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name: model-export-and-checkpoints description: "Load Facenet checkpoint directories or frozen graphs, inspect variables, and export frozen models." metadata: disco-role: operating disable-model-invocation: true license: MIT

Facenet Model Export and Checkpoints

Use this sub-skill when the task is about loading a Facenet model, inspecting checkpoint files, choosing between .meta/checkpoint directories and .pb frozen graphs, or exporting a frozen graph.

When to read

• The user asks how to load a trained Facenet model path.
• A workflow fails because a model directory has missing or multiple .meta files.
• The user needs to freeze a checkpoint into a GraphDef .pb file.
• Tensor names such as input:0, embeddings:0, phase_train:0, or label_batch are missing or mismatched.
• The user wants to list checkpoint variables or understand which file in a model directory is used.

Core behaviors

• facenet.load_model(model, input_map=None) loads either a frozen .pb graph or a checkpoint directory.
• facenet.get_model_filenames(model_dir) expects one .meta file and a checkpoint state or model-*.ckpt-* file.
• facenet.list_variables(filename) lists variable names in a checkpoint file.
• src/freeze_graph.py imports a checkpoint graph, restores weights, converts variables to constants, and writes a frozen .pb graph.

Workflow

1. Determine whether the user has a checkpoint directory or a frozen graph.
2. Read references/model-files.md for path formats and tensor-name expectations.
3. Use scripts/inspect_model_dir.py to summarize .meta and checkpoint files.
4. Build a freeze command with scripts/build_freeze_graph_command.py when exporting a .pb graph.
5. If the model path is invalid, check checkpoint selection, tensor names, and tf.train.get_checkpoint_state() behavior before changing workflows.

Common model-file conventions

• Checkpoint directory: one .meta plus checkpoint files such as model-<name>.ckpt-<step>.
• Frozen graph: single .pb file accepted directly by facenet.load_model().
• Source workflows often expect the graph tensors input:0, embeddings:0, and phase_train:0.
• freeze_graph.py also preserves label_batch and Logits-related nodes when exporting.

Export checklist

Before freezing, confirm the model directory has the intended graph/checkpoint pair, choose a new .pb output path, and preserve the original checkpoint directory. After export, inspect the graph's input/output tensor names before passing it to comparison, classifier, or LFW evaluation.

Route onward

• For face comparison and classifier workflows, continue with ../embeddings-and-classification/SKILL.md.
• For training, continue with ../training/SKILL.md.
• For LFW evaluation, continue with ../evaluation/SKILL.md.

Troubleshooting

Read references/troubleshooting.md for missing .meta files, bad checkpoint paths, tensor-name mismatches, and freeze_graph output errors.