mirror of
https://github.com/immich-app/ml-models.git
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Mert
df0ea33c8a
* feat: torch-free export core, shared graph/IR helpers, and CLI scaffolding * feat: fused InsightFace face exporter * feat: RKNN export path with per-SoC compilation * feat: single-partition CoreML CLIP export and runtime rewrite API * feat: refactor to passes, ep-specific fixes and optimizations * feat: check command diffing committed graph renderings, wired into CI * chore: graph and rewrite-plan renderings for the catalog * use pokedex large
811 lines
56 KiB
Plaintext
811 lines
56 KiB
Plaintext
<
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ir_version: 10,
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opset_import: ["" : 23],
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producer_name: "pytorch"
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>
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main_graph (uint8[batch,224,224,3] image) => (float[batch,512] image_embedding)
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<
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float[batch,1024,14,14] add_1016
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float[batch,1024,14,14] add_1092
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float[batch,1024,14,14] add_1168
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float[batch,1024,14,14] add_1244
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float[batch,1024,14,14] add_1320
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float[batch,1024,14,14] add_1396
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float[batch,256,56,56] add_140
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float[batch,1024,14,14] add_1472
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float[batch,1024,14,14] add_1548
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float[batch,1024,14,14] add_1624
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float[batch,1024,14,14] add_1700
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float[batch,1024,14,14] add_1776
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float[batch,1024,14,14] add_1852
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float[batch,1024,14,14] add_1928
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float[batch,1024,14,14] add_2004
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float[batch,1024,14,14] add_2080
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float[batch,1024,14,14] add_2156
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float[batch,256,56,56] add_216
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float[batch,1024,14,14] add_2232
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float[batch,1024,14,14] add_2308
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float[batch,1024,14,14] add_2384
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float[batch,2048,7,7] add_2480
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float[batch,2048,7,7] add_2556
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float[batch,2048,7,7] add_2632
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float[50,batch,2048] add_2662
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float[batch,256,56,56] add_292
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float[batch,512,28,28] add_388
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float[batch,512,28,28] add_464
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float[batch,512,28,28] add_540
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float[batch,512,28,28] add_616
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float[batch,1024,14,14] add_712
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float[batch,1024,14,14] add_788
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float[batch,1024,14,14] add_864
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float[batch,1024,14,14] add_940
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float[batch,64,56,56] avg_pool2d
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float[batch,128,28,28] avg_pool2d_2
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float[batch,256,28,28] avg_pool2d_3
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float[batch,256,14,14] avg_pool2d_4
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float[batch,512,14,14] avg_pool2d_5
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float[batch,512,7,7] avg_pool2d_6
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float[batch,1024,7,7] avg_pool2d_7
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float[50,batch,2048] cat
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float[batch,1] clamp_min
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float[batch,32,112,112] getitem
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float[batch,256,14,14] getitem_102
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float[batch,1024,14,14] getitem_105
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float[batch,256,14,14] getitem_108
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float[batch,256,14,14] getitem_111
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float[batch,1024,14,14] getitem_114
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float[batch,256,14,14] getitem_117
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float[batch,64,56,56] getitem_12
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float[batch,256,14,14] getitem_120
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float[batch,1024,14,14] getitem_123
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float[batch,256,14,14] getitem_126
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float[batch,256,14,14] getitem_129
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float[batch,1024,14,14] getitem_132
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float[batch,256,14,14] getitem_135
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float[batch,256,14,14] getitem_138
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float[batch,1024,14,14] getitem_141
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float[batch,256,14,14] getitem_144
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float[batch,256,14,14] getitem_147
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float[batch,256,56,56] getitem_15
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float[batch,1024,14,14] getitem_150
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float[batch,256,14,14] getitem_153
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float[batch,256,14,14] getitem_156
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float[batch,1024,14,14] getitem_159
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float[batch,256,14,14] getitem_162
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float[batch,256,14,14] getitem_165
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float[batch,1024,14,14] getitem_168
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float[batch,256,14,14] getitem_171
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float[batch,256,14,14] getitem_174
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float[batch,1024,14,14] getitem_177
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float[batch,256,56,56] getitem_18
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float[batch,256,14,14] getitem_180
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float[batch,256,14,14] getitem_183
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float[batch,1024,14,14] getitem_186
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float[batch,256,14,14] getitem_189
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float[batch,256,14,14] getitem_192
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float[batch,1024,14,14] getitem_195
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float[batch,256,14,14] getitem_198
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float[batch,256,14,14] getitem_201
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float[batch,1024,14,14] getitem_204
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float[batch,256,14,14] getitem_207
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float[batch,64,56,56] getitem_21
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float[batch,256,14,14] getitem_210
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float[batch,1024,14,14] getitem_213
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float[batch,256,14,14] getitem_216
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float[batch,256,14,14] getitem_219
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float[batch,1024,14,14] getitem_222
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float[batch,256,14,14] getitem_225
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float[batch,256,14,14] getitem_228
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float[batch,1024,14,14] getitem_231
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float[batch,256,14,14] getitem_234
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float[batch,256,14,14] getitem_237
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float[batch,64,56,56] getitem_24
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float[batch,1024,14,14] getitem_240
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float[batch,256,14,14] getitem_243
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float[batch,256,14,14] getitem_246
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float[batch,1024,14,14] getitem_249
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float[batch,256,14,14] getitem_252
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float[batch,256,14,14] getitem_255
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float[batch,1024,14,14] getitem_258
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float[batch,256,14,14] getitem_261
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float[batch,256,14,14] getitem_264
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float[batch,1024,14,14] getitem_267
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float[batch,256,56,56] getitem_27
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float[batch,256,14,14] getitem_270
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float[batch,256,14,14] getitem_273
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float[batch,1024,14,14] getitem_276
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float[batch,256,14,14] getitem_279
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float[batch,256,14,14] getitem_282
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float[batch,1024,14,14] getitem_285
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float[batch,512,14,14] getitem_288
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float[batch,512,14,14] getitem_291
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float[batch,2048,7,7] getitem_294
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float[batch,2048,7,7] getitem_297
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float[batch,32,112,112] getitem_3
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float[batch,64,56,56] getitem_30
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float[batch,512,7,7] getitem_300
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float[batch,512,7,7] getitem_303
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float[batch,2048,7,7] getitem_306
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float[batch,512,7,7] getitem_309
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float[batch,512,7,7] getitem_312
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float[batch,2048,7,7] getitem_315
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float[batch,64,56,56] getitem_33
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float[batch,256,56,56] getitem_36
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float[batch,128,56,56] getitem_39
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float[batch,128,56,56] getitem_42
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float[batch,512,28,28] getitem_45
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float[batch,512,28,28] getitem_48
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float[batch,128,28,28] getitem_51
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float[batch,128,28,28] getitem_54
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float[batch,512,28,28] getitem_57
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float[batch,64,112,112] getitem_6
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float[batch,128,28,28] getitem_60
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float[batch,128,28,28] getitem_63
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float[batch,512,28,28] getitem_66
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float[batch,128,28,28] getitem_69
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float[batch,128,28,28] getitem_72
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float[batch,512,28,28] getitem_75
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float[batch,256,28,28] getitem_78
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float[batch,256,28,28] getitem_81
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float[batch,1024,14,14] getitem_84
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float[batch,1024,14,14] getitem_87
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float[batch,64,56,56] getitem_9
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float[batch,256,14,14] getitem_90
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float[batch,256,14,14] getitem_93
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float[batch,1024,14,14] getitem_96
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float[batch,256,14,14] getitem_99
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float[batch,3,224,224] image_chw
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float[batch,224,224,3] image_f32
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float[batch,224,224,3] image_shifted
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float[batch,1] linalg_vector_norm
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float[1,batch,2048] linear
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float[50,batch,2048] linear_1
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float[50,batch,2048] linear_2
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float[batch,512] linear_3
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float[1,batch,2048] mean
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float[1,batch,2048] node_scaled_dot_product_attention_q_row
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float[49,batch,2048] permute_1
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float[1,batch,32,64] permute_2
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float[batch,32,112,112] relu
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float[batch,32,112,112] relu_1
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float[batch,64,56,56] relu_10
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float[batch,512,7,7] relu_100
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float[batch,2048,7,7] relu_101
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float[batch,256,56,56] relu_11
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float[batch,128,56,56] relu_12
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float[batch,128,56,56] relu_13
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float[batch,512,28,28] relu_14
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float[batch,128,28,28] relu_15
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float[batch,128,28,28] relu_16
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float[batch,512,28,28] relu_17
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float[batch,128,28,28] relu_18
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float[batch,128,28,28] relu_19
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float[batch,64,112,112] relu_2
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float[batch,512,28,28] relu_20
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float[batch,128,28,28] relu_21
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float[batch,128,28,28] relu_22
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float[batch,512,28,28] relu_23
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float[batch,256,28,28] relu_24
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float[batch,256,28,28] relu_25
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float[batch,1024,14,14] relu_26
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float[batch,256,14,14] relu_27
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float[batch,256,14,14] relu_28
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float[batch,1024,14,14] relu_29
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float[batch,64,56,56] relu_3
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float[batch,256,14,14] relu_30
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float[batch,256,14,14] relu_31
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float[batch,1024,14,14] relu_32
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float[batch,256,14,14] relu_33
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float[batch,256,14,14] relu_34
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float[batch,1024,14,14] relu_35
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float[batch,256,14,14] relu_36
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float[batch,256,14,14] relu_37
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float[batch,1024,14,14] relu_38
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float[batch,256,14,14] relu_39
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float[batch,64,56,56] relu_4
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float[batch,256,14,14] relu_40
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float[batch,1024,14,14] relu_41
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float[batch,256,14,14] relu_42
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float[batch,256,14,14] relu_43
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float[batch,1024,14,14] relu_44
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float[batch,256,14,14] relu_45
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float[batch,256,14,14] relu_46
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float[batch,1024,14,14] relu_47
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float[batch,256,14,14] relu_48
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float[batch,256,14,14] relu_49
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float[batch,256,56,56] relu_5
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float[batch,1024,14,14] relu_50
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float[batch,256,14,14] relu_51
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float[batch,256,14,14] relu_52
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float[batch,1024,14,14] relu_53
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float[batch,256,14,14] relu_54
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float[batch,256,14,14] relu_55
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float[batch,1024,14,14] relu_56
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float[batch,256,14,14] relu_57
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float[batch,256,14,14] relu_58
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float[batch,1024,14,14] relu_59
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float[batch,64,56,56] relu_6
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float[batch,256,14,14] relu_60
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float[batch,256,14,14] relu_61
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float[batch,1024,14,14] relu_62
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float[batch,256,14,14] relu_63
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float[batch,256,14,14] relu_64
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float[batch,1024,14,14] relu_65
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float[batch,256,14,14] relu_66
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float[batch,256,14,14] relu_67
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float[batch,1024,14,14] relu_68
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float[batch,256,14,14] relu_69
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float[batch,64,56,56] relu_7
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float[batch,256,14,14] relu_70
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float[batch,1024,14,14] relu_71
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float[batch,256,14,14] relu_72
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float[batch,256,14,14] relu_73
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float[batch,1024,14,14] relu_74
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float[batch,256,14,14] relu_75
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float[batch,256,14,14] relu_76
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float[batch,1024,14,14] relu_77
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float[batch,256,14,14] relu_78
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float[batch,256,14,14] relu_79
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float[batch,256,56,56] relu_8
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float[batch,1024,14,14] relu_80
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float[batch,256,14,14] relu_81
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float[batch,256,14,14] relu_82
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float[batch,1024,14,14] relu_83
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float[batch,256,14,14] relu_84
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float[batch,256,14,14] relu_85
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float[batch,1024,14,14] relu_86
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float[batch,256,14,14] relu_87
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float[batch,256,14,14] relu_88
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float[batch,1024,14,14] relu_89
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float[batch,64,56,56] relu_9
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float[batch,256,14,14] relu_90
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float[batch,256,14,14] relu_91
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float[batch,1024,14,14] relu_92
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float[batch,512,14,14] relu_93
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float[batch,512,14,14] relu_94
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float[batch,2048,7,7] relu_95
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float[batch,512,7,7] relu_96
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float[batch,512,7,7] relu_97
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float[batch,2048,7,7] relu_98
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float[batch,512,7,7] relu_99
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float[batch,32,1,64] scaled_dot_product_attention
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float[batch,512] select
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float[2048] split_split_0
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float[2048] split_split_1
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float[2048] split_split_2
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float[unk__1,1,64] transpose
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float[unk__1,50,64] transpose_1
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float[unk__1,50,64] transpose_2
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float[50,1,2048] unsqueeze
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float[1,batch,2048] val_7
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float[50,batch,2048] val_8
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float[50,batch,2048] val_9
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float[1,batch,512] view_10
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float[batch,2048,49] view_2
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float[1,unk__1,64] view_3
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float[50,unk__1,64] view_4
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float[50,unk__1,64] view_5
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float[batch,32,1,64] view_6
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float[batch,32,50,64] view_7
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float[batch,32,50,64] view_8
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float[batch,2048] view_9
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>
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{
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[pre_cast] image_f32 = Cast <to: int = 1> (image)
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[pre_shift] image_shifted = Sub (image_f32, image_shift)
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[pre_nhwc_to_nchw] image_chw = Transpose <perm: ints = [0, 3, 1, 2]> (image_shifted)
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getitem = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [2, 2]> (image_chw, "visual.conv1.weight", "visual.conv1.weight_bias")
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[node_relu] relu = Relu (getitem)
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getitem_3 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu, "visual.conv2.weight", "visual.conv2.weight_bias")
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relu_1 = Relu (getitem_3)
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getitem_6 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_1, "visual.conv3.weight", "visual.conv3.weight_bias")
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relu_2 = Relu (getitem_6)
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[node_avg_pool2d] avg_pool2d = AveragePool <auto_pad: string = "NOTSET", ceil_mode: int = 0, count_include_pad: int = 1, kernel_shape: ints = [2, 2], pads: ints = [0, 0, 0, 0], strides: ints = [2, 2]> (relu_2)
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getitem_9 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (avg_pool2d, "visual.layer1.0.conv1.weight", "visual.layer1.0.conv1.weight_bias")
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relu_3 = Relu (getitem_9)
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getitem_12 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_3, "visual.layer1.0.conv2.weight", "visual.layer1.0.conv2.weight_bias")
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relu_4 = Relu (getitem_12)
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getitem_15 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_4, "visual.layer1.0.conv3.weight", "visual.layer1.0.conv3.weight_bias")
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getitem_18 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (avg_pool2d, "visual.layer1.0.downsample.0.weight", "visual.layer1.0.downsample.0.weight_bias")
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add_140 = Add (getitem_15, getitem_18)
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relu_5 = Relu (add_140)
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getitem_21 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_5, "visual.layer1.1.conv1.weight", "visual.layer1.1.conv1.weight_bias")
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relu_6 = Relu (getitem_21)
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getitem_24 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_6, "visual.layer1.1.conv2.weight", "visual.layer1.1.conv2.weight_bias")
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relu_7 = Relu (getitem_24)
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getitem_27 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_7, "visual.layer1.1.conv3.weight", "visual.layer1.1.conv3.weight_bias")
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add_216 = Add (getitem_27, relu_5)
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relu_8 = Relu (add_216)
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getitem_30 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_8, "visual.layer1.2.conv1.weight", "visual.layer1.2.conv1.weight_bias")
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relu_9 = Relu (getitem_30)
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getitem_33 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_9, "visual.layer1.2.conv2.weight", "visual.layer1.2.conv2.weight_bias")
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relu_10 = Relu (getitem_33)
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getitem_36 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_10, "visual.layer1.2.conv3.weight", "visual.layer1.2.conv3.weight_bias")
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add_292 = Add (getitem_36, relu_8)
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relu_11 = Relu (add_292)
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getitem_39 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_11, "visual.layer2.0.conv1.weight", "visual.layer2.0.conv1.weight_bias")
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relu_12 = Relu (getitem_39)
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getitem_42 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_12, "visual.layer2.0.conv2.weight", "visual.layer2.0.conv2.weight_bias")
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relu_13 = Relu (getitem_42)
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avg_pool2d_2 = AveragePool <auto_pad: string = "NOTSET", ceil_mode: int = 0, count_include_pad: int = 1, kernel_shape: ints = [2, 2], pads: ints = [0, 0, 0, 0], strides: ints = [2, 2]> (relu_13)
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getitem_45 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (avg_pool2d_2, "visual.layer2.0.conv3.weight", "visual.layer2.0.conv3.weight_bias")
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avg_pool2d_3 = AveragePool <auto_pad: string = "NOTSET", ceil_mode: int = 0, count_include_pad: int = 1, kernel_shape: ints = [2, 2], pads: ints = [0, 0, 0, 0], strides: ints = [2, 2]> (relu_11)
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getitem_48 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (avg_pool2d_3, "visual.layer2.0.downsample.0.weight", "visual.layer2.0.downsample.0.weight_bias")
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add_388 = Add (getitem_45, getitem_48)
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relu_14 = Relu (add_388)
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getitem_51 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_14, "visual.layer2.1.conv1.weight", "visual.layer2.1.conv1.weight_bias")
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relu_15 = Relu (getitem_51)
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getitem_54 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_15, "visual.layer2.1.conv2.weight", "visual.layer2.1.conv2.weight_bias")
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relu_16 = Relu (getitem_54)
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getitem_57 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_16, "visual.layer2.1.conv3.weight", "visual.layer2.1.conv3.weight_bias")
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add_464 = Add (getitem_57, relu_14)
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relu_17 = Relu (add_464)
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getitem_60 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_17, "visual.layer2.2.conv1.weight", "visual.layer2.2.conv1.weight_bias")
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relu_18 = Relu (getitem_60)
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getitem_63 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_18, "visual.layer2.2.conv2.weight", "visual.layer2.2.conv2.weight_bias")
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relu_19 = Relu (getitem_63)
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getitem_66 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_19, "visual.layer2.2.conv3.weight", "visual.layer2.2.conv3.weight_bias")
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add_540 = Add (getitem_66, relu_17)
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relu_20 = Relu (add_540)
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getitem_69 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_20, "visual.layer2.3.conv1.weight", "visual.layer2.3.conv1.weight_bias")
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relu_21 = Relu (getitem_69)
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getitem_72 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_21, "visual.layer2.3.conv2.weight", "visual.layer2.3.conv2.weight_bias")
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relu_22 = Relu (getitem_72)
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getitem_75 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_22, "visual.layer2.3.conv3.weight", "visual.layer2.3.conv3.weight_bias")
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add_616 = Add (getitem_75, relu_20)
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relu_23 = Relu (add_616)
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getitem_78 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_23, "visual.layer3.0.conv1.weight", "visual.layer3.0.conv1.weight_bias")
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relu_24 = Relu (getitem_78)
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getitem_81 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_24, "visual.layer3.0.conv2.weight", "visual.layer3.0.conv2.weight_bias")
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relu_25 = Relu (getitem_81)
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avg_pool2d_4 = AveragePool <auto_pad: string = "NOTSET", ceil_mode: int = 0, count_include_pad: int = 1, kernel_shape: ints = [2, 2], pads: ints = [0, 0, 0, 0], strides: ints = [2, 2]> (relu_25)
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getitem_84 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (avg_pool2d_4, "visual.layer3.0.conv3.weight", "visual.layer3.0.conv3.weight_bias")
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avg_pool2d_5 = AveragePool <auto_pad: string = "NOTSET", ceil_mode: int = 0, count_include_pad: int = 1, kernel_shape: ints = [2, 2], pads: ints = [0, 0, 0, 0], strides: ints = [2, 2]> (relu_23)
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|
getitem_87 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (avg_pool2d_5, "visual.layer3.0.downsample.0.weight", "visual.layer3.0.downsample.0.weight_bias")
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add_712 = Add (getitem_84, getitem_87)
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relu_26 = Relu (add_712)
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|
getitem_90 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_26, "visual.layer3.1.conv1.weight", "visual.layer3.1.conv1.weight_bias")
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|
relu_27 = Relu (getitem_90)
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|
getitem_93 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_27, "visual.layer3.1.conv2.weight", "visual.layer3.1.conv2.weight_bias")
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relu_28 = Relu (getitem_93)
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getitem_96 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_28, "visual.layer3.1.conv3.weight", "visual.layer3.1.conv3.weight_bias")
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add_788 = Add (getitem_96, relu_26)
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|
relu_29 = Relu (add_788)
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|
getitem_99 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_29, "visual.layer3.2.conv1.weight", "visual.layer3.2.conv1.weight_bias")
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relu_30 = Relu (getitem_99)
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getitem_102 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_30, "visual.layer3.2.conv2.weight", "visual.layer3.2.conv2.weight_bias")
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|
relu_31 = Relu (getitem_102)
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getitem_105 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_31, "visual.layer3.2.conv3.weight", "visual.layer3.2.conv3.weight_bias")
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|
add_864 = Add (getitem_105, relu_29)
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relu_32 = Relu (add_864)
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|
getitem_108 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_32, "visual.layer3.3.conv1.weight", "visual.layer3.3.conv1.weight_bias")
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relu_33 = Relu (getitem_108)
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|
getitem_111 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_33, "visual.layer3.3.conv2.weight", "visual.layer3.3.conv2.weight_bias")
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|
relu_34 = Relu (getitem_111)
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|
getitem_114 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_34, "visual.layer3.3.conv3.weight", "visual.layer3.3.conv3.weight_bias")
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|
add_940 = Add (getitem_114, relu_32)
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|
relu_35 = Relu (add_940)
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|
getitem_117 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_35, "visual.layer3.4.conv1.weight", "visual.layer3.4.conv1.weight_bias")
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|
relu_36 = Relu (getitem_117)
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|
getitem_120 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_36, "visual.layer3.4.conv2.weight", "visual.layer3.4.conv2.weight_bias")
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|
relu_37 = Relu (getitem_120)
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|
getitem_123 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_37, "visual.layer3.4.conv3.weight", "visual.layer3.4.conv3.weight_bias")
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|
add_1016 = Add (getitem_123, relu_35)
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|
relu_38 = Relu (add_1016)
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|
getitem_126 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_38, "visual.layer3.5.conv1.weight", "visual.layer3.5.conv1.weight_bias")
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|
relu_39 = Relu (getitem_126)
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|
getitem_129 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_39, "visual.layer3.5.conv2.weight", "visual.layer3.5.conv2.weight_bias")
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|
relu_40 = Relu (getitem_129)
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|
getitem_132 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_40, "visual.layer3.5.conv3.weight", "visual.layer3.5.conv3.weight_bias")
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|
add_1092 = Add (getitem_132, relu_38)
|
|
relu_41 = Relu (add_1092)
|
|
getitem_135 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_41, "visual.layer3.6.conv1.weight", "visual.layer3.6.conv1.weight_bias")
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|
relu_42 = Relu (getitem_135)
|
|
getitem_138 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_42, "visual.layer3.6.conv2.weight", "visual.layer3.6.conv2.weight_bias")
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|
relu_43 = Relu (getitem_138)
|
|
getitem_141 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_43, "visual.layer3.6.conv3.weight", "visual.layer3.6.conv3.weight_bias")
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|
add_1168 = Add (getitem_141, relu_41)
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|
relu_44 = Relu (add_1168)
|
|
getitem_144 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_44, "visual.layer3.7.conv1.weight", "visual.layer3.7.conv1.weight_bias")
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|
relu_45 = Relu (getitem_144)
|
|
getitem_147 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_45, "visual.layer3.7.conv2.weight", "visual.layer3.7.conv2.weight_bias")
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|
relu_46 = Relu (getitem_147)
|
|
getitem_150 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_46, "visual.layer3.7.conv3.weight", "visual.layer3.7.conv3.weight_bias")
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add_1244 = Add (getitem_150, relu_44)
|
|
relu_47 = Relu (add_1244)
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|
getitem_153 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_47, "visual.layer3.8.conv1.weight", "visual.layer3.8.conv1.weight_bias")
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|
relu_48 = Relu (getitem_153)
|
|
getitem_156 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_48, "visual.layer3.8.conv2.weight", "visual.layer3.8.conv2.weight_bias")
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|
relu_49 = Relu (getitem_156)
|
|
getitem_159 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_49, "visual.layer3.8.conv3.weight", "visual.layer3.8.conv3.weight_bias")
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|
add_1320 = Add (getitem_159, relu_47)
|
|
relu_50 = Relu (add_1320)
|
|
getitem_162 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_50, "visual.layer3.9.conv1.weight", "visual.layer3.9.conv1.weight_bias")
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|
relu_51 = Relu (getitem_162)
|
|
getitem_165 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_51, "visual.layer3.9.conv2.weight", "visual.layer3.9.conv2.weight_bias")
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|
relu_52 = Relu (getitem_165)
|
|
getitem_168 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_52, "visual.layer3.9.conv3.weight", "visual.layer3.9.conv3.weight_bias")
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|
add_1396 = Add (getitem_168, relu_50)
|
|
relu_53 = Relu (add_1396)
|
|
getitem_171 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_53, "visual.layer3.10.conv1.weight", "visual.layer3.10.conv1.weight_bias")
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|
relu_54 = Relu (getitem_171)
|
|
getitem_174 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_54, "visual.layer3.10.conv2.weight", "visual.layer3.10.conv2.weight_bias")
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|
relu_55 = Relu (getitem_174)
|
|
getitem_177 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_55, "visual.layer3.10.conv3.weight", "visual.layer3.10.conv3.weight_bias")
|
|
add_1472 = Add (getitem_177, relu_53)
|
|
relu_56 = Relu (add_1472)
|
|
getitem_180 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_56, "visual.layer3.11.conv1.weight", "visual.layer3.11.conv1.weight_bias")
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|
relu_57 = Relu (getitem_180)
|
|
getitem_183 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_57, "visual.layer3.11.conv2.weight", "visual.layer3.11.conv2.weight_bias")
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|
relu_58 = Relu (getitem_183)
|
|
getitem_186 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_58, "visual.layer3.11.conv3.weight", "visual.layer3.11.conv3.weight_bias")
|
|
add_1548 = Add (getitem_186, relu_56)
|
|
relu_59 = Relu (add_1548)
|
|
getitem_189 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_59, "visual.layer3.12.conv1.weight", "visual.layer3.12.conv1.weight_bias")
|
|
relu_60 = Relu (getitem_189)
|
|
getitem_192 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_60, "visual.layer3.12.conv2.weight", "visual.layer3.12.conv2.weight_bias")
|
|
relu_61 = Relu (getitem_192)
|
|
getitem_195 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_61, "visual.layer3.12.conv3.weight", "visual.layer3.12.conv3.weight_bias")
|
|
add_1624 = Add (getitem_195, relu_59)
|
|
relu_62 = Relu (add_1624)
|
|
getitem_198 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_62, "visual.layer3.13.conv1.weight", "visual.layer3.13.conv1.weight_bias")
|
|
relu_63 = Relu (getitem_198)
|
|
getitem_201 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_63, "visual.layer3.13.conv2.weight", "visual.layer3.13.conv2.weight_bias")
|
|
relu_64 = Relu (getitem_201)
|
|
getitem_204 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_64, "visual.layer3.13.conv3.weight", "visual.layer3.13.conv3.weight_bias")
|
|
add_1700 = Add (getitem_204, relu_62)
|
|
relu_65 = Relu (add_1700)
|
|
getitem_207 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_65, "visual.layer3.14.conv1.weight", "visual.layer3.14.conv1.weight_bias")
|
|
relu_66 = Relu (getitem_207)
|
|
getitem_210 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_66, "visual.layer3.14.conv2.weight", "visual.layer3.14.conv2.weight_bias")
|
|
relu_67 = Relu (getitem_210)
|
|
getitem_213 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_67, "visual.layer3.14.conv3.weight", "visual.layer3.14.conv3.weight_bias")
|
|
add_1776 = Add (getitem_213, relu_65)
|
|
relu_68 = Relu (add_1776)
|
|
getitem_216 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_68, "visual.layer3.15.conv1.weight", "visual.layer3.15.conv1.weight_bias")
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relu_69 = Relu (getitem_216)
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getitem_219 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_69, "visual.layer3.15.conv2.weight", "visual.layer3.15.conv2.weight_bias")
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relu_70 = Relu (getitem_219)
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getitem_222 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_70, "visual.layer3.15.conv3.weight", "visual.layer3.15.conv3.weight_bias")
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add_1852 = Add (getitem_222, relu_68)
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relu_71 = Relu (add_1852)
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getitem_225 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_71, "visual.layer3.16.conv1.weight", "visual.layer3.16.conv1.weight_bias")
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relu_72 = Relu (getitem_225)
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getitem_228 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_72, "visual.layer3.16.conv2.weight", "visual.layer3.16.conv2.weight_bias")
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relu_73 = Relu (getitem_228)
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getitem_231 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_73, "visual.layer3.16.conv3.weight", "visual.layer3.16.conv3.weight_bias")
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add_1928 = Add (getitem_231, relu_71)
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relu_74 = Relu (add_1928)
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getitem_234 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_74, "visual.layer3.17.conv1.weight", "visual.layer3.17.conv1.weight_bias")
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relu_75 = Relu (getitem_234)
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getitem_237 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_75, "visual.layer3.17.conv2.weight", "visual.layer3.17.conv2.weight_bias")
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relu_76 = Relu (getitem_237)
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getitem_240 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_76, "visual.layer3.17.conv3.weight", "visual.layer3.17.conv3.weight_bias")
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add_2004 = Add (getitem_240, relu_74)
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relu_77 = Relu (add_2004)
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getitem_243 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_77, "visual.layer3.18.conv1.weight", "visual.layer3.18.conv1.weight_bias")
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relu_78 = Relu (getitem_243)
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getitem_246 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_78, "visual.layer3.18.conv2.weight", "visual.layer3.18.conv2.weight_bias")
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relu_79 = Relu (getitem_246)
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getitem_249 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_79, "visual.layer3.18.conv3.weight", "visual.layer3.18.conv3.weight_bias")
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add_2080 = Add (getitem_249, relu_77)
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relu_80 = Relu (add_2080)
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getitem_252 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_80, "visual.layer3.19.conv1.weight", "visual.layer3.19.conv1.weight_bias")
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relu_81 = Relu (getitem_252)
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getitem_255 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_81, "visual.layer3.19.conv2.weight", "visual.layer3.19.conv2.weight_bias")
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relu_82 = Relu (getitem_255)
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getitem_258 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_82, "visual.layer3.19.conv3.weight", "visual.layer3.19.conv3.weight_bias")
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add_2156 = Add (getitem_258, relu_80)
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relu_83 = Relu (add_2156)
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getitem_261 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_83, "visual.layer3.20.conv1.weight", "visual.layer3.20.conv1.weight_bias")
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relu_84 = Relu (getitem_261)
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getitem_264 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_84, "visual.layer3.20.conv2.weight", "visual.layer3.20.conv2.weight_bias")
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|
relu_85 = Relu (getitem_264)
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|
getitem_267 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_85, "visual.layer3.20.conv3.weight", "visual.layer3.20.conv3.weight_bias")
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add_2232 = Add (getitem_267, relu_83)
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relu_86 = Relu (add_2232)
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getitem_270 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_86, "visual.layer3.21.conv1.weight", "visual.layer3.21.conv1.weight_bias")
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|
relu_87 = Relu (getitem_270)
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|
getitem_273 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_87, "visual.layer3.21.conv2.weight", "visual.layer3.21.conv2.weight_bias")
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|
relu_88 = Relu (getitem_273)
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getitem_276 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_88, "visual.layer3.21.conv3.weight", "visual.layer3.21.conv3.weight_bias")
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|
add_2308 = Add (getitem_276, relu_86)
|
|
relu_89 = Relu (add_2308)
|
|
getitem_279 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_89, "visual.layer3.22.conv1.weight", "visual.layer3.22.conv1.weight_bias")
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|
relu_90 = Relu (getitem_279)
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getitem_282 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_90, "visual.layer3.22.conv2.weight", "visual.layer3.22.conv2.weight_bias")
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|
relu_91 = Relu (getitem_282)
|
|
getitem_285 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_91, "visual.layer3.22.conv3.weight", "visual.layer3.22.conv3.weight_bias")
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|
add_2384 = Add (getitem_285, relu_89)
|
|
relu_92 = Relu (add_2384)
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|
getitem_288 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_92, "visual.layer4.0.conv1.weight", "visual.layer4.0.conv1.weight_bias")
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|
relu_93 = Relu (getitem_288)
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|
getitem_291 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_93, "visual.layer4.0.conv2.weight", "visual.layer4.0.conv2.weight_bias")
|
|
relu_94 = Relu (getitem_291)
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|
avg_pool2d_6 = AveragePool <auto_pad: string = "NOTSET", ceil_mode: int = 0, count_include_pad: int = 1, kernel_shape: ints = [2, 2], pads: ints = [0, 0, 0, 0], strides: ints = [2, 2]> (relu_94)
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|
getitem_294 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (avg_pool2d_6, "visual.layer4.0.conv3.weight", "visual.layer4.0.conv3.weight_bias")
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|
avg_pool2d_7 = AveragePool <auto_pad: string = "NOTSET", ceil_mode: int = 0, count_include_pad: int = 1, kernel_shape: ints = [2, 2], pads: ints = [0, 0, 0, 0], strides: ints = [2, 2]> (relu_92)
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|
getitem_297 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (avg_pool2d_7, "visual.layer4.0.downsample.0.weight", "visual.layer4.0.downsample.0.weight_bias")
|
|
add_2480 = Add (getitem_294, getitem_297)
|
|
relu_95 = Relu (add_2480)
|
|
getitem_300 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_95, "visual.layer4.1.conv1.weight", "visual.layer4.1.conv1.weight_bias")
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|
relu_96 = Relu (getitem_300)
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|
getitem_303 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_96, "visual.layer4.1.conv2.weight", "visual.layer4.1.conv2.weight_bias")
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|
relu_97 = Relu (getitem_303)
|
|
getitem_306 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_97, "visual.layer4.1.conv3.weight", "visual.layer4.1.conv3.weight_bias")
|
|
add_2556 = Add (getitem_306, relu_95)
|
|
relu_98 = Relu (add_2556)
|
|
getitem_309 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_98, "visual.layer4.2.conv1.weight", "visual.layer4.2.conv1.weight_bias")
|
|
relu_99 = Relu (getitem_309)
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|
getitem_312 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [1, 1, 1, 1], strides: ints = [1, 1]> (relu_99, "visual.layer4.2.conv2.weight", "visual.layer4.2.conv2.weight_bias")
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|
relu_100 = Relu (getitem_312)
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|
getitem_315 = Conv <auto_pad: string = "NOTSET", dilations: ints = [1, 1], group: int = 1, pads: ints = [0, 0, 0, 0], strides: ints = [1, 1]> (relu_100, "visual.layer4.2.conv3.weight", "visual.layer4.2.conv3.weight_bias")
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|
add_2632 = Add (getitem_315, relu_98)
|
|
relu_101 = Relu (add_2632)
|
|
view_2 = Reshape <allowzero: int = 1> (relu_101, view_2_target)
|
|
permute_1 = Transpose <perm: ints = [2, 0, 1]> (view_2)
|
|
[node_mean] mean = ReduceMean <keepdims: int = 1, noop_with_empty_axes: int = 0> (permute_1, val_0)
|
|
[node_cat] cat = Concat <axis: int = 0> (mean, permute_1)
|
|
[node_unsqueeze] unsqueeze = Unsqueeze ("visual.attnpool.positional_embedding", val_3)
|
|
add_2662 = Add (cat, unsqueeze)
|
|
split_split_0, split_split_1, split_split_2 = Split <axis: int = 0, num_outputs: int = 3> (cat_1)
|
|
node_scaled_dot_product_attention_q_row = Slice (add_2662, val_0, val_3, val_0)
|
|
val_7 = MatMul (node_scaled_dot_product_attention_q_row, val_4)
|
|
[node_linear] linear = Add (val_7, split_split_0)
|
|
val_8 = MatMul (add_2662, val_5)
|
|
linear_1 = Add (val_8, split_split_1)
|
|
val_9 = MatMul (add_2662, val_6)
|
|
linear_2 = Add (val_9, split_split_2)
|
|
view_3 = Reshape <allowzero: int = 1> (linear, node_scaled_dot_product_attention_q_unpack_1)
|
|
[node_transpose] transpose = Transpose <perm: ints = [1, 0, 2]> (view_3)
|
|
view_4 = Reshape <allowzero: int = 1> (linear_1, view_4_target)
|
|
transpose_1 = Transpose <perm: ints = [1, 0, 2]> (view_4)
|
|
view_5 = Reshape <allowzero: int = 1> (linear_2, view_4_target)
|
|
transpose_2 = Transpose <perm: ints = [1, 0, 2]> (view_5)
|
|
view_6 = Reshape <allowzero: int = 1> (transpose, node_scaled_dot_product_attention_q_pack_1)
|
|
view_7 = Reshape <allowzero: int = 1> (transpose_1, view_7_target)
|
|
view_8 = Reshape <allowzero: int = 1> (transpose_2, view_7_target)
|
|
[node_scaled_dot_product_attention] scaled_dot_product_attention = Attention <is_causal: int = 0, qk_matmul_output_mode: int = 0, softcap: float = 0> (view_6, view_7, view_8)
|
|
permute_2 = Transpose <perm: ints = [2, 0, 1, 3]> (scaled_dot_product_attention)
|
|
view_9 = Reshape <allowzero: int = 1> (permute_2, view_9_target)
|
|
linear_3 = Gemm <alpha: float = 1, beta: float = 1, transA: int = 0, transB: int = 1> (view_9, "visual.attnpool.c_proj.weight", "visual.attnpool.c_proj.bias")
|
|
view_10 = Reshape <allowzero: int = 1> (linear_3, node_scaled_dot_product_attention_out_1)
|
|
select = Squeeze (view_10, val_0)
|
|
[node_linalg_vector_norm] linalg_vector_norm = ReduceL2 <keepdims: int = 1, noop_with_empty_axes: int = 0> (select, val_1)
|
|
[node_clamp_min] clamp_min = Clip (linalg_vector_norm, val_2)
|
|
[node_div] image_embedding = Div (select, clamp_min)
|
|
}
|
|
|
|
weights:
|
|
cat_1 FLOAT[6144] 9612b463fd18
|
|
image_shift FLOAT[3] 2f7a50e604ad
|
|
node_scaled_dot_product_attention_out_1 INT64[3] b6639c8d94ec
|
|
node_scaled_dot_product_attention_q_pack_1 INT64[4] f7b9b5620534
|
|
node_scaled_dot_product_attention_q_unpack_1 INT64[3] cfe34a386daf
|
|
val_0 INT64[1] af5570f5a181
|
|
val_1 INT64[1] 12a3ae445661
|
|
val_2 FLOAT[] 6708d9be4956
|
|
val_3 INT64[1] 7c9fa136d441
|
|
val_4 FLOAT[2048,2048] f49f22a8d0d8
|
|
val_5 FLOAT[2048,2048] 457ada9eb2d1
|
|
val_6 FLOAT[2048,2048] 0e8a5b13c951
|
|
view_2_target INT64[3] 68ffb9ecb5ec
|
|
view_4_target INT64[3] c7a3d94c4eb2
|
|
view_7_target INT64[4] e0ea1841387b
|
|
view_9_target INT64[2] 76aecb4697fd
|
|
visual.attnpool.c_proj.bias FLOAT[512] 98e8b869a687
|
|
visual.attnpool.c_proj.weight FLOAT[512,2048] 944a12dec126
|
|
visual.attnpool.positional_embedding FLOAT[50,2048] 55d39d84cefc
|
|
visual.conv1.weight FLOAT[32,3,3,3] 5e7ba945a006
|
|
visual.conv1.weight_bias FLOAT[32] 52637aaf43d8
|
|
visual.conv2.weight FLOAT[32,32,3,3] d36eefdb938f
|
|
visual.conv2.weight_bias FLOAT[32] adc45ce4c237
|
|
visual.conv3.weight FLOAT[64,32,3,3] e5791fd33889
|
|
visual.conv3.weight_bias FLOAT[64] c56c3812a1a4
|
|
visual.layer1.0.conv1.weight FLOAT[64,64,1,1] e1b663b66c67
|
|
visual.layer1.0.conv1.weight_bias FLOAT[64] b726e5a0c77f
|
|
visual.layer1.0.conv2.weight FLOAT[64,64,3,3] c686312f24da
|
|
visual.layer1.0.conv2.weight_bias FLOAT[64] d9e7ed04a69f
|
|
visual.layer1.0.conv3.weight FLOAT[256,64,1,1] d4e03f1cfd56
|
|
visual.layer1.0.conv3.weight_bias FLOAT[256] f45cb5734d5e
|
|
visual.layer1.0.downsample.0.weight FLOAT[256,64,1,1] 16a01f38eaad
|
|
visual.layer1.0.downsample.0.weight_bias FLOAT[256] 5e729a38e7da
|
|
visual.layer1.1.conv1.weight FLOAT[64,256,1,1] aa7f561a446d
|
|
visual.layer1.1.conv1.weight_bias FLOAT[64] 0a59d6df5a74
|
|
visual.layer1.1.conv2.weight FLOAT[64,64,3,3] 0367fb9328b9
|
|
visual.layer1.1.conv2.weight_bias FLOAT[64] b53f76f67606
|
|
visual.layer1.1.conv3.weight FLOAT[256,64,1,1] 6a1a12bc395f
|
|
visual.layer1.1.conv3.weight_bias FLOAT[256] ac60b5e5c569
|
|
visual.layer1.2.conv1.weight FLOAT[64,256,1,1] e7828bc5f0ee
|
|
visual.layer1.2.conv1.weight_bias FLOAT[64] 0561c6ca68e7
|
|
visual.layer1.2.conv2.weight FLOAT[64,64,3,3] 98ab5cdcecc6
|
|
visual.layer1.2.conv2.weight_bias FLOAT[64] 3bb6933213cd
|
|
visual.layer1.2.conv3.weight FLOAT[256,64,1,1] 625465368498
|
|
visual.layer1.2.conv3.weight_bias FLOAT[256] 207071d90d23
|
|
visual.layer2.0.conv1.weight FLOAT[128,256,1,1] c14f6619adbc
|
|
visual.layer2.0.conv1.weight_bias FLOAT[128] d1bad324aaeb
|
|
visual.layer2.0.conv2.weight FLOAT[128,128,3,3] 87278d03cedc
|
|
visual.layer2.0.conv2.weight_bias FLOAT[128] 791077d8b4d8
|
|
visual.layer2.0.conv3.weight FLOAT[512,128,1,1] 2363a34d5577
|
|
visual.layer2.0.conv3.weight_bias FLOAT[512] e84d9fdb7be7
|
|
visual.layer2.0.downsample.0.weight FLOAT[512,256,1,1] 437d06047db7
|
|
visual.layer2.0.downsample.0.weight_bias FLOAT[512] 2cfd5737e175
|
|
visual.layer2.1.conv1.weight FLOAT[128,512,1,1] 16ab309d8f57
|
|
visual.layer2.1.conv1.weight_bias FLOAT[128] d939ad25a1d7
|
|
visual.layer2.1.conv2.weight FLOAT[128,128,3,3] fb7765f84fb8
|
|
visual.layer2.1.conv2.weight_bias FLOAT[128] b66ccc90092d
|
|
visual.layer2.1.conv3.weight FLOAT[512,128,1,1] bd1e0396f2d7
|
|
visual.layer2.1.conv3.weight_bias FLOAT[512] 4b4ffac018ff
|
|
visual.layer2.2.conv1.weight FLOAT[128,512,1,1] 68015521b270
|
|
visual.layer2.2.conv1.weight_bias FLOAT[128] 7f965766b6f9
|
|
visual.layer2.2.conv2.weight FLOAT[128,128,3,3] 306b50e42a19
|
|
visual.layer2.2.conv2.weight_bias FLOAT[128] 74f794024a5f
|
|
visual.layer2.2.conv3.weight FLOAT[512,128,1,1] c24ae32d7efa
|
|
visual.layer2.2.conv3.weight_bias FLOAT[512] b1ecdee8f9f1
|
|
visual.layer2.3.conv1.weight FLOAT[128,512,1,1] 9d019c735f9e
|
|
visual.layer2.3.conv1.weight_bias FLOAT[128] f3b68bbf8413
|
|
visual.layer2.3.conv2.weight FLOAT[128,128,3,3] 4e8f1b2e7adf
|
|
visual.layer2.3.conv2.weight_bias FLOAT[128] 27ed6a9331c3
|
|
visual.layer2.3.conv3.weight FLOAT[512,128,1,1] 218bf38825c3
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