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20 hours ago •
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ClassificationConfig prevents model compression
🐞Describing the bug
I have a pytorch model ,which I'm able to convert to CoreML and compress. If I add ClassificationConfig during conversion, I'm unable to compress the model. The error is:
"identity_1" (op_type: identity) Input x="classLabel_probs" expects list, tensor, or scalar but got dict[str,fp64]"
Stack Trace
"name": "ValueError",
"message": "Op \"identity_1\" (op_type: identity) Input x=\"classLabel_probs\" expects list, tensor, or scalar but got dict[str,fp64]",
"stack": "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)\nCell \u001b[0;32mIn[162], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39m# Quantizing model\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m compress_coreml_program(output_base_path\u001b[39m=\u001b[39;49moutput_folder_path, model_path\u001b[39m=\u001b[39;49mcurrent_model_path)\n\nFile \u001b[0;32m~/Documents/repos/ml-explorations/object_detection/classification_training/../../clip/utils/compression_utils.py:83\u001b[0m, in \u001b[0;36mcompress_coreml_program\u001b[0;34m(output_base_path, model_path, model)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[39mfor\u001b[39;00m interpolation_mode \u001b[39min\u001b[39;00m interpolation_modes:\n\u001b[1;32m 81\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mCompressing model to 8 bits using \u001b[39m\u001b[39m{\u001b[39;00minterpolation_mode\u001b[39m}\u001b[39;00m\u001b[39m interpolation mode\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m---> 83\u001b[0m compressed_model \u001b[39m=\u001b[39m ct\u001b[39m.\u001b[39;49mcompression_utils\u001b[39m.\u001b[39;49maffine_quantize_weights(model, mode \u001b[39m=\u001b[39;49m interpolation_mode)\n\u001b[1;32m 84\u001b[0m out_path \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(output_base_path, \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39maffine_\u001b[39m\u001b[39m{\u001b[39;00minterpolation_mode\u001b[39m}\u001b[39;00m\u001b[39m_model.mlpackage\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 85\u001b[0m os\u001b[39m.\u001b[39mmakedirs(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mdirname(out_path), exist_ok\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\nFile \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/classification_env/lib/python3.10/site-packages/coremltools/models/ml_program/compression_utils.py:243\u001b[0m, in \u001b[0;36maffine_quantize_weights\u001b[0;34m(mlmodel, mode, op_selector, dtype)\u001b[0m\n\u001b[1;32m 241\u001b[0m op_selector \u001b[39m=\u001b[39m _default_op_selector\n\u001b[1;32m 242\u001b[0m affine_weight_quantizer \u001b[39m=\u001b[39m _WeightAffineQuantizer(fake_compression\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, mode\u001b[39m=\u001b[39mmode, op_selector\u001b[39m=\u001b[39mop_selector, dtype\u001b[39m=\u001b[39mdtype)\n\u001b[0;32m--> 243\u001b[0m \u001b[39mreturn\u001b[39;00m _apply_graph_pass(mlmodel, affine_weight_quantizer)\n\nFile \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/classification_env/lib/python3.10/site-packages/coremltools/models/ml_program/compression_utils.py:64\u001b[0m, in \u001b[0;36m_apply_graph_pass\u001b[0;34m(mlmodel, graph_pass)\u001b[0m\n\u001b[1;32m 61\u001b[0m graph_pass\u001b[39m.\u001b[39mapply(prog)\n\u001b[1;32m 63\u001b[0m \u001b[39m# convert the pymil program back to mlmodel\u001b[39;00m\n\u001b[0;32m---> 64\u001b[0m compressed_mlmodel \u001b[39m=\u001b[39m _mil_convert(\n\u001b[1;32m 65\u001b[0m prog,\n\u001b[1;32m 66\u001b[0m convert_to\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mmlprogram\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m 67\u001b[0m convert_from\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mmilinternal\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m 68\u001b[0m specification_version\u001b[39m=\u001b[39;49mspecification_version,\n\u001b[1;32m 69\u001b[0m compute_units\u001b[39m=\u001b[39;49mmlmodel\u001b[39m.\u001b[39;49mcompute_unit,\n\u001b[1;32m 70\u001b[0m model_description\u001b[39m=\u001b[39;49mmodel_spec\u001b[39m.\u001b[39;49mdescription,\n\u001b[1;32m 71\u001b[0m )\n\u001b[1;32m 72\u001b[0m \u001b[39mreturn\u001b[39;00m compressed_mlmodel\n\nFile \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/classification_env/lib/python3.10/site-packages/coremltools/converters/mil/converter.py:188\u001b[0m, in \u001b[0;36mmil_convert\u001b[0;34m(model, convert_from, convert_to, compute_units, **kwargs)\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[39m@_profile\u001b[39m\n\u001b[1;32m 150\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mmil_convert\u001b[39m(\n\u001b[1;32m 151\u001b[0m model,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs\n\u001b[1;32m 156\u001b[0m ):\n\u001b[1;32m 157\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 158\u001b[0m \u001b[39m Convert model from a specified frontend `convert_from` to a specified\u001b[39;00m\n\u001b[1;32m 159\u001b[0m \u001b[39m converter backend `convert_to`.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[39m See `coremltools.converters.convert`\u001b[39;00m\n\u001b[1;32m 187\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 188\u001b[0m \u001b[39mreturn\u001b[39;00m _mil_convert(model, convert_from, convert_to, ConverterRegistry, MLModel, compute_units, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\nFile \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/classification_env/lib/python3.10/site-packages/coremltools/converters/mil/converter.py:212\u001b[0m, in \u001b[0;36m_mil_convert\u001b[0;34m(model, convert_from, convert_to, registry, modelClass, compute_units, **kwargs)\u001b[0m\n\u001b[1;32m 209\u001b[0m weights_dir \u001b[39m=\u001b[39m _tempfile\u001b[39m.\u001b[39mTemporaryDirectory()\n\u001b[1;32m 210\u001b[0m kwargs[\u001b[39m\"\u001b[39m\u001b[39mweights_dir\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m weights_dir\u001b[39m.\u001b[39mname\n\u001b[0;32m--> 212\u001b[0m proto, mil_program \u001b[39m=\u001b[39m mil_convert_to_proto(\n\u001b[1;32m 213\u001b[0m model,\n\u001b[1;32m 214\u001b[0m convert_from,\n\u001b[1;32m 215\u001b[0m convert_to,\n\u001b[1;32m 216\u001b[0m registry,\n\u001b[1;32m 217\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs\n\u001b[1;32m 218\u001b[0m )\n\u001b[1;32m 220\u001b[0m _reset_conversion_state()\n\u001b[1;32m 222\u001b[0m \u001b[39mif\u001b[39;00m convert_to \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mmilinternal\u001b[39m\u001b[39m'\u001b[39m:\n\nFile \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/classification_env/lib/python3.10/site-packages/coremltools/converters/mil/converter.py:288\u001b[0m, in \u001b[0;36mmil_convert_to_proto\u001b[0;34m(model, convert_from, convert_to, converter_registry, main_pipeline, **kwargs)\u001b[0m\n\u001b[1;32m 285\u001b[0m prog \u001b[39m=\u001b[39m frontend_converter(model, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m 286\u001b[0m PipelineManager\u001b[39m.\u001b[39mapply_pipeline(prog, frontend_pipeline)\n\u001b[0;32m--> 288\u001b[0m PipelineManager\u001b[39m.\u001b[39;49mapply_pipeline(prog, main_pipeline)\n\u001b[1;32m 290\u001b[0m prog\u001b[39m.\u001b[39m_check_invalid_tensor_rank()\n\u001b[1;32m 292\u001b[0m \u001b[39mif\u001b[39;00m convert_to \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mmilinternal\u001b[39m\u001b[39m'\u001b[39m:\n\nFile \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/classification_env/lib/python3.10/site-packages/coremltools/converters/mil/mil/passes/pass_pipeline.py:378\u001b[0m, in \u001b[0;36mPipelineManager.apply_pipeline\u001b[0;34m(prog, pass_pipeline)\u001b[0m\n\u001b[1;32m 376\u001b[0m graph_pass \u001b[39m=\u001b[39m PASS_REGISTRY[pass_name]\n\u001b[1;32m 377\u001b[0m graph_pass\u001b[39m.\u001b[39mset_options(pass_options)\n\u001b[0;32m--> 378\u001b[0m graph_pass(prog)\n\u001b[1;32m 379\u001b[0m prog\u001b[39m.\u001b[39mvalidate()\n\u001b[1;32m 380\u001b[0m logger\u001b[39m.\u001b[39mdebug(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mProgram after \u001b[39m\u001b[39m{\u001b[39;00mpass_pipeline\u001b[39m}\u001b[39;00m\u001b[39m pipeline:\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m{\u001b[39;00mprog\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\nFile \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/classification_env/lib/python3.10/site-packages/coremltools/converters/mil/mil/passes/graph_pass.py:55\u001b[0m, in \u001b[0;36mAbstractGraphPass.__call__\u001b[0;34m(self, prog)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, prog: Program):\n\u001b[1;32m 54\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m prog\u001b[39m.\u001b[39mskip_all_passes:\n\u001b[0;32m---> 55\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mapply(prog)\n\nFile \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/classification_env/lib/python3.10/site-packages/coremltools/converters/mil/mil/passes/defs/optimize_repeat_ops.py:1735\u001b[0m, in \u001b[0;36mreduce_transposes.apply\u001b[0;34m(self, prog)\u001b[0m\n\u001b[1;32m 1733\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mapply\u001b[39m(\u001b[39mself\u001b[39m, prog):\n\u001b[1;32m 1734\u001b[0m \u001b[39mfor\u001b[39;00m f \u001b[39min\u001b[39;00m prog\u001b[39m.\u001b[39mfunctions\u001b[39m.\u001b[39mvalues():\n\u001b[0;32m-> 1735\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_reduce_transposes_block(f)\n\nFile \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/classification_env/lib/python3.10/site-packages/coremltools/converters/mil/mil/passes/defs/optimize_repeat_ops.py:1753\u001b[0m, in \u001b[0;36mreduce_transposes._reduce_transposes_block\u001b[0;34m(block)\u001b[0m\n\u001b[1;32m 1750\u001b[0m \u001b[39mreturn\u001b[39;00m\n\u001b[1;32m 1752\u001b[0m \u001b[39mwith\u001b[39;00m block:\n\u001b[0;32m-> 1753\u001b[0m opt_transposes \u001b[39m=\u001b[39m _TransposeOptimization(block)\n\u001b[1;32m 1754\u001b[0m opt_transposes\u001b[39m.\u001b[39mblock_traversal()\n\u001b[1;32m 1755\u001b[0m opt_transposes\u001b[39m.\u001b[39mapply_transform()\n\nFile \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/classification_env/lib/python3.10/site-packages/coremltools/converters/mil/mil/passes/defs/optimize_repeat_ops.py:757\u001b[0m, in \u001b[0;36m_TransposeOptimization.__init__\u001b[0;34m(self, block)\u001b[0m\n\u001b[1;32m 754\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput_sink_ops \u001b[39m=\u001b[39m []\n\u001b[1;32m 756\u001b[0m \u001b[39m# We modify the graph temporarily for outputs\u001b[39;00m\n\u001b[0;32m--> 757\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_add_output_sinks()\n\nFile \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/classification_env/lib/python3.10/site-packages/coremltools/converters/mil/mil/passes/defs/optimize_repeat_ops.py:766\u001b[0m, in \u001b[0;36m_TransposeOptimization._add_output_sinks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 764\u001b[0m \u001b[39mfor\u001b[39;00m out_var \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mblock\u001b[39m.\u001b[39moutputs:\n\u001b[1;32m 765\u001b[0m \u001b[39mif\u001b[39;00m out_var \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m output_sinks_var:\n\u001b[0;32m--> 766\u001b[0m out_sink \u001b[39m=\u001b[39m mb\u001b[39m.\u001b[39;49midentity(x\u001b[39m=\u001b[39;49mout_var)\n\u001b[1;32m 767\u001b[0m output_sinks_var[out_var] \u001b[39m=\u001b[39m out_sink\n\u001b[1;32m 768\u001b[0m \u001b[39melse\u001b[39;00m:\n\nFile \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/classification_env/lib/python3.10/site-packages/coremltools/converters/mil/mil/ops/registry.py:182\u001b[0m, in \u001b[0;36mSSAOpRegistry.register_op.<locals>.class_wrapper.<locals>.add_op\u001b[0;34m(cls, **kwargs)\u001b[0m\n\u001b[1;32m 179\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 180\u001b[0m op_cls_to_add \u001b[39m=\u001b[39m op_reg[op_type]\n\u001b[0;32m--> 182\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mcls\u001b[39;49m\u001b[39m.\u001b[39;49m_add_op(op_cls_to_add, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\nFile \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/classification_env/lib/python3.10/site-packages/coremltools/converters/mil/mil/builder.py:166\u001b[0m, in \u001b[0;36mBuilder._add_op\u001b[0;34m(cls, op_cls, **kwargs)\u001b[0m\n\u001b[1;32m 161\u001b[0m kwargs \u001b[39m=\u001b[39m {k: v \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(v, (\u001b[39mlist\u001b[39m, \u001b[39mtuple\u001b[39m)) \u001b[39melse\u001b[39;00m v[:] \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kwargs\u001b[39m.\u001b[39mitems() \u001b[39mif\u001b[39;00m v \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m}\n\u001b[1;32m 162\u001b[0m kwargs\u001b[39m.\u001b[39mupdate(\u001b[39mcls\u001b[39m\u001b[39m.\u001b[39m_create_vars(\n\u001b[1;32m 163\u001b[0m input_spec\u001b[39m=\u001b[39mop_cls\u001b[39m.\u001b[39minput_spec,\n\u001b[1;32m 164\u001b[0m op_name\u001b[39m=\u001b[39mkwargs[\u001b[39m\"\u001b[39m\u001b[39mname\u001b[39m\u001b[39m\"\u001b[39m], before_op\u001b[39m=\u001b[39mbefore_op,\n\u001b[1;32m 165\u001b[0m candidate_kv\u001b[39m=\u001b[39mkwargs))\n\u001b[0;32m--> 166\u001b[0m new_op \u001b[39m=\u001b[39m op_cls(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 168\u001b[0m \u001b[39m# Initialize optional input Vars if it wasn't in kwargs\u001b[39;00m\n\u001b[1;32m 169\u001b[0m default_inputs \u001b[39m=\u001b[39m new_op\u001b[39m.\u001b[39mdefault_inputs()\n\nFile \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/classification_env/lib/python3.10/site-packages/coremltools/converters/mil/mil/operation.py:187\u001b[0m, in \u001b[0;36mOperation.__init__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[39m# Set inputs from kwargs\u001b[39;00m\n\u001b[1;32m 185\u001b[0m input_kv \u001b[39m=\u001b[39m {k: v \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kwargs\u001b[39m.\u001b[39mitems()\n\u001b[1;32m 186\u001b[0m \u001b[39mif\u001b[39;00m k \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_input_types \u001b[39mand\u001b[39;00m v \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m}\n\u001b[0;32m--> 187\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_and_set_inputs(input_kv)\n\u001b[1;32m 188\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ensure_required_inputs()\n\nFile \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/classification_env/lib/python3.10/site-packages/coremltools/converters/mil/mil/operation.py:496\u001b[0m, in \u001b[0;36mOperation._validate_and_set_inputs\u001b[0;34m(self, input_kvs, no_check_var_types)\u001b[0m\n\u001b[1;32m 493\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(msg\u001b[39m.\u001b[39mformat(v_new\u001b[39m.\u001b[39msym_type, v_old\u001b[39m.\u001b[39msym_type))\n\u001b[1;32m 494\u001b[0m v_old\u001b[39m.\u001b[39mremove_child_op(op, no_check_var_types)\n\u001b[0;32m--> 496\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minput_spec\u001b[39m.\u001b[39;49mvalidate_inputs(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mname, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mop_type, input_kvs)\n\u001b[1;32m 498\u001b[0m \u001b[39mfor\u001b[39;00m name, var \u001b[39min\u001b[39;00m input_kvs\u001b[39m.\u001b[39mitems():\n\u001b[1;32m 499\u001b[0m \u001b[39m# Remove this operation itself from existing input\u001b[39;00m\n\u001b[1;32m 500\u001b[0m \u001b[39m# Var's child_ops\u001b[39;00m\n\u001b[1;32m 501\u001b[0m existing_input_var \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_input_vars[name]\n\nFile \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/classification_env/lib/python3.10/site-packages/coremltools/converters/mil/mil/input_type.py:162\u001b[0m, in \u001b[0;36mInputSpec.validate_inputs\u001b[0;34m(self, op_name, op_type, candidate_kvs)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(var, InternalVar) \u001b[39mand\u001b[39;00m \\\n\u001b[1;32m 159\u001b[0m \u001b[39mnot\u001b[39;00m input_type\u001b[39m.\u001b[39mis_compatible(var):\n\u001b[1;32m 160\u001b[0m msg \u001b[39m=\u001b[39m msg_prefix \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mInput \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m=\u001b[39m\u001b[39m\\\"\u001b[39;00m\u001b[39m{}\u001b[39;00m\u001b[39m\\\"\u001b[39;00m\u001b[39m expects \u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m\\\n\u001b[1;32m 161\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m but got \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m--> 162\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(msg\u001b[39m.\u001b[39mformat(name, var\u001b[39m.\u001b[39mname, input_type\u001b[39m.\u001b[39mtype_str,\n\u001b[1;32m 163\u001b[0m var\u001b[39m.\u001b[39msym_type\u001b[39m.\u001b[39m__type_info__()))\n\n\u001b[0;31mValueError\u001b[0m: Op \"identity_1\" (op_type: identity) Input x=\"classLabel_probs\" expects list, tensor, or scalar but got dict[str,fp64]"
To Reproduce
import timm
import torch
import coremltools as ct
# Create model
model = timm.create_model('mobilenetv3_small_100', pretrained=True, num_classes=3)
# Example input
example_input = torch.randn((1,3,224,224))
# Trace model
traced_model = torch.jit.trace(model, example_input)
classifier_config = ct.ClassifierConfig(class_labels=['dog','cat','unicorn'])
ct_model = ct.convert(
traced_model,
inputs=[ct.ImageType(name="image", shape=example_input.shape, scale=scale, bias=bias)],
outputs=[ct.TensorType(name="probabilities")],
convert_to='mlprogram',
classifier_config=classifier_config
)
# Compressing the model -> Raises the error
compressed_model = ct.compression_utils.affine_quantize_weights(ct_model, mode = 'linear')
System environment (please complete the following information):
- coremltools version: 6.3.0
- OS: MacOS
- Any other relevant version information (e.g. PyTorch or TensorFlow version): pytorch 2.0
Additional context
scale and bias are not defined. So I used 1 and 0.
Here is runnable code that illustrates it is the addition of a classifier config that cause affine_quantize_weights to fail.
import timm
import torch
import coremltools as ct
# Create model
model = timm.create_model('mobilenetv3_small_100', pretrained=True, num_classes=3)
# Example input
example_input = torch.randn((1,3,224,224))
# Trace model
traced_model = torch.jit.trace(model, example_input)
ct_model = ct.convert(
traced_model,
inputs=[ct.ImageType(name="image", shape=example_input.shape, scale=1, bias=0)],
outputs=[ct.TensorType(name="probabilities")],
convert_to='mlprogram',
)
# This works
compressed_model = ct.compression_utils.affine_quantize_weights(ct_model, mode = 'linear')
classifier_config = ct.ClassifierConfig(class_labels=['dog','cat','unicorn'])
ct_model = ct.convert(
traced_model,
inputs=[ct.ImageType(name="image", shape=example_input.shape, scale=1, bias=0)],
outputs=[ct.TensorType(name="probabilities")],
convert_to='mlprogram',
classifier_config=classifier_config
)
# This fails
compressed_model = ct.compression_utils.affine_quantize_weights(ct_model, mode = 'linear')
Using tip of main, gives the following error:
---> 11 compressed_model = ct.compression_utils.affine_quantize_weights(ct_model, mode = 'linear')
File /Volumes/DevData/workspace/coremltools/coremltools/models/_deprecation.py:28, in deprecated.<locals>.decorator_deprecation_warning.<locals>.wrapped(*args, **kwargs)
26 msg += f"; {suffix}"
27 warnings.warn(msg, category=FutureWarning)
---> 28 return obj(*args, **kwargs)
File /Volumes/DevData/workspace/coremltools/coremltools/models/ml_program/compression_utils.py:46, in affine_quantize_weights(mlmodel, mode, op_selector, dtype)
44 op_config = _OpLinearQuantizerConfig(mode=mode, dtype=dtype, weight_threshold=None)
45 config = _OptimizationConfig(global_config=op_config, is_deprecated=True, op_selector=op_selector)
---> 46 return _linear_quantize_weights(mlmodel, config)
File /Volumes/DevData/workspace/coremltools/coremltools/optimize/coreml/_post_training_quantization.py:172, in linear_quantize_weights(mlmodel, config)
62 """
63 Utility function to convert a float precision MLModel of type ``mlprogram``, which uses
64 float-precision weights, into a compressed MLModel that uses 8-bit weights. This is
(...)
168
169 """
171 linear_weight_quantizer = _linear_quantize_weights(config, fake_compression=False)
--> 172 return _apply_graph_pass(mlmodel, linear_weight_quantizer)
File /Volumes/DevData/workspace/coremltools/coremltools/optimize/coreml/_post_training_quantization.py:51, in _apply_graph_pass(mlmodel, graph_pass)
48 graph_pass.apply(prog)
50 # convert the pymil program back to mlmodel
---> 51 compressed_mlmodel = _mil_convert(
52 prog,
53 convert_to="mlprogram",
54 convert_from="milinternal",
55 specification_version=specification_version,
56 compute_units=mlmodel.compute_unit,
57 model_description=model_spec.description,
58 )
59 return compressed_mlmodel
File /Volumes/DevData/workspace/coremltools/coremltools/converters/mil/converter.py:188, in mil_convert(model, convert_from, convert_to, compute_units, **kwargs)
149 @_profile
150 def mil_convert(
151 model,
(...)
155 **kwargs
156 ):
157 """
158 Convert model from a specified frontend `convert_from` to a specified
159 converter backend `convert_to`.
(...)
186 See `coremltools.converters.convert`
187 """
--> 188 return _mil_convert(model, convert_from, convert_to, ConverterRegistry, MLModel, compute_units, **kwargs)
File /Volumes/DevData/workspace/coremltools/coremltools/converters/mil/converter.py:212, in _mil_convert(model, convert_from, convert_to, registry, modelClass, compute_units, **kwargs)
209 weights_dir = _tempfile.TemporaryDirectory()
210 kwargs["weights_dir"] = weights_dir.name
--> 212 proto, mil_program = mil_convert_to_proto(
213 model,
214 convert_from,
215 convert_to,
216 registry,
217 **kwargs
218 )
220 _reset_conversion_state()
222 if convert_to == 'milinternal':
File /Volumes/DevData/workspace/coremltools/coremltools/converters/mil/converter.py:289, in mil_convert_to_proto(model, convert_from, convert_to, converter_registry, main_pipeline, **kwargs)
286 prog = frontend_converter(model, **kwargs)
287 PassPipelineManager.apply_pipeline(prog, frontend_pipeline)
--> 289 PassPipelineManager.apply_pipeline(prog, main_pipeline)
291 prog._check_invalid_tensor_rank()
293 if convert_to == 'milinternal':
File /Volumes/DevData/workspace/coremltools/coremltools/converters/mil/mil/passes/pass_pipeline.py:445, in PassPipelineManager.apply_pipeline(prog, pass_pipeline)
443 graph_pass = PASS_REGISTRY[pass_name]
444 graph_pass.set_options(pass_options)
--> 445 graph_pass(prog)
446 prog.validate()
447 logger.debug(f"Program after {pass_pipeline} pipeline:\n{prog}")
File /Volumes/DevData/workspace/coremltools/coremltools/converters/mil/mil/passes/graph_pass.py:51, in AbstractGraphPass.__call__(self, prog)
49 def __call__(self, prog: Program):
50 if not prog.skip_all_passes:
---> 51 self.apply(prog)
File /Volumes/DevData/workspace/coremltools/coremltools/converters/mil/mil/passes/defs/cleanup/const_deduplication.py:67, in const_deduplication.apply(self, prog)
65 def apply(self, prog) -> None:
66 for f in prog.functions.values():
---> 67 self._constant_deduplication_block(f)
File /Volumes/DevData/workspace/coremltools/coremltools/converters/mil/mil/passes/helper.py:60, in block_context_manager.<locals>.wrapper(*args)
55 raise ValueError(
56 "The function decorated with block_context_manager must have a Block "
57 "type argument as the first input."
58 )
59 with block:
---> 60 return func(*args)
File /Volumes/DevData/workspace/coremltools/coremltools/converters/mil/mil/passes/defs/cleanup/const_deduplication.py:75, in const_deduplication._constant_deduplication_block(self, block)
72 for b in op.blocks:
73 self._constant_deduplication_block(b)
---> 75 unique2duplicates = self.find_constants(block)
76 for unique in unique2duplicates:
77 for duplicate in unique2duplicates[unique]:
File /Volumes/DevData/workspace/coremltools/coremltools/converters/mil/mil/passes/defs/cleanup/const_deduplication.py:105, in const_deduplication.find_constants(self, block)
103 if op_type == "const" or op_type in self.CONSTEXPR_OPS:
104 constant_var = op.outputs[0]
--> 105 shape = constant_var.shape
107 numel = np.prod(shape)
108 if numel < self.NUMEL_THRESH:
File /Volumes/DevData/workspace/coremltools/coremltools/converters/mil/mil/var.py:291, in ListVar.shape(self)
289 @property
290 def shape(self):
--> 291 raise ValueError("shape not applicable to ListVar '{}'.".format(self.name))
ValueError: shape not applicable to ListVar 'const_0'.
