def create_and_check_for_image_classification(self, config, pixel_values,
labels):
config.num_labels = self.type_sequence_label_size
model = TFConvNextForImageClassification(config)
result = model(pixel_values, labels=labels, training=False)
self.parent.assertEqual(
result.logits.shape,
(self.batch_size, self.type_sequence_label_size))
def test_inference_image_classification_head(self):
model = TFConvNextForImageClassification.from_pretrained(
"facebook/convnext-tiny-224")
feature_extractor = self.default_feature_extractor
image = prepare_img()
inputs = feature_extractor(images=image, return_tensors="tf")
# forward pass
outputs = model(**inputs)
# verify the logits
expected_shape = tf.TensorShape((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = tf.constant([-0.0260, -0.4739, 0.1911])
tf.debugging.assert_near(outputs.logits[0, :3],
expected_slice,
atol=1e-4)