chansongoal commited on Jan 2

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Upload the learned transform mappings

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transform_mapping/dinov2_cls/dinov2_cls_kmenas.txt +232 -0
transform_mapping/dinov2_cls/dinov2_cls_kmenas100.txt +210 -0
transform_mapping/dinov2_cls/transform_mapping_cls_kmeans100_bitdepth10.json +1 -0
transform_mapping/dinov2_cls/transform_mapping_cls_kmeans100_bitdepth8.json +1 -0
transform_mapping/dinov2_cls/transform_mapping_cls_kmeans10_bitdepth10.json +1 -0
transform_mapping/dinov2_cls/transform_mapping_cls_kmeans10_bitdepth8.json +1 -0
transform_mapping/dinov2_dpt/dinov2_dpt_kmenas.txt +0 -0
transform_mapping/dinov2_dpt/dinov2_dpt_kmenas_layer10.txt +103 -0
transform_mapping/dinov2_dpt/dinov2_dpt_kmenas_layer20.txt +103 -0
transform_mapping/dinov2_dpt/dinov2_dpt_kmenas_layer30.txt +206 -0
transform_mapping/dinov2_dpt/dinov2_dpt_kmenas_layer40.txt +206 -0
transform_mapping/dinov2_dpt/transform_mapping_dpt_layer10_kmeans100_bitdepth10.json +1 -0
transform_mapping/dinov2_dpt/transform_mapping_dpt_layer10_kmeans100_bitdepth8.json +1 -0
transform_mapping/dinov2_dpt/transform_mapping_dpt_layer10_kmeans10_bitdepth10.json +1 -0
transform_mapping/dinov2_dpt/transform_mapping_dpt_layer10_kmeans10_bitdepth8.json +1 -0
transform_mapping/dinov2_dpt/transform_mapping_dpt_layer20_kmeans100_bitdepth10.json +1 -0
transform_mapping/dinov2_dpt/transform_mapping_dpt_layer20_kmeans100_bitdepth8.json +1 -0
transform_mapping/dinov2_dpt/transform_mapping_dpt_layer20_kmeans10_bitdepth10.json +1 -0
transform_mapping/dinov2_dpt/transform_mapping_dpt_layer20_kmeans10_bitdepth8.json +1 -0
transform_mapping/dinov2_dpt/transform_mapping_dpt_layer30_kmeans100_bitdepth10.json +1 -0
transform_mapping/dinov2_dpt/transform_mapping_dpt_layer30_kmeans100_bitdepth8.json +1 -0
transform_mapping/dinov2_dpt/transform_mapping_dpt_layer30_kmeans10_bitdepth10.json +1 -0
transform_mapping/dinov2_dpt/transform_mapping_dpt_layer30_kmeans10_bitdepth8.json +1 -0
transform_mapping/dinov2_dpt/transform_mapping_dpt_layer40_kmeans100_bitdepth10.json +1 -0
transform_mapping/dinov2_dpt/transform_mapping_dpt_layer40_kmeans100_bitdepth8.json +1 -0
transform_mapping/dinov2_dpt/transform_mapping_dpt_layer40_kmeans10_bitdepth10.json +1 -0
transform_mapping/dinov2_dpt/transform_mapping_dpt_layer40_kmeans10_bitdepth8.json +1 -0
transform_mapping/dinov2_seg/dinov2_seg_kmenas100.txt +107 -0
transform_mapping/dinov2_seg/transform_mapping_seg_kmeans100_bitdepth8.json +1 -0
transform_mapping/dinov2_seg/transform_mapping_seg_kmeans10_bitdepth10.json +1 -0
transform_mapping/dinov2_seg/transform_mapping_seg_kmeans10_bitdepth8.json +1 -0
transform_mapping/llama3_csr/llama3_csr_kmenas.txt +36 -0
transform_mapping/llama3_csr/llama3_csr_kmenas100.txt +206 -0
transform_mapping/llama3_csr/transform_mapping_csr_kmeans100_bitdepth10.json +1 -0
transform_mapping/llama3_csr/transform_mapping_csr_kmeans100_bitdepth8.json +1 -0
transform_mapping/llama3_csr/transform_mapping_csr_kmeans10_bitdepth10.json +1 -0
transform_mapping/llama3_csr/transform_mapping_csr_kmeans10_bitdepth6.json +1 -0
transform_mapping/llama3_csr/transform_mapping_csr_kmeans10_bitdepth8.json +1 -0
transform_mapping/resnet50_cls/transform_mapping_resnet50_cls_kmeans10_bitdepth8.json +1 -0
transform_mapping/sd3_tti/sd3_tti_kmenas.txt +36 -0
transform_mapping/sd3_tti/sd3_tti_kmenas100.txt +206 -0
transform_mapping/sd3_tti/transform_mapping_tti_kmeans100_bitdepth10.json +1 -0
transform_mapping/sd3_tti/transform_mapping_tti_kmeans100_bitdepth8.json +1 -0
transform_mapping/sd3_tti/transform_mapping_tti_kmeans10_bitdepth10.json +1 -0
transform_mapping/sd3_tti/transform_mapping_tti_kmeans10_bitdepth6.json +1 -0
transform_mapping/sd3_tti/transform_mapping_tti_kmeans10_bitdepth8.json +1 -0

transform_mapping/dinov2_cls/dinov2_cls_kmenas.txt ADDED Viewed

	@@ -0,0 +1,232 @@

+dinov2 cls False kmeans 10 94.15 -542.31 94.15 -542.31 8
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+KMeans_quant_time: 161.6999 KMeans Feature MSE: 0.00214467
+Quantization points saved to /gdata1/gaocs/Data_DTUFC/transform_mapping/dinov2_cls/transform_mapping_cls_kmeans10_bitdepth8.json
+dinov2 cls False kmeans 10 94.15 -542.31 94.15 -542.31 10
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+KMeans_quant_time: 642.4359 KMeans Feature MSE: 0.00007715
+Quantization points saved to /gdata1/gaocs/Data_DTUFC/transform_mapping/dinov2_cls/transform_mapping_cls_kmeans10_bitdepth10.json
+dinov2 cls False kmeans 100 94.15 -542.31 94.15 -542.31 8
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+KMeans_quant_time: 833.3995 KMeans Feature MSE: 0.00197164
+Quantization points saved to /gdata1/gaocs/Data_DTUFC/transform_mapping/dinov2_cls/transform_mapping_cls_kmeans100_bitdepth8.json
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+KMeans_quant_time: 3506.3802 KMeans Feature MSE: 0.00012543
+Quantization points saved to /gdata1/gaocs/Data_DTUFC/transform_mapping/dinov2_cls/transform_mapping_cls_kmeans100_bitdepth10.json

transform_mapping/dinov2_cls/dinov2_cls_kmenas100.txt ADDED Viewed

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+KMeans_quant_time: 13031.5314 KMeans Feature MSE: 0.00161281
+Quantization points saved to /gdata1/gaocs/Data_DTUFC/transform_mapping/dinov2_seg/transform_mapping_seg_kmeans100_bitdepth8.json
+dinov2 seg False kmeans 100 105.95 -506.97 105.95 -506.97 10
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+Traceback (most recent call last):
+  File "/ghome/gaocs/FCM-UFC/coding/transform/nonlinear_transform.py", line 422, in <module>
+    nonlinear_fitting(org_feat_path, transform_mapping_path, model_type, task, samples, trun_flag, trun_high, trun_low, transform_type, bit_depth)
+  File "/ghome/gaocs/FCM-UFC/coding/transform/nonlinear_transform.py", line 293, in nonlinear_fitting
+    feat_list_all = np.asarray(feat_list_all)
+ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (100,) + inhomogeneous part.

transform_mapping/dinov2_cls/transform_mapping_cls_kmeans100_bitdepth10.json ADDED Viewed

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transform_mapping/dinov2_dpt/dinov2_dpt_kmenas.txt ADDED Viewed

File without changes

transform_mapping/dinov2_dpt/dinov2_dpt_kmenas_layer10.txt ADDED Viewed

	@@ -0,0 +1,103 @@

+dinov2 dpt False kmeans 100 [3.27, 5.03, 25.05, 100.27] [-2.39, -26.44, -323.3, -504.44] [3.27, 5.03, 25.05, 100.27] [-2.39, -26.44, -323.3, -504.44] 10
+home_office_0005_rgb_00008.npy: 2, 4, 1611, 1536
+home_office_0006_rgb_00109.npy: 2, 4, 1611, 1536
+study_room_0004_rgb_00015.npy: 2, 4, 1611, 1536
+reception_room_0002_rgb_00126.npy: 2, 4, 1611, 1536
+bedroom_0004_rgb_00114.npy: 2, 4, 1611, 1536
+bedroom_0053_rgb_00050.npy: 2, 4, 1611, 1536
+excercise_room_0001_rgb_00034.npy: 2, 4, 1611, 1536
+furniture_store_0001d_rgb_00124.npy: 2, 4, 1611, 1536
+living_room_0019_rgb_00085.npy: 2, 4, 1611, 1536
+furniture_store_0001b_rgb_00009.npy: 2, 4, 1611, 1536
+bedroom_0038_rgb_00002.npy: 2, 4, 1611, 1536
+bedroom_0138_rgb_00019.npy: 2, 4, 1611, 1536
+dining_room_0007_rgb_00088.npy: 2, 4, 1611, 1536
+kitchen_0045a_rgb_00001.npy: 2, 4, 1611, 1536
+bathroom_0007_rgb_00051.npy: 2, 4, 1611, 1536
+bedroom_0017_rgb_00126.npy: 2, 4, 1611, 1536
+playroom_0003_rgb_00134.npy: 2, 4, 1611, 1536
+bedroom_0140_rgb_00172.npy: 2, 4, 1611, 1536
+living_room_0071_rgb_00036.npy: 2, 4, 1611, 1536
+reception_room_0002_rgb_00136.npy: 2, 4, 1611, 1536
+living_room_0086a_rgb_00002.npy: 2, 4, 1611, 1536
+bedroom_0125a_rgb_00015.npy: 2, 4, 1611, 1536
+dining_room_0015_rgb_00148.npy: 2, 4, 1611, 1536
+dining_room_0028_rgb_00040.npy: 2, 4, 1611, 1536
+kitchen_0049_rgb_00069.npy: 2, 4, 1611, 1536
+kitchen_0019a_rgb_00005.npy: 2, 4, 1611, 1536
+reception_room_0002_rgb_00066.npy: 2, 4, 1611, 1536
+living_room_0022_rgb_00308.npy: 2, 4, 1611, 1536
+classroom_0010_rgb_00031.npy: 2, 4, 1611, 1536
+kitchen_0045a_rgb_00061.npy: 2, 4, 1611, 1536
+bedroom_0060_rgb_00059.npy: 2, 4, 1611, 1536
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+office_kitchen_0001a_rgb_00072.npy: 2, 4, 1611, 1536
+living_room_0040_rgb_00069.npy: 2, 4, 1611, 1536
+bookstore_0001i_rgb_00093.npy: 2, 4, 1611, 1536
+bedroom_0072_rgb_00070.npy: 2, 4, 1611, 1536
+playroom_0006_rgb_00124.npy: 2, 4, 1611, 1536
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+kitchen_0052_rgb_00001.npy: 2, 4, 1611, 1536
+kitchen_0051_rgb_00251.npy: 2, 4, 1611, 1536
+living_room_0012_rgb_00069.npy: 2, 4, 1611, 1536
+bedroom_0060_rgb_00010.npy: 2, 4, 1611, 1536
+bedroom_0065_rgb_00003.npy: 2, 4, 1611, 1536
+nyu_office_0_rgb_00334.npy: 2, 4, 1611, 1536
+bedroom_0136_rgb_00008.npy: 2, 4, 1611, 1536
+living_room_0058_rgb_00032.npy: 2, 4, 1611, 1536
+office_kitchen_0001a_rgb_00034.npy: 2, 4, 1611, 1536
+classroom_0022_rgb_00048.npy: 2, 4, 1611, 1536
+bookstore_0001d_rgb_00329.npy: 2, 4, 1611, 1536
+kitchen_0029c_rgb_00131.npy: 2, 4, 1611, 1536
+living_room_0012_rgb_00110.npy: 2, 4, 1611, 1536
+kitchen_0047_rgb_00071.npy: 2, 4, 1611, 1536
+dining_room_0013_rgb_00139.npy: 2, 4, 1611, 1536
+kitchen_0045b_rgb_00137.npy: 2, 4, 1611, 1536
+bedroom_0078_rgb_00126.npy: 2, 4, 1611, 1536
+living_room_0047a_rgb_00062.npy: 2, 4, 1611, 1536
+bedroom_0020_rgb_00111.npy: 2, 4, 1611, 1536
+office_0011_rgb_00161.npy: 2, 4, 1611, 1536
+dining_room_0010_rgb_00056.npy: 2, 4, 1611, 1536
+bathroom_0054_rgb_00026.npy: 2, 4, 1611, 1536
+living_room_0058_rgb_00256.npy: 2, 4, 1611, 1536
+living_room_0069b_rgb_00025.npy: 2, 4, 1611, 1536
+bedroom_0015_rgb_00039.npy: 2, 4, 1611, 1536
+living_room_0069b_rgb_00071.npy: 2, 4, 1611, 1536
+bathroom_0055_rgb_00017.npy: 2, 4, 1611, 1536
+office_0025_rgb_00000.npy: 2, 4, 1611, 1536
+dining_room_0015_rgb_00087.npy: 2, 4, 1611, 1536
+study_0008_rgb_00035.npy: 2, 4, 1611, 1536
+dining_room_0014_rgb_00106.npy: 2, 4, 1611, 1536
+bedroom_0056a_rgb_00096.npy: 2, 4, 1611, 1536
+basement_0001a_rgb_00038.npy: 2, 4, 1611, 1536
+office_0025_rgb_00013.npy: 2, 4, 1611, 1536
+study_room_0005b_rgb_00092.npy: 2, 4, 1611, 1536
+bathroom_0053_rgb_00026.npy: 2, 4, 1611, 1536
+living_room_0069a_rgb_00065.npy: 2, 4, 1611, 1536
+bathroom_0055_rgb_00062.npy: 2, 4, 1611, 1536
+home_office_0007_rgb_00060.npy: 2, 4, 1611, 1536
+bookstore_0001g_rgb_00168.npy: 2, 4, 1611, 1536
+living_room_0005_rgb_00086.npy: 2, 4, 1611, 1536
+cafe_0001c_rgb_00043.npy: 2, 4, 1611, 1536
+bedroom_0039_rgb_00027.npy: 2, 4, 1611, 1536
+dining_room_0010_rgb_00002.npy: 2, 4, 1611, 1536
+living_room_0039_rgb_00136.npy: 2, 4, 1611, 1536
+bedroom_0021_rgb_00072.npy: 2, 4, 1611, 1536
+furniture_store_0002c_rgb_00001.npy: 2, 4, 1611, 1536
+dining_room_0029_rgb_00089.npy: 2, 4, 1611, 1536
+bedroom_0120_rgb_00009.npy: 2, 4, 1611, 1536
+living_room_0018_rgb_00084.npy: 2, 4, 1611, 1536
+reception_room_0001b_rgb_00118.npy: 2, 4, 1611, 1536
+bedroom_0017_rgb_00151.npy: 2, 4, 1611, 1536
+cafe_0001c_rgb_00031.npy: 2, 4, 1611, 1536
+bathroom_0039_rgb_00045.npy: 2, 4, 1611, 1536
+dining_room_0013_rgb_00168.npy: 2, 4, 1611, 1536
+study_0005_rgb_00000.npy: 2, 4, 1611, 1536
+study_room_0005b_rgb_00019.npy: 2, 4, 1611, 1536
+bedroom_0063_rgb_00148.npy: 2, 4, 1611, 1536
+kitchen_0047_rgb_00049.npy: 2, 4, 1611, 1536
+basement_0001a_rgb_00134.npy: 2, 4, 1611, 1536
+bedroom_0106_rgb_00078.npy: 2, 4, 1611, 1536
+bedroom_0053_rgb_00108.npy: 2, 4, 1611, 1536
+KMeans_quant_time: 52400.7904 KMeans Feature MSE: 0.00000114
+Quantization points saved to /gdata1/gaocs/Data_DTUFC/transform_mapping/dinov2_dpt/transform_mapping_dpt_layer10_kmeans100_bitdepth10.json

transform_mapping/dinov2_dpt/dinov2_dpt_kmenas_layer20.txt ADDED Viewed

	@@ -0,0 +1,103 @@

+dinov2 dpt False kmeans 100 [3.27, 5.03, 25.05, 100.27] [-2.39, -26.44, -323.3, -504.44] [3.27, 5.03, 25.05, 100.27] [-2.39, -26.44, -323.3, -504.44] 10
+bathroom_0054_rgb_00026.npy: 2, 4, 1611, 1536
+bathroom_0002_rgb_00008.npy: 2, 4, 1611, 1536
+study_0003_rgb_00012.npy: 2, 4, 1611, 1536
+office_kitchen_0001a_rgb_00082.npy: 2, 4, 1611, 1536
+home_office_0008_rgb_00087.npy: 2, 4, 1611, 1536
+dining_room_0019_rgb_00045.npy: 2, 4, 1611, 1536
+office_0009_rgb_00043.npy: 2, 4, 1611, 1536
+dining_room_0007_rgb_00034.npy: 2, 4, 1611, 1536
+bedroom_0015_rgb_00039.npy: 2, 4, 1611, 1536
+living_room_0058_rgb_00037.npy: 2, 4, 1611, 1536
+conference_room_0002_rgb_00014.npy: 2, 4, 1611, 1536
+furniture_store_0001e_rgb_00015.npy: 2, 4, 1611, 1536
+cafe_0001c_rgb_00031.npy: 2, 4, 1611, 1536
+dining_room_0016_rgb_00001.npy: 2, 4, 1611, 1536
+living_room_0040_rgb_00024.npy: 2, 4, 1611, 1536
+bathroom_0055_rgb_00017.npy: 2, 4, 1611, 1536
+bathroom_0039_rgb_00062.npy: 2, 4, 1611, 1536
+living_room_0042a_rgb_00021.npy: 2, 4, 1611, 1536
+study_room_0005b_rgb_00019.npy: 2, 4, 1611, 1536
+furniture_store_0001c_rgb_00018.npy: 2, 4, 1611, 1536
+bookstore_0001g_rgb_00168.npy: 2, 4, 1611, 1536
+furniture_store_0002a_rgb_00065.npy: 2, 4, 1611, 1536
+bedroom_0140_rgb_00170.npy: 2, 4, 1611, 1536
+furniture_store_0002c_rgb_00013.npy: 2, 4, 1611, 1536
+bookstore_0001d_rgb_00329.npy: 2, 4, 1611, 1536
+nyu_office_0_rgb_00334.npy: 2, 4, 1611, 1536
+kitchen_0035b_rgb_00325.npy: 2, 4, 1611, 1536
+bathroom_0039_rgb_00045.npy: 2, 4, 1611, 1536
+dining_room_0013_rgb_00168.npy: 2, 4, 1611, 1536
+bookstore_0001d_rgb_00011.npy: 2, 4, 1611, 1536
+living_room_0069b_rgb_00025.npy: 2, 4, 1611, 1536
+reception_room_0002_rgb_00126.npy: 2, 4, 1611, 1536
+living_room_0020_rgb_00206.npy: 2, 4, 1611, 1536
+dining_room_0007_rgb_00088.npy: 2, 4, 1611, 1536
+living_room_0040_rgb_00069.npy: 2, 4, 1611, 1536
+bedroom_0053_rgb_00108.npy: 2, 4, 1611, 1536
+study_room_0005a_rgb_00054.npy: 2, 4, 1611, 1536
+kitchen_0048_rgb_00256.npy: 2, 4, 1611, 1536
+kitchen_0028b_rgb_00004.npy: 2, 4, 1611, 1536
+dining_room_0013_rgb_00139.npy: 2, 4, 1611, 1536
+bedroom_0021_rgb_00072.npy: 2, 4, 1611, 1536
+dining_room_0033_rgb_00113.npy: 2, 4, 1611, 1536
+bedroom_0125a_rgb_00015.npy: 2, 4, 1611, 1536
+home_office_0005_rgb_00115.npy: 2, 4, 1611, 1536
+study_0005_rgb_00000.npy: 2, 4, 1611, 1536
+bedroom_0053_rgb_00028.npy: 2, 4, 1611, 1536
+bedroom_0078_rgb_00126.npy: 2, 4, 1611, 1536
+bathroom_0055_rgb_00062.npy: 2, 4, 1611, 1536
+office_0026_rgb_00119.npy: 2, 4, 1611, 1536
+dining_room_0037_rgb_00093.npy: 2, 4, 1611, 1536
+kitchen_0052_rgb_00001.npy: 2, 4, 1611, 1536
+kitchen_0017_rgb_00031.npy: 2, 4, 1611, 1536
+office_kitchen_0001a_rgb_00072.npy: 2, 4, 1611, 1536
+dining_room_0008_rgb_00008.npy: 2, 4, 1611, 1536
+cafe_0001c_rgb_00043.npy: 2, 4, 1611, 1536
+kitchen_0029c_rgb_00131.npy: 2, 4, 1611, 1536
+reception_room_0002_rgb_00136.npy: 2, 4, 1611, 1536
+living_room_0040_rgb_00267.npy: 2, 4, 1611, 1536
+living_room_0078_rgb_00015.npy: 2, 4, 1611, 1536
+living_room_0012_rgb_00110.npy: 2, 4, 1611, 1536
+study_0008_rgb_00035.npy: 2, 4, 1611, 1536
+nyu_office_0_rgb_00186.npy: 2, 4, 1611, 1536
+kitchen_0051_rgb_00293.npy: 2, 4, 1611, 1536
+living_room_0058_rgb_00256.npy: 2, 4, 1611, 1536
+dining_room_0016_rgb_00206.npy: 2, 4, 1611, 1536
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+bedroom_0060_rgb_00010.npy: 2, 4, 1611, 1536
+dining_room_0029_rgb_00089.npy: 2, 4, 1611, 1536
+bedroom_0020_rgb_00111.npy: 2, 4, 1611, 1536
+kitchen_0031_rgb_00051.npy: 2, 4, 1611, 1536
+bedroom_0062_rgb_00079.npy: 2, 4, 1611, 1536
+bedroom_0038_rgb_00002.npy: 2, 4, 1611, 1536
+bathroom_0053_rgb_00026.npy: 2, 4, 1611, 1536
+bedroom_0086_rgb_00013.npy: 2, 4, 1611, 1536
+bedroom_0072_rgb_00070.npy: 2, 4, 1611, 1536
+bathroom_0028_rgb_00027.npy: 2, 4, 1611, 1536
+office_0025_rgb_00000.npy: 2, 4, 1611, 1536
+kitchen_0047_rgb_00049.npy: 2, 4, 1611, 1536
+bedroom_0063_rgb_00148.npy: 2, 4, 1611, 1536
+living_room_0058_rgb_00032.npy: 2, 4, 1611, 1536
+kitchen_0048_rgb_00057.npy: 2, 4, 1611, 1536
+office_kitchen_0003_rgb_00000.npy: 2, 4, 1611, 1536
+bedroom_0056a_rgb_00096.npy: 2, 4, 1611, 1536
+furniture_store_0002c_rgb_00001.npy: 2, 4, 1611, 1536
+bedroom_0019_rgb_00133.npy: 2, 4, 1611, 1536
+living_room_0086a_rgb_00002.npy: 2, 4, 1611, 1536
+reception_room_0002_rgb_00034.npy: 2, 4, 1611, 1536
+living_room_0012_rgb_00069.npy: 2, 4, 1611, 1536
+basement_0001a_rgb_00134.npy: 2, 4, 1611, 1536
+bathroom_0051_rgb_00027.npy: 2, 4, 1611, 1536
+living_room_0038_rgb_00056.npy: 2, 4, 1611, 1536
+bedroom_0065_rgb_00003.npy: 2, 4, 1611, 1536
+study_room_0005b_rgb_00092.npy: 2, 4, 1611, 1536
+living_room_0019_rgb_00085.npy: 2, 4, 1611, 1536
+living_room_0019_rgb_00209.npy: 2, 4, 1611, 1536
+study_room_0004_rgb_00172.npy: 2, 4, 1611, 1536
+kitchen_0051_rgb_00251.npy: 2, 4, 1611, 1536
+bedroom_0017_rgb_00151.npy: 2, 4, 1611, 1536
+bookstore_0001g_rgb_00001.npy: 2, 4, 1611, 1536
+KMeans_quant_time: 58989.7063 KMeans Feature MSE: 0.00000150
+Quantization points saved to /gdata1/gaocs/Data_DTUFC/transform_mapping/dinov2_dpt/transform_mapping_dpt_layer20_kmeans100_bitdepth10.json

transform_mapping/dinov2_dpt/dinov2_dpt_kmenas_layer30.txt ADDED Viewed

	@@ -0,0 +1,206 @@

+dinov2 dpt False kmeans 100 [3.27, 5.03, 25.05, 100.27] [-2.39, -26.44, -323.3, -504.44] [3.27, 5.03, 25.05, 100.27] [-2.39, -26.44, -323.3, -504.44] 8
+bedroom_0060_rgb_00059.npy: 2, 4, 1611, 1536
+bedroom_0060_rgb_00010.npy: 2, 4, 1611, 1536
+office_kitchen_0001a_rgb_00034.npy: 2, 4, 1611, 1536
+kitchen_0045b_rgb_00137.npy: 2, 4, 1611, 1536
+home_office_0006_rgb_00109.npy: 2, 4, 1611, 1536
+living_room_0020_rgb_00206.npy: 2, 4, 1611, 1536
+living_room_0012_rgb_00110.npy: 2, 4, 1611, 1536
+kitchen_0048_rgb_00256.npy: 2, 4, 1611, 1536
+living_room_0019_rgb_00209.npy: 2, 4, 1611, 1536
+student_lounge_0001_rgb_00079.npy: 2, 4, 1611, 1536
+kitchen_0051_rgb_00251.npy: 2, 4, 1611, 1536
+living_room_0012_rgb_00069.npy: 2, 4, 1611, 1536
+dining_room_0007_rgb_00024.npy: 2, 4, 1611, 1536
+bathroom_0039_rgb_00062.npy: 2, 4, 1611, 1536
+bedroom_0004_rgb_00114.npy: 2, 4, 1611, 1536
+bedroom_0104_rgb_00017.npy: 2, 4, 1611, 1536
+dining_room_0015_rgb_00148.npy: 2, 4, 1611, 1536
+bedroom_0053_rgb_00056.npy: 2, 4, 1611, 1536
+playroom_0002_rgb_00099.npy: 2, 4, 1611, 1536
+dining_room_0019_rgb_00045.npy: 2, 4, 1611, 1536
+dining_room_0037_rgb_00093.npy: 2, 4, 1611, 1536
+living_room_0040_rgb_00024.npy: 2, 4, 1611, 1536
+cafe_0001c_rgb_00043.npy: 2, 4, 1611, 1536
+furniture_store_0001e_rgb_00015.npy: 2, 4, 1611, 1536
+living_room_0069b_rgb_00025.npy: 2, 4, 1611, 1536
+kitchen_0028b_rgb_00004.npy: 2, 4, 1611, 1536
+bedroom_0072_rgb_00070.npy: 2, 4, 1611, 1536
+kitchen_0047_rgb_00071.npy: 2, 4, 1611, 1536
+office_kitchen_0001a_rgb_00072.npy: 2, 4, 1611, 1536
+cafe_0001c_rgb_00031.npy: 2, 4, 1611, 1536
+bedroom_0015_rgb_00041.npy: 2, 4, 1611, 1536
+living_room_0005_rgb_00054.npy: 2, 4, 1611, 1536
+kitchen_0029c_rgb_00131.npy: 2, 4, 1611, 1536
+playroom_0003_rgb_00134.npy: 2, 4, 1611, 1536
+reception_room_0002_rgb_00126.npy: 2, 4, 1611, 1536
+furniture_store_0001b_rgb_00083.npy: 2, 4, 1611, 1536
+reception_room_0002_rgb_00034.npy: 2, 4, 1611, 1536
+bathroom_0030_rgb_00011.npy: 2, 4, 1611, 1536
+dining_room_0024_rgb_00083.npy: 2, 4, 1611, 1536
+office_0025_rgb_00000.npy: 2, 4, 1611, 1536
+furniture_store_0001d_rgb_00236.npy: 2, 4, 1611, 1536
+kitchen_0048_rgb_00145.npy: 2, 4, 1611, 1536
+living_room_0018_rgb_00084.npy: 2, 4, 1611, 1536
+home_storage_0001_rgb_00023.npy: 2, 4, 1611, 1536
+bedroom_0020_rgb_00119.npy: 2, 4, 1611, 1536
+kitchen_0048_rgb_00057.npy: 2, 4, 1611, 1536
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+bookstore_0001f_rgb_00029.npy: 2, 4, 1611, 1536
+bedroom_0017_rgb_00151.npy: 2, 4, 1611, 1536
+office_0026_rgb_00119.npy: 2, 4, 1611, 1536
+bookstore_0001d_rgb_00329.npy: 2, 4, 1611, 1536
+study_0005_rgb_00000.npy: 2, 4, 1611, 1536
+office_0011_rgb_00161.npy: 2, 4, 1611, 1536
+basement_0001a_rgb_00134.npy: 2, 4, 1611, 1536
+office_kitchen_0003_rgb_00000.npy: 2, 4, 1611, 1536
+excercise_room_0001_rgb_00034.npy: 2, 4, 1611, 1536
+dining_room_0029_rgb_00089.npy: 2, 4, 1611, 1536
+bedroom_0019_rgb_00133.npy: 2, 4, 1611, 1536
+bedroom_0125b_rgb_00075.npy: 2, 4, 1611, 1536
+bathroom_0051_rgb_00027.npy: 2, 4, 1611, 1536
+bedroom_0017_rgb_00126.npy: 2, 4, 1611, 1536
+nyu_office_0_rgb_00186.npy: 2, 4, 1611, 1536
+dining_room_0010_rgb_00056.npy: 2, 4, 1611, 1536
+living_room_0058_rgb_00037.npy: 2, 4, 1611, 1536
+furniture_store_0002c_rgb_00001.npy: 2, 4, 1611, 1536
+bookstore_0001i_rgb_00093.npy: 2, 4, 1611, 1536
+bathroom_0057_rgb_00029.npy: 2, 4, 1611, 1536
+bathroom_0002_rgb_00008.npy: 2, 4, 1611, 1536
+bathroom_0028_rgb_00027.npy: 2, 4, 1611, 1536
+home_office_0008_rgb_00087.npy: 2, 4, 1611, 1536
+kitchen_0028a_rgb_00039.npy: 2, 4, 1611, 1536
+bedroom_0059_rgb_00092.npy: 2, 4, 1611, 1536
+study_room_0005b_rgb_00019.npy: 2, 4, 1611, 1536
+bedroom_0039_rgb_00027.npy: 2, 4, 1611, 1536
+furniture_store_0002b_rgb_00024.npy: 2, 4, 1611, 1536
+bedroom_0120_rgb_00009.npy: 2, 4, 1611, 1536
+bedroom_0053_rgb_00108.npy: 2, 4, 1611, 1536
+classroom_0016_rgb_00080.npy: 2, 4, 1611, 1536
+study_0008_rgb_00035.npy: 2, 4, 1611, 1536
+bookstore_0001g_rgb_00001.npy: 2, 4, 1611, 1536
+dining_room_0015_rgb_00087.npy: 2, 4, 1611, 1536
+furniture_store_0001d_rgb_00113.npy: 2, 4, 1611, 1536
+office_0025_rgb_00013.npy: 2, 4, 1611, 1536
+living_room_0067_rgb_00019.npy: 2, 4, 1611, 1536
+kitchen_0031_rgb_00051.npy: 2, 4, 1611, 1536
+study_room_0004_rgb_00015.npy: 2, 4, 1611, 1536
+furniture_store_0001a_rgb_00038.npy: 2, 4, 1611, 1536
+bedroom_0136_rgb_00008.npy: 2, 4, 1611, 1536
+living_room_0069b_rgb_00071.npy: 2, 4, 1611, 1536
+kitchen_0006_rgb_00006.npy: 2, 4, 1611, 1536
+bedroom_0041_rgb_00000.npy: 2, 4, 1611, 1536
+bedroom_0074_rgb_00104.npy: 2, 4, 1611, 1536
+study_0003_rgb_00012.npy: 2, 4, 1611, 1536
+kitchen_0047_rgb_00049.npy: 2, 4, 1611, 1536
+furniture_store_0001c_rgb_00018.npy: 2, 4, 1611, 1536
+reception_room_0002_rgb_00136.npy: 2, 4, 1611, 1536
+living_room_0019_rgb_00109.npy: 2, 4, 1611, 1536
+kitchen_0029b_rgb_00041.npy: 2, 4, 1611, 1536
+nyu_office_0_rgb_00334.npy: 2, 4, 1611, 1536
+living_room_0058_rgb_00256.npy: 2, 4, 1611, 1536
+KMeans_quant_time: 12957.7668 KMeans Feature MSE: 0.00029080
+Quantization points saved to /gdata1/gaocs/Data_DTUFC/transform_mapping/dinov2_dpt/transform_mapping_dpt_layer30_kmeans100_bitdepth8.json
+dinov2 dpt False kmeans 100 [3.27, 5.03, 25.05, 100.27] [-2.39, -26.44, -323.3, -504.44] [3.27, 5.03, 25.05, 100.27] [-2.39, -26.44, -323.3, -504.44] 10
+bedroom_0076a_rgb_00211.npy: 2, 4, 1611, 1536
+living_room_0040_rgb_00069.npy: 2, 4, 1611, 1536
+living_room_0019_rgb_00085.npy: 2, 4, 1611, 1536
+bedroom_0038_rgb_00002.npy: 2, 4, 1611, 1536
+living_room_0018_rgb_00084.npy: 2, 4, 1611, 1536
+living_room_0055_rgb_00039.npy: 2, 4, 1611, 1536
+bedroom_0125b_rgb_00075.npy: 2, 4, 1611, 1536
+living_room_0069a_rgb_00065.npy: 2, 4, 1611, 1536
+dining_room_0028_rgb_00040.npy: 2, 4, 1611, 1536
+kitchen_0006_rgb_00006.npy: 2, 4, 1611, 1536
+kitchen_0028a_rgb_00039.npy: 2, 4, 1611, 1536
+home_office_0005_rgb_00008.npy: 2, 4, 1611, 1536
+bedroom_0056a_rgb_00096.npy: 2, 4, 1611, 1536
+study_room_0004_rgb_00172.npy: 2, 4, 1611, 1536
+study_0008_rgb_00035.npy: 2, 4, 1611, 1536
+bedroom_0113_rgb_00045.npy: 2, 4, 1611, 1536
+home_office_0008_rgb_00087.npy: 2, 4, 1611, 1536
+bathroom_0039_rgb_00062.npy: 2, 4, 1611, 1536
+furniture_store_0002c_rgb_00013.npy: 2, 4, 1611, 1536
+kitchen_0041_rgb_00043.npy: 2, 4, 1611, 1536
+bedroom_0120_rgb_00009.npy: 2, 4, 1611, 1536
+living_room_0022_rgb_00308.npy: 2, 4, 1611, 1536
+living_room_0058_rgb_00037.npy: 2, 4, 1611, 1536
+living_room_0042a_rgb_00021.npy: 2, 4, 1611, 1536
+kitchen_0035b_rgb_00325.npy: 2, 4, 1611, 1536
+dining_room_0015_rgb_00148.npy: 2, 4, 1611, 1536
+study_0003_rgb_00012.npy: 2, 4, 1611, 1536
+playroom_0006_rgb_00124.npy: 2, 4, 1611, 1536
+kitchen_0048_rgb_00145.npy: 2, 4, 1611, 1536
+bedroom_0076a_rgb_00179.npy: 2, 4, 1611, 1536
+kitchen_0060_rgb_00037.npy: 2, 4, 1611, 1536
+study_room_0005a_rgb_00054.npy: 2, 4, 1611, 1536
+living_room_0078_rgb_00015.npy: 2, 4, 1611, 1536
+living_room_0012_rgb_00110.npy: 2, 4, 1611, 1536
+dining_room_0037_rgb_00093.npy: 2, 4, 1611, 1536
+living_room_0039_rgb_00136.npy: 2, 4, 1611, 1536
+bathroom_0007_rgb_00051.npy: 2, 4, 1611, 1536
+bedroom_0059_rgb_00092.npy: 2, 4, 1611, 1536
+classroom_0010_rgb_00031.npy: 2, 4, 1611, 1536
+dining_room_0019_rgb_00045.npy: 2, 4, 1611, 1536
+bedroom_0072_rgb_00070.npy: 2, 4, 1611, 1536
+living_room_0038_rgb_00056.npy: 2, 4, 1611, 1536
+nyu_office_0_rgb_00334.npy: 2, 4, 1611, 1536
+bathroom_0039_rgb_00045.npy: 2, 4, 1611, 1536
+basement_0001a_rgb_00134.npy: 2, 4, 1611, 1536
+student_lounge_0001_rgb_00079.npy: 2, 4, 1611, 1536
+living_room_0069b_rgb_00042.npy: 2, 4, 1611, 1536
+kitchen_0047_rgb_00071.npy: 2, 4, 1611, 1536
+dining_room_0024_rgb_00083.npy: 2, 4, 1611, 1536
+reception_room_0001b_rgb_00118.npy: 2, 4, 1611, 1536
+living_room_0020_rgb_00019.npy: 2, 4, 1611, 1536
+dining_room_0014_rgb_00106.npy: 2, 4, 1611, 1536
+kitchen_0049_rgb_00069.npy: 2, 4, 1611, 1536
+kitchen_0048_rgb_00057.npy: 2, 4, 1611, 1536
+bookstore_0001f_rgb_00029.npy: 2, 4, 1611, 1536
+dining_room_0007_rgb_00088.npy: 2, 4, 1611, 1536
+kitchen_0017_rgb_00031.npy: 2, 4, 1611, 1536
+furniture_store_0002b_rgb_00024.npy: 2, 4, 1611, 1536
+furniture_store_0001c_rgb_00018.npy: 2, 4, 1611, 1536
+living_room_0012_rgb_00069.npy: 2, 4, 1611, 1536
+bedroom_0015_rgb_00041.npy: 2, 4, 1611, 1536
+living_room_0019_rgb_00109.npy: 2, 4, 1611, 1536
+living_room_0039_rgb_00060.npy: 2, 4, 1611, 1536
+kitchen_0019a_rgb_00005.npy: 2, 4, 1611, 1536
+living_room_0086a_rgb_00002.npy: 2, 4, 1611, 1536
+bedroom_0140_rgb_00170.npy: 2, 4, 1611, 1536
+bathroom_0051_rgb_00027.npy: 2, 4, 1611, 1536
+bookstore_0001i_rgb_00093.npy: 2, 4, 1611, 1536
+furniture_store_0001b_rgb_00083.npy: 2, 4, 1611, 1536
+bedroom_0020_rgb_00111.npy: 2, 4, 1611, 1536
+bedroom_0020_rgb_00119.npy: 2, 4, 1611, 1536
+dining_room_0008_rgb_00008.npy: 2, 4, 1611, 1536
+kitchen_0048_rgb_00256.npy: 2, 4, 1611, 1536
+dining_room_0007_rgb_00024.npy: 2, 4, 1611, 1536
+living_room_0069b_rgb_00071.npy: 2, 4, 1611, 1536
+bedroom_0053_rgb_00056.npy: 2, 4, 1611, 1536
+bookstore_0001g_rgb_00001.npy: 2, 4, 1611, 1536
+living_room_0071_rgb_00036.npy: 2, 4, 1611, 1536
+reception_room_0002_rgb_00136.npy: 2, 4, 1611, 1536
+bedroom_0086_rgb_00013.npy: 2, 4, 1611, 1536
+office_0026_rgb_00119.npy: 2, 4, 1611, 1536
+classroom_0016_rgb_00080.npy: 2, 4, 1611, 1536
+office_0025_rgb_00013.npy: 2, 4, 1611, 1536
+bedroom_0106_rgb_00078.npy: 2, 4, 1611, 1536
+cafe_0001c_rgb_00043.npy: 2, 4, 1611, 1536
+study_room_0004_rgb_00015.npy: 2, 4, 1611, 1536
+dining_room_0029_rgb_00089.npy: 2, 4, 1611, 1536
+kitchen_0031_rgb_00051.npy: 2, 4, 1611, 1536
+dining_room_0007_rgb_00034.npy: 2, 4, 1611, 1536
+dining_room_0033_rgb_00113.npy: 2, 4, 1611, 1536
+dining_room_0016_rgb_00206.npy: 2, 4, 1611, 1536
+home_office_0007_rgb_00060.npy: 2, 4, 1611, 1536
+bedroom_0053_rgb_00028.npy: 2, 4, 1611, 1536
+dining_room_0010_rgb_00056.npy: 2, 4, 1611, 1536
+study_room_0005a_rgb_00050.npy: 2, 4, 1611, 1536
+dining_room_0013_rgb_00139.npy: 2, 4, 1611, 1536
+home_office_0005_rgb_00115.npy: 2, 4, 1611, 1536
+furniture_store_0001b_rgb_00009.npy: 2, 4, 1611, 1536
+bathroom_0028_rgb_00027.npy: 2, 4, 1611, 1536
+dining_room_0010_rgb_00002.npy: 2, 4, 1611, 1536
+KMeans_quant_time: 122285.3599 KMeans Feature MSE: 0.00001673
+Quantization points saved to /gdata1/gaocs/Data_DTUFC/transform_mapping/dinov2_dpt/transform_mapping_dpt_layer30_kmeans100_bitdepth10.json

transform_mapping/dinov2_dpt/dinov2_dpt_kmenas_layer40.txt ADDED Viewed

	@@ -0,0 +1,206 @@

+dinov2 dpt False kmeans 100 [3.27, 5.03, 25.05, 100.27] [-2.39, -26.44, -323.3, -504.44] [3.27, 5.03, 25.05, 100.27] [-2.39, -26.44, -323.3, -504.44] 8
+classroom_0022_rgb_00048.npy: 2, 4, 1611, 1536
+living_room_0039_rgb_00136.npy: 2, 4, 1611, 1536
+bedroom_0125b_rgb_00075.npy: 2, 4, 1611, 1536
+living_room_0069b_rgb_00071.npy: 2, 4, 1611, 1536
+living_room_0086a_rgb_00002.npy: 2, 4, 1611, 1536
+kitchen_0041_rgb_00043.npy: 2, 4, 1611, 1536
+living_room_0012_rgb_00110.npy: 2, 4, 1611, 1536
+bathroom_0051_rgb_00027.npy: 2, 4, 1611, 1536
+home_office_0007_rgb_00060.npy: 2, 4, 1611, 1536
+office_kitchen_0001a_rgb_00072.npy: 2, 4, 1611, 1536
+furniture_store_0002c_rgb_00001.npy: 2, 4, 1611, 1536
+bedroom_0053_rgb_00050.npy: 2, 4, 1611, 1536
+living_room_0058_rgb_00032.npy: 2, 4, 1611, 1536
+bathroom_0055_rgb_00017.npy: 2, 4, 1611, 1536
+living_room_0012_rgb_00207.npy: 2, 4, 1611, 1536
+home_office_0005_rgb_00115.npy: 2, 4, 1611, 1536
+living_room_0035_rgb_00001.npy: 2, 4, 1611, 1536
+furniture_store_0001c_rgb_00018.npy: 2, 4, 1611, 1536
+kitchen_0006_rgb_00006.npy: 2, 4, 1611, 1536
+dining_room_0013_rgb_00168.npy: 2, 4, 1611, 1536
+study_room_0005b_rgb_00019.npy: 2, 4, 1611, 1536
+bedroom_0063_rgb_00148.npy: 2, 4, 1611, 1536
+bedroom_0025_rgb_00064.npy: 2, 4, 1611, 1536
+kitchen_0047_rgb_00071.npy: 2, 4, 1611, 1536
+dining_room_0033_rgb_00113.npy: 2, 4, 1611, 1536
+bedroom_0076a_rgb_00203.npy: 2, 4, 1611, 1536
+living_room_0020_rgb_00206.npy: 2, 4, 1611, 1536
+living_room_0047a_rgb_00062.npy: 2, 4, 1611, 1536
+living_room_0058_rgb_00037.npy: 2, 4, 1611, 1536
+dining_room_0029_rgb_00089.npy: 2, 4, 1611, 1536
+study_room_0005b_rgb_00092.npy: 2, 4, 1611, 1536
+study_room_0005a_rgb_00054.npy: 2, 4, 1611, 1536
+bedroom_0060_rgb_00059.npy: 2, 4, 1611, 1536
+living_room_0019_rgb_00085.npy: 2, 4, 1611, 1536
+cafe_0001c_rgb_00043.npy: 2, 4, 1611, 1536
+bedroom_0041_rgb_00000.npy: 2, 4, 1611, 1536
+living_room_0005_rgb_00054.npy: 2, 4, 1611, 1536
+bedroom_0076a_rgb_00179.npy: 2, 4, 1611, 1536
+kitchen_0028b_rgb_00004.npy: 2, 4, 1611, 1536
+cafe_0001c_rgb_00031.npy: 2, 4, 1611, 1536
+bedroom_0053_rgb_00028.npy: 2, 4, 1611, 1536
+bookstore_0001f_rgb_00029.npy: 2, 4, 1611, 1536
+dining_room_0016_rgb_00001.npy: 2, 4, 1611, 1536
+playroom_0002_rgb_00099.npy: 2, 4, 1611, 1536
+bathroom_0054_rgb_00026.npy: 2, 4, 1611, 1536
+living_room_0040_rgb_00024.npy: 2, 4, 1611, 1536
+kitchen_0017_rgb_00031.npy: 2, 4, 1611, 1536
+basement_0001a_rgb_00134.npy: 2, 4, 1611, 1536
+living_room_0069a_rgb_00065.npy: 2, 4, 1611, 1536
+student_lounge_0001_rgb_00079.npy: 2, 4, 1611, 1536
+kitchen_0048_rgb_00145.npy: 2, 4, 1611, 1536
+furniture_store_0001e_rgb_00015.npy: 2, 4, 1611, 1536
+bedroom_0140_rgb_00172.npy: 2, 4, 1611, 1536
+kitchen_0029b_rgb_00041.npy: 2, 4, 1611, 1536
+bookstore_0001d_rgb_00011.npy: 2, 4, 1611, 1536
+study_0003_rgb_00038.npy: 2, 4, 1611, 1536
+bedroom_0021_rgb_00072.npy: 2, 4, 1611, 1536
+bedroom_0039_rgb_00027.npy: 2, 4, 1611, 1536
+office_0011_rgb_00161.npy: 2, 4, 1611, 1536
+dining_room_0028_rgb_00040.npy: 2, 4, 1611, 1536
+home_office_0005_rgb_00008.npy: 2, 4, 1611, 1536
+study_0005_rgb_00000.npy: 2, 4, 1611, 1536
+kitchen_0051_rgb_00293.npy: 2, 4, 1611, 1536
+bathroom_0055_rgb_00062.npy: 2, 4, 1611, 1536
+dining_room_0014_rgb_00083.npy: 2, 4, 1611, 1536
+kitchen_0028a_rgb_00039.npy: 2, 4, 1611, 1536
+office_0009_rgb_00043.npy: 2, 4, 1611, 1536
+reception_room_0002_rgb_00126.npy: 2, 4, 1611, 1536
+kitchen_0029c_rgb_00131.npy: 2, 4, 1611, 1536
+living_room_0012_rgb_00069.npy: 2, 4, 1611, 1536
+bathroom_0002_rgb_00008.npy: 2, 4, 1611, 1536
+living_room_0069b_rgb_00025.npy: 2, 4, 1611, 1536
+furniture_store_0002b_rgb_00024.npy: 2, 4, 1611, 1536
+dining_room_0016_rgb_00206.npy: 2, 4, 1611, 1536
+office_0021_rgb_00056.npy: 2, 4, 1611, 1536
+living_room_0071_rgb_00036.npy: 2, 4, 1611, 1536
+study_0008_rgb_00035.npy: 2, 4, 1611, 1536
+bathroom_0057_rgb_00029.npy: 2, 4, 1611, 1536
+bathroom_0053_rgb_00026.npy: 2, 4, 1611, 1536
+bedroom_0060_rgb_00010.npy: 2, 4, 1611, 1536
+dining_room_0010_rgb_00056.npy: 2, 4, 1611, 1536
+bedroom_0015_rgb_00039.npy: 2, 4, 1611, 1536
+bedroom_0038_rgb_00002.npy: 2, 4, 1611, 1536
+bathroom_0039_rgb_00062.npy: 2, 4, 1611, 1536
+office_kitchen_0001a_rgb_00082.npy: 2, 4, 1611, 1536
+bedroom_0136_rgb_00008.npy: 2, 4, 1611, 1536
+dining_room_0007_rgb_00088.npy: 2, 4, 1611, 1536
+bedroom_0050_rgb_00098.npy: 2, 4, 1611, 1536
+furniture_store_0001b_rgb_00083.npy: 2, 4, 1611, 1536
+bedroom_0078_rgb_00126.npy: 2, 4, 1611, 1536
+kitchen_0048_rgb_00256.npy: 2, 4, 1611, 1536
+bedroom_0020_rgb_00111.npy: 2, 4, 1611, 1536
+basement_0001a_rgb_00038.npy: 2, 4, 1611, 1536
+reception_room_0001b_rgb_00118.npy: 2, 4, 1611, 1536
+office_kitchen_0001a_rgb_00034.npy: 2, 4, 1611, 1536
+home_office_0006_rgb_00109.npy: 2, 4, 1611, 1536
+kitchen_0047_rgb_00049.npy: 2, 4, 1611, 1536
+home_office_0008_rgb_00087.npy: 2, 4, 1611, 1536
+dining_room_0015_rgb_00087.npy: 2, 4, 1611, 1536
+living_room_0019_rgb_00209.npy: 2, 4, 1611, 1536
+KMeans_quant_time: 12967.7024 KMeans Feature MSE: 0.00166339
+Quantization points saved to /gdata1/gaocs/Data_DTUFC/transform_mapping/dinov2_dpt/transform_mapping_dpt_layer40_kmeans100_bitdepth8.json
+dinov2 dpt False kmeans 100 [3.27, 5.03, 25.05, 100.27] [-2.39, -26.44, -323.3, -504.44] [3.27, 5.03, 25.05, 100.27] [-2.39, -26.44, -323.3, -504.44] 10
+bedroom_0039_rgb_00027.npy: 2, 4, 1611, 1536
+furniture_store_0002a_rgb_00065.npy: 2, 4, 1611, 1536
+dining_room_0010_rgb_00056.npy: 2, 4, 1611, 1536
+bedroom_0125b_rgb_00075.npy: 2, 4, 1611, 1536
+dining_room_0029_rgb_00089.npy: 2, 4, 1611, 1536
+study_0008_rgb_00035.npy: 2, 4, 1611, 1536
+furniture_store_0002b_rgb_00024.npy: 2, 4, 1611, 1536
+bedroom_0063_rgb_00148.npy: 2, 4, 1611, 1536
+furniture_store_0001d_rgb_00124.npy: 2, 4, 1611, 1536
+living_room_0058_rgb_00254.npy: 2, 4, 1611, 1536
+bathroom_0030_rgb_00011.npy: 2, 4, 1611, 1536
+furniture_store_0001e_rgb_00015.npy: 2, 4, 1611, 1536
+bedroom_0140_rgb_00172.npy: 2, 4, 1611, 1536
+dining_room_0019_rgb_00045.npy: 2, 4, 1611, 1536
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+reception_room_0002_rgb_00034.npy: 2, 4, 1611, 1536
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+bedroom_0015_rgb_00039.npy: 2, 4, 1611, 1536
+living_room_0035_rgb_00001.npy: 2, 4, 1611, 1536
+KMeans_quant_time: 57755.0995 KMeans Feature MSE: 0.00009359
+Quantization points saved to /gdata1/gaocs/Data_DTUFC/transform_mapping/dinov2_dpt/transform_mapping_dpt_layer40_kmeans100_bitdepth10.json

transform_mapping/dinov2_dpt/transform_mapping_dpt_layer10_kmeans100_bitdepth10.json ADDED Viewed

	@@ -0,0 +1 @@

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+Traceback (most recent call last):
+  File "/ghome/gaocs/FCM-UFC/coding/transform/nonlinear_transform.py", line 419, in <module>
+    nonlinear_fitting(org_feat_path, transform_mapping_path, model_type, task, samples, trun_flag, trun_high, trun_low, transform_type, bit_depth)
+  File "/ghome/gaocs/FCM-UFC/coding/transform/nonlinear_transform.py", line 293, in nonlinear_fitting
+    feat_list_all = np.asarray(feat_list_all)
+ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (100,) + inhomogeneous part.

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transform_mapping/llama3_csr/llama3_csr_kmenas.txt ADDED Viewed

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+llama3 csr False kmeans 10 47.75 -78 0 0 10
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+KMeans_quant_time: 35.4178 KMeans Feature MSE: 0.00232841

transform_mapping/llama3_csr/llama3_csr_kmenas100.txt ADDED Viewed

	@@ -0,0 +1,206 @@

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+Quantization points saved to /gdata1/gaocs/Data_DTUFC/transform_mapping/llama3_csr/transform_mapping_csr_kmeans100_bitdepth8.json
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transform_mapping/llama3_csr/transform_mapping_csr_kmeans100_bitdepth10.json ADDED Viewed

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transform_mapping/sd3_tti/sd3_tti_kmenas.txt ADDED Viewed

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+sd3 tti False kmeans 10 4.46 -5.79 0 0 8
+COCO_train2017_000000000192.npy: 1, 16, 128, 128
+COCO_train2017_000000000387.npy: 1, 16, 128, 128
+COCO_train2017_000000000400.npy: 1, 16, 128, 128
+COCO_train2017_000000000459.npy: 1, 16, 128, 128
+COCO_train2017_000000000715.npy: 1, 16, 128, 128
+COCO_train2017_000000000764.npy: 1, 16, 128, 128
+COCO_train2017_000000000831.npy: 1, 16, 128, 128
+COCO_train2017_000000000923.npy: 1, 16, 128, 128
+COCO_train2017_000000000974.npy: 1, 16, 128, 128
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+KMeans_quant_time: 130.6978 KMeans Feature MSE: 0.00019546
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