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)