Duplicate from Atualli/yolox1

.gitattributes

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README.md

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+---
+title: YOLOX is a high-performance anchor-free YOLO.
+emoji: 🌖
+colorFrom: red
+colorTo: red
+sdk: gradio
+sdk_version: 3.15.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: Atualli/yolox1
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import os
+#os.system("pip -qq install yoloxdetect==0.0.7")
+os.system("pip -qq install yoloxdetect")
+import torch
+import json
+
+from yoloxdetect import YoloxDetector
+
+# Images
+torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
+torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
+torch.hub.download_url_to_file('https://raw.githubusercontent.com/Megvii-BaseDetection/YOLOX/main/assets/dog.jpg', 'dog.jpg')
+
+def yolox_inference(
+    image_path: gr.inputs.Image = None,
+    model_path: gr.inputs.Dropdown = 'kadirnar/yolox_s-v0.1.1',
+    config_path: gr.inputs.Textbox = 'configs.yolox_s',
+    image_size: gr.inputs.Slider = 640
+):
+    """
+    YOLOX inference function
+    Args:
+        image: Input image
+        model_path: Path to the model
+        config_path: Path to the config file
+        image_size: Image size
+    Returns:
+        Rendered image
+    """
+
+    model = YoloxDetector(model_path, config_path=config_path, device="cpu", hf_model=True)
+    #pred = model.predict(image_path=image_path, image_size=image_size)
+    model.torchyolo = True
+    pred2 = model.predict(image_path=image_path, image_size=image_size)
+    #text = "Ola"
+    #print (vars(model))
+    #print (pred2[0])
+    #print (pred2[1])
+    #print (pred2[2])
+
+    
+    tensor = {
+      "tensorflow": [ 
+      ]
+    }
+
+    #print (pred2[3])
+    for i, element in enumerate(pred2[0]):
+      object = {}
+      itemclass = round(pred2[2][i].item())
+      object["classe"] = itemclass
+      object["nome"] = pred2[3][itemclass]
+      object["score"] = pred2[1][i].item()
+      object["x"] = element[0].item()
+      object["y"] = element[1].item()
+      object["w"] = element[2].item()
+      object["h"] = element[3].item()
+      tensor["tensorflow"].append(object)
+  
+    #print(tensor)
+
+    text = json.dumps(tensor)
+    return text
+        
+
+inputs = [
+    gr.inputs.Image(type="filepath", label="Input Image"),
+    gr.inputs.Textbox(lines=1, label="Model Path", default="kadirnar/yolox_s-v0.1.1"),
+    gr.inputs.Textbox(lines=1, label="Config Path", default="configs.yolox_s"),
+    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
+]
+
+outputs = gr.outputs.Image(type="filepath", label="Output Image")
+title = "SIMULADOR PARA RECONHECIMENTO DE IMAGEM"
+
+examples = [
+    ["small-vehicles1.jpeg", "kadirnar/yolox_m-v0.1.1", "configs.yolox_m", 640],
+    ["zidane.jpg", "kadirnar/yolox_s-v0.1.1", "configs.yolox_s", 640],
+    ["dog.jpg", "kadirnar/yolox_tiny-v0.1.1", "configs.yolox_tiny", 640],
+]
+
+demo_app = gr.Interface(
+    fn=yolox_inference,
+    inputs=inputs,
+    outputs=["text"],
+    title=title,
+    examples=examples,
+    cache_examples=True,
+    live=True,
+    theme='huggingface',
+)
+demo_app.launch(debug=True, enable_queue=True)

configs/yolov3.py

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+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+import torch.nn as nn
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 1.0
+        self.width = 1.0
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+
+    def get_model(self, sublinear=False):
+        def init_yolo(M):
+            for m in M.modules():
+                if isinstance(m, nn.BatchNorm2d):
+                    m.eps = 1e-3
+                    m.momentum = 0.03
+        if "model" not in self.__dict__:
+            from yolox.models import YOLOX, YOLOFPN, YOLOXHead
+            backbone = YOLOFPN()
+            head = YOLOXHead(self.num_classes, self.width, in_channels=[128, 256, 512], act="lrelu")
+            self.model = YOLOX(backbone, head)
+        self.model.apply(init_yolo)
+        self.model.head.initialize_biases(1e-2)
+
+        return self.model

configs/yolox_l.py

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+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 1.0
+        self.width = 1.0
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

configs/yolox_m.py

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+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 0.67
+        self.width = 0.75
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

configs/yolox_nano.py

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+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+import torch.nn as nn
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 0.33
+        self.width = 0.25
+        self.input_size = (416, 416)
+        self.random_size = (10, 20)
+        self.mosaic_scale = (0.5, 1.5)
+        self.test_size = (416, 416)
+        self.mosaic_prob = 0.5
+        self.enable_mixup = False
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+
+    def get_model(self, sublinear=False):
+
+        def init_yolo(M):
+            for m in M.modules():
+                if isinstance(m, nn.BatchNorm2d):
+                    m.eps = 1e-3
+                    m.momentum = 0.03
+        if "model" not in self.__dict__:
+            from yolox.models import YOLOX, YOLOPAFPN, YOLOXHead
+            in_channels = [256, 512, 1024]
+            # NANO model use depthwise = True, which is main difference.
+            backbone = YOLOPAFPN(
+                self.depth, self.width, in_channels=in_channels,
+                act=self.act, depthwise=True,
+            )
+            head = YOLOXHead(
+                self.num_classes, self.width, in_channels=in_channels,
+                act=self.act, depthwise=True
+            )
+            self.model = YOLOX(backbone, head)
+
+        self.model.apply(init_yolo)
+        self.model.head.initialize_biases(1e-2)
+        return self.model

configs/yolox_s.py

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+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 0.33
+        self.width = 0.50
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

configs/yolox_tiny.py

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+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 0.33
+        self.width = 0.375
+        self.input_size = (416, 416)
+        self.mosaic_scale = (0.5, 1.5)
+        self.random_size = (10, 20)
+        self.test_size = (416, 416)
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
+        self.enable_mixup = False

configs/yolox_x.py

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+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+
+from yolox.exp import Exp as MyExp
+
+
+class Exp(MyExp):
+    def __init__(self):
+        super(Exp, self).__init__()
+        self.depth = 1.33
+        self.width = 1.25
+        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

requirements.txt

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+torch