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Task: dl-residual-connection

DL Residual Connection Block Design

Research Question

Design a novel residual/skip connection block for deep convolutional neural networks that improves test accuracy across different depths and datasets.

Background

Residual connections (He et al., 2016) enabled training of very deep networks by providing identity shortcut paths. The basic residual block adds the input to the output of two stacked 3x3 convolutions. Several improvements have been proposed:

Pre-activation ResBlock (He et al., 2016 v2): BN-ReLU-Conv order instead of Conv-BN-ReLU, enabling cleaner gradient flow
Gated Residual (ReZero / learnable scaling): A learnable scalar gate scales the residual branch before addition, allowing the network to learn optimal residual contribution per block
Dense Shortcut (DenseNet-inspired): Concatenates shortcut and residual features then fuses with 1x1 conv, giving richer feature combination than simple addition
ResNeXt (Xie et al., 2017): Grouped convolutions for multi-branch aggregation
Res2Net (Gao et al., 2019): Multi-scale feature extraction within a single residual block

There is room for novel block designs that better balance gradient flow, feature reuse, and computational efficiency, particularly for varying network depths.

What You Can Modify

The CustomBlock class (lines 31-61) in custom_residual.py. This is the residual block used by the ResNet backbone.

You can modify:

The internal convolution structure (number, kernel sizes, grouping)
The activation function placement and type
The normalization layer placement and type
The shortcut/skip connection design
Channel attention or spatial attention mechanisms
The expansion class attribute (1 for basic, 4 for bottleneck, etc.)
Any additional modules within the block

Constraints:

The block must accept (in_planes, planes, stride) constructor arguments
The block must have an expansion class attribute
forward(x) must return a tensor with channels = planes * expansion
The shortcut must handle dimension mismatches (stride != 1 or channel mismatch)

Evaluation

Metric: Best test accuracy (%, higher is better)
Architectures & datasets:
- ResNet-20 ([3,3,3]) on CIFAR-10 (shallow, 10 classes)
- ResNet-56 ([9,9,9]) on CIFAR-100 (deep, 100 classes)
- ResNet-110 ([18,18,18]) on CIFAR-100 (very deep, 100 classes — tests gradient flow) — hidden, evaluated on final submission only
Training: SGD (lr=0.1, momentum=0.9, wd=5e-4), cosine annealing, 200 epochs
Data augmentation: RandomCrop(32, pad=4) + RandomHorizontalFlip

pytorch-vision/custom_residual.py [EDITABLE — lines 31–61 only]

     1: """CV Residual Connection Benchmark.
     2: 
     3: Train CIFAR ResNets with custom residual blocks to evaluate
     4: skip/residual connection designs.
     5: 
     6: FIXED: ResNet backbone, data pipeline, training loop.
     7: EDITABLE: CustomBlock cl

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "pytorch-vision/custom_residual.py",
4  "start_line": 31,
5  "end_line": 61,
6  "content": "class CustomBlock(nn.Module):\n    \"\"\"Channel-Attention Gated Residual Block with pre-activation.\n    \n    Features:\n    - Pre-activation (BN-ReLU-Conv) for cleaner gradient flow.\n    - Channel attention (squeeze-excitation) on residual branch.\n    - Learnable scalar gate (alpha) initialized at 0 (ReZero style).\n    - Shortcut handling with optional 1x1 conv.\n    \"\"\"\n    expansion = 1\n\n    def __init__(self, in_planes, planes, stride=1, reduction=16):\n        super().__init__()\n        # Main branch (pre-activation)\n        self.bn1 = nn.BatchNorm2d(in_planes)\n        self.conv1 = nn.Conv2d(in_planes, planes, 3, stride=stride, padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)\n        \n        # Channel attention (squeeze-excitation)\n        self.se_pool = nn.AdaptiveAvgPool2d(1)\n        self.se_fc1 = nn.Linear(planes, planes // reduction, bias=False)\n        self.se_relu = nn.ReLU(inplace=True)\n        self.se_fc2 = nn.Linear(planes // reduction, planes, bias=False)\n        self.se_sigmoid = nn.Sigmoid()\n        \n        # Learnable residual gate (scalar)\n        self.alpha = nn.Parameter(torch.zeros(1))\n        \n        # Shortcut\n        self.downsample = None\n        if stride != 1 or in_planes != planes * self.expansion:\n            self.downsample = nn.Sequential(\n                nn.Conv2d(in_planes, planes * self.expansion, 1, stride=stride, bias=False),\n                nn.BatchNorm2d(planes * self.expansion),\n            )\n\n    def forward(self, x):\n        shortcut = self.downsample(x) if self.downsample is not None else x\n        \n        # Pre-activation main branch\n        out = F.relu(self.bn1(x))\n        out = self.conv1(out)\n        out = F.relu(self.bn2(out))\n        out = self.conv2(out)\n        \n        # Channel attention\n        se = self.se_pool(out).squeeze(-1).squeeze(-1)  # (B, C)\n        se = self.se_fc1(se)\n        se = self.se_relu(se)\n        se = self.se_fc2(se)\n        se = self.se_sigmoid(se)  # (B, C)\n        # Reshape to (B, C, 1, 1) for broadcasting\n        se = se.unsqueeze(-1).unsqueeze(-1)\n        out = out * se\n        \n        # Gated residual addition\n        out = shortcut + self.alpha * out\n        return out"
7}

Tool ResultStep 1

OK: Replaced lines 31..61 with 59 line(s) in pytorch-vision/custom_residual.py. Editable range: 31–89.

[Current file: pytorch-vision/custom_residual.py | editable: 31–89 | total: 306 lines]
    28| # ============================================================================
    29| 
    30| # -- EDITABLE REGION START (lines 31-61) ------------------------------------
    31  class CustomBlock(nn.Module):
    32      """Channel-Attention Gated Residual Block with pre-activation.
    33      
    34      Features:
    35      - Pre-activation (BN-ReLU-Conv) for cleaner gradient flow.
    36      - Channel attention (squeeze-excitation) on residual branch.
    37      - Learnable scalar gate (alpha) initialized at 0 (ReZero style).
    38      - Shortcut handling with optional 1x1 conv.
    39      """
    40      expansion = 1
    41  
    42      def __init__(self, in_planes, planes, stride=1, reduction=16):
    43          super().__init__()
    44          # Main branch (pre-activa

AssistantStep 2test

Tool ResultStep 2

[Test #1] (2 tests remaining; call submit(n=N) to choose which test result to submit as final)

### resnet20-cifar10 (scripts/resnet20_cifar10.sh)
Training progress (last epochs):
TRAIN_METRICS: epoch=160 train_loss=0.1591 train_acc=94.39 test_loss=0.3256 test_acc=89.76 lr=0.009549
TRAIN_METRICS: epoch=170 train_loss=0.1132 train_acc=96.05 test_loss=0.3616 test_acc=89.52 lr=0.005450
TRAIN_METRICS: epoch=180 train_loss=0.0680 train_acc=97.59 test_loss=0.3314 test_acc=91.03 lr=0.002447
TRAIN_METRICS: epoch=190 train_loss=0.0414 train_acc=98.65 test_loss=0.3245 test_acc=91.68 lr=0.000616
TRAIN_METRICS: epoch=200 train_loss=0.0365 train_acc=98.83 test_loss=0.3211 test_acc=91.81 lr=0.000000
Final metrics (resnet20-cifar10): test_acc_resnet20-cifar10=91.83

### resnet56-cifar100 (scripts/resnet56_cifar100.sh)
Training progress (last epochs):
TRAIN_METRICS: epoch=160 train_loss=0.3129 train_acc=89.80 test_loss=1.3973 test_acc=67.72 lr=0.009549
TRAIN_METRICS: epoch=170 train_loss=0.1669 train_

AssistantStep 3edit

Tool Input

1{
2  "op": "replace",
3  "filename": "pytorch-vision/custom_residual.py",
4  "start_line": 31,
5  "end_line": 89,
6  "content": "class CustomBlock(nn.Module):\n    \"\"\"Channel-Attention Gated Residual Block with pre-activation.\n    \n    Features:\n    - Pre-activation (BN-ReLU-Conv) for cleaner gradient flow.\n    - Channel attention (squeeze-excitation) on residual branch with adaptive reduction.\n    - Learnable scalar gate (alpha) initialized at 1.0.\n    - Shortcut handling with optional 1x1 conv.\n    \"\"\"\n    expansion = 1\n\n    def __init__(self, in_planes, planes, stride=1):\n        super().__init__()\n        # Main branch (pre-activation)\n        self.bn1 = nn.BatchNorm2d(in_planes)\n        self.conv1 = nn.Conv2d(in_planes, planes, 3, stride=stride, padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)\n        \n        # Channel attention (squeeze-excitation) with adaptive reduction\n        # Ensure reduction ratio is at least 2 and not larger than planes\n        reduction = max(2, planes // 8)\n        self.se_pool = nn.AdaptiveAvgPool2d(1)\n        self.se_fc1 = nn.Linear(planes, reduction, bias=False)\n        self.se_relu = nn.ReLU(inplace=True)\n        self.se_fc2 = nn.Linear(reduction, planes, bias=False)\n        self.se_sigmoid = nn.Sigmoid()\n        \n        # Learnable residual gate (scalar) initialized to 1.0\n        self.alpha = nn.Parameter(torch.ones(1))\n        \n        # Shortcut\n        self.downsample = None\n        if stride != 1 or in_planes != planes * self.expansion:\n            self.downsample = nn.Sequential(\n                nn.Conv2d(in_planes, planes * self.expansion, 1, stride=stride, bias=False),\n                nn.BatchNorm2d(planes * self.expansion),\n            )\n\n    def forward(self, x):\n        shortcut = self.downsample(x) if self.downsample is not None else x\n        \n        # Pre-activation main branch\n        out = F.relu(self.bn1(x))\n        out = self.conv1(out)\n        out = F.relu(self.bn2(out))\n        out = self.conv2(out)\n        \n        # Channel attention\n        se = self.se_pool(out).squeeze(-1).squeeze(-1)  # (B, C)\n        se = self.se_fc1(se)\n        se = self.se_relu(se)\n        se = self.se_fc2(se)\n        se = self.se_sigmoid(se)  # (B, C)\n        # Reshape to (B, C, 1, 1) for broadcasting\n        se = se.unsqueeze(-1).unsqueeze(-1)\n        out = out * se\n        \n        # Gated residual addition\n        out = shortcut + self.alpha * out\n        return out"
7}

Tool ResultStep 3

OK: Replaced lines 31..89 with 61 line(s) in pytorch-vision/custom_residual.py. Editable range: 31–91.

[Current file: pytorch-vision/custom_residual.py | editable: 31–91 | total: 308 lines]
    28| # ============================================================================
    29| 
    30| # -- EDITABLE REGION START (lines 31-61) ------------------------------------
    31  class CustomBlock(nn.Module):
    32      """Channel-Attention Gated Residual Block with pre-activation.
    33      
    34      Features:
    35      - Pre-activation (BN-ReLU-Conv) for cleaner gradient flow.
    36      - Channel attention (squeeze-excitation) on residual branch with adaptive reduction.
    37      - Learnable scalar gate (alpha) initialized at 1.0.
    38      - Shortcut handling with optional 1x1 conv.
    39      """
    40      expansion = 1
    41  
    42      def __init__(self, in_planes, planes, stride=1):
    43          super().__init__()
    44          # Main branch (pre-activatio

AssistantStep 4test

Tool ResultStep 4

[Test #2] (1 test remaining; call submit(n=N) to choose which test result to submit as final)

### resnet20-cifar10 (scripts/resnet20_cifar10.sh)
Training progress (last epochs):
TRAIN_METRICS: epoch=160 train_loss=0.1156 train_acc=95.89 test_loss=0.3400 test_acc=90.18 lr=0.009549
TRAIN_METRICS: epoch=170 train_loss=0.0703 train_acc=97.48 test_loss=0.3405 test_acc=91.07 lr=0.005450
TRAIN_METRICS: epoch=180 train_loss=0.0339 train_acc=98.89 test_loss=0.3433 test_acc=91.82 lr=0.002447
TRAIN_METRICS: epoch=190 train_loss=0.0173 train_acc=99.52 test_loss=0.3380 test_acc=92.10 lr=0.000616
TRAIN_METRICS: epoch=200 train_loss=0.0144 train_acc=99.58 test_loss=0.3425 test_acc=92.24 lr=0.000000
Final metrics (resnet20-cifar10): test_acc_resnet20-cifar10=92.37

### resnet56-cifar100 (scripts/resnet56_cifar100.sh)
Training progress (last epochs):
TRAIN_METRICS: epoch=160 train_loss=0.3394 train_acc=88.82 test_loss=1.4316 test_acc=67.56 lr=0.009549
TRAIN_METRICS: epoch=170 train_loss=0.2016 train_a

AssistantStep 5edit

Tool Input

1{
2  "op": "replace",
3  "filename": "pytorch-vision/custom_residual.py",
4  "start_line": 31,
5  "end_line": 91,
6  "content": "class CustomBlock(nn.Module):\n    \"\"\"Adaptive Channel‑Weighted Residual Block (ACWR).\n    \n    Features:\n    - Pre-activation (BN-ReLU-Conv) for clean gradients.\n    - Per‑channel learnable weights (initialized to 1) after residual branch.\n    - Per‑block learnable residual gate α (initialized to 0, ReZero style).\n    - Shortcut with optional 1x1 conv+BN.\n    \n    This design allows each channel to adapt its contribution, while the\n    global gate α stabilizes very deep networks by letting early layers\n    start with near‑identity mapping.\n    \"\"\"\n    expansion = 1\n\n    def __init__(self, in_planes, planes, stride=1):\n        super().__init__()\n        # Main branch (pre-activation)\n        self.bn1 = nn.BatchNorm2d(in_planes)\n        self.conv1 = nn.Conv2d(in_planes, planes, 3, stride=stride, padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)\n        \n        # Per‑channel scaling weights (one per output channel)\n        self.channel_weights = nn.Parameter(torch.ones(planes, 1, 1))\n        \n        # Global residual gate (initialized to zero)\n        self.alpha = nn.Parameter(torch.zeros(1))\n        \n        # Shortcut\n        self.downsample = None\n        if stride != 1 or in_planes != planes * self.expansion:\n            self.downsample = nn.Sequential(\n                nn.Conv2d(in_planes, planes * self.expansion, 1, stride=stride, bias=False),\n                nn.BatchNorm2d(planes * self.expansion),\n            )\n\n    def forward(self, x):\n        shortcut = self.downsample(x) if self.downsample is not None else x\n        \n        # Pre-activation main branch\n        out = F.relu(self.bn1(x))\n        out = self.conv1(out)\n        out = F.relu(self.bn2(out))\n        out = self.conv2(out)\n        \n        # Channel‑wise scaling\n        out = out * self.channel_weights\n        \n        # Gated residual addition (α starts at 0 → identity)\n        out = shortcut + self.alpha * out\n        return out"
7}

Tool ResultStep 5

OK: Replaced lines 31..91 with 52 line(s) in pytorch-vision/custom_residual.py. Editable range: 31–82.

[Current file: pytorch-vision/custom_residual.py | editable: 31–82 | total: 299 lines]
    28| # ============================================================================
    29| 
    30| # -- EDITABLE REGION START (lines 31-61) ------------------------------------
    31  class CustomBlock(nn.Module):
    32      """Adaptive Channel‑Weighted Residual Block (ACWR).
    33      
    34      Features:
    35      - Pre-activation (BN-ReLU-Conv) for clean gradients.
    36      - Per‑channel learnable weights (initialized to 1) after residual branch.
    37      - Per‑block learnable residual gate α (initialized to 0, ReZero style).
    38      - Shortcut with optional 1x1 conv+BN.
    39      
    40      This design allows each channel to adapt its contribution, while the
    41      global gate α stabilizes very deep networks by letting early layers
    42      start with near‑ide

AssistantStep 6test

Tool ResultStep 6

[Test #3] (0 tests remaining; call submit(n=N) to choose which test result to submit as final)

[NOTE] This was your last test. You MUST now call submit(n=X) to choose which test result to submit as your final answer.

### resnet20-cifar10 (scripts/resnet20_cifar10.sh)
Training progress (last epochs):
TRAIN_METRICS: epoch=160 train_loss=0.1164 train_acc=95.84 test_loss=0.3631 test_acc=89.48 lr=0.009549
TRAIN_METRICS: epoch=170 train_loss=0.0764 train_acc=97.31 test_loss=0.3885 test_acc=90.48 lr=0.005450
TRAIN_METRICS: epoch=180 train_loss=0.0372 train_acc=98.73 test_loss=0.3718 test_acc=91.53 lr=0.002447
TRAIN_METRICS: epoch=190 train_loss=0.0176 train_acc=99.50 test_loss=0.3774 test_acc=92.09 lr=0.000616
TRAIN_METRICS: epoch=200 train_loss=0.0140 train_acc=99.61 test_loss=0.3749 test_acc=92.16 lr=0.000000
Final metrics (resnet20-cifar10): test_acc_resnet20-cifar10=92.24

### resnet56-cifar100 (scripts/resnet56_cifar100.sh)
Training progress (last epochs):
TRAIN_METRICS: epoch=160 trai

AssistantStep 7submit

Tool Input

1{
2  "n": 1
3}

Tool ResultStep 7

[submit] Submitting result from test #1 as final.

### resnet20-cifar10 (scripts/resnet20_cifar10.sh)
Training progress (last epochs):
TRAIN_METRICS: epoch=160 train_loss=0.1591 train_acc=94.39 test_loss=0.3256 test_acc=89.76 lr=0.009549
TRAIN_METRICS: epoch=170 train_loss=0.1132 train_acc=96.05 test_loss=0.3616 test_acc=89.52 lr=0.005450
TRAIN_METRICS: epoch=180 train_loss=0.0680 train_acc=97.59 test_loss=0.3314 test_acc=91.03 lr=0.002447
TRAIN_METRICS: epoch=190 train_loss=0.0414 train_acc=98.65 test_loss=0.3245 test_acc=91.68 lr=0.000616
TRAIN_METRICS: epoch=200 train_loss=0.0365 train_acc=98.83 test_loss=0.3211 test_acc=91.81 lr=0.000000
Final metrics (resnet20-cifar10): test_acc_resnet20-cifar10=91.83

### resnet56-cifar100 (scripts/resnet56_cifar100.sh)
Training progress (last epochs):
TRAIN_METRICS: epoch=160 train_loss=0.3129 train_acc=89.80 test_loss=1.3973 test_acc=67.72 lr=0.009549
TRAIN_METRICS: epoch=170 train_loss=0.1669 train_acc=94.59 test_loss=1.3581 test_acc=70.76 lr=