YOLOF模型整改问题修改 & 仓库模型列表修改-ModelZoo-PyTorch-AtomGit

已合并

YOLOF模型整改问题修改 & 仓库模型列表修改 #3344

shikang2022创建于 2023年1月5日从master 合入到 master

YOLOF模型整改问题修改 & 仓库模型列表修改 #3344

已合并

shikang2022创建于 2023年1月5日

从master合入到master

共 6 个文件变更+21-16

MACL_PyTorch/README.md+0-1

	@@ -353,5 +353,4 @@
353	- [GoogleNet_for_Pytorch](https://gitee.com/ascend/ModelZoo-PyTorch/tree/master/ACL_PyTorch/built-in/cv/GoogleNet_for_Pytorch)	353	- [GoogleNet_for_Pytorch](https://gitee.com/ascend/ModelZoo-PyTorch/tree/master/ACL_PyTorch/built-in/cv/GoogleNet_for_Pytorch)
354	- [PSENet_for_Pytorch](https://gitee.com/ascend/ModelZoo-PyTorch/tree/master/ACL_PyTorch/built-in/cv/PSENet_for_Pytorch)	354	- [PSENet_for_Pytorch](https://gitee.com/ascend/ModelZoo-PyTorch/tree/master/ACL_PyTorch/built-in/cv/PSENet_for_Pytorch)
355	- [RawNet2_for_Pytorch](https://gitee.com/ascend/ModelZoo-PyTorch/tree/master/ACL_PyTorch/built-in/audio/RawNet2_for_Pytorch)	355	- [RawNet2_for_Pytorch](https://gitee.com/ascend/ModelZoo-PyTorch/tree/master/ACL_PyTorch/built-in/audio/RawNet2_for_Pytorch)
356	-- [ResNeXt50_for_Pytorch](https://gitee.com/ascend/ModelZoo-PyTorch/tree/master/ACL_PyTorch/built-in/cv/ResNeXt50_for_Pytorch)
357	- [TransformerXL_for_Pytorch](https://gitee.com/ascend/ModelZoo-PyTorch/tree/master/ACL_PyTorch/built-in/nlp/TransformerXL_for_Pytorch)	356	- [TransformerXL_for_Pytorch](https://gitee.com/ascend/ModelZoo-PyTorch/tree/master/ACL_PyTorch/built-in/nlp/TransformerXL_for_Pytorch)

MACL_PyTorch/contrib/cv/detection/YOLOF/YOLOF_postprocess.py+4-5

	@@ -13,19 +13,18 @@
13	# limitations under the License.	13	# limitations under the License.
14	import numpy as np	14	import numpy as np
15	import argparse	15	import argparse
16	-	16	+import sys
		17	+sys.path.append("./YOLOF/playground/detection/coco/yolof/yolof.cspdarknet53.DC5.9x/")
		18	+sys.path.append("./YOLOF")
		19	+from config import config
17	import torch	20	import torch
18	from cvpods.structures import Boxes, Instances	21	from cvpods.structures import Boxes, Instances
19	from cvpods.modeling.postprocessing import detector_postprocess	22	from cvpods.modeling.postprocessing import detector_postprocess
20	from cvpods.engine import RUNNERS	23	from cvpods.engine import RUNNERS
21	from cvpods.evaluation import build_evaluator	24	from cvpods.evaluation import build_evaluator
22	-
23	import sys	25	import sys
24	import os	26	import os
25		27
26	-sys.path.append("{0}/YOLOF/playground/detection/coco/yolof/yolof.cspdarknet53.DC5.9x/".format(sys.path[0]))
27	-from config import config
28	-
29	const_shape = (608, 608)	28	const_shape = (608, 608)
30	dataset_name = "coco_2017_val"	29	dataset_name = "coco_2017_val"
31		30

MACL_PyTorch/contrib/cv/detection/YOLOF/YOLOF_preprocess.py+0-0

The file is empty

MACL_PyTorch/contrib/cv/detection/YOLOF/YOLOF_pth2onnx.py+4-5

	@@ -15,13 +15,12 @@
15	import torch	15	import torch
16	import argparse	16	import argparse
17	import numpy as np	17	import numpy as np
		18	+import sys
		19	+sys.path.append("./YOLOF")
18	from cvpods.engine import default_setup	20	from cvpods.engine import default_setup
19	from cvpods.checkpoint import DefaultCheckpointer	21	from cvpods.checkpoint import DefaultCheckpointer
20	-	22	+sys.path.append("./YOLOF/playground/detection/coco/yolof/yolof.cspdarknet53.DC5.9x/")
21	-import sys	23	+sys.path.append("./YOLOF/playground/detection/coco/yolof/")
22	-
23	-sys.path.append("{0}/YOLOF/playground/detection/coco/yolof/yolof.cspdarknet53.DC5.9x/".format(sys.path[0]))
24	-sys.path.append("{0}/YOLOF/playground/detection/coco/yolof/".format(sys.path[0]))
25	from net import build_model	24	from net import build_model
26	from config import config	25	from config import config
27		26

MACL_PyTorch/contrib/cv/detection/YOLOF/readme.md+6-4

	@@ -147,8 +147,9 @@ YOLOF引入了一种解决该优化问题的替代方案而无需使用复杂的
147	```	147	```
148	python YOLOF_pth2onnx.py \	148	python YOLOF_pth2onnx.py \
149	--model_config=YOLOF/playground/detection/coco/yolof/yolof.cspdarknet53.DC5.9x \	149	--model_config=YOLOF/playground/detection/coco/yolof/yolof.cspdarknet53.DC5.9x \
150	- --out=yolof.onnx \	150	+ --out=yolof_bs${bs}.onnx \
151	--pth_path=./YOLOF_CSP_D_53_DC5_9x.pth	151	--pth_path=./YOLOF_CSP_D_53_DC5_9x.pth
		152	+ --batch_size=${bs}
152	```	153	```
153	- 参数说明：	154	- 参数说明：
154		155
	@@ -156,7 +157,8 @@ YOLOF引入了一种解决该优化问题的替代方案而无需使用复杂的
156	- --framework：5代表ONNX模型。	157	- --framework：5代表ONNX模型。
157	- --out：输出的Onnx模型。	158	- --out：输出的Onnx模型。
158	- --pth_path：模型权重文件	159	- --pth_path：模型权重文件
159	- 获得yolof.onnx文件。	160	+ - --batch_size：bs大小
		161	+ 获得yolof_bs${bs}.onnx文件。
160		162
161		163
162	3. 使用ATC工具将ONNX模型转OM模型。	164	3. 使用ATC工具将ONNX模型转OM模型。
	@@ -188,7 +190,7 @@ YOLOF引入了一种解决该优化问题的替代方案而无需使用复杂的
188	3. 执行ATC命令。	190	3. 执行ATC命令。
189		191
190	```	192	```
191	- atc --framework=5 --model=yolof.onnx --output=yolof_bs${bs} --input_shape="input:${bs},3,608,608" --log=error --soc_version=Ascend{chip_name}	193	+ atc --framework=5 --model=yolof_bs${bs}.onnx --output=yolof_bs${bs} --input_shape="input:${bs},3,608,608" --log=error --soc_version=Ascend{chip_name}
192	```	194	```
193		195
194	- 参数说明：	196	- 参数说明：
	@@ -230,7 +232,7 @@ YOLOF引入了一种解决该优化问题的替代方案而无需使用复杂的
230	调用脚本YOLOF_postprocess.py计算推理精度	232	调用脚本YOLOF_postprocess.py计算推理精度
231		233
232	```	234	```
233	- python YOLOF_postprocess.py --pth_path YOLOF_CSP_D_53_DC5_9x.pth --bin_data_path result --meta_info_path yolof_meta.info	235	+ python YOLOF_postprocess.py --pth_path YOLOF_CSP_D_53_DC5_9x.pth --bin_data_path result --meta_info_path yolof_meta.info
234	```	236	```
235		237
236	- 参数说明：	238	- 参数说明：

MACL_PyTorch/contrib/cv/detection/YOLOF/requirements.txt+7-1

	@@ -1,6 +1,12 @@
1	torch == 1.7.1	1	torch == 1.7.1
2	torchvision == 0.8.2	2	torchvision == 0.8.2
3	-	3	+portalocker
		4	+cloudpickle
		5	+tabulate
		6	+termcolor
		7	+pycocotools
		8	+easydict
		9	+colorama
4	onnx==1.7.0	10	onnx==1.7.0
5	onnx-simplifier	11	onnx-simplifier
6	opencv-python	12	opencv-python