Add files using upload-large-folder tool
Browse files- .gitattributes +4 -0
- clevr_best/model.cfg.json +115 -0
- clevr_best/model.chkpt +3 -0
- clevr_best/model_best_greedy_pred_val_all_metrics.json +30 -0
- densevid_eval/LICENCE +21 -0
- densevid_eval/__init__.py +0 -0
- densevid_eval/convert_to_coco.py +19 -0
- densevid_eval/evaluate.py +304 -0
- densevid_eval/get_caption_stat.py +70 -0
- densevid_eval/merge_dicts_by_prefix.py +40 -0
- densevid_eval/para-evaluate.py +211 -0
- edit_best/model.cfg.json +114 -0
- edit_best/model.chkpt +3 -0
- edit_best/model_best_greedy_pred_val_all_metrics.json +11 -0
- filter_files/clevr_similarity_scores.json +3 -0
- filter_files/edit_similarity_scores.json +0 -0
- filter_files/spot_similarity_scores.json +0 -0
- filtered-spot-captions/filter_test.json +0 -0
- filtered-spot-captions/filter_train.json +0 -0
- spot_best/model.chkpt +3 -0
- stage1_spot_best/log.txt +1007 -0
.gitattributes
CHANGED
|
@@ -36,3 +36,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 36 |
densevid_eval/clevr_data/total_change_captions_reformat.json filter=lfs diff=lfs merge=lfs -text
|
| 37 |
densevid_eval/clevr_data/change_captions.json filter=lfs diff=lfs merge=lfs -text
|
| 38 |
densevid_eval/clevr_data/train_change_captions.json filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 36 |
densevid_eval/clevr_data/total_change_captions_reformat.json filter=lfs diff=lfs merge=lfs -text
|
| 37 |
densevid_eval/clevr_data/change_captions.json filter=lfs diff=lfs merge=lfs -text
|
| 38 |
densevid_eval/clevr_data/train_change_captions.json filter=lfs diff=lfs merge=lfs -text
|
| 39 |
+
spot_best/model.chkpt filter=lfs diff=lfs merge=lfs -text
|
| 40 |
+
edit_best/model.chkpt filter=lfs diff=lfs merge=lfs -text
|
| 41 |
+
clevr_best/model.chkpt filter=lfs diff=lfs merge=lfs -text
|
| 42 |
+
filter_files/clevr_similarity_scores.json filter=lfs diff=lfs merge=lfs -text
|
clevr_best/model.cfg.json
ADDED
|
@@ -0,0 +1,115 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"config": "./config/mmvid_config.yaml",
|
| 3 |
+
"resume": null,
|
| 4 |
+
"save_model": "./results/clevr_2025_07_04_09_48_seed52_ema-1_mmvid/model",
|
| 5 |
+
"save_mode": "best",
|
| 6 |
+
"res_root_dir": "./results",
|
| 7 |
+
"debug": false,
|
| 8 |
+
"seed": 52,
|
| 9 |
+
"no_cuda": false,
|
| 10 |
+
"no_pin_memory": true,
|
| 11 |
+
"cuda": true,
|
| 12 |
+
"dalle_param": {
|
| 13 |
+
"vae": {
|
| 14 |
+
"which_vae": "vqgan1024",
|
| 15 |
+
"vae_path": "./pretrained_vqgan/clevr_epoch=000035.ckpt",
|
| 16 |
+
"image_size": 224
|
| 17 |
+
},
|
| 18 |
+
"bert": {
|
| 19 |
+
"num_text_tokens": 0,
|
| 20 |
+
"text_seq_len": 24,
|
| 21 |
+
"dim": 768,
|
| 22 |
+
"loss_img_weight": 7,
|
| 23 |
+
"text_feature_dim": 0,
|
| 24 |
+
"fixed_language_model": null,
|
| 25 |
+
"text_emb_bottleneck": null,
|
| 26 |
+
"which_transformer": "openai_clip_visual",
|
| 27 |
+
"num_targets": 4,
|
| 28 |
+
"num_visuals": 0,
|
| 29 |
+
"use_separate_visual_emb": false,
|
| 30 |
+
"beit": true,
|
| 31 |
+
"insert_sep": false,
|
| 32 |
+
"openai_clip_path": "./ckpt/ViT-B-32.pt",
|
| 33 |
+
"vision_layers": 12,
|
| 34 |
+
"rel": true,
|
| 35 |
+
"vid": true
|
| 36 |
+
},
|
| 37 |
+
"skip_params": [
|
| 38 |
+
"to_logits_vid.1.bias",
|
| 39 |
+
"to_logits_vid.1.weight",
|
| 40 |
+
"to_logits_vid.0.bias",
|
| 41 |
+
"to_logits_vid.0.weight",
|
| 42 |
+
"to_logits_rel.1.bias",
|
| 43 |
+
"to_logits_rel.1.weight",
|
| 44 |
+
"to_logits_rel.0.bias",
|
| 45 |
+
"to_logits_rel.0.weight",
|
| 46 |
+
"to_logits.1.bias",
|
| 47 |
+
"to_logits.1.weight",
|
| 48 |
+
"to_logits.0.bias",
|
| 49 |
+
"to_logits.0.weight",
|
| 50 |
+
"to_logits_text.1.bias",
|
| 51 |
+
"to_logits_text.1.weight",
|
| 52 |
+
"to_logits_text.0.bias",
|
| 53 |
+
"to_logits_text.0.weight",
|
| 54 |
+
"image_emb.weight"
|
| 55 |
+
],
|
| 56 |
+
"freeze": false,
|
| 57 |
+
"use_lora": false,
|
| 58 |
+
"lora_config": {
|
| 59 |
+
"r": 8,
|
| 60 |
+
"lora_alpha": 16,
|
| 61 |
+
"lora_dropout": 0.1,
|
| 62 |
+
"bias": "none"
|
| 63 |
+
}
|
| 64 |
+
},
|
| 65 |
+
"decoder_param": {
|
| 66 |
+
"max_n_sen": 12,
|
| 67 |
+
"max_t_len": 24,
|
| 68 |
+
"max_v_len": 4,
|
| 69 |
+
"exp_id": "init",
|
| 70 |
+
"hidden_size": 512,
|
| 71 |
+
"intermediate_size": 2048,
|
| 72 |
+
"num_hidden_layers": 2,
|
| 73 |
+
"num_attention_heads": 8,
|
| 74 |
+
"mask_prob": 0.0,
|
| 75 |
+
"hidden_dropout_prob": 0.1,
|
| 76 |
+
"label_smoothing": 0.1,
|
| 77 |
+
"recurrent": false,
|
| 78 |
+
"untied": false,
|
| 79 |
+
"mtrans": true,
|
| 80 |
+
"use_beam": false,
|
| 81 |
+
"vocab_size": 80,
|
| 82 |
+
"mask_token_id": 7
|
| 83 |
+
},
|
| 84 |
+
"dset_name": "clevr",
|
| 85 |
+
"data_dir": "/home/sunjiayang/VFI4IDC_test/IDC_scratch_model/densevid_eval/clevr_data",
|
| 86 |
+
"video_feature_dir": "./data/clevr/CLEVR_processed",
|
| 87 |
+
"word2idx_path": "./cache/clevr_word2idx.json",
|
| 88 |
+
"glove_path": "./cache/yc2_vocab_glove.pt",
|
| 89 |
+
"eval_tool_dir": "/home/sunjiayang/VFI4IDC_test/IDC_scratch_model/densevid_eval",
|
| 90 |
+
"filtered": true,
|
| 91 |
+
"filter_file_path": "./filter_files/clevr_similarity_scores.json",
|
| 92 |
+
"max_k": 2,
|
| 93 |
+
"num_frames": 9,
|
| 94 |
+
"recurrent": false,
|
| 95 |
+
"untied": false,
|
| 96 |
+
"mtrans": true,
|
| 97 |
+
"use_beam": false,
|
| 98 |
+
"image_size": 224,
|
| 99 |
+
"n_epoch": 40,
|
| 100 |
+
"batch_size": 16,
|
| 101 |
+
"val_batch_size": 32,
|
| 102 |
+
"max_es_cnt": 50,
|
| 103 |
+
"lr": 5e-05,
|
| 104 |
+
"lr_finetune": 5e-05,
|
| 105 |
+
"lr_warmup_proportion": 0.1,
|
| 106 |
+
"grad_clip": 1,
|
| 107 |
+
"weight_decay": 0.01,
|
| 108 |
+
"ema_decay": -1,
|
| 109 |
+
"num_workers": 8,
|
| 110 |
+
"temperature": 0.5,
|
| 111 |
+
"pretrained_model": "./ckpt/img_size224_layer12_clevr_wovisual_softmax/dalle.pt",
|
| 112 |
+
"res_dir": "./results/clevr_2025_07_04_09_48_seed52_ema-1_mmvid",
|
| 113 |
+
"log": "./results/clevr_2025_07_04_09_48_seed52_ema-1_mmvid/model",
|
| 114 |
+
"pin_memory": false
|
| 115 |
+
}
|
clevr_best/model.chkpt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:22c231029ab6d7b13b8c315916337b978c24966978d1918c5dc5274f7c1e083a
|
| 3 |
+
size 1968010418
|
clevr_best/model_best_greedy_pred_val_all_metrics.json
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"total_results": {
|
| 3 |
+
"Bleu_1": 0.8420700803383588,
|
| 4 |
+
"Bleu_2": 0.7579372490465773,
|
| 5 |
+
"Bleu_3": 0.6636594870114211,
|
| 6 |
+
"Bleu_4": 0.5672140810891073,
|
| 7 |
+
"METEOR": 0.4165370010265982,
|
| 8 |
+
"ROUGE_L": 0.7472314257366539,
|
| 9 |
+
"CIDEr": 1.3562307002208966
|
| 10 |
+
},
|
| 11 |
+
"change_results": {
|
| 12 |
+
"Bleu_1": 0.845858001605002,
|
| 13 |
+
"Bleu_2": 0.7597243391607108,
|
| 14 |
+
"Bleu_3": 0.6624295759271439,
|
| 15 |
+
"Bleu_4": 0.5606241976791171,
|
| 16 |
+
"METEOR": 0.3908916952169497,
|
| 17 |
+
"ROUGE_L": 0.7253092620179556,
|
| 18 |
+
"CIDEr": 1.21632819242502
|
| 19 |
+
},
|
| 20 |
+
"unchange_results": {
|
| 21 |
+
"Bleu_1": 0.8266600960945653,
|
| 22 |
+
"Bleu_2": 0.7505478349738592,
|
| 23 |
+
"Bleu_3": 0.6747631146158112,
|
| 24 |
+
"Bleu_4": 0.6366370861460098,
|
| 25 |
+
"METEOR": 0.5254467142365439,
|
| 26 |
+
"ROUGE_L": 0.7691535894553523,
|
| 27 |
+
"CIDEr": 1.1470175404358385
|
| 28 |
+
},
|
| 29 |
+
"type_results": null
|
| 30 |
+
}
|
densevid_eval/LICENCE
ADDED
|
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
MIT License
|
| 2 |
+
|
| 3 |
+
Copyright (c) 2017 Ranjay Krishna
|
| 4 |
+
|
| 5 |
+
Permission is hereby granted, free of charge, to any person obtaining a copy
|
| 6 |
+
of this software and associated documentation files (the "Software"), to deal
|
| 7 |
+
in the Software without restriction, including without limitation the rights
|
| 8 |
+
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
| 9 |
+
copies of the Software, and to permit persons to whom the Software is
|
| 10 |
+
furnished to do so, subject to the following conditions:
|
| 11 |
+
|
| 12 |
+
The above copyright notice and this permission notice shall be included in all
|
| 13 |
+
copies or substantial portions of the Software.
|
| 14 |
+
|
| 15 |
+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
| 16 |
+
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
| 17 |
+
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
| 18 |
+
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
| 19 |
+
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
| 20 |
+
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
| 21 |
+
SOFTWARE.
|
densevid_eval/__init__.py
ADDED
|
File without changes
|
densevid_eval/convert_to_coco.py
ADDED
|
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import json
|
| 3 |
+
|
| 4 |
+
with open("./clevr_data/test_all_captions.json", "r") as f:
|
| 5 |
+
captions = json.load(f)
|
| 6 |
+
|
| 7 |
+
print(captions.keys())
|
| 8 |
+
|
| 9 |
+
coco_res = {"images": [], "annotations": []}
|
| 10 |
+
|
| 11 |
+
caption_id = 0
|
| 12 |
+
for k, v in captions.items():
|
| 13 |
+
coco_res["images"].append({"file_name": k, "id": k})
|
| 14 |
+
for i, caption in enumerate(v):
|
| 15 |
+
coco_res["annotations"].append({"image_id": k, "caption": caption, "id": caption_id})
|
| 16 |
+
caption_id += 1
|
| 17 |
+
|
| 18 |
+
with open("./clevr_data/clevr_total_change_captions_reformat.json", "w") as f:
|
| 19 |
+
json.dump(coco_res, f)
|
densevid_eval/evaluate.py
ADDED
|
@@ -0,0 +1,304 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# --------------------------------------------------------
|
| 2 |
+
# Dense-Captioning Events in Videos Eval
|
| 3 |
+
# Copyright (c) 2017 Ranjay Krishna
|
| 4 |
+
# Licensed under The MIT License [see LICENSE for details]
|
| 5 |
+
# Written by Ranjay Krishna
|
| 6 |
+
# --------------------------------------------------------
|
| 7 |
+
|
| 8 |
+
import argparse
|
| 9 |
+
import string
|
| 10 |
+
import json
|
| 11 |
+
import sys
|
| 12 |
+
sys.path.insert(0, './coco-caption') # Hack to allow the import of pycocoeval
|
| 13 |
+
|
| 14 |
+
from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer
|
| 15 |
+
from pycocoevalcap.bleu.bleu import Bleu
|
| 16 |
+
from pycocoevalcap.meteor.meteor import Meteor
|
| 17 |
+
from pycocoevalcap.rouge.rouge import Rouge
|
| 18 |
+
from pycocoevalcap.cider.cider import Cider
|
| 19 |
+
from pycocoevalcap.spice.spice import Spice
|
| 20 |
+
from sets import Set
|
| 21 |
+
import numpy as np
|
| 22 |
+
|
| 23 |
+
def remove_nonascii(text):
|
| 24 |
+
return ''.join([i if ord(i) < 128 else ' ' for i in text])
|
| 25 |
+
|
| 26 |
+
class ANETcaptions(object):
|
| 27 |
+
PREDICTION_FIELDS = ['results', 'version', 'external_data']
|
| 28 |
+
|
| 29 |
+
def __init__(self, ground_truth_filenames=None, prediction_filename=None,
|
| 30 |
+
tious=None, max_proposals=1000,
|
| 31 |
+
prediction_fields=PREDICTION_FIELDS, verbose=False):
|
| 32 |
+
# Check that the gt and submission files exist and load them
|
| 33 |
+
if len(tious) == 0:
|
| 34 |
+
raise IOError('Please input a valid tIoU.')
|
| 35 |
+
if not ground_truth_filenames:
|
| 36 |
+
raise IOError('Please input a valid ground truth file.')
|
| 37 |
+
if not prediction_filename:
|
| 38 |
+
raise IOError('Please input a valid prediction file.')
|
| 39 |
+
|
| 40 |
+
self.verbose = verbose
|
| 41 |
+
self.tious = tious
|
| 42 |
+
self.max_proposals = max_proposals
|
| 43 |
+
self.pred_fields = prediction_fields
|
| 44 |
+
self.ground_truths = self.import_ground_truths(ground_truth_filenames)
|
| 45 |
+
self.prediction = self.import_prediction(prediction_filename)
|
| 46 |
+
self.tokenizer = PTBTokenizer()
|
| 47 |
+
|
| 48 |
+
# Set up scorers, if not verbose, we only use the one we're
|
| 49 |
+
# testing on: METEOR
|
| 50 |
+
if self.verbose:
|
| 51 |
+
self.scorers = [
|
| 52 |
+
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
|
| 53 |
+
(Meteor(),"METEOR"),
|
| 54 |
+
(Rouge(), "ROUGE_L"),
|
| 55 |
+
(Cider(), "CIDEr"),
|
| 56 |
+
(Spice(), "SPICE")
|
| 57 |
+
]
|
| 58 |
+
else:
|
| 59 |
+
self.scorers = [(Meteor(), "METEOR")]
|
| 60 |
+
|
| 61 |
+
def import_prediction(self, prediction_filename):
|
| 62 |
+
if self.verbose:
|
| 63 |
+
print "| Loading submission..."
|
| 64 |
+
submission = json.load(open(prediction_filename))
|
| 65 |
+
if not all([field in submission.keys() for field in self.pred_fields]):
|
| 66 |
+
raise IOError('Please input a valid ground truth file.')
|
| 67 |
+
# Ensure that every video is limited to the correct maximum number of proposals.
|
| 68 |
+
results = {}
|
| 69 |
+
len_captions = 0
|
| 70 |
+
for vid_id in submission['results']:
|
| 71 |
+
results[vid_id] = submission['results'][vid_id][:self.max_proposals]
|
| 72 |
+
len_captions+= len(submission['results'][vid_id][:self.max_proposals])
|
| 73 |
+
print('len of results:', len(results))
|
| 74 |
+
print('len of captions:', len_captions)
|
| 75 |
+
return results
|
| 76 |
+
|
| 77 |
+
def import_ground_truths(self, filenames):
|
| 78 |
+
gts = []
|
| 79 |
+
self.n_ref_vids = Set()
|
| 80 |
+
for filename in filenames:
|
| 81 |
+
gt = json.load(open(filename))
|
| 82 |
+
self.n_ref_vids.update(gt.keys())
|
| 83 |
+
gts.append(gt)
|
| 84 |
+
if self.verbose:
|
| 85 |
+
print "| Loading GT. #files: %d, #videos: %d" % (len(filenames), len(self.n_ref_vids))
|
| 86 |
+
return gts
|
| 87 |
+
|
| 88 |
+
def iou(self, interval_1, interval_2):
|
| 89 |
+
start_i, end_i = interval_1[0], interval_1[1]
|
| 90 |
+
start, end = interval_2[0], interval_2[1]
|
| 91 |
+
intersection = max(0, min(end, end_i) - max(start, start_i))
|
| 92 |
+
union = min(max(end, end_i) - min(start, start_i), end-start + end_i-start_i)
|
| 93 |
+
iou = float(intersection) / (union + 1e-8)
|
| 94 |
+
return iou
|
| 95 |
+
|
| 96 |
+
def check_gt_exists(self, vid_id):
|
| 97 |
+
for gt in self.ground_truths:
|
| 98 |
+
if vid_id in gt:
|
| 99 |
+
return True
|
| 100 |
+
return False
|
| 101 |
+
|
| 102 |
+
def get_gt_vid_ids(self):
|
| 103 |
+
vid_ids = set([])
|
| 104 |
+
for gt in self.ground_truths:
|
| 105 |
+
vid_ids |= set(gt.keys())
|
| 106 |
+
return list(vid_ids)
|
| 107 |
+
|
| 108 |
+
def evaluate(self):
|
| 109 |
+
aggregator = {}
|
| 110 |
+
self.scores = {}
|
| 111 |
+
for tiou in self.tious:
|
| 112 |
+
scores = self.evaluate_tiou(tiou)
|
| 113 |
+
for metric, score in scores.items():
|
| 114 |
+
if metric not in self.scores:
|
| 115 |
+
self.scores[metric] = []
|
| 116 |
+
self.scores[metric].append(score)
|
| 117 |
+
if self.verbose:
|
| 118 |
+
self.scores['Recall'] = []
|
| 119 |
+
self.scores['Precision'] = []
|
| 120 |
+
for tiou in self.tious:
|
| 121 |
+
precision, recall = self.evaluate_detection(tiou)
|
| 122 |
+
self.scores['Recall'].append(recall)
|
| 123 |
+
self.scores['Precision'].append(precision)
|
| 124 |
+
|
| 125 |
+
def evaluate_detection(self, tiou):
|
| 126 |
+
gt_vid_ids = self.get_gt_vid_ids()
|
| 127 |
+
# Recall is the percentage of ground truth that is covered by the predictions
|
| 128 |
+
# Precision is the percentage of predictions that are valid
|
| 129 |
+
recall = [0] * len(gt_vid_ids)
|
| 130 |
+
precision = [0] * len(gt_vid_ids)
|
| 131 |
+
for vid_i, vid_id in enumerate(gt_vid_ids):
|
| 132 |
+
best_recall = 0
|
| 133 |
+
best_precision = 0
|
| 134 |
+
for gt in self.ground_truths:
|
| 135 |
+
if vid_id not in gt:
|
| 136 |
+
continue
|
| 137 |
+
refs = gt[vid_id]
|
| 138 |
+
ref_set_covered = set([])
|
| 139 |
+
pred_set_covered = set([])
|
| 140 |
+
num_gt = 0
|
| 141 |
+
num_pred = 0
|
| 142 |
+
if vid_id in self.prediction:
|
| 143 |
+
for pred_i, pred in enumerate(self.prediction[vid_id]):
|
| 144 |
+
pred_timestamp = pred['timestamp']
|
| 145 |
+
for ref_i, ref_timestamp in enumerate(refs['timestamps']):
|
| 146 |
+
if self.iou(pred_timestamp, ref_timestamp) > tiou:
|
| 147 |
+
ref_set_covered.add(ref_i)
|
| 148 |
+
pred_set_covered.add(pred_i)
|
| 149 |
+
|
| 150 |
+
new_precision = float(len(pred_set_covered)) / (pred_i + 1)
|
| 151 |
+
best_precision = max(best_precision, new_precision)
|
| 152 |
+
new_recall = float(len(ref_set_covered)) / len(refs['timestamps'])
|
| 153 |
+
best_recall = max(best_recall, new_recall)
|
| 154 |
+
recall[vid_i] = best_recall
|
| 155 |
+
precision[vid_i] = best_precision
|
| 156 |
+
return sum(precision) / len(precision), sum(recall) / len(recall)
|
| 157 |
+
|
| 158 |
+
def evaluate_tiou(self, tiou):
|
| 159 |
+
# This method averages the tIoU precision from METEOR, Bleu, etc. across videos
|
| 160 |
+
res = {}
|
| 161 |
+
gts = {}
|
| 162 |
+
gt_vid_ids = self.get_gt_vid_ids()
|
| 163 |
+
|
| 164 |
+
unique_index = 0
|
| 165 |
+
|
| 166 |
+
# video id to unique caption ids mapping
|
| 167 |
+
vid2capid = {}
|
| 168 |
+
|
| 169 |
+
cur_res = {}
|
| 170 |
+
cur_gts = {}
|
| 171 |
+
|
| 172 |
+
for vid_id in gt_vid_ids:
|
| 173 |
+
|
| 174 |
+
vid2capid[vid_id] = []
|
| 175 |
+
|
| 176 |
+
# If the video does not have a prediction, then Vwe give it no matches
|
| 177 |
+
# We set it to empty, and use this as a sanity check later on
|
| 178 |
+
if vid_id not in self.prediction:
|
| 179 |
+
pass
|
| 180 |
+
|
| 181 |
+
# If we do have a prediction, then we find the scores based on all the
|
| 182 |
+
# valid tIoU overlaps
|
| 183 |
+
else:
|
| 184 |
+
# For each prediction, we look at the tIoU with ground truth
|
| 185 |
+
for i,pred in enumerate(self.prediction[vid_id]):
|
| 186 |
+
has_added = False
|
| 187 |
+
for gt in self.ground_truths:
|
| 188 |
+
if vid_id not in gt:
|
| 189 |
+
print('skipped')
|
| 190 |
+
continue
|
| 191 |
+
gt_captions = gt[vid_id]
|
| 192 |
+
for caption_idx, caption_timestamp in enumerate(gt_captions['timestamps']):
|
| 193 |
+
if True or self.iou(pred['timestamp'], caption_timestamp) >= tiou:
|
| 194 |
+
gt_caption = gt_captions['sentences'][i] # for now we use gt proposal
|
| 195 |
+
cur_res[unique_index] = [{'caption': remove_nonascii(pred['sentence'])}]
|
| 196 |
+
cur_gts[unique_index] = [{'caption': remove_nonascii(gt_caption)}] # for now we use gt proposal
|
| 197 |
+
#cur_gts[unique_index] = [{'caption': remove_nonascii(gt_captions['sentences'][caption_idx])}]
|
| 198 |
+
vid2capid[vid_id].append(unique_index)
|
| 199 |
+
unique_index += 1
|
| 200 |
+
has_added = True
|
| 201 |
+
break # for now we use gt proposal
|
| 202 |
+
|
| 203 |
+
# If the predicted caption does not overlap with any ground truth,
|
| 204 |
+
# we should compare it with garbage
|
| 205 |
+
if not has_added:
|
| 206 |
+
cur_res[unique_index] = [{'caption': remove_nonascii(pred['sentence'])}]
|
| 207 |
+
cur_gts[unique_index] = [{'caption': 'abc123!@#'}]
|
| 208 |
+
vid2capid[vid_id].append(unique_index)
|
| 209 |
+
unique_index += 1
|
| 210 |
+
|
| 211 |
+
# Each scorer will compute across all videos and take average score
|
| 212 |
+
output = {}
|
| 213 |
+
|
| 214 |
+
# call tokenizer here for all predictions and gts
|
| 215 |
+
tokenize_res = self.tokenizer.tokenize(cur_res)
|
| 216 |
+
tokenize_gts = self.tokenizer.tokenize(cur_gts)
|
| 217 |
+
|
| 218 |
+
# reshape back
|
| 219 |
+
for vid in vid2capid.keys():
|
| 220 |
+
res[vid] = {index:tokenize_res[index] for index in vid2capid[vid]}
|
| 221 |
+
gts[vid] = {index:tokenize_gts[index] for index in vid2capid[vid]}
|
| 222 |
+
|
| 223 |
+
for scorer, method in self.scorers:
|
| 224 |
+
if self.verbose:
|
| 225 |
+
print 'computing %s score...'%(scorer.method())
|
| 226 |
+
|
| 227 |
+
# For each video, take all the valid pairs (based from tIoU) and compute the score
|
| 228 |
+
all_scores = {}
|
| 229 |
+
|
| 230 |
+
if method == "SPICE": # don't want to compute spice for 10000 times
|
| 231 |
+
print("getting spice score...")
|
| 232 |
+
score, scores = scorer.compute_score(tokenize_gts, tokenize_res)
|
| 233 |
+
all_scores[0] = score
|
| 234 |
+
else:
|
| 235 |
+
for i,vid_id in enumerate(gt_vid_ids):
|
| 236 |
+
if len(res[vid_id]) == 0 or len(gts[vid_id]) == 0:
|
| 237 |
+
if type(method) == list:
|
| 238 |
+
score = [0] * len(method)
|
| 239 |
+
else:
|
| 240 |
+
score = 0
|
| 241 |
+
else:
|
| 242 |
+
score, scores = scorer.compute_score(gts[vid_id], res[vid_id])
|
| 243 |
+
all_scores[vid_id] = score
|
| 244 |
+
|
| 245 |
+
#print all_scores.values()
|
| 246 |
+
if type(method) == list:
|
| 247 |
+
scores = np.mean(all_scores.values(), axis=0)
|
| 248 |
+
for m in xrange(len(method)):
|
| 249 |
+
output[method[m]] = scores[m]
|
| 250 |
+
if self.verbose:
|
| 251 |
+
print "Calculated tIoU: %1.1f, %s: %0.3f" % (tiou, method[m], output[method[m]])
|
| 252 |
+
else:
|
| 253 |
+
output[method] = np.mean(all_scores.values())
|
| 254 |
+
if self.verbose:
|
| 255 |
+
print "Calculated tIoU: %1.1f, %s: %0.3f" % (tiou, method, output[method])
|
| 256 |
+
return output
|
| 257 |
+
|
| 258 |
+
def main(args):
|
| 259 |
+
# Call coco eval
|
| 260 |
+
evaluator = ANETcaptions(ground_truth_filenames=args.references,
|
| 261 |
+
prediction_filename=args.submission,
|
| 262 |
+
tious=args.tious,
|
| 263 |
+
max_proposals=args.max_proposals_per_video,
|
| 264 |
+
verbose=args.verbose)
|
| 265 |
+
evaluator.evaluate()
|
| 266 |
+
|
| 267 |
+
# Output the results
|
| 268 |
+
if args.verbose:
|
| 269 |
+
for i, tiou in enumerate(args.tious):
|
| 270 |
+
print '-' * 80
|
| 271 |
+
print "tIoU: " , tiou
|
| 272 |
+
print '-' * 80
|
| 273 |
+
for metric in evaluator.scores:
|
| 274 |
+
score = evaluator.scores[metric][i]
|
| 275 |
+
print '| %s: %2.4f'%(metric, 100*score)
|
| 276 |
+
|
| 277 |
+
# Print the averages
|
| 278 |
+
print '-' * 80
|
| 279 |
+
print "Average across all tIoUs"
|
| 280 |
+
print '-' * 80
|
| 281 |
+
output = {}
|
| 282 |
+
for metric in evaluator.scores:
|
| 283 |
+
score = evaluator.scores[metric]
|
| 284 |
+
print '| %s: %2.4f'%(metric, 100 * sum(score) / float(len(score)))
|
| 285 |
+
output[metric] = 100 * sum(score) / float(len(score))
|
| 286 |
+
json.dump(output,open(args.output,'w'))
|
| 287 |
+
print(output)
|
| 288 |
+
if __name__=='__main__':
|
| 289 |
+
parser = argparse.ArgumentParser(description='Evaluate the results stored in a submissions file.')
|
| 290 |
+
parser.add_argument('-s', '--submission', type=str, default='sample_submission.json',
|
| 291 |
+
help='sample submission file for ActivityNet Captions Challenge.')
|
| 292 |
+
parser.add_argument('-r', '--references', type=str, nargs='+', default=['data/val_1.json'],
|
| 293 |
+
help='reference files with ground truth captions to compare results against. delimited (,) str')
|
| 294 |
+
parser.add_argument('-o', '--output', type=str, default='result.json',
|
| 295 |
+
help='output file with final language metrics.')
|
| 296 |
+
parser.add_argument('--tious', type=float, nargs='+', default=[0.3],
|
| 297 |
+
help='Choose the tIoUs to average over.')
|
| 298 |
+
parser.add_argument('-ppv', '--max-proposals-per-video', type=int, default=1000,
|
| 299 |
+
help='maximum propoasls per video.')
|
| 300 |
+
parser.add_argument('-v', '--verbose', action='store_true',
|
| 301 |
+
help='Print intermediate steps.')
|
| 302 |
+
args = parser.parse_args()
|
| 303 |
+
|
| 304 |
+
main(args)
|
densevid_eval/get_caption_stat.py
ADDED
|
@@ -0,0 +1,70 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import json
|
| 2 |
+
import argparse
|
| 3 |
+
import nltk
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
def save_json_pretty(data, file_path):
|
| 7 |
+
"""save formatted json, use this one for some json config files"""
|
| 8 |
+
with open(file_path, "w") as f:
|
| 9 |
+
f.write(json.dumps(data, indent=4, sort_keys=True))
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
def load_json(file_path):
|
| 13 |
+
with open(file_path, "r") as f:
|
| 14 |
+
return json.load(f)
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
def flat_list_of_lists(l):
|
| 18 |
+
"""flatten a list of lists [[1,2], [3,4]] to [1,2,3,4]"""
|
| 19 |
+
return [item for sublist in l for item in sublist]
|
| 20 |
+
|
| 21 |
+
|
| 22 |
+
def get_args():
|
| 23 |
+
parser = argparse.ArgumentParser()
|
| 24 |
+
parser.add_argument("-s", "--submission", type=str, help="submission file")
|
| 25 |
+
parser.add_argument("-v", "--verbose", action="store_true", help="print info")
|
| 26 |
+
parser.add_argument("-r", "--reference", type=str, help="GT reference, used to collect the video ids")
|
| 27 |
+
parser.add_argument("-o", "--output", type=str, help="save path")
|
| 28 |
+
return parser.parse_args()
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
def get_sen_stat(list_of_str):
|
| 32 |
+
"""list_of_str, list(str), str could be a sentence a paragraph"""
|
| 33 |
+
tokenized = [nltk.tokenize.word_tokenize(sen.lower()) for sen in list_of_str]
|
| 34 |
+
num_sen = len(list_of_str)
|
| 35 |
+
lengths = [len(e) for e in tokenized]
|
| 36 |
+
avg_len = 1.0 * sum(lengths) / len(lengths)
|
| 37 |
+
full_vocab = set(flat_list_of_lists(tokenized))
|
| 38 |
+
return {"vocab_size": len(full_vocab), "avg_sen_len": avg_len, "num_sen": num_sen}
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
def eval_cap():
|
| 42 |
+
"""Get vocab size, average length, etc """
|
| 43 |
+
args = get_args()
|
| 44 |
+
|
| 45 |
+
# load data
|
| 46 |
+
sub_data = json.load(open(args.submission, "r"))
|
| 47 |
+
ref_data = json.load(open(args.reference, "r"))
|
| 48 |
+
sub_data = sub_data["results"] if "results" in sub_data else sub_data
|
| 49 |
+
ref_data = ref_data["results"] if "results" in ref_data else ref_data
|
| 50 |
+
sub_data = {k: v for k, v in sub_data.items() if k in ref_data}
|
| 51 |
+
|
| 52 |
+
submission_data_entries = flat_list_of_lists(sub_data.values())
|
| 53 |
+
submission_sentences = [e["sentence"] for e in submission_data_entries]
|
| 54 |
+
submission_stat = get_sen_stat(submission_sentences)
|
| 55 |
+
|
| 56 |
+
if args.verbose:
|
| 57 |
+
for k in submission_stat:
|
| 58 |
+
print("{} submission {}".format(k, submission_stat[k]))
|
| 59 |
+
final_res = {"submission": submission_stat}
|
| 60 |
+
|
| 61 |
+
if "gt_sentence" in submission_data_entries[0]:
|
| 62 |
+
gt_sentences = [e["gt_sentence"] for e in submission_data_entries]
|
| 63 |
+
gt_stat = get_sen_stat(gt_sentences) # only one reference is used here!!!
|
| 64 |
+
final_res["gt_stat"] = gt_stat
|
| 65 |
+
|
| 66 |
+
save_json_pretty(final_res, args.output)
|
| 67 |
+
return final_res
|
| 68 |
+
|
| 69 |
+
if __name__ == '__main__':
|
| 70 |
+
eval_cap()
|
densevid_eval/merge_dicts_by_prefix.py
ADDED
|
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import json
|
| 2 |
+
import glob
|
| 3 |
+
import argparse
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
def merge_dicts(list_dicts):
|
| 7 |
+
merged_dict = list_dicts[0].copy()
|
| 8 |
+
for i in range(1, len(list_dicts)):
|
| 9 |
+
merged_dict.update(list_dicts[i])
|
| 10 |
+
return merged_dict
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
def save_json_pretty(data, file_path):
|
| 14 |
+
"""save formatted json, use this one for some json config files"""
|
| 15 |
+
with open(file_path, "w") as f:
|
| 16 |
+
f.write(json.dumps(data, indent=4, sort_keys=True))
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
def load_json(file_path):
|
| 20 |
+
with open(file_path, "r") as f:
|
| 21 |
+
return json.load(f)
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
def merge_main():
|
| 25 |
+
parser = argparse.ArgumentParser()
|
| 26 |
+
parser.add_argument("-t", "--template", type=str,
|
| 27 |
+
help="path template for glob.glob, all files with the same template will be merged")
|
| 28 |
+
parser.add_argument("-o", "--output", type=str, help="path to the output")
|
| 29 |
+
args = parser.parse_args()
|
| 30 |
+
|
| 31 |
+
print("args.template {}".format(args.template))
|
| 32 |
+
prefix_filepaths = glob.glob(args.template) # list of filepaths
|
| 33 |
+
print("Loading {} files:\n{}".format(len(prefix_filepaths), "\n".join(prefix_filepaths)))
|
| 34 |
+
merged_dict = merge_dicts([load_json(e) for e in prefix_filepaths])
|
| 35 |
+
|
| 36 |
+
save_json_pretty(merged_dict, args.output)
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
if __name__ == '__main__':
|
| 40 |
+
merge_main()
|
densevid_eval/para-evaluate.py
ADDED
|
@@ -0,0 +1,211 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# --------------------------------------------------------
|
| 2 |
+
# Dense-Captioning Events in Videos Eval
|
| 3 |
+
# Copyright (c) 2017 Ranjay Krishna
|
| 4 |
+
# Licensed under The MIT License [see LICENSE for details]
|
| 5 |
+
# Written by Ranjay Krishna
|
| 6 |
+
# --------------------------------------------------------
|
| 7 |
+
|
| 8 |
+
import argparse
|
| 9 |
+
import json
|
| 10 |
+
import sys
|
| 11 |
+
import os
|
| 12 |
+
|
| 13 |
+
from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer
|
| 14 |
+
from pycocoevalcap.bleu.bleu import Bleu
|
| 15 |
+
from pycocoevalcap.meteor.meteor import Meteor
|
| 16 |
+
from pycocoevalcap.rouge.rouge import Rouge
|
| 17 |
+
from pycocoevalcap.cider.cider import Cider
|
| 18 |
+
import numpy as np
|
| 19 |
+
|
| 20 |
+
import re
|
| 21 |
+
def parse_sent(sent):
|
| 22 |
+
res = re.sub('[^a-zA-Z]', ' ', sent)
|
| 23 |
+
res = res.strip().lower().split()
|
| 24 |
+
return res
|
| 25 |
+
|
| 26 |
+
def parse_para(para):
|
| 27 |
+
para = para.replace('..', '.')
|
| 28 |
+
para = para.replace('.', ' endofsent')
|
| 29 |
+
return parse_sent(para)
|
| 30 |
+
|
| 31 |
+
class ANETcaptions(object):
|
| 32 |
+
|
| 33 |
+
def __init__(self, ground_truth_filenames=None, prediction_filename=None,
|
| 34 |
+
verbose=False, all_scorer=False):
|
| 35 |
+
# Check that the gt and submission files exist and load them
|
| 36 |
+
if not ground_truth_filenames:
|
| 37 |
+
raise IOError('Please input a valid ground truth file.')
|
| 38 |
+
if not prediction_filename:
|
| 39 |
+
raise IOError('Please input a valid prediction file.')
|
| 40 |
+
|
| 41 |
+
self.verbose = verbose
|
| 42 |
+
self.all_scorer = all_scorer
|
| 43 |
+
self.ground_truths = self.import_ground_truths(ground_truth_filenames)
|
| 44 |
+
self.prediction = self.import_prediction(prediction_filename)
|
| 45 |
+
self.tokenizer = PTBTokenizer()
|
| 46 |
+
|
| 47 |
+
# Set up scorers, if not verbose, we only use the one we're
|
| 48 |
+
# testing on: METEOR
|
| 49 |
+
if self.verbose or self.all_scorer:
|
| 50 |
+
self.scorers = [
|
| 51 |
+
(Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
|
| 52 |
+
(Meteor(),"METEOR"),
|
| 53 |
+
(Rouge(), "ROUGE_L"),
|
| 54 |
+
(Cider(), "CIDEr")
|
| 55 |
+
]
|
| 56 |
+
else:
|
| 57 |
+
self.scorers = [(Meteor(), "METEOR")]
|
| 58 |
+
|
| 59 |
+
def ensure_caption_key(self, data):
|
| 60 |
+
if len(data) == 0:
|
| 61 |
+
return data
|
| 62 |
+
if not list(data.keys())[0].startswith('v_'):
|
| 63 |
+
data = {'v_' + k: data[k] for k in data}
|
| 64 |
+
return data
|
| 65 |
+
|
| 66 |
+
def import_prediction(self, prediction_filename):
|
| 67 |
+
if self.verbose:
|
| 68 |
+
print("| Loading submission... {}".format(prediction_filename))
|
| 69 |
+
submission = json.load(open(prediction_filename))['results']
|
| 70 |
+
# change to paragraph format
|
| 71 |
+
para_submission = {}
|
| 72 |
+
for id in submission.keys():
|
| 73 |
+
para_submission[id] = ''
|
| 74 |
+
for info in submission[id]:
|
| 75 |
+
para_submission[id] += info['sentence'] + '. '
|
| 76 |
+
for para in para_submission.values():
|
| 77 |
+
assert(type(para) == str or type(para) == unicode)
|
| 78 |
+
# Ensure that every video is limited to the correct maximum number of proposals.
|
| 79 |
+
return self.ensure_caption_key(para_submission)
|
| 80 |
+
|
| 81 |
+
def import_ground_truths(self, filenames):
|
| 82 |
+
gts = []
|
| 83 |
+
self.n_ref_vids = set()
|
| 84 |
+
for filename in filenames:
|
| 85 |
+
gt = json.load(open(filename))
|
| 86 |
+
self.n_ref_vids.update(gt.keys())
|
| 87 |
+
gts.append(self.ensure_caption_key(gt))
|
| 88 |
+
if self.verbose:
|
| 89 |
+
print("| Loading GT. #files: %d, #videos: %d" % (len(filenames), len(self.n_ref_vids)))
|
| 90 |
+
return gts
|
| 91 |
+
|
| 92 |
+
def check_gt_exists(self, vid_id):
|
| 93 |
+
for gt in self.ground_truths:
|
| 94 |
+
if vid_id in gt:
|
| 95 |
+
return True
|
| 96 |
+
return False
|
| 97 |
+
|
| 98 |
+
def get_gt_vid_ids(self):
|
| 99 |
+
vid_ids = set([])
|
| 100 |
+
for gt in self.ground_truths:
|
| 101 |
+
vid_ids |= set(gt.keys())
|
| 102 |
+
return list(vid_ids)
|
| 103 |
+
|
| 104 |
+
def evaluate(self):
|
| 105 |
+
self.scores = self.evaluate_para()
|
| 106 |
+
|
| 107 |
+
def evaluate_para(self):
|
| 108 |
+
# This method averages the tIoU precision from METEOR, Bleu, etc. across videos
|
| 109 |
+
gt_vid_ids = self.get_gt_vid_ids()
|
| 110 |
+
vid2idx = {k: i for i, k in enumerate(gt_vid_ids)}
|
| 111 |
+
gts = {vid2idx[k]: [] for k in gt_vid_ids}
|
| 112 |
+
for i, gt in enumerate(self.ground_truths):
|
| 113 |
+
for k in gt_vid_ids:
|
| 114 |
+
if k not in gt:
|
| 115 |
+
continue
|
| 116 |
+
# gts[vid2idx[k]].append(' '.join(parse_sent(gt[k])))
|
| 117 |
+
for sent in gt[k]:
|
| 118 |
+
gts[vid2idx[k]].append(' '.join(parse_sent(sent)))
|
| 119 |
+
res = {vid2idx[k]: [' '.join(parse_sent(self.prediction[k]))] \
|
| 120 |
+
if k in self.prediction and len(self.prediction[k]) > 0 else [''] for k in gt_vid_ids}
|
| 121 |
+
para_res = {vid2idx[k]: [' '.join(parse_para(self.prediction[k]))] \
|
| 122 |
+
if k in self.prediction and len(self.prediction[k]) > 0 else [''] for k in gt_vid_ids}
|
| 123 |
+
|
| 124 |
+
# Each scorer will compute across all videos and take average score
|
| 125 |
+
output = {}
|
| 126 |
+
num = len(res)
|
| 127 |
+
hard_samples = {}
|
| 128 |
+
easy_samples = {}
|
| 129 |
+
for scorer, method in self.scorers:
|
| 130 |
+
if self.verbose:
|
| 131 |
+
print('computing %s score...'%(scorer.method()))
|
| 132 |
+
|
| 133 |
+
if method != 'Self_Bleu':
|
| 134 |
+
score, scores = scorer.compute_score(gts, res)
|
| 135 |
+
else:
|
| 136 |
+
score, scores = scorer.compute_score(gts, para_res)
|
| 137 |
+
scores = np.asarray(scores)
|
| 138 |
+
|
| 139 |
+
if type(method) == list:
|
| 140 |
+
for m in range(len(method)):
|
| 141 |
+
output[method[m]] = score[m]
|
| 142 |
+
if self.verbose:
|
| 143 |
+
print("%s: %0.3f" % (method[m], output[method[m]]))
|
| 144 |
+
for m, i in enumerate(scores.argmin(1)):
|
| 145 |
+
if i not in hard_samples:
|
| 146 |
+
hard_samples[i] = []
|
| 147 |
+
hard_samples[i].append(method[m])
|
| 148 |
+
for m, i in enumerate(scores.argmax(1)):
|
| 149 |
+
if i not in easy_samples:
|
| 150 |
+
easy_samples[i] = []
|
| 151 |
+
easy_samples[i].append(method[m])
|
| 152 |
+
else:
|
| 153 |
+
output[method] = score
|
| 154 |
+
if self.verbose:
|
| 155 |
+
print("%s: %0.3f" % (method, output[method]))
|
| 156 |
+
i = scores.argmin()
|
| 157 |
+
if i not in hard_samples:
|
| 158 |
+
hard_samples[i] = []
|
| 159 |
+
hard_samples[i].append(method)
|
| 160 |
+
i = scores.argmax()
|
| 161 |
+
if i not in easy_samples:
|
| 162 |
+
easy_samples[i] = []
|
| 163 |
+
easy_samples[i].append(method)
|
| 164 |
+
print('# scored video =', num)
|
| 165 |
+
|
| 166 |
+
self.hard_samples = {gt_vid_ids[i]: v for i, v in hard_samples.items()}
|
| 167 |
+
self.easy_samples = {gt_vid_ids[i]: v for i, v in easy_samples.items()}
|
| 168 |
+
return output
|
| 169 |
+
|
| 170 |
+
def main(args):
|
| 171 |
+
# Call coco eval
|
| 172 |
+
evaluator = ANETcaptions(ground_truth_filenames=args.references,
|
| 173 |
+
prediction_filename=args.submission,
|
| 174 |
+
verbose=args.verbose,
|
| 175 |
+
all_scorer=args.all_scorer)
|
| 176 |
+
evaluator.evaluate()
|
| 177 |
+
output = {}
|
| 178 |
+
# Output the results
|
| 179 |
+
for metric, score in evaluator.scores.items():
|
| 180 |
+
print('| %s: %2.4f'%(metric, 100*score))
|
| 181 |
+
output[metric] = score
|
| 182 |
+
json.dump(output, open(args.output, 'w'))
|
| 183 |
+
print(output)
|
| 184 |
+
|
| 185 |
+
import time
|
| 186 |
+
if __name__=='__main__':
|
| 187 |
+
parser = argparse.ArgumentParser(description='Evaluate the results stored in a submissions file.')
|
| 188 |
+
parser.add_argument('-s', '--submission', type=str, default='sample_submission.json',
|
| 189 |
+
help='sample submission file for ActivityNet Captions Challenge.')
|
| 190 |
+
parser.add_argument('-r', '--references', type=str, nargs='+', required=True,
|
| 191 |
+
help='reference files with ground truth captions to compare results against. delimited (,) str')
|
| 192 |
+
parser.add_argument('-o', '--output', type=str, default=None, help='output file with final language metrics.')
|
| 193 |
+
parser.add_argument('-v', '--verbose', action='store_true',
|
| 194 |
+
help='Print intermediate steps.')
|
| 195 |
+
parser.add_argument('--time', '--t', action = 'store_true',
|
| 196 |
+
help = 'Count running time.')
|
| 197 |
+
parser.add_argument('--all_scorer', '--a', action = 'store_true',
|
| 198 |
+
help = 'Use all scorer.')
|
| 199 |
+
args = parser.parse_args()
|
| 200 |
+
|
| 201 |
+
if args.output is None:
|
| 202 |
+
r_path = args.submission
|
| 203 |
+
r_path_splits = r_path.split(".")
|
| 204 |
+
r_path_splits = r_path_splits[:-1] + ["_metric", r_path_splits[-1]]
|
| 205 |
+
args.output = ".".join(r_path_splits)
|
| 206 |
+
|
| 207 |
+
if args.time:
|
| 208 |
+
start_time = time.time()
|
| 209 |
+
main(args)
|
| 210 |
+
if args.time:
|
| 211 |
+
print('time = %.2f' % (time.time() - start_time))
|
edit_best/model.cfg.json
ADDED
|
@@ -0,0 +1,114 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"config": "./config/mmvid_edit_config.yaml",
|
| 3 |
+
"resume": null,
|
| 4 |
+
"save_model": "./results/edit_2025_07_07_08_55_seed42_ema-1_mmvid/model",
|
| 5 |
+
"save_mode": "best",
|
| 6 |
+
"res_root_dir": "./results",
|
| 7 |
+
"debug": false,
|
| 8 |
+
"seed": 42,
|
| 9 |
+
"no_cuda": false,
|
| 10 |
+
"no_pin_memory": true,
|
| 11 |
+
"cuda": true,
|
| 12 |
+
"dalle_param": {
|
| 13 |
+
"vae": {
|
| 14 |
+
"which_vae": "vqgan1024",
|
| 15 |
+
"vae_path": "./pretrained_vqgan/edit_epoch=000050.ckpt",
|
| 16 |
+
"image_size": 224
|
| 17 |
+
},
|
| 18 |
+
"bert": {
|
| 19 |
+
"num_text_tokens": 0,
|
| 20 |
+
"text_seq_len": 24,
|
| 21 |
+
"dim": 768,
|
| 22 |
+
"loss_img_weight": 7,
|
| 23 |
+
"text_feature_dim": 0,
|
| 24 |
+
"fixed_language_model": null,
|
| 25 |
+
"text_emb_bottleneck": null,
|
| 26 |
+
"which_transformer": "openai_clip_visual",
|
| 27 |
+
"num_targets": 4,
|
| 28 |
+
"num_visuals": 0,
|
| 29 |
+
"beit": true,
|
| 30 |
+
"use_separate_visual_emb": false,
|
| 31 |
+
"insert_sep": false,
|
| 32 |
+
"openai_clip_path": "./ckpt/ViT-B-32.pt",
|
| 33 |
+
"vision_layers": 12
|
| 34 |
+
},
|
| 35 |
+
"skip_params": [
|
| 36 |
+
"to_logits_vid.1.bias",
|
| 37 |
+
"to_logits_vid.1.weight",
|
| 38 |
+
"to_logits_vid.0.bias",
|
| 39 |
+
"to_logits_vid.0.weight",
|
| 40 |
+
"to_logits_rel.1.bias",
|
| 41 |
+
"to_logits_rel.1.weight",
|
| 42 |
+
"to_logits_rel.0.bias",
|
| 43 |
+
"to_logits_rel.0.weight",
|
| 44 |
+
"to_logits.1.bias",
|
| 45 |
+
"to_logits.1.weight",
|
| 46 |
+
"to_logits.0.bias",
|
| 47 |
+
"to_logits.0.weight",
|
| 48 |
+
"to_logits_text.1.bias",
|
| 49 |
+
"to_logits_text.1.weight",
|
| 50 |
+
"to_logits_text.0.bias",
|
| 51 |
+
"to_logits_text.0.weight",
|
| 52 |
+
"image_emb.weight"
|
| 53 |
+
],
|
| 54 |
+
"freeze": false,
|
| 55 |
+
"use_lora": false,
|
| 56 |
+
"lora_config": {
|
| 57 |
+
"r": 8,
|
| 58 |
+
"lora_alpha": 16,
|
| 59 |
+
"lora_dropout": 0.1,
|
| 60 |
+
"bias": "none"
|
| 61 |
+
}
|
| 62 |
+
},
|
| 63 |
+
"decoder_param": {
|
| 64 |
+
"max_n_sen": 12,
|
| 65 |
+
"max_t_len": 24,
|
| 66 |
+
"max_v_len": 4,
|
| 67 |
+
"exp_id": "init",
|
| 68 |
+
"hidden_size": 512,
|
| 69 |
+
"intermediate_size": 2048,
|
| 70 |
+
"num_hidden_layers": 2,
|
| 71 |
+
"num_attention_heads": 8,
|
| 72 |
+
"mask_prob": 0.0,
|
| 73 |
+
"hidden_dropout_prob": 0.1,
|
| 74 |
+
"label_smoothing": 0.1,
|
| 75 |
+
"recurrent": false,
|
| 76 |
+
"untied": false,
|
| 77 |
+
"mtrans": true,
|
| 78 |
+
"use_beam": false,
|
| 79 |
+
"vocab_size": 524,
|
| 80 |
+
"mask_token_id": 7
|
| 81 |
+
},
|
| 82 |
+
"dset_name": "edit",
|
| 83 |
+
"data_dir": "/home/sunjiayang/VFI4IDC_test/IDC_scratch_model/densevid_eval/edit_data",
|
| 84 |
+
"video_feature_dir": "./data/edit/IER_processed",
|
| 85 |
+
"word2idx_path": "./cache/edit_word2idx2.json",
|
| 86 |
+
"glove_path": "./cache/yc2_vocab_glove.pt",
|
| 87 |
+
"eval_tool_dir": "/home/sunjiayang/VFI4IDC_test/IDC_scratch_model/densevid_eval",
|
| 88 |
+
"filtered": true,
|
| 89 |
+
"filter_file_path": "./filter_files/edit_similarity_scores.json",
|
| 90 |
+
"max_k": 5,
|
| 91 |
+
"num_frames": 9,
|
| 92 |
+
"recurrent": false,
|
| 93 |
+
"untied": false,
|
| 94 |
+
"mtrans": true,
|
| 95 |
+
"use_beam": false,
|
| 96 |
+
"image_size": 224,
|
| 97 |
+
"n_epoch": 40,
|
| 98 |
+
"batch_size": 16,
|
| 99 |
+
"val_batch_size": 32,
|
| 100 |
+
"max_es_cnt": 50,
|
| 101 |
+
"lr": 5e-05,
|
| 102 |
+
"lr_finetune": 5e-05,
|
| 103 |
+
"lr_warmup_proportion": 0.1,
|
| 104 |
+
"grad_clip": 1,
|
| 105 |
+
"weight_decay": 0.01,
|
| 106 |
+
"ema_decay": -1,
|
| 107 |
+
"num_workers": 4,
|
| 108 |
+
"temperature": 0.5,
|
| 109 |
+
"metric_reference": "CIDEr",
|
| 110 |
+
"pretrained_model": "./ckpt/img_size224_layer12_edit_wovisual_beit_softmax/dalle.pt",
|
| 111 |
+
"res_dir": "./results/edit_2025_07_07_08_55_seed42_ema-1_mmvid",
|
| 112 |
+
"log": "./results/edit_2025_07_07_08_55_seed42_ema-1_mmvid/model",
|
| 113 |
+
"pin_memory": false
|
| 114 |
+
}
|
edit_best/model.chkpt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3062766ac2cb2af3b75e426ec50f7768e6cb32453ae6c1ce1f2c071f90fc8a64
|
| 3 |
+
size 1970745522
|
edit_best/model_best_greedy_pred_val_all_metrics.json
ADDED
|
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"total_results": {
|
| 3 |
+
"Bleu_1": 0.4647026246276841,
|
| 4 |
+
"Bleu_2": 0.3235925596450469,
|
| 5 |
+
"Bleu_3": 0.19645683528000984,
|
| 6 |
+
"Bleu_4": 0.11655278004974493,
|
| 7 |
+
"METEOR": 0.1590985553848711,
|
| 8 |
+
"ROUGE_L": 0.43158686120935513,
|
| 9 |
+
"CIDEr": 0.40558600060220157
|
| 10 |
+
}
|
| 11 |
+
}
|
filter_files/clevr_similarity_scores.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:feabafa5ce69279a1db8763d3c01ddda14317da6aa20f96ccfe1beb897683946
|
| 3 |
+
size 189315048
|
filter_files/edit_similarity_scores.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
filter_files/spot_similarity_scores.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
filtered-spot-captions/filter_test.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
filtered-spot-captions/filter_train.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
spot_best/model.chkpt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:020adc4d56fcc9bbf8865558970a86160c2ead44d58ebd82846c53ce8037a7e0
|
| 3 |
+
size 1321505210
|
stage1_spot_best/log.txt
ADDED
|
@@ -0,0 +1,1007 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Name: train_image_text_4layer_spot_novisual_beit_softmax Time: 2025-07-05 17:06:52.060263
|
| 2 |
+
--------------------------------------------------
|
| 3 |
+
Name: train_image_text_4layer_spot_novisual_beit_softmax Time: 2025-07-05 17:09:48.177979
|
| 4 |
+
--------------------------------------------------
|
| 5 |
+
iter 0000000; MSM 7.0730; REL 1.4800; VID 2.8448; CONST 0.0000; lr 1e-06
|
| 6 |
+
Name: train_image_text_4layer_spot_novisual_beit_softmax Time: 2025-07-05 17:13:42.644630
|
| 7 |
+
--------------------------------------------------
|
| 8 |
+
iter 0000000; MSM 7.0757; REL 1.4800; VID 2.8448; CONST 0.0000; lr 1e-06
|
| 9 |
+
iter 0000200; MSM 4.7919; REL 1.3864; VID 1.3869; CONST 0.0000; lr 6.258524381211508e-05
|
| 10 |
+
iter 0000400; MSM 4.6439; REL 1.3864; VID 2.7761; CONST 0.0000; lr 7.064207302019181e-05
|
| 11 |
+
iter 0000600; MSM 2.7617; REL 1.3869; VID 1.2018; CONST 0.0000; lr 7.535501598163163e-05
|
| 12 |
+
iter 0000800; MSM 2.8117; REL 1.3871; VID 1.2757; CONST 0.0000; lr 7.869890222826853e-05
|
| 13 |
+
iter 0001000; MSM 0.4446; REL 1.3863; VID 1.4332; CONST 0.0000; lr 8.129262190605753e-05
|
| 14 |
+
iter 0001200; MSM 1.4418; REL 1.3863; VID 1.4829; CONST 0.0000; lr 8.341184518970836e-05
|
| 15 |
+
iter 0001400; MSM 1.3585; REL 1.3866; VID 1.9270; CONST 0.0000; lr 8.520362294558676e-05
|
| 16 |
+
iter 0001600; MSM 0.9381; REL 1.3865; VID 1.1143; CONST 0.0000; lr 8.675573143634527e-05
|
| 17 |
+
iter 0001800; MSM 0.3504; REL 1.3866; VID 0.8501; CONST 0.0000; lr 8.812478815114817e-05
|
| 18 |
+
iter 0002000; MSM 2.7011; REL 1.3820; VID 2.2949; CONST 0.0000; lr 8.934945111413428e-05
|
| 19 |
+
iter 0002200; MSM 1.0522; REL 1.4052; VID 3.1450; CONST 0.0000; lr 9.045729352049622e-05
|
| 20 |
+
iter 0002400; MSM 0.3224; REL 1.0380; VID 0.8708; CONST 0.0000; lr 9.146867439778508e-05
|
| 21 |
+
iter 0002600; MSM 1.4655; REL 2.2905; VID 1.3866; CONST 0.0000; lr 9.239905461596146e-05
|
| 22 |
+
iter 0002800; MSM 1.3275; REL 1.2480; VID 1.0370; CONST 0.0000; lr 9.326045215366348e-05
|
| 23 |
+
iter 0003000; MSM 0.9741; REL 1.0739; VID 2.3977; CONST 0.0000; lr 9.406239407557407e-05
|
| 24 |
+
iter 0003200; MSM 0.6732; REL 0.9766; VID 1.3614; CONST 0.0000; lr 9.4812560644422e-05
|
| 25 |
+
iter 0003400; MSM 0.6303; REL 0.8554; VID 0.5234; CONST 0.0000; lr 9.551723381842911e-05
|
| 26 |
+
iter 0003600; MSM 0.3827; REL 1.1000; VID 1.4225; CONST 0.0000; lr 9.61816173592249e-05
|
| 27 |
+
iter 0003800; MSM 0.2617; REL 1.4248; VID 0.4668; CONST 0.0000; lr 9.681007027622012e-05
|
| 28 |
+
iter 0004000; MSM 0.7107; REL 0.7835; VID 0.9838; CONST 0.0000; lr 9.7406280322211e-05
|
| 29 |
+
iter 0004200; MSM 0.2569; REL 0.3413; VID 1.0890; CONST 0.0000; lr 9.797339511510329e-05
|
| 30 |
+
iter 0004400; MSM 0.3244; REL 1.3934; VID 1.3817; CONST 0.0000; lr 9.851412272857295e-05
|
| 31 |
+
iter 0004600; MSM 0.6503; REL 0.1564; VID 3.7332; CONST 0.0000; lr 9.903080990421993e-05
|
| 32 |
+
iter 0004800; MSM 1.8135; REL 1.8582; VID 2.8121; CONST 0.0000; lr 9.952550360586183e-05
|
| 33 |
+
iter 0005000; MSM 1.1037; REL 1.6146; VID 0.9576; CONST 0.0000; lr 0.0001
|
| 34 |
+
iter 0005200; MSM 0.3554; REL 1.7847; VID 0.7454; CONST 0.0000; lr 0.0001
|
| 35 |
+
iter 0005400; MSM 0.3313; REL 0.3185; VID 0.8664; CONST 0.0000; lr 0.0001
|
| 36 |
+
iter 0005600; MSM 0.2058; REL 0.5306; VID 0.6826; CONST 0.0000; lr 0.0001
|
| 37 |
+
iter 0005800; MSM 1.0962; REL 1.6637; VID 1.7308; CONST 0.0000; lr 0.0001
|
| 38 |
+
iter 0006000; MSM 0.2766; REL 0.6253; VID 0.9339; CONST 0.0000; lr 0.0001
|
| 39 |
+
iter 0006200; MSM 0.6157; REL 2.1663; VID 0.8669; CONST 0.0000; lr 0.0001
|
| 40 |
+
iter 0006400; MSM 1.2666; REL 1.0583; VID 1.1395; CONST 0.0000; lr 0.0001
|
| 41 |
+
iter 0006600; MSM 0.6589; REL 1.0784; VID 0.3453; CONST 0.0000; lr 0.0001
|
| 42 |
+
iter 0006800; MSM 1.0229; REL 3.6412; VID 3.8820; CONST 0.0000; lr 0.0001
|
| 43 |
+
iter 0007000; MSM 0.2894; REL 1.0493; VID 0.2894; CONST 0.0000; lr 0.0001
|
| 44 |
+
iter 0007200; MSM 0.4485; REL 1.5747; VID 1.7021; CONST 0.0000; lr 0.0001
|
| 45 |
+
iter 0007400; MSM 0.5064; REL 0.3376; VID 0.6103; CONST 0.0000; lr 0.0001
|
| 46 |
+
iter 0007600; MSM 1.3031; REL 0.4443; VID 1.7458; CONST 0.0000; lr 0.0001
|
| 47 |
+
iter 0007800; MSM 0.5332; REL 0.2147; VID 0.9363; CONST 0.0000; lr 0.0001
|
| 48 |
+
iter 0008000; MSM 0.2202; REL 0.4927; VID 0.9525; CONST 0.0000; lr 0.0001
|
| 49 |
+
iter 0008200; MSM 2.4804; REL 1.6103; VID 2.6614; CONST 0.0000; lr 0.0001
|
| 50 |
+
iter 0008400; MSM 0.3719; REL 0.9540; VID 1.2691; CONST 0.0000; lr 0.0001
|
| 51 |
+
iter 0008600; MSM 0.6137; REL 1.0957; VID 0.5131; CONST 0.0000; lr 0.0001
|
| 52 |
+
iter 0008800; MSM 0.4576; REL 1.0892; VID 0.3289; CONST 0.0000; lr 0.0001
|
| 53 |
+
iter 0009000; MSM 0.8317; REL 1.1622; VID 0.9327; CONST 0.0000; lr 0.0001
|
| 54 |
+
iter 0009200; MSM 2.4190; REL 1.7703; VID 2.8440; CONST 0.0000; lr 0.0001
|
| 55 |
+
iter 0009400; MSM 0.8552; REL 0.5874; VID 1.7203; CONST 0.0000; lr 0.0001
|
| 56 |
+
iter 0009600; MSM 0.2766; REL 1.6716; VID 0.9973; CONST 0.0000; lr 0.0001
|
| 57 |
+
iter 0009800; MSM 0.5350; REL 0.5707; VID 0.4255; CONST 0.0000; lr 0.0001
|
| 58 |
+
iter 0010000; MSM 0.1899; REL 0.2301; VID 1.4898; CONST 0.0000; lr 0.0001
|
| 59 |
+
iter 0010200; MSM 0.2483; REL 0.8169; VID 1.5314; CONST 0.0000; lr 0.0001
|
| 60 |
+
iter 0010400; MSM 0.8956; REL 0.0000; VID 2.7740; CONST 0.0000; lr 0.0001
|
| 61 |
+
iter 0010600; MSM 0.3726; REL 0.8434; VID 0.4145; CONST 0.0000; lr 0.0001
|
| 62 |
+
iter 0010800; MSM 0.4895; REL 0.8228; VID 0.4207; CONST 0.0000; lr 0.0001
|
| 63 |
+
iter 0011000; MSM 0.4797; REL 1.0054; VID 0.4407; CONST 0.0000; lr 0.0001
|
| 64 |
+
iter 0011200; MSM 0.2132; REL 0.0983; VID 0.9128; CONST 0.0000; lr 0.0001
|
| 65 |
+
iter 0011400; MSM 0.2876; REL 1.0793; VID 0.9140; CONST 0.0000; lr 0.0001
|
| 66 |
+
iter 0011600; MSM 2.9885; REL 0.6229; VID 2.2220; CONST 0.0000; lr 0.0001
|
| 67 |
+
iter 0011800; MSM 0.2088; REL 0.2241; VID 0.3234; CONST 0.0000; lr 0.0001
|
| 68 |
+
iter 0012000; MSM 0.3776; REL 0.5590; VID 0.7056; CONST 0.0000; lr 0.0001
|
| 69 |
+
iter 0012200; MSM 0.3856; REL 1.9053; VID 0.6509; CONST 0.0000; lr 0.0001
|
| 70 |
+
iter 0012400; MSM 0.1166; REL 2.3510; VID 1.4147; CONST 0.0000; lr 0.0001
|
| 71 |
+
iter 0012600; MSM 0.3117; REL 0.2127; VID 0.8640; CONST 0.0000; lr 0.0001
|
| 72 |
+
iter 0012800; MSM 2.3298; REL 3.9129; VID 1.8764; CONST 0.0000; lr 0.0001
|
| 73 |
+
iter 0013000; MSM 0.1975; REL 0.9485; VID 1.0797; CONST 0.0000; lr 0.0001
|
| 74 |
+
iter 0013200; MSM 0.2000; REL 1.8044; VID 3.0530; CONST 0.0000; lr 0.0001
|
| 75 |
+
iter 0013400; MSM 0.2540; REL 0.7120; VID 0.5403; CONST 0.0000; lr 0.0001
|
| 76 |
+
iter 0013600; MSM 0.2135; REL 0.1636; VID 1.6278; CONST 0.0000; lr 0.0001
|
| 77 |
+
iter 0013800; MSM 0.3446; REL 1.2313; VID 0.5073; CONST 0.0000; lr 0.0001
|
| 78 |
+
iter 0014000; MSM 0.3298; REL 1.3379; VID 0.9271; CONST 0.0000; lr 0.0001
|
| 79 |
+
iter 0014200; MSM 0.2288; REL 1.3080; VID 0.8094; CONST 0.0000; lr 0.0001
|
| 80 |
+
iter 0014400; MSM 0.4463; REL 0.3531; VID 0.9008; CONST 0.0000; lr 0.0001
|
| 81 |
+
iter 0014600; MSM 0.3779; REL 0.3339; VID 1.3171; CONST 0.0000; lr 0.0001
|
| 82 |
+
iter 0014800; MSM 1.0337; REL 0.3407; VID 1.6345; CONST 0.0000; lr 0.0001
|
| 83 |
+
iter 0015000; MSM 0.3770; REL 0.4420; VID 0.3488; CONST 0.0000; lr 0.0001
|
| 84 |
+
iter 0015200; MSM 0.2443; REL 0.7286; VID 0.9572; CONST 0.0000; lr 0.0001
|
| 85 |
+
iter 0015400; MSM 0.2940; REL 0.8966; VID 0.3009; CONST 0.0000; lr 0.0001
|
| 86 |
+
iter 0015600; MSM 0.4583; REL 0.1569; VID 1.5456; CONST 0.0000; lr 0.0001
|
| 87 |
+
iter 0015800; MSM 0.4134; REL 0.6012; VID 1.4440; CONST 0.0000; lr 0.0001
|
| 88 |
+
iter 0016000; MSM 0.4663; REL 1.5631; VID 1.4455; CONST 0.0000; lr 0.0001
|
| 89 |
+
iter 0016200; MSM 0.2149; REL 1.7674; VID 0.4534; CONST 0.0000; lr 0.0001
|
| 90 |
+
iter 0016400; MSM 0.1929; REL 1.7164; VID 0.7870; CONST 0.0000; lr 0.0001
|
| 91 |
+
iter 0016600; MSM 0.1500; REL 1.9928; VID 1.5772; CONST 0.0000; lr 0.0001
|
| 92 |
+
iter 0016800; MSM 0.2135; REL 0.2991; VID 0.3275; CONST 0.0000; lr 0.0001
|
| 93 |
+
iter 0017000; MSM 0.3169; REL 1.0706; VID 0.6995; CONST 0.0000; lr 0.0001
|
| 94 |
+
iter 0017200; MSM 0.2815; REL 0.2055; VID 1.0044; CONST 0.0000; lr 0.0001
|
| 95 |
+
iter 0017400; MSM 0.3545; REL 0.3181; VID 0.2980; CONST 0.0000; lr 0.0001
|
| 96 |
+
iter 0017600; MSM 0.2859; REL 0.0989; VID 0.9992; CONST 0.0000; lr 0.0001
|
| 97 |
+
iter 0017800; MSM 0.9475; REL 0.1905; VID 1.6072; CONST 0.0000; lr 0.0001
|
| 98 |
+
iter 0018000; MSM 0.3241; REL 0.6478; VID 0.2976; CONST 0.0000; lr 0.0001
|
| 99 |
+
iter 0018200; MSM 0.6847; REL 0.4397; VID 0.2075; CONST 0.0000; lr 0.0001
|
| 100 |
+
iter 0018400; MSM 2.4486; REL 0.3789; VID 3.0943; CONST 0.0000; lr 0.0001
|
| 101 |
+
iter 0018600; MSM 0.2235; REL 0.6783; VID 1.0172; CONST 0.0000; lr 0.0001
|
| 102 |
+
iter 0018800; MSM 1.9115; REL 0.0000; VID 2.8250; CONST 0.0000; lr 0.0001
|
| 103 |
+
iter 0019000; MSM 0.2144; REL 1.9256; VID 0.3589; CONST 0.0000; lr 0.0001
|
| 104 |
+
iter 0019200; MSM 0.2384; REL 1.0311; VID 0.9828; CONST 0.0000; lr 0.0001
|
| 105 |
+
iter 0019400; MSM 0.4263; REL 0.6469; VID 0.5428; CONST 0.0000; lr 0.0001
|
| 106 |
+
iter 0019600; MSM 1.1722; REL 0.5901; VID 2.8788; CONST 0.0000; lr 0.0001
|
| 107 |
+
iter 0019800; MSM 0.2699; REL 0.2980; VID 0.4833; CONST 0.0000; lr 0.0001
|
| 108 |
+
iter 0020000; MSM 0.2520; REL 0.6872; VID 0.3875; CONST 0.0000; lr 0.0001
|
| 109 |
+
iter 0020200; MSM 0.3237; REL 0.0843; VID 0.5529; CONST 0.0000; lr 0.0001
|
| 110 |
+
iter 0020400; MSM 0.5807; REL 0.3822; VID 1.0104; CONST 0.0000; lr 0.0001
|
| 111 |
+
iter 0020600; MSM 0.2252; REL 2.0080; VID 0.8988; CONST 0.0000; lr 0.0001
|
| 112 |
+
iter 0020800; MSM 0.2260; REL 2.1123; VID 0.8389; CONST 0.0000; lr 0.0001
|
| 113 |
+
iter 0021000; MSM 0.2632; REL 0.2790; VID 3.2646; CONST 0.0000; lr 0.0001
|
| 114 |
+
iter 0021200; MSM 0.2097; REL 0.9169; VID 0.4345; CONST 0.0000; lr 0.0001
|
| 115 |
+
iter 0021400; MSM 0.4724; REL 0.5627; VID 1.0635; CONST 0.0000; lr 0.0001
|
| 116 |
+
iter 0021600; MSM 0.2035; REL 0.5681; VID 1.1287; CONST 0.0000; lr 0.0001
|
| 117 |
+
iter 0021800; MSM 0.2310; REL 2.3132; VID 0.3801; CONST 0.0000; lr 0.0001
|
| 118 |
+
iter 0022000; MSM 0.7849; REL 2.0101; VID 0.5182; CONST 0.0000; lr 0.0001
|
| 119 |
+
iter 0022200; MSM 0.2900; REL 0.6502; VID 1.1098; CONST 0.0000; lr 0.0001
|
| 120 |
+
iter 0022400; MSM 0.1469; REL 0.7023; VID 0.9765; CONST 0.0000; lr 0.0001
|
| 121 |
+
iter 0022600; MSM 2.0483; REL 0.8741; VID 2.9738; CONST 0.0000; lr 0.0001
|
| 122 |
+
iter 0022800; MSM 0.6756; REL 0.5077; VID 1.0173; CONST 0.0000; lr 0.0001
|
| 123 |
+
iter 0023000; MSM 0.3537; REL 0.8035; VID 1.0680; CONST 0.0000; lr 0.0001
|
| 124 |
+
iter 0023200; MSM 0.4817; REL 1.6575; VID 1.2945; CONST 0.0000; lr 0.0001
|
| 125 |
+
iter 0023400; MSM 0.1260; REL 0.2181; VID 0.3230; CONST 0.0000; lr 0.0001
|
| 126 |
+
iter 0023600; MSM 0.5853; REL 0.5854; VID 0.9243; CONST 0.0000; lr 0.0001
|
| 127 |
+
iter 0023800; MSM 0.2148; REL 1.1743; VID 0.2236; CONST 0.0000; lr 0.0001
|
| 128 |
+
iter 0024000; MSM 0.1482; REL 1.6082; VID 1.0063; CONST 0.0000; lr 0.0001
|
| 129 |
+
iter 0024200; MSM 0.4739; REL 0.7397; VID 1.3144; CONST 0.0000; lr 0.0001
|
| 130 |
+
iter 0024400; MSM 0.6448; REL 1.3809; VID 0.9448; CONST 0.0000; lr 0.0001
|
| 131 |
+
iter 0024600; MSM 1.1193; REL 0.0000; VID 2.7761; CONST 0.0000; lr 0.0001
|
| 132 |
+
iter 0024800; MSM 0.6300; REL 0.5439; VID 0.9948; CONST 0.0000; lr 0.0001
|
| 133 |
+
iter 0025000; MSM 0.2843; REL 0.2302; VID 0.7646; CONST 0.0000; lr 0.0001
|
| 134 |
+
iter 0025200; MSM 0.3149; REL 0.8952; VID 1.0985; CONST 0.0000; lr 0.0001
|
| 135 |
+
iter 0025400; MSM 0.3048; REL 1.6427; VID 1.5607; CONST 0.0000; lr 0.0001
|
| 136 |
+
iter 0025600; MSM 0.5708; REL 0.2974; VID 1.6238; CONST 0.0000; lr 0.0001
|
| 137 |
+
iter 0025800; MSM 0.2794; REL 0.1766; VID 0.2592; CONST 0.0000; lr 0.0001
|
| 138 |
+
iter 0026000; MSM 0.2837; REL 0.9592; VID 0.9325; CONST 0.0000; lr 0.0001
|
| 139 |
+
iter 0026200; MSM 0.3282; REL 0.6374; VID 0.7388; CONST 0.0000; lr 0.0001
|
| 140 |
+
iter 0026400; MSM 2.0561; REL 2.4098; VID 1.6629; CONST 0.0000; lr 0.0001
|
| 141 |
+
iter 0026600; MSM 0.5074; REL 1.6541; VID 0.2501; CONST 0.0000; lr 0.0001
|
| 142 |
+
iter 0026800; MSM 1.8140; REL 1.1230; VID 2.8734; CONST 0.0000; lr 0.0001
|
| 143 |
+
iter 0027000; MSM 0.2153; REL 0.4860; VID 0.6740; CONST 0.0000; lr 0.0001
|
| 144 |
+
iter 0027200; MSM 0.3578; REL 0.9660; VID 1.2618; CONST 0.0000; lr 0.0001
|
| 145 |
+
iter 0027400; MSM 0.4565; REL 0.0700; VID 0.5977; CONST 0.0000; lr 0.0001
|
| 146 |
+
iter 0027600; MSM 2.0818; REL 0.8742; VID 1.6976; CONST 0.0000; lr 0.0001
|
| 147 |
+
iter 0027800; MSM 0.2175; REL 0.0672; VID 0.3340; CONST 0.0000; lr 0.0001
|
| 148 |
+
iter 0028000; MSM 0.2710; REL 0.2936; VID 0.9741; CONST 0.0000; lr 0.0001
|
| 149 |
+
iter 0028200; MSM 0.1594; REL 0.1652; VID 0.9551; CONST 0.0000; lr 0.0001
|
| 150 |
+
iter 0028400; MSM 0.8783; REL 1.0196; VID 1.8466; CONST 0.0000; lr 0.0001
|
| 151 |
+
iter 0028600; MSM 0.3330; REL 0.3634; VID 0.5030; CONST 0.0000; lr 0.0001
|
| 152 |
+
iter 0028800; MSM 0.3025; REL 0.2050; VID 0.2730; CONST 0.0000; lr 0.0001
|
| 153 |
+
iter 0029000; MSM 0.2212; REL 0.2095; VID 1.0778; CONST 0.0000; lr 0.0001
|
| 154 |
+
iter 0029200; MSM 0.6297; REL 0.3338; VID 1.4930; CONST 0.0000; lr 0.0001
|
| 155 |
+
iter 0029400; MSM 1.0917; REL 1.1364; VID 1.6394; CONST 0.0000; lr 0.0001
|
| 156 |
+
iter 0029600; MSM 0.3731; REL 0.2116; VID 0.5634; CONST 0.0000; lr 0.0001
|
| 157 |
+
iter 0029800; MSM 0.3331; REL 2.1349; VID 0.6663; CONST 0.0000; lr 0.0001
|
| 158 |
+
iter 0030000; MSM 0.6178; REL 1.4554; VID 0.8163; CONST 0.0000; lr 0.0001
|
| 159 |
+
iter 0030200; MSM 0.3069; REL 0.2612; VID 0.9159; CONST 0.0000; lr 0.0001
|
| 160 |
+
iter 0030400; MSM 0.2265; REL 0.2654; VID 0.7697; CONST 0.0000; lr 0.0001
|
| 161 |
+
iter 0030600; MSM 0.2109; REL 0.6389; VID 0.8434; CONST 0.0000; lr 0.0001
|
| 162 |
+
iter 0030800; MSM 0.2306; REL 0.1347; VID 1.0749; CONST 0.0000; lr 0.0001
|
| 163 |
+
iter 0031000; MSM 0.2399; REL 0.1131; VID 1.7893; CONST 0.0000; lr 0.0001
|
| 164 |
+
iter 0031200; MSM 0.5107; REL 0.1130; VID 0.7939; CONST 0.0000; lr 0.0001
|
| 165 |
+
iter 0031400; MSM 0.3441; REL 0.8955; VID 0.3252; CONST 0.0000; lr 0.0001
|
| 166 |
+
iter 0031600; MSM 1.1189; REL 1.2925; VID 1.6127; CONST 0.0000; lr 0.0001
|
| 167 |
+
iter 0031800; MSM 0.2982; REL 0.7791; VID 0.9954; CONST 0.0000; lr 0.0001
|
| 168 |
+
iter 0032000; MSM 2.1692; REL 0.3525; VID 2.1896; CONST 0.0000; lr 0.0001
|
| 169 |
+
iter 0032200; MSM 0.1245; REL 0.8456; VID 1.0500; CONST 0.0000; lr 0.0001
|
| 170 |
+
iter 0032400; MSM 0.0998; REL 0.2649; VID 0.2901; CONST 0.0000; lr 0.0001
|
| 171 |
+
iter 0032600; MSM 0.2850; REL 0.2154; VID 1.0254; CONST 0.0000; lr 0.0001
|
| 172 |
+
iter 0032800; MSM 0.2877; REL 1.9792; VID 1.1796; CONST 0.0000; lr 0.0001
|
| 173 |
+
iter 0033000; MSM 0.8585; REL 0.8982; VID 1.5561; CONST 0.0000; lr 0.0001
|
| 174 |
+
iter 0033200; MSM 0.4718; REL 0.1400; VID 0.2913; CONST 0.0000; lr 0.0001
|
| 175 |
+
iter 0033400; MSM 0.3997; REL 0.5137; VID 0.9151; CONST 0.0000; lr 0.0001
|
| 176 |
+
iter 0033600; MSM 0.1043; REL 0.4395; VID 0.4546; CONST 0.0000; lr 0.0001
|
| 177 |
+
iter 0033800; MSM 0.3710; REL 1.8411; VID 1.4668; CONST 0.0000; lr 0.0001
|
| 178 |
+
iter 0034000; MSM 0.4416; REL 1.0474; VID 1.5934; CONST 0.0000; lr 0.0001
|
| 179 |
+
iter 0034200; MSM 0.4298; REL 0.2680; VID 0.3962; CONST 0.0000; lr 0.0001
|
| 180 |
+
iter 0034400; MSM 0.1245; REL 1.3203; VID 0.1845; CONST 0.0000; lr 0.0001
|
| 181 |
+
iter 0034600; MSM 0.1764; REL 0.1697; VID 0.8702; CONST 0.0000; lr 0.0001
|
| 182 |
+
iter 0034800; MSM 0.3926; REL 0.5636; VID 1.2856; CONST 0.0000; lr 0.0001
|
| 183 |
+
iter 0035000; MSM 0.2018; REL 0.1563; VID 0.5392; CONST 0.0000; lr 0.0001
|
| 184 |
+
iter 0035200; MSM 0.2156; REL 0.8723; VID 1.4545; CONST 0.0000; lr 0.0001
|
| 185 |
+
iter 0035400; MSM 0.2920; REL 0.1380; VID 1.6641; CONST 0.0000; lr 0.0001
|
| 186 |
+
iter 0035600; MSM 2.0885; REL 0.0000; VID 2.7761; CONST 0.0000; lr 0.0001
|
| 187 |
+
iter 0035800; MSM 0.2602; REL 0.0708; VID 1.1498; CONST 0.0000; lr 0.0001
|
| 188 |
+
iter 0036000; MSM 0.1616; REL 0.1360; VID 0.2327; CONST 0.0000; lr 0.0001
|
| 189 |
+
iter 0036200; MSM 1.0899; REL 0.2818; VID 1.8223; CONST 0.0000; lr 0.0001
|
| 190 |
+
iter 0036400; MSM 0.1014; REL 2.0884; VID 0.9522; CONST 0.0000; lr 0.0001
|
| 191 |
+
iter 0036600; MSM 0.3058; REL 0.0886; VID 1.1741; CONST 0.0000; lr 0.0001
|
| 192 |
+
iter 0036800; MSM 0.3641; REL 0.0965; VID 1.8257; CONST 0.0000; lr 0.0001
|
| 193 |
+
iter 0037000; MSM 0.1786; REL 0.0468; VID 0.6969; CONST 0.0000; lr 0.0001
|
| 194 |
+
iter 0037200; MSM 0.1166; REL 0.0847; VID 0.2963; CONST 0.0000; lr 0.0001
|
| 195 |
+
iter 0037400; MSM 0.0970; REL 1.0098; VID 1.2836; CONST 0.0000; lr 0.0001
|
| 196 |
+
iter 0037600; MSM 0.1757; REL 1.2895; VID 0.7466; CONST 0.0000; lr 0.0001
|
| 197 |
+
iter 0037800; MSM 1.3240; REL 0.1697; VID 1.8783; CONST 0.0000; lr 0.0001
|
| 198 |
+
iter 0038000; MSM 0.2008; REL 0.0711; VID 0.4620; CONST 0.0000; lr 0.0001
|
| 199 |
+
iter 0038200; MSM 0.2117; REL 0.0383; VID 0.9631; CONST 0.0000; lr 0.0001
|
| 200 |
+
iter 0038400; MSM 0.6702; REL 1.1070; VID 1.0502; CONST 0.0000; lr 0.0001
|
| 201 |
+
iter 0038600; MSM 0.1663; REL 0.2700; VID 0.3322; CONST 0.0000; lr 0.0001
|
| 202 |
+
iter 0038800; MSM 0.3163; REL 0.0537; VID 1.9460; CONST 0.0000; lr 0.0001
|
| 203 |
+
iter 0039000; MSM 0.3771; REL 0.0825; VID 0.2488; CONST 0.0000; lr 0.0001
|
| 204 |
+
iter 0039200; MSM 0.1047; REL 1.1368; VID 0.3072; CONST 0.0000; lr 0.0001
|
| 205 |
+
iter 0039400; MSM 0.2024; REL 0.1401; VID 0.2925; CONST 0.0000; lr 0.0001
|
| 206 |
+
iter 0039600; MSM 0.4978; REL 0.2796; VID 0.1993; CONST 0.0000; lr 0.0001
|
| 207 |
+
iter 0039800; MSM 0.4164; REL 0.2482; VID 0.9372; CONST 0.0000; lr 0.0001
|
| 208 |
+
iter 0040000; MSM 0.2964; REL 0.1792; VID 0.3884; CONST 0.0000; lr 0.0001
|
| 209 |
+
iter 0040200; MSM 0.3335; REL 1.1086; VID 0.3755; CONST 0.0000; lr 0.0001
|
| 210 |
+
iter 0040400; MSM 0.1613; REL 1.7044; VID 0.9992; CONST 0.0000; lr 0.0001
|
| 211 |
+
iter 0040600; MSM 0.2170; REL 0.1091; VID 0.2677; CONST 0.0000; lr 0.0001
|
| 212 |
+
iter 0040800; MSM 0.5294; REL 0.0804; VID 1.7153; CONST 0.0000; lr 0.0001
|
| 213 |
+
iter 0041000; MSM 0.1491; REL 0.1947; VID 0.1855; CONST 0.0000; lr 0.0001
|
| 214 |
+
iter 0041200; MSM 0.3730; REL 0.0837; VID 0.6405; CONST 0.0000; lr 0.0001
|
| 215 |
+
iter 0041400; MSM 0.2375; REL 0.7878; VID 0.8974; CONST 0.0000; lr 0.0001
|
| 216 |
+
iter 0041600; MSM 0.1338; REL 0.8492; VID 1.1418; CONST 0.0000; lr 0.0001
|
| 217 |
+
iter 0041800; MSM 0.5818; REL 0.0709; VID 0.9931; CONST 0.0000; lr 0.0001
|
| 218 |
+
iter 0042000; MSM 0.3204; REL 0.1150; VID 0.2943; CONST 0.0000; lr 0.0001
|
| 219 |
+
iter 0042200; MSM 0.3045; REL 0.4012; VID 0.3916; CONST 0.0000; lr 0.0001
|
| 220 |
+
iter 0042400; MSM 0.2084; REL 0.2145; VID 1.5859; CONST 0.0000; lr 0.0001
|
| 221 |
+
iter 0042600; MSM 0.4936; REL 0.3377; VID 1.1153; CONST 0.0000; lr 0.0001
|
| 222 |
+
iter 0042800; MSM 0.3661; REL 0.3209; VID 1.4593; CONST 0.0000; lr 0.0001
|
| 223 |
+
iter 0043000; MSM 0.1173; REL 0.8237; VID 0.3957; CONST 0.0000; lr 0.0001
|
| 224 |
+
iter 0043200; MSM 0.1545; REL 2.2155; VID 0.8706; CONST 0.0000; lr 0.0001
|
| 225 |
+
iter 0043400; MSM 0.6946; REL 0.2137; VID 1.6675; CONST 0.0000; lr 0.0001
|
| 226 |
+
iter 0043600; MSM 2.3268; REL 0.0310; VID 1.6265; CONST 0.0000; lr 0.0001
|
| 227 |
+
iter 0043800; MSM 0.2751; REL 0.1512; VID 0.5811; CONST 0.0000; lr 0.0001
|
| 228 |
+
iter 0044000; MSM 0.3523; REL 0.1600; VID 1.9177; CONST 0.0000; lr 0.0001
|
| 229 |
+
iter 0044200; MSM 0.2154; REL 0.1211; VID 1.7617; CONST 0.0000; lr 0.0001
|
| 230 |
+
iter 0044400; MSM 0.4151; REL 0.3375; VID 0.9726; CONST 0.0000; lr 0.0001
|
| 231 |
+
iter 0044600; MSM 0.1969; REL 0.0394; VID 0.6435; CONST 0.0000; lr 0.0001
|
| 232 |
+
iter 0044800; MSM 0.5615; REL 0.0567; VID 1.6656; CONST 0.0000; lr 0.0001
|
| 233 |
+
iter 0045000; MSM 0.2290; REL 0.1270; VID 1.5453; CONST 0.0000; lr 0.0001
|
| 234 |
+
iter 0045200; MSM 0.3737; REL 0.2420; VID 1.5312; CONST 0.0000; lr 0.0001
|
| 235 |
+
iter 0045400; MSM 0.5837; REL 0.1842; VID 1.1838; CONST 0.0000; lr 0.0001
|
| 236 |
+
iter 0045600; MSM 0.1932; REL 0.1102; VID 0.2399; CONST 0.0000; lr 0.0001
|
| 237 |
+
iter 0045800; MSM 0.1535; REL 0.2558; VID 0.1229; CONST 0.0000; lr 0.0001
|
| 238 |
+
iter 0046000; MSM 0.3849; REL 0.1034; VID 0.2137; CONST 0.0000; lr 0.0001
|
| 239 |
+
iter 0046200; MSM 1.6337; REL 0.1054; VID 1.5380; CONST 0.0000; lr 0.0001
|
| 240 |
+
iter 0046400; MSM 1.8598; REL 3.9875; VID 1.4863; CONST 0.0000; lr 0.0001
|
| 241 |
+
iter 0046600; MSM 0.1342; REL 2.3059; VID 0.2146; CONST 0.0000; lr 0.0001
|
| 242 |
+
iter 0046800; MSM 0.1964; REL 0.6942; VID 0.3521; CONST 0.0000; lr 0.0001
|
| 243 |
+
iter 0047000; MSM 0.4918; REL 0.4390; VID 1.0087; CONST 0.0000; lr 0.0001
|
| 244 |
+
iter 0047200; MSM 0.2874; REL 1.4248; VID 0.1797; CONST 0.0000; lr 0.0001
|
| 245 |
+
iter 0047400; MSM 0.2045; REL 2.4232; VID 1.2766; CONST 0.0000; lr 0.0001
|
| 246 |
+
iter 0047600; MSM 0.2747; REL 0.1146; VID 0.3473; CONST 0.0000; lr 0.0001
|
| 247 |
+
iter 0047800; MSM 0.1346; REL 0.1010; VID 0.1437; CONST 0.0000; lr 0.0001
|
| 248 |
+
iter 0048000; MSM 0.5086; REL 0.0884; VID 1.3990; CONST 0.0000; lr 0.0001
|
| 249 |
+
iter 0048200; MSM 0.2988; REL 0.0930; VID 1.3904; CONST 0.0000; lr 0.0001
|
| 250 |
+
iter 0048400; MSM 0.3792; REL 0.1110; VID 1.0042; CONST 0.0000; lr 0.0001
|
| 251 |
+
iter 0048600; MSM 0.1660; REL 0.0523; VID 1.1858; CONST 0.0000; lr 0.0001
|
| 252 |
+
iter 0048800; MSM 0.1302; REL 0.0924; VID 0.2201; CONST 0.0000; lr 0.0001
|
| 253 |
+
iter 0049000; MSM 0.1310; REL 0.2289; VID 0.2615; CONST 0.0000; lr 0.0001
|
| 254 |
+
iter 0049200; MSM 0.4556; REL 0.0352; VID 2.9775; CONST 0.0000; lr 0.0001
|
| 255 |
+
iter 0049400; MSM 0.0834; REL 1.3691; VID 0.0998; CONST 0.0000; lr 0.0001
|
| 256 |
+
iter 0049600; MSM 0.3564; REL 0.1547; VID 1.1860; CONST 0.0000; lr 0.0001
|
| 257 |
+
iter 0049800; MSM 0.3634; REL 0.2633; VID 2.8900; CONST 0.0000; lr 0.0001
|
| 258 |
+
iter 0050000; MSM 0.4595; REL 0.0350; VID 1.1908; CONST 0.0000; lr 0.0001
|
| 259 |
+
iter 0050200; MSM 0.2407; REL 0.2518; VID 0.8214; CONST 0.0000; lr 0.0001
|
| 260 |
+
iter 0050400; MSM 0.4157; REL 0.1018; VID 0.5101; CONST 0.0000; lr 0.0001
|
| 261 |
+
iter 0050600; MSM 0.5158; REL 0.0474; VID 0.4313; CONST 0.0000; lr 0.0001
|
| 262 |
+
iter 0050800; MSM 0.2283; REL 0.1867; VID 0.8181; CONST 0.0000; lr 0.0001
|
| 263 |
+
iter 0051000; MSM 0.1125; REL 0.0825; VID 1.7602; CONST 0.0000; lr 0.0001
|
| 264 |
+
iter 0051200; MSM 0.2395; REL 0.0514; VID 1.1012; CONST 0.0000; lr 0.0001
|
| 265 |
+
iter 0051400; MSM 0.1685; REL 0.1136; VID 0.7468; CONST 0.0000; lr 0.0001
|
| 266 |
+
iter 0051600; MSM 0.3284; REL 0.0547; VID 0.8058; CONST 0.0000; lr 0.0001
|
| 267 |
+
iter 0051800; MSM 0.2528; REL 0.0291; VID 0.3037; CONST 0.0000; lr 0.0001
|
| 268 |
+
iter 0052000; MSM 0.2270; REL 0.1106; VID 0.7876; CONST 0.0000; lr 0.0001
|
| 269 |
+
iter 0052200; MSM 0.1518; REL 0.1157; VID 0.8686; CONST 0.0000; lr 0.0001
|
| 270 |
+
iter 0052400; MSM 0.8709; REL 0.0553; VID 1.5737; CONST 0.0000; lr 0.0001
|
| 271 |
+
iter 0052600; MSM 0.4361; REL 0.7190; VID 1.5912; CONST 0.0000; lr 0.0001
|
| 272 |
+
iter 0052800; MSM 0.2629; REL 0.0838; VID 0.1777; CONST 0.0000; lr 0.0001
|
| 273 |
+
iter 0053000; MSM 0.3807; REL 0.1099; VID 0.8048; CONST 0.0000; lr 0.0001
|
| 274 |
+
iter 0053200; MSM 0.6462; REL 0.0103; VID 1.7104; CONST 0.0000; lr 0.0001
|
| 275 |
+
iter 0053400; MSM 0.1965; REL 0.2704; VID 0.4104; CONST 0.0000; lr 0.0001
|
| 276 |
+
iter 0053600; MSM 0.1184; REL 0.0408; VID 0.8957; CONST 0.0000; lr 0.0001
|
| 277 |
+
iter 0053800; MSM 0.1683; REL 0.0323; VID 1.1083; CONST 0.0000; lr 0.0001
|
| 278 |
+
iter 0054000; MSM 0.2816; REL 0.3281; VID 0.9505; CONST 0.0000; lr 0.0001
|
| 279 |
+
iter 0054200; MSM 0.4918; REL 0.2931; VID 1.5245; CONST 0.0000; lr 0.0001
|
| 280 |
+
iter 0054400; MSM 0.3160; REL 0.0392; VID 0.9707; CONST 0.0000; lr 0.0001
|
| 281 |
+
iter 0054600; MSM 0.4206; REL 0.0313; VID 0.1869; CONST 0.0000; lr 0.0001
|
| 282 |
+
iter 0054800; MSM 0.1354; REL 0.0487; VID 0.3519; CONST 0.0000; lr 0.0001
|
| 283 |
+
iter 0055000; MSM 0.2608; REL 0.0691; VID 0.3629; CONST 0.0000; lr 0.0001
|
| 284 |
+
iter 0055200; MSM 0.7191; REL 0.1452; VID 2.9512; CONST 0.0000; lr 0.0001
|
| 285 |
+
iter 0055400; MSM 0.2236; REL 0.0507; VID 0.8342; CONST 0.0000; lr 0.0001
|
| 286 |
+
iter 0055600; MSM 0.1596; REL 0.0938; VID 0.2318; CONST 0.0000; lr 0.0001
|
| 287 |
+
iter 0055800; MSM 0.2508; REL 0.1853; VID 0.5456; CONST 0.0000; lr 0.0001
|
| 288 |
+
iter 0056000; MSM 1.1815; REL 0.1416; VID 1.5157; CONST 0.0000; lr 0.0001
|
| 289 |
+
iter 0056200; MSM 0.1914; REL 0.5154; VID 0.2544; CONST 0.0000; lr 0.0001
|
| 290 |
+
iter 0056400; MSM 0.2713; REL 0.1309; VID 1.4742; CONST 0.0000; lr 0.0001
|
| 291 |
+
iter 0056600; MSM 0.2233; REL 0.0426; VID 0.3315; CONST 0.0000; lr 0.0001
|
| 292 |
+
iter 0056800; MSM 0.1505; REL 0.7601; VID 3.0650; CONST 0.0000; lr 0.0001
|
| 293 |
+
iter 0057000; MSM 0.2394; REL 0.0826; VID 0.2570; CONST 0.0000; lr 0.0001
|
| 294 |
+
iter 0057200; MSM 0.7969; REL 0.9186; VID 2.5618; CONST 0.0000; lr 0.0001
|
| 295 |
+
iter 0057400; MSM 0.2655; REL 0.8793; VID 0.1375; CONST 0.0000; lr 0.0001
|
| 296 |
+
iter 0057600; MSM 0.4987; REL 0.2134; VID 4.1898; CONST 0.0000; lr 0.0001
|
| 297 |
+
iter 0057800; MSM 0.9575; REL 0.0322; VID 1.4977; CONST 0.0000; lr 0.0001
|
| 298 |
+
iter 0058000; MSM 0.1485; REL 0.2753; VID 0.3628; CONST 0.0000; lr 0.0001
|
| 299 |
+
iter 0058200; MSM 0.1397; REL 1.9203; VID 0.8112; CONST 0.0000; lr 0.0001
|
| 300 |
+
iter 0058400; MSM 0.6494; REL 0.0106; VID 0.4084; CONST 0.0000; lr 0.0001
|
| 301 |
+
iter 0058600; MSM 0.3587; REL 0.0779; VID 0.9720; CONST 0.0000; lr 0.0001
|
| 302 |
+
iter 0058800; MSM 0.1810; REL 0.1786; VID 0.2299; CONST 0.0000; lr 0.0001
|
| 303 |
+
iter 0059000; MSM 0.2342; REL 2.8636; VID 1.8148; CONST 0.0000; lr 0.0001
|
| 304 |
+
iter 0059200; MSM 0.1962; REL 0.2796; VID 0.1462; CONST 0.0000; lr 0.0001
|
| 305 |
+
iter 0059400; MSM 0.3524; REL 0.1809; VID 0.4860; CONST 0.0000; lr 0.0001
|
| 306 |
+
iter 0059600; MSM 3.6547; REL 0.0000; VID 2.7728; CONST 0.0000; lr 0.0001
|
| 307 |
+
iter 0059800; MSM 0.9702; REL 0.0200; VID 2.7781; CONST 0.0000; lr 0.0001
|
| 308 |
+
iter 0060000; MSM 0.1322; REL 1.2893; VID 1.5498; CONST 0.0000; lr 0.0001
|
| 309 |
+
iter 0060200; MSM 0.1090; REL 0.1137; VID 0.3294; CONST 0.0000; lr 0.0001
|
| 310 |
+
iter 0060400; MSM 0.4203; REL 0.3037; VID 2.0297; CONST 0.0000; lr 0.0001
|
| 311 |
+
iter 0060600; MSM 0.2291; REL 0.1162; VID 0.8692; CONST 0.0000; lr 0.0001
|
| 312 |
+
iter 0060800; MSM 0.1355; REL 0.0716; VID 1.0386; CONST 0.0000; lr 0.0001
|
| 313 |
+
iter 0061000; MSM 0.2266; REL 1.7051; VID 0.2001; CONST 0.0000; lr 0.0001
|
| 314 |
+
iter 0061200; MSM 0.1419; REL 0.1208; VID 0.3047; CONST 0.0000; lr 0.0001
|
| 315 |
+
iter 0061400; MSM 0.9151; REL 0.0000; VID 2.7967; CONST 0.0000; lr 0.0001
|
| 316 |
+
iter 0061600; MSM 0.2875; REL 0.0284; VID 3.1319; CONST 0.0000; lr 0.0001
|
| 317 |
+
iter 0061800; MSM 0.2798; REL 0.0533; VID 1.3649; CONST 0.0000; lr 0.0001
|
| 318 |
+
iter 0062000; MSM 0.1349; REL 1.2456; VID 0.0837; CONST 0.0000; lr 0.0001
|
| 319 |
+
iter 0062200; MSM 0.2333; REL 0.1638; VID 1.4654; CONST 0.0000; lr 0.0001
|
| 320 |
+
iter 0062400; MSM 1.1439; REL 0.1193; VID 1.5378; CONST 0.0000; lr 0.0001
|
| 321 |
+
iter 0062600; MSM 0.1840; REL 1.8393; VID 0.1114; CONST 0.0000; lr 0.0001
|
| 322 |
+
iter 0062800; MSM 0.1933; REL 0.1813; VID 0.1438; CONST 0.0000; lr 0.0001
|
| 323 |
+
iter 0063000; MSM 0.6531; REL 0.9283; VID 2.1462; CONST 0.0000; lr 0.0001
|
| 324 |
+
iter 0063200; MSM 0.2340; REL 0.0875; VID 0.1749; CONST 0.0000; lr 0.0001
|
| 325 |
+
iter 0063400; MSM 0.4191; REL 0.6158; VID 0.4845; CONST 0.0000; lr 0.0001
|
| 326 |
+
iter 0063600; MSM 0.2611; REL 0.7739; VID 1.5793; CONST 0.0000; lr 0.0001
|
| 327 |
+
iter 0063800; MSM 0.2066; REL 0.0378; VID 1.0221; CONST 0.0000; lr 0.0001
|
| 328 |
+
iter 0064000; MSM 0.4950; REL 1.0546; VID 0.9668; CONST 0.0000; lr 0.0001
|
| 329 |
+
iter 0064200; MSM 0.1336; REL 0.1448; VID 0.8931; CONST 0.0000; lr 0.0001
|
| 330 |
+
iter 0064400; MSM 0.2220; REL 0.1034; VID 0.8076; CONST 0.0000; lr 0.0001
|
| 331 |
+
iter 0064600; MSM 0.2433; REL 0.0494; VID 1.0516; CONST 0.0000; lr 0.0001
|
| 332 |
+
iter 0064800; MSM 0.3284; REL 0.2215; VID 0.8044; CONST 0.0000; lr 0.0001
|
| 333 |
+
iter 0065000; MSM 0.2929; REL 0.1044; VID 0.3654; CONST 0.0000; lr 0.0001
|
| 334 |
+
iter 0065200; MSM 0.1737; REL 0.6641; VID 0.1763; CONST 0.0000; lr 0.0001
|
| 335 |
+
iter 0065400; MSM 0.2336; REL 0.1267; VID 1.5977; CONST 0.0000; lr 0.0001
|
| 336 |
+
iter 0065600; MSM 0.1276; REL 0.0426; VID 0.3794; CONST 0.0000; lr 0.0001
|
| 337 |
+
iter 0065800; MSM 0.3315; REL 0.2828; VID 0.9682; CONST 0.0000; lr 0.0001
|
| 338 |
+
iter 0066000; MSM 0.2078; REL 0.0163; VID 1.5780; CONST 0.0000; lr 0.0001
|
| 339 |
+
iter 0066200; MSM 0.1071; REL 0.1196; VID 0.7652; CONST 0.0000; lr 0.0001
|
| 340 |
+
iter 0066400; MSM 0.0786; REL 0.0609; VID 0.2094; CONST 0.0000; lr 0.0001
|
| 341 |
+
iter 0066600; MSM 0.1007; REL 0.7179; VID 1.1258; CONST 0.0000; lr 0.0001
|
| 342 |
+
iter 0066800; MSM 0.2281; REL 0.1682; VID 0.1921; CONST 0.0000; lr 0.0001
|
| 343 |
+
iter 0067000; MSM 0.4915; REL 0.0248; VID 0.8706; CONST 0.0000; lr 0.0001
|
| 344 |
+
iter 0067200; MSM 0.1582; REL 0.0785; VID 0.2274; CONST 0.0000; lr 0.0001
|
| 345 |
+
iter 0067400; MSM 0.1409; REL 0.0303; VID 1.1074; CONST 0.0000; lr 0.0001
|
| 346 |
+
iter 0067600; MSM 0.1401; REL 0.1855; VID 0.2028; CONST 0.0000; lr 0.0001
|
| 347 |
+
iter 0067800; MSM 0.1351; REL 0.1008; VID 0.1336; CONST 0.0000; lr 0.0001
|
| 348 |
+
iter 0068000; MSM 0.1612; REL 0.1175; VID 0.3736; CONST 0.0000; lr 0.0001
|
| 349 |
+
iter 0068200; MSM 0.1379; REL 0.1494; VID 0.2687; CONST 0.0000; lr 0.0001
|
| 350 |
+
iter 0068400; MSM 0.3115; REL 0.0373; VID 0.5351; CONST 0.0000; lr 0.0001
|
| 351 |
+
iter 0068600; MSM 0.5074; REL 0.0348; VID 1.9871; CONST 0.0000; lr 0.0001
|
| 352 |
+
iter 0068800; MSM 0.1250; REL 0.0565; VID 1.0438; CONST 0.0000; lr 0.0001
|
| 353 |
+
iter 0069000; MSM 0.1263; REL 0.0260; VID 0.7479; CONST 0.0000; lr 0.0001
|
| 354 |
+
iter 0069200; MSM 0.1687; REL 0.2817; VID 0.0966; CONST 0.0000; lr 0.0001
|
| 355 |
+
iter 0069400; MSM 0.2373; REL 0.0225; VID 1.5371; CONST 0.0000; lr 0.0001
|
| 356 |
+
iter 0069600; MSM 0.2246; REL 0.0797; VID 1.0101; CONST 0.0000; lr 0.0001
|
| 357 |
+
iter 0069800; MSM 0.0774; REL 0.0723; VID 0.1215; CONST 0.0000; lr 0.0001
|
| 358 |
+
iter 0070000; MSM 0.1217; REL 0.1239; VID 0.1534; CONST 0.0000; lr 0.0001
|
| 359 |
+
iter 0070200; MSM 0.1308; REL 0.1281; VID 0.2317; CONST 0.0000; lr 0.0001
|
| 360 |
+
iter 0070400; MSM 0.1597; REL 0.1493; VID 0.0630; CONST 0.0000; lr 0.0001
|
| 361 |
+
iter 0070600; MSM 0.6963; REL 0.8653; VID 1.5229; CONST 0.0000; lr 0.0001
|
| 362 |
+
iter 0070800; MSM 0.2345; REL 0.0438; VID 0.9137; CONST 0.0000; lr 0.0001
|
| 363 |
+
iter 0071000; MSM 0.6111; REL 0.0545; VID 0.3146; CONST 0.0000; lr 0.0001
|
| 364 |
+
iter 0071200; MSM 0.0927; REL 0.1672; VID 1.1258; CONST 0.0000; lr 0.0001
|
| 365 |
+
iter 0071400; MSM 0.4616; REL 0.3474; VID 1.1199; CONST 0.0000; lr 0.0001
|
| 366 |
+
iter 0071600; MSM 0.4739; REL 0.0658; VID 1.4936; CONST 0.0000; lr 0.0001
|
| 367 |
+
iter 0071800; MSM 0.3197; REL 0.0465; VID 0.3776; CONST 0.0000; lr 0.0001
|
| 368 |
+
iter 0072000; MSM 0.2673; REL 0.0295; VID 1.0154; CONST 0.0000; lr 0.0001
|
| 369 |
+
iter 0072200; MSM 0.1834; REL 0.1052; VID 1.1064; CONST 0.0000; lr 0.0001
|
| 370 |
+
iter 0072400; MSM 0.3628; REL 2.2151; VID 0.1519; CONST 0.0000; lr 0.0001
|
| 371 |
+
iter 0072600; MSM 0.3487; REL 0.0216; VID 2.3243; CONST 0.0000; lr 0.0001
|
| 372 |
+
iter 0072800; MSM 0.1386; REL 0.7350; VID 0.7377; CONST 0.0000; lr 0.0001
|
| 373 |
+
iter 0073000; MSM 0.3602; REL 0.0311; VID 1.4292; CONST 0.0000; lr 0.0001
|
| 374 |
+
iter 0073200; MSM 0.1005; REL 0.6532; VID 0.7445; CONST 0.0000; lr 0.0001
|
| 375 |
+
iter 0073400; MSM 0.1030; REL 0.0809; VID 0.1127; CONST 0.0000; lr 0.0001
|
| 376 |
+
iter 0073600; MSM 0.2096; REL 0.0373; VID 1.1275; CONST 0.0000; lr 0.0001
|
| 377 |
+
iter 0073800; MSM 0.5706; REL 1.0271; VID 1.7496; CONST 0.0000; lr 0.0001
|
| 378 |
+
iter 0074000; MSM 0.1679; REL 0.0353; VID 0.2028; CONST 0.0000; lr 0.0001
|
| 379 |
+
iter 0074200; MSM 0.1228; REL 0.1226; VID 0.1606; CONST 0.0000; lr 0.0001
|
| 380 |
+
iter 0074400; MSM 0.1630; REL 0.2573; VID 1.9732; CONST 0.0000; lr 0.0001
|
| 381 |
+
iter 0074600; MSM 0.2728; REL 0.2824; VID 2.6848; CONST 0.0000; lr 0.0001
|
| 382 |
+
iter 0074800; MSM 0.3308; REL 0.0263; VID 1.6656; CONST 0.0000; lr 0.0001
|
| 383 |
+
iter 0075000; MSM 0.1456; REL 0.4290; VID 1.1038; CONST 0.0000; lr 0.0001
|
| 384 |
+
iter 0075200; MSM 0.1261; REL 0.1121; VID 0.2578; CONST 0.0000; lr 0.0001
|
| 385 |
+
iter 0075400; MSM 0.0633; REL 0.0291; VID 0.0788; CONST 0.0000; lr 0.0001
|
| 386 |
+
iter 0075600; MSM 0.4426; REL 0.0389; VID 1.5891; CONST 0.0000; lr 0.0001
|
| 387 |
+
iter 0075800; MSM 0.3703; REL 0.0643; VID 0.4108; CONST 0.0000; lr 0.0001
|
| 388 |
+
iter 0076000; MSM 0.5367; REL 0.0836; VID 1.4781; CONST 0.0000; lr 0.0001
|
| 389 |
+
iter 0076200; MSM 0.2521; REL 0.0312; VID 0.8343; CONST 0.0000; lr 0.0001
|
| 390 |
+
iter 0076400; MSM 0.6211; REL 0.1083; VID 1.4375; CONST 0.0000; lr 0.0001
|
| 391 |
+
iter 0076600; MSM 0.5155; REL 0.0451; VID 1.5829; CONST 0.0000; lr 0.0001
|
| 392 |
+
iter 0076800; MSM 0.1130; REL 0.0295; VID 0.1113; CONST 0.0000; lr 0.0001
|
| 393 |
+
iter 0077000; MSM 0.3882; REL 0.0663; VID 0.8233; CONST 0.0000; lr 0.0001
|
| 394 |
+
iter 0077200; MSM 0.0551; REL 0.0401; VID 1.2035; CONST 0.0000; lr 0.0001
|
| 395 |
+
iter 0077400; MSM 0.1185; REL 0.0749; VID 0.1963; CONST 0.0000; lr 0.0001
|
| 396 |
+
iter 0077600; MSM 0.1638; REL 0.0319; VID 1.6515; CONST 0.0000; lr 0.0001
|
| 397 |
+
iter 0077800; MSM 0.4669; REL 0.0576; VID 3.7660; CONST 0.0000; lr 0.0001
|
| 398 |
+
iter 0078000; MSM 0.1914; REL 3.4608; VID 0.7793; CONST 0.0000; lr 0.0001
|
| 399 |
+
iter 0078200; MSM 0.1809; REL 0.0100; VID 0.4310; CONST 0.0000; lr 0.0001
|
| 400 |
+
iter 0078400; MSM 0.1214; REL 0.1499; VID 0.8953; CONST 0.0000; lr 0.0001
|
| 401 |
+
iter 0078600; MSM 1.9513; REL 0.0180; VID 1.5311; CONST 0.0000; lr 0.0001
|
| 402 |
+
iter 0078800; MSM 0.1522; REL 0.0318; VID 0.8873; CONST 0.0000; lr 0.0001
|
| 403 |
+
iter 0079000; MSM 0.1263; REL 0.0492; VID 0.5786; CONST 0.0000; lr 0.0001
|
| 404 |
+
iter 0079200; MSM 0.3397; REL 0.0165; VID 2.9871; CONST 0.0000; lr 0.0001
|
| 405 |
+
iter 0079400; MSM 0.3686; REL 0.0842; VID 1.4974; CONST 0.0000; lr 0.0001
|
| 406 |
+
iter 0079600; MSM 0.1495; REL 0.0636; VID 0.4816; CONST 0.0000; lr 0.0001
|
| 407 |
+
iter 0079800; MSM 2.7186; REL 0.0040; VID 2.8585; CONST 0.0000; lr 0.0001
|
| 408 |
+
iter 0080000; MSM 0.2503; REL 0.0121; VID 0.4291; CONST 0.0000; lr 0.0001
|
| 409 |
+
iter 0080200; MSM 0.1738; REL 0.0308; VID 0.6737; CONST 0.0000; lr 0.0001
|
| 410 |
+
iter 0080400; MSM 0.1321; REL 0.0200; VID 0.5653; CONST 0.0000; lr 0.0001
|
| 411 |
+
iter 0080600; MSM 0.1217; REL 0.0565; VID 0.1056; CONST 0.0000; lr 0.0001
|
| 412 |
+
iter 0080800; MSM 0.2062; REL 0.0232; VID 2.3123; CONST 0.0000; lr 0.0001
|
| 413 |
+
iter 0081000; MSM 0.0725; REL 0.1012; VID 1.0990; CONST 0.0000; lr 0.0001
|
| 414 |
+
iter 0081200; MSM 0.1602; REL 0.0785; VID 0.3768; CONST 0.0000; lr 0.0001
|
| 415 |
+
iter 0081400; MSM 0.3067; REL 0.0078; VID 0.1405; CONST 0.0000; lr 0.0001
|
| 416 |
+
iter 0081600; MSM 0.1858; REL 0.1607; VID 0.3062; CONST 0.0000; lr 0.0001
|
| 417 |
+
iter 0081800; MSM 1.2420; REL 0.0122; VID 1.5962; CONST 0.0000; lr 0.0001
|
| 418 |
+
iter 0082000; MSM 0.1778; REL 0.1498; VID 1.4397; CONST 0.0000; lr 0.0001
|
| 419 |
+
iter 0082200; MSM 0.1620; REL 0.0095; VID 1.2541; CONST 0.0000; lr 0.0001
|
| 420 |
+
iter 0082400; MSM 0.1690; REL 0.1042; VID 0.8939; CONST 0.0000; lr 0.0001
|
| 421 |
+
iter 0082600; MSM 0.2510; REL 0.2106; VID 0.2027; CONST 0.0000; lr 0.0001
|
| 422 |
+
iter 0082800; MSM 0.3443; REL 0.0978; VID 0.1627; CONST 0.0000; lr 0.0001
|
| 423 |
+
iter 0083000; MSM 1.5963; REL 1.9108; VID 1.9441; CONST 0.0000; lr 0.0001
|
| 424 |
+
iter 0083200; MSM 0.3299; REL 0.0076; VID 1.1817; CONST 0.0000; lr 0.0001
|
| 425 |
+
iter 0083400; MSM 0.2582; REL 0.1532; VID 2.2818; CONST 0.0000; lr 0.0001
|
| 426 |
+
iter 0083600; MSM 0.1326; REL 0.1110; VID 0.3382; CONST 0.0000; lr 0.0001
|
| 427 |
+
iter 0083800; MSM 0.3437; REL 0.0152; VID 0.9567; CONST 0.0000; lr 0.0001
|
| 428 |
+
iter 0084000; MSM 0.1132; REL 0.0680; VID 0.0625; CONST 0.0000; lr 0.0001
|
| 429 |
+
iter 0084200; MSM 0.1243; REL 0.0558; VID 0.1880; CONST 0.0000; lr 0.0001
|
| 430 |
+
iter 0084400; MSM 0.1898; REL 0.0192; VID 0.9412; CONST 0.0000; lr 0.0001
|
| 431 |
+
iter 0084600; MSM 0.1059; REL 0.0494; VID 1.0532; CONST 0.0000; lr 0.0001
|
| 432 |
+
iter 0084800; MSM 0.0828; REL 0.2375; VID 0.8972; CONST 0.0000; lr 0.0001
|
| 433 |
+
iter 0085000; MSM 0.1246; REL 0.0072; VID 0.2820; CONST 0.0000; lr 0.0001
|
| 434 |
+
iter 0085200; MSM 0.2474; REL 0.3097; VID 0.3155; CONST 0.0000; lr 0.0001
|
| 435 |
+
iter 0085400; MSM 0.0841; REL 0.0353; VID 0.1116; CONST 0.0000; lr 0.0001
|
| 436 |
+
iter 0085600; MSM 0.0510; REL 0.0395; VID 1.5153; CONST 0.0000; lr 0.0001
|
| 437 |
+
iter 0085800; MSM 0.2124; REL 0.1593; VID 1.1127; CONST 0.0000; lr 0.0001
|
| 438 |
+
iter 0086000; MSM 0.1306; REL 0.0901; VID 0.1097; CONST 0.0000; lr 0.0001
|
| 439 |
+
iter 0086200; MSM 0.1483; REL 0.0409; VID 0.1952; CONST 0.0000; lr 0.0001
|
| 440 |
+
iter 0086400; MSM 0.0774; REL 0.0668; VID 0.8386; CONST 0.0000; lr 0.0001
|
| 441 |
+
iter 0086600; MSM 0.1232; REL 0.0688; VID 0.6267; CONST 0.0000; lr 0.0001
|
| 442 |
+
iter 0086800; MSM 0.1593; REL 0.0294; VID 0.3732; CONST 0.0000; lr 0.0001
|
| 443 |
+
iter 0087000; MSM 0.2314; REL 0.1159; VID 0.2857; CONST 0.0000; lr 0.0001
|
| 444 |
+
iter 0087200; MSM 0.0953; REL 0.0578; VID 1.0062; CONST 0.0000; lr 0.0001
|
| 445 |
+
iter 0087400; MSM 0.4802; REL 0.0381; VID 1.4888; CONST 0.0000; lr 0.0001
|
| 446 |
+
iter 0087600; MSM 0.1139; REL 0.0617; VID 0.2098; CONST 0.0000; lr 0.0001
|
| 447 |
+
iter 0087800; MSM 0.3021; REL 0.0441; VID 1.4492; CONST 0.0000; lr 0.0001
|
| 448 |
+
iter 0088000; MSM 0.0905; REL 0.0371; VID 1.7579; CONST 0.0000; lr 0.0001
|
| 449 |
+
iter 0088200; MSM 0.2020; REL 0.0466; VID 1.7955; CONST 0.0000; lr 0.0001
|
| 450 |
+
iter 0088400; MSM 0.1695; REL 0.0348; VID 1.8897; CONST 0.0000; lr 0.0001
|
| 451 |
+
iter 0088600; MSM 0.2439; REL 0.1600; VID 0.2146; CONST 0.0000; lr 0.0001
|
| 452 |
+
iter 0088800; MSM 0.1100; REL 0.0245; VID 0.1584; CONST 0.0000; lr 0.0001
|
| 453 |
+
iter 0089000; MSM 2.3451; REL 0.0774; VID 3.8645; CONST 0.0000; lr 0.0001
|
| 454 |
+
iter 0089200; MSM 0.0758; REL 0.0448; VID 0.3017; CONST 0.0000; lr 0.0001
|
| 455 |
+
iter 0089400; MSM 0.1623; REL 0.1155; VID 1.5907; CONST 0.0000; lr 0.0001
|
| 456 |
+
iter 0089600; MSM 0.2560; REL 0.0202; VID 1.4958; CONST 0.0000; lr 0.0001
|
| 457 |
+
iter 0089800; MSM 0.2287; REL 0.2301; VID 0.1279; CONST 0.0000; lr 0.0001
|
| 458 |
+
iter 0090000; MSM 0.0975; REL 0.0547; VID 0.8280; CONST 0.0000; lr 0.0001
|
| 459 |
+
iter 0090200; MSM 0.0953; REL 0.0489; VID 0.1985; CONST 0.0000; lr 0.0001
|
| 460 |
+
iter 0090400; MSM 0.1527; REL 0.0530; VID 0.1949; CONST 0.0000; lr 0.0001
|
| 461 |
+
iter 0090600; MSM 0.2545; REL 0.0225; VID 0.0972; CONST 0.0000; lr 0.0001
|
| 462 |
+
iter 0090800; MSM 0.1631; REL 0.0496; VID 0.2026; CONST 0.0000; lr 0.0001
|
| 463 |
+
iter 0091000; MSM 0.2208; REL 0.3182; VID 0.4892; CONST 0.0000; lr 0.0001
|
| 464 |
+
iter 0091200; MSM 0.1796; REL 0.0084; VID 0.0481; CONST 0.0000; lr 0.0001
|
| 465 |
+
iter 0091400; MSM 0.1196; REL 1.9991; VID 0.6475; CONST 0.0000; lr 0.0001
|
| 466 |
+
iter 0091600; MSM 0.1800; REL 0.0580; VID 0.7540; CONST 0.0000; lr 0.0001
|
| 467 |
+
iter 0091800; MSM 0.2249; REL 0.0050; VID 0.1308; CONST 0.0000; lr 0.0001
|
| 468 |
+
iter 0092000; MSM 0.0597; REL 0.0415; VID 0.1197; CONST 0.0000; lr 0.0001
|
| 469 |
+
iter 0092200; MSM 0.2252; REL 0.0503; VID 0.7766; CONST 0.0000; lr 0.0001
|
| 470 |
+
iter 0092400; MSM 0.2250; REL 0.0139; VID 0.1988; CONST 0.0000; lr 0.0001
|
| 471 |
+
iter 0092600; MSM 0.1107; REL 0.1311; VID 0.0583; CONST 0.0000; lr 0.0001
|
| 472 |
+
iter 0092800; MSM 0.1854; REL 0.1301; VID 0.1133; CONST 0.0000; lr 0.0001
|
| 473 |
+
iter 0093000; MSM 0.0509; REL 0.6888; VID 0.0900; CONST 0.0000; lr 0.0001
|
| 474 |
+
iter 0093200; MSM 0.1713; REL 0.0270; VID 1.1206; CONST 0.0000; lr 0.0001
|
| 475 |
+
iter 0093400; MSM 0.1447; REL 0.0220; VID 0.0922; CONST 0.0000; lr 0.0001
|
| 476 |
+
iter 0093600; MSM 0.2419; REL 0.0277; VID 0.3953; CONST 0.0000; lr 0.0001
|
| 477 |
+
iter 0093800; MSM 0.2006; REL 0.0074; VID 0.7209; CONST 0.0000; lr 0.0001
|
| 478 |
+
iter 0094000; MSM 0.0832; REL 0.0161; VID 0.6854; CONST 0.0000; lr 0.0001
|
| 479 |
+
iter 0094200; MSM 0.1076; REL 0.0104; VID 0.2902; CONST 0.0000; lr 0.0001
|
| 480 |
+
iter 0094400; MSM 0.1373; REL 0.0635; VID 1.5509; CONST 0.0000; lr 0.0001
|
| 481 |
+
iter 0094600; MSM 0.2656; REL 0.0378; VID 1.4192; CONST 0.0000; lr 0.0001
|
| 482 |
+
iter 0094800; MSM 0.0899; REL 0.0332; VID 0.5917; CONST 0.0000; lr 0.0001
|
| 483 |
+
iter 0095000; MSM 0.1038; REL 0.0156; VID 1.1567; CONST 0.0000; lr 0.0001
|
| 484 |
+
iter 0095200; MSM 0.2355; REL 0.5361; VID 1.5478; CONST 0.0000; lr 0.0001
|
| 485 |
+
iter 0095400; MSM 0.0722; REL 0.1295; VID 0.1185; CONST 0.0000; lr 0.0001
|
| 486 |
+
iter 0095600; MSM 0.1373; REL 0.0384; VID 1.6698; CONST 0.0000; lr 0.0001
|
| 487 |
+
iter 0095800; MSM 0.0669; REL 0.0436; VID 0.2136; CONST 0.0000; lr 0.0001
|
| 488 |
+
iter 0096000; MSM 0.1081; REL 0.0717; VID 1.5628; CONST 0.0000; lr 0.0001
|
| 489 |
+
iter 0096200; MSM 0.1370; REL 0.0420; VID 0.9538; CONST 0.0000; lr 0.0001
|
| 490 |
+
iter 0096400; MSM 0.0931; REL 0.0567; VID 0.2482; CONST 0.0000; lr 0.0001
|
| 491 |
+
iter 0096600; MSM 0.1192; REL 0.0322; VID 0.9623; CONST 0.0000; lr 0.0001
|
| 492 |
+
iter 0096800; MSM 1.5356; REL 0.0283; VID 1.4409; CONST 0.0000; lr 0.0001
|
| 493 |
+
iter 0097000; MSM 4.9526; REL 0.1196; VID 2.4906; CONST 0.0000; lr 0.0001
|
| 494 |
+
iter 0097200; MSM 0.1141; REL 1.9540; VID 0.5000; CONST 0.0000; lr 0.0001
|
| 495 |
+
iter 0097400; MSM 0.1017; REL 0.0646; VID 0.4613; CONST 0.0000; lr 0.0001
|
| 496 |
+
iter 0097600; MSM 0.1852; REL 0.0182; VID 0.9707; CONST 0.0000; lr 0.0001
|
| 497 |
+
iter 0097800; MSM 0.0734; REL 0.1156; VID 0.2208; CONST 0.0000; lr 0.0001
|
| 498 |
+
iter 0098000; MSM 0.2251; REL 0.0212; VID 0.2419; CONST 0.0000; lr 0.0001
|
| 499 |
+
iter 0098200; MSM 0.1399; REL 0.1137; VID 0.1530; CONST 0.0000; lr 0.0001
|
| 500 |
+
iter 0098400; MSM 0.3367; REL 0.0204; VID 1.7062; CONST 0.0000; lr 0.0001
|
| 501 |
+
iter 0098600; MSM 0.1721; REL 0.0019; VID 0.5544; CONST 0.0000; lr 0.0001
|
| 502 |
+
iter 0098800; MSM 0.1332; REL 0.0082; VID 0.9030; CONST 0.0000; lr 0.0001
|
| 503 |
+
iter 0099000; MSM 0.2818; REL 0.0121; VID 1.2793; CONST 0.0000; lr 0.0001
|
| 504 |
+
iter 0099200; MSM 0.1047; REL 0.0481; VID 0.4390; CONST 0.0000; lr 0.0001
|
| 505 |
+
iter 0099400; MSM 0.1257; REL 0.0333; VID 0.2337; CONST 0.0000; lr 0.0001
|
| 506 |
+
iter 0099600; MSM 0.0771; REL 0.1897; VID 0.8797; CONST 0.0000; lr 0.0001
|
| 507 |
+
iter 0099800; MSM 0.1474; REL 0.9226; VID 1.0361; CONST 0.0000; lr 0.0001
|
| 508 |
+
iter 0100000; MSM 0.2348; REL 0.0216; VID 0.5811; CONST 0.0000; lr 0.0001
|
| 509 |
+
iter 0100200; MSM 0.2013; REL 0.0013; VID 1.7048; CONST 0.0000; lr 0.0001
|
| 510 |
+
iter 0100400; MSM 0.2474; REL 0.0106; VID 1.4942; CONST 0.0000; lr 0.0001
|
| 511 |
+
iter 0100600; MSM 0.1476; REL 0.0109; VID 1.5348; CONST 0.0000; lr 0.0001
|
| 512 |
+
iter 0100800; MSM 0.0914; REL 0.1437; VID 0.3633; CONST 0.0000; lr 0.0001
|
| 513 |
+
iter 0101000; MSM 0.1057; REL 0.3614; VID 0.1206; CONST 0.0000; lr 0.0001
|
| 514 |
+
iter 0101200; MSM 0.1281; REL 0.1209; VID 0.0391; CONST 0.0000; lr 0.0001
|
| 515 |
+
iter 0101400; MSM 0.0860; REL 0.0455; VID 0.2057; CONST 0.0000; lr 0.0001
|
| 516 |
+
iter 0101600; MSM 0.2226; REL 0.0267; VID 1.6831; CONST 0.0000; lr 0.0001
|
| 517 |
+
iter 0101800; MSM 0.0929; REL 0.0740; VID 1.3019; CONST 0.0000; lr 0.0001
|
| 518 |
+
iter 0102000; MSM 0.0457; REL 0.0743; VID 0.1615; CONST 0.0000; lr 0.0001
|
| 519 |
+
iter 0102200; MSM 0.1323; REL 0.1914; VID 0.7163; CONST 0.0000; lr 0.0001
|
| 520 |
+
iter 0102400; MSM 0.1144; REL 5.5983; VID 1.3788; CONST 0.0000; lr 0.0001
|
| 521 |
+
iter 0102600; MSM 0.0764; REL 0.0097; VID 0.3208; CONST 0.0000; lr 0.0001
|
| 522 |
+
iter 0102800; MSM 0.1295; REL 0.0356; VID 0.2902; CONST 0.0000; lr 0.0001
|
| 523 |
+
iter 0103000; MSM 0.1504; REL 0.1523; VID 1.2867; CONST 0.0000; lr 0.0001
|
| 524 |
+
iter 0103200; MSM 0.0615; REL 0.0291; VID 0.8247; CONST 0.0000; lr 0.0001
|
| 525 |
+
iter 0103400; MSM 0.0740; REL 0.0599; VID 0.9390; CONST 0.0000; lr 0.0001
|
| 526 |
+
iter 0103600; MSM 0.0665; REL 0.1769; VID 0.8991; CONST 0.0000; lr 0.0001
|
| 527 |
+
iter 0103800; MSM 0.0555; REL 0.0478; VID 0.0754; CONST 0.0000; lr 0.0001
|
| 528 |
+
iter 0104000; MSM 0.1266; REL 0.0661; VID 0.1363; CONST 0.0000; lr 0.0001
|
| 529 |
+
iter 0104200; MSM 0.1407; REL 0.0092; VID 0.1252; CONST 0.0000; lr 0.0001
|
| 530 |
+
iter 0104400; MSM 0.1391; REL 0.0094; VID 0.2390; CONST 0.0000; lr 0.0001
|
| 531 |
+
iter 0104600; MSM 0.0823; REL 0.0110; VID 0.5543; CONST 0.0000; lr 0.0001
|
| 532 |
+
iter 0104800; MSM 0.2342; REL 0.0116; VID 1.9164; CONST 0.0000; lr 0.0001
|
| 533 |
+
iter 0105000; MSM 0.0708; REL 0.0284; VID 2.3336; CONST 0.0000; lr 0.0001
|
| 534 |
+
iter 0105200; MSM 2.6157; REL 0.2920; VID 4.1099; CONST 0.0000; lr 0.0001
|
| 535 |
+
iter 0105400; MSM 0.1465; REL 0.8518; VID 1.0360; CONST 0.0000; lr 0.0001
|
| 536 |
+
iter 0105600; MSM 0.1287; REL 0.0264; VID 1.6051; CONST 0.0000; lr 0.0001
|
| 537 |
+
iter 0105800; MSM 0.0769; REL 0.0763; VID 0.0890; CONST 0.0000; lr 0.0001
|
| 538 |
+
iter 0106000; MSM 0.2052; REL 0.0140; VID 0.1550; CONST 0.0000; lr 0.0001
|
| 539 |
+
iter 0106200; MSM 0.1119; REL 0.7789; VID 1.1660; CONST 0.0000; lr 0.0001
|
| 540 |
+
iter 0106400; MSM 0.1354; REL 1.4049; VID 0.8848; CONST 0.0000; lr 0.0001
|
| 541 |
+
iter 0106600; MSM 0.1946; REL 0.0769; VID 0.9067; CONST 0.0000; lr 0.0001
|
| 542 |
+
iter 0106800; MSM 0.0857; REL 0.0248; VID 1.0716; CONST 0.0000; lr 0.0001
|
| 543 |
+
iter 0107000; MSM 0.1200; REL 0.0513; VID 1.4612; CONST 0.0000; lr 0.0001
|
| 544 |
+
iter 0107200; MSM 0.1576; REL 0.2329; VID 0.6212; CONST 0.0000; lr 0.0001
|
| 545 |
+
iter 0107400; MSM 0.1327; REL 0.3410; VID 0.9379; CONST 0.0000; lr 0.0001
|
| 546 |
+
iter 0107600; MSM 0.1375; REL 0.0148; VID 0.5686; CONST 0.0000; lr 0.0001
|
| 547 |
+
iter 0107800; MSM 0.0862; REL 0.0303; VID 0.1083; CONST 0.0000; lr 0.0001
|
| 548 |
+
iter 0108000; MSM 0.0601; REL 0.0766; VID 0.1946; CONST 0.0000; lr 0.0001
|
| 549 |
+
iter 0108200; MSM 0.8302; REL 2.7557; VID 3.6759; CONST 0.0000; lr 0.0001
|
| 550 |
+
iter 0108400; MSM 0.1176; REL 0.1749; VID 0.0498; CONST 0.0000; lr 0.0001
|
| 551 |
+
iter 0108600; MSM 0.1329; REL 0.1629; VID 0.2573; CONST 0.0000; lr 0.0001
|
| 552 |
+
iter 0108800; MSM 0.0365; REL 0.0140; VID 0.1266; CONST 0.0000; lr 0.0001
|
| 553 |
+
iter 0109000; MSM 0.1303; REL 0.0294; VID 0.0470; CONST 0.0000; lr 0.0001
|
| 554 |
+
iter 0109200; MSM 0.0799; REL 0.0126; VID 0.4764; CONST 0.0000; lr 0.0001
|
| 555 |
+
iter 0109400; MSM 0.1630; REL 0.0342; VID 0.8460; CONST 0.0000; lr 0.0001
|
| 556 |
+
iter 0109600; MSM 0.0866; REL 0.1005; VID 0.1220; CONST 0.0000; lr 0.0001
|
| 557 |
+
iter 0109800; MSM 0.0986; REL 0.0032; VID 0.0985; CONST 0.0000; lr 0.0001
|
| 558 |
+
iter 0110000; MSM 0.1011; REL 0.0147; VID 0.1426; CONST 0.0000; lr 0.0001
|
| 559 |
+
iter 0110200; MSM 0.0864; REL 0.0603; VID 1.4389; CONST 0.0000; lr 0.0001
|
| 560 |
+
iter 0110400; MSM 0.1799; REL 3.7839; VID 2.2857; CONST 0.0000; lr 0.0001
|
| 561 |
+
iter 0110600; MSM 0.0864; REL 0.0095; VID 0.6009; CONST 0.0000; lr 0.0001
|
| 562 |
+
iter 0110800; MSM 0.0553; REL 0.0394; VID 0.0718; CONST 0.0000; lr 0.0001
|
| 563 |
+
iter 0111000; MSM 0.1011; REL 0.1859; VID 1.0348; CONST 0.0000; lr 0.0001
|
| 564 |
+
iter 0111200; MSM 0.1262; REL 0.0597; VID 0.6537; CONST 0.0000; lr 0.0001
|
| 565 |
+
iter 0111400; MSM 0.0601; REL 0.0420; VID 1.0663; CONST 0.0000; lr 0.0001
|
| 566 |
+
iter 0111600; MSM 0.0859; REL 0.0319; VID 0.1507; CONST 0.0000; lr 0.0001
|
| 567 |
+
iter 0111800; MSM 0.0887; REL 0.0964; VID 1.2177; CONST 0.0000; lr 0.0001
|
| 568 |
+
iter 0112000; MSM 0.0758; REL 0.0842; VID 0.0847; CONST 0.0000; lr 0.0001
|
| 569 |
+
iter 0112200; MSM 0.0784; REL 2.9657; VID 0.0840; CONST 0.0000; lr 0.0001
|
| 570 |
+
iter 0112400; MSM 0.0588; REL 0.0775; VID 1.2626; CONST 0.0000; lr 0.0001
|
| 571 |
+
iter 0112600; MSM 0.1284; REL 0.0164; VID 0.1911; CONST 0.0000; lr 0.0001
|
| 572 |
+
iter 0112800; MSM 0.1142; REL 0.0000; VID 2.7756; CONST 0.0000; lr 0.0001
|
| 573 |
+
iter 0113000; MSM 0.0500; REL 0.6277; VID 1.0464; CONST 0.0000; lr 0.0001
|
| 574 |
+
iter 0113200; MSM 0.0873; REL 0.0527; VID 0.6144; CONST 0.0000; lr 0.0001
|
| 575 |
+
iter 0113400; MSM 0.2370; REL 0.0055; VID 1.4563; CONST 0.0000; lr 0.0001
|
| 576 |
+
iter 0113600; MSM 0.1625; REL 0.0379; VID 0.2853; CONST 0.0000; lr 0.0001
|
| 577 |
+
iter 0113800; MSM 0.0590; REL 0.4949; VID 0.2764; CONST 0.0000; lr 0.0001
|
| 578 |
+
iter 0114000; MSM 0.1259; REL 1.6043; VID 0.1290; CONST 0.0000; lr 0.0001
|
| 579 |
+
iter 0114200; MSM 0.0726; REL 0.1664; VID 0.1251; CONST 0.0000; lr 0.0001
|
| 580 |
+
iter 0114400; MSM 0.1434; REL 0.0078; VID 0.1348; CONST 0.0000; lr 0.0001
|
| 581 |
+
iter 0114600; MSM 0.1492; REL 0.0771; VID 1.7968; CONST 0.0000; lr 0.0001
|
| 582 |
+
iter 0114800; MSM 0.0869; REL 0.0338; VID 0.1128; CONST 0.0000; lr 0.0001
|
| 583 |
+
iter 0115000; MSM 0.1512; REL 0.5939; VID 1.7655; CONST 0.0000; lr 0.0001
|
| 584 |
+
iter 0115200; MSM 0.0725; REL 0.0399; VID 4.4615; CONST 0.0000; lr 0.0001
|
| 585 |
+
iter 0115400; MSM 0.0380; REL 0.0167; VID 0.9072; CONST 0.0000; lr 0.0001
|
| 586 |
+
iter 0115600; MSM 0.0847; REL 1.7158; VID 0.5814; CONST 0.0000; lr 0.0001
|
| 587 |
+
iter 0115800; MSM 0.0517; REL 0.0150; VID 0.2015; CONST 0.0000; lr 0.0001
|
| 588 |
+
iter 0116000; MSM 0.1666; REL 0.0133; VID 1.4352; CONST 0.0000; lr 0.0001
|
| 589 |
+
iter 0116200; MSM 0.2070; REL 2.9748; VID 1.4149; CONST 0.0000; lr 0.0001
|
| 590 |
+
iter 0116400; MSM 0.0477; REL 0.0561; VID 0.3989; CONST 0.0000; lr 0.0001
|
| 591 |
+
iter 0116600; MSM 0.0943; REL 0.0210; VID 0.0914; CONST 0.0000; lr 0.0001
|
| 592 |
+
iter 0116800; MSM 0.1289; REL 0.0140; VID 0.0659; CONST 0.0000; lr 0.0001
|
| 593 |
+
iter 0117000; MSM 0.0619; REL 1.6535; VID 0.2206; CONST 0.0000; lr 0.0001
|
| 594 |
+
iter 0117200; MSM 0.1893; REL 0.0171; VID 0.6069; CONST 0.0000; lr 0.0001
|
| 595 |
+
iter 0117400; MSM 0.0589; REL 0.0159; VID 1.2866; CONST 0.0000; lr 0.0001
|
| 596 |
+
iter 0117600; MSM 0.0607; REL 0.0171; VID 0.0735; CONST 0.0000; lr 0.0001
|
| 597 |
+
iter 0117800; MSM 0.0666; REL 0.0580; VID 1.2547; CONST 0.0000; lr 0.0001
|
| 598 |
+
iter 0118000; MSM 0.1268; REL 0.0053; VID 0.8404; CONST 0.0000; lr 0.0001
|
| 599 |
+
iter 0118200; MSM 0.1677; REL 0.0091; VID 0.1298; CONST 0.0000; lr 0.0001
|
| 600 |
+
iter 0118400; MSM 0.1712; REL 0.0070; VID 1.4527; CONST 0.0000; lr 0.0001
|
| 601 |
+
iter 0118600; MSM 0.0662; REL 0.0733; VID 0.3211; CONST 0.0000; lr 0.0001
|
| 602 |
+
iter 0118800; MSM 0.1193; REL 1.5129; VID 0.1572; CONST 0.0000; lr 0.0001
|
| 603 |
+
iter 0119000; MSM 0.1100; REL 0.0605; VID 1.4894; CONST 0.0000; lr 0.0001
|
| 604 |
+
iter 0119200; MSM 0.0984; REL 0.0124; VID 0.8921; CONST 0.0000; lr 0.0001
|
| 605 |
+
iter 0119400; MSM 0.0961; REL 0.1456; VID 0.6038; CONST 0.0000; lr 0.0001
|
| 606 |
+
iter 0119600; MSM 0.0464; REL 0.0659; VID 0.2924; CONST 0.0000; lr 0.0001
|
| 607 |
+
iter 0119800; MSM 0.1522; REL 0.0179; VID 1.4520; CONST 0.0000; lr 0.0001
|
| 608 |
+
iter 0120000; MSM 0.6393; REL 0.0533; VID 1.6183; CONST 0.0000; lr 0.0001
|
| 609 |
+
iter 0120200; MSM 0.0457; REL 0.0284; VID 0.4973; CONST 0.0000; lr 0.0001
|
| 610 |
+
iter 0120400; MSM 0.1324; REL 0.0309; VID 0.2104; CONST 0.0000; lr 0.0001
|
| 611 |
+
iter 0120600; MSM 0.0648; REL 0.0235; VID 1.1017; CONST 0.0000; lr 0.0001
|
| 612 |
+
iter 0120800; MSM 0.0372; REL 0.9831; VID 1.2620; CONST 0.0000; lr 0.0001
|
| 613 |
+
iter 0121000; MSM 0.0681; REL 0.0145; VID 0.1059; CONST 0.0000; lr 0.0001
|
| 614 |
+
iter 0121200; MSM 0.0389; REL 0.0265; VID 0.0997; CONST 0.0000; lr 0.0001
|
| 615 |
+
iter 0121400; MSM 0.0953; REL 0.1102; VID 0.0930; CONST 0.0000; lr 0.0001
|
| 616 |
+
iter 0121600; MSM 0.2308; REL 0.0355; VID 2.4932; CONST 0.0000; lr 0.0001
|
| 617 |
+
iter 0121800; MSM 0.0830; REL 0.0632; VID 1.1852; CONST 0.0000; lr 0.0001
|
| 618 |
+
iter 0122000; MSM 0.0351; REL 0.2387; VID 1.6595; CONST 0.0000; lr 0.0001
|
| 619 |
+
iter 0122200; MSM 0.0348; REL 0.0226; VID 0.9679; CONST 0.0000; lr 0.0001
|
| 620 |
+
iter 0122400; MSM 0.1593; REL 0.0112; VID 0.2774; CONST 0.0000; lr 0.0001
|
| 621 |
+
iter 0122600; MSM 0.0675; REL 0.0193; VID 1.1810; CONST 0.0000; lr 0.0001
|
| 622 |
+
iter 0122800; MSM 0.0518; REL 0.0616; VID 0.9757; CONST 0.0000; lr 0.0001
|
| 623 |
+
iter 0123000; MSM 0.0492; REL 0.0073; VID 1.4338; CONST 0.0000; lr 0.0001
|
| 624 |
+
iter 0123200; MSM 0.0799; REL 0.0190; VID 0.0475; CONST 0.0000; lr 0.0001
|
| 625 |
+
iter 0123400; MSM 0.1988; REL 0.0057; VID 1.4384; CONST 0.0000; lr 0.0001
|
| 626 |
+
iter 0123600; MSM 0.1329; REL 0.1182; VID 1.5091; CONST 0.0000; lr 0.0001
|
| 627 |
+
iter 0123800; MSM 0.1033; REL 0.0019; VID 1.2019; CONST 0.0000; lr 0.0001
|
| 628 |
+
iter 0124000; MSM 0.1150; REL 0.0047; VID 0.1341; CONST 0.0000; lr 0.0001
|
| 629 |
+
iter 0124200; MSM 0.0915; REL 0.0227; VID 0.1983; CONST 0.0000; lr 0.0001
|
| 630 |
+
iter 0124400; MSM 0.2170; REL 0.0121; VID 1.4858; CONST 0.0000; lr 0.0001
|
| 631 |
+
iter 0124600; MSM 0.0900; REL 0.0041; VID 1.4657; CONST 0.0000; lr 0.0001
|
| 632 |
+
iter 0124800; MSM 0.0463; REL 0.0291; VID 1.2212; CONST 0.0000; lr 0.0001
|
| 633 |
+
iter 0125000; MSM 0.0384; REL 0.0663; VID 0.1693; CONST 0.0000; lr 0.0001
|
| 634 |
+
iter 0125200; MSM 0.0885; REL 0.0120; VID 0.3462; CONST 0.0000; lr 0.0001
|
| 635 |
+
iter 0125400; MSM 0.1394; REL 0.0208; VID 0.5666; CONST 0.0000; lr 0.0001
|
| 636 |
+
iter 0125600; MSM 0.1926; REL 0.0300; VID 1.0200; CONST 0.0000; lr 0.0001
|
| 637 |
+
iter 0125800; MSM 0.1222; REL 0.0065; VID 0.1446; CONST 0.0000; lr 0.0001
|
| 638 |
+
iter 0126000; MSM 0.0816; REL 0.0385; VID 0.2734; CONST 0.0000; lr 0.0001
|
| 639 |
+
iter 0126200; MSM 0.7201; REL 0.0384; VID 3.2974; CONST 0.0000; lr 0.0001
|
| 640 |
+
iter 0126400; MSM 0.1071; REL 0.0206; VID 0.0721; CONST 0.0000; lr 0.0001
|
| 641 |
+
iter 0126600; MSM 0.3389; REL 0.0000; VID 2.7731; CONST 0.0000; lr 0.0001
|
| 642 |
+
iter 0126800; MSM 0.1181; REL 0.0258; VID 0.1457; CONST 0.0000; lr 0.0001
|
| 643 |
+
iter 0127000; MSM 0.0774; REL 0.0289; VID 0.0553; CONST 0.0000; lr 0.0001
|
| 644 |
+
iter 0127200; MSM 0.0265; REL 0.0375; VID 0.0325; CONST 0.0000; lr 0.0001
|
| 645 |
+
iter 0127400; MSM 0.2119; REL 0.0066; VID 1.4923; CONST 0.0000; lr 0.0001
|
| 646 |
+
iter 0127600; MSM 0.1246; REL 0.0844; VID 0.1313; CONST 0.0000; lr 0.0001
|
| 647 |
+
iter 0127800; MSM 0.1047; REL 0.0339; VID 0.9134; CONST 0.0000; lr 0.0001
|
| 648 |
+
iter 0128000; MSM 0.1483; REL 0.0359; VID 1.8064; CONST 0.0000; lr 0.0001
|
| 649 |
+
iter 0128200; MSM 0.2191; REL 0.0000; VID 2.7734; CONST 0.0000; lr 0.0001
|
| 650 |
+
iter 0128400; MSM 0.0705; REL 0.0389; VID 0.2748; CONST 0.0000; lr 0.0001
|
| 651 |
+
iter 0128600; MSM 0.1403; REL 0.0324; VID 0.0710; CONST 0.0000; lr 0.0001
|
| 652 |
+
iter 0128800; MSM 0.1133; REL 0.0215; VID 0.3470; CONST 0.0000; lr 0.0001
|
| 653 |
+
iter 0129000; MSM 0.1673; REL 0.0074; VID 1.8140; CONST 0.0000; lr 0.0001
|
| 654 |
+
iter 0129200; MSM 0.2364; REL 0.0133; VID 1.4454; CONST 0.0000; lr 0.0001
|
| 655 |
+
iter 0129400; MSM 0.0633; REL 0.0120; VID 1.4659; CONST 0.0000; lr 0.0001
|
| 656 |
+
iter 0129600; MSM 0.0263; REL 0.8587; VID 0.0702; CONST 0.0000; lr 0.0001
|
| 657 |
+
iter 0129800; MSM 0.0677; REL 0.0249; VID 0.2897; CONST 0.0000; lr 0.0001
|
| 658 |
+
iter 0130000; MSM 0.0730; REL 0.0943; VID 0.7247; CONST 0.0000; lr 0.0001
|
| 659 |
+
iter 0130200; MSM 0.0364; REL 0.0206; VID 0.8306; CONST 0.0000; lr 0.0001
|
| 660 |
+
iter 0130400; MSM 0.0530; REL 0.0399; VID 0.0461; CONST 0.0000; lr 0.0001
|
| 661 |
+
iter 0130600; MSM 0.0544; REL 0.2726; VID 0.2666; CONST 0.0000; lr 0.0001
|
| 662 |
+
iter 0130800; MSM 0.1090; REL 0.1594; VID 0.1367; CONST 0.0000; lr 0.0001
|
| 663 |
+
iter 0131000; MSM 0.0680; REL 0.1300; VID 0.0803; CONST 0.0000; lr 0.0001
|
| 664 |
+
iter 0131200; MSM 0.0731; REL 0.0179; VID 0.0734; CONST 0.0000; lr 0.0001
|
| 665 |
+
iter 0131400; MSM 0.0342; REL 0.0292; VID 0.0934; CONST 0.0000; lr 0.0001
|
| 666 |
+
iter 0131600; MSM 0.0806; REL 0.0778; VID 1.6701; CONST 0.0000; lr 0.0001
|
| 667 |
+
iter 0131800; MSM 0.0716; REL 0.0180; VID 0.2600; CONST 0.0000; lr 0.0001
|
| 668 |
+
iter 0132000; MSM 0.1771; REL 0.0018; VID 0.0609; CONST 0.0000; lr 0.0001
|
| 669 |
+
iter 0132200; MSM 0.0328; REL 0.0068; VID 0.1186; CONST 0.0000; lr 0.0001
|
| 670 |
+
iter 0132400; MSM 0.4320; REL 0.0336; VID 1.5058; CONST 0.0000; lr 0.0001
|
| 671 |
+
iter 0132600; MSM 0.1017; REL 0.0011; VID 3.4442; CONST 0.0000; lr 0.0001
|
| 672 |
+
iter 0132800; MSM 0.0905; REL 0.0066; VID 0.9660; CONST 0.0000; lr 0.0001
|
| 673 |
+
iter 0133000; MSM 0.0836; REL 0.0087; VID 0.1026; CONST 0.0000; lr 0.0001
|
| 674 |
+
iter 0133200; MSM 0.0967; REL 0.0199; VID 1.0753; CONST 0.0000; lr 0.0001
|
| 675 |
+
iter 0133400; MSM 0.0274; REL 0.0623; VID 1.3595; CONST 0.0000; lr 0.0001
|
| 676 |
+
iter 0133600; MSM 0.0297; REL 0.0031; VID 0.1592; CONST 0.0000; lr 0.0001
|
| 677 |
+
iter 0133800; MSM 0.0845; REL 0.0604; VID 0.8617; CONST 0.0000; lr 0.0001
|
| 678 |
+
iter 0134000; MSM 0.0236; REL 0.0294; VID 0.2266; CONST 0.0000; lr 0.0001
|
| 679 |
+
iter 0134200; MSM 0.0743; REL 0.0360; VID 0.0679; CONST 0.0000; lr 0.0001
|
| 680 |
+
iter 0134400; MSM 0.0374; REL 2.9017; VID 0.0864; CONST 0.0000; lr 0.0001
|
| 681 |
+
iter 0134600; MSM 0.0771; REL 0.0125; VID 1.7894; CONST 0.0000; lr 0.0001
|
| 682 |
+
iter 0134800; MSM 0.0534; REL 0.0271; VID 0.0216; CONST 0.0000; lr 0.0001
|
| 683 |
+
iter 0135000; MSM 0.0895; REL 0.0142; VID 1.9902; CONST 0.0000; lr 0.0001
|
| 684 |
+
iter 0135200; MSM 0.0608; REL 0.0310; VID 1.6914; CONST 0.0000; lr 0.0001
|
| 685 |
+
iter 0135400; MSM 0.1221; REL 0.0287; VID 0.3220; CONST 0.0000; lr 0.0001
|
| 686 |
+
iter 0135600; MSM 0.0804; REL 0.0821; VID 0.1279; CONST 0.0000; lr 0.0001
|
| 687 |
+
iter 0135800; MSM 0.0569; REL 0.0202; VID 0.1385; CONST 0.0000; lr 0.0001
|
| 688 |
+
iter 0136000; MSM 0.0743; REL 0.1184; VID 0.3618; CONST 0.0000; lr 0.0001
|
| 689 |
+
iter 0136200; MSM 0.2137; REL 0.0012; VID 4.3003; CONST 0.0000; lr 0.0001
|
| 690 |
+
iter 0136400; MSM 0.0369; REL 0.0062; VID 0.0921; CONST 0.0000; lr 0.0001
|
| 691 |
+
iter 0136600; MSM 0.0545; REL 1.3999; VID 0.1254; CONST 0.0000; lr 0.0001
|
| 692 |
+
iter 0136800; MSM 0.0191; REL 1.3728; VID 0.2800; CONST 0.0000; lr 0.0001
|
| 693 |
+
iter 0137000; MSM 0.1203; REL 0.2287; VID 0.0617; CONST 0.0000; lr 0.0001
|
| 694 |
+
iter 0137200; MSM 0.0353; REL 0.0669; VID 0.0605; CONST 0.0000; lr 0.0001
|
| 695 |
+
iter 0137400; MSM 0.0915; REL 0.1235; VID 0.1534; CONST 0.0000; lr 0.0001
|
| 696 |
+
iter 0137600; MSM 0.0389; REL 0.0060; VID 1.0249; CONST 0.0000; lr 0.0001
|
| 697 |
+
iter 0137800; MSM 0.1577; REL 0.0136; VID 1.8998; CONST 0.0000; lr 0.0001
|
| 698 |
+
iter 0138000; MSM 0.0642; REL 0.0175; VID 0.1880; CONST 0.0000; lr 0.0001
|
| 699 |
+
iter 0138200; MSM 0.0996; REL 0.0686; VID 0.6848; CONST 0.0000; lr 0.0001
|
| 700 |
+
iter 0138400; MSM 0.0727; REL 0.0069; VID 0.8341; CONST 0.0000; lr 0.0001
|
| 701 |
+
iter 0138600; MSM 0.0340; REL 0.0093; VID 0.0885; CONST 0.0000; lr 0.0001
|
| 702 |
+
iter 0138800; MSM 0.1170; REL 0.0232; VID 0.1074; CONST 0.0000; lr 0.0001
|
| 703 |
+
iter 0139000; MSM 0.0348; REL 0.0272; VID 0.9927; CONST 0.0000; lr 0.0001
|
| 704 |
+
iter 0139200; MSM 0.0396; REL 0.0135; VID 0.1523; CONST 0.0000; lr 0.0001
|
| 705 |
+
iter 0139400; MSM 0.0170; REL 0.0476; VID 0.0492; CONST 0.0000; lr 0.0001
|
| 706 |
+
iter 0139600; MSM 0.0831; REL 0.0101; VID 0.1606; CONST 0.0000; lr 0.0001
|
| 707 |
+
iter 0139800; MSM 0.0829; REL 0.0198; VID 1.5026; CONST 0.0000; lr 0.0001
|
| 708 |
+
iter 0140000; MSM 0.0868; REL 0.0076; VID 0.1458; CONST 0.0000; lr 0.0001
|
| 709 |
+
iter 0140200; MSM 0.1018; REL 0.0423; VID 0.5995; CONST 0.0000; lr 0.0001
|
| 710 |
+
iter 0140400; MSM 0.0941; REL 0.1442; VID 0.0954; CONST 0.0000; lr 0.0001
|
| 711 |
+
iter 0140600; MSM 0.0784; REL 0.0031; VID 1.4524; CONST 0.0000; lr 0.0001
|
| 712 |
+
iter 0140800; MSM 0.0552; REL 0.0146; VID 0.4184; CONST 0.0000; lr 0.0001
|
| 713 |
+
iter 0141000; MSM 0.0729; REL 0.0720; VID 0.1914; CONST 0.0000; lr 0.0001
|
| 714 |
+
iter 0141200; MSM 0.0615; REL 0.0525; VID 1.4071; CONST 0.0000; lr 0.0001
|
| 715 |
+
iter 0141400; MSM 0.0671; REL 0.0128; VID 1.3496; CONST 0.0000; lr 0.0001
|
| 716 |
+
iter 0141600; MSM 0.1042; REL 0.0070; VID 0.1891; CONST 0.0000; lr 0.0001
|
| 717 |
+
iter 0141800; MSM 0.4333; REL 0.0235; VID 1.4829; CONST 0.0000; lr 0.0001
|
| 718 |
+
iter 0142000; MSM 0.1423; REL 0.0364; VID 0.5372; CONST 0.0000; lr 0.0001
|
| 719 |
+
iter 0142200; MSM 0.0736; REL 0.0947; VID 1.8423; CONST 0.0000; lr 0.0001
|
| 720 |
+
iter 0142400; MSM 0.0544; REL 0.0970; VID 0.9126; CONST 0.0000; lr 0.0001
|
| 721 |
+
iter 0142600; MSM 0.0705; REL 0.0153; VID 1.1580; CONST 0.0000; lr 0.0001
|
| 722 |
+
iter 0142800; MSM 0.0699; REL 0.0086; VID 0.2594; CONST 0.0000; lr 0.0001
|
| 723 |
+
iter 0143000; MSM 0.0612; REL 0.0035; VID 0.1692; CONST 0.0000; lr 0.0001
|
| 724 |
+
iter 0143200; MSM 0.1212; REL 0.0189; VID 1.7768; CONST 0.0000; lr 0.0001
|
| 725 |
+
iter 0143400; MSM 0.1024; REL 0.0146; VID 1.5418; CONST 0.0000; lr 0.0001
|
| 726 |
+
iter 0143600; MSM 0.0593; REL 0.0590; VID 0.2966; CONST 0.0000; lr 0.0001
|
| 727 |
+
iter 0143800; MSM 0.1136; REL 4.1799; VID 0.8346; CONST 0.0000; lr 0.0001
|
| 728 |
+
iter 0144000; MSM 0.1177; REL 0.0054; VID 0.0536; CONST 0.0000; lr 0.0001
|
| 729 |
+
iter 0144200; MSM 0.1616; REL 0.0218; VID 1.5042; CONST 0.0000; lr 0.0001
|
| 730 |
+
iter 0144400; MSM 0.0450; REL 0.1144; VID 1.4894; CONST 0.0000; lr 0.0001
|
| 731 |
+
iter 0144600; MSM 0.0676; REL 0.0192; VID 0.0630; CONST 0.0000; lr 0.0001
|
| 732 |
+
iter 0144800; MSM 0.0812; REL 5.0188; VID 0.2276; CONST 0.0000; lr 0.0001
|
| 733 |
+
iter 0145000; MSM 0.0274; REL 0.0024; VID 0.1637; CONST 0.0000; lr 0.0001
|
| 734 |
+
iter 0145200; MSM 0.2054; REL 0.0490; VID 0.1473; CONST 0.0000; lr 0.0001
|
| 735 |
+
iter 0145400; MSM 0.0247; REL 0.3075; VID 0.0852; CONST 0.0000; lr 0.0001
|
| 736 |
+
iter 0145600; MSM 0.0547; REL 0.0096; VID 1.9674; CONST 0.0000; lr 0.0001
|
| 737 |
+
iter 0145800; MSM 0.1133; REL 0.2978; VID 0.3298; CONST 0.0000; lr 0.0001
|
| 738 |
+
iter 0146000; MSM 0.0832; REL 0.0341; VID 0.2010; CONST 0.0000; lr 0.0001
|
| 739 |
+
iter 0146200; MSM 0.0419; REL 0.0101; VID 0.0637; CONST 0.0000; lr 0.0001
|
| 740 |
+
iter 0146400; MSM 0.0793; REL 0.0037; VID 0.0624; CONST 0.0000; lr 0.0001
|
| 741 |
+
iter 0146600; MSM 0.1299; REL 0.0052; VID 1.4691; CONST 0.0000; lr 0.0001
|
| 742 |
+
iter 0146800; MSM 0.1417; REL 0.0071; VID 1.4826; CONST 0.0000; lr 0.0001
|
| 743 |
+
iter 0147000; MSM 0.0983; REL 0.0208; VID 0.0805; CONST 0.0000; lr 0.0001
|
| 744 |
+
iter 0147200; MSM 0.2197; REL 0.0934; VID 0.0811; CONST 0.0000; lr 0.0001
|
| 745 |
+
iter 0147400; MSM 0.0970; REL 0.1439; VID 1.6085; CONST 0.0000; lr 0.0001
|
| 746 |
+
iter 0147600; MSM 0.0694; REL 0.0261; VID 0.0746; CONST 0.0000; lr 0.0001
|
| 747 |
+
iter 0147800; MSM 0.0290; REL 0.0019; VID 0.3193; CONST 0.0000; lr 0.0001
|
| 748 |
+
iter 0148000; MSM 0.0412; REL 0.0016; VID 0.0927; CONST 0.0000; lr 0.0001
|
| 749 |
+
iter 0148200; MSM 0.1322; REL 0.0107; VID 0.0233; CONST 0.0000; lr 0.0001
|
| 750 |
+
iter 0148400; MSM 0.0263; REL 0.0271; VID 0.1557; CONST 0.0000; lr 0.0001
|
| 751 |
+
iter 0148600; MSM 0.0399; REL 0.0670; VID 1.2888; CONST 0.0000; lr 0.0001
|
| 752 |
+
iter 0148800; MSM 0.1661; REL 0.0148; VID 1.5827; CONST 0.0000; lr 0.0001
|
| 753 |
+
iter 0149000; MSM 0.0553; REL 0.0096; VID 0.1610; CONST 0.0000; lr 0.0001
|
| 754 |
+
iter 0149200; MSM 0.0624; REL 0.0272; VID 0.0726; CONST 0.0000; lr 0.0001
|
| 755 |
+
iter 0149400; MSM 0.1446; REL 0.0166; VID 1.5069; CONST 0.0000; lr 0.0001
|
| 756 |
+
iter 0149600; MSM 0.0464; REL 0.0024; VID 0.3466; CONST 0.0000; lr 0.0001
|
| 757 |
+
iter 0149800; MSM 0.0668; REL 0.0464; VID 0.1150; CONST 0.0000; lr 0.0001
|
| 758 |
+
iter 0150000; MSM 0.0421; REL 1.1264; VID 1.5628; CONST 0.0000; lr 0.0001
|
| 759 |
+
iter 0150200; MSM 0.0666; REL 0.1063; VID 0.7016; CONST 0.0000; lr 0.0001
|
| 760 |
+
iter 0150400; MSM 0.0401; REL 0.0058; VID 0.1962; CONST 0.0000; lr 0.0001
|
| 761 |
+
iter 0150600; MSM 0.0543; REL 0.1340; VID 1.2892; CONST 0.0000; lr 0.0001
|
| 762 |
+
iter 0150800; MSM 0.0537; REL 0.0282; VID 0.0744; CONST 0.0000; lr 0.0001
|
| 763 |
+
iter 0151000; MSM 1.0867; REL 0.0234; VID 1.4358; CONST 0.0000; lr 0.0001
|
| 764 |
+
iter 0151200; MSM 0.0739; REL 0.0350; VID 1.4985; CONST 0.0000; lr 0.0001
|
| 765 |
+
iter 0151400; MSM 0.1033; REL 0.0362; VID 1.4699; CONST 0.0000; lr 0.0001
|
| 766 |
+
iter 0151600; MSM 0.8906; REL 0.0117; VID 1.7246; CONST 0.0000; lr 0.0001
|
| 767 |
+
iter 0151800; MSM 0.0820; REL 0.8196; VID 0.8952; CONST 0.0000; lr 0.0001
|
| 768 |
+
iter 0152000; MSM 0.2034; REL 0.0623; VID 1.5229; CONST 0.0000; lr 0.0001
|
| 769 |
+
iter 0152200; MSM 0.0516; REL 0.0096; VID 0.3455; CONST 0.0000; lr 0.0001
|
| 770 |
+
iter 0152400; MSM 0.1203; REL 0.0105; VID 3.0471; CONST 0.0000; lr 0.0001
|
| 771 |
+
iter 0152600; MSM 0.0854; REL 0.7374; VID 0.3528; CONST 0.0000; lr 0.0001
|
| 772 |
+
iter 0152800; MSM 0.0571; REL 0.2931; VID 1.5018; CONST 0.0000; lr 0.0001
|
| 773 |
+
iter 0153000; MSM 0.0786; REL 0.0173; VID 0.0649; CONST 0.0000; lr 0.0001
|
| 774 |
+
iter 0153200; MSM 0.0664; REL 0.0194; VID 0.1336; CONST 0.0000; lr 0.0001
|
| 775 |
+
iter 0153400; MSM 2.4369; REL 0.0066; VID 1.4948; CONST 0.0000; lr 0.0001
|
| 776 |
+
iter 0153600; MSM 0.0764; REL 0.0000; VID 2.7740; CONST 0.0000; lr 0.0001
|
| 777 |
+
iter 0153800; MSM 0.0658; REL 0.0341; VID 0.0748; CONST 0.0000; lr 0.0001
|
| 778 |
+
iter 0154000; MSM 0.1000; REL 4.6567; VID 1.8417; CONST 0.0000; lr 0.0001
|
| 779 |
+
iter 0154200; MSM 0.0441; REL 4.7187; VID 0.0617; CONST 0.0000; lr 0.0001
|
| 780 |
+
iter 0154400; MSM 0.0279; REL 0.0154; VID 0.0506; CONST 0.0000; lr 0.0001
|
| 781 |
+
iter 0154600; MSM 0.0480; REL 0.0080; VID 0.6881; CONST 0.0000; lr 0.0001
|
| 782 |
+
iter 0154800; MSM 0.0933; REL 0.0232; VID 0.1348; CONST 0.0000; lr 0.0001
|
| 783 |
+
iter 0155000; MSM 0.0480; REL 0.0154; VID 1.0776; CONST 0.0000; lr 0.0001
|
| 784 |
+
iter 0155200; MSM 0.0546; REL 0.0497; VID 0.5891; CONST 0.0000; lr 0.0001
|
| 785 |
+
iter 0155400; MSM 0.1643; REL 0.0672; VID 1.4545; CONST 0.0000; lr 0.0001
|
| 786 |
+
iter 0155600; MSM 0.1469; REL 0.0161; VID 0.9593; CONST 0.0000; lr 0.0001
|
| 787 |
+
iter 0155800; MSM 0.0452; REL 0.0107; VID 1.2100; CONST 0.0000; lr 0.0001
|
| 788 |
+
iter 0156000; MSM 0.1025; REL 0.0268; VID 0.1108; CONST 0.0000; lr 0.0001
|
| 789 |
+
iter 0156200; MSM 0.1049; REL 0.0025; VID 1.6169; CONST 0.0000; lr 0.0001
|
| 790 |
+
iter 0156400; MSM 0.0666; REL 0.0109; VID 3.1788; CONST 0.0000; lr 0.0001
|
| 791 |
+
iter 0156600; MSM 0.0656; REL 0.0116; VID 0.6463; CONST 0.0000; lr 0.0001
|
| 792 |
+
iter 0156800; MSM 0.0271; REL 1.9066; VID 0.0831; CONST 0.0000; lr 0.0001
|
| 793 |
+
iter 0157000; MSM 0.0543; REL 0.0146; VID 0.1439; CONST 0.0000; lr 0.0001
|
| 794 |
+
iter 0157200; MSM 0.0564; REL 0.0071; VID 0.1247; CONST 0.0000; lr 0.0001
|
| 795 |
+
iter 0157400; MSM 0.1461; REL 0.0257; VID 2.0726; CONST 0.0000; lr 0.0001
|
| 796 |
+
iter 0157600; MSM 0.1105; REL 0.0047; VID 0.2599; CONST 0.0000; lr 0.0001
|
| 797 |
+
iter 0157800; MSM 0.0631; REL 0.0037; VID 0.2186; CONST 0.0000; lr 0.0001
|
| 798 |
+
iter 0158000; MSM 0.0621; REL 0.0157; VID 0.0811; CONST 0.0000; lr 0.0001
|
| 799 |
+
iter 0158200; MSM 0.0595; REL 0.0239; VID 1.4139; CONST 0.0000; lr 0.0001
|
| 800 |
+
iter 0158400; MSM 1.5939; REL 0.0099; VID 1.4268; CONST 0.0000; lr 0.0001
|
| 801 |
+
iter 0158600; MSM 0.0368; REL 0.0043; VID 0.1559; CONST 0.0000; lr 0.0001
|
| 802 |
+
iter 0158800; MSM 0.0222; REL 0.0245; VID 0.1042; CONST 0.0000; lr 0.0001
|
| 803 |
+
iter 0159000; MSM 0.0239; REL 0.0056; VID 0.1683; CONST 0.0000; lr 0.0001
|
| 804 |
+
iter 0159200; MSM 0.0153; REL 0.0172; VID 1.2905; CONST 0.0000; lr 0.0001
|
| 805 |
+
iter 0159400; MSM 0.0753; REL 0.3953; VID 0.0768; CONST 0.0000; lr 0.0001
|
| 806 |
+
iter 0159600; MSM 0.0740; REL 2.6695; VID 1.4393; CONST 0.0000; lr 0.0001
|
| 807 |
+
iter 0159800; MSM 0.0325; REL 3.4335; VID 1.2947; CONST 0.0000; lr 0.0001
|
| 808 |
+
iter 0160000; MSM 0.0779; REL 0.0235; VID 1.5435; CONST 0.0000; lr 0.0001
|
| 809 |
+
iter 0160200; MSM 0.0450; REL 0.0519; VID 0.0782; CONST 0.0000; lr 0.0001
|
| 810 |
+
iter 0160400; MSM 0.1021; REL 0.0039; VID 2.7838; CONST 0.0000; lr 0.0001
|
| 811 |
+
iter 0160600; MSM 0.0169; REL 0.8952; VID 0.0439; CONST 0.0000; lr 0.0001
|
| 812 |
+
iter 0160800; MSM 0.0841; REL 0.0089; VID 0.1548; CONST 0.0000; lr 0.0001
|
| 813 |
+
iter 0161000; MSM 0.0272; REL 0.0302; VID 0.1882; CONST 0.0000; lr 0.0001
|
| 814 |
+
iter 0161200; MSM 1.1727; REL 2.4885; VID 1.4915; CONST 0.0000; lr 0.0001
|
| 815 |
+
iter 0161400; MSM 0.0293; REL 0.0499; VID 0.2037; CONST 0.0000; lr 0.0001
|
| 816 |
+
iter 0161600; MSM 0.0979; REL 0.0616; VID 0.7780; CONST 0.0000; lr 0.0001
|
| 817 |
+
iter 0161800; MSM 0.1259; REL 0.0064; VID 0.3771; CONST 0.0000; lr 0.0001
|
| 818 |
+
iter 0162000; MSM 0.1210; REL 0.0437; VID 1.4384; CONST 0.0000; lr 0.0001
|
| 819 |
+
iter 0162200; MSM 0.0315; REL 0.0081; VID 1.5461; CONST 0.0000; lr 0.0001
|
| 820 |
+
iter 0162400; MSM 0.0583; REL 0.0296; VID 1.4656; CONST 0.0000; lr 0.0001
|
| 821 |
+
iter 0162600; MSM 0.0534; REL 0.0192; VID 1.3845; CONST 0.0000; lr 0.0001
|
| 822 |
+
iter 0162800; MSM 0.0437; REL 0.0235; VID 0.0961; CONST 0.0000; lr 0.0001
|
| 823 |
+
iter 0163000; MSM 0.1043; REL 0.0110; VID 0.1283; CONST 0.0000; lr 0.0001
|
| 824 |
+
iter 0163200; MSM 0.0834; REL 0.0088; VID 1.4613; CONST 0.0000; lr 0.0001
|
| 825 |
+
iter 0163400; MSM 0.0583; REL 0.0111; VID 1.4308; CONST 0.0000; lr 0.0001
|
| 826 |
+
iter 0163600; MSM 0.0525; REL 0.0171; VID 0.9069; CONST 0.0000; lr 0.0001
|
| 827 |
+
iter 0163800; MSM 0.0343; REL 0.3697; VID 0.0477; CONST 0.0000; lr 0.0001
|
| 828 |
+
iter 0164000; MSM 0.0513; REL 0.0075; VID 1.4269; CONST 0.0000; lr 0.0001
|
| 829 |
+
iter 0164200; MSM 0.1727; REL 0.0389; VID 0.0628; CONST 0.0000; lr 0.0001
|
| 830 |
+
iter 0164400; MSM 0.0781; REL 0.0409; VID 0.0974; CONST 0.0000; lr 0.0001
|
| 831 |
+
iter 0164600; MSM 0.0657; REL 0.0223; VID 0.2472; CONST 0.0000; lr 0.0001
|
| 832 |
+
iter 0164800; MSM 0.0214; REL 0.0092; VID 1.5831; CONST 0.0000; lr 0.0001
|
| 833 |
+
iter 0165000; MSM 0.0629; REL 0.0604; VID 0.8094; CONST 0.0000; lr 0.0001
|
| 834 |
+
iter 0165200; MSM 0.0944; REL 0.0785; VID 1.4297; CONST 0.0000; lr 0.0001
|
| 835 |
+
iter 0165400; MSM 0.0902; REL 0.0087; VID 1.4210; CONST 0.0000; lr 0.0001
|
| 836 |
+
iter 0165600; MSM 0.0454; REL 0.0083; VID 0.8410; CONST 0.0000; lr 0.0001
|
| 837 |
+
iter 0165800; MSM 0.0549; REL 0.0027; VID 0.1422; CONST 0.0000; lr 0.0001
|
| 838 |
+
iter 0166000; MSM 0.0558; REL 0.0074; VID 0.0355; CONST 0.0000; lr 0.0001
|
| 839 |
+
iter 0166200; MSM 0.0309; REL 0.0168; VID 0.6831; CONST 0.0000; lr 0.0001
|
| 840 |
+
iter 0166400; MSM 0.1133; REL 0.3074; VID 0.0450; CONST 0.0000; lr 0.0001
|
| 841 |
+
iter 0166600; MSM 0.1140; REL 0.0135; VID 0.8225; CONST 0.0000; lr 0.0001
|
| 842 |
+
iter 0166800; MSM 0.0710; REL 0.0595; VID 0.0315; CONST 0.0000; lr 0.0001
|
| 843 |
+
iter 0167000; MSM 0.0574; REL 0.0055; VID 0.0362; CONST 0.0000; lr 0.0001
|
| 844 |
+
iter 0167200; MSM 0.0428; REL 0.0132; VID 0.0933; CONST 0.0000; lr 0.0001
|
| 845 |
+
iter 0167400; MSM 0.0470; REL 0.2155; VID 0.1351; CONST 0.0000; lr 0.0001
|
| 846 |
+
iter 0167600; MSM 0.0339; REL 0.0218; VID 0.0452; CONST 0.0000; lr 0.0001
|
| 847 |
+
iter 0167800; MSM 0.0828; REL 0.0170; VID 0.1198; CONST 0.0000; lr 0.0001
|
| 848 |
+
iter 0168000; MSM 0.0729; REL 0.2375; VID 0.2014; CONST 0.0000; lr 0.0001
|
| 849 |
+
iter 0168200; MSM 0.0477; REL 3.7902; VID 0.8823; CONST 0.0000; lr 0.0001
|
| 850 |
+
iter 0168400; MSM 0.0208; REL 0.0056; VID 0.1950; CONST 0.0000; lr 0.0001
|
| 851 |
+
iter 0168600; MSM 0.0348; REL 0.0163; VID 0.0495; CONST 0.0000; lr 0.0001
|
| 852 |
+
iter 0168800; MSM 0.0535; REL 0.0175; VID 0.0692; CONST 0.0000; lr 0.0001
|
| 853 |
+
iter 0169000; MSM 0.0483; REL 3.2906; VID 0.0588; CONST 0.0000; lr 0.0001
|
| 854 |
+
iter 0169200; MSM 0.0598; REL 0.0094; VID 0.0691; CONST 0.0000; lr 0.0001
|
| 855 |
+
iter 0169400; MSM 0.0368; REL 0.0027; VID 0.1106; CONST 0.0000; lr 0.0001
|
| 856 |
+
iter 0169600; MSM 0.0452; REL 0.3810; VID 2.7093; CONST 0.0000; lr 0.0001
|
| 857 |
+
iter 0169800; MSM 0.1090; REL 0.0238; VID 1.4785; CONST 0.0000; lr 0.0001
|
| 858 |
+
iter 0170000; MSM 0.0612; REL 3.2868; VID 0.1274; CONST 0.0000; lr 0.0001
|
| 859 |
+
iter 0170200; MSM 0.0976; REL 0.0159; VID 1.1900; CONST 0.0000; lr 0.0001
|
| 860 |
+
iter 0170400; MSM 0.0551; REL 0.0152; VID 0.6120; CONST 0.0000; lr 0.0001
|
| 861 |
+
iter 0170600; MSM 0.0420; REL 0.0117; VID 0.1324; CONST 0.0000; lr 0.0001
|
| 862 |
+
iter 0170800; MSM 0.0503; REL 0.0187; VID 0.1270; CONST 0.0000; lr 0.0001
|
| 863 |
+
iter 0171000; MSM 0.0479; REL 0.0134; VID 0.8867; CONST 0.0000; lr 0.0001
|
| 864 |
+
iter 0171200; MSM 0.0515; REL 0.0097; VID 0.1166; CONST 0.0000; lr 0.0001
|
| 865 |
+
iter 0171400; MSM 0.0811; REL 0.1205; VID 0.3153; CONST 0.0000; lr 0.0001
|
| 866 |
+
iter 0171600; MSM 0.0375; REL 0.0189; VID 0.3177; CONST 0.0000; lr 0.0001
|
| 867 |
+
iter 0171800; MSM 0.0975; REL 4.6046; VID 1.6741; CONST 0.0000; lr 0.0001
|
| 868 |
+
iter 0172000; MSM 0.0630; REL 0.0138; VID 0.0538; CONST 0.0000; lr 0.0001
|
| 869 |
+
iter 0172200; MSM 0.0462; REL 0.0048; VID 0.1541; CONST 0.0000; lr 0.0001
|
| 870 |
+
iter 0172400; MSM 0.0394; REL 0.0752; VID 0.0506; CONST 0.0000; lr 0.0001
|
| 871 |
+
iter 0172600; MSM 0.0801; REL 0.0293; VID 1.4325; CONST 0.0000; lr 0.0001
|
| 872 |
+
iter 0172800; MSM 0.0691; REL 0.3758; VID 0.2156; CONST 0.0000; lr 0.0001
|
| 873 |
+
iter 0173000; MSM 0.0475; REL 0.0288; VID 0.7728; CONST 0.0000; lr 0.0001
|
| 874 |
+
iter 0173200; MSM 0.0790; REL 0.0039; VID 0.8106; CONST 0.0000; lr 0.0001
|
| 875 |
+
iter 0173400; MSM 0.0753; REL 0.0055; VID 0.8349; CONST 0.0000; lr 0.0001
|
| 876 |
+
iter 0173600; MSM 0.0327; REL 0.0094; VID 0.3839; CONST 0.0000; lr 0.0001
|
| 877 |
+
iter 0173800; MSM 0.1078; REL 0.0050; VID 0.0253; CONST 0.0000; lr 0.0001
|
| 878 |
+
iter 0174000; MSM 0.0649; REL 0.0174; VID 0.3197; CONST 0.0000; lr 0.0001
|
| 879 |
+
iter 0174200; MSM 0.0227; REL 0.0200; VID 0.1190; CONST 0.0000; lr 0.0001
|
| 880 |
+
iter 0174400; MSM 0.0555; REL 0.0211; VID 0.7340; CONST 0.0000; lr 0.0001
|
| 881 |
+
iter 0174600; MSM 0.0446; REL 0.2769; VID 1.8779; CONST 0.0000; lr 0.0001
|
| 882 |
+
iter 0174800; MSM 0.0342; REL 0.0027; VID 0.0380; CONST 0.0000; lr 0.0001
|
| 883 |
+
iter 0175000; MSM 0.0410; REL 0.0076; VID 0.6792; CONST 0.0000; lr 0.0001
|
| 884 |
+
iter 0175200; MSM 0.0530; REL 0.0099; VID 0.6733; CONST 0.0000; lr 0.0001
|
| 885 |
+
iter 0175400; MSM 0.0320; REL 0.0426; VID 1.4602; CONST 0.0000; lr 0.0001
|
| 886 |
+
iter 0175600; MSM 0.0796; REL 0.0432; VID 1.7816; CONST 0.0000; lr 0.0001
|
| 887 |
+
iter 0175800; MSM 0.1312; REL 0.0184; VID 0.0910; CONST 0.0000; lr 0.0001
|
| 888 |
+
iter 0176000; MSM 0.0432; REL 0.0124; VID 0.5986; CONST 0.0000; lr 0.0001
|
| 889 |
+
iter 0176200; MSM 0.0790; REL 0.0015; VID 1.5356; CONST 0.0000; lr 0.0001
|
| 890 |
+
iter 0176400; MSM 0.0723; REL 0.0242; VID 0.0389; CONST 0.0000; lr 0.0001
|
| 891 |
+
iter 0176600; MSM 0.0404; REL 0.4054; VID 0.2268; CONST 0.0000; lr 0.0001
|
| 892 |
+
iter 0176800; MSM 0.0874; REL 0.0255; VID 0.0597; CONST 0.0000; lr 0.0001
|
| 893 |
+
iter 0177000; MSM 0.3272; REL 0.0032; VID 0.2147; CONST 0.0000; lr 0.0001
|
| 894 |
+
iter 0177200; MSM 0.2376; REL 0.0620; VID 0.1163; CONST 0.0000; lr 0.0001
|
| 895 |
+
iter 0177400; MSM 0.0744; REL 0.0507; VID 0.0606; CONST 0.0000; lr 0.0001
|
| 896 |
+
iter 0177600; MSM 0.1170; REL 0.0276; VID 0.7705; CONST 0.0000; lr 0.0001
|
| 897 |
+
iter 0177800; MSM 0.0978; REL 0.0021; VID 0.0236; CONST 0.0000; lr 0.0001
|
| 898 |
+
iter 0178000; MSM 1.0003; REL 0.0021; VID 1.4051; CONST 0.0000; lr 0.0001
|
| 899 |
+
iter 0178200; MSM 0.0654; REL 0.0135; VID 1.2558; CONST 0.0000; lr 0.0001
|
| 900 |
+
iter 0178400; MSM 0.0965; REL 0.0106; VID 0.7536; CONST 0.0000; lr 0.0001
|
| 901 |
+
iter 0178600; MSM 0.1293; REL 0.0133; VID 0.0449; CONST 0.0000; lr 0.0001
|
| 902 |
+
iter 0178800; MSM 0.0529; REL 0.1190; VID 1.4007; CONST 0.0000; lr 0.0001
|
| 903 |
+
iter 0179000; MSM 0.0688; REL 0.0271; VID 0.0666; CONST 0.0000; lr 0.0001
|
| 904 |
+
iter 0179200; MSM 0.1270; REL 0.0360; VID 1.6120; CONST 0.0000; lr 0.0001
|
| 905 |
+
iter 0179400; MSM 0.2628; REL 0.0056; VID 0.0962; CONST 0.0000; lr 0.0001
|
| 906 |
+
iter 0179600; MSM 0.0499; REL 0.0229; VID 0.1274; CONST 0.0000; lr 0.0001
|
| 907 |
+
iter 0179800; MSM 0.1644; REL 0.0049; VID 0.0551; CONST 0.0000; lr 0.0001
|
| 908 |
+
iter 0180000; MSM 0.0672; REL 0.3354; VID 1.4426; CONST 0.0000; lr 0.0001
|
| 909 |
+
iter 0180200; MSM 0.0525; REL 0.0021; VID 1.0858; CONST 0.0000; lr 0.0001
|
| 910 |
+
iter 0180400; MSM 0.0701; REL 0.0056; VID 0.0488; CONST 0.0000; lr 0.0001
|
| 911 |
+
iter 0180600; MSM 0.0215; REL 0.0134; VID 0.1066; CONST 0.0000; lr 0.0001
|
| 912 |
+
iter 0180800; MSM 0.0413; REL 0.0023; VID 0.1553; CONST 0.0000; lr 0.0001
|
| 913 |
+
iter 0181000; MSM 0.0990; REL 0.3896; VID 0.0808; CONST 0.0000; lr 0.0001
|
| 914 |
+
iter 0181200; MSM 0.0463; REL 0.0059; VID 1.4351; CONST 0.0000; lr 0.0001
|
| 915 |
+
iter 0181400; MSM 0.0520; REL 0.0454; VID 0.1060; CONST 0.0000; lr 0.0001
|
| 916 |
+
iter 0181600; MSM 0.1780; REL 0.0087; VID 0.0898; CONST 0.0000; lr 0.0001
|
| 917 |
+
iter 0181800; MSM 0.0742; REL 0.0037; VID 0.0945; CONST 0.0000; lr 0.0001
|
| 918 |
+
iter 0182000; MSM 0.0770; REL 0.0099; VID 0.0902; CONST 0.0000; lr 0.0001
|
| 919 |
+
iter 0182200; MSM 0.0180; REL 0.0055; VID 1.5491; CONST 0.0000; lr 0.0001
|
| 920 |
+
iter 0182400; MSM 0.0116; REL 0.1137; VID 0.0553; CONST 0.0000; lr 0.0001
|
| 921 |
+
iter 0182600; MSM 0.1037; REL 0.0235; VID 0.0214; CONST 0.0000; lr 0.0001
|
| 922 |
+
iter 0182800; MSM 0.0399; REL 0.0056; VID 1.7640; CONST 0.0000; lr 0.0001
|
| 923 |
+
iter 0183000; MSM 0.0467; REL 3.8594; VID 0.2215; CONST 0.0000; lr 0.0001
|
| 924 |
+
iter 0183200; MSM 0.0488; REL 0.0101; VID 0.0693; CONST 0.0000; lr 0.0001
|
| 925 |
+
iter 0183400; MSM 0.0940; REL 0.0461; VID 4.5127; CONST 0.0000; lr 0.0001
|
| 926 |
+
iter 0183600; MSM 0.0639; REL 0.1391; VID 0.1513; CONST 0.0000; lr 0.0001
|
| 927 |
+
iter 0183800; MSM 0.0970; REL 0.0047; VID 0.1835; CONST 0.0000; lr 0.0001
|
| 928 |
+
iter 0184000; MSM 0.0426; REL 0.0278; VID 0.8590; CONST 0.0000; lr 0.0001
|
| 929 |
+
iter 0184200; MSM 0.1157; REL 0.0747; VID 0.0608; CONST 0.0000; lr 0.0001
|
| 930 |
+
iter 0184400; MSM 0.1133; REL 0.0207; VID 1.2683; CONST 0.0000; lr 0.0001
|
| 931 |
+
iter 0184600; MSM 0.0259; REL 0.0056; VID 0.0408; CONST 0.0000; lr 0.0001
|
| 932 |
+
iter 0184800; MSM 0.1004; REL 2.9718; VID 4.0035; CONST 0.0000; lr 0.0001
|
| 933 |
+
iter 0185000; MSM 0.0289; REL 0.0161; VID 0.0838; CONST 0.0000; lr 0.0001
|
| 934 |
+
iter 0185200; MSM 0.0465; REL 0.0076; VID 0.0415; CONST 0.0000; lr 0.0001
|
| 935 |
+
iter 0185400; MSM 0.0370; REL 0.0042; VID 0.7021; CONST 0.0000; lr 0.0001
|
| 936 |
+
iter 0185600; MSM 0.0572; REL 0.0102; VID 0.1164; CONST 0.0000; lr 0.0001
|
| 937 |
+
iter 0185800; MSM 0.0492; REL 0.0157; VID 0.0531; CONST 0.0000; lr 0.0001
|
| 938 |
+
iter 0186000; MSM 0.0427; REL 0.0093; VID 1.7909; CONST 0.0000; lr 0.0001
|
| 939 |
+
iter 0186200; MSM 0.0230; REL 0.0076; VID 1.1720; CONST 0.0000; lr 0.0001
|
| 940 |
+
iter 0186400; MSM 0.0350; REL 0.0142; VID 0.0234; CONST 0.0000; lr 0.0001
|
| 941 |
+
iter 0186600; MSM 0.0547; REL 0.0313; VID 0.3184; CONST 0.0000; lr 0.0001
|
| 942 |
+
iter 0186800; MSM 0.0617; REL 0.0079; VID 0.0705; CONST 0.0000; lr 0.0001
|
| 943 |
+
iter 0187000; MSM 0.0295; REL 0.0162; VID 0.0423; CONST 0.0000; lr 0.0001
|
| 944 |
+
iter 0187200; MSM 0.0906; REL 0.0093; VID 0.0793; CONST 0.0000; lr 0.0001
|
| 945 |
+
iter 0187400; MSM 0.0139; REL 0.0051; VID 0.0538; CONST 0.0000; lr 0.0001
|
| 946 |
+
iter 0187600; MSM 0.0308; REL 0.0272; VID 0.2298; CONST 0.0000; lr 0.0001
|
| 947 |
+
iter 0187800; MSM 0.1130; REL 0.0162; VID 0.0288; CONST 0.0000; lr 0.0001
|
| 948 |
+
iter 0188000; MSM 0.0840; REL 0.0214; VID 0.1676; CONST 0.0000; lr 0.0001
|
| 949 |
+
iter 0188200; MSM 0.0388; REL 0.0093; VID 0.0625; CONST 0.0000; lr 0.0001
|
| 950 |
+
iter 0188400; MSM 0.0892; REL 0.0451; VID 0.8465; CONST 0.0000; lr 0.0001
|
| 951 |
+
iter 0188600; MSM 0.1160; REL 0.0082; VID 0.0504; CONST 0.0000; lr 0.0001
|
| 952 |
+
iter 0188800; MSM 0.0381; REL 0.1701; VID 0.0271; CONST 0.0000; lr 0.0001
|
| 953 |
+
iter 0189000; MSM 0.0498; REL 0.0223; VID 1.3956; CONST 0.0000; lr 0.0001
|
| 954 |
+
iter 0189200; MSM 0.0473; REL 0.0078; VID 0.4238; CONST 0.0000; lr 0.0001
|
| 955 |
+
iter 0189400; MSM 0.0686; REL 0.0237; VID 1.6829; CONST 0.0000; lr 0.0001
|
| 956 |
+
iter 0189600; MSM 0.0190; REL 0.5529; VID 1.6591; CONST 0.0000; lr 0.0001
|
| 957 |
+
iter 0189800; MSM 0.0542; REL 0.0043; VID 0.0402; CONST 0.0000; lr 0.0001
|
| 958 |
+
iter 0190000; MSM 0.0214; REL 0.6901; VID 0.0321; CONST 0.0000; lr 0.0001
|
| 959 |
+
iter 0190200; MSM 0.0749; REL 0.0293; VID 0.0694; CONST 0.0000; lr 0.0001
|
| 960 |
+
iter 0190400; MSM 0.1258; REL 0.0139; VID 0.0294; CONST 0.0000; lr 0.0001
|
| 961 |
+
iter 0190600; MSM 0.0265; REL 0.0058; VID 0.0959; CONST 0.0000; lr 0.0001
|
| 962 |
+
iter 0190800; MSM 0.1472; REL 0.0149; VID 1.9790; CONST 0.0000; lr 0.0001
|
| 963 |
+
iter 0191000; MSM 0.1249; REL 0.0163; VID 0.8729; CONST 0.0000; lr 0.0001
|
| 964 |
+
iter 0191200; MSM 0.0262; REL 0.0126; VID 0.1434; CONST 0.0000; lr 0.0001
|
| 965 |
+
iter 0191400; MSM 0.0446; REL 0.0143; VID 0.0433; CONST 0.0000; lr 0.0001
|
| 966 |
+
iter 0191600; MSM 0.0457; REL 0.0053; VID 0.0880; CONST 0.0000; lr 0.0001
|
| 967 |
+
iter 0191800; MSM 0.0539; REL 0.0112; VID 0.2351; CONST 0.0000; lr 0.0001
|
| 968 |
+
iter 0192000; MSM 0.0643; REL 0.0043; VID 0.0635; CONST 0.0000; lr 0.0001
|
| 969 |
+
iter 0192200; MSM 0.0199; REL 0.0203; VID 1.4245; CONST 0.0000; lr 0.0001
|
| 970 |
+
iter 0192400; MSM 0.0471; REL 0.1410; VID 0.0813; CONST 0.0000; lr 0.0001
|
| 971 |
+
iter 0192600; MSM 0.1839; REL 0.3834; VID 0.0854; CONST 0.0000; lr 0.0001
|
| 972 |
+
iter 0192800; MSM 0.1191; REL 0.0271; VID 1.4159; CONST 0.0000; lr 0.0001
|
| 973 |
+
iter 0193000; MSM 0.0784; REL 0.0051; VID 1.4866; CONST 0.0000; lr 0.0001
|
| 974 |
+
iter 0193200; MSM 0.1098; REL 0.0200; VID 0.0329; CONST 0.0000; lr 0.0001
|
| 975 |
+
iter 0193400; MSM 0.0381; REL 0.0297; VID 0.1591; CONST 0.0000; lr 0.0001
|
| 976 |
+
iter 0193600; MSM 0.2797; REL 0.0381; VID 1.8439; CONST 0.0000; lr 0.0001
|
| 977 |
+
iter 0193800; MSM 0.0544; REL 0.0254; VID 3.6698; CONST 0.0000; lr 0.0001
|
| 978 |
+
iter 0194000; MSM 0.0514; REL 0.0204; VID 0.0352; CONST 0.0000; lr 0.0001
|
| 979 |
+
iter 0194200; MSM 0.0908; REL 0.0026; VID 1.4729; CONST 0.0000; lr 0.0001
|
| 980 |
+
iter 0194400; MSM 0.0691; REL 0.0085; VID 0.8850; CONST 0.0000; lr 0.0001
|
| 981 |
+
iter 0194600; MSM 0.0614; REL 0.0111; VID 0.0714; CONST 0.0000; lr 0.0001
|
| 982 |
+
iter 0194800; MSM 0.0978; REL 0.0095; VID 0.1037; CONST 0.0000; lr 0.0001
|
| 983 |
+
iter 0195000; MSM 0.0591; REL 0.1987; VID 0.1044; CONST 0.0000; lr 0.0001
|
| 984 |
+
iter 0195200; MSM 0.0656; REL 0.0034; VID 1.5324; CONST 0.0000; lr 0.0001
|
| 985 |
+
iter 0195400; MSM 0.0198; REL 0.0699; VID 0.0337; CONST 0.0000; lr 0.0001
|
| 986 |
+
iter 0195600; MSM 0.0669; REL 0.0030; VID 1.4273; CONST 0.0000; lr 0.0001
|
| 987 |
+
iter 0195800; MSM 0.0126; REL 0.0660; VID 0.0267; CONST 0.0000; lr 0.0001
|
| 988 |
+
iter 0196000; MSM 0.2760; REL 0.0620; VID 0.0308; CONST 0.0000; lr 0.0001
|
| 989 |
+
iter 0196200; MSM 0.0880; REL 2.2863; VID 0.1108; CONST 0.0000; lr 0.0001
|
| 990 |
+
iter 0196400; MSM 0.0398; REL 0.0170; VID 0.5002; CONST 0.0000; lr 0.0001
|
| 991 |
+
iter 0196600; MSM 0.0302; REL 0.0080; VID 0.1573; CONST 0.0000; lr 0.0001
|
| 992 |
+
iter 0196800; MSM 0.1055; REL 0.0188; VID 1.4155; CONST 0.0000; lr 0.0001
|
| 993 |
+
iter 0197000; MSM 0.0606; REL 0.0409; VID 0.1642; CONST 0.0000; lr 0.0001
|
| 994 |
+
iter 0197200; MSM 0.0754; REL 0.0110; VID 0.0648; CONST 0.0000; lr 0.0001
|
| 995 |
+
iter 0197400; MSM 0.0534; REL 0.0051; VID 0.6536; CONST 0.0000; lr 0.0001
|
| 996 |
+
iter 0197600; MSM 0.1112; REL 0.0021; VID 2.1521; CONST 0.0000; lr 0.0001
|
| 997 |
+
iter 0197800; MSM 0.0525; REL 0.1024; VID 0.0552; CONST 0.0000; lr 0.0001
|
| 998 |
+
iter 0198000; MSM 0.0643; REL 0.0387; VID 0.9123; CONST 0.0000; lr 0.0001
|
| 999 |
+
iter 0198200; MSM 0.0766; REL 2.6710; VID 0.0567; CONST 0.0000; lr 0.0001
|
| 1000 |
+
iter 0198400; MSM 0.0344; REL 0.0064; VID 1.2951; CONST 0.0000; lr 0.0001
|
| 1001 |
+
iter 0198600; MSM 0.0273; REL 0.0040; VID 0.0699; CONST 0.0000; lr 0.0001
|
| 1002 |
+
iter 0198800; MSM 0.0251; REL 0.0099; VID 0.0374; CONST 0.0000; lr 0.0001
|
| 1003 |
+
iter 0199000; MSM 0.1095; REL 0.0027; VID 1.4446; CONST 0.0000; lr 0.0001
|
| 1004 |
+
iter 0199200; MSM 0.0620; REL 0.0289; VID 0.0491; CONST 0.0000; lr 0.0001
|
| 1005 |
+
iter 0199400; MSM 0.0697; REL 0.0153; VID 1.4263; CONST 0.0000; lr 0.0001
|
| 1006 |
+
iter 0199600; MSM 0.2364; REL 0.0023; VID 0.0216; CONST 0.0000; lr 0.0001
|
| 1007 |
+
iter 0199800; MSM 0.0348; REL 2.8578; VID 1.4539; CONST 0.0000; lr 0.0001
|