Fix ci for mmocr (#144)

* fix mmocr unittests

* remove useless

* lock mmdet maximum version to 2.20

* pip install -U numpy

Author: AllentDan

.github/workflows/build.yml

@@ -44,6 +44,7 @@ jobs:
       - name: Install unittest dependencies
         run: |
           pip install -r requirements.txt
+          pip install -U numpy
       - name: Build and install
         run: rm -rf .eggs && pip install -e .
       - name: Run unittests and generate coverage report
@@ -85,6 +86,7 @@ jobs:
           python -V
           python -m pip install mmcv-full==${{matrix.mmcv}} -f https://download.openmmlab.com/mmcv/dist/cu102/${{matrix.torch_version}}/index.html
           python -m pip install -r requirements.txt
+          python -m pip install -U numpy
       - name: Build and install
         run: |
           rm -rf .eggs && python -m pip install -e .
@@ -128,6 +130,7 @@ jobs:
           python -V
           python -m pip install mmcv-full==${{matrix.mmcv}} -f https://download.openmmlab.com/mmcv/dist/cu111/${{matrix.torch_version}}/index.html
           python -m pip install -r requirements.txt
+          python -m pip install -U numpy
       - name: Build and install
         run: |
           rm -rf .eggs && python -m pip install -e .

requirements/optional.txt

@@ -1,7 +1,7 @@
 mmcls>=0.15.0,<=0.19.0
-mmdet>=2.19.0
+mmdet>=2.19.0,<=2.20.0
 mmedit
-mmocr==0.3.0
+mmocr>=0.3.0
 mmsegmentation
 onnxruntime>=1.8.0
 openvino-dev

tests/test_codebase/test_mmocr/test_mmocr_models.py

@@ -74,7 +74,7 @@ def get_encode_decode_recognizer_model():
     cfg = dict(
         preprocessor=None,
         backbone=dict(type='VeryDeepVgg', leaky_relu=False, input_channels=1),
-        encoder=dict(type='TFEncoder'),
+        encoder=None,
         decoder=dict(type='CRNNDecoder', in_channels=512, rnn_flag=True),
         loss=dict(type='CTCLoss'),
         label_convertor=dict(
@@ -394,12 +394,15 @@ def test_forward_of_fpnc(backend: Backend):
                 model_inputs=[
                     dict(
                         input_shapes=dict(
-                            input=dict(
+                            inputs=dict(
                                 min_shape=[1, 3, 64, 64],
                                 opt_shape=[1, 3, 64, 64],
                                 max_shape=[1, 3, 64, 64])))
                 ]),
-            onnx_config=dict(input_shape=[64, 64], output_names=['output']),
+            onnx_config=dict(
+                input_shape=None,
+                input_names=['inputs'],
+                output_names=['output']),
             codebase_config=dict(type='mmocr', task='TextDetection')))
 
     input = torch.rand(1, 3, 64, 64).cuda()

tests/test_codebase/test_mmocr/test_text_detection_models.py

@@ -60,6 +60,7 @@ def test_forward(self, ori_shape):
             'ori_shape': ori_shape,
             'img_shape': [IMAGE_SIZE, IMAGE_SIZE, 3],
             'scale_factor': [1., 1., 1., 1.],
+            'filename': ''
         }]]
         results = self.end2end_model.forward(imgs, img_metas)
         assert results is not None, 'failed to get output using '\