rpitonak

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rpitonak

GSOC 2018

Summer is not over yet, but coding part of Google summer of code yes. This is my final report.

Quick recap

I was working with fedora organization on conu - python API for your containers. Before GSOC, you could use library just to work with standalone containers. Goal for GSOC was to implement support also for orchestration tools like Kubernetes and OpenShift. For more information see my proposal.

rpitonak

# Adapted from this StackOverflow answer: https://gis.stackexchange.com/a/14717

"""
Usage:

file_path = "/tmp/file.tiff"
split_tiff("/tmp/file.tiff", 256, 256)

The resulting output will be chunks /tmp/file_0_0.tiff ...

rpitonak

import onnx

# !wget https://github.com/onnx/models/raw/main/vision/classification/squeezenet/model/squeezenet1.0-12.onnx

model1 = onnx.load('resizer.onnx')
model2 = onnx.load('squeezenet1.0-12.onnx')


combined_model = onnx.compose.merge_models(
    model1, model2,

rpitonak

model_input_name = "X"
X = onnx.helper.make_tensor_value_info(
    model_input_name,
    onnx.TensorProto.FLOAT,
    [None, None, None, 3]) # notice that we are leaving just channels dimension fixed, Batch size, width and heigh is not known in advance


model_output_name = "Y"
Y = onnx.helper.make_tensor_value_info(
    model_output_name,

rpitonak

# pip install wget
# pip install -i https://test.pypi.org/simple/ onnx-weekly
# pip install onnxruntime

import onnx
import numpy as np
import onnxruntime as rt
import matplotlib.pyplot as plt
from PIL import Image

rpitonak

resizer_node_output_name = "Resize_Y"
Y_resize = onnx.helper.make_tensor_value_info(resizer_node_output_name,
                                       onnx.TensorProto.FLOAT,
                                       [None, 224, 224, 3])



roi = onnx.helper.make_node("Constant", inputs=[], outputs=["roi"], name="roi-constant",
                           value=onnx.helper.make_tensor(name="roi-values",
                                                         data_type=onnx.TensorProto.FLOAT,

rpitonak

# Permute dimension, after resize they are in this order
# N W H C
# 0 1 2 3
# After transpose we want them in shape
# N C W H
# 0 3 1 2
perm = onnx.helper.make_node("Constant", inputs=[], outputs=["perm"], name="perm-constant",
                           value=onnx.helper.make_tensor(name="perm-values",
                                                         data_type=onnx.TensorProto.INT64,
                                                         dims=np.array([0,3,1,2]).shape,

rpitonak

resizer_node = onnx.helper.make_node("Resize", inputs=[model_input_name, "roi", "scales", "output_size"], outputs=[resizer_node_output_name])
transpose_node = onnx.helper.make_node("Transpose", inputs=[resizer_node_output_name], outputs=[model_output_name], perm=[0,3,1,2])

rpitonak

graph = onnx.helper.make_graph([roi, scales, output_size, resizer_node, transpose_node], "resizer", [X], [Y])

print(graph)

# Create the model (ModelProto instance)
model_def = onnx.helper.make_model(g, producer_name="onnx-zaitra")

# these are optional, just to show what you can set here
model_def.opset_import[0].version = 12
model_def.ir_version = 7