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.azure-pipelines/docker/Dockerfile.devel

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@@ -36,7 +36,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends --fix-missing \
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RUN ln -sf $(which python3) /usr/bin/python
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RUN python -m pip --no-cache-dir install --upgrade pip
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RUN python -m pip install pip==24.0
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RUN python -m pip install --no-cache-dir setuptools
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RUN pip list

.azure-pipelines/model-test.yml

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@@ -57,7 +57,7 @@ parameters:
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- name: PyTorchModelList
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type: object
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default:
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- resnet18
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# - resnet18
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- resnet18_fx
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- name: ONNXModelList
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type: object

.github/checkgroup.yml

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@@ -51,7 +51,6 @@ subprojects:
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- "Model-Test"
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- "Model-Test (Generate Report GenerateReport)"
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- "Model-Test (Run ONNX Model resnet50-v1-12)"
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- "Model-Test (Run PyTorch Model resnet18)"
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- "Model-Test (Run PyTorch Model resnet18_fx)"
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- "Model-Test (Run TensorFlow Model resnet50v1.5)"
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- "Model-Test (Run TensorFlow Model ssd_resnet50_v1)"

examples/onnxrt/nlp/huggingface_model/token_classification/layoutlmv2/quantization/ptq_dynamic/main.py

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@@ -216,8 +216,6 @@ def _process_dataset(self):
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self.label = []
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self.onnx_inputs = []
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for inputs in self.dataset:
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# import pdb;
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# pdb.set_trace()
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onnx_inputs = []
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has_labels = all(inputs.get(k) is not None for k in self.label_names)
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if has_labels:
@@ -237,8 +235,6 @@ def _process_dataset(self):
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}
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"""
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for key in self.onnx_input_names:
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# import pdb;
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# pdb.set_trace()
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if key in inputs:
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# onnx_inputs[key] = np.array([inputs[key]])
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onnx_inputs.append(np.array(inputs[key]))

examples/onnxrt/nlp/huggingface_model/token_classification/layoutlmv2/quantization/ptq_static/main.py

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@@ -216,8 +216,6 @@ def _process_dataset(self):
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self.label = []
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self.onnx_inputs = []
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for inputs in self.dataset:
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# import pdb;
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# pdb.set_trace()
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onnx_inputs = []
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has_labels = all(inputs.get(k) is not None for k in self.label_names)
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if has_labels:
@@ -237,8 +235,6 @@ def _process_dataset(self):
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}
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"""
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for key in self.onnx_input_names:
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# import pdb;
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# pdb.set_trace()
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if key in inputs:
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# onnx_inputs[key] = np.array([inputs[key]])
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onnx_inputs.append(np.array(inputs[key]))

examples/pytorch/object_detection/ssd_resnet34/quantization/ptq/fx/python/models/utils.py

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@@ -59,7 +59,6 @@ def _compute_padding(self, input, dim):
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return additional_padding, total_padding
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def forward(self, input):
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#import pdb; pdb.set_trace()
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if self.padding == "VALID":
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return F.conv2d(
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input,
@@ -180,7 +179,6 @@ def decode_boxes(rel_codes, boxes, weights):
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dh = dh / wh
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pred_ctr_x = dx * widths + ctr_x
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#import pdb; pdb.set_trace()
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pred_ctr_y = dy * heights + ctr_y
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pred_w = torch.exp(dw) * widths
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pred_h = torch.exp(dh) * heights
@@ -194,5 +192,4 @@ def decode_boxes(rel_codes, boxes, weights):
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],
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dim=2,
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)
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#import pdb; pdb.set_trace()
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return pred_boxes

neural_coder/coders/tensorflow/amp.py

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@@ -22,8 +22,6 @@ def __init__(self, file) -> None:
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self.keras_edited_flag = False
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def transform(self):
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# import pdb
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# pdb.set_trace()
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lines = self.file.split("\n")
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for line in lines:
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if self.is_modify(line):

neural_coder/coders/tensorflow/inc.py

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@@ -21,8 +21,6 @@ def __init__(self, file) -> None:
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self.result = []
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def transform(self):
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# import pdb
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# pdb.set_trace()
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lines = self.file.split("\n")
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for line in lines:
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if self.is_modify(line):

neural_compressor/adaptor/keras.py

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@@ -24,7 +24,6 @@
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import numpy as np
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import yaml
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from ..conf.dotdict import deep_get
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from ..data.dataloaders.base_dataloader import BaseDataLoader
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from ..utils import logger
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from ..utils.utility import (
@@ -34,6 +33,7 @@
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Dequantize,
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LazyImport,
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Statistics,
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deep_get,
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dump_elapsed_time,
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singleton,
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version1_lt_version2,

neural_compressor/adaptor/mxnet.py

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@@ -446,13 +446,6 @@ def _one_shot_query(self):
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raise ValueError(
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"Please check if the format of {} follows Neural Compressor yaml schema.".format(self.cfg)
448448
)
449-
self._update_cfg_with_usr_definition()
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def _update_cfg_with_usr_definition(self):
452-
from neural_compressor.conf.pythonic_config import mxnet_config
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if mxnet_config.precisions is not None:
455-
self.cur_config["precisions"]["names"] = ",".join(mxnet_config.precisions)
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457450
def _get_specified_version_cfg(self, data):
458451
"""Get the configuration for the current runtime.

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