Spaces:
Runtime error
Runtime error
retrrain on 30090
Browse files- .github/workflows/main.yml +21 -0
- README.md +37 -0
- app.py +73 -0
- gradioapp.ipynb +262 -0
- requirements.txt +9 -0
- train.ipynb +0 -0
.github/workflows/main.yml
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name: Sync to Hugging Face hub
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on:
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push:
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branches: [main]
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# to run this workflow manually from the Actions tab
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workflow_dispatch:
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jobs:
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sync-to-hub:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v2
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with:
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fetch-depth: 0
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- name: Push to hub
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env:
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HF_TOKEN: ${{ secrets.HF_TOKEN }}
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run: git push --force https://dnth:$HF_TOKEN@huggingface.co/spaces/dnth/webdemo-fridge-detection main
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README.md
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---
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title: webdemo-fridge-detection
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emoji: 🍿
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colorFrom: red
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colorTo: purple
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sdk: gradio
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app_file: app.py
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pinned: false
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---
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# Configuration
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`title`: _string_
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Display title for the Space
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`emoji`: _string_
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Space emoji (emoji-only character allowed)
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`colorFrom`: _string_
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Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
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`colorTo`: _string_
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Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
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`sdk`: _string_
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Can be either `gradio` or `streamlit`
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`sdk_version` : _string_
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Only applicable for `streamlit` SDK.
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See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
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`app_file`: _string_
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Path to your main application file (which contains either `gradio` or `streamlit` Python code).
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Path is relative to the root of the repository.
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`pinned`: _boolean_
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Whether the Space stays on top of your list.
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app.py
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from icevision.all import *
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import icedata
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import PIL, requests
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import torch
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from torchvision import transforms
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import gradio as gr
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# Download the dataset
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url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip"
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dest_dir = "fridge"
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data_dir = icedata.load_data(url, dest_dir)
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# Create the parser
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parser = parsers.VOCBBoxParser(annotations_dir=data_dir / "odFridgeObjects/annotations", images_dir=data_dir / "odFridgeObjects/images")
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# Parse annotations to create records
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train_records, valid_records = parser.parse()
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class_map = parser.class_map
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extra_args = {}
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model_type = models.torchvision.retinanet
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backbone = model_type.backbones.resnet50_fpn
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# Instantiate the model
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model = model_type.model(backbone=backbone(pretrained=True), num_classes=len(parser.class_map), **extra_args)
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# Transforms
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# size is set to 384 because EfficientDet requires its inputs to be divisible by 128
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image_size = 384
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train_tfms = tfms.A.Adapter([*tfms.A.aug_tfms(size=image_size, presize=512), tfms.A.Normalize()])
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valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(image_size), tfms.A.Normalize()])
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# Datasets
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train_ds = Dataset(train_records, train_tfms)
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valid_ds = Dataset(valid_records, valid_tfms)
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# Data Loaders
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train_dl = model_type.train_dl(train_ds, batch_size=8, num_workers=4, shuffle=True)
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valid_dl = model_type.valid_dl(valid_ds, batch_size=8, num_workers=4, shuffle=False)
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metrics = [COCOMetric(metric_type=COCOMetricType.bbox)]
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learn = model_type.fastai.learner(dls=[train_dl, valid_dl], model=model, metrics=metrics)
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learn = learn.load('model')
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def show_preds(input_image, display_label, display_bbox, detection_threshold):
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if detection_threshold==0: detection_threshold=0.5
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img = PIL.Image.fromarray(input_image, 'RGB')
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pred_dict = model_type.end2end_detect(img, valid_tfms, model, class_map=class_map, detection_threshold=detection_threshold,
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display_label=display_label, display_bbox=display_bbox, return_img=True,
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font_size=16, label_color="#FF59D6")
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return pred_dict['img']
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# display_chkbox = gr.inputs.CheckboxGroup(["Label", "BBox"], label="Display", default=True)
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display_chkbox_label = gr.inputs.Checkbox(label="Label", default=True)
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display_chkbox_box = gr.inputs.Checkbox(label="Box", default=True)
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detection_threshold_slider = gr.inputs.Slider(minimum=0, maximum=1, step=0.1, default=0.5, label="Detection Threshold")
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outputs = gr.outputs.Image(type="pil")
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# Option 1: Get an image from local drive
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gr_interface = gr.Interface(fn=show_preds, inputs=["image", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - Fridge Object')
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# # Option 2: Grab an image from a webcam
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# gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=False)
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# # Option 3: Continuous image stream from the webcam
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# gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=True)
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gr_interface.launch(inline=False, share=True, debug=True)
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gradioapp.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "ee7e0c23-3fa5-4547-8598-7df27a3876c5",
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"metadata": {},
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"outputs": [],
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"source": [
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"from icevision.all import *\n",
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"import icedata\n",
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"import PIL, requests\n",
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"import torch\n",
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"from torchvision import transforms\n",
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"import gradio as gr"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "646cc218-f7de-4f32-a3d9-fccdc9b54592",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Download the dataset\n",
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"url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip\"\n",
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"dest_dir = \"fridge\"\n",
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"data_dir = icedata.load_data(url, dest_dir)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "96184ca0-0b0a-4a20-8ab9-30dee6096588",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"# Create the parser\n",
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"parser = parsers.VOCBBoxParser(annotations_dir=data_dir / \"odFridgeObjects/annotations\", images_dir=data_dir / \"odFridgeObjects/images\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "dfa20f76-4970-479a-9497-871fe4cfd170",
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"metadata": {},
|
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"outputs": [
|
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{
|
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"data": {
|
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"application/vnd.jupyter.widget-view+json": {
|
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"model_id": "fc8e676815314038a40c884c8c7f5b67",
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"version_major": 2,
|
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/128 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
|
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"output_type": "display_data"
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},
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
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"\u001b[1m\u001b[1mINFO \u001b[0m\u001b[1m\u001b[0m - \u001b[1m\u001b[34m\u001b[1mAutofixing records\u001b[0m\u001b[1m\u001b[34m\u001b[0m\u001b[1m\u001b[0m | \u001b[36micevision.parsers.parser\u001b[0m:\u001b[36mparse\u001b[0m:\u001b[36m122\u001b[0m\n"
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]
|
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+
},
|
69 |
+
{
|
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+
"data": {
|
71 |
+
"application/vnd.jupyter.widget-view+json": {
|
72 |
+
"model_id": "3db4bc1ae388495eb3b62289459a5c00",
|
73 |
+
"version_major": 2,
|
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/128 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
|
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{
|
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"data": {
|
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"text/plain": [
|
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"<ClassMap: {'background': 0, 'carton': 1, 'milk_bottle': 2, 'can': 3, 'water_bottle': 4}>"
|
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]
|
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+
},
|
89 |
+
"execution_count": 4,
|
90 |
+
"metadata": {},
|
91 |
+
"output_type": "execute_result"
|
92 |
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}
|
93 |
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],
|
94 |
+
"source": [
|
95 |
+
"# Parse annotations to create records\n",
|
96 |
+
"train_records, valid_records = parser.parse()\n",
|
97 |
+
"parser.class_map"
|
98 |
+
]
|
99 |
+
},
|
100 |
+
{
|
101 |
+
"cell_type": "code",
|
102 |
+
"execution_count": 5,
|
103 |
+
"id": "26d4f2f7-db51-413c-838f-f80c5898ab52",
|
104 |
+
"metadata": {},
|
105 |
+
"outputs": [],
|
106 |
+
"source": [
|
107 |
+
"class_map = parser.class_map"
|
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]
|
109 |
+
},
|
110 |
+
{
|
111 |
+
"cell_type": "code",
|
112 |
+
"execution_count": 6,
|
113 |
+
"id": "007b2e97-d546-4178-84e7-d4fe597f3731",
|
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"metadata": {},
|
115 |
+
"outputs": [],
|
116 |
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"source": [
|
117 |
+
"extra_args = {}\n",
|
118 |
+
"model_type = models.torchvision.retinanet\n",
|
119 |
+
"backbone = model_type.backbones.resnet50_fpn\n",
|
120 |
+
"# Instantiate the model\n",
|
121 |
+
"model = model_type.model(backbone=backbone(pretrained=True), num_classes=len(parser.class_map), **extra_args) "
|
122 |
+
]
|
123 |
+
},
|
124 |
+
{
|
125 |
+
"cell_type": "code",
|
126 |
+
"execution_count": 7,
|
127 |
+
"id": "7b664cbf-3ab0-46df-a9d0-c4eb5c3c026d",
|
128 |
+
"metadata": {},
|
129 |
+
"outputs": [],
|
130 |
+
"source": [
|
131 |
+
"# Transforms\n",
|
132 |
+
"# size is set to 384 because EfficientDet requires its inputs to be divisible by 128\n",
|
133 |
+
"image_size = 384\n",
|
134 |
+
"train_tfms = tfms.A.Adapter([*tfms.A.aug_tfms(size=image_size, presize=512), tfms.A.Normalize()])\n",
|
135 |
+
"valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(image_size), tfms.A.Normalize()])\n",
|
136 |
+
"# Datasets\n",
|
137 |
+
"train_ds = Dataset(train_records, train_tfms)\n",
|
138 |
+
"valid_ds = Dataset(valid_records, valid_tfms)\n",
|
139 |
+
"# Data Loaders\n",
|
140 |
+
"train_dl = model_type.train_dl(train_ds, batch_size=8, num_workers=4, shuffle=True)\n",
|
141 |
+
"valid_dl = model_type.valid_dl(valid_ds, batch_size=8, num_workers=4, shuffle=False)\n",
|
142 |
+
"metrics = [COCOMetric(metric_type=COCOMetricType.bbox)]\n",
|
143 |
+
"learn = model_type.fastai.learner(dls=[train_dl, valid_dl], model=model, metrics=metrics)"
|
144 |
+
]
|
145 |
+
},
|
146 |
+
{
|
147 |
+
"cell_type": "code",
|
148 |
+
"execution_count": 8,
|
149 |
+
"id": "7bddb248-215d-4998-9d90-14ea6989c236",
|
150 |
+
"metadata": {},
|
151 |
+
"outputs": [
|
152 |
+
{
|
153 |
+
"name": "stderr",
|
154 |
+
"output_type": "stream",
|
155 |
+
"text": [
|
156 |
+
"/home/dnth/anaconda3/envs/icevision-gradio/lib/python3.8/site-packages/fastai/learner.py:56: UserWarning: Saved filed doesn't contain an optimizer state.\n",
|
157 |
+
" elif with_opt: warn(\"Saved filed doesn't contain an optimizer state.\")\n"
|
158 |
+
]
|
159 |
+
}
|
160 |
+
],
|
161 |
+
"source": [
|
162 |
+
"learn = learn.load('model')"
|
163 |
+
]
|
164 |
+
},
|
165 |
+
{
|
166 |
+
"cell_type": "code",
|
167 |
+
"execution_count": 9,
|
168 |
+
"id": "745315f6-8aa5-486e-a7bc-e11348bec6a6",
|
169 |
+
"metadata": {},
|
170 |
+
"outputs": [],
|
171 |
+
"source": [
|
172 |
+
"def show_preds(input_image, display_label, display_bbox, detection_threshold):\n",
|
173 |
+
"\n",
|
174 |
+
" if detection_threshold==0: detection_threshold=0.5\n",
|
175 |
+
"\n",
|
176 |
+
" img = PIL.Image.fromarray(input_image, 'RGB')\n",
|
177 |
+
"\n",
|
178 |
+
" pred_dict = model_type.end2end_detect(img, valid_tfms, model, class_map=class_map, detection_threshold=detection_threshold,\n",
|
179 |
+
" display_label=display_label, display_bbox=display_bbox, return_img=True, \n",
|
180 |
+
" font_size=16, label_color=\"#FF59D6\")\n",
|
181 |
+
"\n",
|
182 |
+
" return pred_dict['img']"
|
183 |
+
]
|
184 |
+
},
|
185 |
+
{
|
186 |
+
"cell_type": "code",
|
187 |
+
"execution_count": null,
|
188 |
+
"id": "63ac7fab-2068-4dbc-a464-0551b6fc12b2",
|
189 |
+
"metadata": {},
|
190 |
+
"outputs": [
|
191 |
+
{
|
192 |
+
"name": "stdout",
|
193 |
+
"output_type": "stream",
|
194 |
+
"text": [
|
195 |
+
"Running on local URL: http://127.0.0.1:7860/\n",
|
196 |
+
"Running on public URL: https://11839.gradio.app\n",
|
197 |
+
"\n",
|
198 |
+
"This share link will expire in 72 hours. To get longer links, send an email to: support@gradio.app\n"
|
199 |
+
]
|
200 |
+
},
|
201 |
+
{
|
202 |
+
"name": "stderr",
|
203 |
+
"output_type": "stream",
|
204 |
+
"text": [
|
205 |
+
"/home/dnth/anaconda3/envs/icevision-gradio/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)\n",
|
206 |
+
" return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n"
|
207 |
+
]
|
208 |
+
}
|
209 |
+
],
|
210 |
+
"source": [
|
211 |
+
"# display_chkbox = gr.inputs.CheckboxGroup([\"Label\", \"BBox\"], label=\"Display\", default=True)\n",
|
212 |
+
"display_chkbox_label = gr.inputs.Checkbox(label=\"Label\", default=True)\n",
|
213 |
+
"display_chkbox_box = gr.inputs.Checkbox(label=\"Box\", default=True)\n",
|
214 |
+
"\n",
|
215 |
+
"detection_threshold_slider = gr.inputs.Slider(minimum=0, maximum=1, step=0.1, default=0.5, label=\"Detection Threshold\")\n",
|
216 |
+
"\n",
|
217 |
+
"outputs = gr.outputs.Image(type=\"pil\")\n",
|
218 |
+
"\n",
|
219 |
+
"# Option 1: Get an image from local drive\n",
|
220 |
+
"gr_interface = gr.Interface(fn=show_preds, inputs=[\"image\", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - COCO')\n",
|
221 |
+
"\n",
|
222 |
+
"# # Option 2: Grab an image from a webcam\n",
|
223 |
+
"# gr_interface = gr.Interface(fn=show_preds, inputs=[\"webcam\", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=False)\n",
|
224 |
+
"\n",
|
225 |
+
"# # Option 3: Continuous image stream from the webcam\n",
|
226 |
+
"# gr_interface = gr.Interface(fn=show_preds, inputs=[\"webcam\", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=True)\n",
|
227 |
+
"\n",
|
228 |
+
"\n",
|
229 |
+
"gr_interface.launch(inline=False, share=True, debug=True)\n"
|
230 |
+
]
|
231 |
+
},
|
232 |
+
{
|
233 |
+
"cell_type": "code",
|
234 |
+
"execution_count": null,
|
235 |
+
"id": "727a3589-364b-4bfd-9c32-bef5ebe34dbe",
|
236 |
+
"metadata": {},
|
237 |
+
"outputs": [],
|
238 |
+
"source": []
|
239 |
+
}
|
240 |
+
],
|
241 |
+
"metadata": {
|
242 |
+
"kernelspec": {
|
243 |
+
"display_name": "Python 3",
|
244 |
+
"language": "python",
|
245 |
+
"name": "python3"
|
246 |
+
},
|
247 |
+
"language_info": {
|
248 |
+
"codemirror_mode": {
|
249 |
+
"name": "ipython",
|
250 |
+
"version": 3
|
251 |
+
},
|
252 |
+
"file_extension": ".py",
|
253 |
+
"mimetype": "text/x-python",
|
254 |
+
"name": "python",
|
255 |
+
"nbconvert_exporter": "python",
|
256 |
+
"pygments_lexer": "ipython3",
|
257 |
+
"version": "3.8.12"
|
258 |
+
}
|
259 |
+
},
|
260 |
+
"nbformat": 4,
|
261 |
+
"nbformat_minor": 5
|
262 |
+
}
|
requirements.txt
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#icevision[all]
|
2 |
+
|
3 |
+
#opencv-python-headless
|
4 |
+
|
5 |
+
git+https://github.com/dnth/icevision.git
|
6 |
+
icedata
|
7 |
+
|
8 |
+
#fastai
|
9 |
+
#scikit-image
|
train.ipynb
ADDED
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