♻️ [Refactor] Code of examples, use PostProccess
Browse files- examples/notebook_inference.ipynb +31 -11
- examples/notebook_smallobject.ipynb +11 -11
examples/notebook_inference.ipynb
CHANGED
@@ -6,15 +6,18 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"from hydra import compose, initialize\n",
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"from PIL import Image \n",
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"\n",
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"from yolo import
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{
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@@ -25,7 +28,7 @@
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"source": [
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"CONFIG_PATH = \"../yolo/config\"\n",
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"CONFIG_NAME = \"config\"\n",
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"MODEL = \"
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"\n",
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"DEVICE = 'cuda:0'\n",
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"CLASS_NUM = 80\n",
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@@ -45,7 +48,9 @@
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" cfg: Config = compose(config_name=CONFIG_NAME, overrides=[\"task=inference\", f\"task.data.source={IMAGE_PATH}\", f\"model={MODEL}\"])\n",
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" model = create_model(cfg.model, class_num=CLASS_NUM).to(device)\n",
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" transform = AugmentationComposer([], cfg.image_size)\n",
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},
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{
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@@ -57,7 +62,7 @@
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"pil_image = Image.open(IMAGE_PATH)\n",
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"image, bbox, rev_tensor = transform(pil_image)\n",
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"image = image.to(device)[None]\n",
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-
"rev_tensor = rev_tensor.to(device)"
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]
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},
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{
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@@ -68,10 +73,8 @@
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"source": [
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"with torch.no_grad():\n",
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" predict = model(image)\n",
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"
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"\n",
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"pred_bbox = (pred_bbox / rev_tensor[0] - rev_tensor[None, None, 1:]) \n",
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"pred_bbox = bbox_nms(pred_class, pred_bbox, cfg.task.nms)\n",
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"draw_bboxes(pil_image, pred_bbox, idx2label=cfg.class_list)"
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]
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},
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@@ -83,6 +86,23 @@
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"\n",
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""
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]
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}
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],
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"metadata": {
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"from pathlib import Path\n",
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"\n",
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"import torch\n",
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"from hydra import compose, initialize\n",
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"from PIL import Image \n",
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"\n",
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"project_root = Path().resolve().parent\n",
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"sys.path.append(str(project_root))\n",
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"\n",
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"from yolo import AugmentationComposer, Config, create_model, custom_logger, draw_bboxes, Vec2Box, PostProccess\n",
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"from yolo.utils.bounding_box_utils import Anc2Box"
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]
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},
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{
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"source": [
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"CONFIG_PATH = \"../yolo/config\"\n",
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"CONFIG_NAME = \"config\"\n",
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"MODEL = \"v7-base\"\n",
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"\n",
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"DEVICE = 'cuda:0'\n",
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"CLASS_NUM = 80\n",
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" cfg: Config = compose(config_name=CONFIG_NAME, overrides=[\"task=inference\", f\"task.data.source={IMAGE_PATH}\", f\"model={MODEL}\"])\n",
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" model = create_model(cfg.model, class_num=CLASS_NUM).to(device)\n",
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" transform = AugmentationComposer([], cfg.image_size)\n",
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" converter = Anc2Box(model, cfg.model.anchor, cfg.image_size, device)\n",
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" # converter = Vec2Box(model, cfg.model.anchor, cfg.image_size, device)\n",
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" post_proccess = PostProccess(converter, cfg.task.nms)"
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]
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},
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{
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"pil_image = Image.open(IMAGE_PATH)\n",
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"image, bbox, rev_tensor = transform(pil_image)\n",
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"image = image.to(device)[None]\n",
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"rev_tensor = rev_tensor.to(device)[None]"
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]
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},
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{
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"source": [
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"with torch.no_grad():\n",
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" predict = model(image)\n",
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" pred_bbox = post_proccess(predict, rev_tensor)\n",
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"\n",
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"draw_bboxes(pil_image, pred_bbox, idx2label=cfg.class_list)"
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]
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},
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"\n",
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""
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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examples/notebook_smallobject.ipynb
CHANGED
@@ -22,7 +22,6 @@
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"import torch\n",
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"from hydra import compose, initialize\n",
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"from PIL import Image \n",
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-
"from einops import rearrange\n",
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"\n",
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"# Ensure that the necessary repository is cloned and installed. You may need to run: \n",
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"# git clone [email protected]:WongKinYiu/YOLO.git\n",
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@@ -30,8 +29,8 @@
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"# pip install .\n",
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"project_root = Path().resolve().parent\n",
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"sys.path.append(str(project_root))\n",
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-
"
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"from yolo import AugmentationComposer, bbox_nms, Config, create_model, custom_logger, draw_bboxes, Vec2Box"
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]
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},
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{
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@@ -63,7 +62,9 @@
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" cfg: Config = compose(config_name=CONFIG_NAME, overrides=[\"task=inference\", f\"task.data.source={IMAGE_PATH}\", f\"model={MODEL}\"])\n",
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" model = create_model(cfg.model, class_num=CLASS_NUM).to(device)\n",
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" transform = AugmentationComposer([], cfg.image_size)\n",
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" vec2box = Vec2Box(model, cfg.image_size, device)"
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]
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},
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{
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@@ -75,7 +76,7 @@
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"pil_image = Image.open(IMAGE_PATH)\n",
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"image, bbox, rev_tensor = transform(pil_image)\n",
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"image = image.to(device)[None]\n",
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"rev_tensor = rev_tensor.to(device)"
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]
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},
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{
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@@ -114,7 +115,9 @@
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" pred_class, _, pred_bbox = vec2box(predict[\"Main\"])\n",
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"pred_bbox[1:] = (pred_bbox[1: ] + total_shift[:, None]) / SLIDE\n",
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"pred_bbox = pred_bbox.view(1, -1, 4)\n",
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"pred_class = pred_class.view(1, -1, 80)"
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]
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},
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{
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@@ -123,7 +126,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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-
"
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]
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},
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{
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@@ -131,10 +134,7 @@
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"predict_box = bbox_nms(pred_class, pred_bbox, NMSConfig(0.5, 0.5))\n",
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"draw_bboxes(pil_image, predict_box, idx2label=cfg.class_list)"
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]
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}
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],
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"metadata": {
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"import torch\n",
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"from hydra import compose, initialize\n",
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"from PIL import Image \n",
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"\n",
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"# Ensure that the necessary repository is cloned and installed. You may need to run: \n",
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"# git clone [email protected]:WongKinYiu/YOLO.git\n",
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"# pip install .\n",
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"project_root = Path().resolve().parent\n",
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"sys.path.append(str(project_root))\n",
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"\n",
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"from yolo import AugmentationComposer, bbox_nms, Config, create_model, custom_logger, draw_bboxes, Vec2Box, NMSConfig, PostProccess"
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]
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},
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{
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" cfg: Config = compose(config_name=CONFIG_NAME, overrides=[\"task=inference\", f\"task.data.source={IMAGE_PATH}\", f\"model={MODEL}\"])\n",
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" model = create_model(cfg.model, class_num=CLASS_NUM).to(device)\n",
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" transform = AugmentationComposer([], cfg.image_size)\n",
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" vec2box = Vec2Box(model, cfg.image_size, device)\n",
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" post_proccess = PostProccess(vec2box, NMSConfig(0.5, 0.9))\n",
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" "
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]
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},
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{
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"pil_image = Image.open(IMAGE_PATH)\n",
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"image, bbox, rev_tensor = transform(pil_image)\n",
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"image = image.to(device)[None]\n",
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"rev_tensor = rev_tensor.to(device)[None]"
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]
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},
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{
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" pred_class, _, pred_bbox = vec2box(predict[\"Main\"])\n",
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"pred_bbox[1:] = (pred_bbox[1: ] + total_shift[:, None]) / SLIDE\n",
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"pred_bbox = pred_bbox.view(1, -1, 4)\n",
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"pred_class = pred_class.view(1, -1, 80)\n",
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"pred_bbox = (pred_bbox - rev_tensor[:, None, 1:]) / rev_tensor[:, 0:1, None]\n",
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"predict_box = bbox_nms(pred_class, pred_bbox, NMSConfig(0.3, 0.5))\n"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"draw_bboxes(pil_image, predict_box, idx2label=cfg.class_list)"
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]
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},
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{
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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