From cc44712021ef8c7dbc9ab60662d962230f74bf32 Mon Sep 17 00:00:00 2001 From: Theodoros Katzalis Date: Tue, 27 Aug 2024 01:25:41 +0200 Subject: [PATCH 1/8] Remove test data and unused code originated from ModelInfo --- proto/inference.proto | 34 +----- tests/data/__init__.py | 0 tests/data/dummy/Dummy.model.yaml | 56 --------- tests/data/dummy/dummy.md | 0 tests/data/dummy/dummy.py | 7 -- tests/data/dummy/dummy_in.npy | Bin 65664 -> 0 bytes tests/data/dummy/dummy_out.npy | Bin 65664 -> 0 bytes tests/data/dummy/environment.yaml | 0 tests/data/dummy/weights | Bin 232 -> 0 bytes tests/data/dummy_param/Dummy.model_param.yaml | 57 ---------- tests/data/dummy_param/dummy.md | 0 tests/data/dummy_param/dummy.py | 7 -- tests/data/dummy_param/dummy_in.npy | Bin 65664 -> 0 bytes tests/data/dummy_param/dummy_out.npy | Bin 65664 -> 0 bytes tests/data/dummy_param/environment.yaml | 0 tests/data/dummy_param/weights | Bin 232 -> 0 bytes tests/data/dummy_tensorflow/cover.png | Bin 3166 -> 0 bytes .../dummy_tensorflow/dummy-model-weights.zip | Bin 2499 -> 0 bytes tests/data/dummy_tensorflow/rdf.yaml | 34 ------ .../data/dummy_tensorflow/tensorflow-dummy.md | 74 ------------ tests/data/dummy_tensorflow/test_input.npy | Bin 1152 -> 0 bytes tests/data/dummy_tensorflow/test_output.npy | Bin 4224 -> 0 bytes tests/data/tiny_models.py | 102 ----------------- tests/data/unet2d/UNet2DNucleiBroad.md | 0 .../data/unet2d/UNet2DNucleiBroad.model.yaml | 76 ------------- tests/data/unet2d/environment.yaml | 8 -- tests/data/unet2d/test_input.npy | Bin 524416 -> 0 bytes tests/data/unet2d/test_output.npy | Bin 1048704 -> 0 bytes tests/data/unet2d/unet2d.py | 73 ------------ tests/data/unet2d_onnx/UNet2DNucleiBroad.md | 0 .../unet2d_onnx/UNet2DNucleiBroad.model.yaml | 73 ------------ tests/data/unet2d_onnx/cover0.png | Bin 110847 -> 0 bytes tests/data/unet2d_onnx/sample_input.tif | Bin 726350 -> 0 bytes tests/data/unet2d_onnx/sample_output.tif | Bin 1448000 -> 0 bytes tests/data/unet2d_onnx/test_input.npy | Bin 524416 -> 0 bytes tests/data/unet2d_onnx/test_output.npy | Bin 1048704 -> 0 bytes tests/data/unet2d_onnx/weights.onnx | Bin 1802974 -> 0 bytes .../unet2d_torchscript/UNet2DNucleiBroad.md | 0 .../UNet2DNucleiBroad.model.yaml | 66 ----------- tests/data/unet2d_torchscript/cover0.png | Bin 110847 -> 0 bytes tests/data/unet2d_torchscript/test_input.npy | Bin 524416 -> 0 bytes tests/data/unet2d_torchscript/test_output.npy | Bin 1048704 -> 0 bytes tests/data/unet2d_torchscript/weights.pt | Bin 1861779 -> 0 bytes tests/test_converters.py | 106 +----------------- tiktorch/converters.py | 63 +---------- 45 files changed, 3 insertions(+), 833 deletions(-) delete mode 100644 tests/data/__init__.py delete mode 100644 tests/data/dummy/Dummy.model.yaml delete mode 100644 tests/data/dummy/dummy.md delete mode 100644 tests/data/dummy/dummy.py delete mode 100644 tests/data/dummy/dummy_in.npy delete mode 100644 tests/data/dummy/dummy_out.npy delete mode 100644 tests/data/dummy/environment.yaml delete mode 100644 tests/data/dummy/weights delete mode 100644 tests/data/dummy_param/Dummy.model_param.yaml delete mode 100644 tests/data/dummy_param/dummy.md delete mode 100644 tests/data/dummy_param/dummy.py delete mode 100644 tests/data/dummy_param/dummy_in.npy delete mode 100644 tests/data/dummy_param/dummy_out.npy delete mode 100644 tests/data/dummy_param/environment.yaml delete mode 100644 tests/data/dummy_param/weights delete mode 100644 tests/data/dummy_tensorflow/cover.png delete mode 100644 tests/data/dummy_tensorflow/dummy-model-weights.zip delete mode 100644 tests/data/dummy_tensorflow/rdf.yaml delete mode 100644 tests/data/dummy_tensorflow/tensorflow-dummy.md delete mode 100644 tests/data/dummy_tensorflow/test_input.npy delete mode 100644 tests/data/dummy_tensorflow/test_output.npy delete mode 100644 tests/data/tiny_models.py delete mode 100644 tests/data/unet2d/UNet2DNucleiBroad.md delete mode 100644 tests/data/unet2d/UNet2DNucleiBroad.model.yaml delete mode 100644 tests/data/unet2d/environment.yaml delete mode 100644 tests/data/unet2d/test_input.npy delete mode 100644 tests/data/unet2d/test_output.npy delete mode 100644 tests/data/unet2d/unet2d.py delete mode 100644 tests/data/unet2d_onnx/UNet2DNucleiBroad.md delete mode 100644 tests/data/unet2d_onnx/UNet2DNucleiBroad.model.yaml delete mode 100644 tests/data/unet2d_onnx/cover0.png delete mode 100644 tests/data/unet2d_onnx/sample_input.tif delete mode 100644 tests/data/unet2d_onnx/sample_output.tif delete mode 100644 tests/data/unet2d_onnx/test_input.npy delete mode 100644 tests/data/unet2d_onnx/test_output.npy delete mode 100644 tests/data/unet2d_onnx/weights.onnx delete mode 100644 tests/data/unet2d_torchscript/UNet2DNucleiBroad.md delete mode 100644 tests/data/unet2d_torchscript/UNet2DNucleiBroad.model.yaml delete mode 100644 tests/data/unet2d_torchscript/cover0.png delete mode 100644 tests/data/unet2d_torchscript/test_input.npy delete mode 100644 tests/data/unet2d_torchscript/test_output.npy delete mode 100644 tests/data/unet2d_torchscript/weights.pt diff --git a/proto/inference.proto b/proto/inference.proto index 39845dc5..59ae4d26 100644 --- a/proto/inference.proto +++ b/proto/inference.proto @@ -2,6 +2,7 @@ syntax = "proto3"; service Inference { rpc CreateModelSession(CreateModelSessionRequest) returns (ModelSession) {} + rpc CloseModelSession(ModelSession) returns (Empty) {} rpc CreateDatasetDescription(CreateDatasetDescriptionRequest) returns (DatasetDescription) {} @@ -56,34 +57,6 @@ message NamedFloats { } -/* InputShape will always be expected to have `shape` set. - * For `ShapeType` PARAMETRIZED, also a `stepShape` has to be given. - * ref: https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/model_spec_latest.md */ -message InputShape { - enum ShapeType { - EXPLICIT = 0; - PARAMETRIZED = 1; - } - - ShapeType shapeType = 1; - // shape is min, when PARAMETRIZED - NamedInts shape = 2; - NamedInts stepShape = 4; -} - -message OutputShape { - enum ShapeType { - EXPLICIT = 0; - IMPLICIT = 1; - } - ShapeType shapeType = 1; - NamedInts shape = 2; - NamedInts halo = 3; - string referenceTensor = 4; - NamedFloats scale = 5; - NamedFloats offset = 6; -} - message ModelSession { string id = 1; } @@ -136,16 +109,11 @@ message PredictResponse { message Empty {} - service FlightControl { rpc Ping(Empty) returns (Empty) {} rpc Shutdown(Empty) returns (Empty) {} } -message ModelInfo { - repeated string deviceIds = 1; -} - message CreateModelSessionChunkedRequest { oneof data { ModelInfo info = 1; diff --git a/tests/data/__init__.py b/tests/data/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/data/dummy/Dummy.model.yaml b/tests/data/dummy/Dummy.model.yaml deleted file mode 100644 index 1aa8fe90..00000000 --- a/tests/data/dummy/Dummy.model.yaml +++ /dev/null @@ -1,56 +0,0 @@ -format_version: 0.3.3 -language: python -framework: pytorch - -name: UNet2DNucleiBroad -description: A 2d U-Net pretrained on broad nucleus dataset. -cite: - - text: "Ronneberger, Olaf et al. U-net: Convolutional networks for biomedical image segmentation. MICCAI 2015." - doi: https://doi.org/10.1007/978-3-319-24574-4_28 -authors: - - name: "ilastik-team" - affiliation: "EMBL Heidelberg" - -documentation: dummy.md -tags: [pytorch, nucleus-segmentation] -license: MIT -git_repo: https://github.com/ilastik/tiktorch -covers: [] - -source: dummy.py::Dummy -sha256: 00ffb1647cf7ec524892206dce6258d9da498fe040c62838f31b501a09bfd573 -timestamp: 2019-12-11T12:22:32Z # ISO 8601 - -test_inputs: [dummy_in.npy] -test_outputs: [dummy_out.npy] - -weights: - pytorch_state_dict: - source: ./weights - sha256: 518cb80bad2eb3ec3dfbe6bab74920951391ce8fb24e15cf59b9b9f052a575a6 - authors: - - name: "ilastik-team" - affiliation: "EMBL Heidelberg" - - -# TODO double check inputs/outputs -inputs: - - name: input - axes: bcyx - data_type: float32 - data_range: [-inf, inf] - shape: [1, 1, 128, 128] - - -outputs: - - name: output - axes: bcyx - data_type: float32 - data_range: [0, 1] - shape: - reference_tensor: input - scale: [1, 1, 1, 1] - offset: [0, 0, 0, 0] - halo: [0, 0, 32, 32] - -type: model diff --git a/tests/data/dummy/dummy.md b/tests/data/dummy/dummy.md deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/data/dummy/dummy.py b/tests/data/dummy/dummy.py deleted file mode 100644 index 195e98e3..00000000 --- a/tests/data/dummy/dummy.py +++ /dev/null @@ -1,7 +0,0 @@ -from torch import nn - - -class Dummy(nn.Module): - def forward(self, input): - x = input - return x + 1 diff --git a/tests/data/dummy/dummy_in.npy b/tests/data/dummy/dummy_in.npy deleted file mode 100644 index 96a78a7b87dbeef0611f2cf9c97f5b0f72cf8396..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 65664 zcmeIuF$%&!6a>)NdW!8ic9?hT-PR-do;;I-_!YTjq~sd zf7UR|-Yeq=3^2d|0}L?000Rs#zyJdbFu(u<3^2d|0}L?000Rs#zyJdbFu(u<3^2d| z0}L?000Rs#zyJdbFu(u<3^2d|0}L?000Rs#zyJdbFu(u<3^2d|0}L?000Rs#zyJdb zFu(u<3^2d|0}L?000Rs#zyJdbFu(u<3^2d|0}L?000Rs#zyJdbFu(u<3^2d|0}L?0 z00Rs#zyJdbFu(u<3^2d|0}L?000Rs#zyJdbFu(u<3^2d|0}L?000Rs#zyJdbFu(u< z3^2d|0}L?000Rs#zyJdbFu(u<3^2d|0}L?000Rs#zyJdbFu(u<3^2d|0}L?000Rs# zzyJdbFu(u<3^2d|0}L?000Rs#zyJdbFu(u<3^2d|0}L?000Rs#zyJdbFu(u<3^2d| P0}L?000Rs#zyJeZi2B~R diff --git a/tests/data/dummy/environment.yaml b/tests/data/dummy/environment.yaml deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/data/dummy/weights b/tests/data/dummy/weights deleted file mode 100644 index da14f34253e8e85c58d498b94b7ed3b933de0dc6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 232 zcmXwz!D<3Q42EZCXI)$ndhM-8z4#R3Wf7&PTx8kq1|1n^%uEz4EO;zFLm!~8)<-Dx z(0J%2B;TL>e{T9-x!=#_&&%O!^E|)l)pss+AT@<2rPL_~4qb4~1!JA5ye?% zhYkAPwx09{R08>W!Y0{wOq35~rcek>`w*FmT0<_A^-QDMfO&WXzoW7?=d)yX663IA zNlDc@_875W*p4ewvscOn(lC~r=7`+(Ew_~KAA$jaii&|oP)hgzXjx||r8hC&lA8Yk D#+O8X diff --git a/tests/data/dummy_param/Dummy.model_param.yaml b/tests/data/dummy_param/Dummy.model_param.yaml deleted file mode 100644 index 87ddd885..00000000 --- a/tests/data/dummy_param/Dummy.model_param.yaml +++ /dev/null @@ -1,57 +0,0 @@ -format_version: 0.3.3 -language: python -framework: pytorch - -name: UNet2DNucleiBroad -description: A 2d U-Net pretrained on broad nucleus dataset. -cite: - - text: "Ronneberger, Olaf et al. U-net: Convolutional networks for biomedical image segmentation. MICCAI 2015." - doi: https://doi.org/10.1007/978-3-319-24574-4_28 -authors: - - name: "ilastik-team" - affiliation: "EMBL Heidelberg" - -documentation: dummy.md -tags: [pytorch, nucleus-segmentation] -license: MIT -git_repo: https://github.com/ilastik/tiktorch -covers: [] - -source: dummy.py::Dummy -sha256: 00ffb1647cf7ec524892206dce6258d9da498fe040c62838f31b501a09bfd573 -timestamp: 2019-12-11T12:22:32Z # ISO 8601 - -test_inputs: [dummy_in.npy] -test_outputs: [dummy_out.npy] - -weights: - pytorch_state_dict: - source: ./weights - sha256: 518cb80bad2eb3ec3dfbe6bab74920951391ce8fb24e15cf59b9b9f052a575a6 - authors: - - name: "ilastik-team" - affiliation: "EMBL Heidelberg" - - -# TODO double check inputs/outputs -inputs: - - name: param - axes: bcyx - data_type: float32 - data_range: [-inf, inf] - shape: - min: [1, 1, 64, 64] - step: [0, 0, 2, 1] - -outputs: - - name: output - axes: bcyx - data_type: float32 - data_range: [0, 1] - shape: - reference_tensor: param - scale: [1, 1, 1, 1] - offset: [0, 0, 0, 0] - halo: [0, 0, 8, 8] - -type: model diff --git a/tests/data/dummy_param/dummy.md b/tests/data/dummy_param/dummy.md deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/data/dummy_param/dummy.py b/tests/data/dummy_param/dummy.py deleted file mode 100644 index 195e98e3..00000000 --- a/tests/data/dummy_param/dummy.py +++ /dev/null @@ -1,7 +0,0 @@ -from torch import nn - - -class Dummy(nn.Module): - def forward(self, input): - x = input - return x + 1 diff --git a/tests/data/dummy_param/dummy_in.npy b/tests/data/dummy_param/dummy_in.npy deleted file mode 100644 index 96a78a7b87dbeef0611f2cf9c97f5b0f72cf8396..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 65664 zcmeIuF$%&!6a>)NdW!8ic9?hT-PR-do;;I-_!YTjq~sd zf7UR|-Yeq=3^2d|0}L?000Rs#zyJdbFu(u<3^2d|0}L?000Rs#zyJdbFu(u<3^2d| z0}L?000Rs#zyJdbFu(u<3^2d|0}L?000Rs#zyJdbFu(u<3^2d|0}L?000Rs#zyJdb zFu(u<3^2d|0}L?000Rs#zyJdbFu(u<3^2d|0}L?000Rs#zyJdbFu(u<3^2d|0}L?0 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z0si*n#^HqOxBr$-+3dTSvm3sTghRzf%&*$_7`z#DGrcxI<0ZZi`h{$pVK%d217?@x nw=f&r*o?6Gx^Eyv3lV;B`SDl@;YR>qi}2|Ya%FOTMg#l-V_w}s diff --git a/tests/data/dummy_tensorflow/rdf.yaml b/tests/data/dummy_tensorflow/rdf.yaml deleted file mode 100644 index e5a8597f..00000000 --- a/tests/data/dummy_tensorflow/rdf.yaml +++ /dev/null @@ -1,34 +0,0 @@ -authors: -- {name: ilastik-team} -cite: -- {text: text, url: pass} -covers: [cover.png] -description: simple model that increments input by 1 -documentation: tensorflow-dummy.md -format_version: 0.4.3 -inputs: -- axes: bcyx - data_range: [0.0, 255.0] - data_type: uint8 - name: input0 - shape: [1, 1, 32, 32] -license: CC-BY-4.0 -name: dummy-keras -outputs: -- axes: bcyx - data_range: [-.inf, .inf] - data_type: float32 - halo: [0, 0, 0, 0] - name: output0 - shape: - offset: [0.0, 0.0, 0.0, 0.0] - reference_tensor: input0 - scale: [1.0, 1.0, 1.0, 1.0] -tags: [testing] -test_inputs: [test_input.npy] -test_outputs: [test_output.npy] -timestamp: '2022-02-02T11:19:07.303147' -type: model -weights: - tensorflow_saved_model_bundle: {sha256: a3c86e3b2ec3766d29b635ae4422e0c12912be5af571a88ad3e44ba6c93f737c, - source: dummy-model-weights.zip, tensorflow_version: '1.14'} diff --git a/tests/data/dummy_tensorflow/tensorflow-dummy.md b/tests/data/dummy_tensorflow/tensorflow-dummy.md deleted file mode 100644 index 8b76d7aa..00000000 --- a/tests/data/dummy_tensorflow/tensorflow-dummy.md +++ /dev/null @@ -1,74 +0,0 @@ -# Simple tensorflow test model - -## Code to generate the model - -```python -# Simple tensorflow test model -# inverts input. - -## Code to generate the model - -```python -import tensorflow as tf -from tensorflow.keras.models import Sequential -from tensorflow.keras.layers import Input, Lambda -import numpy -import xarray -import shutil - -shutil.rmtree("./dummy-model") - -model = Sequential() -model.add(Input(shape=(1, 32, 32), name="input0")) -model.add(Lambda(lambda x: x * -1, name="output0")) -tf.keras.experimental.export_saved_model(model, "./dummy-model") - -input_data = numpy.random.randint(0, 255, (1, 1, 32, 32), dtype="uint8") -input_ = xarray.DataArray(input_data, dims=("b", "c", "y", "x")) - -output_ = model.predict(input_) - -numpy.save("test_input.npy", input_) -numpy.save("test_output.npy", output_) -``` - - -```Python -# generate a model zoo model out of it -from bioimageio.core.build_spec import build_model -import shutil - -weight_file = "./dummy-model-weights.zip" -name = "dummy-keras" -input_axes = ["bcyx"] -output_axes = ["bcyx"] -zip_path = "./dummy-model-tensorflow.zip" - - -shutil.make_archive("dummy-model-weights", "zip", root_dir="dummy-model") - - -new_mod_raw = build_model( - weight_uri=weight_file, - test_inputs=["./test_input.npy"], - test_outputs=["./test_output.npy"], - input_axes=input_axes, - output_axes=output_axes, - output_path=zip_path, - name=name, - input_names=["input0"], - description="simple model that increments input by 1", - authors=[{"name": "ilastik-team"}], - documentation="./tensorflow-dummy.md", - tags=["testing"], - cite={"text": "pass"}, - tensorflow_version="1.14", - weight_type="tensorflow_saved_model_bundle", - halo=[[0, 0, 0, 0]], - output_offset=[[0, 0, 0, 0]], - output_reference=["input0"], - output_scale=[[1.0, 1.0, 1.0, 1.0]], - output_names=["output0"], -) - -``` diff --git a/tests/data/dummy_tensorflow/test_input.npy b/tests/data/dummy_tensorflow/test_input.npy deleted file mode 100644 index 048f67e519c4e24b0cb5cc69449931af2feb7b76..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1152 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1JQ);NLqoAIaUsO_*m=~X4l#&V(cT3DEP6dh= zXCxM+0{I$-ItoB!Yy`rZItsN4Bm=G~KA9m))^3`XS;;36a%+WA`HFSNvK;1=yE&dQ zuB(qKw>Ljt#`m4c^x#Q5-B&AK%gVQj?y!0y?y^Y4-<)qtUe5`)XHu&Tw@lwOUp!oX z#`U`kQdD?0HC&m;bmPfW4hJD!QD4nnJ4;`R#%ddR#3@FE3aoJx`!;p?e5XsDTc-rJ z1^<2ZBJNGgPWXr~^L3}23_?C3=cShQ@0mPtYrbV;^5mMlLPp)YH)q~fDcpB;)dH_ir@ErE 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D_out) - - torch.manual_seed(0) - self.x = torch.randn(N, D_in) - self.y = self.forward(self.x).detach() - - def forward(self, x): - h_relu = self.linear1(x).clamp(min=0) - y_pred = self.linear2(h_relu) - return y_pred - - -class TinyConvNet2d(torch.nn.Module): - def __init__(self, in_channels=1, out_channels=1): - super().__init__() - self.conv1 = torch.nn.Conv2d(in_channels, 16, 1) - self.nlin1 = torch.nn.ReLU() - self.conv2 = torch.nn.Conv2d(16, 64, 1) - self.nlin2 = torch.nn.ReLU() - self.conv3 = torch.nn.Conv2d(64, out_channels, 1) - self.nlin3 = torch.nn.Sigmoid() - - def forward(self, x): - return torch.nn.Sequential(self.conv1, self.nlin1, self.conv2, self.nlin2, self.conv3, self.nlin3)(x) - - -class TinyConvNet3d(torch.nn.Module): - def __init__(self, in_channels=1, out_channels=1): - super().__init__() - self.conv1 = torch.nn.Conv3d(in_channels, 16, 1) - self.nlin1 = torch.nn.ReLU() - self.conv2 = torch.nn.Conv3d(16, 64, 1) - self.nlin2 = torch.nn.ReLU() - self.conv3 = torch.nn.Conv3d(64, out_channels, 1) - self.nlin3 = torch.nn.Sigmoid() - - def forward(self, x): - return torch.nn.Sequential(self.conv1, self.nlin1, self.conv2, self.nlin2, self.conv3, self.nlin3)(x) - - -def train(model, x, y): - loss_fn = torch.nn.MSELoss(reduction="sum") - optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) - - for t in range(10): - y_pred = model(x) - - loss = loss_fn(y_pred, y) - logger.debug(t, loss.item()) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - -if __name__ == "__main__": - test1 = TestModel0() - print("train", test1.training) - # test.to(dtype=torch.float) - y = test1(test1.x) - assert y.allclose(test1.y) - - test2 = test1.__class__() - test2.eval() - test2.load_state_dict(test1.state_dict()) - print("here train2", test2.training) - print(id(test1.state_dict())) - print(id(test2.state_dict())) - - sd = test2.state_dict() - for key, value in sd.items(): - sd[key] = torch.zeros_like(value) - - print("train2", test2.training) - - print(id(test1) == id(test2)) - print(id(test1.state_dict()["linear1.weight"]) == id(test2.state_dict()["linear1.weight"])) - print(id(test1.state_dict()), test1.state_dict()["linear1.weight"]) - print(id(test2.state_dict()), test2.state_dict()["linear1.weight"]) - test2.load_state_dict(sd) - print(id(test1) == id(test2)) - magic1 = test1.state_dict() - magic2 = test2.state_dict() - print(id(test1.state_dict()) == id(test2.state_dict())) - print(id(magic1) == id(magic2)) - # print(id(magic1['linear1.weight']) == id(magic2['linear1.weight'])) - # print(id(test1.state_dict()['linear1.weight']) == id(test2.state_dict()['linear1.weight'])) - print(id(test1.state_dict()), test1.state_dict()["linear1.weight"]) - print(id(test2.state_dict()), test2.state_dict()["linear1.weight"]) - - print("train2", test2.training) diff --git a/tests/data/unet2d/UNet2DNucleiBroad.md b/tests/data/unet2d/UNet2DNucleiBroad.md deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/data/unet2d/UNet2DNucleiBroad.model.yaml b/tests/data/unet2d/UNet2DNucleiBroad.model.yaml deleted file mode 100644 index 2da3d352..00000000 --- a/tests/data/unet2d/UNet2DNucleiBroad.model.yaml +++ /dev/null @@ -1,76 +0,0 @@ -# TODO physical scale of the data -format_version: 0.3.3 -type: model - -name: UNet 2D Nuclei Broad -description: A 2d U-Net trained on the nuclei broad dataset. -authors: - - name: "Constantin Pape;@bioimage-io" - affiliation: "EMBL Heidelberg" - orcid: "0000-0001-6562-7187" - - name: "Fynn Beuttenmueller" - affiliation: "EMBL Heidelberg" - orcid: "0000-0002-8567-6389" - -# we allow for multiple citations. Each citation contains TEXT, DOI and URL. One of DOI or URL needs to be given. -cite: - - text: "Ronneberger, Olaf et al. U-net: Convolutional networks for biomedical image segmentation. MICCAI 2015." - doi: https://doi.org/10.1007/978-3-319-24574-4_28 - - text: "2018 Data Science Bowl" - url: https://www.kaggle.com/c/data-science-bowl-2018 - -git_repo: https://github.com/bioimage-io/spec-bioimage-io/tree/main/example_specs/models/unet2d_nuclei_broad -tags: [unet2d, pytorch, nucleus, segmentation, dsb2018] -license: MIT - -documentation: UNet2DNucleiBroad.md -covers: [] -attachments: {} -timestamp: 2019-12-11T12:22:32Z # ISO 8601 - -inputs: - - name: raw - description: raw input - axes: bcyx # letters of axes in btczyx - data_type: float32 - data_range: [-.inf, .inf] - shape: [1, 1, 512, 512] - preprocessing: # list of preprocessing steps - - name: zero_mean_unit_variance # name of preprocessing step - kwargs: - mode: per_sample # mode in [fixed, per_dataset, per_sample] - axes: yx # subset of axes to normalize jointly, batch ('b') is not a valid axis key here! - -outputs: - - name: probability - description: probability in [0,1] - axes: bcyx - data_type: float32 - data_range: [-.inf, .inf] - halo: [0, 0, 32, 32] - shape: - reference_tensor: raw - scale: [1.0, 1.0, 1.0, 1.0] - offset: [0, 0, 0, 0] - -language: python -framework: pytorch -source: unet2d.py:UNet2d -sha256: cf42a6d86adeb4eb6e8e37b539a20e5413866b183bed88f4e2e26ad1639761ed -kwargs: {input_channels: 1, output_channels: 1} -dependencies: conda:./environment.yaml - -test_inputs: [test_input.npy] -test_outputs: [test_output.npy] - -sample_inputs: [test_input.npy] -sample_outputs: [test_output.npy] - -weights: - pytorch_state_dict: - authors: - - name: "Constantin Pape;@bioimage-io" - affiliation: "EMBL Heidelberg" - orcid: "0000-0001-6562-7187" - sha256: e4d3885bccbe41cbf6c1d825f3cd2b707c7021ead5593156007e407a16b27cf2 - source: https://zenodo.org/record/3446812/files/unet2d_weights.torch \ No newline at end of file diff --git a/tests/data/unet2d/environment.yaml b/tests/data/unet2d/environment.yaml deleted file mode 100644 index 4297c6ca..00000000 --- a/tests/data/unet2d/environment.yaml +++ /dev/null @@ -1,8 +0,0 @@ -name: - unet2d_nuclei_broad -channels: - - conda-forge - - defaults -dependencies: - - pytorch - - numpy diff --git a/tests/data/unet2d/test_input.npy b/tests/data/unet2d/test_input.npy deleted file mode 100644 index 228057f8d10bebd96bf464a2dd9d2f1f436f051a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 524416 zcmb51b=*|d_qNYDgDof~2B4yV-GY8?#lUXt!tPF_8|m&wy1P@l5kV9&2th>@8-1_q zET7Ns@7HmLc_z-;XYY02>t1WGy{FC1cinXBy=n4h@?M4NPrUZ{s}(LfuEND{oKxYn z<0?G)(yOn(`sj-fzw~PLCw%>yN1uP~30{Bg=|^9Fg1;-CecEySfBxC$EH6OS#qx962kc^mv@dp_GSZ$~rUYr*Rt#m~0k=&d2*sm!~(UH}O~X^r7_D^qTaXbWgG*8I}x8deXLz zNjI*cP11yWJ9@7J_s03XPJVTb^Va%WJMIm*wc_9A^lsCnA^$e!TAJ~^5l3jwvql`V zA>XOTYfZQ}&5!jV*X^?we7a#$pMT>REqT@V+RzKl@;+$b zlJo*@52tU?uXXd@eJXvD9=kd{IjxkQl9o%4O^-?sNsFh2^p|8s(hD20PnvRcTUU>} z=W4>U+WB9e$$ql;TJzbC^!kkCpQJ*1dwK^WR6Tv2(XPSyp5aVS^6G<}|Hky5v+x;_{`KAH z&xZNcYWE+~vYzzZN6EBgWwJdvGW{P{P$wOnzJ*n`PTQqj)0eou8uVnt^kuH~Zu;_K 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