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!21864 fix graph input 

Merge pull request !21864 from hangq/graph_inout2
This commit is contained in:
i-robot 2021-08-18 08:02:06 +00:00 committed by Gitee
parent 1c7c7dc6da c7345cd311
commit 9ccfee288b
21 changed files with 208 additions and 128 deletions
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2
mindspore/lite/test/config/models_caffe.cfg
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@ -122,6 +122,6 @@ ml_face_emotion
hdc_ocr_recog_horizontal
ml_Heatmap_depth_240180;2
ml_Heatmap_depth_180240;2
ml_video_edit_person_divison_video;2
ml_video_edit_person_divison_video;2:2,1
ml_video_edit_hair_dyeing_segmodel_v2
ml_video_edit_hairline_segmentation;3

20
mindspore/lite/test/config/models_npu.cfg
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@ -31,16 +31,16 @@ ml_video_edit_style_transfer_autoportrait.onnx 9
ml_video_edit_style_transfer_candy.onnx 11
ml_video_edit_style_transfer_gongnongbing.onnx 10
ml_video_edit_style_transfer_starry.onnx 11
porseg_tmp.onnx;2 1
porseg_tmp.onnx;2:2,1 1
ml_video_edit_Mnet 1.5
ml_video_edit_hairSeg_have_imageProcessLayer_interpTo145 0.5
ml_video_edit_img_segment 1
ml_video_edit_video_segment_gauss_adaptis_part1 2
ml_video_edit_generate_filter.pb 1
ml_video_edit_img_segment_adaptise.pb;2 0.5
ml_video_edit_video_segment_gauss_adaptis_part2.pb;2 10
ml_video_edit_img_segment_adaptise.pb;2:2,1 0.5
ml_video_edit_video_segment_gauss_adaptis_part2.pb;2:2,1 10
ml_video_edit_person_divison_pic 0.5
ml_video_edit_person_divison_video;2 13
ml_video_edit_person_divison_video;2:2,1 13
ml_video_edit_judge.onnx 5
ml_video_edit_vignet.onnx 0.5
hdc_Face_Aesthetic_MTI_Aesthetic 0.5
@ -67,12 +67,12 @@ hdc_ocr_attention.onnx 0.5 #too many subgraphs
# hdc_ocr_detect.onnx 30 #too many subgraphs
ml_edu_kit_hand_detection.onnx 1
ml_edu_kit_hand_key_position.onnx 2
ml_video_edit_oneclick_adaptis.pb;3 2.4
ml_video_edit_oneclick_adaptis.pb;3:2,1,3 2.4
densenet.tflite 3
resnet_v2_101_299.tflite 1
ml_video_edit_enhance.pb 2
ml_video_edit_video_segment_gauss_adaptis_part2_pb2tflite.tflite;2 10
ml_video_edit_img_segment_adaptise_pb2tflite.tflite;2 0.5
ml_video_edit_video_segment_gauss_adaptis_part2_pb2tflite.tflite;2:2,1 10
ml_video_edit_img_segment_adaptise_pb2tflite.tflite;2:2,1 0.5
#the fifth value of the ml_video_edit_imitate_filter.onnx's output is very small (10-5).
ml_video_edit_imitate_filter.onnx 200
hdc_mobilenet_1w_class.onnx 20
@ -83,12 +83,12 @@ ml_video_edit_art_generate.onnx 0.5
ml_video_edit_art_transfer.onnx;3 3
ml_video_edit_enhance_update_tmp.onnx 0.5
ml_video_edit_art_generate_20210513.onnx 0.5
ml_video_edit_art_transfer_20210513.onnx;3 0.5
ml_video_edit_art_transfer_20210513.onnx;3:1,3,2 0.5
ml_video_edit_hair_dyeing_segmodel_v2 0.5
ml_video_edit_makeup_mobilenetv203.onnx 2
ml_video_edit_hairline_segmentation;3 0.5
ml_video_edit_hair_dyeing_migrate_v2.onnx;4 0.5
ml_audio_kit_encoder_v5.pb;6;1,32:1,32:1,32:1,32:1:1
ml_video_edit_hair_dyeing_migrate_v2.onnx;4:3,4,1,2 0.5
ml_audio_kit_encoder_v5.pb;6:5,2,1,4,6,3;1:1,32:1,32:1,32:1:1,32
fsr_270_mindspore.pb 1
fsr_360_mindspore.pb 1
fsr_720_mindspore.pb 1

16
mindspore/lite/test/config/models_npu_fp16.cfg
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@ -16,16 +16,16 @@ ml_video_edit_style_transfer_autoportrait.onnx 9
ml_video_edit_style_transfer_candy.onnx 11
ml_video_edit_style_transfer_gongnongbing.onnx 11
ml_video_edit_style_transfer_starry.onnx 11
porseg_tmp.onnx;2 1
porseg_tmp.onnx;2:2,1 1
ml_video_edit_Mnet 1.5
ml_video_edit_hairSeg_have_imageProcessLayer_interpTo145 0.5
ml_video_edit_img_segment 1
ml_video_edit_video_segment_gauss_adaptis_part1 2
ml_video_edit_generate_filter.pb 1
ml_video_edit_img_segment_adaptise.pb;2 0.5
ml_video_edit_video_segment_gauss_adaptis_part2.pb;2 10
ml_video_edit_img_segment_adaptise.pb;2:2,1 0.5
ml_video_edit_video_segment_gauss_adaptis_part2.pb;2:2,1 10
ml_video_edit_person_divison_pic 0.5
ml_video_edit_person_divison_video;2 13
ml_video_edit_person_divison_video;2:2,1 13
ml_video_edit_judge.onnx 5
ml_video_edit_vignet.onnx 0.5
hdc_Face_Aesthetic_MTI_Aesthetic 0.5
@ -52,12 +52,12 @@ ml_video_edit_v10_best_model_nomean_20200723 8
# hdc_ocr_detect.onnx 30 #too many subgraphs
ml_edu_kit_hand_detection.onnx 1
ml_edu_kit_hand_key_position.onnx 2
ml_video_edit_oneclick_adaptis.pb;3 2.4
ml_video_edit_oneclick_adaptis.pb;3:2,1,3 2.4
densenet.tflite 3
resnet_v2_101_299.tflite 1
ml_video_edit_enhance.pb 2
ml_video_edit_video_segment_gauss_adaptis_part2_pb2tflite.tflite;2 10
ml_video_edit_img_segment_adaptise_pb2tflite.tflite;2 0.5
ml_video_edit_video_segment_gauss_adaptis_part2_pb2tflite.tflite;2:2,1 10
ml_video_edit_img_segment_adaptise_pb2tflite.tflite;2:2,1 0.5
#the fifth value of the ml_video_edit_imitate_filter.onnx's output is very small (10-5).
ml_video_edit_imitate_filter.onnx 200
hdc_mobilenet_1w_class.onnx 20
@ -69,6 +69,6 @@ ml_video_edit_art_transfer.onnx;3 3
ml_video_edit_enhance_update_tmp.onnx 0.5
#ml_video_edit_art_generate_20210513.onnx, output is out of range
# ConstructSubgraph change, adjust threshold(3->29) for nlu temporary
ml_video_edit_art_transfer_20210513.onnx;3 29
ml_video_edit_art_transfer_20210513.onnx;3:1,3,2 29
ml_video_edit_hair_dyeing_segmodel_v2 0.5
ml_video_edit_makeup_mobilenetv203.onnx 2

10
mindspore/lite/test/config/models_onnx.cfg
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@ -85,14 +85,14 @@ ml_asr_encoder_int8_202103.onnx
rpnt_pdr_conv2d_16_fixed_last.onnx
hdc_efficientnet_b3_1w_class.onnx
yolov5s.onnx
porseg_tmp.onnx;2
hiai_nlu_onnx_model_v1_0.onnx;3
hiai_nlu_onnx_model_v1_1.onnx;3
ml_video_edit_art_transfer_20210513.onnx;3
porseg_tmp.onnx;2:2,1
hiai_nlu_onnx_model_v1_0.onnx;3:3,1,2
hiai_nlu_onnx_model_v1_1.onnx;3:2,1,3
ml_video_edit_art_transfer_20210513.onnx;3:1,3,2
ml_asr_decoder_202103.onnx;2;1,64,512:1,64
decoder.onnx;2;1,7,512:1,7
ml_video_edit_makeup_mobilenetv203.onnx
ml_video_edit_hair_dyeing_migrate_v2.onnx;4
ml_video_edit_hair_dyeing_migrate_v2.onnx;4:3,4,1,2
# cur acc for ml_audio_kit_vocals_test is 1.7% because the softmax's output of the last op has very small numbers.
ml_audio_kit_vocals_test.onnx;1;1,512,1024,2 2
gender_lstm_scd.onnx

4
mindspore/lite/test/config/models_onnx_fp16.cfg
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@ -97,9 +97,9 @@ hdc_efficientnet_b3_1w_class.onnx 18
yolov5s.onnx 2
ml_video_edit_art_transfer.onnx;3 3
decoder.onnx;2;1,7,512:1,7 113
ml_video_edit_art_transfer_20210513.onnx;3 1
ml_video_edit_art_transfer_20210513.onnx;3:1,3,2 1
ml_asr_decoder_202103.onnx;2;1,64,512:1,64 0.5
ml_video_edit_makeup_mobilenetv203.onnx 4
# The input of ml_video_edit_hair_dyeing_migrate_v2.onnx should be between [0, 1]
ml_video_edit_hair_dyeing_migrate_v2.onnx;4 2.5
ml_video_edit_hair_dyeing_migrate_v2.onnx;4:3,4,1,2 2.5
Q888_CV_face_recognition_self.onnx 3.5

4
mindspore/lite/test/config/models_posttraining.cfg
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@ -1,5 +1,5 @@
ml_face_mnet 105
ml_face_landmark_2 2
mobilenet.tflite 0.5
transformer_20200831_encoder_fp32.tflite;36 70
transformer_20200831_decoder_fp32.tflite;11 35
#transformer_20200831_encoder_fp32.tflite;36 70
#transformer_20200831_decoder_fp32.tflite;11 35

40
mindspore/lite/test/config/models_tf.cfg
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@ -72,11 +72,11 @@ siteAI_trans_nonlinear40g.pb;1;1,271
siteAI_trans_nonlinear134g.pb;1;1,137
siteAI_trans_nonlinear134g_nrz.pb;1;1,182
ml_vision_guide_detection2.pb;1;1,320,320,1
ml_tts_encoder.pb;4;1:1,44:1:1;;input_dependent
ml_tts_encoder.pb;4:2,4,3,1;1,44:1:1:1;;input_dependent
# encoder_0111_control_flow.pb is same as ml_tts_encoder_control_flow.pb
#encoder_0111_control_flow.pb;4;1:1,44:1:1;;input_dependent
ml_video_edit_img_segment_adaptise.pb;2
ml_video_edit_video_segment_gauss_adaptis_part2.pb;2
ml_video_edit_img_segment_adaptise.pb;2:2,1
ml_video_edit_video_segment_gauss_adaptis_part2.pb;2:2,1
#fasterrcnn_crop.pb is the same model as gts_object_detect_Ics.pb.
#fasterrcnn_crop.pb;1;420,630,3
#decoder_step_201217.pb is the same model as ml_tts_decoder.pb.
@ -85,25 +85,25 @@ ml_video_edit_video_segment_gauss_adaptis_part2.pb;2
#decoder_step_201217_modified.pb;5
#encoder_0111.pb is the same model as ml_tts_encoder.pb.
#encoder_0111.pb;4;1:1,44:1:1
encoder_201228.pb;3;1:1,22:1;;input_dependent
ml_video_edit_oneclick_adaptis.pb;3
tacotron_encoder_stf.pb;5;1:1,62:1,62:1,62:1,62;;input_dependent
female_model_step2_int16_noiseout.pb;66
ml_female_model_step6_noiseout.pb;66
ml_male_model_step6_noiseout.pb;66
ml_tts_decoder_control_flow.pb;5
ml_tts_decoder.pb;5
ml_tts_encoder_control_flow.pb;4;1:1,22:1:1;;input_dependent
ml_tts_vocoder.pb;66
encoder_201228.pb;3:2,3,1;1,22:1:1;;input_dependent
ml_video_edit_oneclick_adaptis.pb;3:2,1,3
tacotron_encoder_stf.pb;5:2,3,5,4,1;1,62:1,62:1,62:1,62:1;;input_dependent
female_model_step2_int16_noiseout.pb;66:2,7,6,1,3,4,5,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,66,65,64,63,62,61,60,59,58,57,56,55,54,53,52,51,50,49,48,47,46,45,44,43,42,41,40,39,38
ml_female_model_step6_noiseout.pb;66:2,7,6,1,3,4,5,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,66,65,64,63,62,61,60,59,58,57,56,55,54,53,52,51,50,49,48,47,46,45,44,43,42,41,40,39,38
ml_male_model_step6_noiseout.pb;66:2,7,6,1,3,4,5,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,66,65,64,63,62,61,60,59,58,57,56,55,54,53,52,51,50,49,48,47,46,45,44,43,42,41,40,39,38
ml_tts_decoder_control_flow.pb;5:5,4,3,1,2
ml_tts_decoder.pb;5:4,5,2,1,3
ml_tts_encoder_control_flow.pb;4:2,4,3,1;1,22:1:1:1;;input_dependent
ml_tts_vocoder.pb;66:2,7,6,1,3,4,5,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,66,65,64,63,62,61,60,59,58,57,56,55,54,53,52,51,50,49,48,47,46,45,44,43,42,41,40,39,38
hiai_nlu_model.pb;3;1,16:1,16:1,16
gts_object_detect_Ics.pb;1;420,630,3;;input_dependent
hiai_transformer_encoder.pb;15
decoder_step_nocumsum_v5.pb;13;1:1,512:1,1429,2:1,127:1,127:1,127:1,127,320:1,80:1,512:1,512:1,512:1,512:1,512
ml_audio_kit_encoder_v5.pb;6;1,32:1,32:1,32:1,32:1:1
hiai_nlu_model_v1.pb;3;1,16:1,16:1,16 2.0
hiai_nlu_model_v2.pb;7;1,5:1,6:1,174:1,98:1,5:1,5:1,5
hiai_nlu_model_multi.pb;6;1,32:1,32:1,6:1,11:1,74:1,32
hiai_nlu_model_single.pb;3;1,32:1,32:1,32
hiai_transformer_encoder.pb;15:1,3,4,5,6,7,8,9,10,11,12,13,14,15,2
decoder_step_nocumsum_v5.pb;13:11,2,13,12,10,7,3,5,1,4,9,8,6;1,512:1,512:1,512:1,512:1,512:1,127,320:1,1429,2:1,127:1:1,127:1,512:1,80:1,127
ml_audio_kit_encoder_v5.pb;6:5,2,1,4,6,3;1:1,32:1,32:1,32:1:1,32
hiai_nlu_model_v1.pb;3:1,3,2;1,16:1,16:1,16 2.0
hiai_nlu_model_v2.pb;7:5,7,6,4,3,2,1;1,5:1,5:1,5:1,98:1,174:1,6:1,5
hiai_nlu_model_multi.pb;6:1,6,2,5,4,3;1,32:1,32:1,32:1,74:1,11:1,6
hiai_nlu_model_single.pb;3:1,3,2;1,32:1,32:1,32
fsr_270_mindspore.pb
fsr_360_mindspore.pb
fsr_720_mindspore.pb

30
mindspore/lite/test/config/models_tf_fp16.cfg
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@ -65,29 +65,29 @@ siteAI_trans_nonlinear134g.pb;1;1,137 0.5
siteAI_trans_nonlinear134g_nrz.pb;1;1,182 0.6
ml_vision_guide_detection2.pb;1;1,320,320,1 1
# ml_tts_encoder.pb has a round op, which will cause round-off error when the decimal of input value is near 0.5
ml_tts_encoder.pb;4;1:1,44:1:1 9
ml_tts_encoder.pb;4:2,4,3,1;1,44:1:1:1 9
# encoder_0111_control_flow.pb is same as ml_tts_encoder_control_flow.pb
#encoder_0111_control_flow.pb;4;1:1,44:1:1 10
ml_video_edit_video_segment_gauss_adaptis_part2.pb;2 11
ml_video_edit_img_segment_adaptise.pb;2 40
ml_video_edit_person_divison_video;2 38
ml_video_edit_oneclick_adaptis.pb;3 6
ml_video_edit_video_segment_gauss_adaptis_part2.pb;2:2,1 11
ml_video_edit_img_segment_adaptise.pb;2:2,1 40
ml_video_edit_person_divison_video;2:2,1 38
ml_video_edit_oneclick_adaptis.pb;3:2,1,3 6
#decoder_step_201217.pb is the same model as ml_tts_decoder.pb.
#decoder_step_201217.pb;5 187
#decoder_step_201217_modified.pb is the same model as ml_tts_decoder_control_flow.pb.
#decoder_step_201217_modified.pb;5 0.5
#encoder_0111.pb is the same model as ml_tts_encoder.pb.
#encoder_0111.pb;4;1:1,44:1:1
ml_female_model_step6_noiseout.pb;66 2
ml_male_model_step6_noiseout.pb;66 2.5
ml_tts_encoder_control_flow.pb;4;1:1,22:1:1 1.5
ml_tts_decoder_control_flow.pb;5 1
ml_tts_decoder.pb;5 2.5
ml_tts_vocoder.pb;66 53
hiai_transformer_encoder.pb;15 4
decoder_step_nocumsum_v5.pb;13;1:1,512:1,1429,2:1,127:1,127:1,127:1,127,320:1,80:1,512:1,512:1,512:1,512:1,512 1.2
hiai_nlu_model_multi.pb;6;1,32:1,32:1,6:1,11:1,74:1,32 25
hiai_nlu_model_single.pb;3;1,32:1,32:1,32 2470
ml_female_model_step6_noiseout.pb;66:2,7,6,1,3,4,5,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,66,65,64,63,62,61,60,59,58,57,56,55,54,53,52,51,50,49,48,47,46,45,44,43,42,41,40,39,38 2
ml_male_model_step6_noiseout.pb;66:2,7,6,1,3,4,5,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,66,65,64,63,62,61,60,59,58,57,56,55,54,53,52,51,50,49,48,47,46,45,44,43,42,41,40,39,38 2.5
ml_tts_encoder_control_flow.pb;4:2,4,3,1;1,22:1:1:1 1.5
ml_tts_decoder_control_flow.pb;5:5,4,3,1,2 1
ml_tts_decoder.pb;5:4,5,2,1,3 2.5
ml_tts_vocoder.pb;66:2,7,6,1,3,4,5,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,66,65,64,63,62,61,60,59,58,57,56,55,54,53,52,51,50,49,48,47,46,45,44,43,42,41,40,39,38 53
hiai_transformer_encoder.pb;15:1,3,4,5,6,7,8,9,10,11,12,13,14,15,2 4
decoder_step_nocumsum_v5.pb;13:11,2,13,12,10,7,3,5,1,4,9,8,6;1,512:1,512:1,512:1,512:1,512:1,127,320:1,1429,2:1,127:1:1,127:1,512:1,80:1,127 1.2
hiai_nlu_model_multi.pb;6:1,6,2,5,4,3;1,32:1,32:1,32:1,74:1,11:1,6 25
hiai_nlu_model_single.pb;3:1,3,2;1,32:1,32:1,32 2470
fsr_270_mindspore.pb 6.0
fsr_360_mindspore.pb 6.5
fsr_720_mindspore.pb 2.0

22
mindspore/lite/test/config/models_tflite.cfg
View File

@ -185,18 +185,18 @@ bloom_isface.tflite
hiai_object_detect_814.tflite
hiai_object_tflite_graph_8bit.tflite
lma_tsec_shallow_channels16_ds2.1.1_model-best-f1.tflite
lite-model_arbitrary-image-stylization-inceptionv3_fp16_transfer_1.tflite;2
magenta_arbitrary-image-stylization-v1-256_fp16_transfer_1.tflite;2
albert_lite_base_squadv1_1.tflite;3
mobilebert_1_default_1.tflite;3
ml_video_edit_img_segment_adaptise_pb2tflite.tflite;2
ml_video_edit_video_segment_gauss_adaptis_part2_pb2tflite.tflite;2
hdc_tb_cn_neg.tflite;3
hiai_cv_labelDetectorModel_v3.tflite;2
lite-model_arbitrary-image-stylization-inceptionv3_fp16_transfer_1.tflite;2:2,1
magenta_arbitrary-image-stylization-v1-256_fp16_transfer_1.tflite;2:2,1
albert_lite_base_squadv1_1.tflite;3:2,3,1
mobilebert_1_default_1.tflite;3:2,3,1
ml_video_edit_img_segment_adaptise_pb2tflite.tflite;2:2,1
ml_video_edit_video_segment_gauss_adaptis_part2_pb2tflite.tflite;2:2,1
hdc_tb_cn_neg.tflite;3:3,1,2 0.5
hiai_cv_labelDetectorModel_v3.tflite;2:2,1
Modify_Out_ml_tacotron_decoder_step_stf.tflite;9;1,80:1,256:1,1024:1,1024:1,1024:1,1024:1,8:1,1,256:1
ml_headpose_pb2tflite.tflite;3;16:1,64,64,3:16
ml_ei_headpose_pb2tflite.tflite;3;16:1,64,64,3:16
lite-model_albert_lite_base_squadv1_metadata_1.tflite;3
ml_headpose_pb2tflite.tflite;3:2,3,1;1,64,64,3:16:16
ml_ei_headpose_pb2tflite.tflite;3:2,3,1;1,64,64,3:16:16
lite-model_albert_lite_base_squadv1_metadata_1.tflite;3:2,3,1
lite-model_mobilebert_1_metadata_1.tflite;3
Modify_Out_hiai_vad.tflite;2
add_uint8.tflite;2

12
mindspore/lite/test/config/models_tflite_fp16.cfg
View File

@ -213,10 +213,10 @@ bloom_isface.tflite 0.5
# The output values of conv layers range from -e±5 to e±5, which almost reaches the representation limit of fp16. In
# this range, the fp16 data will has big bias. And the accumulation of this bias lowers the final precision.
hiai_object_detect_814.tflite 14
ml_video_edit_video_segment_gauss_adaptis_part2_pb2tflite.tflite;2 11
ml_video_edit_img_segment_adaptise_pb2tflite.tflite;2 0.5
hdc_tb_cn_neg.tflite;3 295
ml_video_edit_video_segment_gauss_adaptis_part2_pb2tflite.tflite;2:2,1 11
ml_video_edit_img_segment_adaptise_pb2tflite.tflite;2:2,1 0.5
hdc_tb_cn_neg.tflite;3:3,1,2 295
# The input of hiai_cv_labelDetectorModel_v3.tflite is between 0-255.
hiai_cv_labelDetectorModel_v3.tflite;2 2
ml_headpose_pb2tflite.tflite;3;16:1,64,64,3:16 1
ml_ei_headpose_pb2tflite.tflite;3;16:1,64,64,3:16 0.6
hiai_cv_labelDetectorModel_v3.tflite;2:2,1 2
ml_headpose_pb2tflite.tflite;3:2,3,1;1,64,64,3:16:16 1
ml_ei_headpose_pb2tflite.tflite;3:2,3,1;1,64,64,3:16:16 0.6

23
mindspore/lite/test/st/scripts/base_functions.sh
View File

@ -133,7 +133,7 @@ function Run_Benchmark() {
model_info=`echo ${line_info}|awk -F ' ' '{print $1}'`
spec_acc_limit=`echo ${line_info}|awk -F ' ' '{print $2}'`
model_name=`echo ${model_info}|awk -F ';' '{print $1}'`
input_num=`echo ${model_info} | awk -F ';' '{print $2}'`
input_config=`echo ${model_info} | awk -F ';' '{print $2}'`
input_shapes=`echo ${model_info} | awk -F ';' '{print $3}'`
spec_threads=`echo ${model_info} | awk -F ';' '{print $4}'`
extra_info=`echo ${model_info} | awk -F ';' '{print $5}'`
@ -172,13 +172,24 @@ function Run_Benchmark() {
input_files=""
output_file=""
data_path=$3"/input_output/"
if [[ ${input_num} == "" || ${input_num} == 1 ]]; then
if [[ ${input_config} == "" || ${input_config} == 1 ]]; then
input_files=${data_path}'input/'${model_name}'.ms.bin'
else
for i in $(seq 1 $input_num)
do
input_files=${input_files}${data_path}'input/'${model_name}'.ms.bin_'$i','
done
input_num=`echo ${input_config} | awk -F ':' '{print $1}'`
input_seq=`echo ${input_config} | awk -F ':' '{print $2}'`
if [[ ${input_seq} == "" ]]; then
for i in $(seq 1 $input_num)
do
input_files=${input_files}${data_path}'input/'${model_name}'.ms.bin_'$i','
done
else
for i in $(seq 1 $input_num)
do
cur_input_num=${input_seq%%,*}
input_seq=${input_seq#*,}
input_files=${input_files}${data_path}'input/'${model_name}'.ms.bin_'$cur_input_num','
done
fi
fi
output_file=${data_path}'output/'${model_name}'.ms.out'
# adjust threads

39
mindspore/lite/tools/anf_exporter/anf_exporter.cc
View File

@ -532,6 +532,23 @@ FuncGraphPtr GetFinalGraph(const FuncGraphPtr &func_graph) {
return nullptr;
}
int AnfExporter::SetMetaGraphInput(const FuncGraphPtr &func_graph,
const std::unique_ptr<schema::MetaGraphT> &meta_graphT) {
MS_ASSERT(func_graph != nullptr);
if (!reorder_input_) {
return RET_OK;
}
meta_graphT->inputIndex.clear();
for (const auto &input : func_graph->get_inputs()) {
auto iter = graph_inputs_map_.find(input);
if (iter == graph_inputs_map_.end()) {
return RET_ERROR;
}
meta_graphT->inputIndex.emplace_back(iter->second);
}
return RET_OK;
}
int AnfExporter::SetMetaGraphOutput(const FuncGraphPtr &func_graph,
const std::unique_ptr<schema::MetaGraphT> &meta_graphT) {
auto final_fg = GetFinalGraph(func_graph);
@ -554,6 +571,9 @@ int AnfExporter::SetMetaGraphOutput(const FuncGraphPtr &func_graph,
schema::MetaGraphT *AnfExporter::Export(const FuncGraphPtr &func_graph, bool keep_graph, bool copy_primitive,
bool train_flag) {
this->train_flag_ = train_flag;
// hardcode for nnie and train
this->reorder_input_ = !(train_flag) && !(ConverterContext::GetInstance()->GetGraphInputTensorNames().empty());
this->graph_inputs_map_.clear();
auto meta_graphT = std::make_unique<schema::MetaGraphT>();
auto fmk = func_graph->get_attr("fmk");
MS_ASSERT(fmk != nullptr);
@ -568,7 +588,18 @@ schema::MetaGraphT *AnfExporter::Export(const FuncGraphPtr &func_graph, bool kee
return nullptr;
}
SetMetaGraphOutput(func_graph, meta_graphT);
ret = SetMetaGraphInput(func_graph, meta_graphT);
if (ret != RET_OK) {
MS_LOG(ERROR) << "SetMetaGraphInput failed.";
ReturnCode::GetSingleReturnCode()->UpdateReturnCode(ret);
return nullptr;
}
ret = SetMetaGraphOutput(func_graph, meta_graphT);
if (ret != RET_OK) {
MS_LOG(ERROR) << "SetMetaGraphOutput failed.";
ReturnCode::GetSingleReturnCode()->UpdateReturnCode(ret);
return nullptr;
}
return meta_graphT.release();
}
@ -749,7 +780,11 @@ int AnfExporter::SetOpInputNode(const CNodePtr &cnode, const std::unique_ptr<sch
if (IsContain(graph_inputs_, input_node->cast<AnfNodePtr>()) &&
graph_inputs_has_exported_.find(input_node) == graph_inputs_has_exported_.end()) {
graph_inputs_has_exported_.insert(input_node);
meta_graphT->inputIndex.push_back(meta_graphT->allTensors.size() - 1);
if (reorder_input_) {
graph_inputs_map_[input_node] = meta_graphT->allTensors.size() - 1;
} else {
meta_graphT->inputIndex.push_back(meta_graphT->allTensors.size() - 1);
}
}
} else if (input_node->isa<ValueNode>()) {
auto ret = ConvertInputValueNode(cnode, i, primitive_c, meta_graphT, fb_node);

3
mindspore/lite/tools/anf_exporter/anf_exporter.h
View File

@ -82,6 +82,7 @@ class AnfExporter {
const bool &copy_primitive, const CNodePtr &partial_cnode,
const std::unique_ptr<schema::CNodeT> &schema_cnode);
std::list<CNodePtr> InsertCallNode(const FuncGraphPtr &func_graph);
int SetMetaGraphInput(const FuncGraphPtr &func_graph, const std::unique_ptr<schema::MetaGraphT> &meta_graphT);
int SetMetaGraphOutput(const FuncGraphPtr &func_graph, const std::unique_ptr<schema::MetaGraphT> &meta_graphT);
bool IsCall(const AnfNodePtr node);
int CreateNewTensorForParameter(const std::unique_ptr<schema::MetaGraphT> &meta_graphT, const AnfNodePtr &input);
@ -93,8 +94,10 @@ class AnfExporter {
std::map<FuncGraphPtr, size_t> fg_subgraph_map_;
std::vector<AnfNodePtr> graph_inputs_;
std::set<AnfNodePtr> graph_inputs_has_exported_;
std::map<AnfNodePtr, int> graph_inputs_map_;
uint32_t node_idx_ = 0;
bool train_flag_ = false;
bool reorder_input_ = false;
};
// by default, copy_primitive is false, which means that the MetaGraph and func_graph share the same schema::PrimitiveT.
// but in PostQuantization, the func_graph need to transfer to MetaGraph first and do MetaGraph pass, which may modify

8
mindspore/lite/tools/converter/converter_context.h
View File

@ -109,7 +109,12 @@ class ConverterContext {
void SetGraphOutputTensorNames(const std::vector<std::string> &output_names) {
graph_output_tensor_names_ = output_names;
}
const std::vector<std::string> GetGraphOutputTensorNames() { return graph_output_tensor_names_; }
const std::vector<std::string> GetGraphOutputTensorNames() const { return graph_output_tensor_names_; }
void AddGraphInputTensorNames(const std::string &input_name) { graph_input_tensor_names_.emplace_back(input_name); }
const std::vector<std::string> GetGraphInputTensorNames() const { return graph_input_tensor_names_; }
private:
ConverterContext() {}
@ -118,6 +123,7 @@ class ConverterContext {
std::map<int32_t, int32_t> graph_input_data_type_map_;
std::map<int32_t, int32_t> graph_output_data_type_map_;
std::map<std::string, std::vector<int64_t>> graph_input_tensor_shape_map_;
std::vector<std::string> graph_input_tensor_names_;
std::vector<std::string> graph_output_tensor_names_;
};
} // namespace lite

10
mindspore/lite/tools/converter/parser/caffe/caffe_inspector.cc
View File

@ -16,6 +16,7 @@
#include "tools/converter/parser/caffe/caffe_inspector.h"
#include "src/common/log_adapter.h"
#include "src/common/utils.h"
namespace mindspore {
namespace lite {
@ -48,13 +49,12 @@ STATUS CaffeInspector::ParseInput() {
STATUS CaffeInspector::FindGraphInputsAndOutputs() {
for (const auto &iter : layerBottoms) {
if (layerTops.find(iter) == layerTops.end()) {
if (!IsContain(layerTops, iter)) {
graphInput.insert(iter);
}
}
for (const auto &iter : layerTops) {
if (layerBottoms.find(iter) == layerBottoms.end() &&
std::find(graphOutput.begin(), graphOutput.end(), iter) == graphOutput.end()) {
if (layerBottoms.find(iter) == layerBottoms.end() && !IsContain(graphOutput, iter)) {
graphOutput.push_back(iter);
}
}
@ -71,7 +71,9 @@ STATUS CaffeInspector::SetLayerTopsAndBottoms() {
graphInput.insert(layer.top(0));
}
for (int j = 0; j < layer.top_size(); j++) {
layerTops.insert(layer.top(j));
if (!IsContain(layerTops, layer.top(j))) {
layerTops.push_back(layer.top(j));
}
}
for (int j = 0; j < layer.bottom_size(); j++) {
layerBottoms.insert(layer.bottom(j));

2
mindspore/lite/tools/converter/parser/caffe/caffe_inspector.h
View File

@ -43,7 +43,7 @@ class CaffeInspector {
private:
caffe::NetParameter net;
std::set<std::string> layerTops;
std::vector<std::string> layerTops;
std::set<std::string> layerBottoms;
std::set<std::string> graphInput;

28
mindspore/lite/tools/converter/parser/caffe/caffe_model_parser.cc
View File

@ -243,6 +243,10 @@ STATUS CaffeModelParser::ConvertGraphInputsOfLayer() {
for (int i = 0; i < caffe_model_.layer_size(); i++) {
auto layer = caffe_model_.layer(i);
if (layer.type() == "Input") {
if (layer.bottom_size() != 0) {
MS_LOG(ERROR) << "The input layer should not have inputs";
return RET_ERROR;
}
auto parameter = res_graph_->add_parameter();
std::vector<int64_t> shape = ConverterContext::GetInstance()->GetGraphInputTensorShape(layer.name());
if (ConverterContext::GetInstance()->GetGraphInputTensorShapeMapSize() > 0 && shape.empty()) {
@ -259,7 +263,8 @@ STATUS CaffeModelParser::ConvertGraphInputsOfLayer() {
return RET_ERROR;
}
parameter->set_abstract(abstract);
parameter->set_name("graph_input-" + std::to_string(i));
parameter->set_name(layer.name());
ConverterContext::GetInstance()->AddGraphInputTensorNames(layer.name());
nodes_.insert(std::pair(layer.top(0), parameter));
}
}
@ -291,7 +296,8 @@ STATUS CaffeModelParser::ConvertGraphInputsOfShape() {
return RET_ERROR;
}
parameter->set_abstract(abstract);
parameter->set_name("graph_input-" + caffe_model_.input(i));
parameter->set_name(caffe_model_.input(i));
ConverterContext::GetInstance()->AddGraphInputTensorNames(caffe_model_.input(i));
nodes_.insert(std::pair(caffe_model_.input(i), parameter));
}
return RET_OK;
@ -323,7 +329,8 @@ STATUS CaffeModelParser::ConvertGraphInputsOfDim() {
return RET_ERROR;
}
parameter->set_abstract(abstract);
parameter->set_name("graph_input-" + caffe_model_.input(i));
parameter->set_name(caffe_model_.input(i));
ConverterContext::GetInstance()->AddGraphInputTensorNames(caffe_model_.input(i));
nodes_.insert(std::pair(caffe_model_.input(i), parameter));
}
return RET_OK;
@ -334,12 +341,17 @@ STATUS CaffeModelParser::ConvertGraphInputs() {
if (ret != RET_OK) {
return ret;
}
if (caffe_model_.input_dim_size() > 0) {
return ConvertGraphInputsOfDim();
} else {
return ConvertGraphInputsOfShape();
ret = ConvertGraphInputsOfShape();
if (ret != RET_OK) {
return ret;
}
return ret;
if (caffe_model_.input_dim_size() > 0) {
ret = ConvertGraphInputsOfDim();
if (ret != RET_OK) {
return ret;
}
}
return RET_OK;
}
STATUS CaffeModelParser::ConvertGraphOutputs() {

1
mindspore/lite/tools/converter/parser/onnx/onnx_model_parser.cc
View File

@ -221,6 +221,7 @@ STATUS OnnxModelParser::ConvertGraphInputs(const onnx::GraphProto &onnx_graph, c
}
parameter->set_abstract(abstract_tensor);
parameter->set_name(input_value.name());
ConverterContext::GetInstance()->AddGraphInputTensorNames(input_value.name());
anf_nodes_map->emplace(input_value.name(), parameter);
}
return RET_OK;

51
mindspore/lite/tools/converter/parser/tf/tf_model_parser.cc
View File

@ -414,7 +414,7 @@ STATUS TFModelParser::ConvertConstTensor(const tensorflow::NodeDef &node_def, co
}
STATUS TFModelParser::ConvertParameter(const tensorflow::NodeDef &node, const ParameterPtr &parameter,
std::unordered_map<std::string, AnfNodePtr> *anf_node_map) {
std::unordered_map<std::string, AnfNodePtr> *anf_node_map, bool root_graph) {
MS_ASSERT(node != nullptr);
MS_ASSERT(parameter != nullptr);
@ -446,7 +446,10 @@ STATUS TFModelParser::ConvertParameter(const tensorflow::NodeDef &node, const Pa
return status;
}
} else {
graph_input_names_.emplace_back(node.name()); // only root graph need set graph input names
if (root_graph) {
graph_input_names_.emplace_back(node.name()); // only root graph need set graph input names
ConverterContext::GetInstance()->AddGraphInputTensorNames(node.name());
}
}
type = (type == kNumberTypeInt64) ? kNumberTypeInt32 : type;
@ -463,13 +466,14 @@ STATUS TFModelParser::ConvertParameter(const tensorflow::NodeDef &node, const Pa
return RET_OK;
}
STATUS TFModelParser::ConvertGraphInputsAndConsts(
const std::map<std::string, const tensorflow::NodeDef *> &tf_graph_nodes, const FuncGraphPtr &anf_graph,
std::unordered_map<std::string, AnfNodePtr> *anf_node_map) {
for (auto &pair : tf_graph_nodes) {
STATUS TFModelParser::ConvertGraphInputsAndConsts(const std::vector<const tensorflow::NodeDef *> &tf_graph_nodes,
const FuncGraphPtr &anf_graph,
std::unordered_map<std::string, AnfNodePtr> *anf_node_map,
bool root_graph) {
for (auto &node : tf_graph_nodes) {
bool have_data_depend = false;
for (int i = 0; i < pair.second->input_size(); ++i) {
auto name = pair.second->input(i);
for (int i = 0; i < node->input_size(); ++i) {
auto name = node->input(i);
if (!name.empty() && name[0] != '^') { // control_depend input start with "^"
have_data_depend = true;
break;
@ -477,7 +481,7 @@ STATUS TFModelParser::ConvertGraphInputsAndConsts(
}
if (!have_data_depend) {
auto parameter = anf_graph->add_parameter();
if (ConvertParameter(*pair.second, parameter, anf_node_map) != RET_OK) {
if (ConvertParameter(*node, parameter, anf_node_map, root_graph) != RET_OK) {
MS_LOG(ERROR) << "convert Parameter Node failed";
return RET_ERROR;
}
@ -520,9 +524,10 @@ FuncGraphPtr TFModelParser::Parse(const converter::ConverterParameters &flag) {
for (int i = 0; i < tf_root_graph_->node_size(); i++) {
auto &node_def = tf_root_graph_->node(i);
tf_root_graph_nodes_[node_def.name()] = &node_def;
tf_root_graph_nodes_vec_.emplace_back(&node_def);
}
status = ConvertGraphInputsAndConsts(tf_root_graph_nodes_, res_graph_, &anf_root_node_map_);
status = ConvertGraphInputsAndConsts(tf_root_graph_nodes_vec_, res_graph_, &anf_root_node_map_, true);
if (status != RET_OK) {
ReturnCode::GetSingleReturnCode()->UpdateReturnCode(status);
return nullptr;
@ -607,11 +612,13 @@ STATUS TFModelParser::ConvertSubgraphInputs(std::map<std::string, const tensorfl
}
sub_graph_inputs.emplace_back(parameter);
}
std::vector<const tensorflow::NodeDef *> subgraph_tf_node_vec;
for (int j = 0; j < tf_sub_fuction.node_def_size(); j++) {
auto &node_def = tf_sub_fuction.node_def(j);
(*tf_sub_node_map)[node_def.name()] = &node_def;
subgraph_tf_node_vec.emplace_back(&node_def);
}
if (ConvertGraphInputsAndConsts(*tf_sub_node_map, sub_func_graph, anf_sub_node_map) != RET_OK) {
if (ConvertGraphInputsAndConsts(subgraph_tf_node_vec, sub_func_graph, anf_sub_node_map, false) != RET_OK) {
MS_LOG(ERROR) << "Convert subgraph consts failed";
return RET_ERROR;
}
@ -1029,23 +1036,23 @@ STATUS TFModelParser::ConvertRootGraphOutputs() {
// tf_root_graph_nodes_ but not anf_root_node_map_
std::set<std::string> all_node_inputs;
std::vector<AnfNodePtr> output_nodes;
for (auto &pair : tf_root_graph_nodes_) {
for (int i = 0; i < pair.second->input_size(); ++i) {
all_node_inputs.insert(TensorFlowUtils::GetNodeName(pair.second->input(i)));
auto input_name = pair.second->input(i);
for (auto &node : tf_root_graph_nodes_vec_) {
for (int i = 0; i < node->input_size(); ++i) {
all_node_inputs.insert(TensorFlowUtils::GetNodeName(node->input(i)));
auto input_name = node->input(i);
if (input_name[0] == '^') {
input_name.erase(0, 1);
}
all_node_inputs.insert(input_name);
}
}
for (auto &pair : tf_root_graph_nodes_) {
if (pair.second->op() == "Assert") {
for (auto &node : tf_root_graph_nodes_vec_) {
if (node->op() == "Assert") {
continue;
}
auto it = all_node_inputs.find(pair.first);
if (it == all_node_inputs.end() && pair.second->input_size() > 0) { // output node not constraint to Identity
auto origin_name = GetOriginInputName(*(pair.second), tf_root_graph_nodes_);
auto it = all_node_inputs.find(node->name());
if (it == all_node_inputs.end() && node->input_size() > 0) { // output node not constraint to Identity
auto origin_name = GetOriginInputName(*(node), tf_root_graph_nodes_);
// node with multiple outputs has been changed to tupleGetItem, and the original name changes to be name:idx.
for (int i = 0; i < node_output_num_[origin_name]; i++) {
auto anf_node = GetAnfNode(origin_name, anf_root_node_map_, i);
@ -1055,8 +1062,8 @@ STATUS TFModelParser::ConvertRootGraphOutputs() {
}
output_nodes.push_back(anf_node);
// Get the name of node 'Identity' and 'StopGradient'.
if (pair.second->op() == "Identity" || pair.second->op() == "StopGradient") {
auto tmp_node = pair.second;
if (node->op() == "Identity" || node->op() == "StopGradient") {
auto tmp_node = node;
bool found_input = true;
while (tmp_node->name().empty() && (tmp_node->op() == "Identity" || tmp_node->op() == "StopGradient")) {
auto flatten_input_name = TensorFlowUtils::GetFlattenNodeName(tmp_node->input(0));

8
mindspore/lite/tools/converter/parser/tf/tf_model_parser.h
View File

@ -51,10 +51,11 @@ class TFModelParser : public converter::ModelParser {
std::vector<int64_t> *shape_vector);
static STATUS SetTensorInfoFromType(const tensorflow::TensorProto &tensor_proto, tensor::TensorPtr *tensor_info);
STATUS ConvertParameter(const tensorflow::NodeDef &node, const ParameterPtr &parameter,
std::unordered_map<std::string, AnfNodePtr> *anf_node_map);
STATUS ConvertGraphInputsAndConsts(const std::map<std::string, const tensorflow::NodeDef *> &tf_graph_nodes,
std::unordered_map<std::string, AnfNodePtr> *anf_node_map, bool root_graph = false);
STATUS ConvertGraphInputsAndConsts(const std::vector<const tensorflow::NodeDef *> &tf_graph_nodes,
const FuncGraphPtr &anf_graph,
std::unordered_map<std::string, AnfNodePtr> *anf_node_map);
std::unordered_map<std::string, AnfNodePtr> *anf_node_map,
bool root_graph = false);
static STATUS ConvertInputNodes(const tensorflow::NodeDef &node_def, const std::vector<std::string> &input_names,
const std::map<std::string, const tensorflow::NodeDef *> &tf_node_map,
const std::unordered_map<std::string, AnfNodePtr> &anf_node_map,
@ -97,6 +98,7 @@ class TFModelParser : public converter::ModelParser {
std::unique_ptr<tensorflow::GraphDef> tf_root_graph_; // tf root graph def
std::map<std::string, const tensorflow::NodeDef *> tf_root_graph_nodes_; // tf root graph node map
std::vector<const tensorflow::NodeDef *> tf_root_graph_nodes_vec_;
std::unordered_map<std::string, AnfNodePtr> anf_root_node_map_;
std::vector<std::string> graph_input_names_;
std::vector<std::string> graph_output_names_;

3
mindspore/lite/tools/converter/parser/tflite/tflite_model_parser.cc
View File

@ -336,7 +336,8 @@ STATUS TfliteModelParser::ConvertGraphInputs() {
return RET_ERROR;
}
parameter->set_abstract(abstract_tensor);
parameter->set_name("graph_input-" + std::to_string(tflite_graph_input));
parameter->set_name(tensor->name);
ConverterContext::GetInstance()->AddGraphInputTensorNames(tensor->name);
nodes_.insert(std::pair(tflite_graph_input, parameter));
}
return RET_OK;