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Fix inception score computation to accept arbitrary batch sizes. #40

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Fix inception score computation to accept arbitrary batch sizes. #40

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11 changes: 8 additions & 3 deletions inception_score/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@ def get_inception_score(images, splits=10):
for img in images:
img = img.astype(np.float32)
inps.append(np.expand_dims(img, 0))
bs = 1
bs = 100
with tf.Session() as sess:
preds = []
n_batches = int(math.ceil(float(len(inps)) / float(bs)))
Expand All @@ -40,7 +40,7 @@ def get_inception_score(images, splits=10):
sys.stdout.flush()
inp = inps[(i * bs):min((i + 1) * bs, len(inps))]
inp = np.concatenate(inp, 0)
pred = sess.run(softmax, {'ExpandDims:0': inp})
pred = sess.run(softmax, {'InputTensor:0': inp})
preds.append(pred)
preds = np.concatenate(preds, 0)
scores = []
Expand Down Expand Up @@ -72,7 +72,12 @@ def _progress(count, block_size, total_size):
MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
# Import model with a modification in the input tensor to accept arbitrary
# batch size.
input_tensor = tf.placeholder(tf.float32, shape=[None, None, None, 3],
name='InputTensor')
_ = tf.import_graph_def(graph_def, name='',
input_map={'ExpandDims:0':input_tensor})
# Works with an arbitrary minibatch size.
with tf.Session() as sess:
pool3 = sess.graph.get_tensor_by_name('pool_3:0')
Expand Down