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app.py

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+import gradio as gr
+import torch
+from torchvision import models, transforms
+
+# -- get torch and cuda version
+TORCH_VERSION = ".".join(torch.__version__.split(".")[:2])
+CUDA_VERSION = torch.__version__.split("+")[-1]
+'''
+# -- install pre-build detectron2
+!pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/{CUDA_VERSION}/{TORCH_VERSION}/index.html
+
+import detectron2
+from detectron2.utils.logger import setup_logger # ????
+
+from detectron2 import model_zoo
+from detectron2.engine import DefaultPredictor
+from detectron2.config import get_cfg
+
+# ????
+setup_logger()
+
+# -- load rcnn model
+cfg = get_cfg()
+# add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library
+cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
+cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5  # set threshold for this model
+# Find a model from detectron2's model zoo. You can use the https://dl.fbaipublicfiles... url as well
+cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
+predictor = DefaultPredictor(cfg)
+
+!wget http://images.cocodataset.org/val2017/000000439715.jpg -q -O input.jpg
+im = cv2.imread("./input.jpg")
+cv2_imshow(im)
+
+outputs = predictor(im)
+
+print(outputs["instances"].pred_classes)
+print(outputs["instances"].pred_boxes)
+'''
+# -- load Mask R-CNN model for segmentation
+DesignModernityModel = torch.load("DesignModernityModel.pt")
+
+#INPUT_FEATURES = DesignModernityModel.fc.in_features
+#linear = nn.linear(INPUT_FEATURES, 5)
+
+DesignModernityModel.eval() # set state of the model to inference
+
+LABELS = ['2000-2004', '2006-2008', '2009-2011', '2012-2015', '2016-2018']
+
+carTransforms = transforms.Compose([transforms.Resize(224)])
+
+def classifyCar(im):
+  im = Image.fromarray(im.astype('uint8'), 'RGB')
+  im = carTransforms(im).unsqueeze(0)  # transform and add batch dimension
+  with torch.no_grad():
+    scores = torch.nn.functional.softmax(model(im)[0])
+  return {LABELS[i]: float(scores[i]) for i in range(2)}
+
+examples = [[example_img.jpg], [example_img2.jpg]]  # must be uploaded in repo
+
+# create interface for model
+interface = gr.Interface(classifyCar, inputs='Image', outputs='label', cache_examples=False, title='VW Up or Fiat 500', example=examples)
+interface.launch()
+
+