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name: tfhub-conversion description: "Converts legacy DeepMind TFHub BigGAN generator modules at 128, 256, or 512 resolution into BigGAN-PyTorch state dictionaries and optionally generates CUDA sample images through the repository's reference converter." disable-model-invocation: true metadata: disco-role: operating license: MIT

TFHub conversion

Use this route only for the repository's legacy TFHub/converter.py workflow: porting a DeepMind BigGAN generator downloaded from TensorFlow Hub into a PyTorch .pth state dictionary. The supported resolutions are 128, 256, and 512. This is not a general TensorFlow-to-PyTorch conversion tool and it does not convert a discriminator or a complete training checkpoint.

Reference-only status. This workflow is reference-only unless an isolated environment with legacy TensorFlow 1.x-compatible APIs, TensorFlow Hub, the required Python packages, network access (or a suitable existing HDF5 intermediate), and a compatible CUDA GPU for sampling is deliberately available. It was not runtime verified. In particular, do not report a successful conversion merely because the Python files parse or because load_state_dict(..., strict=False) returns.

Read before acting

• Read references/workflows.md for the end-to-end command sequence, working directory requirement, download/cache behavior, outputs, and sample checks.
• Read references/api-reference.md for the converter functions, resolution table, TensorFlow-to-PyTorch mappings, and every command-line flag.
• Read references/troubleshooting.md before changing environments or interpreting a failure.

Prerequisites and hard constraints

The converter imports all of the following: PyTorch, torchvision, h5py, TensorFlow, tensorflow_hub, and parse. The adjacent TFHub/biggan_v1.py also imports NumPy and SciPy. The repository's normal PyTorch dependencies therefore are not sufficient by themselves. Keep the legacy TensorFlow/TFHub dependencies isolated from the normal training environment.

The source uses TensorFlow 1.x graph/session APIs, including tf.reset_default_graph(), tf.global_variables_initializer(), and tf.Session(), plus the TF1-style hub.Module(...) API. TensorFlow 2.x is not a drop-in replacement. Do not “fix” this route by silently substituting TF2 or by claiming compatibility through tf.compat.v1 without separately reviewing the TFHub module and the complete conversion behavior.

The converter has two important layout assumptions:

1. Launch it from the TFHub directory, for example cd /path/to/BigGAN-PyTorch/TFHub. It imports the sibling biggan_v1.py and appends .. to find the repository-level BigGAN.py. Running from another directory can produce relative-import or module-path failures.
2. DEVICE is hard-coded to cuda. Conversion itself reads weights and builds a PyTorch generator, but the optional sample path moves the generator and tensors to CUDA. There is no supported CPU sampling fallback in this script.

Operational outline

1. Choose one resolution first. The --resolution/-r flag accepts exactly 128, 256, or 512. Omitting it converts all three in sequence.
2. Ensure the TFHub URL can be reached, or place the matching intermediate biggan-<resolution>.h5 in the selected weights directory. If the HDF5 exists and --redownload is absent, the converter reuses it without contacting TFHub.
3. Run converter.py from TFHub. It loads the module with tensorflow_hub, initializes the TF graph, writes every TF global variable to HDF5, maps the HDF5 variables through the legacy biggan_v1.py model and convert_from_v1, then instantiates the current BigGAN.Generator.
4. It saves the converted generator state dictionary as biggan-<resolution>.pth under --weights_dir. This is not a complete checkpoint containing optimizer, discriminator, or EMA training objects.
5. If --generate_samples is supplied, it samples random latent vectors and random ImageNet class ids on CUDA and writes biggan<resolution>_samples.jpg under --samples_dir. Use --parallel only when the visible GPUs and the legacy nn.parallel.data_parallel path are appropriate.
6. Treat output existence as only a file-level check. A meaningful acceptance check requires loading the intended state dictionary into the matching current generator and visually or numerically checking generated images; neither conversion nor strict=False loading proves semantic equivalence.

Truncation limitation

The TFHub README explicitly says that this port is currently set up to run without truncation. The converter's generate_sample does not call biggan_v1.truncated_z_sample; it uses torch.randn and does not establish standing statistics for truncation levels. The missing per-truncation standing statistics mean that truncation curves or quality claims at non-default truncation values are not reproduced by this route. Accumulate and validate standing statistics separately before attempting truncation experiments, and record that extra procedure with the result.

Scope and non-goals

This route covers only the checked-in TFHub conversion script and its adjacent legacy reference model. It does not modernize the code, download weights on the user's behalf, guarantee TF2 operation, provide a CPU implementation, create standing statistics, or certify image quality. Do not import this sub-skill into an automatic runtime router without preserving its explicit reference-only and not-runtime-verified warnings.