test wildcard imports for models

Author: rstojnic

modelindex/models/Model.py

@@ -26,7 +26,7 @@ class Model(BaseModelIndex):
  ]
 
  def __init__(self,
- name: str,
+ name: str = None,
  metadata: Union[Dict, Metadata, str] = None,
  results: Union[List, ResultList, Result, Dict, str] = None,
  paper: Union[str, Dict] = None,
@@ -145,7 +145,7 @@ def from_dict(cls, d: Dict, _filepath: str = None, _path_to_readme: str = None):
  dd[field_name] = dd.pop(lc_keys[key])
 
  if _path_to_readme:
- dd["README"] = _path_to_readme
+ dd["readme"] = _path_to_readme
 
  return cls(
  _filepath=_filepath,

tests/test-mi/13_wildcard_model_imports/mi8.yml

@@ -0,0 +1,19 @@
+Collections:
+ - Name: Inception v3
+ Metadata:
+ Training Data: ImageNet 
+ Training Techniques: 
+ - RMSProp
+ - Weight Decay
+ - Gradient Clipping
+ - Label Smoothing
+ Training Resources: 8x V100 GPUs
+ Architecture:
+ - Auxiliary Classifier
+ - Inception-v3 Module
+ Paper: https://arxiv.org/abs/1512.00567v3
+ Code: https://github.com/rwightman/pytorch-image-models/blob/timm/models/inception_v3.py#L442
+ README: docs/inception-v3-readme.md
+
+Models:
+ - models_md/*.md

tests/test-mi/13_wildcard_model_imports/models_md/m1.md

@@ -0,0 +1,16 @@
+<!--
+Models:
+ - Name: Inception v3 - 90 epochs
+ In Collection: Inception v3 
+ Metadata:
+ Epochs: 90
+ Results:
+ - Task: Image Classification
+ Dataset: ImageNet
+ Metrics:
+ Top 1 Accuracy: 74.67%
+ Top 5 Accuracy: 92.1%
+ Weights: https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth
+-->
+
+something here. 

tests/test-mi/13_wildcard_model_imports/models_md/m2.md

@@ -0,0 +1,16 @@
+<!--
+Models:
+ - Name: Inception v3 - 120 epochs
+ In Collection: Inception v3
+ Metadata:
+ Epochs: 120
+ Results:
+ - Task: Image Classification
+ Dataset: ImageNet
+ Metrics:
+ Top 1 Accuracy: 75.1%
+ Top 5 Accuracy: 93.1%
+ Weights: https://download.pytorch.org/models/inception_v3_google-120-1a9a5afd.pth
+-->
+
+# Second here

tests/test_import.py

@@ -142,3 +142,12 @@ def test_wildcard_model_import():
  assert mi.models[0].results[0].metrics["Top 1 Accuracy"] == "11%"
  assert mi.models[0].results[1].metrics["Top 1 Accuracy"] == "21%"
 
+
+def test_models_import_wildcard():
+ mi = modelindex.load("tests/test-mi/13_wildcard_model_imports/mi8.yml")
+
+ assert len(mi.collections) == 1
+ assert len(mi.models) == 2
+
+ assert mi.models[0].readme == "models_md/m1.md"
+ assert mi.models[1].readme == "models_md/m2.md"