Commit 68661967 authored by limm's avatar limm
Browse files

add config module

parent 4353fa59
Pipeline #2808 canceled with stages
_base_ = [
'../_base_/base_dynamic.py', '../../_base_/backends/tensorrt-fp16.py'
]
backend_config = dict(
common_config=dict(max_workspace_size=1 << 30),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 64, 64],
opt_shape=[1, 3, 800, 800],
max_shape=[1, 3, 800, 800])))
])
_base_ = ['../_base_/base_static.py', '../../_base_/backends/tensorrt-fp16.py']
onnx_config = dict(input_shape=(320, 320))
backend_config = dict(
common_config=dict(max_workspace_size=1 << 30),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 320, 320],
opt_shape=[1, 3, 320, 320],
max_shape=[1, 3, 320, 320])))
])
_base_ = ['../_base_/base_tensorrt-fp16_static-800x1344.py']
_base_ = ['../_base_/base_tensorrt-int8_dynamic-300x300-512x512.py']
_base_ = ['../_base_/base_tensorrt-int8_dynamic-320x320-1344x1344.py']
_base_ = [
'../_base_/base_dynamic.py', '../../_base_/backends/tensorrt-int8.py'
]
backend_config = dict(
common_config=dict(max_workspace_size=1 << 30),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 64, 64],
opt_shape=[1, 3, 608, 608],
max_shape=[1, 3, 608, 608])))
])
_base_ = [
'../_base_/base_dynamic.py', '../../_base_/backends/tensorrt-int8.py'
]
backend_config = dict(
common_config=dict(max_workspace_size=1 << 30),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 64, 64],
opt_shape=[1, 3, 800, 800],
max_shape=[1, 3, 800, 800])))
])
_base_ = ['../_base_/base_static.py', '../../_base_/backends/tensorrt-int8.py']
onnx_config = dict(input_shape=(320, 320))
backend_config = dict(
common_config=dict(max_workspace_size=1 << 30),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 320, 320],
opt_shape=[1, 3, 320, 320],
max_shape=[1, 3, 320, 320])))
])
_base_ = ['../_base_/base_tensorrt-int8_static-800x1344.py']
_base_ = ['../_base_/base_tensorrt_dynamic-300x300-512x512.py']
_base_ = ['../_base_/base_tensorrt_dynamic-320x320-1344x1344.py']
_base_ = ['../_base_/base_dynamic.py', '../../_base_/backends/tensorrt.py']
backend_config = dict(
common_config=dict(max_workspace_size=1 << 30),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 64, 64],
opt_shape=[1, 3, 608, 608],
max_shape=[1, 3, 608, 608])))
])
_base_ = ['../_base_/base_dynamic.py', '../../_base_/backends/tensorrt.py']
backend_config = dict(
common_config=dict(max_workspace_size=1 << 30),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 64, 64],
opt_shape=[1, 3, 800, 800],
max_shape=[1, 3, 800, 800])))
])
_base_ = ['../_base_/base_tensorrt_static-300x300.py']
_base_ = ['../_base_/base_static.py', '../../_base_/backends/tensorrt.py']
onnx_config = dict(input_shape=(320, 320))
backend_config = dict(
common_config=dict(max_workspace_size=1 << 30),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 320, 320],
opt_shape=[1, 3, 320, 320],
max_shape=[1, 3, 320, 320])))
])
_base_ = ['../_base_/base_tensorrt_static-300x300.py']
onnx_config = dict(input_shape=(640, 640))
backend_config = dict(
common_config=dict(max_workspace_size=1 << 30),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 640, 640],
opt_shape=[1, 3, 640, 640],
max_shape=[1, 3, 640, 640])))
])
_base_ = ['../_base_/base_tensorrt_static-800x1344.py']
_base_ = [
'../_base_/base_torchscript.py', '../../_base_/backends/torchscript.py'
]
_base_ = ['../_base_/base_static.py', '../../_base_/backends/tvm.py']
onnx_config = dict(input_shape=[1344, 800])
backend_config = dict(model_inputs=[
dict(
use_vm=True,
shape=dict(input=[1, 3, 800, 1344]),
dtype=dict(input='float32'),
tuner=dict(
type='AutoScheduleTuner',
log_file='tvm_tune_log.log',
num_measure_trials=2000))
])
_base_ = ['../_base_/base_static.py', '../../_base_/backends/tvm.py']
onnx_config = dict(input_shape=[300, 300])
backend_config = dict(model_inputs=[
dict(
use_vm=True,
shape=dict(input=[1, 3, 300, 300]),
dtype=dict(input='float32'),
tuner=dict(
type='AutoTVMTuner',
log_file='tvm_tune_log.log',
n_trial=1000,
tuner=dict(type='XGBTuner'),
))
])
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