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Unsorted segmented max with eager execution is not working.

segmented_max_error.py

importpandasaspd

importtensorflowastf

importnumpyasnp

fromsklearn.preprocessingimportMultiLabelBinarizer

classSegmentedMean(tf.keras.layers.Layer):

def__init__(self, *args, **kwargs):

super(SegmentedMean, self).__init__(*args, **kwargs)

defcall(self, inputs, **kwargs):

features, segments, num_segments=inputs

# return tf.math.segment_mean(features, segments)

returntf.math.unsorted_segment_mean(features, segments, num_segments)

classSegmentedMax(tf.keras.layers.Layer):

def__init__(self, none_val=None, *args, **kwargs):

super(SegmentedMax, self).__init__(*args, **kwargs)

self.none_val=none_val

defcall(self, inputs, **kwargs):

features, segments, num_segments=inputs

# return tf.math.segment_max(features, segments)

returntf.math.unsorted_segment_max(features, segments, num_segments)

df=pd.DataFrame({'list': [

[[1, 2, 3, 4], [2, 3, 6]],

[[1, 2], [1, 5, 8]],

[[6, 7, 8], [2, 4, 10], [1, 6], [5]],

[[3, 4, 6, 8], [1, 8], [2], [7]],

[[3, 6, 8]],

[[4, 2, 2], [8, 1, 5, 6]],

[[3, 2, 1], [9, 8], [4]],

'label': [0, 0, 1, 1, 1, 0, 0]})

df['list_len'] =df['list'].apply(len)

batch_size=8

# list of all unique numbers used

nums_used=list(set([iforjindf['list'].apply(lambdak: [iforjinkforiinj]) foriinj]))

list_enc=MultiLabelBinarizer().fit([[i] foriinnums_used])

defcreate_batch():

batch_ub=df.sample(batch_size//2) # upper bound, will trim so the batch size is correct after expansion

batch_end= (batch_ub['list_len'].cumsum() <=batch_size)[::-1].idxmax()

batch=batch_ub.loc[:batch_end].copy()

batch['id'] =np.arange(0, len(batch))

feat_bags=batch['list'].apply(list_enc.transform)

feats=np.concatenate(feat_bags.values)

# so I know which data to group together during segmentation

segments=batch['id'].repeat(batch['list_len']).values

labels=batch['label'].values.astype(np.int32)

return (feats, segments, np.array([len(labels)], dtype=np.int32)), labels

settings= {'k': 40, 'steps': 10}

feats_len=len(nums_used)

inputs=tf.keras.Input(shape=(feats_len,), name='features')

segments=tf.keras.Input(shape=(), name='segments', dtype=tf.int32)

samples_num=tf.keras.Input(shape=(), name='samples_num', dtype=tf.int32)

x=tf.keras.layers.Dense(settings['k'], activation=tf.nn.relu)(inputs)

x=tf.keras.layers.Dense(settings['k'])(x)

# x = SegmentedMean()((x, segments, samples_num[0])) # unsorted segmented mean works without problems

x=SegmentedMax()((x, segments, samples_num[0]))

x=tf.keras.layers.Dense(settings['k'], activation=tf.nn.relu)(x)

logits=tf.keras.layers.Dense(2, name='output_logits')(x)

probs=tf.keras.layers.Softmax()(logits)

model=tf.keras.Model(inputs=(inputs, segments, samples_num), outputs=(logits, probs), name='mil_model')

optimizer=tf.keras.optimizers.Adam()

loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

forstepinrange(settings['steps']):

x_train, y_train=create_batch()

withtf.GradientTape() astape:

logits, probs=model(x_train)

loss_value=loss(y_train, logits)

grads=tape.gradient(loss_value, model.trainable_weights)

optimizer.apply_gradients(zip(grads, model.trainable_weights))

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