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name: sampling description: "Operate BigGAN-PyTorch sampling from compatible checkpoints, including EMA and batch-normalization statistics, image sheets, NPZ export, truncation curves, and repository-specific Inception evaluation." disable-model-invocation: true metadata: disco-role: operating license: MIT

BigGAN-PyTorch sampling

Use this skill when the task is to load a trained BigGAN-PyTorch generator, produce random or conditional/interpolated images, export a TensorFlow-compatible NPZ, or compare the repository's PyTorch Inception metrics. Work from the repository root so that sample.py, utils.py, inception_utils.py, and the model module can be imported.

This is a CUDA sampling path: sample.py constructs G with .cuda(), uses CUDA distributions for z and y, and the metric wrapper also moves Inception to CUDA. Confirm the CUDA, PyTorch, torchvision, and checkpoint environment before launching a large run. Read all three bundled references for exact flag semantics and recovery procedures.

Operating procedure

1. Resolve the checkpoint before choosing outputs. Identify the exact weights_root/<experiment_name>/ directory and the suffix to load. An empty suffix means G.pth and state_dict.pth; best0 and copy0 mean G_best0.pth/state_dict_best0.pth and G_copy0.pth/state_dict_copy0.pth, respectively. A usable normal-G run needs the generator and state dict; a --use_ema run additionally needs G_ema[_suffix].pth. --load_weights selects a file suffix, not a directory.
2. Make the architecture agree with the saved config. Prefer an explicit --experiment_name NAME --load_weights SUFFIX --config_from_name when the checkpoint has a saved state dict. The bootstrap state-dict load happens before update_config_roots, so if the checkpoint is outside the default weights/, pass its actual --weights_root explicitly as well as --base_root if desired. Without --config_from_name, supply the training architecture flags yourself; strict=False in sample.py is not a remedy for incompatible tensor shapes.
3. Select normal or EMA parameters deliberately. Omit --use_ema to load G[_suffix].pth. Use --use_ema only when the checkpoint was saved with an EMA generator and the matching G_ema[_suffix].pth exists. --ema describes the training/configuration and contributes to auto-generated experiment names; --use_ema controls which generator state is loaded for evaluation.
4. Fix the normalization/statistics mode before comparing samples. --G_eval_mode calls G.eval() and uses running BatchNorm estimates. With the custom myBN implementation, --accumulate_stats runs --num_standing_accumulations random forward passes in training mode and then switches to eval. Keep G_eval_mode, mybn, accumulation count, EMA choice, and z_var fixed for fair comparisons. The BigGAN recipe uses --mybn --accumulate_stats --num_standing_accumulations 32 when standing statistics are wanted; the provided main sampling recipe instead uses --use_ema --G_eval_mode.
5. Set noise and batch size explicitly. --z_var controls the var field of the custom normal Distribution. In this code that field is passed as the second argument to Tensor.normal_, i.e. as the normal scale despite the historical “variance” name. G_batch_size is set to max(--G_batch_size, --batch_size), so lower both flags when reducing GPU memory. --seed seeds PyTorch CUDA and NumPy RNGs but does not make every multi-GPU or cuDNN operation deterministic.
6. Choose one or more output actions and check their prerequisites. --sample_random writes a normalized random_samples.jpg; --sample_sheets writes class-conditional sheets; --sample_interps writes Z/Y interpolation sheets; --sample_npz --sample_num_npz N writes samples.npz with x uint8 CHW images and y integer labels; and metric flags compute repository-specific scores. Create the samples root first: the script does not call utils.prepare_root, and NPZ/random output assumes the experiment directory already exists. See references/workflows.md for exact layout and commands.
7. Treat metric names as implementation-specific. The built-in path uses torchvision Inception, ImageNet normalization, 299x299 resizing, and <base-dataset>_inception_moments.npz. Its IS/FID are explicitly unofficial and not numerically interchangeable with the legacy TensorFlow metric. To obtain the TensorFlow-style IS, first produce the NPZ and then run the repository's TensorFlow 1.3-or-earlier evaluator. Record the implementation, weights (normal/EMA), mode, z_var, image count, and moments used with every score.

Standard command shape

For a checkpoint with a saved config, use a command of this form (replace the paths and name; do not invent a name from a changed sampling batch size):

mkdir -p /path/to/samples /path/to/samples/EXPERIMENT
python sample.py \
  --experiment_name EXPERIMENT --weights_root /path/to/weights \
  --load_weights best0 --config_from_name \
  --batch_size 32 --G_batch_size 32 --seed 0 \
  --use_ema --G_eval_mode --sample_random

Add only the required output flags. A truncation sweep, for example, adds --sample_inception_metrics --sample_trunc_curves 0.05_0.05_1.0; this is a metric run, not a cheap image smoke test. Do not start with 50,000 images or multiple sheets when first validating a checkpoint.

Required recovery behavior

• For missing weights, inspect the experiment directory and repair the --weights_root, --experiment_name, or --load_weights selection. A missing state_dict[_suffix].pth prevents --config_from_name; recover the saved config or provide every architecture argument explicitly.
• For an EMA load failure, remove --use_ema only if a normal-G evaluation is acceptable; otherwise locate a checkpoint containing the matching G_ema[_suffix].pth. Never label a normal-G result as an EMA result.
• For missing Inception moments, generate the dataset moments using the data preparation/moments script, put the file where inception_utils.py will search, or restrict the run to checkpoint/image loading after applying the lazy-metrics source fix described in references/troubleshooting.md. --no_fid is not a missing-moments workaround because moments are loaded before the metric flags are consulted.
• For incompatible configs, load the state dict's saved config, use --config_from_name, and keep only documented runtime overrides. A shape mismatch means the checkpoint and model are genuinely incompatible; do not paper over it with strict=False.
• For CUDA OOM, reduce both --batch_size and --G_batch_size, disable --parallel only if it reduces overhead on the available topology, and reduce image counts or run outputs separately. NPZ sampling accumulates all generated arrays in host memory before writing, so large --sample_num_npz values can also exhaust RAM.
• For sample.py --help crashing with ValueError: unsupported format character '#', the --logstyle help text in utils.py contains literal %#.#f/%#.#e strings. Escape the percent signs as %%#.#f and %%#.#e (or remove the formatting examples), then retry help. Until that source-only fix is made, use known flags directly rather than treating the help crash as a checkpoint failure.

For the complete flag contract, file names, metric caveats, multi-GPU behavior, and failure matrix, read references/api-reference.md, references/workflows.md, and references/troubleshooting.md.