feat: Add MODNet model configuration and quantization settings

- Added config.json file to configure MODNet model type and data type
- Added ONNX model files, including FP32, FP16, and quantized versions
- Added preprocessor configuration and quantization configuration files
- Removed unnecessary requirements.txt file
- Deleted test and training scripts to simplify project structure

config.json

@@ -0,0 +1,6 @@
+{
+  "model_type": "modnet",
+  "transformers.js_config": {
+    "dtype": "fp32"
+  }
+}

onnx/model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07c308cf0fc7e6e8b2065a12ed7fc07e1de8febb7dc7839d7b7f15dd66584df9
+size 25888640

onnx/model_fp16.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:25f165da9bfd30830a575f1f0490f1acd995975cb349bc02f3d79332e1fe5cf6
+size 12984781

onnx/model_quantized.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:92e49898c3e05a6d7a944fc67a8cb87c4aad754ffb6ebd949528c7d1105fee3a
+size 6632188

preprocessor_config.json

@@ -0,0 +1,23 @@
+{
+  "do_normalize": true,
+  "do_pad": false,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "feature_extractor_type": "ImageFeatureExtractor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 512
+  },
+  "size_divisibility": 32
+}

quantize_config.json

@@ -0,0 +1,30 @@
+{
+  "per_channel": false,
+  "reduce_range": false,
+  "per_model_config": {
+    "model": {
+      "op_types": [
+        "Sigmoid",
+        "Constant",
+        "Resize",
+        "Gather",
+        "MatMul",
+        "Clip",
+        "BatchNormalization",
+        "Concat",
+        "Conv",
+        "GlobalAveragePool",
+        "Expand",
+        "Add",
+        "Slice",
+        "Shape",
+        "Unsqueeze",
+        "Reshape",
+        "InstanceNormalization",
+        "Relu",
+        "Mul"
+      ],
+      "weight_type": "QUInt8"
+    }
+  }
+}

requirements.txt

@@ -1,3 +0,0 @@
-# onnx==1.17.0
-torch==2.7.0
-swanlab==0.5.7

test.py

@@ -1,6 +0,0 @@
-import torch
-x = torch.rand(5, 3)
-print(x)
-
-print(torch.__version__)
-print(torch.cuda.is_available())

train.py

@@ -1,29 +0,0 @@
-import swanlab 
-import random 
-
-# 初始化一个新的swanlab run类来跟踪这个脚本 
-swanlab.init( 
-  # 设置将记录此次运行的项目信息 
-  project="MODNet", 
-  workspace="wudi", 
-  # 跟踪超参数和运行元数据 
-  config={ 
-    "learning_rate": 0.02, 
-    "architecture": "CNN", 
-    "dataset": "CIFAR-100", 
-    "epochs": 10 
-  } 
-) 
-
-# 模拟训练 
-epochs = 10 
-offset = random.random() / 5 
-for epoch in range(2, epochs): 
-  acc = 1 - 2 ** -epoch - random.random() / epoch - offset 
-  loss = 2 ** -epoch + random.random() / epoch + offset 
-
-  # 向swanlab上传训练指标 
-  swanlab.log({"acc": acc, "loss": loss}) 
-
-# [可选] 完成训练，这在notebook环境中是必要的 
-swanlab.finish()