NOTE: The Jupyter Notebook below is included in the Chimera SDK and can be run interactively by running the following CLI command:
$ quadric sdk notebook
From the Jupyter Notebook window in your browser, select the notebook named /quadric/sdk-cli/examples/models/unet/unet.ipynb.
PATIENT_LIMIT = 1
LIMIT_PER_PATIENT = 3
TUMOR_THRESHOLD_FOR_ISS = 200
THREADS = 6
LIMITED_DATA = True
if LIMITED_DATA:
!unzip -qo kaggle_cache.zip
else:
!pip3 install kaggle -q
#!KAGGLE_USERNAME=xxx KAGGLE_KEY=yyy kaggle datasets download -d mateuszbuda/lgg-mri-segmentation
!kaggle datasets download -d mateuszbuda/lgg-mri-segmentation
!if [ ! -d "kaggle_3m" ]; then unzip lgg-mri-segmentation.zip; fi
import torch
model = torch.hub.load(
"mateuszbuda/brain-segmentation-pytorch",
"unet",
in_channels=3,
out_channels=1,
init_features=32,
pretrained=True,
)
prefix = model.__class__.__name__
fp_onnx_name = f"{prefix}_float32.onnx"
Using cache found in /github/home/.cache/torch/hub/mateuszbuda_brain-segmentation-pytorch_master
## Init input tensor used for exporting in ONNX in NCHW
## assumptions for all imagenet-trained models. for user-supplied models change the following:
dataset_mean = [0.485, 0.456, 0.406]
dataset_std = [0.229, 0.224, 0.225]
dataset_input_size = (224, 224) # an (W, H) tuple
## include quadric's cli helpers and instantiate a module to help
from sdk_cli.utils import model_helpers
import tvm.contrib.epu.chimera_job.constants as sdk_constants
mh = model_helpers.ModelHelper(dataset_input_size, dataset_mean, dataset_std)
x = torch.randn(1, 3, mh.size[1], mh.size[0], requires_grad=True)
## Export the model
torch.onnx.export(
model, # model being run
x, # model input (or a tuple for multiple inputs)
fp_onnx_name, # where to save the model (can be a file or file-like object)
export_params=True, # store the trained parameter weights inside the model file
do_constant_folding=True, # whether to execute constant folding for optimization
opset_version=sdk_constants.DEFAULT_ONNX_OPSET,
input_names=["input"], # the model's input names
output_names=["output"],
) # the model's output names
print(f"Model exported to ONNX with name '{fp_onnx_name}'!")
Model exported to ONNX with name 'UNet_float32.onnx'!
/tmp/ipykernel_34319/2902265974.py:12: DeprecationWarning: Call to deprecated class ModelHelper. ('ModelHelper' class is being deprecated. Quadric APIs have been updated to use PyTorch datasets and transforms instead.) -- Deprecated since version 24.01.
mh = model_helpers.ModelHelper(dataset_input_size, dataset_mean, dataset_std)
from torch.utils.data import DataLoader
from tqdm import tqdm
import torch
from dataset import BrainSegmentationDataset as Dataset
import onnxruntime as ort
from dataclasses import dataclass
import numpy as np
from skimage.io import imsave
from inference_onnx import (
makedirs,
data_loader,
postprocess_per_volume,
dsc_distribution,
plot_dsc,
)
from utils import dsc, gray2rgb, outline
import os
from onnxruntime.quantization import CalibrationDataReader
@dataclass
class Arguments:
onnx: str = fp_onnx_name
images: str = "./kaggle_3m/"
image_size: int = 224
figure: str = "./dsc.png"
predictions: str = "./predictions_onnx"
batch_size: int = 1
args = Arguments()
makedirs(args)
loader = data_loader(args)
reading validation images...
preprocessing validation volumes...
cropping validation volumes...
padding validation volumes...
resizing validation volumes...
normalizing validation volumes...
done creating validation dataset
ort_sess = ort.InferenceSession(args.onnx)
input_list = []
pred_list = []
true_list = []
ort_data_reader = []
for i, data in tqdm(enumerate(loader)):
x, y_true = data
x_np = x.detach().cpu().numpy()
ort_inp = {"input": x_np}
ort_data_reader.append(ort_inp)
y_pred_np = ort_sess.run(None, ort_inp)
y_pred_np = np.array(y_pred_np[0])
pred_list.extend([y_pred_np[s] for s in range(y_pred_np.shape[0])])
y_true_np = y_true.detach().cpu().numpy()
true_list.extend([y_true_np[s] for s in range(y_true_np.shape[0])])
x_np = x.detach().cpu().numpy()
input_list.extend([x_np[s] for s in range(x_np.shape[0])])
311it [00:13, 23.46it/s]
volumes = postprocess_per_volume(
input_list,
pred_list,
true_list,
loader.dataset.patient_slice_index,
loader.dataset.patients,
)
for p in volumes:
x = volumes[p][0]
y_pred = volumes[p][1]
y_true = volumes[p][2]
for s in range(x.shape[0]):
image = gray2rgb(x[s, 1]) # channel 1 is for FLAIR
image = outline(image, y_pred[s, 0], color=[255, 0, 0])
image = outline(image, y_true[s, 0], color=[0, 255, 0])
filename = "{}-{}.png".format(p, str(s).zfill(2))
filepath = os.path.join(args.predictions, filename)
imsave(filepath, image)
/tmp/ipykernel_34319/1936848120.py:19: UserWarning: ./predictions_onnx/TCGA_DU_5854_19951104-33.png is a low contrast image
imsave(filepath, image)
/tmp/ipykernel_34319/1936848120.py:19: UserWarning: ./predictions_onnx/TCGA_DU_5872_19950223-66.png is a low contrast image
imsave(filepath, image)
/tmp/ipykernel_34319/1936848120.py:19: UserWarning: ./predictions_onnx/TCGA_DU_6399_19830416-50.png is a low contrast image
imsave(filepath, image)
/tmp/ipykernel_34319/1936848120.py:19: UserWarning: ./predictions_onnx/TCGA_DU_5851_19950428-34.png is a low contrast image
imsave(filepath, image)
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import glob
img_width, img_height = 400, 400
def resize_img_to_array(img, img_shape=(244, 244)):
img_array = np.array(img.resize(img_shape, Image.LANCZOS))
return img_array
def image_grid(fn_images: list, text: list = [], top: int = 12, per_row: int = 4):
"""
fn_images is a list of image paths.
text is a list of annotations.
top is how many images you want to display
per_row is the number of images to show per row.
"""
for i in range(len(fn_images[:top])):
if i % 4 == 0:
_, ax = plt.subplots(1, per_row, sharex="col", sharey="row", figsize=(20, 6))
j = i % 4
image = Image.open(fn_images[i])
image = resize_img_to_array(image, img_shape=(img_width, img_height))
ax[j].imshow(image)
ax[j].axis("off")
if text:
ax[j].annotate(
text[i],
(0, 0),
(0, -32),
xycoords="axes fraction",
textcoords="offset points",
va="top",
)
image_grid(
fn_images=glob.glob(f"{args.predictions}/*.png"),
text=glob.glob(f"{args.predictions}/*.png"),
)
import tvm.contrib.epu.chimera_job.quantize as quant
import onnx
inf_onnx = onnx.load(args.onnx)
class UnetDr(CalibrationDataReader):
def __init__(self, array_of_inputs):
self._enum_data_dicts = array_of_inputs
self.reset()
def get_next(self):
return next(self.enum_data_dicts, None)
def reset(self):
self.enum_data_dicts = iter(self._enum_data_dicts)
unet_dr = UnetDr(ort_data_reader)
##optimize the model ahead of quantization
optimized_model_path, _ = quant.optimize_onnx(args.onnx)
quant_result = quant.quantize_float_onnx(optimized_model_path, unet_dr)
2026-09-22 03:12 - INFO - epu - quantize - Optimized model to opset
2026-09-22 03:12 - INFO - epu - quantize - Saved optimized model to UNet_float32_float32_opt.onnx
2026-09-22 03:12 - INFO - epu - quantize - Input shapes: [1, 3, 224, 224]. Input names: input
2026-09-22 03:12 - INFO - epu - quantize - Output shapes: [[1, 1, 224, 224]]. Output names: ['output']
2026-09-22 03:12 - DEBUG - epu - quantize - Full exclusion set for quantization: ['Softmax', 'Sigmoid', 'QuadricCustomOp']
2026-09-22 03:12 - DEBUG - epu - quantize - excl_nodes ['/Sigmoid']
2026-09-22 03:12 - INFO - epu - quantize - Quantization started...
WARNING:root:Please use QuantFormat.QDQ for activation type QInt8 and weight type QInt8. Or it will lead to bad performance on x64.
2026-09-22 03:13 - INFO - epu - quantize - Quantization done succesfully!
2026-09-22 03:13 - INFO - epu - quantize - ONNX full precision model size: 29.61 MB
2026-09-22 03:13 - INFO - epu - quantize - ONNX quantized model size: 7.45 MB
2026-09-22 03:13 - INFO - epu - quantize - Saved quantized model to /quadric/sdk-cli/examples/models/unet/UNet_opt_sym_int8_q.onnx
2026-09-22 03:13 - INFO - epu - quantize - Saved shape inferenced model to /quadric/sdk-cli/examples/models/unet/UNet_opt_sym_int8_q.onnx
2026-09-22 03:13 - INFO - epu - quantize - Checking for remaining FLOAT/FLOAT16 types.
2026-09-22 03:13 - INFO - epu - quantize - Model still has FLOAT/FLOAT16 types. Creating ranges for floating point tensors using calibration data
Custom quantization code for ConvTranspose
Custom quantization code for ConvTranspose
Custom quantization code for ConvTranspose
Custom quantization code for ConvTranspose
2026-09-22 03:13 - INFO - epu - quantize - Saved tensor ranges to /quadric/sdk-cli/examples/models/unet/UNet_opt_sym_int8_q.onnx.tranges
quant_result
QuantizeResult(qmodel_path='/quadric/sdk-cli/examples/models/unet/UNet_opt_sym_int8_q.onnx', tranges_path='/quadric/sdk-cli/examples/models/unet/UNet_opt_sym_int8_q.onnx.tranges')
from tvm.contrib.epu.chimera_job.chimera_job import ChimeraJob
from tvm.relay.backend.contrib.epu.util import logger as tvm_logger
import logging, warnings, sys
cgc_job = ChimeraJob(model_p=quant_result.qmodel_path, quiet_iss=False)
tvm_logger.setLevel(logging.INFO)
warnings.filterwarnings("ignore")
sys.tracebacklimit = 0
cgc_job.compile(quiet=True)
experiments = []
for patient, val in volumes.items():
print(patient)
relpath = f"./iss_results/{patient}"
patient_iss_runs = 0
for i in range(len(val[0])):
patient_root = f"{patient}_{i}"
inp = val[0][i]
inp = np.expand_dims(inp, axis=0)
onnx_output = ort_sess.run([], {"input": inp})[0][0]
onnx_output = onnx_output.squeeze()
if np.count_nonzero(np.round(onnx_output)) > TUMOR_THRESHOLD_FOR_ISS:
experiment = {"patient": patient, "input": inp}
experiments.append(experiment)
experiments = experiments[:THREADS]
TCGA_DU_5854_19951104
TCGA_CS_4944_20010208
TCGA_CS_4941_19960909
TCGA_DU_5872_19950223
TCGA_CS_6186_20000601
TCGA_CS_5397_20010315
TCGA_DU_5855_19951217
TCGA_CS_5393_19990606
TCGA_DU_6399_19830416
TCGA_DU_5851_19950428
ort_sess = ort.InferenceSession(args.onnx)
ort_sess_q = ort.InferenceSession(quant_result.qmodel_path)
from sdk_cli.visualizers.unet_brain import UnetBrainVisualizer
%matplotlib inline
from matplotlib.gridspec import GridSpec
import matplotlib.patches as mpatches
from sdk_cli.utils.model_helpers import calcualate_pixelwise_iou
## change these to run more patients and more images per patient
vis = UnetBrainVisualizer()
def run_iss_patient(experiment, unet_pixelvalue_threshold=0.75):
print(f"patient: {experiment['patient']}")
input = experiment["input"]
onnx_output_q = ort_sess_q.run([], {"input": input})[0][0]
onnx_output_q = onnx_output_q.squeeze()
experiment["ort"] = onnx_output_q
cgc_output = cgc_job.run_inference_harness(inputs={"input": input})["output"].reshape(
onnx_output_q.shape
)
experiment["iss"] = cgc_output
experiment["cgc_job"] = cgc_job
experiment["iou"] = calcualate_pixelwise_iou(
onnx_output_q > unet_pixelvalue_threshold,
cgc_output > unet_pixelvalue_threshold,
)
return experiment
def plot_result(experiment):
fig = plt.figure(constrained_layout=True, figsize=(12, 8))
fig.suptitle(f"Patient {experiment['patient']}", y=0.92)
gs = GridSpec(1, 2, figure=fig)
ax = []
ax.append(fig.add_subplot(gs[0, 0]))
ax.append(fig.add_subplot(gs[0, 1]))
ax[0].set_axis_off()
ax[0].set_title("Brain Scan Data mapped to RGB")
ax[1].set_title(f"Quantized ort and Chimera ISS. IoU: {experiment['iou']:0.2f}")
legend = [
mpatches.Patch(color="blue", label="ort"),
mpatches.Patch(color="red", label="chimera iss"),
]
ax[1].legend(handles=legend)
input = experiment["input"]
ax[0].imshow(UnetBrainVisualizer.input_img_to_display(input))
onnx_output_q = experiment["ort"]
cgc_output = experiment["iss"]
image = vis.gray2rgb(input.squeeze()[1])
image = vis.outline(image, onnx_output_q, color=[0, 0, 205])
image = vis.outline(image, cgc_output, color=[255, 0, 0])
experiment["image"] = image
ax[1].imshow(image)
## output = run_iss_patient(experiments[0])
import multiprocess
with multiprocess.Pool(processes=THREADS) as pool:
# process data in parallel
results = pool.map(run_iss_patient, experiments)
patient: TCGA_CS_4944_20010208patient: TCGA_CS_4941_19960909
patient: TCGA_CS_4941_19960909
patient: TCGA_CS_4941_19960909patient: TCGA_CS_4941_19960909
patient: TCGA_DU_5872_19950223
2026-09-22 03:14 - INFO - epu - iss_testing - No tranges found for input, use default float range: <tvm.contrib.epu.interval.Interval object at 0x78088cfe1c30>
2026-09-22 03:14 - INFO - epu - iss_testing - No tranges found for input, use default float range: <tvm.contrib.epu.interval.Interval object at 0x78088cee8c40>
2026-09-22 03:14 - INFO - epu - iss_testing - No tranges found for input, use default float range: <tvm.contrib.epu.interval.Interval object at 0x780922ceab90>
2026-09-22 03:14 - INFO - epu - iss_testing - No tranges found for input, use default float range: <tvm.contrib.epu.interval.Interval object at 0x78088ceea050>
2026-09-22 03:14 - INFO - epu - iss_testing - No tranges found for input, use default float range: <tvm.contrib.epu.interval.Interval object at 0x78088cfe14e0>
2026-09-22 03:14 - INFO - epu - iss_testing - No tranges found for input, use default float range: <tvm.contrib.epu.interval.Interval object at 0x78088cee87c0>
FILM 16/16: 100%|███████████████████████████████████████████████████| 16/16 [00:19<00:00, 1.20s/it]
2026-09-22 03:15 - INFO - epu - iss_testing - No tranges found for input, use default float range: <tvm.contrib.epu.interval.Interval object at 0x78088ceea050>
2026-09-22 03:15 - INFO - epu - iss_testing - Started Executing Onnxruntime...
2026-09-22 03:15 - INFO - epu - iss_testing - Done 0:00:00.243114
FILM 16/16: 100%|███████████████████████████████████████████████████| 16/16 [00:19<00:00, 1.22s/it]
2026-09-22 03:15 - INFO - epu - iss_testing - No tranges found for input, use default float range: <tvm.contrib.epu.interval.Interval object at 0x78088cee87c0>
2026-09-22 03:15 - INFO - epu - iss_testing - Started Executing Onnxruntime...
FILM 16/16: 100%|███████████████████████████████████████████████████| 16/16 [00:19<00:00, 1.22s/it]
2026-09-22 03:15 - INFO - epu - iss_testing - No tranges found for input, use default float range: <tvm.contrib.epu.interval.Interval object at 0x78088cee8c40>
2026-09-22 03:15 - INFO - epu - iss_testing - Started Executing Onnxruntime...
FILM 16/16: 100%|███████████████████████████████████████████████████| 16/16 [00:19<00:00, 1.23s/it]
FILM 15/16: 100%|███████████████████████████████████████████████████| 16/16 [00:19<00:00, 1.74s/it]2026-09-22 03:15 - INFO - epu - iss_testing - No tranges found for input, use default float range: <tvm.contrib.epu.interval.Interval object at 0x780922ceab90>
FILM 16/16: 100%|███████████████████████████████████████████████████| 16/16 [00:19<00:00, 1.74s/it]2026-09-22 03:15 - INFO - epu - iss_testing - Started Executing Onnxruntime...
FILM 16/16: 100%|███████████████████████████████████████████████████| 16/16 [00:19<00:00, 1.23s/it]
2026-09-22 03:15 - INFO - epu - iss_testing - No tranges found for input, use default float range: <tvm.contrib.epu.interval.Interval object at 0x78088cfe14e0>
2026-09-22 03:15 - INFO - epu - iss_testing - Started Executing Onnxruntime...
2026-09-22 03:15 - INFO - epu - iss_testing - Done 0:00:00.513445
2026-09-22 03:15 - INFO - epu - iss_testing - Done 0:00:00.553574
2026-09-22 03:15 - INFO - epu - iss_testing - Done 0:00:00.431742
2026-09-22 03:15 - INFO - epu - iss_testing - Done 0:00:00.513926
FILM 16/16: 100%|███████████████████████████████████████████████████| 16/16 [00:20<00:00, 1.27s/it]
2026-09-22 03:15 - INFO - epu - iss_testing - No tranges found for input, use default float range: <tvm.contrib.epu.interval.Interval object at 0x78088cee83d0>
2026-09-22 03:15 - INFO - epu - iss_testing - Started Executing Onnxruntime...
2026-09-22 03:15 - INFO - epu - iss_testing - Done 0:00:00.241194
## print(output['input'])
for res in results:
plot_result(res)
plt.show()
print(results[0]["cgc_job"])
results[0]["cgc_job"].plot_run_statistics()
╒═════════════════════╤════════════════════════════════════════════════════════════════════╕
│ Module Name │ UNet_opt_sym_int8_q_QC_U_1d7_16MB_4kB_128GBps_128GBps_16_OFF_x1_x1 │
├─────────────────────┼────────────────────────────────────────────────────────────────────┤
│ ONNX File │ /quadric/sdk-cli/examples/models/unet/UNet_opt_sym_int8_q.onnx │
├─────────────────────┼────────────────────────────────────────────────────────────────────┤
│ Product Target │ QC-U │
├─────────────────────┼────────────────────────────────────────────────────────────────────┤
│ Number of Cores │ 1 │
├─────────────────────┼────────────────────────────────────────────────────────────────────┤
│ ISS Clock Frequency │ 1.700 │
├─────────────────────┼────────────────────────────────────────────────────────────────────┤
│ L2M Size │ 16MB │
├─────────────────────┼────────────────────────────────────────────────────────────────────┤
│ LRM Size │ 4kB │
├─────────────────────┼────────────────────────────────────────────────────────────────────┤
│ External Read BW │ 128GBps │
├─────────────────────┼────────────────────────────────────────────────────────────────────┤
│ External Write BW │ 128GBps │
├─────────────────────┼────────────────────────────────────────────────────────────────────┤
│ MACS per PE │ 16 │
├─────────────────────┼────────────────────────────────────────────────────────────────────┤
│ Max L2M │ 10.608MB │
├─────────────────────┼────────────────────────────────────────────────────────────────────┤
│ Max LRM │ 0.750kB │
├─────────────────────┼────────────────────────────────────────────────────────────────────┤
│ Max Temp Ext Bytes │ 0.000MB │
├─────────────────────┼────────────────────────────────────────────────────────────────────┤
│ Network GMACs │ 9.242 │
╘═════════════════════╧════════════════════════════════════════════════════════════════════╛
╒════╤════════╤════════╤══════════════════╤══════════════════════════╤═══════╕
│ │ Type │ Name │ shape │ type │ mse │
╞════╪════════╪════════╪══════════════════╪══════════════════════════╪═══════╡
│ 0 │ Input │ input │ [1, 3, 224, 224] │ tensor[FixedPoint32<27>] │ n/a │
├────┼────────┼────────┼──────────────────┼──────────────────────────┼───────┤
│ 1 │ Output │ output │ [1, 1, 224, 224] │ tensor[FixedPoint32<31>] │ 0.000 │
╘════╧════════╧════════╧══════════════════╧══════════════════════════╧═══════╛
Post-ISS Report 1.7 GHz ***
Fully placed-and-routed gate simulation:
╒══════════════════════════════════╤═════════╕
│ Latency (ms) │ 0.88 │
├──────────────────────────────────┼─────────┤
│ FPS │ 1140.67 │
├──────────────────────────────────┼─────────┤
│ Average Power @ 3nm SSGNP (mW) │ 3624.77 │
├──────────────────────────────────┼─────────┤
│ FPS per Watt @ 3nm SSGNP (FPS/W) │ 314.69 │
├──────────────────────────────────┼─────────┤
│ Ext Rd Bytes (MB) │ 8.02 │
├──────────────────────────────────┼─────────┤
│ Ext Wr Bytes (MB) │ 0.19 │
├──────────────────────────────────┼─────────┤
│ Avg Ext Rd BW (GBps) │ 8.94 │
├──────────────────────────────────┼─────────┤
│ Avg Ext Wr BW (GBps) │ 0.21 │
├──────────────────────────────────┼─────────┤
│ MAC Utilization │ 37.85% │
╘══════════════════════════════════╧═════════╛
*** Data generated using 7nm SSGNP gatesim and scaled to 3nm
[SDK-CLI] : TotalCycles: 1,490,358
[SDK-CLI] : Executions/second: 1,140.67
compute : ▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 225.017K
data_array : ▇▇▇▇▇ 80.6K
mac : ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 791.048K
data_external: ▏ 7.985K
data_ocm : ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 384.914K
for more information check run directory: /quadric/sdk-cli/examples/models/unet/ccl_build/UNet_opt_sym_int8_q_QC_U_1d7_16MB_4kB_128GBps_128GBps_16_OFF_x1_x1/run/20260922_031453_924176
2026-09-22 03:15 - INFO - epu - chimera_job - Combined plots generated and saved to:
/quadric/sdk-cli/examples/models/unet/ccl_build/UNet_opt_sym_int8_q_QC_U_1d7_16MB_4kB_128GBps_128GBps_16_OFF_x1_x1/run/20260922_031453_924176/data/UNet_opt_sym_int8_q_QC_U_1d7_16MB_4kB_128GBps_128GBps_16_OFF_x1_x1.combined.png
'/quadric/sdk-cli/examples/models/unet/ccl_build/UNet_opt_sym_int8_q_QC_U_1d7_16MB_4kB_128GBps_128GBps_16_OFF_x1_x1/run/20260922_031453_924176/data'
