
----------------------------------------------------
- The Traceback of Net Construct Code:
----------------------------------------------------
# 0 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:115, 19~41
return train_step_func(*args)
^~~~~~~~~~~~~~~~~~~~~~
# 1 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:97~98, 12~99
if clip_grad:
# 2 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:92, 40~62
(loss, loss_items), grads = grad_fn(x, label, seg)
^~~~~~~~~~~~~~~~~~~~~~
# 3 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/ops/composite/base.py:597, 31~56
return grad_(fn, weights)(*args)
^~~~~~~~~~~~~~~~~~~~~~~~~
# 4 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:84, 19~29
pred = network(x)
^~~~~~~~~~
# 5 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~28
return self.model(x)
^~~~~~~~~~~~~
# 6 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~25
return self.model(x)
^~~~~~~~~~
# 7 In file /opt/mindyolo/mindyolo/models/model_factory.py:67~85, 8~51
for i in range(len(self.model)):
# 8 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~25
return self.model(x)
^~~~~~~~~~
# 9 In file /opt/mindyolo/mindyolo/models/model_factory.py:83, 16~20
x = m(x) # run
^~~~
# 10 In file /opt/mindyolo/mindyolo/models/layers/conv.py:59, 32~44
return self.act(self.bn(self.conv(x)))
^~~~~~~~~~~~
# 11 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/nn/layer/conv.py:367~368, 8~53
if self.has_bias:
# 12 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/nn/layer/conv.py:366, 17~44
output = self.conv2d(x, self.weight)
^~~~~~~~~~~~~~~~~~~~~~~~~~~
(See file '/opt/mindyolo/rank_0/om/analyze_fail.ir' for more details. Get instructions about `analyze_fail.ir` at https://www.mindspore.cn/search?inputValue=analyze_fail.ir)
(mindyolomaster) root@autodl-container-6ce943a632-58447938:/opt/mindyolo# git pull
Password for 'https://wangqiangqiang@bitbucket.bs2y.com':
remote: Counting objects: 6, done.
remote: Compressing objects: 100% (6/6), done.
remote: Total 6 (delta 5), reused 0 (delta 0)
Unpacking objects: 100% (6/6), 476 bytes | 238.00 KiB/s, done.
From https://bitbucket.bs2y.com/scm/zstp/mindyolo
d044490..a6ae59b master -> origin/master
Updating d044490..a6ae59b
Fast-forward
configs/yolov8/seg/yolov8x-seg.yaml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
(mindyolomaster) root@autodl-container-6ce943a632-58447938:/opt/mindyolo# python train.py --task segment --weight weight/yolov8-x-seg_300e_mAP_mask_429-b4920557.ckpt --config datasets/seg_cat_dog/seg_cat_dog.yaml --epochs 20
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:10:53.710.337 [mindspore/run_check/_check_version.py:324] MindSpore version 2.6.0 and Ascend AI software package (Ascend Data Center Solution)version 8.2 does not match, the version of software package expect one of ['7.6', '7.7']. Please refer to the match info on: https://www.mindspore.cn/install
/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.
setattr(self, word, getattr(machar, word).flat[0])
/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.
return self._float_to_str(self.smallest_subnormal)
/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.
setattr(self, word, getattr(machar, word).flat[0])
/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.
return self._float_to_str(self.smallest_subnormal)
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:10:55.924.466 [mindspore/run_check/_check_version.py:342] MindSpore version 2.6.0 and "te" wheel package version 8.2 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:10:55.924.624 [mindspore/run_check/_check_version.py:355] Please pay attention to the above warning, countdown: 3
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:10:56.925.767 [mindspore/run_check/_check_version.py:355] Please pay attention to the above warning, countdown: 2
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:10:57.926.607 [mindspore/run_check/_check_version.py:355] Please pay attention to the above warning, countdown: 1
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:11:00.956.646 [mindspore/run_check/_check_version.py:324] MindSpore version 2.6.0 and Ascend AI software package (Ascend Data Center Solution)version 8.2 does not match, the version of software package expect one of ['7.6', '7.7']. Please refer to the match info on: https://www.mindspore.cn/install
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:11:00.957.438 [mindspore/run_check/_check_version.py:342] MindSpore version 2.6.0 and "te" wheel package version 8.2 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:11:00.957.519 [mindspore/run_check/_check_version.py:355] Please pay attention to the above warning, countdown: 3
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:11:01.958.607 [mindspore/run_check/_check_version.py:355] Please pay attention to the above warning, countdown: 2
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:11:02.959.901 [mindspore/run_check/_check_version.py:355] Please pay attention to the above warning, countdown: 1
2025-08-13 17:11:03,978 [INFO] parse_args:
2025-08-13 17:11:03,978 [INFO] task segment
2025-08-13 17:11:03,978 [INFO] device_target Ascend
2025-08-13 17:11:03,978 [INFO] save_dir ./runs/2025.08.13-17.11.03
2025-08-13 17:11:03,978 [INFO] log_level INFO
2025-08-13 17:11:03,978 [INFO] is_parallel False
2025-08-13 17:11:03,978 [INFO] ms_mode 0
2025-08-13 17:11:03,978 [INFO] max_call_depth 2000
2025-08-13 17:11:03,978 [INFO] ms_amp_level O0
2025-08-13 17:11:03,978 [INFO] keep_loss_fp32 True
2025-08-13 17:11:03,978 [INFO] anchor_base False
2025-08-13 17:11:03,978 [INFO] ms_loss_scaler static
2025-08-13 17:11:03,978 [INFO] ms_loss_scaler_value 1024.0
2025-08-13 17:11:03,978 [INFO] ms_jit True
2025-08-13 17:11:03,978 [INFO] ms_enable_graph_kernel False
2025-08-13 17:11:03,978 [INFO] ms_datasink False
2025-08-13 17:11:03,978 [INFO] overflow_still_update False
2025-08-13 17:11:03,978 [INFO] clip_grad True
2025-08-13 17:11:03,978 [INFO] clip_grad_value 10.0
2025-08-13 17:11:03,978 [INFO] ema True
2025-08-13 17:11:03,978 [INFO] weight weight/yolov8-x-seg_300e_mAP_mask_429-b4920557.ckpt
2025-08-13 17:11:03,978 [INFO] ema_weight
2025-08-13 17:11:03,978 [INFO] freeze []
2025-08-13 17:11:03,978 [INFO] epochs 20
2025-08-13 17:11:03,978 [INFO] per_batch_size 1
2025-08-13 17:11:03,978 [INFO] img_size 640
2025-08-13 17:11:03,978 [INFO] nbs 64
2025-08-13 17:11:03,978 [INFO] accumulate 1
2025-08-13 17:11:03,978 [INFO] auto_accumulate False
2025-08-13 17:11:03,978 [INFO] log_interval 10
2025-08-13 17:11:03,978 [INFO] single_cls False
2025-08-13 17:11:03,978 [INFO] sync_bn False
2025-08-13 17:11:03,978 [INFO] keep_checkpoint_max 100
2025-08-13 17:11:03,978 [INFO] run_eval False
2025-08-13 17:11:03,978 [INFO] conf_thres 0.001
2025-08-13 17:11:03,978 [INFO] iou_thres 0.7
2025-08-13 17:11:03,978 [INFO] conf_free True
2025-08-13 17:11:03,978 [INFO] rect False
2025-08-13 17:11:03,978 [INFO] nms_time_limit 20.0
2025-08-13 17:11:03,978 [INFO] recompute True
2025-08-13 17:11:03,978 [INFO] recompute_layers 2
2025-08-13 17:11:03,978 [INFO] seed 2
2025-08-13 17:11:03,978 [INFO] summary True
2025-08-13 17:11:03,978 [INFO] profiler False
2025-08-13 17:11:03,978 [INFO] profiler_step_num 1
2025-08-13 17:11:03,978 [INFO] opencv_threads_num 0
2025-08-13 17:11:03,978 [INFO] strict_load False
2025-08-13 17:11:03,978 [INFO] enable_modelarts False
2025-08-13 17:11:03,978 [INFO] data_url
2025-08-13 17:11:03,978 [INFO] ckpt_url
2025-08-13 17:11:03,978 [INFO] multi_data_url
2025-08-13 17:11:03,978 [INFO] pretrain_url
2025-08-13 17:11:03,978 [INFO] train_url
2025-08-13 17:11:03,978 [INFO] data_dir /cache/data/
2025-08-13 17:11:03,978 [INFO] ckpt_dir /cache/pretrain_ckpt/
2025-08-13 17:11:03,978 [INFO] network.model_name yolov8
2025-08-13 17:11:03,978 [INFO] network.nc 80
2025-08-13 17:11:03,978 [INFO] network.reg_max 16
2025-08-13 17:11:03,978 [INFO] network.stride [8, 16, 32]
2025-08-13 17:11:03,978 [INFO] network.backbone [[-1, 1, 'ConvNormAct', [64, 3, 2]], [-1, 1, 'ConvNormAct', [128, 3, 2]], [-1, 3, 'C2f', [128, True]], [-1, 1, 'ConvNormAct', [256, 3, 2]], [-1, 6, 'C2f', [256, True]], [-1, 1, 'ConvNormAct', [512, 3, 2]], [-1, 6, 'C2f', [512, True]], [-1, 1, 'ConvNormAct', [1024, 3, 2]], [-1, 3, 'C2f', [1024, True]], [-1, 1, 'SPPF', [1024, 5]]]
2025-08-13 17:11:03,978 [INFO] network.head [[-1, 1, 'Upsample', ['None', 2, 'nearest']], [[-1, 6], 1, 'Concat', [1]], [-1, 3, 'C2f', [512]], [-1, 1, 'Upsample', ['None', 2, 'nearest']], [[-1, 4], 1, 'Concat', [1]], [-1, 3, 'C2f', [256]], [-1, 1, 'ConvNormAct', [256, 3, 2]], [[-1, 12], 1, 'Concat', [1]], [-1, 3, 'C2f', [512]], [-1, 1, 'ConvNormAct', [512, 3, 2]], [[-1, 9], 1, 'Concat', [1]], [-1, 3, 'C2f', [1024]], [[15, 18, 21], 1, 'YOLOv8SegHead', ['nc', 'reg_max', 32, 256, 'stride']]]
2025-08-13 17:11:03,978 [INFO] network.depth_multiple 1.0
2025-08-13 17:11:03,978 [INFO] network.width_multiple 1.25
2025-08-13 17:11:03,978 [INFO] network.max_channels 512
2025-08-13 17:11:03,978 [INFO] optimizer.optimizer momentum
2025-08-13 17:11:03,978 [INFO] optimizer.lr_init 0.001
2025-08-13 17:11:03,978 [INFO] optimizer.momentum 0.937
2025-08-13 17:11:03,978 [INFO] optimizer.nesterov True
2025-08-13 17:11:03,978 [INFO] optimizer.loss_scale 1.0
2025-08-13 17:11:03,978 [INFO] optimizer.warmup_epochs 3
2025-08-13 17:11:03,978 [INFO] optimizer.warmup_momentum 0.8
2025-08-13 17:11:03,978 [INFO] optimizer.warmup_bias_lr 0.1
2025-08-13 17:11:03,978 [INFO] optimizer.min_warmup_step 1000
2025-08-13 17:11:03,978 [INFO] optimizer.group_param yolov8
2025-08-13 17:11:03,978 [INFO] optimizer.gp_weight_decay 0.0010078125
2025-08-13 17:11:03,978 [INFO] optimizer.start_factor 1.0
2025-08-13 17:11:03,978 [INFO] optimizer.end_factor 0.01
2025-08-13 17:11:03,978 [INFO] optimizer.epochs 20
2025-08-13 17:11:03,978 [INFO] optimizer.nbs 64
2025-08-13 17:11:03,978 [INFO] optimizer.accumulate 1
2025-08-13 17:11:03,978 [INFO] optimizer.total_batch_size 1
2025-08-13 17:11:03,978 [INFO] loss.name YOLOv8SegLoss
2025-08-13 17:11:03,978 [INFO] loss.box 7.5
2025-08-13 17:11:03,978 [INFO] loss.cls 0.5
2025-08-13 17:11:03,978 [INFO] loss.dfl 1.5
2025-08-13 17:11:03,978 [INFO] loss.reg_max 16
2025-08-13 17:11:03,978 [INFO] loss.nm 32
2025-08-13 17:11:03,978 [INFO] loss.overlap True
2025-08-13 17:11:03,978 [INFO] loss.max_object_num 600
2025-08-13 17:11:03,978 [INFO] data.num_parallel_workers 8
2025-08-13 17:11:03,978 [INFO] data.train_transforms []
2025-08-13 17:11:03,978 [INFO] data.test_transforms []
2025-08-13 17:11:03,978 [INFO] data.dataset_name cat_dog_seg
2025-08-13 17:11:03,978 [INFO] data.train_set /opt/mindyolo/datasets/seg_cat_dog/train.txt
2025-08-13 17:11:03,978 [INFO] data.val_set /opt/mindyolo/datasets/seg_cat_dog/val.txt
2025-08-13 17:11:03,978 [INFO] data.test_set /opt/mindyolo/datasets/seg_cat_dog/test.txt
2025-08-13 17:11:03,978 [INFO] data.nc 2
2025-08-13 17:11:03,978 [INFO] data.names ['cat', 'dog']
2025-08-13 17:11:03,978 [INFO] config datasets/seg_cat_dog/seg_cat_dog.yaml
2025-08-13 17:11:03,978 [INFO] rank 0
2025-08-13 17:11:03,978 [INFO] rank_size 1
2025-08-13 17:11:03,978 [INFO] total_batch_size 1
2025-08-13 17:11:03,978 [INFO] callback []
2025-08-13 17:11:03,978 [INFO]
2025-08-13 17:11:03,980 [INFO] Please check the above information for the configurations
2025-08-13 17:11:04,754 [WARNING] Parse Model, args: nearest, keep str type
2025-08-13 17:11:04,906 [WARNING] Parse Model, args: nearest, keep str type
2025-08-13 17:11:05,707 [INFO] number of network params, total: 71.813161M, trainable: 71.752758M
2025-08-13 17:11:05,716 [INFO] Turn on recompute, and the results of the first 2 layers will be recomputed.
[WARNING] DEVICE(845031,ffff982ff020,python):2025-08-13-17:11:06.865.050 [mindspore/ccsrc/plugin/res_manager/ascend/mem_manager/ascend_vmm_adapter.h:152] CheckVmmDriverVersion] Open file /etc/ascend_install.info failed.
[WARNING] DEVICE(845031,ffff982ff020,python):2025-08-13-17:11:06.865.135 [mindspore/ccsrc/plugin/res_manager/ascend/mem_manager/ascend_vmm_adapter.h:180] CheckVmmDriverVersion] Open file /usr/local/Ascend/driver/version.info failed.
2025-08-13 17:11:14,933 [WARNING] Parse Model, args: nearest, keep str type
2025-08-13 17:11:15,084 [WARNING] Parse Model, args: nearest, keep str type
2025-08-13 17:11:15,879 [INFO] number of network params, total: 71.813161M, trainable: 71.752758M
2025-08-13 17:11:15,888 [INFO] Turn on recompute, and the results of the first 2 layers will be recomputed.
2025-08-13 17:11:17,727 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.0.2.weight` with shape `(80, 320, 1, 1)`, which is inconsistent with cell shape `(2, 320, 1, 1)`
2025-08-13 17:11:17,727 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.0.2.bias` with shape `(80,)`, which is inconsistent with cell shape `(2,)`
2025-08-13 17:11:17,728 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.1.2.weight` with shape `(80, 320, 1, 1)`, which is inconsistent with cell shape `(2, 320, 1, 1)`
2025-08-13 17:11:17,728 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.1.2.bias` with shape `(80,)`, which is inconsistent with cell shape `(2,)`
2025-08-13 17:11:17,728 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.2.2.weight` with shape `(80, 320, 1, 1)`, which is inconsistent with cell shape `(2, 320, 1, 1)`
2025-08-13 17:11:17,728 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.2.2.bias` with shape `(80,)`, which is inconsistent with cell shape `(2,)`
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:11:17.750.623 [mindspore/train/serialization.py:1770] For 'load_param_into_net', 6 parameters in the 'net' are not loaded, because they are not in the 'parameter_dict', please check whether the network structure is consistent when training and loading checkpoint.
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:11:17.750.856 [mindspore/train/serialization.py:1774] ['model.model.22.cv3.0.2.weight', 'model.model.22.cv3.0.2.bias', 'model.model.22.cv3.1.2.weight', 'model.model.22.cv3.1.2.bias', 'model.model.22.cv3.2.2.weight', 'model.model.22.cv3.2.2.bias'] are not loaded.
2025-08-13 17:11:17,750 [INFO] Pretrain model load from "weight/yolov8-x-seg_300e_mAP_mask_429-b4920557.ckpt" success.
.2025-08-13 17:11:23,895 [INFO] ema_weight not exist, default pretrain weight is currently used.
2025-08-13 17:11:23,898 [INFO] Dataset Cache file hash/version check success.
2025-08-13 17:11:23,898 [INFO] Load dataset cache from [/opt/mindyolo/datasets/seg_cat_dog/train.cache.npy] success.
Scanning '/opt/mindyolo/datasets/seg_cat_dog/train.cache.npy' images and labels... 46 found, 0 missing, 0 empty, 0 corrupted: 100%|████████████████████████████████████████| 46/46 [00:00<?, ?it/s]
2025-08-13 17:11:23,901 [INFO] Dataloader num parallel workers: [8]
2025-08-13 17:11:25,120 [INFO] Registry(name=callback, total=4)
2025-08-13 17:11:25,120 [INFO] (0): YoloxSwitchTrain in mindyolo/utils/callback.py
2025-08-13 17:11:25,120 [INFO] (1): EvalWhileTrain in mindyolo/utils/callback.py
2025-08-13 17:11:25,120 [INFO] (2): SummaryCallback in mindyolo/utils/callback.py
2025-08-13 17:11:25,120 [INFO] (3): ProfilerCallback in mindyolo/utils/callback.py
2025-08-13 17:11:25,120 [INFO]
2025-08-13 17:11:25,129 [INFO] got 1 active callback as follows:
2025-08-13 17:11:25,129 [INFO] SummaryCallback()
2025-08-13 17:11:25,129 [WARNING] The first epoch will be compiled for the graph, which may take a long time; You can come back later :).
[ERROR] ANALYZER(845031,ffff982ff020,python):2025-08-13-17:11:26.474.019 [mindspore/ccsrc/pipeline/jit/ps/static_analysis/evaluator.cc:717] Run] Primitive: <StandardPrimEvaluator_Conv2D> infer failed, failed info: For primitive[Conv2D], the input type must be same.
name:[w]:Ref[Tensor[Float32]].
name:[x]:Tensor[UInt8].
----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
mindspore/core/utils/check_convert_utils.cc:1137 CheckTypeSame
----------------------------------------------------
- The Traceback of Net Construct Code:
----------------------------------------------------
# 0 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:115, 19~41
return train_step_func(*args)
^~~~~~~~~~~~~~~~~~~~~~
# 1 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:97~98, 12~99
if clip_grad:
# 2 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:92, 40~62
(loss, loss_items), grads = grad_fn(x, label, seg)
^~~~~~~~~~~~~~~~~~~~~~
# 3 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/ops/composite/base.py:597, 31~56
return grad_(fn, weights)(*args)
^~~~~~~~~~~~~~~~~~~~~~~~~
# 4 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:84, 19~29
pred = network(x)
^~~~~~~~~~
# 5 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~28
return self.model(x)
^~~~~~~~~~~~~
# 6 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~25
return self.model(x)
^~~~~~~~~~
# 7 In file /opt/mindyolo/mindyolo/models/model_factory.py:67~85, 8~51
for i in range(len(self.model)):
# 8 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~25
return self.model(x)
^~~~~~~~~~
# 9 In file /opt/mindyolo/mindyolo/models/model_factory.py:83, 16~20
x = m(x) # run
^~~~
# 10 In file /opt/mindyolo/mindyolo/models/layers/conv.py:59, 32~44
return self.act(self.bn(self.conv(x)))
^~~~~~~~~~~~
# 11 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/nn/layer/conv.py:367~368, 8~53
if self.has_bias:
# 12 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/nn/layer/conv.py:366, 17~44
output = self.conv2d(x, self.weight)
^~~~~~~~~~~~~~~~~~~~~~~~~~~
(See file '/opt/mindyolo/rank_0/om/analyze_fail.ir' for more details. Get instructions about `analyze_fail.ir` at https://www.mindspore.cn/search?inputValue=analyze_fail.ir)
Traceback (most recent call last):
File "/opt/mindyolo/train.py", line 336, in <module>
train(args)
File "/opt/mindyolo/train.py", line 291, in train
trainer.train(
File "/opt/mindyolo/mindyolo/utils/trainer_factory.py", line 170, in train
run_context.loss, run_context.lr = self.train_step(imgs, labels, segments,
File "/opt/mindyolo/mindyolo/utils/trainer_factory.py", line 368, in train_step
loss, loss_item, _, grads_finite = self.train_step_fn(imgs, labels, segments, True)
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 1224, in staging_specialize
out = ms_function_executor(*args, **kwargs)
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 180, in wrapper
results = fn(*arg, **kwargs)
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 669, in __call__
raise err
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 666, in __call__
phase = self.compile(self.fn.__name__, *args_list, **kwargs)
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 772, in compile
is_compile = self._graph_executor.compile(self.fn, compile_args, kwargs, phase)
TypeError: For primitive[Conv2D], the input type must be same.
name:[w]:Ref[Tensor[Float32]].
name:[x]:Tensor[UInt8].
----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
mindspore/core/utils/check_convert_utils.cc:1137 CheckTypeSame
----------------------------------------------------
- The Traceback of Net Construct Code:
----------------------------------------------------
# 0 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:115, 19~41
return train_step_func(*args)
^~~~~~~~~~~~~~~~~~~~~~
# 1 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:97~98, 12~99
if clip_grad:
# 2 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:92, 40~62
(loss, loss_items), grads = grad_fn(x, label, seg)
^~~~~~~~~~~~~~~~~~~~~~
# 3 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/ops/composite/base.py:597, 31~56
return grad_(fn, weights)(*args)
^~~~~~~~~~~~~~~~~~~~~~~~~
# 4 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:84, 19~29
pred = network(x)
^~~~~~~~~~
# 5 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~28
return self.model(x)
^~~~~~~~~~~~~
# 6 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~25
return self.model(x)
^~~~~~~~~~
# 7 In file /opt/mindyolo/mindyolo/models/model_factory.py:67~85, 8~51
for i in range(len(self.model)):
# 8 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~25
return self.model(x)
^~~~~~~~~~
# 9 In file /opt/mindyolo/mindyolo/models/model_factory.py:83, 16~20
x = m(x) # run
^~~~
# 10 In file /opt/mindyolo/mindyolo/models/layers/conv.py:59, 32~44
return self.act(self.bn(self.conv(x)))
^~~~~~~~~~~~
# 11 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/nn/layer/conv.py:367~368, 8~53
if self.has_bias:
# 12 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/nn/layer/conv.py:366, 17~44
output = self.conv2d(x, self.weight)
^~~~~~~~~~~~~~~~~~~~~~~~~~~
(See file '/opt/mindyolo/rank_0/om/analyze_fail.ir' for more details. Get instructions about `analyze_fail.ir` at https://www.mindspore.cn/search?inputValue=analyze_fail.ir)
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2025-08-13 17:02:10,708 [WARNING] The first epoch will be compiled for the graph, which may take a long time; You can come back later :).
[ERROR] ANALYZER(819407,ffffb78d8020,python):2025-08-13-17:02:11.942.296 [mindspore/ccsrc/pipeline/jit/ps/static_analysis/evaluator.cc:717] Run] Primitive: <StandardPrimEvaluator_Conv2D> infer failed, failed info: For primitive[Conv2D], the input type must be same.
name:[w]:Ref[Tensor[Float32]].
name:[x]:Tensor[UInt8].
----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
mindspore/core/utils/check_convert_utils.cc:1137 CheckTypeSame
----------------------------------------------------
- The Traceback of Net Construct Code:
----------------------------------------------------
# 0 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:115, 19~41
return train_step_func(*args)
^~~~~~~~~~~~~~~~~~~~~~
# 1 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:97~98, 12~99
if clip_grad:
# 2 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:92, 40~62
(loss, loss_items), grads = grad_fn(x, label, seg)
^~~~~~~~~~~~~~~~~~~~~~
# 3 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/ops/composite/base.py:597, 31~56
return grad_(fn, weights)(*args)
^~~~~~~~~~~~~~~~~~~~~~~~~
# 4 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:84, 19~29
pred = network(x)
^~~~~~~~~~
# 5 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~28
return self.model(x)
^~~~~~~~~~~~~
# 6 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~25
return self.model(x)
^~~~~~~~~~
# 7 In file /opt/mindyolo/mindyolo/models/model_factory.py:67~85, 8~51
for i in range(len(self.model)):
# 8 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~25
return self.model(x)
^~~~~~~~~~
# 9 In file /opt/mindyolo/mindyolo/models/model_factory.py:83, 16~20
x = m(x) # run
^~~~
# 10 In file /opt/mindyolo/mindyolo/models/layers/conv.py:59, 32~44
return self.act(self.bn(self.conv(x)))
^~~~~~~~~~~~
# 11 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/nn/layer/conv.py:367~368, 8~53
if self.has_bias:
# 12 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/nn/layer/conv.py:366, 17~44
output = self.conv2d(x, self.weight)
^~~~~~~~~~~~~~~~~~~~~~~~~~~
(See file '/opt/mindyolo/rank_0/om/analyze_fail.ir' for more details. Get instructions about `analyze_fail.ir` at https://www.mindspore.cn/search?inputValue=analyze_fail.ir)
Traceback (most recent call last):
File "/opt/mindyolo/train.py", line 336, in <module>
train(args)
File "/opt/mindyolo/train.py", line 291, in train
trainer.train(
File "/opt/mindyolo/mindyolo/utils/trainer_factory.py", line 170, in train
run_context.loss, run_context.lr = self.train_step(imgs, labels, segments,
File "/opt/mindyolo/mindyolo/utils/trainer_factory.py", line 368, in train_step
loss, loss_item, _, grads_finite = self.train_step_fn(imgs, labels, segments, True)
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 1224, in staging_specialize
out = ms_function_executor(*args, **kwargs)
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 180, in wrapper
results = fn(*arg, **kwargs)
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 669, in __call__
raise err
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 666, in __call__
phase = self.compile(self.fn.__name__, *args_list, **kwargs)
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 772, in compile
is_compile = self._graph_executor.compile(self.fn, compile_args, kwargs, phase)
TypeError: For primitive[Conv2D], the input type must be same.
name:[w]:Ref[Tensor[Float32]].
name:[x]:Tensor[UInt8].
----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
mindspore/core/utils/check_convert_utils.cc:1137 CheckTypeSame
----------------------------------------------------
- The Traceback of Net Construct Code:
----------------------------------------------------
# 0 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:115, 19~41
return train_step_func(*args)
^~~~~~~~~~~~~~~~~~~~~~
# 1 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:97~98, 12~99
if clip_grad:
# 2 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:92, 40~62
(loss, loss_items), grads = grad_fn(x, label, seg)
^~~~~~~~~~~~~~~~~~~~~~
# 3 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/ops/composite/base.py:597, 31~56
return grad_(fn, weights)(*args)
^~~~~~~~~~~~~~~~~~~~~~~~~
# 4 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:84, 19~29
pred = network(x)
^~~~~~~~~~
# 5 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~28
return self.model(x)
^~~~~~~~~~~~~
# 6 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~25
return self.model(x)
^~~~~~~~~~
# 7 In file /opt/mindyolo/mindyolo/models/model_factory.py:67~85, 8~51
for i in range(len(self.model)):
# 8 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~25
return self.model(x)
^~~~~~~~~~
# 9 In file /opt/mindyolo/mindyolo/models/model_factory.py:83, 16~20
x = m(x) # run
^~~~
# 10 In file /opt/mindyolo/mindyolo/models/layers/conv.py:59, 32~44
return self.act(self.bn(self.conv(x)))
^~~~~~~~~~~~
# 11 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/nn/layer/conv.py:367~368, 8~53
if self.has_bias:
# 12 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/nn/layer/conv.py:366, 17~44
output = self.conv2d(x, self.weight)
^~~~~~~~~~~~~~~~~~~~~~~~~~~
(See file '/opt/mindyolo/rank_0/om/analyze_fail.ir' for more details. Get instructions about `analyze_fail.ir` at https://www.mindspore.cn/search?inputValue=analyze_fail.ir)
(mindyolomaster) root@autodl-container-6ce943a632-58447938:/opt/mindyolo# git pull
Password for 'https://wangqiangqiang@bitbucket.bs2y.com':
remote: Counting objects: 6, done.
remote: Compressing objects: 100% (6/6), done.
remote: Total 6 (delta 5), reused 0 (delta 0)
Unpacking objects: 100% (6/6), 476 bytes | 238.00 KiB/s, done.
From https://bitbucket.bs2y.com/scm/zstp/mindyolo
d044490..a6ae59b master -> origin/master
Updating d044490..a6ae59b
Fast-forward
configs/yolov8/seg/yolov8x-seg.yaml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
(mindyolomaster) root@autodl-container-6ce943a632-58447938:/opt/mindyolo# python train.py --task segment --weight weight/yolov8-x-seg_300e_mAP_mask_429-b4920557.ckpt --config datasets/seg_cat_dog/seg_cat_dog.yaml --epochs 20
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:10:53.710.337 [mindspore/run_check/_check_version.py:324] MindSpore version 2.6.0 and Ascend AI software package (Ascend Data Center Solution)version 8.2 does not match, the version of software package expect one of ['7.6', '7.7']. Please refer to the match info on: https://www.mindspore.cn/install
/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.
setattr(self, word, getattr(machar, word).flat[0])
/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.
return self._float_to_str(self.smallest_subnormal)
/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.
setattr(self, word, getattr(machar, word).flat[0])
/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.
return self._float_to_str(self.smallest_subnormal)
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:10:55.924.466 [mindspore/run_check/_check_version.py:342] MindSpore version 2.6.0 and "te" wheel package version 8.2 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:10:55.924.624 [mindspore/run_check/_check_version.py:355] Please pay attention to the above warning, countdown: 3
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:10:56.925.767 [mindspore/run_check/_check_version.py:355] Please pay attention to the above warning, countdown: 2
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:10:57.926.607 [mindspore/run_check/_check_version.py:355] Please pay attention to the above warning, countdown: 1
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:11:00.956.646 [mindspore/run_check/_check_version.py:324] MindSpore version 2.6.0 and Ascend AI software package (Ascend Data Center Solution)version 8.2 does not match, the version of software package expect one of ['7.6', '7.7']. Please refer to the match info on: https://www.mindspore.cn/install
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:11:00.957.438 [mindspore/run_check/_check_version.py:342] MindSpore version 2.6.0 and "te" wheel package version 8.2 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:11:00.957.519 [mindspore/run_check/_check_version.py:355] Please pay attention to the above warning, countdown: 3
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:11:01.958.607 [mindspore/run_check/_check_version.py:355] Please pay attention to the above warning, countdown: 2
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:11:02.959.901 [mindspore/run_check/_check_version.py:355] Please pay attention to the above warning, countdown: 1
2025-08-13 17:11:03,978 [INFO] parse_args:
2025-08-13 17:11:03,978 [INFO] task segment
2025-08-13 17:11:03,978 [INFO] device_target Ascend
2025-08-13 17:11:03,978 [INFO] save_dir ./runs/2025.08.13-17.11.03
2025-08-13 17:11:03,978 [INFO] log_level INFO
2025-08-13 17:11:03,978 [INFO] is_parallel False
2025-08-13 17:11:03,978 [INFO] ms_mode 0
2025-08-13 17:11:03,978 [INFO] max_call_depth 2000
2025-08-13 17:11:03,978 [INFO] ms_amp_level O0
2025-08-13 17:11:03,978 [INFO] keep_loss_fp32 True
2025-08-13 17:11:03,978 [INFO] anchor_base False
2025-08-13 17:11:03,978 [INFO] ms_loss_scaler static
2025-08-13 17:11:03,978 [INFO] ms_loss_scaler_value 1024.0
2025-08-13 17:11:03,978 [INFO] ms_jit True
2025-08-13 17:11:03,978 [INFO] ms_enable_graph_kernel False
2025-08-13 17:11:03,978 [INFO] ms_datasink False
2025-08-13 17:11:03,978 [INFO] overflow_still_update False
2025-08-13 17:11:03,978 [INFO] clip_grad True
2025-08-13 17:11:03,978 [INFO] clip_grad_value 10.0
2025-08-13 17:11:03,978 [INFO] ema True
2025-08-13 17:11:03,978 [INFO] weight weight/yolov8-x-seg_300e_mAP_mask_429-b4920557.ckpt
2025-08-13 17:11:03,978 [INFO] ema_weight
2025-08-13 17:11:03,978 [INFO] freeze []
2025-08-13 17:11:03,978 [INFO] epochs 20
2025-08-13 17:11:03,978 [INFO] per_batch_size 1
2025-08-13 17:11:03,978 [INFO] img_size 640
2025-08-13 17:11:03,978 [INFO] nbs 64
2025-08-13 17:11:03,978 [INFO] accumulate 1
2025-08-13 17:11:03,978 [INFO] auto_accumulate False
2025-08-13 17:11:03,978 [INFO] log_interval 10
2025-08-13 17:11:03,978 [INFO] single_cls False
2025-08-13 17:11:03,978 [INFO] sync_bn False
2025-08-13 17:11:03,978 [INFO] keep_checkpoint_max 100
2025-08-13 17:11:03,978 [INFO] run_eval False
2025-08-13 17:11:03,978 [INFO] conf_thres 0.001
2025-08-13 17:11:03,978 [INFO] iou_thres 0.7
2025-08-13 17:11:03,978 [INFO] conf_free True
2025-08-13 17:11:03,978 [INFO] rect False
2025-08-13 17:11:03,978 [INFO] nms_time_limit 20.0
2025-08-13 17:11:03,978 [INFO] recompute True
2025-08-13 17:11:03,978 [INFO] recompute_layers 2
2025-08-13 17:11:03,978 [INFO] seed 2
2025-08-13 17:11:03,978 [INFO] summary True
2025-08-13 17:11:03,978 [INFO] profiler False
2025-08-13 17:11:03,978 [INFO] profiler_step_num 1
2025-08-13 17:11:03,978 [INFO] opencv_threads_num 0
2025-08-13 17:11:03,978 [INFO] strict_load False
2025-08-13 17:11:03,978 [INFO] enable_modelarts False
2025-08-13 17:11:03,978 [INFO] data_url
2025-08-13 17:11:03,978 [INFO] ckpt_url
2025-08-13 17:11:03,978 [INFO] multi_data_url
2025-08-13 17:11:03,978 [INFO] pretrain_url
2025-08-13 17:11:03,978 [INFO] train_url
2025-08-13 17:11:03,978 [INFO] data_dir /cache/data/
2025-08-13 17:11:03,978 [INFO] ckpt_dir /cache/pretrain_ckpt/
2025-08-13 17:11:03,978 [INFO] network.model_name yolov8
2025-08-13 17:11:03,978 [INFO] network.nc 80
2025-08-13 17:11:03,978 [INFO] network.reg_max 16
2025-08-13 17:11:03,978 [INFO] network.stride [8, 16, 32]
2025-08-13 17:11:03,978 [INFO] network.backbone [[-1, 1, 'ConvNormAct', [64, 3, 2]], [-1, 1, 'ConvNormAct', [128, 3, 2]], [-1, 3, 'C2f', [128, True]], [-1, 1, 'ConvNormAct', [256, 3, 2]], [-1, 6, 'C2f', [256, True]], [-1, 1, 'ConvNormAct', [512, 3, 2]], [-1, 6, 'C2f', [512, True]], [-1, 1, 'ConvNormAct', [1024, 3, 2]], [-1, 3, 'C2f', [1024, True]], [-1, 1, 'SPPF', [1024, 5]]]
2025-08-13 17:11:03,978 [INFO] network.head [[-1, 1, 'Upsample', ['None', 2, 'nearest']], [[-1, 6], 1, 'Concat', [1]], [-1, 3, 'C2f', [512]], [-1, 1, 'Upsample', ['None', 2, 'nearest']], [[-1, 4], 1, 'Concat', [1]], [-1, 3, 'C2f', [256]], [-1, 1, 'ConvNormAct', [256, 3, 2]], [[-1, 12], 1, 'Concat', [1]], [-1, 3, 'C2f', [512]], [-1, 1, 'ConvNormAct', [512, 3, 2]], [[-1, 9], 1, 'Concat', [1]], [-1, 3, 'C2f', [1024]], [[15, 18, 21], 1, 'YOLOv8SegHead', ['nc', 'reg_max', 32, 256, 'stride']]]
2025-08-13 17:11:03,978 [INFO] network.depth_multiple 1.0
2025-08-13 17:11:03,978 [INFO] network.width_multiple 1.25
2025-08-13 17:11:03,978 [INFO] network.max_channels 512
2025-08-13 17:11:03,978 [INFO] optimizer.optimizer momentum
2025-08-13 17:11:03,978 [INFO] optimizer.lr_init 0.001
2025-08-13 17:11:03,978 [INFO] optimizer.momentum 0.937
2025-08-13 17:11:03,978 [INFO] optimizer.nesterov True
2025-08-13 17:11:03,978 [INFO] optimizer.loss_scale 1.0
2025-08-13 17:11:03,978 [INFO] optimizer.warmup_epochs 3
2025-08-13 17:11:03,978 [INFO] optimizer.warmup_momentum 0.8
2025-08-13 17:11:03,978 [INFO] optimizer.warmup_bias_lr 0.1
2025-08-13 17:11:03,978 [INFO] optimizer.min_warmup_step 1000
2025-08-13 17:11:03,978 [INFO] optimizer.group_param yolov8
2025-08-13 17:11:03,978 [INFO] optimizer.gp_weight_decay 0.0010078125
2025-08-13 17:11:03,978 [INFO] optimizer.start_factor 1.0
2025-08-13 17:11:03,978 [INFO] optimizer.end_factor 0.01
2025-08-13 17:11:03,978 [INFO] optimizer.epochs 20
2025-08-13 17:11:03,978 [INFO] optimizer.nbs 64
2025-08-13 17:11:03,978 [INFO] optimizer.accumulate 1
2025-08-13 17:11:03,978 [INFO] optimizer.total_batch_size 1
2025-08-13 17:11:03,978 [INFO] loss.name YOLOv8SegLoss
2025-08-13 17:11:03,978 [INFO] loss.box 7.5
2025-08-13 17:11:03,978 [INFO] loss.cls 0.5
2025-08-13 17:11:03,978 [INFO] loss.dfl 1.5
2025-08-13 17:11:03,978 [INFO] loss.reg_max 16
2025-08-13 17:11:03,978 [INFO] loss.nm 32
2025-08-13 17:11:03,978 [INFO] loss.overlap True
2025-08-13 17:11:03,978 [INFO] loss.max_object_num 600
2025-08-13 17:11:03,978 [INFO] data.num_parallel_workers 8
2025-08-13 17:11:03,978 [INFO] data.train_transforms []
2025-08-13 17:11:03,978 [INFO] data.test_transforms []
2025-08-13 17:11:03,978 [INFO] data.dataset_name cat_dog_seg
2025-08-13 17:11:03,978 [INFO] data.train_set /opt/mindyolo/datasets/seg_cat_dog/train.txt
2025-08-13 17:11:03,978 [INFO] data.val_set /opt/mindyolo/datasets/seg_cat_dog/val.txt
2025-08-13 17:11:03,978 [INFO] data.test_set /opt/mindyolo/datasets/seg_cat_dog/test.txt
2025-08-13 17:11:03,978 [INFO] data.nc 2
2025-08-13 17:11:03,978 [INFO] data.names ['cat', 'dog']
2025-08-13 17:11:03,978 [INFO] config datasets/seg_cat_dog/seg_cat_dog.yaml
2025-08-13 17:11:03,978 [INFO] rank 0
2025-08-13 17:11:03,978 [INFO] rank_size 1
2025-08-13 17:11:03,978 [INFO] total_batch_size 1
2025-08-13 17:11:03,978 [INFO] callback []
2025-08-13 17:11:03,978 [INFO]
2025-08-13 17:11:03,980 [INFO] Please check the above information for the configurations
2025-08-13 17:11:04,754 [WARNING] Parse Model, args: nearest, keep str type
2025-08-13 17:11:04,906 [WARNING] Parse Model, args: nearest, keep str type
2025-08-13 17:11:05,707 [INFO] number of network params, total: 71.813161M, trainable: 71.752758M
2025-08-13 17:11:05,716 [INFO] Turn on recompute, and the results of the first 2 layers will be recomputed.
[WARNING] DEVICE(845031,ffff982ff020,python):2025-08-13-17:11:06.865.050 [mindspore/ccsrc/plugin/res_manager/ascend/mem_manager/ascend_vmm_adapter.h:152] CheckVmmDriverVersion] Open file /etc/ascend_install.info failed.
[WARNING] DEVICE(845031,ffff982ff020,python):2025-08-13-17:11:06.865.135 [mindspore/ccsrc/plugin/res_manager/ascend/mem_manager/ascend_vmm_adapter.h:180] CheckVmmDriverVersion] Open file /usr/local/Ascend/driver/version.info failed.
2025-08-13 17:11:14,933 [WARNING] Parse Model, args: nearest, keep str type
2025-08-13 17:11:15,084 [WARNING] Parse Model, args: nearest, keep str type
2025-08-13 17:11:15,879 [INFO] number of network params, total: 71.813161M, trainable: 71.752758M
2025-08-13 17:11:15,888 [INFO] Turn on recompute, and the results of the first 2 layers will be recomputed.
2025-08-13 17:11:17,727 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.0.2.weight` with shape `(80, 320, 1, 1)`, which is inconsistent with cell shape `(2, 320, 1, 1)`
2025-08-13 17:11:17,727 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.0.2.bias` with shape `(80,)`, which is inconsistent with cell shape `(2,)`
2025-08-13 17:11:17,728 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.1.2.weight` with shape `(80, 320, 1, 1)`, which is inconsistent with cell shape `(2, 320, 1, 1)`
2025-08-13 17:11:17,728 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.1.2.bias` with shape `(80,)`, which is inconsistent with cell shape `(2,)`
2025-08-13 17:11:17,728 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.2.2.weight` with shape `(80, 320, 1, 1)`, which is inconsistent with cell shape `(2, 320, 1, 1)`
2025-08-13 17:11:17,728 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.2.2.bias` with shape `(80,)`, which is inconsistent with cell shape `(2,)`
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:11:17.750.623 [mindspore/train/serialization.py:1770] For 'load_param_into_net', 6 parameters in the 'net' are not loaded, because they are not in the 'parameter_dict', please check whether the network structure is consistent when training and loading checkpoint.
[WARNING] ME(845031:281473235021856,MainProcess):2025-08-13-17:11:17.750.856 [mindspore/train/serialization.py:1774] ['model.model.22.cv3.0.2.weight', 'model.model.22.cv3.0.2.bias', 'model.model.22.cv3.1.2.weight', 'model.model.22.cv3.1.2.bias', 'model.model.22.cv3.2.2.weight', 'model.model.22.cv3.2.2.bias'] are not loaded.
2025-08-13 17:11:17,750 [INFO] Pretrain model load from "weight/yolov8-x-seg_300e_mAP_mask_429-b4920557.ckpt" success.
.2025-08-13 17:11:23,895 [INFO] ema_weight not exist, default pretrain weight is currently used.
2025-08-13 17:11:23,898 [INFO] Dataset Cache file hash/version check success.
2025-08-13 17:11:23,898 [INFO] Load dataset cache from [/opt/mindyolo/datasets/seg_cat_dog/train.cache.npy] success.
Scanning '/opt/mindyolo/datasets/seg_cat_dog/train.cache.npy' images and labels... 46 found, 0 missing, 0 empty, 0 corrupted: 100%|████████████████████████████████████████| 46/46 [00:00<?, ?it/s]
2025-08-13 17:11:23,901 [INFO] Dataloader num parallel workers: [8]
2025-08-13 17:11:25,120 [INFO] Registry(name=callback, total=4)
2025-08-13 17:11:25,120 [INFO] (0): YoloxSwitchTrain in mindyolo/utils/callback.py
2025-08-13 17:11:25,120 [INFO] (1): EvalWhileTrain in mindyolo/utils/callback.py
2025-08-13 17:11:25,120 [INFO] (2): SummaryCallback in mindyolo/utils/callback.py
2025-08-13 17:11:25,120 [INFO] (3): ProfilerCallback in mindyolo/utils/callback.py
2025-08-13 17:11:25,120 [INFO]
2025-08-13 17:11:25,129 [INFO] got 1 active callback as follows:
2025-08-13 17:11:25,129 [INFO] SummaryCallback()
2025-08-13 17:11:25,129 [WARNING] The first epoch will be compiled for the graph, which may take a long time; You can come back later :).
[ERROR] ANALYZER(845031,ffff982ff020,python):2025-08-13-17:11:26.474.019 [mindspore/ccsrc/pipeline/jit/ps/static_analysis/evaluator.cc:717] Run] Primitive: <StandardPrimEvaluator_Conv2D> infer failed, failed info: For primitive[Conv2D], the input type must be same.
name:[w]:Ref[Tensor[Float32]].
name:[x]:Tensor[UInt8].
----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
mindspore/core/utils/check_convert_utils.cc:1137 CheckTypeSame
----------------------------------------------------
- The Traceback of Net Construct Code:
----------------------------------------------------
# 0 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:115, 19~41
return train_step_func(*args)
^~~~~~~~~~~~~~~~~~~~~~
# 1 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:97~98, 12~99
if clip_grad:
# 2 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:92, 40~62
(loss, loss_items), grads = grad_fn(x, label, seg)
^~~~~~~~~~~~~~~~~~~~~~
# 3 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/ops/composite/base.py:597, 31~56
return grad_(fn, weights)(*args)
^~~~~~~~~~~~~~~~~~~~~~~~~
# 4 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:84, 19~29
pred = network(x)
^~~~~~~~~~
# 5 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~28
return self.model(x)
^~~~~~~~~~~~~
# 6 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~25
return self.model(x)
^~~~~~~~~~
# 7 In file /opt/mindyolo/mindyolo/models/model_factory.py:67~85, 8~51
for i in range(len(self.model)):
# 8 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~25
return self.model(x)
^~~~~~~~~~
# 9 In file /opt/mindyolo/mindyolo/models/model_factory.py:83, 16~20
x = m(x) # run
^~~~
# 10 In file /opt/mindyolo/mindyolo/models/layers/conv.py:59, 32~44
return self.act(self.bn(self.conv(x)))
^~~~~~~~~~~~
# 11 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/nn/layer/conv.py:367~368, 8~53
if self.has_bias:
# 12 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/nn/layer/conv.py:366, 17~44
output = self.conv2d(x, self.weight)
^~~~~~~~~~~~~~~~~~~~~~~~~~~
(See file '/opt/mindyolo/rank_0/om/analyze_fail.ir' for more details. Get instructions about `analyze_fail.ir` at https://www.mindspore.cn/search?inputValue=analyze_fail.ir)
Traceback (most recent call last):
File "/opt/mindyolo/train.py", line 336, in <module>
train(args)
File "/opt/mindyolo/train.py", line 291, in train
trainer.train(
File "/opt/mindyolo/mindyolo/utils/trainer_factory.py", line 170, in train
run_context.loss, run_context.lr = self.train_step(imgs, labels, segments,
File "/opt/mindyolo/mindyolo/utils/trainer_factory.py", line 368, in train_step
loss, loss_item, _, grads_finite = self.train_step_fn(imgs, labels, segments, True)
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 1224, in staging_specialize
out = ms_function_executor(*args, **kwargs)
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 180, in wrapper
results = fn(*arg, **kwargs)
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 669, in __call__
raise err
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 666, in __call__
phase = self.compile(self.fn.__name__, *args_list, **kwargs)
File "/root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/common/api.py", line 772, in compile
is_compile = self._graph_executor.compile(self.fn, compile_args, kwargs, phase)
TypeError: For primitive[Conv2D], the input type must be same.
name:[w]:Ref[Tensor[Float32]].
name:[x]:Tensor[UInt8].
----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
mindspore/core/utils/check_convert_utils.cc:1137 CheckTypeSame
----------------------------------------------------
- The Traceback of Net Construct Code:
----------------------------------------------------
# 0 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:115, 19~41
return train_step_func(*args)
^~~~~~~~~~~~~~~~~~~~~~
# 1 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:97~98, 12~99
if clip_grad:
# 2 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:92, 40~62
(loss, loss_items), grads = grad_fn(x, label, seg)
^~~~~~~~~~~~~~~~~~~~~~
# 3 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/ops/composite/base.py:597, 31~56
return grad_(fn, weights)(*args)
^~~~~~~~~~~~~~~~~~~~~~~~~
# 4 In file /opt/mindyolo/mindyolo/utils/train_step_factory.py:84, 19~29
pred = network(x)
^~~~~~~~~~
# 5 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~28
return self.model(x)
^~~~~~~~~~~~~
# 6 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~25
return self.model(x)
^~~~~~~~~~
# 7 In file /opt/mindyolo/mindyolo/models/model_factory.py:67~85, 8~51
for i in range(len(self.model)):
# 8 In file /opt/mindyolo/mindyolo/models/yolov8.py:35, 15~25
return self.model(x)
^~~~~~~~~~
# 9 In file /opt/mindyolo/mindyolo/models/model_factory.py:83, 16~20
x = m(x) # run
^~~~
# 10 In file /opt/mindyolo/mindyolo/models/layers/conv.py:59, 32~44
return self.act(self.bn(self.conv(x)))
^~~~~~~~~~~~
# 11 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/nn/layer/conv.py:367~368, 8~53
if self.has_bias:
# 12 In file /root/ENTER/envs/mindyolomaster/lib/python3.9/site-packages/mindspore/nn/layer/conv.py:366, 17~44
output = self.conv2d(x, self.weight)
^~~~~~~~~~~~~~~~~~~~~~~~~~~
(See file '/opt/mindyolo/rank_0/om/analyze_fail.ir' for more details. Get instructions about `analyze_fail.ir` at https://www.mindspore.cn/search?inputValue=analyze_fail.ir)