Open joseph-cv opened 1 year ago
cwhgn
commented
1 year ago The model trained by SOLIDER is mainly used for downstream human task fine-tuning, so we do not involve onnx yet. If you want to export onnx, you may use some 3rd-party functions or tools to do it, such as torch.onnx.export.
22ema
commented
8 months ago I share the tips I learned while converting ONNX. If you use dynamic batch when converting Swin transformer to onnx, you must change the code below. Even if you change the model, there will be no change in performance.
def window_reverse(self, windows, H, W):
"""
Args:
windows: (num_windows*B, window_size, window_size, C)
H (int): Height of image
W (int): Width of image
Returns:
x: (B, H, W, C)
"""
window_size = self.window_size
# B = int(windows.shape[0] / (H * W / window_size / window_size))
# x = windows.view(B, H // window_size, W // window_size, window_size,
# window_size, -1)
# x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
C = int(windows.shape[-1])
x = windows.view(-1, H // window_size, W // window_size, window_size, window_size, C)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, H, W, C)
return x
rhett-ye
commented
8 months ago 我分享了我在转换 ONNX 时学到的技巧。如果在将 Swin transformer 转换为 onnx 时使用动态批处理，则必须更改以下代码。即使您更改模型，性能也不会发生变化。
def window_reverse(self, windows, H, W): """ Args: windows: (num_windows*B, window_size, window_size, C) H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ window_size = self.window_size # B = int(windows.shape[0] / (H * W / window_size / window_size)) # x = windows.view(B, H // window_size, W // window_size, window_size, # window_size, -1) # x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) C = int(windows.shape[-1]) x = windows.view(-1, H // window_size, W // window_size, window_size, window_size, C) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, H, W, C) return x
Can you provide more details about torch to onnx ? When I was inferencing with the transformed onnx model, the results were completely different from the torch results, and I had made sure the inputs were the same. And it will prompt when export:
TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
And I also get errors when I try to use onnx-simplify. is this a problem with the model?
22ema
commented
8 months ago 我分享了我在转换 ONNX 时学到的技巧。如果在将 Swin transformer 转换为 onnx 时使用动态批处理，则必须更改以下代码。即使您更改模型，性能也不会发生变化。
def window_reverse(self, windows, H, W): """ Args: windows: (num_windows*B, window_size, window_size, C) H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ window_size = self.window_size # B = int(windows.shape[0] / (H * W / window_size / window_size)) # x = windows.view(B, H // window_size, W // window_size, window_size, # window_size, -1) # x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) C = int(windows.shape[-1]) x = windows.view(-1, H // window_size, W // window_size, window_size, window_size, C) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, H, W, C) return xCan you provide more details about torch to onnx ? When I was inferencing with the transformed onnx model, the results were completely different from the torch results, and I had made sure the inputs were the same. And it will prompt when export:
TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!And I also get errors when I try to use onnx-simplify. is this a problem with the model?
Hello. @rhett-ye . happy new year. Okay. I provide more details about convert to onnx. First, i share convert pth to onnx code.
import os
import torch
import torchvision.models as models
from utils.Util import pretrained_model
from config.Config import Config
from net.Swin_Transform.SOLIDER import SOLIDER
from net.Swin_Transform.backbone import swin_transformer
from net.SoliderReid import make_model
def load_model(model, nLabels, pretrained_model_path, activate_func):
torch_model = None
model_desc = model.lower()
print(model_desc)
if "solider_reid_swin_s" in model_desc:
torch_model = make_model(0.2)
# assert torch_model == None, "torch_model must not be None!!!"
torch_model = pretrained_model(torch_model, pretrained_model_path, False)
if activate_func == "sigmoid":
torch_model = torch.nn.Sequential(torch_model, torch.nn.Sigmoid())
elif activate_func == "softmax":
torch_model = torch.nn.Sequential(torch_model, torch.nn.Softmax())
elif activate_func == None:
pass
return torch_model
def convert_onnx(torch_model, onnx_path, sizes):
torch_model.to("cuda:0")
batch, channels, height, width = sizes
input_names = ["input_0"]
output_names = ["output_0"]
dynamic_axes = {"input_0" : {0 : 'batch_size'}, "output_0" : {0 : 'batch_size'}}
if not os.path.isfile(onnx_path):
dummy_input = torch.randn(batch, channels, height, width, device='cuda')
torch.onnx.export(
torch_model,
dummy_input,
onnx_path,
input_names = input_names,
output_names = output_names,
dynamic_axes = dynamic_axes,
opset_version=11
)
print(f"ONNX model exported to {onnx_path}.")
else:
print(f"ONNX model {onnx_path} already exists.")
if __name__ == "__main__":
torch.set_grad_enabled(False)
dummy_size = (Config['batch_size'], Config['channels'], Config['height'], Config['width'])
torch_model = load_model(Config['model_name'],
Config['label_number'],
Config['pth_path'],
Config['fn_activate_func'])
convert_onnx(torch_model, Config['onnx_path'], dummy_size)load_model() function load a model. and convert_onnx() fuction convert model to onnx format. config's information is below.
Config = \
{
'pth_path': './model/pth/p_reid.solider.swin_s.i384x128.sb.v1.0.0.pth',
'onnx_path': './model/onnx/p_reid.solider.swin_s.i384x128.sb.v1.0.0_b.onnx',
'fn_activate_func': None,
'model_name': 'solider_reid_swin_s',
'label_number': 768,
'batch_size': 1,
'channels': 3,
'width': 128,
'height': 384
}My convert result is below. Tested using several images from Market 1501. (ReID)
## PTH Result
Label=3: predict=3 score=0.24012675881385803 True
Label=2: predict=2 score=0.251444011926651 True
Label=3: predict=3 score=0.18431055545806885 True
Label=1: predict=1 score=0.14189831912517548 True
Label=4: predict=4 score=0.16967739164829254 True
Label=4: predict=4 score=0.1858203113079071 True
Label=2: predict=2 score=0.19251590967178345 True
Label=3: predict=3 score=0.18236778676509857 True
Label=2: predict=2 score=0.18501074612140656 True
Label=4: predict=4 score=0.18582163751125336 True
Label=4: predict=4 score=0.15165238082408905 True
Label=1: predict=1 score=0.18481244146823883 True
Label=3: predict=3 score=0.1856241375207901 True
Label=1: predict=1 score=0.33886465430259705 True
Label=1: predict=1 score=0.17545929551124573 True
Label=2: predict=2 score=0.2006452977657318 True
Label=1: predict=1 score=0.16112568974494934 True
Label=1: predict=1 score=0.135588601231575 True
Label=1: predict=1 score=0.2757149636745453 True
Label=3: predict=3 score=0.19793693721294403 True
Label=2: predict=2 score=0.23355929553508759 True## ONNX result
Label=3: predict=3 score=0.24011623859405518 True
Label=2: predict=2 score=0.2514563202857971 True
Label=3: predict=3 score=0.18432889878749847 True
Label=1: predict=1 score=0.14190667867660522 True
Label=4: predict=4 score=0.16966880857944489 True
Label=4: predict=4 score=0.18584184348583221 True
Label=2: predict=2 score=0.192528635263443 True
Label=3: predict=3 score=0.18238039314746857 True
Label=2: predict=2 score=0.18498477339744568 True
Label=4: predict=4 score=0.18582601845264435 True
Label=4: predict=4 score=0.1516398787498474 True
Label=1: predict=1 score=0.18481333553791046 True
Label=3: predict=3 score=0.185637429356575 True
Label=1: predict=1 score=0.33886411786079407 True
Label=1: predict=1 score=0.17545419931411743 True
Label=2: predict=2 score=0.20063801109790802 True
Label=1: predict=1 score=0.1611219197511673 True
Label=1: predict=1 score=0.1355796605348587 True
Label=1: predict=1 score=0.27569296956062317 True
Label=3: predict=3 score=0.19792437553405762 True
Label=2: predict=2 score=0.23357689380645752 TrueLastly, the evaluation results using market1501 are as follows. Both ONNX and pth show the same results.
pth
onnx
User Warning is displayed in the same way. Since it is a warning, there does not appear to be any problem. (because the result is the same)
If the above does not solve the problem, please share your code.
rhett-ye
commented
8 months ago 我分享了我在转换 ONNX 时学到的技巧。如果在将 Swin transformer 转换为 onnx 时使用动态批处理，则必须更改以下代码。即使您更改模型，性能也不会发生变化。
def window_reverse(self, windows, H, W): """ Args: windows: (num_windows*B, window_size, window_size, C) H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ window_size = self.window_size # B = int(windows.shape[0] / (H * W / window_size / window_size)) # x = windows.view(B, H // window_size, W // window_size, window_size, # window_size, -1) # x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) C = int(windows.shape[-1]) x = windows.view(-1, H // window_size, W // window_size, window_size, window_size, C) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, H, W, C) return xCan you provide more details about torch to onnx ? When I was inferencing with the transformed onnx model, the results were completely different from the torch results, and I had made sure the inputs were the same. And it will prompt when export:
TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!And I also get errors when I try to use onnx-simplify. is this a problem with the model?Hello. @rhett-ye . happy new year. Okay. I provide more details about convert to onnx. First, i share convert pth to onnx code.
import os import torch import torchvision.models as models from utils.Util import pretrained_model from config.Config import Config from net.Swin_Transform.SOLIDER import SOLIDER from net.Swin_Transform.backbone import swin_transformer from net.SoliderReid import make_model def load_model(model, nLabels, pretrained_model_path, activate_func): torch_model = None model_desc = model.lower() print(model_desc) if "solider_reid_swin_s" in model_desc: torch_model = make_model(0.2) # assert torch_model == None, "torch_model must not be None!!!" torch_model = pretrained_model(torch_model, pretrained_model_path, False) if activate_func == "sigmoid": torch_model = torch.nn.Sequential(torch_model, torch.nn.Sigmoid()) elif activate_func == "softmax": torch_model = torch.nn.Sequential(torch_model, torch.nn.Softmax()) elif activate_func == None: pass return torch_model def convert_onnx(torch_model, onnx_path, sizes): torch_model.to("cuda:0") batch, channels, height, width = sizes input_names = ["input_0"] output_names = ["output_0"] dynamic_axes = {"input_0" : {0 : 'batch_size'}, "output_0" : {0 : 'batch_size'}} if not os.path.isfile(onnx_path): dummy_input = torch.randn(batch, channels, height, width, device='cuda') torch.onnx.export( torch_model, dummy_input, onnx_path, input_names = input_names, output_names = output_names, dynamic_axes = dynamic_axes, opset_version=11 ) print(f"ONNX model exported to {onnx_path}.") else: print(f"ONNX model {onnx_path} already exists.") if __name__ == "__main__": torch.set_grad_enabled(False) dummy_size = (Config['batch_size'], Config['channels'], Config['height'], Config['width']) torch_model = load_model(Config['model_name'], Config['label_number'], Config['pth_path'], Config['fn_activate_func']) convert_onnx(torch_model, Config['onnx_path'], dummy_size)load_model() function load a model. and convert_onnx() fuction convert model to onnx format. config's information is below.
Config = \ { 'pth_path': './model/pth/p_reid.solider.swin_s.i384x128.sb.v1.0.0.pth', 'onnx_path': './model/onnx/p_reid.solider.swin_s.i384x128.sb.v1.0.0_b.onnx', 'fn_activate_func': None, 'model_name': 'solider_reid_swin_s', 'label_number': 768, 'batch_size': 1, 'channels': 3, 'width': 128, 'height': 384 }My convert result is below. Tested using several images from Market 1501. (ReID)
## PTH Result Label=3: predict=3 score=0.24012675881385803 True Label=2: predict=2 score=0.251444011926651 True Label=3: predict=3 score=0.18431055545806885 True Label=1: predict=1 score=0.14189831912517548 True Label=4: predict=4 score=0.16967739164829254 True Label=4: predict=4 score=0.1858203113079071 True Label=2: predict=2 score=0.19251590967178345 True Label=3: predict=3 score=0.18236778676509857 True Label=2: predict=2 score=0.18501074612140656 True Label=4: predict=4 score=0.18582163751125336 True Label=4: predict=4 score=0.15165238082408905 True Label=1: predict=1 score=0.18481244146823883 True Label=3: predict=3 score=0.1856241375207901 True Label=1: predict=1 score=0.33886465430259705 True Label=1: predict=1 score=0.17545929551124573 True Label=2: predict=2 score=0.2006452977657318 True Label=1: predict=1 score=0.16112568974494934 True Label=1: predict=1 score=0.135588601231575 True Label=1: predict=1 score=0.2757149636745453 True Label=3: predict=3 score=0.19793693721294403 True Label=2: predict=2 score=0.23355929553508759 True## ONNX result Label=3: predict=3 score=0.24011623859405518 True Label=2: predict=2 score=0.2514563202857971 True Label=3: predict=3 score=0.18432889878749847 True Label=1: predict=1 score=0.14190667867660522 True Label=4: predict=4 score=0.16966880857944489 True Label=4: predict=4 score=0.18584184348583221 True Label=2: predict=2 score=0.192528635263443 True Label=3: predict=3 score=0.18238039314746857 True Label=2: predict=2 score=0.18498477339744568 True Label=4: predict=4 score=0.18582601845264435 True Label=4: predict=4 score=0.1516398787498474 True Label=1: predict=1 score=0.18481333553791046 True Label=3: predict=3 score=0.185637429356575 True Label=1: predict=1 score=0.33886411786079407 True Label=1: predict=1 score=0.17545419931411743 True Label=2: predict=2 score=0.20063801109790802 True Label=1: predict=1 score=0.1611219197511673 True Label=1: predict=1 score=0.1355796605348587 True Label=1: predict=1 score=0.27569296956062317 True Label=3: predict=3 score=0.19792437553405762 True Label=2: predict=2 score=0.23357689380645752 TrueLastly, the evaluation results using market1501 are as follows. Both ONNX and pth show the same results.
- pth
- onnx
User Warning is displayed in the same way. Since it is a warning, there does not appear to be any problem. (because the result is the same)
If the above does not solve the problem, please share your code.
Thanks for your reply! I solved my problem, which was model loading error. On the other hand, when I used opset version = 11, I got an exception: torch.roll operation was not supported (opset 14 is already supported), so I re-implemented this part.
chenscottus
commented
6 months ago When run it, there is an error: from utils.Util import pretrained_model ModuleNotFoundError: No module named 'utils.Util'
22ema
commented
6 months ago https://github.com/tinyvision/SOLIDER/issues/9#issuecomment-1960522884
I'm sorry The pretrained_model module was implemented by me. You will need to implement the module yourself or modify the code.
I just provided some of the onnx conversion code.
chenscottus
commented
6 months ago Is that possible you can share the code? Or just the minimum code that works?
22ema
commented
6 months ago Okay, I share the code. :D
def check_keys(model, pretrained_state_dict):
ckpt_keys = set(pretrained_state_dict.keys())
model_keys = set(model.state_dict().keys())
used_pretrained_keys = model_keys & ckpt_keys
unused_pretrained_keys = ckpt_keys - model_keys
missing_keys = model_keys - ckpt_keys
print('Missing keys:{}'.format(len(missing_keys)))
print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
print('Used keys:{}'.format(len(used_pretrained_keys)))
assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
return True
def remove_prefix(state_dict, prefix):
''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
print('remove prefix \'{}\''.format(prefix))
f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
return {f(key): value for key, value in state_dict.items()}
def pretrained_model(model, pretrained_path, load_to_cpu):
print('Loading pretrained model from {}'.format(pretrained_path))
if load_to_cpu:
pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
else:
device = torch.cuda.current_device()
pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
# with open(pretrained_path, 'rb') as f:
# buffer = io.BytesIO(f.read())
# pretrained_dict = torch.jit.load(buffer)
if "state_dicts" in pretrained_dict.keys():
pretrained_dict = remove_prefix(pretrained_dict['state_dicts'], 'module.')
else:
pretrained_dict = remove_prefix(pretrained_dict, 'module.')
# print(model.state_dict().keys())
check_keys(model, pretrained_dict)
model.load_state_dict(pretrained_dict, strict=False)
return modelSince the Config file is simply a file consisting of constant values, it is recommended that you modify the relevant constants before use.
chenscottus
commented
6 months ago Many thanks for your sharing! I will try it.
On Thu, Feb 22, 2024 at 5:22 PM yunsunghan @.***> wrote:
Okay, I share the code. :D
def check_keys(model, pretrained_state_dict): ckpt_keys = set(pretrained_state_dict.keys()) model_keys = set(model.state_dict().keys()) used_pretrained_keys = model_keys & ckpt_keys unused_pretrained_keys = ckpt_keys - model_keys missing_keys = model_keys - ckpt_keys print('Missing keys:{}'.format(len(missing_keys))) print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys))) print('Used keys:{}'.format(len(used_pretrained_keys))) assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint' return True
def remove_prefix(state_dict, prefix): ''' Old style model is stored with all names of parameters sharing common prefix 'module.' ''' print('remove prefix \'{}\''.format(prefix)) f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x return {f(key): value for key, value in state_dict.items()}
def pretrained_model(model, pretrained_path, load_to_cpu): print('Loading pretrained model from {}'.format(pretrained_path)) if load_to_cpu: pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage) else: device = torch.cuda.current_device() pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
with open(pretrained_path, 'rb') as f:
# buffer = io.BytesIO(f.read()) # pretrained_dict = torch.jit.load(buffer) if "state_dicts" in pretrained_dict.keys(): pretrained_dict = remove_prefix(pretrained_dict['state_dicts'], 'module.') else: pretrained_dict = remove_prefix(pretrained_dict, 'module.') # print(model.state_dict().keys()) check_keys(model, pretrained_dict) model.load_state_dict(pretrained_dict, strict=False) return modelSince the Config file is simply a file consisting of constant values, it is recommended that you modify the relevant constants before use.
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onismera
commented
2 months ago 我分享了我在转换 ONNX 时学到的技巧。如果在将 Swin transformer 转换为 onnx 时使用动态批处理，则必须更改以下代码。即使您更改模型，性能也不会发生变化。
def window_reverse(self, windows, H, W): """ Args: windows: (num_windows*B, window_size, window_size, C) H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) """ window_size = self.window_size # B = int(windows.shape[0] / (H * W / window_size / window_size)) # x = windows.view(B, H // window_size, W // window_size, window_size, # window_size, -1) # x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) C = int(windows.shape[-1]) x = windows.view(-1, H // window_size, W // window_size, window_size, window_size, C) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, H, W, C) return xCan you provide more details about torch to onnx ? When I was inferencing with the transformed onnx model, the results were completely different from the torch results, and I had made sure the inputs were the same. And it will prompt when export:
TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!And I also get errors when I try to use onnx-simplify. is this a problem with the model?Hello. @rhett-ye . happy new year. Okay. I provide more details about convert to onnx. First, i share convert pth to onnx code.
import os import torch import torchvision.models as models from utils.Util import pretrained_model from config.Config import Config from net.Swin_Transform.SOLIDER import SOLIDER from net.Swin_Transform.backbone import swin_transformer from net.SoliderReid import make_model def load_model(model, nLabels, pretrained_model_path, activate_func): torch_model = None model_desc = model.lower() print(model_desc) if "solider_reid_swin_s" in model_desc: torch_model = make_model(0.2) # assert torch_model == None, "torch_model must not be None!!!" torch_model = pretrained_model(torch_model, pretrained_model_path, False) if activate_func == "sigmoid": torch_model = torch.nn.Sequential(torch_model, torch.nn.Sigmoid()) elif activate_func == "softmax": torch_model = torch.nn.Sequential(torch_model, torch.nn.Softmax()) elif activate_func == None: pass return torch_model def convert_onnx(torch_model, onnx_path, sizes): torch_model.to("cuda:0") batch, channels, height, width = sizes input_names = ["input_0"] output_names = ["output_0"] dynamic_axes = {"input_0" : {0 : 'batch_size'}, "output_0" : {0 : 'batch_size'}} if not os.path.isfile(onnx_path): dummy_input = torch.randn(batch, channels, height, width, device='cuda') torch.onnx.export( torch_model, dummy_input, onnx_path, input_names = input_names, output_names = output_names, dynamic_axes = dynamic_axes, opset_version=11 ) print(f"ONNX model exported to {onnx_path}.") else: print(f"ONNX model {onnx_path} already exists.") if __name__ == "__main__": torch.set_grad_enabled(False) dummy_size = (Config['batch_size'], Config['channels'], Config['height'], Config['width']) torch_model = load_model(Config['model_name'], Config['label_number'], Config['pth_path'], Config['fn_activate_func']) convert_onnx(torch_model, Config['onnx_path'], dummy_size)load_model() function load a model. and convert_onnx() fuction convert model to onnx format. config's information is below.
Config = \ { 'pth_path': './model/pth/p_reid.solider.swin_s.i384x128.sb.v1.0.0.pth', 'onnx_path': './model/onnx/p_reid.solider.swin_s.i384x128.sb.v1.0.0_b.onnx', 'fn_activate_func': None, 'model_name': 'solider_reid_swin_s', 'label_number': 768, 'batch_size': 1, 'channels': 3, 'width': 128, 'height': 384 }My convert result is below. Tested using several images from Market 1501. (ReID)
## PTH Result Label=3: predict=3 score=0.24012675881385803 True Label=2: predict=2 score=0.251444011926651 True Label=3: predict=3 score=0.18431055545806885 True Label=1: predict=1 score=0.14189831912517548 True Label=4: predict=4 score=0.16967739164829254 True Label=4: predict=4 score=0.1858203113079071 True Label=2: predict=2 score=0.19251590967178345 True Label=3: predict=3 score=0.18236778676509857 True Label=2: predict=2 score=0.18501074612140656 True Label=4: predict=4 score=0.18582163751125336 True Label=4: predict=4 score=0.15165238082408905 True Label=1: predict=1 score=0.18481244146823883 True Label=3: predict=3 score=0.1856241375207901 True Label=1: predict=1 score=0.33886465430259705 True Label=1: predict=1 score=0.17545929551124573 True Label=2: predict=2 score=0.2006452977657318 True Label=1: predict=1 score=0.16112568974494934 True Label=1: predict=1 score=0.135588601231575 True Label=1: predict=1 score=0.2757149636745453 True Label=3: predict=3 score=0.19793693721294403 True Label=2: predict=2 score=0.23355929553508759 True## ONNX result Label=3: predict=3 score=0.24011623859405518 True Label=2: predict=2 score=0.2514563202857971 True Label=3: predict=3 score=0.18432889878749847 True Label=1: predict=1 score=0.14190667867660522 True Label=4: predict=4 score=0.16966880857944489 True Label=4: predict=4 score=0.18584184348583221 True Label=2: predict=2 score=0.192528635263443 True Label=3: predict=3 score=0.18238039314746857 True Label=2: predict=2 score=0.18498477339744568 True Label=4: predict=4 score=0.18582601845264435 True Label=4: predict=4 score=0.1516398787498474 True Label=1: predict=1 score=0.18481333553791046 True Label=3: predict=3 score=0.185637429356575 True Label=1: predict=1 score=0.33886411786079407 True Label=1: predict=1 score=0.17545419931411743 True Label=2: predict=2 score=0.20063801109790802 True Label=1: predict=1 score=0.1611219197511673 True Label=1: predict=1 score=0.1355796605348587 True Label=1: predict=1 score=0.27569296956062317 True Label=3: predict=3 score=0.19792437553405762 True Label=2: predict=2 score=0.23357689380645752 TrueLastly, the evaluation results using market1501 are as follows. Both ONNX and pth show the same results.
- pth
- onnx
User Warning is displayed in the same way. Since it is a warning, there does not appear to be any problem. (because the result is the same)
If the above does not solve the problem, please share your code.
Hello, may I ask how can I see the mAP and rank values? These two parameters are not displayed in my training log. Thank you very much for your review!
22ema
commented
2 months ago @onismera
I implemented and used a separate evaluation code myself. The evaluation code and logic provided in the code were implemented identically.
I implemented it using the code below.
def do_inference(cfg,
model,
val_loader,
num_query):
device = "cuda"
logger = logging.getLogger("transreid.test")
logger.info("Enter inferencing")
evaluator = R1_mAP_eval(num_query, max_rank=50, feat_norm=cfg.TEST.FEAT_NORM, reranking=cfg.TEST.RE_RANKING)
evaluator.reset()
if device:
if torch.cuda.device_count() > 1:
print('Using {} GPUs for inference'.format(torch.cuda.device_count()))
model = nn.DataParallel(model)
model.to(device)
model.eval()
img_path_list = []
for n_iter, (img, pid, camid, camids, target_view, imgpath) in enumerate(val_loader):
with torch.no_grad():
img = img.to(device)
camids = camids.to(device)
target_view = target_view.to(device)
feat, _ = model(img, cam_label=camids, view_label=target_view)
evaluator.update((feat, pid, camid))
img_path_list.extend(imgpath)
cmc, mAP, _, _, _, _, _ = evaluator.compute()
logger.info("Validation Results ")
logger.info("mAP: {:.1%}".format(mAP))
for r in [1, 5, 10]:
logger.info("CMC curve, Rank-{:<3}:{:.1%}".format(r, cmc[r - 1]))
return cmc[0], cmc[4]@onismera
I implemented and used a separate evaluation code myself. The evaluation code and logic provided in the code were implemented identically.
I implemented it using the code below.
def do_inference(cfg, model, val_loader, num_query): device = "cuda" logger = logging.getLogger("transreid.test") logger.info("Enter inferencing") evaluator = R1_mAP_eval(num_query, max_rank=50, feat_norm=cfg.TEST.FEAT_NORM, reranking=cfg.TEST.RE_RANKING) evaluator.reset() if device: if torch.cuda.device_count() > 1: print('Using {} GPUs for inference'.format(torch.cuda.device_count())) model = nn.DataParallel(model) model.to(device) model.eval() img_path_list = [] for n_iter, (img, pid, camid, camids, target_view, imgpath) in enumerate(val_loader): with torch.no_grad(): img = img.to(device) camids = camids.to(device) target_view = target_view.to(device) feat, _ = model(img, cam_label=camids, view_label=target_view) evaluator.update((feat, pid, camid)) img_path_list.extend(imgpath) cmc, mAP, _, _, _, _, _ = evaluator.compute() logger.info("Validation Results ") logger.info("mAP: {:.1%}".format(mAP)) for r in [1, 5, 10]: logger.info("CMC curve, Rank-{:<3}:{:.1%}".format(r, cmc[r - 1])) return cmc[0], cmc[4]
Thank you for sharing and replying! May I ask if this part of the code is placed in main_solider.py? Which part is it specifically placed in?
22ema
commented
2 months ago @onismera
https://github.com/tinyvision/SOLIDER-REID
You can check in the above code. I understand that the code in this repository is for learning the solider model, and for the downstream code related to reid, please refer to the git repository above.
To clearly indicate the location of the code, you can check the code below.
https://github.com/tinyvision/SOLIDER-REID/blob/master/test.py
Within that code, there is code that calls the do_inference() function.
onismera
commented
2 months ago @onismera
https://github.com/tinyvision/SOLIDER-REID
You can check in the above code. I understand that the code in this repository is for learning the solider model, and for the downstream code related to reid, please refer to the git repository above.
To clearly indicate the location of the code, you can check the code below.
https://github.com/tinyvision/SOLIDER-REID/blob/master/test.py
Within that code, there is code that calls the do_inference() function.
Thank you for your reply! I have found the downstream task for reid, so may I ask again? SOLIDER main is used to form a pre trained model, and SOLIDER ReID is used for further evaluation, right?
22ema
commented
2 months ago @onismera
yes. that's right.
https://github.com/tinyvision/SOLIDER
I used the model provided by git above in the downstream. Of course, additional learning was conducted downstream.
onismera
commented
2 months ago @onismera
yes. that's right.
https://github.com/tinyvision/SOLIDER
I used the model provided by git above in the downstream. Of course, additional learning was conducted downstream.
Thank you very much for your answer! Wishing you a happy life!
Hello, how could I export the model to onnx.