add code

mindspore-jina/Dockerfile

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+FROM mindspore/mindspore-cpu:1.0.0
+
+# setup the workspace
+COPY . /workspace
+WORKDIR /workspace
+
+# fix: add to path
+ENV PATH="/root/.local/bin:${PATH}"
+
+# install the third-party requirements
+RUN pip install --user -r requirements.txt
+
+RUN cd lenet && \
+    python train.py --data_path data/fashion/ --ckpt_path ckpt --device_target="CPU" && \
+    cd -
+
+# for testing the image
+RUN pip install --user pytest && pytest -s
+
+ENTRYPOINT ["jina", "pod", "--uses", "config.yml"]

mindspore-jina/__init__.py

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+import numpy as np
+from jina.executors.encoders.frameworks import BaseMindsporeEncoder
+
+
+class MindsporeLeNet(BaseMindsporeEncoder):
+    """
+    :class:`MindsporeLeNet` Encoding image into vectors using mindspore.
+    """
+
+    def encode(self, data, *args, **kwargs):
+        # data is B x D, where D = 28 * 28
+        # LeNet only accepts BCHW format where H=W=32
+        # hence we need to do some simple transform
+        from mindspore import Tensor
+
+        data = np.pad(data.reshape([-1, 1, 28, 28]),
+                      [(0, 0), (0, 0), (0, 4), (0, 4)]).astype('float32')
+        return self.model(Tensor(data)).asnumpy()
+
+    def get_cell(self):
+        from .lenet.src.lenet import LeNet5
+        class LeNet5Embed(LeNet5):
+            def construct(self, x):
+                x = self.conv1(x)
+                x = self.relu(x)
+                x = self.max_pool2d(x)
+                x = self.conv2(x)
+                x = self.relu(x)
+                x = self.max_pool2d(x)
+                x = self.flatten(x)
+                x = self.fc1(x)
+                x = self.relu(x)
+                x = self.fc2(x)
+                x = self.relu(x)
+                return x
+
+        return LeNet5Embed()

mindspore-jina/config.yml

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+!MindsporeLeNet
+with:
+  model_path: lenet/ckpt/checkpoint_lenet-1_1875.ckpt
+metas:
+  py_modules: 
+    - __init__.py
+    # - You can put more dependencies here
+requests:
+  on:
+    [IndexRequest, SearchRequest]:
+      - !Blob2PngURI {}
+      - !EncodeDriver {}
+      - !ExcludeQL
+        with:
+          fields:
+            - buffer
+            - chunks

mindspore-jina/manifest.yml

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+manifest_version: 1
+name: MindsporeLeNet
+kind: encoder
+description:
+  |
+  Encoding image into vectors using mindspore
+author: Jina AI Dev-Team (dev-team@jina.ai)
+url: https://jina.ai
+vendor: Jina AI Limited
+documentation: https://github.com/jina-ai/jina-hub
+version: 0.0.2
+license: apache-2.0
+keywords: [mindspore, lenet]
+type: pod

mindspore-jina/tests/__init__.py

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+##

mindspore-jina/tests/test_mindsporelenet.py

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+import numpy as np
+
+from .. import MindsporeLeNet
+
+
+def test_mindsporelenet():
+    """here is my test code
+
+    https://docs.pytest.org/en/stable/getting-started.html#create-your-first-test
+    """
+    mln = MindsporeLeNet('lenet/ckpt/checkpoint_lenet-1_1875.ckpt')
+    tmp = np.random.random([4, 28 * 28])
+
+    # The sixth layer is a fully connected layer (F6) with 84 units.
+    # it is the last layer before the output
+    assert mln.encode(tmp).shape == (4, 84)