Model Zoo API

model-zoo-api/README.md

@@ -0,0 +1,60 @@
+# Python-API-Script
+Onnx Python API Script used to download and save pretrained models from [onnx model zoo](https://github.com/onnx/models). Retrieves metadata after the model is successfully downloaded. 
+
+## Features 
+
+
+```get_model_versions(MODEL_FOLDER_NAME)``` - Retrieves an array with strings values of all the versions of the specified model in the ONNX Model Zoo. 
+```
+'bert-squad': ['bertsquad-10', 'bertsquad-8']
+```
+
+```get_pretrained()``` - Downloads and saves specific onnx models to desired path.
+
+
+```get_metadata()``` - Retrieves metadata of the onnx model. 
+
+## Intialization and Usage
+Initiate the object by calling onnx_zoo class name.
+```
+OBJECT_NAME = onnx_zoo()
+```
+The Python scipt will then ask to input the model folder name. When inputting the name, it should be in all lowercase. 
+
+```
+Enter Model Name: resnet
+```
+
+After model folder name is received from input, the script will display all the model versions that exist in the folder.
+
+```
+['resnet101-v1-7', 'resnet101-v2-7', 'resnet152-v1-7', 'resnet152-v2-7', 'resnet18-v1-7', 'resnet18-v2-7', 'resnet34-v1-7', 'resnet34-v2-7', 'resnet50-caffe2-v1-3', 'resnet50-caffe2-v1-6', 'resnet50-caffe2-v1-7', 'resnet50-caffe2-v1-8', 'resnet50-caffe2-v1-9', 'resnet50-v1-7', 'resnet50-v2-7']
+```
+
+The CLI script then prompts the user to input the version (from the array of model versions) that will be downloaded and input the local directory that the model will be saved in.
+
+``` 
+Enter model name from options: resnet101-v1-7 
+Enter output directory name to save model: /Users/name/Downloads
+```
+
+To download the model and output its metadata, run the following functions:
+
+``` 
+OBJECT_NAME.get_pretrained()
+OBJECT_NAME.get_metadata()
+```
+
+## Installation 
+Install onnx to check models
+
+```pip install onnx```
+
+Install [onnxruntime](https://github.com/microsoft/onnxruntime) to run onnx models
+
+```pip install onnxruntime```
+
+## Contributors 
+* [Kundana Pillari](https://github.com/kundanapillari)
+* [Shirley Su](https://github.com/shirleysu8)
+* [Jennifer Wang](https://github.com/jennifererwangg)

model-zoo-api/dict/generate_model_versions.py

@@ -0,0 +1,39 @@
+from pathlib import Path
+import re
+from collections import defaultdict
+
+
+# identify list of onnx models in model Zoo
+model_list = []
+
+# generate default dictionary
+dict = defaultdict(list)
+
+for path in Path('text').rglob('*.onnx'):
+    model_list.append(str(path))
+    # obtains model name
+    pattern1 = re.compile(".*/([^/]+)/model")
+    m1 = pattern1.match(str(path))
+    if m1 != None:
+        model_name = m1.group(1)
+        # obtains file name
+        pattern2 = re.compile(".*/([^/]+\\.*).onnx")
+        m2 = pattern2.match(str(path))
+        file_name = m2.group(1)
+        dict[model_name].append(file_name)
+
+for path in Path('vision').rglob('*.onnx'):
+    model_list.append(str(path))
+    # obtains model name
+    pattern1 = re.compile(".*/([^/]+)/model")
+    m1 = pattern1.match(str(path))
+    if m1 != None:
+        model_name = m1.group(1)
+        # obtains file name
+        pattern2 = re.compile(".*/([^/]+\\.*).onnx")
+        m2 = pattern2.match(str(path))
+        file_name = m2.group(1)
+        dict[model_name].append(file_name)
+
+
+print(dict)

model-zoo-api/dict/generate_models.py

@@ -0,0 +1,31 @@
+from pathlib import Path
+import re
+
+model_list = []
+dict = {}   # ex: "vgg" : "https://github.com/onnx/models/blob/master/vision/classification/vgg/model/vgg16-7.onnx?raw=true"
+folder_names = ["text", "vision"]            
+
+def generate_paths(folder_names):
+    """
+    Generates a list of onnx models and
+    stores them in `dict` and `model_list`
+    """
+    for path in Path(folder_names).rglob('*.onnx'):
+        model_list.append(str(path))
+        if path != None:
+            # Generate onnx model URL
+            url_paths = 'https://github.com/onnx/models/blob/master/' + str(path)  + '?raw=true'
+            # Obtain model name
+            pattern = re.compile(".*/([^/]+\\.*).onnx")
+            m = pattern.match(url_paths)
+            if m != None:
+                file_name = m.group(1)
+                dict[file_name] = url_paths
+
+# generate paths to text or vision onnx models
+for model_type in folder_names:
+    generate_paths(model_type)
+
+# print out the keys and values from dict 
+for keys,values in dict.items():
+    print (f'"{keys}"' + " : " + f'"{values}"' + " ,")

model-zoo-api/dict/model_versions.py

@@ -0,0 +1 @@
+versionDict = {'bert-squad': ['bertsquad-10', 'bertsquad-8'], 'bidirectional_attention_flow': ['bidaf-9'], 'gpt-2': ['gpt2-10', 'gpt2-lm-head-10'], 'arcface': ['arcfaceresnet100-8'], 'emotion_ferplus': ['emotion-ferplus-2', 'emotion-ferplus-7', 'emotion-ferplus-8'], 'alexnet': ['bvlcalexnet-3', 'bvlcalexnet-6', 'bvlcalexnet-7', 'bvlcalexnet-8', 'bvlcalexnet-9'], 'caffenet': ['caffenet-3', 'caffenet-6', 'caffenet-7', 'caffenet-8', 'caffenet-9'], 'densenet-121': ['densenet-3', 'densenet-6', 'densenet-7', 'densenet-8', 'densenet-9'], 'efficientnet-lite4': ['efficientnet-lite4'], 'googlenet': ['googlenet-3', 'googlenet-6', 'googlenet-7', 'googlenet-8', 'googlenet-9'], 'inception_v1': ['inception-v1-3', 'inception-v1-6', 'inception-v1-7', 'inception-v1-8', 'inception-v1-9'], 'inception_v2': ['inception-v2-3', 'inception-v2-6', 'inception-v2-7', 'inception-v2-8', 'inception-v2-9'], 'mnist': ['mnist-1', 'mnist-7', 'mnist-8'], 'mobilenet': ['mobilenetv2-7'], 'rcnn_ilsvrc13': ['rcnn-ilsvrc13-3', 'rcnn-ilsvrc13-6', 'rcnn-ilsvrc13-7', 'rcnn-ilsvrc13-8', 'rcnn-ilsvrc13-9'], 'resnet': ['resnet101-v1-7', 'resnet101-v2-7', 'resnet152-v1-7', 'resnet152-v2-7', 'resnet18-v1-7', 'resnet18-v2-7', 'resnet34-v1-7', 'resnet34-v2-7', 'resnet50-caffe2-v1-3', 'resnet50-caffe2-v1-6', 'resnet50-caffe2-v1-7', 'resnet50-caffe2-v1-8', 'resnet50-caffe2-v1-9', 'resnet50-v1-7', 'resnet50-v2-7'], 'shufflenet': ['shufflenet-3', 'shufflenet-6', 'shufflenet-7', 'shufflenet-8', 'shufflenet-9', 'shufflenet-v2-10'], 'squeezenet': ['squeezenet1.0-3', 'squeezenet1.0-6', 'squeezenet1.0-7', 'squeezenet1.0-8', 'squeezenet1.0-9', 'squeezenet1.1-7'], 'vgg': ['vgg16-7', 'vgg16-bn-7', 'vgg19-7', 'vgg19-bn-7', 'vgg19-caffe2-3', 'vgg19-caffe2-6', 'vgg19-caffe2-7', 'vgg19-caffe2-8', 'vgg19-caffe2-9'], 'zfnet-512': ['zfnet512-3', 'zfnet512-6', 'zfnet512-7', 'zfnet512-8', 'zfnet512-9'], 'duc': ['ResNet101-DUC-7'], 'faster-rcnn': ['FasterRCNN-10'], 'mask-rcnn': ['MaskRCNN-10'], 'retinanet': ['retinanet-9'], 'ssd': ['ssd-10'], 'ssd-mobilenetv1': ['ssd_mobilenet_v1_10'], 'tiny-yolov2': ['tinyyolov2-1', 'tinyyolov2-7', 'tinyyolov2-8'], 'tiny-yolov3': ['tiny-yolov3-11'], 'yolov2': ['yolov2-voc-8'], 'yolov2-coco': ['yolov2-coco-9'], 'yolov3': ['yolov3-10'], 'yolov4': ['yolov4'], 'fast_neural_style': ['candy-8', 'candy-9', 'mosaic-8', 'mosaic-9', 'pointilism-8', 'pointilism-9', 'rain-princess-8', 'rain-princess-9', 'udnie-8', 'udnie-9'], 'sub_pixel_cnn_2016': ['super-resolution-10'], 'roberta': ['roberta-base-11', 'roberta-sequence-classification-9']}