Conv Shape Calculator

A deterministic helper for planning or debugging a CNN. Given an input shape and a list of layer specs, trace shapes, receptive fields, and parameter counts without running the model.

When to use

• Designing a new CNN and you want to verify every downsample lands on a clean size.
• Reading a paper and translating its architecture table into code.
• A pretrained backbone crashes with a shape mismatch at the classifier head and you need to know which layer changed the spatial size.
• Comparing two backbones on parameter efficiency before training either.

Inputs

• input_shape: (C, H, W).
• layers: ordered list of layer dicts. Each supports:
  • \{type: "conv", c_out, k, s, p, groups=1, bias=true\}
  • \{type: "pool", mode: "max"|"avg", k, s, p=0\}
  • \{type: "adaptive_pool", out_h, out_w\}
  • \{type: "flatten"\}
  • \{type: "linear", out_features, bias=true\}

Steps

1. Initialise trace with (C, H, W), receptive field 1, effective stride 1, cumulative params 0.

2. For each layer, update in this order:

   • Compute C_out (conv/linear), or carry C_in through (pool).
   • Compute spatial output using (H + 2P - K) / S + 1 for conv and pool, out_h/out_w for adaptive pool, (1, 1) for flatten output shape (C * H * W, 1, 1) before the linear, and scalar 1x1 for linear.
   • Update receptive field and effective stride:
     • Conv/pool: RF_new = RF_old + (K - 1) * effective_stride, effective_stride *= S.
     • Adaptive pool: treat as a pool with effective S = H_in / out_h (round down). RF_new = RF_old + (H_in - 1) * effective_stride_old; effective_stride *= S. Note that adaptive pool's RF equals the full previous spatial extent.
     • Flatten / linear: RF and effective stride are no longer meaningful; freeze them to the values before the flatten and omit from subsequent rows.
   • Compute params:
     • Conv: C_out * (C_in / groups) * K * K + (C_out if bias else 0).
     • Linear: out_features * in_features + (out_features if bias else 0).
     • Pool and flatten: 0.

3. Detect problems and flag them:

   • Non-integer output size (misaligned stride/padding).
   • H_out <= 0 before the end of the stack.
   • Receptive field exceeding input size (possible wasted compute after that point).
   • Sudden 10x jumps in per-layer params that suggest the wrong channel plan.

4. Report as a single table:

idx  layer                C_in  C_out  K  S  P  H_out  W_out  RF    params     cum_params
1    conv 3x3 s=1 p=1     3     32     3  1  1  224    224    3     896        896
2    conv 3x3 s=2 p=1     32    64     3  2  1  112    112    7     18,496     19,392
3    pool max 2x2         64    64     2  2  0  56     56     11    0          19,392
...

5. Summary line: final (C, H, W), final receptive field, total params, warnings.

Rules

• Always return integers for spatial sizes. If the formula produces a non-integer, flag as an error and do not silently floor.
• When groups > 1, verify C_in % groups == 0 and C_out % groups == 0; otherwise error.
• For depthwise conv (groups == C_in), label it in the layer column so the reader sees why params are low.
• If the user provides BatchNorm or activation layers, ignore them for shape purposes but carry params forward (2 * C per BatchNorm).
• Never guess defaults for missing fields. Require k, s, p on every conv and pool.