Add p0p1 int8 model.py (#304)

 - onnx-import : 486
 - iree-compile : 433
 - inference      : 352

e2eshark/onnx/models/adv_inception_v3_vaiq/model.py

@@ -0,0 +1,43 @@
+import numpy, torch, sys
+import onnxruntime
+
+# import from e2eshark/tools to allow running in current dir, for run through
+# run.pl, commutils is symbolically linked to allow any rundir to work
+sys.path.insert(0, "../../../tools/stubs")
+from commonutils import E2ESHARK_CHECK_DEF, to_numpy, setup_test_image
+
+# Create an instance of it for this test
+E2ESHARK_CHECK = dict(E2ESHARK_CHECK_DEF)
+
+
+# The generated or checked in onnx file must always be called model.onnx
+# the tools/stubs/onnxmodel.py is appended to model.py
+# to form runmodel.py in the rundirectory which is then taken
+# through flow
+
+
+# start an onnxrt session
+session = onnxruntime.InferenceSession("model.onnx", None)
+
+# Even if model is quantized, the inputs and outputs are
+# not, so apply float32
+# Get and process the image
+img_ycbcr = setup_test_image(299, 299)
+
+model_input_X = to_numpy(img_ycbcr)
+
+# gets X in inputs[0] and Y in inputs[1]
+inputs = session.get_inputs()
+# gets Z in outputs[0]
+outputs = session.get_outputs()
+
+
+model_output = session.run(
+    [outputs[0].name],
+    {inputs[0].name: model_input_X},
+)[0]
+E2ESHARK_CHECK["input"] = [torch.from_numpy(model_input_X)]
+E2ESHARK_CHECK["output"] = [torch.from_numpy(arr) for arr in model_output]
+
+print("Input:", E2ESHARK_CHECK["input"])
+print("Output:", E2ESHARK_CHECK["output"])

e2eshark/onnx/models/cs3darknet_focus_l_train_vaiq/model.py

@@ -0,0 +1,43 @@
+import numpy, torch, sys
+import onnxruntime
+
+# import from e2eshark/tools to allow running in current dir, for run through
+# run.pl, commutils is symbolically linked to allow any rundir to work
+sys.path.insert(0, "../../../tools/stubs")
+from commonutils import E2ESHARK_CHECK_DEF, to_numpy, setup_test_image
+
+# Create an instance of it for this test
+E2ESHARK_CHECK = dict(E2ESHARK_CHECK_DEF)
+
+
+# The generated or checked in onnx file must always be called model.onnx
+# the tools/stubs/onnxmodel.py is appended to model.py
+# to form runmodel.py in the rundirectory which is then taken
+# through flow
+
+
+# start an onnxrt session
+session = onnxruntime.InferenceSession("model.onnx", None)
+
+# Even if model is quantized, the inputs and outputs are
+# not, so apply float32
+# Get and process the image
+img_ycbcr = setup_test_image(256, 256)
+
+model_input_X = to_numpy(img_ycbcr)
+
+# gets X in inputs[0] and Y in inputs[1]
+inputs = session.get_inputs()
+# gets Z in outputs[0]
+outputs = session.get_outputs()
+
+
+model_output = session.run(
+    [outputs[0].name],
+    {inputs[0].name: model_input_X},
+)[0]
+E2ESHARK_CHECK["input"] = [torch.from_numpy(model_input_X)]
+E2ESHARK_CHECK["output"] = [torch.from_numpy(arr) for arr in model_output]
+
+print("Input:", E2ESHARK_CHECK["input"])
+print("Output:", E2ESHARK_CHECK["output"])

e2eshark/onnx/models/cs3darknet_focus_l_vaiq/model.py

@@ -0,0 +1,43 @@
+import numpy, torch, sys
+import onnxruntime
+
+# import from e2eshark/tools to allow running in current dir, for run through
+# run.pl, commutils is symbolically linked to allow any rundir to work
+sys.path.insert(0, "../../../tools/stubs")
+from commonutils import E2ESHARK_CHECK_DEF, to_numpy, setup_test_image
+
+# Create an instance of it for this test
+E2ESHARK_CHECK = dict(E2ESHARK_CHECK_DEF)
+
+
+# The generated or checked in onnx file must always be called model.onnx
+# the tools/stubs/onnxmodel.py is appended to model.py
+# to form runmodel.py in the rundirectory which is then taken
+# through flow
+
+
+# start an onnxrt session
+session = onnxruntime.InferenceSession("model.onnx", None)
+
+# Even if model is quantized, the inputs and outputs are
+# not, so apply float32
+# Get and process the image
+img_ycbcr = setup_test_image(288, 288)
+
+model_input_X = to_numpy(img_ycbcr)
+
+# gets X in inputs[0] and Y in inputs[1]
+inputs = session.get_inputs()
+# gets Z in outputs[0]
+outputs = session.get_outputs()
+
+
+model_output = session.run(
+    [outputs[0].name],
+    {inputs[0].name: model_input_X},
+)[0]
+E2ESHARK_CHECK["input"] = [torch.from_numpy(model_input_X)]
+E2ESHARK_CHECK["output"] = [torch.from_numpy(arr) for arr in model_output]
+
+print("Input:", E2ESHARK_CHECK["input"])
+print("Output:", E2ESHARK_CHECK["output"])

e2eshark/onnx/models/cs3darknet_focus_m_train_vaiq/model.py

@@ -0,0 +1,43 @@
+import numpy, torch, sys
+import onnxruntime
+
+# import from e2eshark/tools to allow running in current dir, for run through
+# run.pl, commutils is symbolically linked to allow any rundir to work
+sys.path.insert(0, "../../../tools/stubs")
+from commonutils import E2ESHARK_CHECK_DEF, to_numpy, setup_test_image
+
+# Create an instance of it for this test
+E2ESHARK_CHECK = dict(E2ESHARK_CHECK_DEF)
+
+
+# The generated or checked in onnx file must always be called model.onnx
+# the tools/stubs/onnxmodel.py is appended to model.py
+# to form runmodel.py in the rundirectory which is then taken
+# through flow
+
+
+# start an onnxrt session
+session = onnxruntime.InferenceSession("model.onnx", None)
+
+# Even if model is quantized, the inputs and outputs are
+# not, so apply float32
+# Get and process the image
+img_ycbcr = setup_test_image(256, 256)
+
+model_input_X = to_numpy(img_ycbcr)
+
+# gets X in inputs[0] and Y in inputs[1]
+inputs = session.get_inputs()
+# gets Z in outputs[0]
+outputs = session.get_outputs()
+
+
+model_output = session.run(
+    [outputs[0].name],
+    {inputs[0].name: model_input_X},
+)[0]
+E2ESHARK_CHECK["input"] = [torch.from_numpy(model_input_X)]
+E2ESHARK_CHECK["output"] = [torch.from_numpy(arr) for arr in model_output]
+
+print("Input:", E2ESHARK_CHECK["input"])
+print("Output:", E2ESHARK_CHECK["output"])

e2eshark/onnx/models/cs3darknet_focus_m_vaiq/model.py

@@ -0,0 +1,43 @@
+import numpy, torch, sys
+import onnxruntime
+
+# import from e2eshark/tools to allow running in current dir, for run through
+# run.pl, commutils is symbolically linked to allow any rundir to work
+sys.path.insert(0, "../../../tools/stubs")
+from commonutils import E2ESHARK_CHECK_DEF, to_numpy, setup_test_image
+
+# Create an instance of it for this test
+E2ESHARK_CHECK = dict(E2ESHARK_CHECK_DEF)
+
+
+# The generated or checked in onnx file must always be called model.onnx
+# the tools/stubs/onnxmodel.py is appended to model.py
+# to form runmodel.py in the rundirectory which is then taken
+# through flow
+
+
+# start an onnxrt session
+session = onnxruntime.InferenceSession("model.onnx", None)
+
+# Even if model is quantized, the inputs and outputs are
+# not, so apply float32
+# Get and process the image
+img_ycbcr = setup_test_image(288, 288)
+
+model_input_X = to_numpy(img_ycbcr)
+
+# gets X in inputs[0] and Y in inputs[1]
+inputs = session.get_inputs()
+# gets Z in outputs[0]
+outputs = session.get_outputs()
+
+
+model_output = session.run(
+    [outputs[0].name],
+    {inputs[0].name: model_input_X},
+)[0]
+E2ESHARK_CHECK["input"] = [torch.from_numpy(model_input_X)]
+E2ESHARK_CHECK["output"] = [torch.from_numpy(arr) for arr in model_output]
+
+print("Input:", E2ESHARK_CHECK["input"])
+print("Output:", E2ESHARK_CHECK["output"])

e2eshark/onnx/models/cs3darknet_l_train_vaiq/model.py

@@ -0,0 +1,43 @@
+import numpy, torch, sys
+import onnxruntime
+
+# import from e2eshark/tools to allow running in current dir, for run through
+# run.pl, commutils is symbolically linked to allow any rundir to work
+sys.path.insert(0, "../../../tools/stubs")
+from commonutils import E2ESHARK_CHECK_DEF, to_numpy, setup_test_image
+
+# Create an instance of it for this test
+E2ESHARK_CHECK = dict(E2ESHARK_CHECK_DEF)
+
+
+# The generated or checked in onnx file must always be called model.onnx
+# the tools/stubs/onnxmodel.py is appended to model.py
+# to form runmodel.py in the rundirectory which is then taken
+# through flow
+
+
+# start an onnxrt session
+session = onnxruntime.InferenceSession("model.onnx", None)
+
+# Even if model is quantized, the inputs and outputs are
+# not, so apply float32
+# Get and process the image
+img_ycbcr = setup_test_image(256, 256)
+
+model_input_X = to_numpy(img_ycbcr)
+
+# gets X in inputs[0] and Y in inputs[1]
+inputs = session.get_inputs()
+# gets Z in outputs[0]
+outputs = session.get_outputs()
+
+
+model_output = session.run(
+    [outputs[0].name],
+    {inputs[0].name: model_input_X},
+)[0]
+E2ESHARK_CHECK["input"] = [torch.from_numpy(model_input_X)]
+E2ESHARK_CHECK["output"] = [torch.from_numpy(arr) for arr in model_output]
+
+print("Input:", E2ESHARK_CHECK["input"])
+print("Output:", E2ESHARK_CHECK["output"])

e2eshark/onnx/models/cs3darknet_l_vaiq/model.py

@@ -0,0 +1,43 @@
+import numpy, torch, sys
+import onnxruntime
+
+# import from e2eshark/tools to allow running in current dir, for run through
+# run.pl, commutils is symbolically linked to allow any rundir to work
+sys.path.insert(0, "../../../tools/stubs")
+from commonutils import E2ESHARK_CHECK_DEF, to_numpy, setup_test_image
+
+# Create an instance of it for this test
+E2ESHARK_CHECK = dict(E2ESHARK_CHECK_DEF)
+
+
+# The generated or checked in onnx file must always be called model.onnx
+# the tools/stubs/onnxmodel.py is appended to model.py
+# to form runmodel.py in the rundirectory which is then taken
+# through flow
+
+
+# start an onnxrt session
+session = onnxruntime.InferenceSession("model.onnx", None)
+
+# Even if model is quantized, the inputs and outputs are
+# not, so apply float32
+# Get and process the image
+img_ycbcr = setup_test_image(288, 288)
+
+model_input_X = to_numpy(img_ycbcr)
+
+# gets X in inputs[0] and Y in inputs[1]
+inputs = session.get_inputs()
+# gets Z in outputs[0]
+outputs = session.get_outputs()
+
+
+model_output = session.run(
+    [outputs[0].name],
+    {inputs[0].name: model_input_X},
+)[0]
+E2ESHARK_CHECK["input"] = [torch.from_numpy(model_input_X)]
+E2ESHARK_CHECK["output"] = [torch.from_numpy(arr) for arr in model_output]
+
+print("Input:", E2ESHARK_CHECK["input"])
+print("Output:", E2ESHARK_CHECK["output"])

e2eshark/onnx/models/cs3darknet_m_train_vaiq/model.py

@@ -0,0 +1,43 @@
+import numpy, torch, sys
+import onnxruntime
+
+# import from e2eshark/tools to allow running in current dir, for run through
+# run.pl, commutils is symbolically linked to allow any rundir to work
+sys.path.insert(0, "../../../tools/stubs")
+from commonutils import E2ESHARK_CHECK_DEF, to_numpy, setup_test_image
+
+# Create an instance of it for this test
+E2ESHARK_CHECK = dict(E2ESHARK_CHECK_DEF)
+
+
+# The generated or checked in onnx file must always be called model.onnx
+# the tools/stubs/onnxmodel.py is appended to model.py
+# to form runmodel.py in the rundirectory which is then taken
+# through flow
+
+
+# start an onnxrt session
+session = onnxruntime.InferenceSession("model.onnx", None)
+
+# Even if model is quantized, the inputs and outputs are
+# not, so apply float32
+# Get and process the image
+img_ycbcr = setup_test_image(256, 256)
+
+model_input_X = to_numpy(img_ycbcr)
+
+# gets X in inputs[0] and Y in inputs[1]
+inputs = session.get_inputs()
+# gets Z in outputs[0]
+outputs = session.get_outputs()
+
+
+model_output = session.run(
+    [outputs[0].name],
+    {inputs[0].name: model_input_X},
+)[0]
+E2ESHARK_CHECK["input"] = [torch.from_numpy(model_input_X)]
+E2ESHARK_CHECK["output"] = [torch.from_numpy(arr) for arr in model_output]
+
+print("Input:", E2ESHARK_CHECK["input"])
+print("Output:", E2ESHARK_CHECK["output"])

e2eshark/onnx/models/cs3darknet_m_vaiq/model.py

@@ -0,0 +1,43 @@
+import numpy, torch, sys
+import onnxruntime
+
+# import from e2eshark/tools to allow running in current dir, for run through
+# run.pl, commutils is symbolically linked to allow any rundir to work
+sys.path.insert(0, "../../../tools/stubs")
+from commonutils import E2ESHARK_CHECK_DEF, to_numpy, setup_test_image
+
+# Create an instance of it for this test
+E2ESHARK_CHECK = dict(E2ESHARK_CHECK_DEF)
+
+
+# The generated or checked in onnx file must always be called model.onnx
+# the tools/stubs/onnxmodel.py is appended to model.py
+# to form runmodel.py in the rundirectory which is then taken
+# through flow
+
+
+# start an onnxrt session
+session = onnxruntime.InferenceSession("model.onnx", None)
+
+# Even if model is quantized, the inputs and outputs are
+# not, so apply float32
+# Get and process the image
+img_ycbcr = setup_test_image(288, 288)
+
+model_input_X = to_numpy(img_ycbcr)
+
+# gets X in inputs[0] and Y in inputs[1]
+inputs = session.get_inputs()
+# gets Z in outputs[0]
+outputs = session.get_outputs()
+
+
+model_output = session.run(
+    [outputs[0].name],
+    {inputs[0].name: model_input_X},
+)[0]
+E2ESHARK_CHECK["input"] = [torch.from_numpy(model_input_X)]
+E2ESHARK_CHECK["output"] = [torch.from_numpy(arr) for arr in model_output]
+
+print("Input:", E2ESHARK_CHECK["input"])
+print("Output:", E2ESHARK_CHECK["output"])