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Build error
Matteo Sirri
commited on
Commit
·
cf40191
1
Parent(s):
3e01e59
fix: fix typo
Browse files
app.py
CHANGED
@@ -8,6 +8,8 @@ from torchvision.models.detection.faster_rcnn import fasterrcnn_resnet50_fpn
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from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
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from src.detection.graph_utils import add_bbox
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from src.detection.vision import presets
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logging.getLogger('PIL').setLevel(logging.CRITICAL)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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@@ -36,8 +38,9 @@ def frcnn_motsynth(image):
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image_tensor = image_tensor.to(device)
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prediction = model([image_tensor])[0]
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image_w_bbox = add_bbox(image_tensor, prediction, 0.80)
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-
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def frcnn_coco(image):
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@@ -47,8 +50,9 @@ def frcnn_coco(image):
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image_tensor = image_tensor.to(device)
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prediction = model([image_tensor])[0]
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image_w_bbox = add_bbox(image_tensor, prediction, 0.80)
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title = "Domain shift adaption on pedestrian detection with Faster R-CNN"
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@@ -57,10 +61,10 @@ examples = ["001.jpg", "002.jpg", "003.jpg",
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"004.jpg", "005.jpg", "006.jpg", "007.jpg", ]
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io_baseline = gr.Interface(frcnn_coco, gr.Image(type="pil"), gr.Image(
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type="
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io_custom = gr.Interface(frcnn_motsynth,
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type="
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gr.Parallel(io_baseline, io_custom, title=title,
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description=description, examples=examples).launch(enable_queue=True)
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from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
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from src.detection.graph_utils import add_bbox
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from src.detection.vision import presets
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import torchvision.transforms as T
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logging.getLogger('PIL').setLevel(logging.CRITICAL)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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image_tensor = image_tensor.to(device)
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prediction = model([image_tensor])[0]
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image_w_bbox = add_bbox(image_tensor, prediction, 0.80)
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transform = T.ToPILImage()
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output = transform(image_w_bbox)
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return output
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def frcnn_coco(image):
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image_tensor = image_tensor.to(device)
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prediction = model([image_tensor])[0]
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image_w_bbox = add_bbox(image_tensor, prediction, 0.80)
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transform = T.ToPILImage()
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output = transform(image_w_bbox)
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return output
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title = "Domain shift adaption on pedestrian detection with Faster R-CNN"
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"004.jpg", "005.jpg", "006.jpg", "007.jpg", ]
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io_baseline = gr.Interface(frcnn_coco, gr.Image(type="pil"), gr.Image(
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type="pil", label="Baseline Model trained on COCO + FT on MOT17"))
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io_custom = gr.Interface(frcnn_motsynth, inputs=examples, outputs=gr.Image(
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type="pil", label="Faster R-CNN trained on MOTSynth + FT on MOT17"))
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gr.Parallel(io_baseline, io_custom, title=title,
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description=description, examples=examples).launch(enable_queue=True)
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