Add SqueezeNet Fire Module

build/190.json

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+{
+  "id": "190",
+  "title": "Implement SqueezeNet Fire Module",
+  "difficulty": "medium",
+  "category": "Deep Learning",
+  "video": "",
+  "likes": "0",
+  "dislikes": "0",
+  "contributor": [
+    {
+      "profile_link": "https://github.com/syed-nazmus-sakib",
+      "name": "Syed Nazmus Sakib"
+    }
+  ],
+  "description": "Implement the Fire Module from SqueezeNet, a highly parameter-efficient convolutional neural network architecture. The Fire Module consists of a \"squeeze\" layer (1x1 convolutions) that feeds into an \"expand\" layer (a mix of 1x1 and 3x3 convolutions).\n\nYour task is to implement the forward pass of the Fire Module using NumPy. Given an input tensor and weights/biases for the squeeze and expand layers, compute the output.\n\n**Functions required:**\n\n1.  `squeeze`: Applies 1x1 convolution to reduce channels.\n2.  `expand`: Applies 1x1 and 3x3 convolutions in parallel and concatenates results.\n3.  `fire_module`: Combines squeeze and expand.\n\nAssume stride=1 and padding='same' for 3x3 convolutions (so spatial dimensions are preserved).",
+  "learn_section": "## SqueezeNet Fire Module Explained\n\nSqueezeNet achieves AlexNet-level accuracy with 50x fewer parameters through its \"Fire Module\".\n\n### Architecture\n\nThe Fire Module consists of two layers:\n\n1. **Squeeze Layer**: A convolution layer with only 1x1 filters.\n   - Purpose: Reduce the number of input channels before feeding them to larger 3x3 filters. This minimizes computation and parameters.\n   - Notation: $s_{1x1}$ is the number of 1x1 filters in the squeeze layer.\n\n2. **Expand Layer**: A mix of 1x1 and 3x3 convolution filters.\n   - Purpose: Capture both local details (3x3) and pointwise features (1x1).\n   - Notation:\n     - $e_{1x1}$: Number of 1x1 filters in expand layer.\n     - $e_{3x3}$: Number of 3x3 filters in expand layer.\n   - The outputs of these two parallel convolutions are concatenated along the channel dimension.\n\n### Operation\n\nThe flow is:\n`Input -> Squeeze (1x1 conv + ReLU) -> Expand ( (1x1 conv + ReLU) || (3x3 conv + ReLU) ) -> Concatenate -> Output`\n\n### Why it works?\n\nStandard 3x3 convolution on $C_{in}$ channels with $C_{out}$ filters has parameters:\n$P_{standard} = 3 \\times 3 \\times C_{in} \\times C_{out}$\n\nIn Fire Module:\n\n1. Squeeze layer reduces $C_{in}$ to $s_{1x1}$ (small). Parameters: $1 \\times 1 \\times C_{in} \\times s_{1x1}$.\n2. Expand 3x3 layer operates on only $s_{1x1}$ channels. Parameters: $3 \\times 3 \\times s_{1x1} \\times e_{3x3}$.\n3. Expand 1x1 layer adds more features cheaply. Parameters: $1 \\times 1 \\times s_{1x1} \\times e_{1x1}$.\n\nTotal parameters are significantly lower if $s_{1x1} \\ll C_{in}$.\n\n### Implementation Details\n\n- **Padding**: Use 'same' padding for 3x3 convolutions so spatial dimensions match 1x1 outputs.\n- **Concatenation**: Join along the channel axis (axis=-1 or 1 depending on format, here we assume (H, W, C)).",
+  "starter_code": "import numpy as np\n\ndef fire_module_forward(input_tensor, \n                        squeeze_weights, squeeze_bias,\n                        expand1x1_weights, expand1x1_bias,\n                        expand3x3_weights, expand3x3_bias):\n    \"\"\"\n    Implements the forward pass of a SqueezeNet Fire Module.\n    \n    Args:\n        input_tensor: (H, W, C_in)\n        squeeze_weights: (1, 1, C_in, s1x1)\n        squeeze_bias: (s1x1,)\n        expand1x1_weights: (1, 1, s1x1, e1x1)\n        expand1x1_bias: (e1x1,)\n        expand3x3_weights: (3, 3, s1x1, e3x3)\n        expand3x3_bias: (e3x3,)\n        \n    Returns:\n        output_tensor: (H, W, e1x1 + e3x3)\n    \n    Note:\n    - All convolutions use stride=1.\n    - 3x3 convolution uses padding='same' (pad=1).\n    - Apply ReLU activation after each convolution (max(0, x)).\n    \"\"\"\n    # Your code here\n    pass",
+  "solution": "import numpy as np\n\ndef relu(x):\n    \"\"\"ReLU activation.\"\"\"\n    return np.maximum(0, x)\n\ndef conv2d(x, w, b, padding=0):\n    \"\"\"\n    Apply 2D convolution.\n    x: (H, W, C_in)\n    w: (kh, kw, C_in, C_out)\n    b: (C_out,)\n    padding: int (symmetrical padding on H and W)\n    \"\"\"\n    H, W, C_in = x.shape\n    kh, kw, _, C_out = w.shape\n    \n    # Apply padding if needed\n    if padding > 0:\n        x_padded = np.pad(x, ((padding, padding), (padding, padding), (0, 0)), mode='constant')\n    else:\n        x_padded = x\n    \n    # Output dimensions (assuming stride=1)\n    H_out = H\n    W_out = W\n    \n    output = np.zeros((H_out, W_out, C_out))\n    \n    # Optimization for 1x1 convolution\n    if kh == 1 and kw == 1:\n        # Flatten spatial dimensions: (H*W, C_in)\n        x_flat = x.reshape(-1, C_in)\n        # Weights: (C_in, C_out)\n        w_flat = w.reshape(C_in, C_out)\n        # Matrix multiplication + bias\n        out_flat = np.dot(x_flat, w_flat) + b\n        return out_flat.reshape(H, W, C_out)\n    \n    # General convolution (loops)\n    for h in range(H_out):\n        for w_idx in range(W_out):\n            # Extract patch\n            patch = x_padded[h:h+kh, w_idx:w_idx+kw, :]\n            # Convolution for each output channel\n            for c in range(C_out):\n                kernel = w[:, :, :, c]\n                output[h, w_idx, c] = np.sum(patch * kernel) + b[c]\n                \n    return output\n\ndef fire_module_forward(input_tensor, \n                        squeeze_weights, squeeze_bias,\n                        expand1x1_weights, expand1x1_bias,\n                        expand3x3_weights, expand3x3_bias):\n    \"\"\"\n    Implements the forward pass of a SqueezeNet Fire Module.\n    \"\"\"\n    # 1. Squeeze Layer (1x1 conv)\n    squeeze_out = conv2d(input_tensor, squeeze_weights, squeeze_bias)\n    squeeze_act = relu(squeeze_out)\n    \n    # 2. Expand Layer\n    # Branch 1: 1x1 conv\n    expand1x1_out = conv2d(squeeze_act, expand1x1_weights, expand1x1_bias)\n    expand1x1_act = relu(expand1x1_out)\n    \n    # Branch 2: 3x3 conv (with padding='same' -> pad=1)\n    expand3x3_out = conv2d(squeeze_act, expand3x3_weights, expand3x3_bias, padding=1)\n    expand3x3_act = relu(expand3x3_out)\n    \n    # 3. Concatenate along channel axis\n    output = np.concatenate([expand1x1_act, expand3x3_act], axis=-1)\n    \n    return output",
+  "example": {
+    "input": "input_tensor: (H=32, W=32, C_in=3)\nSqueeze 1x1: s1x1=16 filters\nExpand 1x1: e1x1=64 filters\nExpand 3x3: e3x3=64 filters",
+    "output": "Output Tensor Shape: (32, 32, 128)\nValues: Concatenation of [ReLU(Expand1x1), ReLU(Expand3x3)]",
+    "reasoning": "1. Squeeze layer reduces 3 channels to 16 channels using 1x1 convolution + ReLU.\n2. Expand layer splits into two branches:\n   - Branch A: Apply 64 1x1 filters to the 16-channel squeeze output -> Output shape (32, 32, 64).\n   - Branch B: Apply 64 3x3 filters (with padding) to the 16-channel squeeze output -> Output shape (32, 32, 64).\n3. Concatenate the outputs (64 + 64) to get a final depth of 128. Spatial dimensions (32x32) remain unchanged."
+  },
+  "test_cases": [
+    {
+      "test": "import numpy as np\ninput = np.ones((5, 5, 2))\ns_w = np.ones((1, 1, 2, 1))\ns_b = np.zeros(1)\ne1_w = np.ones((1, 1, 1, 2))\ne1_b = np.zeros(2)\ne3_w = np.ones((3, 3, 1, 3))\ne3_b = np.zeros(3)\nres = fire_module_forward(input, s_w, s_b, e1_w, e1_b, e3_w, e3_b)\nprint(res.shape)",
+      "expected_output": "(5, 5, 5)"
+    },
+    {
+      "test": "import numpy as np\n# Simple value test\n# Input 1s (shape 3,3,1)\ninput = np.ones((3, 3, 1))\n# Squeeze: 1 filter, weight 2, bias 0 -> Output 2s\ns_w = np.full((1, 1, 1, 1), 2.0)\ns_b = np.zeros(1)\n# Expand 1x1: 1 filter, weight 0.5, bias 0 -> Output 2*0.5 = 1s\ne1_w = np.full((1, 1, 1, 1), 0.5)\ne1_b = np.zeros(1)\n# Expand 3x3: 1 filter, weight 0, bias 0 -> Output 0s\ne3_w = np.zeros((3, 3, 1, 1))\ne3_b = np.zeros(1)\nres = fire_module_forward(input, s_w, s_b, e1_w, e1_b, e3_w, e3_b)\n# Check first channel (Expand 1x1) at middle pixel\nprint(res[1, 1, 0])",
+      "expected_output": "1.0"
+    },
+    {
+      "test": "import numpy as np\n# Padding test for 3x3\ninput = np.ones((3, 3, 1))\n# Squeeze: weight 1 -> Output 1s\ns_w = np.ones((1, 1, 1, 1))\ns_b = np.zeros(1)\n# Expand 1x1: weights 0 (ignore)\ne1_w = np.zeros((1, 1, 1, 1))\ne1_b = np.zeros(1)\n# Expand 3x3: weight 1 -> Sum neighbors\n# At corner (0,0), with 'same' padding (zeros), only 4 neighbors are non-zero (1s)\n# Sum = 4 * 1 = 4\ne3_w = np.ones((3, 3, 1, 1))\ne3_b = np.zeros(1)\nres = fire_module_forward(input, s_w, s_b, e1_w, e1_b, e3_w, e3_b)\n# Check second channel (Expand 3x3) at corner (0,0)\nprint(res[0, 0, 1])",
+      "expected_output": "4.0"
+    },
+    {
+      "test": "import numpy as np\n# Middle pixel full sum\n# Same setup as above, check middle pixel (1,1)\n# 9 neighbors are 1s -> Sum = 9\ninput = np.ones((3, 3, 1))\ns_w = np.ones((1, 1, 1, 1))\ns_b = np.zeros(1)\ne1_w = np.zeros((1, 1, 1, 1))\ne1_b = np.zeros(1)\ne3_w = np.ones((3, 3, 1, 1))\ne3_b = np.zeros(1)\nres = fire_module_forward(input, s_w, s_b, e1_w, e1_b, e3_w, e3_b)\nprint(res[1, 1, 1])",
+      "expected_output": "9.0"
+    },
+    {
+      "test": "import numpy as np\n# Non-zero bias test\ninput = np.ones((2, 2, 1))\ns_w = np.zeros((1, 1, 1, 1))\ns_b = np.array([1.5])\ne1_w = np.zeros((1, 1, 1, 1))\ne1_b = np.array([2.0])\ne3_w = np.zeros((3, 3, 1, 1))\ne3_b = np.array([3.0])\nres = fire_module_forward(input, s_w, s_b, e1_w, e1_b, e3_w, e3_b)\nprint(f\"{res[0, 0, 0]}, {res[0, 0, 1]}\")",
+      "expected_output": "2.0, 3.0"
+    },
+    {
+      "test": "import numpy as np\n# ReLU test\ninput = np.ones((2, 2, 1))\ns_w = np.ones((1, 1, 1, 1))\ns_b = np.array([-5.0])\ne1_w = np.ones((1, 1, 1, 1))\ne1_b = np.array([2.0])\ne3_w = np.ones((3, 3, 1, 1))\ne3_b = np.array([-1.0])\nres = fire_module_forward(input, s_w, s_b, e1_w, e1_b, e3_w, e3_b)\nprint(f\"{res[0, 0, 0]}, {res[0, 0, 1]}\")",
+      "expected_output": "2.0, 0.0"
+    }
+  ]
+}

questions/190_implement-squeezenet-fire-module/description.md

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+Implement the Fire Module from SqueezeNet, a highly parameter-efficient convolutional neural network architecture. The Fire Module consists of a "squeeze" layer (1x1 convolutions) that feeds into an "expand" layer (a mix of 1x1 and 3x3 convolutions).
+
+Your task is to implement the forward pass of the Fire Module using NumPy. Given an input tensor and weights/biases for the squeeze and expand layers, compute the output.
+
+**Functions required:**
+
+1.  `squeeze`: Applies 1x1 convolution to reduce channels.
+2.  `expand`: Applies 1x1 and 3x3 convolutions in parallel and concatenates results.
+3.  `fire_module`: Combines squeeze and expand.
+
+Assume stride=1 and padding='same' for 3x3 convolutions (so spatial dimensions are preserved).

questions/190_implement-squeezenet-fire-module/example.json

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+{
+  "input": "input_tensor: (H=32, W=32, C_in=3)\nSqueeze 1x1: s1x1=16 filters\nExpand 1x1: e1x1=64 filters\nExpand 3x3: e3x3=64 filters",
+  "output": "Output Tensor Shape: (32, 32, 128)\nValues: Concatenation of [ReLU(Expand1x1), ReLU(Expand3x3)]",
+  "reasoning": "1. Squeeze layer reduces 3 channels to 16 channels using 1x1 convolution + ReLU.\n2. Expand layer splits into two branches:\n   - Branch A: Apply 64 1x1 filters to the 16-channel squeeze output -> Output shape (32, 32, 64).\n   - Branch B: Apply 64 3x3 filters (with padding) to the 16-channel squeeze output -> Output shape (32, 32, 64).\n3. Concatenate the outputs (64 + 64) to get a final depth of 128. Spatial dimensions (32x32) remain unchanged."
+}

questions/190_implement-squeezenet-fire-module/learn.md

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+## SqueezeNet Fire Module Explained
+
+SqueezeNet achieves AlexNet-level accuracy with 50x fewer parameters through its "Fire Module".
+
+### Architecture
+
+The Fire Module consists of two layers:
+
+1. **Squeeze Layer**: A convolution layer with only 1x1 filters.
+   - Purpose: Reduce the number of input channels before feeding them to larger 3x3 filters. This minimizes computation and parameters.
+   - Notation: $s_{1x1}$ is the number of 1x1 filters in the squeeze layer.
+
+2. **Expand Layer**: A mix of 1x1 and 3x3 convolution filters.
+   - Purpose: Capture both local details (3x3) and pointwise features (1x1).
+   - Notation:
+     - $e_{1x1}$: Number of 1x1 filters in expand layer.
+     - $e_{3x3}$: Number of 3x3 filters in expand layer.
+   - The outputs of these two parallel convolutions are concatenated along the channel dimension.
+
+### Operation
+
+The flow is:
+`Input -> Squeeze (1x1 conv + ReLU) -> Expand ( (1x1 conv + ReLU) || (3x3 conv + ReLU) ) -> Concatenate -> Output`
+
+### Why it works?
+
+Standard 3x3 convolution on $C_{in}$ channels with $C_{out}$ filters has parameters:
+$P_{standard} = 3 \times 3 \times C_{in} \times C_{out}$
+
+In Fire Module:
+
+1. Squeeze layer reduces $C_{in}$ to $s_{1x1}$ (small). Parameters: $1 \times 1 \times C_{in} \times s_{1x1}$.
+2. Expand 3x3 layer operates on only $s_{1x1}$ channels. Parameters: $3 \times 3 \times s_{1x1} \times e_{3x3}$.
+3. Expand 1x1 layer adds more features cheaply. Parameters: $1 \times 1 \times s_{1x1} \times e_{1x1}$.
+
+Total parameters are significantly lower if $s_{1x1} \ll C_{in}$.
+
+### Implementation Details
+
+- **Padding**: Use 'same' padding for 3x3 convolutions so spatial dimensions match 1x1 outputs.
+- **Concatenation**: Join along the channel axis (axis=-1 or 1 depending on format, here we assume (H, W, C)).

questions/190_implement-squeezenet-fire-module/meta.json

@@ -0,0 +1,15 @@
+{
+  "id": "190",
+  "title": "Implement SqueezeNet Fire Module",
+  "difficulty": "medium",
+  "category": "Deep Learning",
+  "video": "",
+  "likes": "0",
+  "dislikes": "0",
+  "contributor": [
+    {
+      "profile_link": "https://github.com/syed-nazmus-sakib",
+      "name": "Syed Nazmus Sakib"
+    }
+  ]
+}

questions/190_implement-squeezenet-fire-module/solution.py

@@ -0,0 +1,75 @@
+import numpy as np
+
+def relu(x):
+    """ReLU activation."""
+    return np.maximum(0, x)
+
+def conv2d(x, w, b, padding=0):
+    """
+    Apply 2D convolution.
+    x: (H, W, C_in)
+    w: (kh, kw, C_in, C_out)
+    b: (C_out,)
+    padding: int (symmetrical padding on H and W)
+    """
+    H, W, C_in = x.shape
+    kh, kw, _, C_out = w.shape
+
+    # Apply padding if needed
+    if padding > 0:
+        x_padded = np.pad(x, ((padding, padding), (padding, padding), (0, 0)), mode='constant')
+    else:
+        x_padded = x
+
+    # Output dimensions (assuming stride=1)
+    H_out = H
+    W_out = W
+
+    output = np.zeros((H_out, W_out, C_out))
+
+    # Optimization for 1x1 convolution
+    if kh == 1 and kw == 1:
+        # Flatten spatial dimensions: (H*W, C_in)
+        x_flat = x.reshape(-1, C_in)
+        # Weights: (C_in, C_out)
+        w_flat = w.reshape(C_in, C_out)
+        # Matrix multiplication + bias
+        out_flat = np.dot(x_flat, w_flat) + b
+        return out_flat.reshape(H, W, C_out)
+
+    # General convolution (loops)
+    for h in range(H_out):
+        for w_idx in range(W_out):
+            # Extract patch
+            patch = x_padded[h:h+kh, w_idx:w_idx+kw, :]
+            # Convolution for each output channel
+            for c in range(C_out):
+                kernel = w[:, :, :, c]
+                output[h, w_idx, c] = np.sum(patch * kernel) + b[c]
+
+    return output
+
+def fire_module_forward(input_tensor, 
+                        squeeze_weights, squeeze_bias,
+                        expand1x1_weights, expand1x1_bias,
+                        expand3x3_weights, expand3x3_bias):
+    """
+    Implements the forward pass of a SqueezeNet Fire Module.
+    """
+    # 1. Squeeze Layer (1x1 conv)
+    squeeze_out = conv2d(input_tensor, squeeze_weights, squeeze_bias)
+    squeeze_act = relu(squeeze_out)
+
+    # 2. Expand Layer
+    # Branch 1: 1x1 conv
+    expand1x1_out = conv2d(squeeze_act, expand1x1_weights, expand1x1_bias)
+    expand1x1_act = relu(expand1x1_out)
+
+    # Branch 2: 3x3 conv (with padding='same' -> pad=1)
+    expand3x3_out = conv2d(squeeze_act, expand3x3_weights, expand3x3_bias, padding=1)
+    expand3x3_act = relu(expand3x3_out)
+
+    # 3. Concatenate along channel axis
+    output = np.concatenate([expand1x1_act, expand3x3_act], axis=-1)
+
+    return output

questions/190_implement-squeezenet-fire-module/starter_code.py

@@ -0,0 +1,28 @@
+import numpy as np
+
+def fire_module_forward(input_tensor, 
+                        squeeze_weights, squeeze_bias,
+                        expand1x1_weights, expand1x1_bias,
+                        expand3x3_weights, expand3x3_bias):
+    """
+    Implements the forward pass of a SqueezeNet Fire Module.
+
+    Args:
+        input_tensor: (H, W, C_in)
+        squeeze_weights: (1, 1, C_in, s1x1)
+        squeeze_bias: (s1x1,)
+        expand1x1_weights: (1, 1, s1x1, e1x1)
+        expand1x1_bias: (e1x1,)
+        expand3x3_weights: (3, 3, s1x1, e3x3)
+        expand3x3_bias: (e3x3,)
+
+    Returns:
+        output_tensor: (H, W, e1x1 + e3x3)
+
+    Note:
+    - All convolutions use stride=1.
+    - 3x3 convolution uses padding='same' (pad=1).
+    - Apply ReLU activation after each convolution (max(0, x)).
+    """
+    # Your code here
+    pass

questions/190_implement-squeezenet-fire-module/tests.json

@@ -0,0 +1,26 @@
+[
+  {
+    "test": "import numpy as np\ninput = np.ones((5, 5, 2))\ns_w = np.ones((1, 1, 2, 1))\ns_b = np.zeros(1)\ne1_w = np.ones((1, 1, 1, 2))\ne1_b = np.zeros(2)\ne3_w = np.ones((3, 3, 1, 3))\ne3_b = np.zeros(3)\nres = fire_module_forward(input, s_w, s_b, e1_w, e1_b, e3_w, e3_b)\nprint(res.shape)",
+    "expected_output": "(5, 5, 5)"
+  },
+  {
+    "test": "import numpy as np\n# Simple value test\n# Input 1s (shape 3,3,1)\ninput = np.ones((3, 3, 1))\n# Squeeze: 1 filter, weight 2, bias 0 -> Output 2s\ns_w = np.full((1, 1, 1, 1), 2.0)\ns_b = np.zeros(1)\n# Expand 1x1: 1 filter, weight 0.5, bias 0 -> Output 2*0.5 = 1s\ne1_w = np.full((1, 1, 1, 1), 0.5)\ne1_b = np.zeros(1)\n# Expand 3x3: 1 filter, weight 0, bias 0 -> Output 0s\ne3_w = np.zeros((3, 3, 1, 1))\ne3_b = np.zeros(1)\nres = fire_module_forward(input, s_w, s_b, e1_w, e1_b, e3_w, e3_b)\n# Check first channel (Expand 1x1) at middle pixel\nprint(res[1, 1, 0])",
+    "expected_output": "1.0"
+  },
+  {
+    "test": "import numpy as np\n# Padding test for 3x3\ninput = np.ones((3, 3, 1))\n# Squeeze: weight 1 -> Output 1s\ns_w = np.ones((1, 1, 1, 1))\ns_b = np.zeros(1)\n# Expand 1x1: weights 0 (ignore)\ne1_w = np.zeros((1, 1, 1, 1))\ne1_b = np.zeros(1)\n# Expand 3x3: weight 1 -> Sum neighbors\n# At corner (0,0), with 'same' padding (zeros), only 4 neighbors are non-zero (1s)\n# Sum = 4 * 1 = 4\ne3_w = np.ones((3, 3, 1, 1))\ne3_b = np.zeros(1)\nres = fire_module_forward(input, s_w, s_b, e1_w, e1_b, e3_w, e3_b)\n# Check second channel (Expand 3x3) at corner (0,0)\nprint(res[0, 0, 1])",
+    "expected_output": "4.0"
+  },
+  {
+    "test": "import numpy as np\n# Middle pixel full sum\n# Same setup as above, check middle pixel (1,1)\n# 9 neighbors are 1s -> Sum = 9\ninput = np.ones((3, 3, 1))\ns_w = np.ones((1, 1, 1, 1))\ns_b = np.zeros(1)\ne1_w = np.zeros((1, 1, 1, 1))\ne1_b = np.zeros(1)\ne3_w = np.ones((3, 3, 1, 1))\ne3_b = np.zeros(1)\nres = fire_module_forward(input, s_w, s_b, e1_w, e1_b, e3_w, e3_b)\nprint(res[1, 1, 1])",
+    "expected_output": "9.0"
+  },
+  {
+    "test": "import numpy as np\n# Non-zero bias test\ninput = np.ones((2, 2, 1))\ns_w = np.zeros((1, 1, 1, 1))\ns_b = np.array([1.5])\ne1_w = np.zeros((1, 1, 1, 1))\ne1_b = np.array([2.0])\ne3_w = np.zeros((3, 3, 1, 1))\ne3_b = np.array([3.0])\nres = fire_module_forward(input, s_w, s_b, e1_w, e1_b, e3_w, e3_b)\nprint(f\"{res[0, 0, 0]}, {res[0, 0, 1]}\")",
+    "expected_output": "2.0, 3.0"
+  },
+  {
+    "test": "import numpy as np\n# ReLU test\ninput = np.ones((2, 2, 1))\ns_w = np.ones((1, 1, 1, 1))\ns_b = np.array([-5.0])\ne1_w = np.ones((1, 1, 1, 1))\ne1_b = np.array([2.0])\ne3_w = np.ones((3, 3, 1, 1))\ne3_b = np.array([-1.0])\nres = fire_module_forward(input, s_w, s_b, e1_w, e1_b, e3_w, e3_b)\nprint(f\"{res[0, 0, 0]}, {res[0, 0, 1]}\")",
+    "expected_output": "2.0, 0.0"
+  }
+]

utils/build_bundle.py

@@ -37,7 +37,7 @@ def bundle_one(folder: pathlib.Path):
                 meta[f"{lang}_test_cases"] = load_json(sub / "tests.json")
 
     out_path = OUTDIR / f"{meta['id']}.json"
-    out_path.write_text(json.dumps(meta, indent=2, ensure_ascii=False))
+    out_path.write_text(json.dumps(meta, indent=2, ensure_ascii=False), encoding="utf-8")
     print(f"✓ bundled {out_path.name}")
 
 def main():