Hello,
I’m trying to auto-tune MobileNet using the following program which is mostly copied from this tutorial.
import os
import numpy as np
import tvm.relay.testing
from tvm import autotvm
from tvm import relay
from tvm.autotvm.tuner import XGBTuner
target = 'llvm'
ctx = tvm.context(str(target), 0)
# Tuning parameters
# n_trial = 2000
n_trial = 1
early_stopping = 600
logfile = "mobilenet.log"
measure_option = autotvm.measure_option(
builder=autotvm.LocalBuilder(timeout=10),
runner=autotvm.LocalRunner(number=20, repeat=3, timeout=4, min_repeat_ms=150))
# Model parameters
dtype = 'float32'
batch_size = 1
input_shape = (batch_size, 3, 224, 224)
output_shape = (batch_size, 1000)
mod, params = relay.testing.mobilenet.get_workload(batch_size=batch_size, dtype=dtype)
tasks = autotvm.task.extract_from_program(mod["main"], target=target, params=params,
ops=(relay.op.nn.conv2d, relay.op.nn.dense))
if os.path.exists(logfile):
os.remove(logfile)
for i, tsk in enumerate(reversed(tasks)):
prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))
tuner = XGBTuner(tsk, loss_type='rank')
tuner.tune(n_trial=min(n_trial, len(tsk.config_space)),
early_stopping=early_stopping,
measure_option=measure_option,
callbacks=[
autotvm.callback.progress_bar(n_trial, prefix=prefix),
autotvm.callback.log_to_file(logfile)
])
with autotvm.apply_history_best(logfile):
with relay.build_config(opt_level=3):
executor = relay.build_module.create_executor('graph', mod, ctx, target)
evaluate = executor.evaluate()
image = np.ones(input_shape, np.float32)
image = tvm.nd.array(image.astype(dtype))
evaluate(image, **params)
The tuning itself seems to work but when it’s finished and I try to call executor.evaluate() I get the following error:
[Task 1/20] Current/Best: 11.37/ 11.37 GFLOPS | Progress: (1/1) | 2.16 s Done.
[Task 2/20] Current/Best: 17.00/ 17.00 GFLOPS | Progress: (1/1) | 1.86 s Done.
[Task 3/20] Current/Best: 31.33/ 31.33 GFLOPS | Progress: (1/1) | 1.91 s Done.
[Task 4/20] Current/Best: 11.17/ 11.17 GFLOPS | Progress: (1/1) | 1.82 s Done.
[Task 5/20] Current/Best: 13.52/ 13.52 GFLOPS | Progress: (1/1) | 1.89 s Done.
[Task 6/20] Current/Best: 17.69/ 17.69 GFLOPS | Progress: (1/1) | 1.74 s Done.
[Task 7/20] Current/Best: 29.99/ 29.99 GFLOPS | Progress: (1/1) | 2.14 s Done.
[Task 8/20] Current/Best: 10.63/ 10.63 GFLOPS | Progress: (1/1) | 1.83 s Done.
[Task 9/20] Current/Best: 29.27/ 29.27 GFLOPS | Progress: (1/1) | 2.81 s Done.
[Task 10/20] Current/Best: 26.26/ 26.26 GFLOPS | Progress: (1/1) | 1.64 s Done.
[Task 11/20] Current/Best: 28.11/ 28.11 GFLOPS | Progress: (1/1) | 3.41 s Done.
[Task 12/20] Current/Best: 12.99/ 12.99 GFLOPS | Progress: (1/1) | 1.84 s Done.
[Task 13/20] Current/Best: 0.00/ 0.00 GFLOPS | Progress: (1/1) | 10.65 s Done.
[Task 14/20] Current/Best: 8.03/ 8.03 GFLOPS | Progress: (1/1) | 1.82 s Done.
[Task 15/20] Current/Best: 18.30/ 18.30 GFLOPS | Progress: (1/1) | 1.83 s Done.
[Task 16/20] Current/Best: 13.78/ 13.78 GFLOPS | Progress: (1/1) | 1.63 s Done.
[Task 17/20] Current/Best: 25.38/ 25.38 GFLOPS | Progress: (1/1) | 1.98 s Done.
[Task 18/20] Current/Best: 8.63/ 8.63 GFLOPS | Progress: (1/1) | 1.68 s Done.
[Task 19/20] Current/Best: 12.88/ 12.88 GFLOPS | Progress: (1/1) | 1.45 s Done.
[Task 20/20] Current/Best: 9.12/ 9.12 GFLOPS | Progress: (1/1) | 1.95 s Done.
Traceback (most recent call last):
File ".../tvm_mobilenet_tuning.py", line 49, in <module>
evaluate = executor.evaluate()
File "/home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/relay/backend/interpreter.py", line 240, in evaluate
return self._make_executor()
File "/home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/relay/build_module.py", line 239, in _make_executor
graph_json, mod, params = build(self.mod, target=self.target)
File "/home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/relay/build_module.py", line 207, in build
graph_json, mod, params = bld_mod.build(func, target, target_host, params)
File "/home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/relay/build_module.py", line 108, in build
self._build(func, target, target_host)
File "/home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/_ffi/_ctypes/function.py", line 210, in __call__
raise get_last_ffi_error()
tvm._ffi.base.TVMError: Traceback (most recent call last):
[bt] (8) /home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ForwardRewriter::GetTempExpr(tvm::relay::Expr const&)+0x14d) [0x7ff2a02f342d]
[bt] (7) /home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ExprMutator::VisitExpr(tvm::relay::Expr const&)+0x9e) [0x7ff2a0129c1e]
[bt] (6) /home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ExprFunctor<tvm::relay::Expr (tvm::relay::Expr const&)>::VisitExpr(tvm::relay::Expr const&)+0xc5) [0x7ff2a0130075]
[bt] (5) /home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(std::_Function_handler<tvm::relay::Expr (tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::relay::Expr (tvm::relay::Expr const&)>*), tvm::relay::ExprFunctor<tvm::relay::Expr (tvm::relay::Expr const&)>::InitVTable()::{lambda(tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::relay::Expr (tvm::relay::Expr const&)>*)#6}>::_M_invoke(std::_Any_data const&, tvm::NodeRef const&, tvm::relay::ExprFunctor<tvm::relay::Expr (tvm::relay::Expr const&)>*&&)+0x2f) [0x7ff2a012baff]
[bt] (4) /home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::ForwardRewriter::VisitExpr_(tvm::relay::CallNode const*)+0x5d8) [0x7ff2a02f4468]
[bt] (3) /home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), tvm::runtime::TypedPackedFunc<tvm::relay::Expr (tvm::relay::Call const&, tvm::Array<tvm::relay::Expr, void> const&, tvm::NodeRef const&)>::AssignTypedLambda<tvm::relay::Expr (*)(tvm::relay::Call const&, tvm::Array<tvm::relay::Expr, void> const&, tvm::NodeRef const&)>(tvm::relay::Expr (*)(tvm::relay::Call const&, tvm::Array<tvm::relay::Expr, void> const&, tvm::NodeRef const&))::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)+0xb0) [0x7ff2a02be980]
[bt] (2) /home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::alter_op_layout::AlterOpLayoutRewrite(tvm::relay::Call const&, tvm::Array<tvm::relay::Expr, void> const&, tvm::NodeRef const&)+0x1000) [0x7ff2a02b9a50]
[bt] (1) /home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(tvm::relay::alter_op_layout::CallAlter(tvm::relay::Call const&, std::vector<tvm::relay::Expr, std::allocator<tvm::relay::Expr> > const&)+0x864) [0x7ff2a02b8874]
[bt] (0) /home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/libtvm.so(+0xb44e6b) [0x7ff2a040de6b]
File "/home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/_ffi/_ctypes/function.py", line 72, in cfun
rv = local_pyfunc(*pyargs)
File "/home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/relay/op/nn/_nn.py", line 205, in alter_op_layout_conv2d
return topi.nn.conv2d_alter_layout(attrs, inputs, tinfos, op)
File "</home/arne/.local/lib/python3.6/site-packages/decorator-4.4.0-py3.6.egg/decorator.py:decorator-gen-24>", line 2, in conv2d_alter_layout
File "/home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/target.py", line 372, in dispatch_func
return dispatch_dict[k](*args, **kwargs)
File "/home/arne/.local/lib/python3.6/site-packages/topi-0.6.dev0-py3.6.egg/topi/x86/conv2d.py", line 464, in _alter_conv2d_layout
ic_bn, oc_bn = cfg["tile_ic"].size[-1], cfg["tile_oc"].size[-1]
File "/home/arne/.local/lib/python3.6/site-packages/tvm-0.6.dev0-py3.6-linux-x86_64.egg/tvm/autotvm/task/space.py", line 773, in __getitem__
return self._entity_map[name]
KeyError: 'tile_ic'
I set num_trials=1 to reproduce the error but it happens with num_trials=2000 as well.
The resulting logfile looks like this, in case that’s of any help. It does include values for tile_ic.
{"i": ["llvm", "topi_nn_conv2d", [["TENSOR", [1, 3, 224, 224], "float32"], ["TENSOR", [32, 3, 3, 3], "float32"], [2, 2], [1, 1], [1, 1], "NCHW", "float32"], {}, ["conv2d", [1, 3, 224, 224, "float32"], [32, 3, 3, 3, "float32"], [2, 2], [1, 1], [1, 1], "NCHW", "float32"], {"i": 204, "t": "direct", "c": null, "e": [["tile_ic", "sp", [3, 1]], ["tile_oc", "sp", [32, 1]], ["tile_ow", "sp", [2, 56]], ["unroll_kw", "ot", false]]}], "r": [[0.0020006725163934425], 0, 1.3565866947174072, 1566210100.143439], "v": 0.1}
{"i": ["llvm", "topi_nn_depthwise_conv2d_nchw", [["TENSOR", [1, 32, 112, 112], "float32"], ["TENSOR", [32, 1, 3, 3], "float32"], [1, 1], [1, 1], [1, 1], "float32"], {}, ["depthwise_conv2d_nchw", [1, 32, 112, 112, "float32"], [32, 1, 3, 3, "float32"], [1, 1], [1, 1], [1, 1], "float32"], {"i": 0, "t": "direct", "c": null, "e": []}], "r": [[0.0005962612872340425], 0, 1.0861918926239014, 1566210102.0591521], "v": 0.1}
{"i": ["llvm", "topi_nn_conv2d", [["TENSOR", [1, 32, 112, 112], "float32"], ["TENSOR", [64, 32, 1, 1], "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {}, ["conv2d", [1, 32, 112, 112, "float32"], [64, 32, 1, 1, "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {"i": 138, "t": "direct", "c": null, "e": [["tile_ic", "sp", [32, 1]], ["tile_oc", "sp", [16, 4]], ["tile_ow", "sp", [16, 7]], ["tile_oh", "ot", 1]]}], "r": [[0.0016398973741496598], 0, 1.1583552360534668, 1566210104.131863], "v": 0.1}
{"i": ["llvm", "topi_nn_depthwise_conv2d_nchw", [["TENSOR", [1, 64, 112, 112], "float32"], ["TENSOR", [64, 1, 3, 3], "float32"], [2, 2], [1, 1], [1, 1], "float32"], {}, ["depthwise_conv2d_nchw", [1, 64, 112, 112, "float32"], [64, 1, 3, 3, "float32"], [2, 2], [1, 1], [1, 1], "float32"], {"i": 0, "t": "direct", "c": null, "e": []}], "r": [[0.0008446549763779528], 0, 1.0396904945373535, 1566210106.075094], "v": 0.1}
{"i": ["llvm", "topi_nn_conv2d", [["TENSOR", [1, 64, 56, 56], "float32"], ["TENSOR", [128, 64, 1, 1], "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {}, ["conv2d", [1, 64, 56, 56, "float32"], [128, 64, 1, 1, "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {"i": 457, "t": "direct", "c": null, "e": [["tile_ic", "sp", [16, 4]], ["tile_oc", "sp", [64, 2]], ["tile_ow", "sp", [56, 1]], ["tile_oh", "ot", 2]]}], "r": [[0.0038014127936507936], 0, 1.1446666717529297, 1566210108.0896251], "v": 0.1}
{"i": ["llvm", "topi_nn_depthwise_conv2d_nchw", [["TENSOR", [1, 128, 56, 56], "float32"], ["TENSOR", [128, 1, 3, 3], "float32"], [1, 1], [1, 1], [1, 1], "float32"], {}, ["depthwise_conv2d_nchw", [1, 128, 56, 56, "float32"], [128, 1, 3, 3, "float32"], [1, 1], [1, 1], [1, 1], "float32"], {"i": 0, "t": "direct", "c": null, "e": []}], "r": [[0.0005787340282485875], 0, 0.9943633079528809, 1566210109.9836464], "v": 0.1}
{"i": ["llvm", "topi_nn_conv2d", [["TENSOR", [1, 128, 56, 56], "float32"], ["TENSOR", [128, 128, 1, 1], "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {}, ["conv2d", [1, 128, 56, 56, "float32"], [128, 128, 1, 1, "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {"i": 921, "t": "direct", "c": null, "e": [["tile_ic", "sp", [64, 2]], ["tile_oc", "sp", [16, 8]], ["tile_ow", "sp", [2, 28]], ["tile_oh", "ot", 2]]}], "r": [[0.003426708086956522], 0, 1.3976550102233887, 1566210112.203216], "v": 0.1}
{"i": ["llvm", "topi_nn_depthwise_conv2d_nchw", [["TENSOR", [1, 128, 56, 56], "float32"], ["TENSOR", [128, 1, 3, 3], "float32"], [2, 2], [1, 1], [1, 1], "float32"], {}, ["depthwise_conv2d_nchw", [1, 128, 56, 56, "float32"], [128, 1, 3, 3, "float32"], [2, 2], [1, 1], [1, 1], "float32"], {"i": 0, "t": "direct", "c": null, "e": []}], "r": [[0.00045367376822429904], 0, 1.0605945587158203, 1566210114.1285105], "v": 0.1}
{"i": ["llvm", "topi_nn_conv2d", [["TENSOR", [1, 128, 28, 28], "float32"], ["TENSOR", [256, 128, 1, 1], "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {}, ["conv2d", [1, 128, 28, 28, "float32"], [256, 128, 1, 1, "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {"i": 627, "t": "direct", "c": null, "e": [["tile_ic", "sp", [16, 8]], ["tile_oc", "sp", [4, 64]], ["tile_ow", "sp", [7, 4]], ["tile_oh", "ot", 2]]}], "r": [[0.001755604953488372], 0, 2.067134141921997, 1566210117.046407], "v": 0.1}
{"i": ["llvm", "topi_nn_depthwise_conv2d_nchw", [["TENSOR", [1, 256, 28, 28], "float32"], ["TENSOR", [256, 1, 3, 3], "float32"], [1, 1], [1, 1], [1, 1], "float32"], {}, ["depthwise_conv2d_nchw", [1, 256, 28, 28, "float32"], [256, 1, 3, 3, "float32"], [1, 1], [1, 1], [1, 1], "float32"], {"i": 0, "t": "direct", "c": null, "e": []}], "r": [[0.00019901406954689148], 0, 0.8972055912017822, 1566210118.7935557], "v": 0.1}
{"i": ["llvm", "topi_nn_conv2d", [["TENSOR", [1, 256, 28, 28], "float32"], ["TENSOR", [256, 256, 1, 1], "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {}, ["conv2d", [1, 256, 28, 28, "float32"], [256, 256, 1, 1, "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {"i": 946, "t": "direct", "c": null, "e": [["tile_ic", "sp", [128, 2]], ["tile_oc", "sp", [4, 64]], ["tile_ow", "sp", [1, 28]], ["tile_oh", "ot", 2]]}], "r": [[0.0036556534696969697], 0, 2.6669557094573975, 1566210122.2847552], "v": 0.1}
{"i": ["llvm", "topi_nn_depthwise_conv2d_nchw", [["TENSOR", [1, 256, 28, 28], "float32"], ["TENSOR", [256, 1, 3, 3], "float32"], [2, 2], [1, 1], [1, 1], "float32"], {}, ["depthwise_conv2d_nchw", [1, 256, 28, 28, "float32"], [256, 1, 3, 3, "float32"], [2, 2], [1, 1], [1, 1], "float32"], {"i": 0, "t": "direct", "c": null, "e": []}], "r": [[0.00019364018270944742], 0, 1.0722415447235107, 1566210124.2563193], "v": 0.1}
{"i": ["llvm", "topi_nn_conv2d", [["TENSOR", [1, 256, 14, 14], "float32"], ["TENSOR", [512, 256, 1, 1], "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {}, ["conv2d", [1, 256, 14, 14, "float32"], [512, 256, 1, 1, "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {"i": 714, "t": "direct", "c": null, "e": [["tile_ic", "sp", [32, 8]], ["tile_oc", "sp", [1, 512]], ["tile_ow", "sp", [1, 14]], ["tile_oh", "ot", 2]]}], "r": [[1000000000.0], 6, 10, 1566210135.108564], "v": 0.1}
{"i": ["llvm", "topi_nn_depthwise_conv2d_nchw", [["TENSOR", [1, 512, 14, 14], "float32"], ["TENSOR", [512, 1, 3, 3], "float32"], [1, 1], [1, 1], [1, 1], "float32"], {}, ["depthwise_conv2d_nchw", [1, 512, 14, 14, "float32"], [512, 1, 3, 3, "float32"], [1, 1], [1, 1], [1, 1], "float32"], {"i": 0, "t": "direct", "c": null, "e": []}], "r": [[0.00033902526347305394], 0, 1.0559625625610352, 1566210136.8925824], "v": 0.1}
{"i": ["llvm", "topi_nn_conv2d", [["TENSOR", [1, 512, 14, 14], "float32"], ["TENSOR", [512, 512, 1, 1], "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {}, ["conv2d", [1, 512, 14, 14, "float32"], [512, 512, 1, 1, "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {"i": 603, "t": "direct", "c": null, "e": [["tile_ic", "sp", [64, 8]], ["tile_oc", "sp", [512, 1]], ["tile_ow", "sp", [2, 7]], ["tile_oh", "ot", 2]]}], "r": [[0.0056157913125], 0, 1.065453052520752, 1566210138.8708098], "v": 0.1}
{"i": ["llvm", "topi_nn_depthwise_conv2d_nchw", [["TENSOR", [1, 512, 14, 14], "float32"], ["TENSOR", [512, 1, 3, 3], "float32"], [2, 2], [1, 1], [1, 1], "float32"], {}, ["depthwise_conv2d_nchw", [1, 512, 14, 14, "float32"], [512, 1, 3, 3, "float32"], [2, 2], [1, 1], [1, 1], "float32"], {"i": 0, "t": "direct", "c": null, "e": []}], "r": [[9.936914162162162e-05], 0, 0.8887343406677246, 1566210140.5857086], "v": 0.1}
{"i": ["llvm", "topi_nn_conv2d", [["TENSOR", [1, 512, 7, 7], "float32"], ["TENSOR", [1024, 512, 1, 1], "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {}, ["conv2d", [1, 512, 7, 7, "float32"], [1024, 512, 1, 1, "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {"i": 32, "t": "direct", "c": null, "e": [["tile_ic", "sp", [128, 4]], ["tile_oc", "sp", [128, 8]], ["tile_ow", "sp", [7, 1]], ["tile_oh", "ot", 1]]}], "r": [[0.0020242687844827588], 0, 1.2413175106048584, 1566210142.6582541], "v": 0.1}
{"i": ["llvm", "topi_nn_depthwise_conv2d_nchw", [["TENSOR", [1, 1024, 7, 7], "float32"], ["TENSOR", [1024, 1, 3, 3], "float32"], [1, 1], [1, 1], [1, 1], "float32"], {}, ["depthwise_conv2d_nchw", [1, 1024, 7, 7, "float32"], [1024, 1, 3, 3, "float32"], [1, 1], [1, 1], [1, 1], "float32"], {"i": 0, "t": "direct", "c": null, "e": []}], "r": [[0.0001718663723887375], 0, 0.9298725128173828, 1566210144.474663], "v": 0.1}
{"i": ["llvm", "topi_nn_conv2d", [["TENSOR", [1, 1024, 7, 7], "float32"], ["TENSOR", [1024, 1024, 1, 1], "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {}, ["conv2d", [1, 1024, 7, 7, "float32"], [1024, 1024, 1, 1, "float32"], [1, 1], [0, 0], [1, 1], "NCHW", "float32"], {"i": 32, "t": "direct", "c": null, "e": [["tile_ic", "sp", [1, 1024]], ["tile_oc", "sp", [256, 4]], ["tile_ow", "sp", [7, 1]], ["tile_oh", "ot", 1]]}], "r": [[0.0079785429], 0, 0.7092256546020508, 1566210146.0702164], "v": 0.1}
{"i": ["llvm", "topi_nn_dense", [["TENSOR", [1, 1024], "float32"], ["TENSOR", [1000, 1024], "float32"], null, "float32"], {}, ["dense", [1, 1024, "float32"], [1000, 1024, "float32"], 0, "float32"], {"i": 43, "t": "direct", "c": null, "e": [["tile_x", "sp", [8, 125]], ["tile_y", "sp", [1, 1]], ["tile_k", "sp", [256, 4]]]}], "r": [[0.00022471214302325582], 0, 1.2047827243804932, 1566210148.0866144], "v": 0.1}
I ran the exact same code with ResNet-50 and VGG-16 and I don’t get any errors. So this seems to be specific to MobileNet.
Does anyone have an idea how to solve this error?