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add new models using cifar10 as dataset
1 parent caebd90 commit 5d58254

15 files changed

+266
-5
lines changed

‎python/mrt/tfm_ops.py

Lines changed: 3 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -722,6 +722,7 @@ def reduce(self, op, **kwargs):
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op = self._matrix_decomposition(op, params, infer_shapes)
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else:
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fc_name = N.n("reduced_fc")
725+
# TODO(ryt): apply infer_batch_axis
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default_batch_axis = 0
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batch_axis = \
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kwargs.get("batch_axes", {}).get(name, default_batch_axis)
@@ -3124,12 +3125,9 @@ class Mean(Transformer):
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def fuse_transpose(self, op, **kwargs):
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return fuse_transpose_reduce(op, kwargs["infer_shapes"])
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def rewrite(self, op, **kwargs):
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name = op.attr('name')
3128-
return self._decompose_axis(op, kwargs['infer_shapes'])
3129-
3130-
def _decompose_axis(self, op, infer_shapes):
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name = op.attr('name')
31323129
attr, childs = op.list_attr(), sym_iter(op.get_children())
3130+
infer_shapes = kwargs['infer_shapes']
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31343132
axis = eval(attr['axis'])
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if isinstance(axis, int):
@@ -3162,7 +3160,7 @@ def _decompose_axis(self, op, infer_shapes):
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op_type='right_shift')
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return op
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3165-
# TODO(ryt): select batch_axis
3163+
# TODO(ryt): apply infer_batch_axis
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axis_set = set(axis)
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oaxes = list(range(len(shp)))
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naxes = sorted(axis)

‎tests/mrt/model_zoo/cifar10/cifar_resnet110_v1.yaml

Lines changed: 19 additions & 0 deletions
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COMMON:
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MODEL_NAME: cifar_resnet110_v1
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PREPARE:
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INPUT_SHAPE: [-1,3,32,32]
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CALIBRATE:
6+
NUM_CALIB: 1
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DATASET_NAME: cifar10
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DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
10+
QUANTIZE:
11+
INPUT_PRECISION: 8
12+
OUTPUT_PRECISION: 8
13+
DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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EVALUATE:
16+
BATCH: 160
17+
DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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ITER_NUM: 500

‎tests/mrt/model_zoo/cifar10/cifar_resnet110_v2.yaml

Lines changed: 19 additions & 0 deletions
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COMMON:
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MODEL_NAME: cifar_resnet20_v2
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PREPARE:
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INPUT_SHAPE: [-1,3,32,32]
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CALIBRATE:
6+
NUM_CALIB: 1
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DATASET_NAME: cifar10
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DEVICE_TYPE: gpu
9+
DEVICE_IDS: [0]
10+
QUANTIZE:
11+
INPUT_PRECISION: 8
12+
OUTPUT_PRECISION: 8
13+
DEVICE_TYPE: gpu
14+
DEVICE_IDS: [0]
15+
EVALUATE:
16+
BATCH: 160
17+
DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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ITER_NUM: 500

‎tests/mrt/model_zoo/cifar10/cifar_resnet20_v1.yaml

Lines changed: 19 additions & 0 deletions
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COMMON:
2+
MODEL_NAME: cifar_resnet20_v1
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PREPARE:
4+
INPUT_SHAPE: [-1,3,32,32]
5+
CALIBRATE:
6+
NUM_CALIB: 1
7+
DATASET_NAME: cifar10
8+
DEVICE_TYPE: gpu
9+
DEVICE_IDS: [0]
10+
QUANTIZE:
11+
INPUT_PRECISION: 8
12+
OUTPUT_PRECISION: 8
13+
DEVICE_TYPE: gpu
14+
DEVICE_IDS: [0]
15+
EVALUATE:
16+
BATCH: 160
17+
DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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ITER_NUM: 500

‎tests/mrt/model_zoo/cifar10/cifar_resnet20_v2.yaml

Lines changed: 19 additions & 0 deletions
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COMMON:
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MODEL_NAME: cifar_resnet20_v2
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PREPARE:
4+
INPUT_SHAPE: [-1,3,32,32]
5+
CALIBRATE:
6+
NUM_CALIB: 1
7+
DATASET_NAME: cifar10
8+
DEVICE_TYPE: gpu
9+
DEVICE_IDS: [0]
10+
QUANTIZE:
11+
INPUT_PRECISION: 8
12+
OUTPUT_PRECISION: 8
13+
DEVICE_TYPE: gpu
14+
DEVICE_IDS: [0]
15+
EVALUATE:
16+
BATCH: 160
17+
DEVICE_TYPE: gpu
18+
DEVICE_IDS: [0]
19+
ITER_NUM: 500

‎tests/mrt/model_zoo/cifar10/cifar_resnet56_v1.yaml

Lines changed: 19 additions & 0 deletions
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COMMON:
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MODEL_NAME: cifar_resnet56_v1
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PREPARE:
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INPUT_SHAPE: [-1,3,32,32]
5+
CALIBRATE:
6+
NUM_CALIB: 1
7+
DATASET_NAME: cifar10
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DEVICE_TYPE: gpu
9+
DEVICE_IDS: [0]
10+
QUANTIZE:
11+
INPUT_PRECISION: 8
12+
OUTPUT_PRECISION: 8
13+
DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
15+
EVALUATE:
16+
BATCH: 160
17+
DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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ITER_NUM: 500

‎tests/mrt/model_zoo/cifar10/cifar_resnet56_v2.yaml

Lines changed: 19 additions & 0 deletions
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COMMON:
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MODEL_NAME: cifar_resnet56_v2
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PREPARE:
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INPUT_SHAPE: [-1,3,32,32]
5+
CALIBRATE:
6+
NUM_CALIB: 1
7+
DATASET_NAME: cifar10
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DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
10+
QUANTIZE:
11+
INPUT_PRECISION: 8
12+
OUTPUT_PRECISION: 8
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DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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EVALUATE:
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BATCH: 160
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DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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ITER_NUM: 500

‎tests/mrt/model_zoo/cifar10/cifar_resnext29_16x64d.yaml

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COMMON:
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MODEL_NAME: cifar_resnext29_16x64d
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PREPARE:
4+
INPUT_SHAPE: [-1,3,32,32]
5+
CALIBRATE:
6+
NUM_CALIB: 1
7+
DATASET_NAME: cifar10
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DEVICE_TYPE: gpu
9+
DEVICE_IDS: [0]
10+
QUANTIZE:
11+
INPUT_PRECISION: 8
12+
OUTPUT_PRECISION: 8
13+
DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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EVALUATE:
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BATCH: 160
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DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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ITER_NUM: 500

‎tests/mrt/model_zoo/cifar10/cifar_wideresnet16_10.yaml

Lines changed: 19 additions & 0 deletions
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COMMON:
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MODEL_NAME: cifar_wideresnet16_10
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PREPARE:
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INPUT_SHAPE: [-1,3,32,32]
5+
CALIBRATE:
6+
NUM_CALIB: 1
7+
DATASET_NAME: cifar10
8+
DEVICE_TYPE: gpu
9+
DEVICE_IDS: [0]
10+
QUANTIZE:
11+
INPUT_PRECISION: 8
12+
OUTPUT_PRECISION: 8
13+
DEVICE_TYPE: gpu
14+
DEVICE_IDS: [0]
15+
EVALUATE:
16+
BATCH: 160
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DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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ITER_NUM: 500

‎tests/mrt/model_zoo/cifar10/cifar_wideresnet28_10.yaml

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COMMON:
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MODEL_NAME: cifar_wideresnet28_10
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PREPARE:
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INPUT_SHAPE: [-1,3,32,32]
5+
CALIBRATE:
6+
NUM_CALIB: 1
7+
DATASET_NAME: cifar10
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DEVICE_TYPE: gpu
9+
DEVICE_IDS: [0]
10+
QUANTIZE:
11+
INPUT_PRECISION: 8
12+
OUTPUT_PRECISION: 8
13+
DEVICE_TYPE: gpu
14+
DEVICE_IDS: [0]
15+
EVALUATE:
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BATCH: 160
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DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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ITER_NUM: 500

‎tests/mrt/model_zoo/cifar10/cifar_wideresnet40_8.yaml

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COMMON:
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MODEL_NAME: cifar_wideresnet40_8
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PREPARE:
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INPUT_SHAPE: [-1,3,32,32]
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CALIBRATE:
6+
NUM_CALIB: 1
7+
DATASET_NAME: cifar10
8+
DEVICE_TYPE: gpu
9+
DEVICE_IDS: [0]
10+
QUANTIZE:
11+
INPUT_PRECISION: 8
12+
OUTPUT_PRECISION: 8
13+
DEVICE_TYPE: gpu
14+
DEVICE_IDS: [0]
15+
EVALUATE:
16+
BATCH: 160
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DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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ITER_NUM: 500

‎tests/mrt/model_zoo/tune_org/resnet18_v1b_kinetics400.yaml

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COMMON:
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MODEL_NAME: resnet18_v1b_kinetics400
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VERBOSITY: debug
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RUN_EVALUATE: True
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CALIBRATE:
6+
BATCH: 16
7+
NUM_CALIB: 1
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DATASET_NAME: imagenet
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DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
11+
QUANTIZE:
12+
INPUT_PRECISION: 8
13+
OUTPUT_PRECISION: 8
14+
DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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EVALUATE:
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BATCH: 160
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DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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ITER_NUM: 500

‎tests/mrt/model_zoo/tune_org/resnet34_v1b_kinetics400.yaml

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COMMON:
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MODEL_NAME: resnet34_v1b_kinetics400
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CALIBRATE:
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NUM_CALIB: 1
5+
LAMBD: 16
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DATASET_NAME: imagenet
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DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
9+
QUANTIZE:
10+
INPUT_PRECISION: 8
11+
OUTPUT_PRECISION: 8
12+
DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
14+
EVALUATE:
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BATCH: 160
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DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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ITER_NUM: 500

‎tests/mrt/model_zoo/tune_org/resnet50_v1b_hmdb51.yaml

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COMMON:
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MODEL_NAME: resnet50_v1b_hmdb51
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CALIBRATE:
4+
NUM_CALIB: 1
5+
LAMBD: 16
6+
DATASET_NAME: imagenet
7+
DEVICE_TYPE: gpu
8+
DEVICE_IDS: [0]
9+
QUANTIZE:
10+
INPUT_PRECISION: 8
11+
OUTPUT_PRECISION: 8
12+
DEVICE_TYPE: gpu
13+
DEVICE_IDS: [0]
14+
EVALUATE:
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BATCH: 160
16+
DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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ITER_NUM: 500

‎tests/mrt/model_zoo/tune_org/vgg16_ucf101.yaml

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COMMON:
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MODEL_NAME: vgg16_ucf101
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CALIBRATE:
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NUM_CALIB: 1
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DATASET_NAME: imagenet
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DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
8+
QUANTIZE:
9+
INPUT_PRECISION: 8
10+
OUTPUT_PRECISION: 8
11+
DEVICE_TYPE: gpu
12+
DEVICE_IDS: [0]
13+
EVALUATE:
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BATCH: 16
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DEVICE_TYPE: gpu
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DEVICE_IDS: [0]
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ITER_NUM: 10000

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