interact script for conditional samples

src/generate_unconditional_samples.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+
+import fire
+import json
+import os
+import numpy as np
+import tensorflow as tf
+
+from src import model, sample, encoder
+
+def sample_model(
+    model_name='117M',
+    seed=None,
+    nsamples=0,
+    batch_size=1,
+    length=None,
+    temperature=1,
+    top_k=0,
+):
+    np.random.seed(seed)
+    tf.set_random_seed(seed)
+
+    enc = encoder.get_encoder(model_name)
+    hparams = model.default_hparams()
+    with open(os.path.join('models', model_name, 'hparams.json')) as f:
+        hparams.override_from_dict(json.load(f))
+
+    if length is None:
+        length = hparams.n_ctx
+    elif length > hparams.n_ctx:
+        raise ValueError(f"can't get samples longer than window size: {hparams.n_ctx}")
+
+    with tf.Session(graph=tf.Graph()) as sess:
+        output = sample.sample_sequence(
+            hparams=hparams, length=length,
+            start_token=enc.encoder['<|endoftext|>'],
+            batch_size=batch_size,
+            temperature=temperature, top_k=top_k
+        )[:, 1:]
+
+        saver = tf.train.Saver()
+        ckpt = tf.train.latest_checkpoint(os.path.join('models', model_name))
+        saver.restore(sess, ckpt)
+
+        generated = 0
+        while nsamples == 0 or generated < nsamples:
+            out = sess.run(output)
+            for i in range(batch_size):
+                generated += batch_size
+                text = enc.decode(out[i])
+                print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
+                print(f"{text}")
+
+if __name__ == '__main__':
+    fire.Fire(sample_model)
+