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Support for latent_space=constant #10

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Support for latent_space=constant #10

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16 changes: 15 additions & 1 deletion export_model.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
import os
import argparse
import tensorflow as tf
import numpy as np
import tf2onnx

from pathlib import Path
Expand All @@ -20,7 +21,7 @@ def main():

parser.add_argument('--latent_space', choices=['normal', 'uniform', 'constant', 'none'], default='normal')
parser.add_argument('--latent_dim', type=int, default=32, required=False)
parser.add_argument('--constant_latent', type=float, default=None)
parser.add_argument('--constant_latent', type=float, default=0.5)

parser.add_argument('--export_format', choices=['pbtxt', 'onnx'], default='pbtxt')

Expand Down Expand Up @@ -87,6 +88,14 @@ def to_save(x):
output_path=Path(args.output_path) / f'{args.checkpoint_name}.onnx',
)

if args.test_input:
test_output = to_save(tf.convert_to_tensor([args.test_input]))
print('Model test output:')
print('Input:')
print(args.test_input)
print('Output')
print(*(f'{num:.4f}' if num > 1e-16 else 0 for num in test_output.numpy().flatten()))

if args.upload_to_mlflow:
import mlflow

Expand Down Expand Up @@ -114,6 +123,11 @@ def latent_input_gen(batch_size):
def latent_input_gen(batch_size):
return tf.random.uniform(shape=(batch_size, args.latent_dim), dtype='float32')

elif args.latent_space == 'constant':

def latent_input_gen(batch_size):
return tf.fill(dims=(batch_size, args.latent_dim), value=np.float32(args.constant_latent))

if latent_input_gen is None:
input_signature = [tf.TensorSpec(shape=[predefined_batch_size, 36], dtype=tf.float32)]

Expand Down