Added Antique and Chicken

Individual-Contest/Antique/Antique.ipynb

@@ -0,0 +1,236 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6fe78d59-1f8b-41fb-b8db-9927b8ed049e",
+   "metadata": {},
+   "source": [
+    "<img src=\"./figs/IOAI-Logo.png\" alt=\"IOAI Logo\" width=\"200\" height=\"auto\">\n",
+    "\n",
+    "[IOAI 2025 (Beijing, China), Individual Contest](https://ioai-official.org/china-2025)\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/IOAI-official/IOAI-2025/blob/main/Individual-Contest/Antique/Antique.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4c6054c-d42b-4c2b-bb79-deb64d936c24",
+   "metadata": {},
+   "source": [
+    "# Antique Painting Authentication\n",
+    "\n",
+    "## 1. Problem Description\n",
+    "\n",
+    "You have studied Artificial Intelligence for quite some time. Old friend of your father, famous archeologist and art critic, heard about this and asked for your help. You need to design an algorithm that can classify antique paintings as either authentic or replica pieces.\n",
+    "\n",
+    "Because professional authentication is expensive, the research team has only obtained authenticity labels for a small portion of the paintings. For the majority of samples, the authenticity remains unknown. It is known that the paintings' digital features exhibit strong structural patterns. You are tasked with leveraging all available samples — including those with unknown labels — to train a model for classifying the authenticity of antique paintings.\n",
+    "\n",
+    "## 2. Dataset\n",
+    "\n",
+    "The dataset consists of a training set, a validation set and a test set, each of them has 500 independent samples. \n",
+    "\n",
+    "1. **Training Set (`training_set.csv`)**:\n",
+    "\n",
+    "   - The first five columns represent the digital features of each antique painting.\n",
+    "   - The sixth column contains the label: 1 for authentic, -1 for replica, and 0 for unknown.\n",
+    "\n",
+    "   The training set is used for training your models and can be accessed and downloaded directly during the competition.\n",
+    "\n",
+    "2. **Validation Set (`validation_set.csv`)**: \n",
+    "   - These are similar to the training set format but do not contain the label column.\n",
+    "\n",
+    "   The validation set is used to calculate the Leaderboard A score and is not directly accessible during the competition.\n",
+    "\n",
+    "3. **Test Set (`test_set.csv`)**: \n",
+    "   - These are similar to the training set format but do not contain the label column.\n",
+    "\n",
+    "   The test set is used to calculate the Leaderboard B score and is not directly accessible during the competition.\n",
+    "\n",
+    "## 3. Task\n",
+    "\n",
+    "Your task is to train an appropriate model capable of predicting the authenticity of paintings in the test sets, despite the large number of unlabeled samples.\n",
+    "\n",
+    "## 4. Submission\n",
+    "\n",
+    "Contestants need to submit a notebook file named `submission.ipynb`. The file should output a zip file named `submission.zip`, which should contain the following two files:\n",
+    "\n",
+    "1. `submissionA.csv`: Contains the model's predicted label results on the validation set, with each line being a -1 or 1 and no header.\n",
+    "2. `submissionB.csv`: Contains the model's predicted label results on the test set, with each line being a -1 or 1 and no header.\n",
+    "\n",
+    "The testing machine will read `submission.zip` and calculate the scores. The submission files must strictly follow the above format and naming; otherwise, the system will not be able to read them correctly. \n",
+    "\n",
+    "Details about the submission procedure are provided in the baseline notebook. Contestants are encouraged to refer to it for guidance.\n",
+    "\n",
+    "## 5. Score\n",
+    "\n",
+    "The evaluation metric will be **classification accuracy**, defined as the proportion of correctly predicted samples over the total number of evaluated samples.\n",
+    "\n",
+    "## 6. Baseline and Training Set\n",
+    "\n",
+    "- Below you can find the baseline solution.\n",
+    "- The dataset is in `training_set` folder.\n",
+    "- The highest score by the Scientific Committee for this task is 0.98 in Leaderboard B,  this score is used for score unification.\n",
+    "- The baseline score by the Scientific Committee for this task is 0.46 in Leaderboard B, this score is used for score unification."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44bf0dce",
+   "metadata": {},
+   "source": [
+    "### Train Your Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3acb09be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "# 1. Get the current working directory\n",
+    "current_dir = os.getcwd()\n",
+    "\n",
+    "# 2. Check if the path contains \"Individual-Contest/Antique\" and trim it to that point\n",
+    "if \"Individual-Contest/Antique\" in current_dir:\n",
+    "    root_index = current_dir.index(\"Individual-Contest/Antique\") + len(\"Individual-Contest/Antique\")\n",
+    "    project_root = current_dir[:root_index]\n",
+    "else:\n",
+    "    raise Exception(\"Project root directory not found. Please check the folder structure.\")\n",
+    "\n",
+    "# 3. Change working directory to the project root\n",
+    "os.chdir(project_root)\n",
+    "print(\"Working directory set to:\", os.getcwd())\n",
+    "\n",
+    "# 4. Add module search path (e.g., where metrics.py is located)\n",
+    "sys.path.append(os.path.join(project_root, \"Scoring\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03dae883",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "TRAIN_PATH = \"./training_set/\"\n",
+    "train = pd.read_csv(TRAIN_PATH + \"training_set.csv\")\n",
+    "\n",
+    "X = np.array(train.iloc[:,:5])\n",
+    "y = np.array(train.iloc[:,5])\n",
+    "\n",
+    "np.random.seed(42)\n",
+    "y[y == 0] = np.random.choice([-1, 1], size=(y == 0).sum())\n",
+    "\n",
+    "svm_binary_model = SVC(kernel='rbf', C=1.0, gamma='scale', random_state=42)\n",
+    "svm_binary_model.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2049ba4",
+   "metadata": {},
+   "source": [
+    "### Make Predictions on the Validation and Test Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c69d9d92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "VAL_DATA_PATH = \"./Solution/validation_set/\"\n",
+    "TEST_DATA_PATH = \"./Solution/test_set/\"\n",
+    "\n",
+    "testA = np.array(pd.read_csv(VAL_DATA_PATH + \"validation_set.csv\"))\n",
+    "testB = np.array(pd.read_csv(TEST_DATA_PATH + \"test_set.csv\"))\n",
+    "\n",
+    "predA = svm_binary_model.predict(testA)\n",
+    "predB = svm_binary_model.predict(testB)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e2141d8",
+   "metadata": {},
+   "source": [
+    "### Generate `submission.zip` for Submission"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "342e6ddb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import zipfile\n",
+    "import os\n",
+    "\n",
+    "submissionA = pd.DataFrame(predA)\n",
+    "submissionA.to_csv(\"./Scoring/submissionA.csv\", index=False, header=False)\n",
+    "\n",
+    "submissionB = pd.DataFrame(predB)\n",
+    "submissionB.to_csv(\"./Scoring/submissionB.csv\", index=False, header=False)\n",
+    "\n",
+    "files_to_zip = ['./Scoring/submissionA.csv', './Scoring/submissionB.csv']\n",
+    "zip_filename = './Scoring/submission.zip'\n",
+    "\n",
+    "with zipfile.ZipFile(zip_filename, 'w') as zipf:\n",
+    "    for file in files_to_zip:\n",
+    "        zipf.write(file, os.path.basename(file))\n",
+    "\n",
+    "print(f'{zip_filename} is created succefully!')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25da701b",
+   "metadata": {},
+   "source": [
+    "### Evaluate the Model Performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "269246ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run Scoring/metrics.py"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py9",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.23"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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Individual-Contest/Antique/Scoring/metrics.py

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+# -*- coding: utf-8 -*-
+import numpy as np
+import pandas as pd
+import json
+from sklearn.metrics import accuracy_score
+import zipfile
+import os
+
+if __name__ == '__main__':
+    with zipfile.ZipFile('./Scoring/submission.zip', 'r') as zip_ref:
+        zip_ref.extractall('./Scoring/submission/')
+
+    ANSWER_PATH = "./Scoring/"  # local testing
+    # A榜
+    predA_dir = ANSWER_PATH + "submission/submissionA.csv"
+    test_dir = ANSWER_PATH + "label.csv"
+    y_predA = pd.read_csv(predA_dir, header=None)
+    y_test = pd.read_csv(test_dir)
+    accuracy_A = accuracy_score(y_predA, y_test['validation_label'])
+    if accuracy_A > 1:
+        accuracy_A = 0
+    print(f"Accuracy for test A: {accuracy_A:.2f}")
+    # B榜
+    predB_dir = ANSWER_PATH + "submission/submissionB.csv"
+    y_predB = pd.read_csv(predB_dir, header=None)
+    accuracy_B = accuracy_score(y_predB, y_test['testing_label'])
+    if accuracy_B > 1:
+        accuracy_B = 0
+    print(f"Accuracy for test B: {accuracy_B:.2f}")
+    #----------calculate the score on the leaderboard------------#
+    score = {
+        "public_a": accuracy_A,
+        "public_detail": {
+            "Accuracy": accuracy_A,
+        },
+        "private_b": accuracy_B,
+        "private_detail":{
+            "Accuracy": accuracy_B,
+        },
+    }
+    #print(score)
+    ret_json = {
+        "status": True,
+        "score": score,
+        "msg": "Success!",
+    }
+    with open('score.json', 'w') as f:
+        f.write(json.dumps(ret_json))    
+    

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+size 2724

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Individual-Contest/Antique/Solution/Antique_Solution.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7c130e37-a029-4011-b741-14adb0bc15bb",
+   "metadata": {},
+   "source": [
+    "<img src=\"./figs/IOAI-Logo.png\" alt=\"IOAI Logo\" width=\"200\" height=\"auto\">\n",
+    "\n",
+    "[IOAI 2025 (Beijing, China), Individual Contest](https://ioai-official.org/china-2025)\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/IOAI-official/IOAI-2025/blob/main/Individual-Contest/Antique/Solution/Antique_Solution.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ae71b15-8e97-4896-90a2-000c9cd6e683",
+   "metadata": {},
+   "source": [
+    "# Antique Painting Authentication: Reference Solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44bf0dce",
+   "metadata": {},
+   "source": [
+    "## Step 1: Train Your Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5bd6db06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "# 1. Get the current working directory\n",
+    "current_dir = os.getcwd()\n",
+    "\n",
+    "# 2. Check if the path contains \"Individual-Contest/Antique\" and trim it to that point\n",
+    "if \"Individual-Contest/Antique\" in current_dir:\n",
+    "    root_index = current_dir.index(\"Individual-Contest/Antique\") + len(\"Individual-Contest/Antique\")\n",
+    "    project_root = current_dir[:root_index]\n",
+    "else:\n",
+    "    raise Exception(\"Project root directory not found. Please check the folder structure.\")\n",
+    "\n",
+    "# 3. Change working directory to the project root\n",
+    "os.chdir(project_root)\n",
+    "print(\"Working directory set to:\", os.getcwd())\n",
+    "\n",
+    "# 4. Add module search path (e.g., where metrics.py is located)\n",
+    "sys.path.append(os.path.join(project_root, \"Scoring\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03dae883",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.cluster import SpectralClustering\n",
+    "from collections import Counter\n",
+    "from sklearn.svm import SVC\n",
+    "import os\n",
+    "\n",
+    "TRAIN_PATH = \"./training_set/\" # The address of trainig set\n",
+    "\n",
+    "train = pd.read_csv(TRAIN_PATH + \"training_set.csv\")\n",
+    "\n",
+    "X = np.array(train.iloc[:,:5])\n",
+    "y = np.array(train.iloc[:,5])\n",
+    "\n",
+    "labeled_mask = y != 0\n",
+    "unlabeled_mask = y == 0\n",
+    "X_labeled = X[labeled_mask]\n",
+    "y_labeled = y[labeled_mask]\n",
+    "X_unlabeled = X[unlabeled_mask]\n",
+    "\n",
+    "n_clusters = 2\n",
+    "spectral = SpectralClustering(n_clusters=n_clusters, affinity='rbf', gamma=10, random_state=42)\n",
+    "cluster_labels = spectral.fit_predict(X) \n",
+    "\n",
+    "cluster_to_label = {}\n",
+    "for cluster in range(n_clusters):\n",
+    "\n",
+    "    labeled_in_cluster = y_labeled[cluster_labels[labeled_mask] == cluster]\n",
+    "\n",
+    "    if len(labeled_in_cluster) > 0:\n",
+    "        most_common_label = Counter(labeled_in_cluster).most_common(1)[0][0]\n",
+    "        cluster_to_label[cluster] = most_common_label\n",
+    "\n",
+    "pseudo_labels = np.array([cluster_to_label[cluster] for cluster in cluster_labels])\n",
+    "\n",
+    "svm = SVC(kernel='rbf', C=1.0, gamma='scale', random_state=42)\n",
+    "svm.fit(X, pseudo_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2049ba4",
+   "metadata": {},
+   "source": [
+    "## Step 2: Make Predictions on the Validation and Test Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c69d9d92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "VAL_DATA_PATH = \"./Solution/validation_set/\"\n",
+    "TEST_DATA_PATH = \"./Solution/test_set/\"\n",
+    "\n",
+    "testA = np.array(pd.read_csv(VAL_DATA_PATH + \"validation_set.csv\"))\n",
+    "testB = np.array(pd.read_csv(TEST_DATA_PATH + \"test_set.csv\"))\n",
+    "\n",
+    "predA = svm.predict(testA)\n",
+    "predB = svm.predict(testB)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e2141d8",
+   "metadata": {},
+   "source": [
+    "## Step 3: Generate `submission.zip` for Submission"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "342e6ddb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import zipfile\n",
+    "import os\n",
+    "\n",
+    "submissionA = pd.DataFrame(predA)\n",
+    "submissionA.to_csv(\"./Scoring/submissionA.csv\", index=False, header=False)\n",
+    "\n",
+    "submissionB = pd.DataFrame(predB)\n",
+    "submissionB.to_csv(\"./Scoring/submissionB.csv\", index=False, header=False)\n",
+    "\n",
+    "files_to_zip = ['./Scoring/submissionA.csv', './Scoring/submissionB.csv']\n",
+    "zip_filename = './Scoring/submission.zip'\n",
+    "\n",
+    "with zipfile.ZipFile(zip_filename, 'w') as zipf:\n",
+    "    for file in files_to_zip:\n",
+    "        zipf.write(file, os.path.basename(file))\n",
+    "\n",
+    "print(f'{zip_filename} is created succefully!')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e65766d9",
+   "metadata": {},
+   "source": [
+    "### Evaluate the Model Performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04d9f1be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run Scoring/metrics.py"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Individual-Contest/Antique/Solution/test_set/test_set.csv

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Individual-Contest/Chicken_Counting/Chicken_Counting.ipynb

@@ -0,0 +1,619 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"./figs/IOAI-Logo.png\" alt=\"IOAI Logo\" width=\"200\" height=\"auto\">\n",
+    "\n",
+    "[IOAI 2025 (Beijing, China), Individual Contest](https://ioai-official.org/china-2025)\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/IOAI-official/IOAI-2025/blob/main/Individual-Contest/Chicken_Counting/Chicken_Counting.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chicken Counting\n",
+    "\n",
+    "## **1. Problem Description**\n",
+    "\n",
+    "As the leader of an AI research team collaborating with Silkie chicken farmers, you are tasked with solving a critical challenge in traditional free-range farming. Accurate counting of livestock is crucial for both farmers and insurance companies, as factors like disease outbreaks and predator invasions can significantly impact the survival rate of these chickens in a short time. While insurance coverage helps mitigate farming risks, the claims process requires precise counting of livestock losses. Your farmers have approached your team for help in developing more accurate, automated counting systems. The challenge before your research team is to develop an optimized Silkie chicken counting model using density estimation techniques that can provide reliable counts to support both farm management and insurance processes.\n",
+    "\n",
+    "Your team has access to a pretrained feature extractor for Silkie chicken images, but you'll need to design and train the density estimation decoder to create a complete counting solution. Your task is to build upon this foundation by developing an effective decoder architecture and training strategy to achieve accurate chicken counts that farmers and insurance companies can rely on.\n",
+    "\n",
+    "The below figure shows an image in the dataset, as well as the corresponding true density distribution and a predicted density distribution generated by the baseline model. The total density (sum of densities across all areas) is labeled.\n",
+    "\n",
+    "<img src=\"./figs/Chicken Counting Fig 1.png\" width=\"800\">\n",
+    "\n",
+    "## **2. Dataset**\n",
+    "\n",
+    "The structure of the provided Silkie chicken image dataset is as follows:\n",
+    "\n",
+    "```\n",
+    "datasets/\n",
+    "├── train/\n",
+    "│   └── A dataset with features:\n",
+    "│       ├── `image`: `PIL.Image` with RGB channels (3x720x1280)\n",
+    "│       └── `density`: a 2D array of shape 180x320\n",
+    "└── base.pth (Pretrained Model)\n",
+    "\n",
+    "\n",
+    "os.environ.get(\"DATA_PATH\")/\n",
+    "├── test_a/\n",
+    "│   └── A dataset with features:\n",
+    "│       └── `image`: `PIL.Image` with RGB channels (3x720x1280)\n",
+    "└── test_b/\n",
+    "    └── A dataset with features:\n",
+    "        └── `image`: `PIL.Image` with RGB channels (3x720x1280)\n",
+    "```\n",
+    "\n",
+    "(1) Training set location: `datasets`, files in this folder are used for model fine-tuning. It contains a train folder, which stores a dataset with 100 images and their corresponding density maps.\n",
+    "\n",
+    "(2) Validation set (test_a) and Test set (test_b): These will be used to evaluate scores on Leaderboard A and Leaderboard B, respectively. They will be inaccessible to contestants. Only the score achieved on test set B will be used for final scoring. \n",
+    "\n",
+    "(3) Datasets size:\n",
+    "\n",
+    "- Training set: 100 images.\n",
+    "- Validation set: 100 images.\n",
+    "- Test set: 100 images.\n",
+    "\n",
+    "(4) Validation set (test_a) and Test set (test_b) are not visible.\n",
+    "\n",
+    "(5) Due to limits of computing resources, training density maps are reshaped to $1\\times 180 \\times 320$. **NOTE** the output density map can be viewed as a 2D real number matrix with shape $180\\times 320$, the sum of all matrix values is the count of chickens.\n",
+    "\n",
+    "## **3. Task**\n",
+    "\n",
+    "Your task is to train your own model using the training data to predict density maps, thereby serving the purpose of chicken counting.\n",
+    "\n",
+    "You may extend and optimize the given pretrained model to improve its count prediction accuracy. The pretrained model `base.pth` only contains the weights of the first four layers of the model (the feature extraction model), and the function `load_pretrained_weights_partial` in the baseline code can be used to load these partial weights into your model. You may construct a density decoder `DensityDecoder` and combine it with the pretrained feature extraction module to form a complete data prediction model.\n",
+    "\n",
+    "```\n",
+    "class DensityDecoder(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        #################################################\n",
+    "        # Your code here\n",
+    "        #################################################\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        #################################################\n",
+    "        # Your code here\n",
+    "        #################################################\n",
+    "        return x\n",
+    "```\n",
+    "\n",
+    "You can also build your own model without the pretrained model we provided.\n",
+    "\n",
+    "This task is the continuation of Satellite Weather Forecasting. A kind remind is the UNET is easily to full GPU memory without any feature engineering. Then, the GPU memory error message will be reported. \n",
+    "\n",
+    "Please follow these rules to achieve a score normally:\n",
+    "\n",
+    "(1) Your model must output the predicted density map.\n",
+    "\n",
+    "(2) Due to limits of computing resources, your output density map should be reshaped to $180 \\times 320$. This is also the shape of target density maps provided in the train dataset.\n",
+    "\n",
+    "## 4. Submission\n",
+    "\n",
+    "Please submit a **submission.ipynb** that includes the following components:\n",
+    "\n",
+    "（1）**Training Code**  \n",
+    "\n",
+    "- Include the full training pipeline.\n",
+    "\n",
+    "（2）**Evaluation Code**\n",
+    "- Evaluate your model on the validation set and test set. \n",
+    "\n",
+    "- The output should be saved as **`submission.npz`**. This must be a valid `npz` file containing two arrays `pred_a` and `pred_b`, each with shape `100x1x180x320` (The evaluation script will also accept predictions in the shape of `100x180x320`, if you decide to squeeze the channel dimension).\n",
+    "\n",
+    "  **Any result that does not meet the specified size will be considered invalid, resulting in an assessment score of zero.**\n",
+    "  \n",
+    "- Each element of the density map should be **no less than zero**, otherwise will result in an assessment score of zero.\n",
+    "\n",
+    "## **5. Scoring**\n",
+    "\n",
+    "You will be scored based on the mean relative error of your model. Relative error is defined by:\n",
+    "\n",
+    "$$\n",
+    "\\text{Relative Error} = \\frac{|y_i - \\hat{y}_i|}{|y_i|}\n",
+    "$$\n",
+    "\n",
+    "where $y_i$ is the true total density for the $i$-th sample, and $\\hat{y}_i$ is the predicted total density for the $i$-th sample.\n",
+    "\n",
+    "Your final score before normalization will be calculated based on your mean relative error, as follows:\n",
+    "\n",
+    "$$\n",
+    "\\text{Score} = \\exp(-\\frac{1}{n} \\sum_{i=1}^{n} \\frac{|y_i - \\hat{y}_i|}{y_i})\n",
+    "$$\n",
+    "\n",
+    "## **6. Baseline & Training Set**\n",
+    "\n",
+    "- Below you can find the baseline solution.\n",
+    "- The dataset is in `training_set` folder.\n",
+    "- The highest score by the Scientific Committee for this task is 0.89,  this score is used for score unification.\n",
+    "- The baseline score by the Scientific Committee for this task is 0.71, this score is used for score unification."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "\n",
+    "seed = 42\n",
+    "\n",
+    "random.seed(seed)                  # Python built-in random\n",
+    "np.random.seed(seed)               # NumPy\n",
+    "torch.manual_seed(seed)            # PyTorch (CPU)\n",
+    "torch.cuda.manual_seed(seed)       # PyTorch (single GPU)\n",
+    "torch.cuda.manual_seed_all(seed)   # PyTorch (all GPUs)\n",
+    "\n",
+    "# Ensures deterministic behavior\n",
+    "torch.backends.cudnn.deterministic = True\n",
+    "torch.backends.cudnn.benchmark = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torch.optim as optim\n",
+    "from torch.utils.data import DataLoader\n",
+    "from datasets import load_from_disk\n",
+    "import logging\n",
+    "from torchvision import transforms\n",
+    "from tqdm import tqdm\n",
+    "import numpy as np\n",
+    "import math\n",
+    "\n",
+    "#Contestants should mount \"counting_problem_train(v1)\" datasets while creating the node.\n",
+    "TRAIN_PATH = \"/bohr/train-adnz/v1/\"  #Address of the training set and base.pth\n",
+    "# The training set is deployed automatically in the testing machine. \n",
+    "# You notebook can access the TRAIN_PATH even if you do not mount it along with notebook.\n",
+    "TRAINING_SET = TRAIN_PATH + \"train\" #Address of the traninig set \n",
+    "BASE_MODEL_PATH = TRAIN_PATH + \"base.pth\"#Address of the .pth file\n",
+    "DTYPE = torch.float32\n",
+    "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "scale = 100.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Logging Utilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def logging_level(level='info'):\n",
+    "    str_format = '%(asctime)s - %(levelname)s: %(message)s'\n",
+    "    if level == 'debug':\n",
+    "        logging.basicConfig(level=logging.DEBUG, format=str_format, datefmt='%Y-%m-%d %H:%M:%S')\n",
+    "    elif level == 'info':\n",
+    "        logging.basicConfig(level=logging.INFO, format=str_format, datefmt='%Y-%m-%d %H:%M:%S')\n",
+    "    return logging\n",
+    "\n",
+    "\n",
+    "class BatchLossLogger:\n",
+    "    def __init__(self, log_interval=100):\n",
+    "        self.losses = []\n",
+    "        self.batch_number = 0\n",
+    "        self.log_interval = log_interval\n",
+    "\n",
+    "    def log(self, loss):\n",
+    "        self.losses.append(loss)\n",
+    "        self.batch_number += 1\n",
+    "        if self.batch_number % self.log_interval == 0:\n",
+    "            logging.info(f'Batch No. {self.batch_number:7d} - loss: {loss:.6f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training Your Model\n",
+    "#### Model Definition\n",
+    "Pretrained weights of the `FeatureExtraction` model is provided in `base.pth` in the training set, along with a function to load them.\n",
+    "`ChickenCounting` is the completed model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class FeatureExtraction(nn.Module):\n",
+    "    def __init__(self, in_channels=3):\n",
+    "        super(FeatureExtraction, self).__init__()\n",
+    "        self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=3, padding=2, dilation=2)\n",
+    "        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=2, dilation=2)\n",
+    "        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
+    "        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=2, dilation=2)\n",
+    "        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=2, dilation=2)\n",
+    "        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
+    "\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = F.relu(self.conv1(x))\n",
+    "        x = F.relu(self.conv2(x))\n",
+    "        x = self.pool2(x)\n",
+    "        x = F.relu(self.conv3(x))\n",
+    "        x = F.relu(self.conv4(x))\n",
+    "        x = self.pool4(x)\n",
+    "\n",
+    "        return x\n",
+    "\n",
+    "    def load_pretrained_weights_partial(self, weights_path, num_layers=4):\n",
+    "        save_model = torch.load(weights_path)\n",
+    "        partial_state_dict = {}\n",
+    "        expected_layers = [\n",
+    "            'feature_extraction.conv1.weight', 'feature_extraction.conv1.bias',\n",
+    "            'feature_extraction.conv2.weight', 'feature_extraction.conv2.bias',\n",
+    "            'feature_extraction.conv3.weight', 'feature_extraction.conv3.bias',\n",
+    "            'feature_extraction.conv4.weight', 'feature_extraction.conv4.bias',\n",
+    "        ]\n",
+    "        state_dict = {k.split('.', 1)[-1]: v for k, v in save_model.items() if k in expected_layers[:2 * num_layers]}\n",
+    "        model_dict = self.state_dict()\n",
+    "\n",
+    "        for k in state_dict:\n",
+    "            if k in model_dict:\n",
+    "                partial_state_dict[k] = state_dict[k]\n",
+    "                print(k)\n",
+    "\n",
+    "        self.load_state_dict(partial_state_dict)\n",
+    "\n",
+    "\n",
+    "class DensityDecoder(nn.Module): # Define your decoder model here.\n",
+    "    def __init__(self):\n",
+    "        super(DensityDecoder, self).__init__()\n",
+    "        self.conv5 = nn.Conv2d(in_channels=128, out_channels=1, kernel_size=3, padding=2, dilation=2)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = F.relu(self.conv5(x))\n",
+    "        return x\n",
+    "\n",
+    "\n",
+    "class ChickenCounting(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(ChickenCounting, self).__init__()\n",
+    "        self.feature_extraction = FeatureExtraction()\n",
+    "        self.feature_decoder = DensityDecoder()\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.feature_extraction(x)\n",
+    "        x = self.feature_decoder(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reading the Dataset\n",
+    "Read the train dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_dataset = load_from_disk(TRAINING_SET)\n",
+    "\n",
+    "image_transform = transforms.Compose([\n",
+    "    transforms.ToTensor(),\n",
+    "])\n",
+    "\n",
+    "def collate_fn(batch, scale = scale):\n",
+    "    return {\n",
+    "        \"image\": torch.stack([image_transform(item[\"image\"]) for item in batch]),\n",
+    "        \"density\": torch.stack([torch.tensor(item[\"density\"], dtype=DTYPE).unsqueeze(0) * scale for item in batch]) # Multiply by scale for faster training\n",
+    "    }\n",
+    "\n",
+    "train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True, collate_fn=collate_fn)\n",
+    "val_loader = DataLoader(train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Definition of the training process\n",
+    "def train_chickenfcn(model, train_loader, val_loader, optimizer, scheduler, num_epochs, device, save_path):\n",
+    "    model.train()\n",
+    "    criterion_mse = torch.nn.MSELoss(reduction='sum').to(device)\n",
+    "    criterion_mae = torch.nn.L1Loss(reduction='sum').to(device)\n",
+    "    best_loss = float('inf')\n",
+    "    print(train_loader.__len__())\n",
+    "\n",
+    "    for epoch in range(num_epochs):\n",
+    "        train_loss_mse = 0.0\n",
+    "        train_loss_mae = 0.0\n",
+    "\n",
+    "        train_loader_tqdm = tqdm(train_loader, desc=f'Epoch {epoch + 1}/{num_epochs}', leave=False)\n",
+    "\n",
+    "        for i, data in enumerate(train_loader_tqdm, 0):\n",
+    "            inputs, targets = data[\"image\"], data[\"density\"]\n",
+    "            inputs = inputs.to(device).float()\n",
+    "            targets = targets.to(device).float()\n",
+    "            # print(targets.shape)\n",
+    "            # t = np.sum((targets[0] / scale).cpu().numpy().squeeze())\n",
+    "            # print(t)\n",
+    "\n",
+    "            optimizer.zero_grad()\n",
+    "            \n",
+    "            outputs = model(inputs)\n",
+    "            \n",
+    "            loss_mse = criterion_mse(outputs, targets)\n",
+    "            loss_mae = criterion_mae(outputs, targets)\n",
+    "            loss_mae.backward()\n",
+    "\n",
+    "            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=2.0)\n",
+    "\n",
+    "            optimizer.step()\n",
+    "            train_loss_mse += loss_mse.item()\n",
+    "            train_loss_mae += loss_mae.item()\n",
+    "\n",
+    "            train_loader_tqdm.set_postfix({'Train MSE Loss': loss_mse.item(), 'Train MAE Loss': loss_mae.item()})\n",
+    "\n",
+    "        train_loss_mse /= (len(train_loader))\n",
+    "        train_loss_mae /= (len(train_loader))\n",
+    "        logging.info(\n",
+    "            f'Epoch [{epoch + 1}/{num_epochs}], Train MSE loss: {train_loss_mse:.8f}, MAE loss: {train_loss_mae:.8f}')\n",
+    "\n",
+    "        scheduler.step()\n",
+    "\n",
+    "        # Validation\n",
+    "        model.eval()\n",
+    "        val_loss_mse = 0.0\n",
+    "        val_loss_mae = 0.0\n",
+    "\n",
+    "        val_loader_tqdm = tqdm(val_loader, desc=f'Validation Epoch {epoch + 1}/{num_epochs}', leave=False)\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            for i, data in enumerate(val_loader_tqdm, 0):\n",
+    "                inputs, targets = data[\"image\"], data[\"density\"]\n",
+    "                inputs = inputs.to(device).float()\n",
+    "                targets = targets.to(device).float()\n",
+    "\n",
+    "                outputs = model(inputs)\n",
+    "                mse_loss = criterion_mse(outputs, targets)\n",
+    "                mae_loss = criterion_mae(outputs, targets)\n",
+    "                val_loss_mse += mse_loss.item()\n",
+    "                val_loss_mae += mae_loss.item()\n",
+    "\n",
+    "                val_loader_tqdm.set_postfix(\n",
+    "                    {'Validation MSE Loss': mse_loss.item(), 'Validation MAE Loss': mae_loss.item()})\n",
+    "\n",
+    "        val_loss_mse /= (len(val_loader))\n",
+    "        val_loss_mae /= (len(val_loader))\n",
+    "        logging.info(\n",
+    "            f'Epoch [{epoch + 1}/{num_epochs}], Validation MSE Loss: {val_loss_mse:.8f}, MAE Loss: {val_loss_mae:.8f}')\n",
+    "\n",
+    "        # Save Model\n",
+    "        if val_loss_mae < best_loss:\n",
+    "            best_loss = val_loss_mae\n",
+    "            torch.save(model.state_dict(), save_path)\n",
+    "\n",
+    "    print('Finished Training ChickenFCN')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logging = logging_level('info')\n",
+    "logging.debug('use debug level logging setting')\n",
+    "\n",
+    "################################################################################\n",
+    "# Experiment Settings\n",
+    "################################################################################\n",
+    "learning_rate = 1e-4\n",
+    "lr_decay = 1e-5\n",
+    "weight_decay = 0.0001\n",
+    "save_path = \"model.pth\"\n",
+    "\n",
+    "epochs = 20\n",
+    "\n",
+    "# Training\n",
+    "model = ChickenCounting().to(DEVICE)\n",
+    "model.feature_extraction.load_pretrained_weights_partial(BASE_MODEL_PATH)\n",
+    "print('load model success')\n",
+    "\n",
+    "optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)\n",
+    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=1 - lr_decay)\n",
+    "\n",
+    "logging.info('Begin training single view model...')\n",
+    "train_chickenfcn(model, train_loader, val_loader, optimizer, scheduler, epochs, DEVICE, save_path=save_path)\n",
+    "logging.info('Finished training single view model.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluate Model\n",
+    "This section validates the model on the train set, which helps contestants understand whether the model is usable and calculate the score on the train set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Definition of the evaluation function\n",
+    "def evaluate(model, val_loader, device, scale): # Function used for final scoring.\n",
+    "    model.eval()  # Set the model to evaluation mode\n",
+    "\n",
+    "    # Initialize metrics\n",
+    "    mse = 0.0\n",
+    "    mae = 0.0\n",
+    "    predict_num = 0.0\n",
+    "    true_num = 0.0\n",
+    "    rate = 0.0\n",
+    "\n",
+    "    with torch.no_grad():  # Disable gradient calculation for inference\n",
+    "        for i, data in enumerate(val_loader, 0):\n",
+    "            inputs, targets = data[\"image\"], data[\"density\"]\n",
+    "            inputs = inputs.to(device).float()  # Move inputs to device and convert to float\n",
+    "            targets = targets.to(device).float()  # Move targets to device and convert to float\n",
+    "\n",
+    "            # Get the model predictions\n",
+    "            outputs = model(inputs) / scale  # Adjusting for the scaling factor\n",
+    "\n",
+    "            # Convert tensors to numpy for visualization and metrics calculation\n",
+    "            inputs_np = inputs.cpu().numpy()  # Convert inputs to numpy\n",
+    "            targets_np = targets.cpu().numpy()  # Convert targets to numpy\n",
+    "            outputs_np = outputs.cpu().numpy()  # Convert outputs to numpy\n",
+    "            # imshow_res(inputs_np, targets_np, outputs_np, scale)  # Uncomment to visualize results\n",
+    "\n",
+    "            # Calculate true and predicted sums for comparison\n",
+    "            t = np.sum((targets[0] / scale).cpu().numpy().squeeze())  # Ground truth sum\n",
+    "            g = np.sum(outputs.cpu().numpy().squeeze())  # Predicted sum\n",
+    "            print(f'NO.{i}   true_sum={t}, get_sum={g}, abs={abs(t - g)}, rate={abs(1 - g / t)}')\n",
+    "\n",
+    "            # Update metrics\n",
+    "            predict_num += g\n",
+    "            true_num += t\n",
+    "            rate += abs(1 - g / t)\n",
+    "            mae += abs(t - g)\n",
+    "            mse += abs(t - g) * abs(t - g)\n",
+    "\n",
+    "    # Calculate average metrics across all batches\n",
+    "    mae /= len(val_loader)\n",
+    "    mse /= len(val_loader)\n",
+    "    predict_num /= len(val_loader)\n",
+    "    true_num /= len(val_loader)\n",
+    "    rate /= len(val_loader)\n",
+    "\n",
+    "    # Log the results\n",
+    "    logging.info(\n",
+    "        f'test ---- Score: {math.exp(-rate):.3f}, MSE: {mse:.4f}, MAE: {mae:.4f}, Chicken_avg: {predict_num:.4f}')\n",
+    "    return math.exp(-rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.load_state_dict(torch.load(save_path, map_location=DEVICE))\n",
+    "model.to(DEVICE)\n",
+    "evaluate(model, val_loader, DEVICE, scale)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Submission\n",
+    "This part is to generate the result files for testing and scoring.\n",
+    "Contestants couldn't access the validation set(test_a) and the test set(test_b) locally.\n",
+    "Please read through the following code carefully. Make sure to following the file naming conventions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#DATA_PATH is the secret environment variable to point the address of the validation set and test set on the testing machine. \n",
+    "#Contestants cannot access this address locally.\n",
+    "if os.environ.get('DATA_PATH'):  \n",
+    "    DATA_PATH = os.environ.get(\"DATA_PATH\") + \"/\" \n",
+    "else:\n",
+    "    DATA_PATH = \"\"  # Fallback for local testing\n",
+    "def collate_fn(batch): # The test datasets will not provide target densities\n",
+    "    return torch.stack([image_transform(item[\"image\"]) for item in batch])\n",
+    "\n",
+    "test_dataset = load_from_disk(os.path.join(DATA_PATH, \"test_a\"))\n",
+    "test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False, collate_fn=collate_fn)\n",
+    "\n",
+    "predictions = []\n",
+    "model.eval()\n",
+    "with torch.no_grad():\n",
+    "    for batch in tqdm(test_loader):\n",
+    "        outputs = model(batch.to(DEVICE)) / scale\n",
+    "        predictions.append(outputs.cpu().numpy())\n",
+    "\n",
+    "pred_a = np.concatenate(predictions, axis=0)\n",
+    "\n",
+    "del test_dataset\n",
+    "del test_loader\n",
+    "del predictions\n",
+    "\n",
+    "test_dataset = load_from_disk(os.path.join(DATA_PATH, \"test_b\"))\n",
+    "test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False, collate_fn=collate_fn)\n",
+    "\n",
+    "predictions = []\n",
+    "with torch.no_grad():\n",
+    "    for batch in tqdm(test_loader):\n",
+    "        outputs = model(batch.to(DEVICE)) / scale\n",
+    "        predictions.append(outputs.cpu().numpy())\n",
+    "\n",
+    "pred_b = np.concatenate(predictions, axis=0)\n",
+    "\n",
+    "np.savez('submission.npz', pred_a=pred_a, pred_b=pred_b) # save your submissions in `submission.npz` file with the keys `pred_a` and `pred_b`"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Individual-Contest/Chicken_Counting/Solution/Chicken_Counting_Solution.ipynb

@@ -0,0 +1,723 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"./figs/IOAI-Logo.png\" alt=\"IOAI Logo\" width=\"200\" height=\"auto\">\n",
+    "\n",
+    "[IOAI 2025 (Beijing, China), Individual Contest](https://ioai-official.org/china-2025)\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/IOAI-official/IOAI-2025/blob/main/Individual-Contest/Chicken_Counting/Solution/Chicken_Counting_Solution.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chicken Counting: Reference Solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 5616,
+     "status": "ok",
+     "timestamp": 1753592556976,
+     "user": {
+      "displayName": "陈红涛",
+      "userId": "16480124546172497377"
+     },
+     "user_tz": -480
+    },
+    "id": "t-7tiKDEs5N7"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torch.optim as optim\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from datasets import load_from_disk\n",
+    "import logging\n",
+    "from torchvision import transforms\n",
+    "from tqdm import tqdm\n",
+    "import numpy as np\n",
+    "import math\n",
+    "from PIL import Image\n",
+    "import zipfile\n",
+    "from typing import Optional\n",
+    "import random\n",
+    "\n",
+    "np.random.seed(42)\n",
+    "random.seed(42)\n",
+    "torch.manual_seed(42)  \n",
+    "torch.cuda.manual_seed(42) \n",
+    "torch.cuda.manual_seed_all(42)\n",
+    "\n",
+    "TRAIN_PATH = \"/bohr/train-adnz/v1/\"\n",
+    "TRAINING_SET = TRAIN_PATH + \"train\"\n",
+    "BASE_MODEL_PATH = TRAIN_PATH + \"base.pth\"\n",
+    "DTYPE = torch.float32\n",
+    "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "scale = 300.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Logging Utilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 28,
+     "status": "ok",
+     "timestamp": 1753592562308,
+     "user": {
+      "displayName": "陈红涛",
+      "userId": "16480124546172497377"
+     },
+     "user_tz": -480
+    },
+    "id": "SMii-32Qs5N9"
+   },
+   "outputs": [],
+   "source": [
+    "def logging_level(level='info'):\n",
+    "    str_format = '%(asctime)s - %(levelname)s: %(message)s'\n",
+    "    if level == 'debug':\n",
+    "        logging.basicConfig(level=logging.DEBUG, format=str_format, datefmt='%Y-%m-%d %H:%M:%S')\n",
+    "    elif level == 'info':\n",
+    "        logging.basicConfig(level=logging.INFO, format=str_format, datefmt='%Y-%m-%d %H:%M:%S')\n",
+    "    return logging\n",
+    "\n",
+    "\n",
+    "class BatchLossLogger:\n",
+    "    def __init__(self, log_interval=100):\n",
+    "        self.losses = []\n",
+    "        self.batch_number = 0\n",
+    "        self.log_interval = log_interval\n",
+    "\n",
+    "    def log(self, loss):\n",
+    "        self.losses.append(loss)\n",
+    "        self.batch_number += 1\n",
+    "        if self.batch_number % self.log_interval == 0:\n",
+    "            logging.info(f'Batch No. {self.batch_number:7d} - loss: {loss:.6f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Traininng Your Model\n",
+    "### Model Definition\n",
+    "Pretrained weights of the FeatureExtraction model is provided, along with a function to load them. And a completed UNet structure is applied."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 113,
+     "status": "ok",
+     "timestamp": 1753592562424,
+     "user": {
+      "displayName": "陈红涛",
+      "userId": "16480124546172497377"
+     },
+     "user_tz": -480
+    },
+    "id": "VWPcNgjUvXq5"
+   },
+   "outputs": [],
+   "source": [
+    "class DoubleConv(nn.Module):\n",
+    "    \"\"\"(convolution => [BN] => ReLU) * 2\"\"\"\n",
+    "\n",
+    "    def __init__(self, in_channels, out_channels, mid_channels=None):\n",
+    "        super().__init__()\n",
+    "        if not mid_channels:\n",
+    "            mid_channels = out_channels\n",
+    "        self.double_conv = nn.Sequential(\n",
+    "            nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False),\n",
+    "            nn.BatchNorm2d(mid_channels),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),\n",
+    "            nn.BatchNorm2d(out_channels),\n",
+    "            nn.ReLU(inplace=True)\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return self.double_conv(x)\n",
+    "\n",
+    "\n",
+    "class Down(nn.Module):\n",
+    "    \"\"\"Downscaling with maxpool then double conv\"\"\"\n",
+    "\n",
+    "    def __init__(self, in_channels, out_channels):\n",
+    "        super().__init__()\n",
+    "        self.maxpool_conv = nn.Sequential(\n",
+    "            nn.MaxPool2d(2),\n",
+    "            DoubleConv(in_channels, out_channels)\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return self.maxpool_conv(x)\n",
+    "\n",
+    "\n",
+    "class Up(nn.Module):\n",
+    "    \"\"\"Upscaling then double conv\"\"\"\n",
+    "\n",
+    "    def __init__(self, in_channels, out_channels, bilinear=True):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # if bilinear, use the normal convolutions to reduce the number of channels\n",
+    "        if bilinear:\n",
+    "            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)\n",
+    "            self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)\n",
+    "        else:\n",
+    "            self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)\n",
+    "            self.conv = DoubleConv(in_channels, out_channels)\n",
+    "\n",
+    "    def forward(self, x1, x2):\n",
+    "        x1 = self.up(x1)\n",
+    "        # input is CHW\n",
+    "        diffY = x2.size()[2] - x1.size()[2]\n",
+    "        diffX = x2.size()[3] - x1.size()[3]\n",
+    "\n",
+    "        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,\n",
+    "                        diffY // 2, diffY - diffY // 2])\n",
+    "        # if you have padding issues, see\n",
+    "        # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a\n",
+    "        # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd\n",
+    "        x = torch.cat([x2, x1], dim=1)\n",
+    "        return self.conv(x)\n",
+    "\n",
+    "\n",
+    "class OutConv(nn.Module):\n",
+    "    def __init__(self, in_channels, out_channels):\n",
+    "        super(OutConv, self).__init__()\n",
+    "        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return self.conv(x)\n",
+    "\n",
+    "class UNet(nn.Module):\n",
+    "    def __init__(self, n_channels, n_classes, bilinear=False):\n",
+    "        super(UNet, self).__init__()\n",
+    "        self.n_channels = n_channels\n",
+    "        self.n_classes = n_classes\n",
+    "        self.bilinear = bilinear\n",
+    "\n",
+    "        self.inc = (DoubleConv(n_channels, 64))\n",
+    "        self.down1 = (Down(64, 128))\n",
+    "        self.down2 = (Down(128, 256))\n",
+    "        self.down3 = (Down(256, 512))\n",
+    "        factor = 2 if bilinear else 1\n",
+    "        self.down4 = (Down(512, 1024 // factor))\n",
+    "        self.up1 = (Up(1024, 512 // factor, bilinear))\n",
+    "        self.up2 = (Up(512, 256 // factor, bilinear))\n",
+    "        # self.up2 = (Up(512, 64, bilinear))\n",
+    "        self.up3 = (Up(256, 128 // factor, bilinear))\n",
+    "        self.up4 = (Up(128, 64, bilinear))\n",
+    "        self.outc = (OutConv(64, n_classes))\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x1 = self.inc(x)\n",
+    "        x2 = self.down1(x1)\n",
+    "        x3 = self.down2(x2)\n",
+    "        x4 = self.down3(x3)\n",
+    "        x5 = self.down4(x4)\n",
+    "        x = self.up1(x5, x4)\n",
+    "        x = self.up2(x, x3)\n",
+    "        x = self.up3(x, x2)\n",
+    "        x = self.up4(x, x1)\n",
+    "        logits = self.outc(x)\n",
+    "        logits = F.relu(logits)\n",
+    "        return logits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 13,
+     "status": "ok",
+     "timestamp": 1753592562452,
+     "user": {
+      "displayName": "陈红涛",
+      "userId": "16480124546172497377"
+     },
+     "user_tz": -480
+    },
+    "id": "5glRHs1Fs5N_"
+   },
+   "outputs": [],
+   "source": [
+    "class FeatureExtraction(nn.Module):\n",
+    "    def __init__(self, in_channels=3):\n",
+    "        super(FeatureExtraction, self).__init__()\n",
+    "        self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=3, padding=2, dilation=2)\n",
+    "        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=2, dilation=2)\n",
+    "        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
+    "\n",
+    "        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=2, dilation=2)\n",
+    "        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=2, dilation=2)\n",
+    "        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
+    "\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = F.relu(self.conv1(x))\n",
+    "        x = F.relu(self.conv2(x))\n",
+    "        x = self.pool2(x)\n",
+    "        x = F.relu(self.conv3(x))\n",
+    "        x = F.relu(self.conv4(x))\n",
+    "        x = self.pool4(x)\n",
+    "\n",
+    "        return x\n",
+    "\n",
+    "    def load_pretrained_weights_partial(self, weights_path, num_layers=4):\n",
+    "        save_model = torch.load(weights_path)\n",
+    "        partial_state_dict = {}\n",
+    "        expected_layers = [\n",
+    "            'feature_extraction.conv1.weight', 'feature_extraction.conv1.bias',\n",
+    "            'feature_extraction.conv2.weight', 'feature_extraction.conv2.bias',\n",
+    "            'feature_extraction.conv3.weight', 'feature_extraction.conv3.bias',\n",
+    "            'feature_extraction.conv4.weight', 'feature_extraction.conv4.bias',\n",
+    "        ]\n",
+    "        state_dict = {k.split('.', 1)[-1]: v for k, v in save_model.items() if k in expected_layers[:2 * num_layers]}\n",
+    "        model_dict = self.state_dict()\n",
+    "\n",
+    "        for k in state_dict:\n",
+    "            if k in model_dict:\n",
+    "                partial_state_dict[k] = state_dict[k]\n",
+    "                print(k)\n",
+    "\n",
+    "        self.load_state_dict(partial_state_dict)\n",
+    "\n",
+    "\n",
+    "class ChickenCounting(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(ChickenCounting, self).__init__()\n",
+    "        self.feature_extraction = FeatureExtraction()\n",
+    "        self.unet = UNet(n_channels=128, n_classes=1)\n",
+    "\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.feature_extraction(x)\n",
+    "        x = self.unet(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reading the Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 14,
+     "status": "ok",
+     "timestamp": 1753592562479,
+     "user": {
+      "displayName": "陈红涛",
+      "userId": "16480124546172497377"
+     },
+     "user_tz": -480
+    },
+    "id": "u-Pb0-1Ps5OA"
+   },
+   "outputs": [],
+   "source": [
+    "train_dataset = load_from_disk(TRAINING_SET)\n",
+    "\n",
+    "image_transform = transforms.Compose([\n",
+    "    transforms.ToTensor(),\n",
+    "])\n",
+    "\n",
+    "def collate_fn(batch, scale = scale):\n",
+    "    return {\n",
+    "        \"image\": torch.stack([image_transform(item[\"image\"]) for item in batch]),\n",
+    "        \"density\": torch.stack([torch.tensor(item[\"density\"], dtype=DTYPE).unsqueeze(0) * scale for item in batch])\n",
+    "    }\n",
+    "\n",
+    "train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True, collate_fn=collate_fn)\n",
+    "val_loader = DataLoader(train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 35,
+     "status": "ok",
+     "timestamp": 1753592562517,
+     "user": {
+      "displayName": "陈红涛",
+      "userId": "16480124546172497377"
+     },
+     "user_tz": -480
+    },
+    "id": "RN2VW6yTs5OB"
+   },
+   "outputs": [],
+   "source": [
+    "def train_chickenfcn(model, train_loader, val_loader, optimizer, scheduler, num_epochs, device, save_path):\n",
+    "    model.train()\n",
+    "    criterion_mse = torch.nn.MSELoss(reduction='sum').to(device)\n",
+    "    criterion_mae = torch.nn.L1Loss(reduction='sum').to(device)\n",
+    "    best_loss = float('inf')\n",
+    "    print(train_loader.__len__())\n",
+    "\n",
+    "    for epoch in range(num_epochs):\n",
+    "        train_loss_mse = 0.0\n",
+    "        train_loss_mae = 0.0\n",
+    "\n",
+    "        train_loader_tqdm = tqdm(train_loader, desc=f'Epoch {epoch + 1}/{num_epochs}', leave=False)\n",
+    "\n",
+    "        for i, data in enumerate(train_loader_tqdm, 0):\n",
+    "            inputs, targets = data[\"image\"], data[\"density\"]\n",
+    "            inputs = inputs.to(device).float()\n",
+    "            targets = targets.to(device).float()\n",
+    "            # print(targets.shape)\n",
+    "            # t = np.sum((targets[0] / scale).cpu().numpy().squeeze())\n",
+    "            # print(t)\n",
+    "\n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "            outputs = model(inputs)\n",
+    "\n",
+    "            loss_mse = criterion_mse(outputs, targets)\n",
+    "            loss_mae = criterion_mae(outputs, targets)\n",
+    "            loss_mae.backward()\n",
+    "\n",
+    "            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=2.0)\n",
+    "\n",
+    "            optimizer.step()\n",
+    "            train_loss_mse += loss_mse.item()\n",
+    "            train_loss_mae += loss_mae.item()\n",
+    "\n",
+    "            train_loader_tqdm.set_postfix({'Train MSE Loss': loss_mse.item(), 'Train MAE Loss': loss_mae.item()})\n",
+    "\n",
+    "        train_loss_mse /= (len(train_loader))\n",
+    "        train_loss_mae /= (len(train_loader))\n",
+    "        logging.info(\n",
+    "            f'Epoch [{epoch + 1}/{num_epochs}], Train MSE loss: {train_loss_mse:.8f}, MAE loss: {train_loss_mae:.8f}')\n",
+    "\n",
+    "        scheduler.step()\n",
+    "\n",
+    "        # Validation\n",
+    "        model.eval()\n",
+    "        val_loss_mse = 0.0\n",
+    "        val_loss_mae = 0.0\n",
+    "\n",
+    "        val_loader_tqdm = tqdm(val_loader, desc=f'Validation Epoch {epoch + 1}/{num_epochs}', leave=False)\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            for i, data in enumerate(val_loader_tqdm, 0):\n",
+    "                inputs, targets = data[\"image\"], data[\"density\"]\n",
+    "                inputs = inputs.to(device).float()\n",
+    "                targets = targets.to(device).float()\n",
+    "\n",
+    "                outputs = model(inputs)\n",
+    "                mse_loss = criterion_mse(outputs, targets)\n",
+    "                mae_loss = criterion_mae(outputs, targets)\n",
+    "                val_loss_mse += mse_loss.item()\n",
+    "                val_loss_mae += mae_loss.item()\n",
+    "\n",
+    "                val_loader_tqdm.set_postfix(\n",
+    "                    {'Validation MSE Loss': mse_loss.item(), 'Validation MAE Loss': mae_loss.item()})\n",
+    "\n",
+    "        val_loss_mse /= (len(val_loader))\n",
+    "        val_loss_mae /= (len(val_loader))\n",
+    "        logging.info(\n",
+    "            f'Epoch [{epoch + 1}/{num_epochs}], Validation MSE Loss: {val_loss_mse:.8f}, MAE Loss: {val_loss_mae:.8f}')\n",
+    "\n",
+    "        # Save Model\n",
+    "        if val_loss_mae < best_loss:\n",
+    "            best_loss = val_loss_mae\n",
+    "            torch.save(model.state_dict(), save_path)\n",
+    "\n",
+    "    print('Finished Training ChickenFCN')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 917027,
+     "status": "ok",
+     "timestamp": 1753593479556,
+     "user": {
+      "displayName": "陈红涛",
+      "userId": "16480124546172497377"
+     },
+     "user_tz": -480
+    },
+    "id": "oAoNLtC5s5OC",
+    "outputId": "f700e0dc-5a4c-4f5f-bdc9-2aa074bc3d60"
+   },
+   "outputs": [],
+   "source": [
+    "logging = logging_level('info')\n",
+    "logging.debug('use debug level logging setting')\n",
+    "\n",
+    "################################################################################\n",
+    "# Experiment Settings\n",
+    "################################################################################\n",
+    "learning_rate = 1e-4\n",
+    "lr_decay = 1e-5\n",
+    "weight_decay = 0.0001\n",
+    "save_path = \"model.pth\"\n",
+    "\n",
+    "epochs = 30\n",
+    "\n",
+    "################################################################################\n",
+    "# Dataset paths\n",
+    "################################################################################\n",
+    "\n",
+    "model = ChickenCounting().to(DEVICE)\n",
+    "model.feature_extraction.load_pretrained_weights_partial(BASE_MODEL_PATH)\n",
+    "print('load model success')\n",
+    "\n",
+    "optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)\n",
+    "# optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum, weight_decay=weight_decay)\n",
+    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=1 - lr_decay)\n",
+    "\n",
+    "logging.info('Begin training single view model...')\n",
+    "train_chickenfcn(model, train_loader, val_loader, optimizer, scheduler, epochs, DEVICE, save_path=save_path)\n",
+    "logging.info('Finished training single view model.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluate Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 3,
+     "status": "ok",
+     "timestamp": 1753593479563,
+     "user": {
+      "displayName": "陈红涛",
+      "userId": "16480124546172497377"
+     },
+     "user_tz": -480
+    },
+    "id": "_PuMTMDKs5OE"
+   },
+   "outputs": [],
+   "source": [
+    "def evaluate(model, val_loader, device, scale): # Function used for final scoring.\n",
+    "    model.eval()  # Set the model to evaluation mode\n",
+    "\n",
+    "    # Initialize metrics\n",
+    "    mse = 0.0\n",
+    "    mae = 0.0\n",
+    "    predict_num = 0.0\n",
+    "    true_num = 0.0\n",
+    "    rate = 0.0\n",
+    "\n",
+    "    with torch.no_grad():  # Disable gradient calculation for inference\n",
+    "        for i, data in enumerate(val_loader, 0):\n",
+    "            inputs, targets = data[\"image\"], data[\"density\"]\n",
+    "            inputs = inputs.to(device).float()  # Move inputs to device and convert to float\n",
+    "            targets = targets.to(device).float()  # Move targets to device and convert to float\n",
+    "\n",
+    "            # Get the model predictions\n",
+    "            outputs = model(inputs) / scale  # Adjusting for the scaling factor\n",
+    "\n",
+    "            # Convert tensors to numpy for visualization and metrics calculation\n",
+    "            inputs_np = inputs.cpu().numpy()  # Convert inputs to numpy\n",
+    "            targets_np = targets.cpu().numpy()  # Convert targets to numpy\n",
+    "            outputs_np = outputs.cpu().numpy()  # Convert outputs to numpy\n",
+    "            # imshow_res(inputs_np, targets_np, outputs_np, scale)  # Uncomment to visualize results\n",
+    "\n",
+    "            # Calculate true and predicted sums for comparison\n",
+    "            t = np.sum((targets[0] / scale).cpu().numpy().squeeze())  # Ground truth sum\n",
+    "            g = np.sum(outputs.cpu().numpy().squeeze())  # Predicted sum\n",
+    "            print(f'NO.{i}   true_sum={t}, get_sum={g}, abs={abs(t - g)}, rate={abs(1 - g / t)}')\n",
+    "\n",
+    "            # Update metrics\n",
+    "            predict_num += g\n",
+    "            true_num += t\n",
+    "            rate += abs(1 - g / t)\n",
+    "            mae += abs(t - g)\n",
+    "            mse += abs(t - g) * abs(t - g)\n",
+    "\n",
+    "    # Calculate average metrics across all batches\n",
+    "    mae /= len(val_loader)\n",
+    "    mse /= len(val_loader)\n",
+    "    predict_num /= len(val_loader)\n",
+    "    true_num /= len(val_loader)\n",
+    "    rate /= len(val_loader)\n",
+    "\n",
+    "    # Log the results\n",
+    "    logging.info(\n",
+    "        f'test ---- Score: {math.exp(-rate):.3f}, MSE: {mse:.4f}, MAE: {mae:.4f}, Chicken_avg: {predict_num:.4f}')\n",
+    "    return math.exp(-rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 12124,
+     "status": "ok",
+     "timestamp": 1753593491690,
+     "user": {
+      "displayName": "陈红涛",
+      "userId": "16480124546172497377"
+     },
+     "user_tz": -480
+    },
+    "id": "0yFgonMZs5OF",
+    "outputId": "f4a0fe9d-d185-4133-8f8e-4bc2e2c8fa77"
+   },
+   "outputs": [],
+   "source": [
+    "model.load_state_dict(torch.load(save_path, map_location=DEVICE))\n",
+    "model.to(DEVICE)\n",
+    "evaluate(model, val_loader, DEVICE, scale)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xVQtmZ55s5OG"
+   },
+   "source": [
+    "## Submission\n",
+    "Please read through the following code carefully. Make sure to following the file naming conventions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 22942,
+     "status": "ok",
+     "timestamp": 1753593514634,
+     "user": {
+      "displayName": "陈红涛",
+      "userId": "16480124546172497377"
+     },
+     "user_tz": -480
+    },
+    "id": "siCztiUEs5OI",
+    "outputId": "79ea01ce-a3c9-45fb-a511-01815386db75"
+   },
+   "outputs": [],
+   "source": [
+    "test_path = os.environ.get(\"DATA_PATH\", \"datasets\")\n",
+    "\n",
+    "def collate_fn(batch): # The test datasets will not provide target densities\n",
+    "    return torch.stack([image_transform(item[\"image\"]) for item in batch])\n",
+    "\n",
+    "test_dataset = load_from_disk(os.path.join(test_path, \"test_a\"))\n",
+    "test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False, collate_fn=collate_fn)\n",
+    "\n",
+    "predictions = []\n",
+    "model.eval()\n",
+    "with torch.no_grad():\n",
+    "    for batch in tqdm(test_loader):\n",
+    "        outputs = model(batch.to(DEVICE)) / scale\n",
+    "        predictions.append(outputs.cpu().numpy())\n",
+    "\n",
+    "pred_a = np.concatenate(predictions, axis=0)\n",
+    "\n",
+    "del test_dataset\n",
+    "del test_loader\n",
+    "del predictions\n",
+    "\n",
+    "test_dataset = load_from_disk(os.path.join(test_path, \"test_b\"))\n",
+    "test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False, collate_fn=collate_fn)\n",
+    "\n",
+    "predictions = []\n",
+    "with torch.no_grad():\n",
+    "    for batch in tqdm(test_loader):\n",
+    "        outputs = model(batch.to(DEVICE)) / scale\n",
+    "        predictions.append(outputs.cpu().numpy())\n",
+    "\n",
+    "pred_b = np.concatenate(predictions, axis=0)\n",
+    "\n",
+    "np.savez('submission.npz', pred_a=pred_a, pred_b=pred_b) # save your submissions in an npz file with the keys `pred_a` and `pred_b`"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Individual-Contest/Chicken_Counting/Solution/Scoring/metrics.py

@@ -0,0 +1,271 @@
+import numpy as np
+import logging
+import math
+import json
+from datasets import load_from_disk
+import os
+
+# Function to configure logging levels
+def logging_level(level='info'):
+    str_format = '%(asctime)s - %(levelname)s: %(message)s'
+    if level == 'debug':
+        logging.basicConfig(level=logging.DEBUG, format=str_format, datefmt='%Y-%m-%d %H:%M:%S')
+    elif level == 'info':
+        logging.basicConfig(level=logging.INFO, format=str_format, datefmt='%Y-%m-%d %H:%M:%S')
+    return logging
+
+
+def validate_predictions_shape(predictions_np, expected_shape_suffix):
+    """
+    Validate that predictions have the correct shape.
+    Accepts either (n, 1, 180, 320) or (n, 180, 320) for this competition.
+    Number of samples (n) must be exactly 100.
+    """
+    if not isinstance(predictions_np, np.ndarray):
+        return False, "Prediction data must be a numpy array"
+    
+    # Accept (n, 1, 180, 320) or (n, 180, 320)
+    if len(predictions_np.shape) == 4:
+        # (n, 1, 180, 320)
+        if predictions_np.shape[1:] != expected_shape_suffix:
+            return False, "Prediction data has incorrect dimensions"
+        if predictions_np.shape[1] != 1:
+            return False, "Prediction data must have exactly 1 channel"
+        if predictions_np.shape[0] != 100:
+            return False, "Invalid number of samples in prediction data"
+    elif len(predictions_np.shape) == 3:
+        # (n, 180, 320)
+        if predictions_np.shape[1:] != expected_shape_suffix[1:]:
+            return False, "Prediction data has incorrect dimensions"
+        if predictions_np.shape[0] != 100:
+            return False, "Invalid number of samples in prediction data"
+    else:
+        return False, "Incorrect dimensions in prediction data"
+    
+    return True, None
+
+
+def validate_predictions_values(predictions_np):
+    """
+    Validate that prediction values are reasonable (real, non-negative, finite).
+    """
+    if np.iscomplexobj(predictions_np):
+        return False, "Prediction data contains complex values"
+    
+    if not np.isfinite(predictions_np).all():
+        return False, "Prediction data contains non-finite values"
+    
+    if (predictions_np < 0).any():
+        return False, "Prediction data contains negative values"
+    
+    return True, None
+
+
+def safe_evaluate_predictions(predictions_np, targets_np):
+    """
+    Safely evaluate predictions with error handling for mathematical operations.
+    """
+    try:
+        N = predictions_np.shape[0]
+        preds_sum = predictions_np.reshape(N, -1).sum(axis=1)
+        true_sum = targets_np.reshape(N, -1).sum(axis=1)
+        
+        # Check for invalid sums
+        if not np.isfinite(preds_sum).all() or not np.isfinite(true_sum).all():
+            return None, "Invalid sum values detected"
+        
+        diffs = np.abs(preds_sum - true_sum)
+        
+        # Safe division - handle division by zero
+        with np.errstate(divide='ignore', invalid='ignore'):
+            rates = np.abs(1 - preds_sum / true_sum)
+            # Replace inf and nan values with a high penalty
+            rates = np.where(np.isfinite(rates), rates, 1.0)
+        
+        mae = diffs.mean()
+        mse = (diffs**2).mean()
+        rate = rates.mean()
+        predict_num_avg = preds_sum.mean()
+        true_num_avg = true_sum.mean()
+        
+        # Check for invalid intermediate results
+        if not all(np.isfinite([mae, mse, rate, predict_num_avg, true_num_avg])):
+            return None, "Invalid intermediate calculation results"
+        
+        # Safe exponential calculation
+        if rate > 100:  # Prevent exp overflow
+            score = 0.0
+        else:
+            score = math.exp(-rate)
+        
+        logging.info(f'test ---- Score: {score:.3f}, MSE: {mse:.4f}, MAE: {mae:.4f}, Chicken_avg: {predict_num_avg:.4f}')
+        return score, None
+        
+    except Exception as e:
+        logging.error(f"Error in evaluation: {str(e)}")
+        return None, "Evaluation calculation failed"
+
+
+# Add function to evaluate predictions and targets arrays
+def evaluate_predictions(predictions_np, targets_np):
+    score, error = safe_evaluate_predictions(predictions_np, targets_np)
+    if error:
+        raise ValueError(error)
+    return score
+
+
+def safe_test(preds, test_path, expected_shape):
+    """
+    Safely run the test with comprehensive error handling.
+    """
+    try:
+        # Validate prediction shape
+        valid_shape, shape_error = validate_predictions_shape(preds, expected_shape)
+        if not valid_shape:
+            return None, shape_error
+        
+        # Validate prediction values
+        valid_values, values_error = validate_predictions_values(preds)
+        if not valid_values:
+            return None, values_error
+        
+        # Load target dataset
+        test_dataset = load_from_disk(test_path)
+        
+        # Extract density data directly as numpy arrays without torch
+        targets = []
+        for item in test_dataset:
+            density = np.array(item["density"], dtype=np.float32)
+            # Add batch dimension to match expected shape
+            targets.append(density[np.newaxis, :])
+        
+        # Concatenate all targets into a single numpy array
+        targets = np.concatenate(targets, axis=0)
+        
+        # Remove channel dimension from predictions for shape comparison and evaluation
+        # Predictions are (n, 1, 180, 320), targets are (n, 180, 320)
+        if len(preds.shape) == 4 and preds.shape[1] == 1:
+            preds_squeezed = preds.squeeze(axis=1)  # Remove channel dimension
+        else:
+            return None, "Invalid prediction format for evaluation"
+        
+        # Validate that prediction and target shapes match after removing channel
+        if preds_squeezed.shape != targets.shape:
+            return None, "Prediction and target data shape mismatch"
+        
+        # Safely evaluate predictions (using squeezed predictions without channel dim)
+        score, eval_error = safe_evaluate_predictions(preds_squeezed, targets)
+        if eval_error:
+            return None, eval_error
+        
+        # Final safety check: clamp score to [0.0, 1.0]
+        if score < 0.0 or score > 1.0:
+            logging.warning(f"Score {score} out of valid range, setting to 0.0")
+            score = 0.0
+        
+        return score, None
+        
+    except Exception as e:
+        logging.error(f"Error in test function: {str(e)}")
+        return None, "Test execution failed"
+
+
+# Main function to run the validation
+def test(preds, test_path):
+    score, error = safe_test(preds, test_path, (1, 180, 320))
+    if error:
+        raise ValueError(error)
+    return score
+
+
+def create_error_response(error_message):
+    """Create standardized error response."""
+    return {
+        "status": False,
+        "score": {
+            "public_a": 0.0,
+            "private_b": 0.0,
+        },
+        "msg": f"Error: {error_message}",
+    }
+
+
+def create_success_response(score_a, score_b):
+    """Create standardized success response."""
+    # 处理 NaN 和 inf，替换为 0.0
+    if not np.isfinite(score_a):  # np.isfinite 同时检查 NaN 和 inf
+        score_a = 0.0
+    if not np.isfinite(score_b):
+        score_b = 0.0
+    return {
+        "status": True,
+        "score": {
+            "public_a": score_a,
+            "private_b": score_b,
+        },
+        "msg": "Success!",
+    }
+
+
+if __name__ == '__main__':
+    ################################################################################
+    # Dataset paths
+    if os.environ.get('METRIC_PATH'):
+        METRIC_PATH = os.environ.get("METRIC_PATH") + "/" 
+    else:
+        METRIC_PATH = ""  # Fallback for local testing
+    testA_path = METRIC_PATH + "test_a_targets"
+    testB_path = METRIC_PATH + "test_b_targets"
+    
+    try:
+        # Safely load the npz file
+        try:
+            preds = np.load("submission.npz", allow_pickle=False)
+        except FileNotFoundError:
+            ret_json = create_error_response("Submission file not found")
+        except Exception as e:
+            ret_json = create_error_response("Failed to load submission file")
+        else:
+            # Check for required keys
+            required_keys = ['pred_a', 'pred_b']
+            missing_keys = [key for key in required_keys if key not in preds.files]
+            
+            if missing_keys:
+                ret_json = create_error_response(f"Missing required keys in submission file")
+            else:
+                try:
+                    # Extract predictions safely
+                    pred_a = preds['pred_a']
+                    pred_b = preds['pred_b']
+                    
+                    logging = logging_level('info')
+                    
+                    # Test both predictions with error handling
+                    score_a, error_a = safe_test(pred_a, testA_path, (1, 180, 320))
+                    if error_a:
+                        ret_json = create_error_response(f"Error in test A evaluation: {error_a}")
+                    else:
+                        score_b, error_b = safe_test(pred_b, testB_path, (1, 180, 320))
+                        if error_b:
+                            ret_json = create_error_response(f"Error in test B evaluation: {error_b}")
+                        else:
+                            # Final safety check on scores
+                            score_a = max(0.0, min(1.0, score_a))
+                            score_b = max(0.0, min(1.0, score_b))
+                            
+                            ret_json = create_success_response(score_a, score_b)
+                            
+                except Exception as e:
+                    logging.error(f"Unexpected error during evaluation: {str(e)}")
+                    ret_json = create_error_response("Evaluation failed due to invalid submission format")
+    
+    except Exception as e:
+        logging.error(f"Critical error: {str(e)}")
+        ret_json = create_error_response("Critical evaluation error")
+    
+    # Write result to file
+    try:
+        with open('score.json', 'w') as f:
+            f.write(json.dumps(ret_json))
+    except Exception as e:
+        logging.error(f"Failed to write score file: {str(e)}")

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+  "citation": "",
+  "description": "",
+  "features": {
+    "image": {
+      "_type": "Image"
+    }
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+  "license": ""
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+  "description": "",
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+        180,
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+      "_type": "Array2D"
+    }
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+  "license": ""
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+  "description": "",
+  "features": {
+    "image": {
+      "_type": "Image"
+    }
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+  "license": ""
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+  "license": ""
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+  "citation": "",
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