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c376f3c
1
Parent(s):
a63e231
Autoload parameters
Browse files- InferenceConfig.py +13 -1
- gradio_scripts/upload_ui.py +14 -12
InferenceConfig.py
CHANGED
@@ -5,6 +5,11 @@ class TrackerType(Enum):
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CONF_BOOST = 1
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BYTETRACK = 2
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### Configuration options
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WEIGHTS = 'models/v5m_896_300best.pt'
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# will need to configure these based on GPU hardware
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@@ -16,7 +21,7 @@ MAX_AGE = 20 # time until missing fish get's new id
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MIN_HITS = 11 # minimum number of frames with a specific fish for it to count
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MIN_LENGTH = 0.3 # minimum fish length, in meters
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IOU_THRES = 0.01 # IOU threshold for tracking
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MIN_TRAVEL =
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DEFAULT_TRACKER = TrackerType.BYTETRACK
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class InferenceConfig:
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@@ -50,6 +55,13 @@ class InferenceConfig:
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self.byte_low_conf = low
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self.byte_high_conf = high
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def to_dict(self):
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dict = {
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'weights': self.weights,
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CONF_BOOST = 1
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BYTETRACK = 2
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def toString(val):
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if val == TrackerType.NONE: return "None"
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if val == TrackerType.CONF_BOOST: return "Confidence Boost"
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if val == TrackerType.BYTETRACK: return "ByteTrack"
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### Configuration options
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WEIGHTS = 'models/v5m_896_300best.pt'
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# will need to configure these based on GPU hardware
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MIN_HITS = 11 # minimum number of frames with a specific fish for it to count
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MIN_LENGTH = 0.3 # minimum fish length, in meters
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IOU_THRES = 0.01 # IOU threshold for tracking
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MIN_TRAVEL = 0 # Minimum distance a track has to travel
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DEFAULT_TRACKER = TrackerType.BYTETRACK
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class InferenceConfig:
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self.byte_low_conf = low
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self.byte_high_conf = high
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def find_model(self, model_list):
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for model_name, model_path in enumerate(model_list):
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if model_path == self.weights:
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return model_name
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return None
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def to_dict(self):
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dict = {
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'weights': self.weights,
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gradio_scripts/upload_ui.py
CHANGED
@@ -1,5 +1,6 @@
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import gradio as gr
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from gradio_scripts.file_reader import File
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models = {
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@@ -17,35 +18,36 @@ def Upload_Gradio(gradio_components):
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gr.HTML("<p align='center' style='font-size: large;font-style: italic;'>Submit an .aris file to analyze result.</p>")
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settings = []
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with gr.Accordion("Advanced Settings", open=False):
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settings.append(gr.Dropdown(label="Model", value=
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gr.Markdown("Detection Parameters")
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with gr.Row():
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settings.append(gr.Slider(0, 1, value=
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settings.append(gr.Slider(0, 1, value=
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gr.Markdown("Tracking Parameters")
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with gr.Row():
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settings.append(gr.Slider(0, 100, value=
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settings.append(gr.Slider(0, 100, value=
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tracker = gr.Dropdown(["None", "Confidence Boost", "ByteTrack"], label="Associative Tracking"
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settings.append(tracker)
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with gr.Row(visible=False) as track_row:
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settings.append(gr.Slider(0, 5, value=
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settings.append(gr.Slider(0, 1, value=
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tracker.change(lambda x: gr.update(visible=(x=="Confidence Boost")), tracker, track_row)
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with gr.Row(visible=False) as track_row:
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settings.append(gr.Slider(0, 1, value=
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settings.append(gr.Slider(0, 1, value=
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tracker.change(lambda x: gr.update(visible=(x=="ByteTrack")), tracker, track_row)
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gr.Markdown("Other")
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with gr.Row():
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settings.append(gr.Slider(0, 3, value=
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settings.append(gr.Slider(0, 5, value=
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gradio_components['hyperparams'] = settings
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import gradio as gr
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from gradio_scripts.file_reader import File
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from InferenceConfig import InferenceConfig, TrackerType
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models = {
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gr.HTML("<p align='center' style='font-size: large;font-style: italic;'>Submit an .aris file to analyze result.</p>")
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default_settings = InferenceConfig()
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settings = []
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with gr.Accordion("Advanced Settings", open=False):
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settings.append(gr.Dropdown(label="Model", value=default_settings.find_model(models), choices=list(models.keys())))
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gr.Markdown("Detection Parameters")
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with gr.Row():
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settings.append(gr.Slider(0, 1, value=default_settings.conf_thresh, label="Confidence Threshold", info="Confidence cutoff for detection boxes"))
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settings.append(gr.Slider(0, 1, value=default_settings.nms_iou, label="NMS IoU", info="IoU threshold for non-max suppression"))
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gr.Markdown("Tracking Parameters")
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with gr.Row():
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settings.append(gr.Slider(0, 100, value=default_settings.min_hits, label="Min Hits", info="Minimum number of frames a fish has to appear in to count"))
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settings.append(gr.Slider(0, 100, value=default_settings.max_age, label="Max Age", info="Max age of occlusion before track is split"))
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tracker = gr.Dropdown(["None", "Confidence Boost", "ByteTrack"], value=TrackerType.toString(default_settings.associative_tracker), label="Associative Tracking")
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settings.append(tracker)
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with gr.Row(visible=False) as track_row:
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settings.append(gr.Slider(0, 5, value=default_settings.boost_power, label="Boost Power", info=""))
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settings.append(gr.Slider(0, 1, value=default_settings.boost_decay, label="Boost Decay", info=""))
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tracker.change(lambda x: gr.update(visible=(x=="Confidence Boost")), tracker, track_row)
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with gr.Row(visible=False) as track_row:
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settings.append(gr.Slider(0, 1, value=default_settings.byte_low_conf, label="Low Conf Threshold", info=""))
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settings.append(gr.Slider(0, 1, value=default_settings.byte_high_conf, label="High Conf Threshold", info=""))
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tracker.change(lambda x: gr.update(visible=(x=="ByteTrack")), tracker, track_row)
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gr.Markdown("Other")
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with gr.Row():
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settings.append(gr.Slider(0, 3, value=default_settings.min_length, label="Min Length", info="Minimum length of fish (meters) in order for it to count"))
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settings.append(gr.Slider(0, 5, value=default_settings.min_travel, label="Min Travel", info="Minimum travel distance of track (meters) in order for it to count"))
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gradio_components['hyperparams'] = settings
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