transformers/tests/models/segformer/test_modeling_segformer.py

# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch SegFormer model."""

import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        SegformerForImageClassification,
        SegformerForSemanticSegmentation,
        SegformerModel,
    )
    from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES


if is_vision_available():
    from PIL import Image

    from transformers import SegformerImageProcessor


class SegformerConfigTester(ConfigTester):
    def create_and_test_config_common_properties(self):
        config = self.config_class(**self.inputs_dict)
        self.parent.assertTrue(hasattr(config, "hidden_sizes"))
        self.parent.assertTrue(hasattr(config, "num_attention_heads"))
        self.parent.assertTrue(hasattr(config, "num_encoder_blocks"))


class SegformerModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=64,
        num_channels=3,
        num_encoder_blocks=4,
        depths=[1, 1, 1, 1],
        sr_ratios=[8, 4, 2, 1],
        hidden_sizes=[8, 8, 16, 16],
        downsampling_rates=[1, 4, 8, 16],
        num_attention_heads=[1, 1, 2, 2],
        is_training=True,
        use_labels=True,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        initializer_range=0.02,
        num_labels=3,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.num_channels = num_channels
        self.num_encoder_blocks = num_encoder_blocks
        self.sr_ratios = sr_ratios
        self.depths = depths
        self.hidden_sizes = hidden_sizes
        self.downsampling_rates = downsampling_rates
        self.num_attention_heads = num_attention_heads
        self.is_training = is_training
        self.use_labels = use_labels
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.initializer_range = initializer_range
        self.num_labels = num_labels
        self.scope = scope

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        labels = None
        if self.use_labels:
            labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)

        config = self.get_config()
        return config, pixel_values, labels

    def get_config(self):
        return SegformerConfig(
            image_size=self.image_size,
            num_channels=self.num_channels,
            num_encoder_blocks=self.num_encoder_blocks,
            depths=self.depths,
            hidden_sizes=self.hidden_sizes,
            num_attention_heads=self.num_attention_heads,
            hidden_act=self.hidden_act,
            hidden_dropout_prob=self.hidden_dropout_prob,
            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = SegformerModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        expected_height = expected_width = self.image_size // (self.downsampling_rates[-1] * 2)
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)
        )

    def create_and_check_for_image_segmentation(self, config, pixel_values, labels):
        config.num_labels = self.num_labels
        model = SegformerForSemanticSegmentation(config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(
            result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)
        )
        result = model(pixel_values, labels=labels)
        self.parent.assertEqual(
            result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)
        )
        self.parent.assertGreater(result.loss, 0.0)

    def create_and_check_for_binary_image_segmentation(self, config, pixel_values, labels):
        config.num_labels = 1
        model = SegformerForSemanticSegmentation(config=config)
        model.to(torch_device)
        model.eval()
        labels = torch.randint(0, 1, (self.batch_size, self.image_size, self.image_size)).to(torch_device)
        result = model(pixel_values, labels=labels)
        self.parent.assertGreater(result.loss, 0.0)

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, pixel_values, labels = config_and_inputs
        inputs_dict = {"pixel_values": pixel_values}
        return config, inputs_dict


@require_torch
class SegformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (
        (
            SegformerModel,
            SegformerForSemanticSegmentation,
            SegformerForImageClassification,
        )
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = (
        {
            "image-feature-extraction": SegformerModel,
            "image-classification": SegformerForImageClassification,
            "image-segmentation": SegformerForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    fx_compatible = True
    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False

    def setUp(self):
        self.model_tester = SegformerModelTester(self)
        self.config_tester = SegformerConfigTester(self, config_class=SegformerConfig)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_for_binary_image_segmentation(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_binary_image_segmentation(*config_and_inputs)

    def test_for_image_segmentation(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_segmentation(*config_and_inputs)

    @unittest.skip("SegFormer does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods")
    def test_model_get_set_embeddings(self):
        pass

    def test_attention_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        for model_class in self.all_model_classes:
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = False
            config.return_dict = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.attentions

            expected_num_attentions = sum(self.model_tester.depths)
            self.assertEqual(len(attentions), expected_num_attentions)

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            config.output_attentions = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.attentions

            self.assertEqual(len(attentions), expected_num_attentions)

            # verify the first attentions (first block, first layer)
            expected_seq_len = (self.model_tester.image_size // 4) ** 2
            expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
            self.assertListEqual(
                list(attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],
            )

            # verify the last attentions (last block, last layer)
            expected_seq_len = (self.model_tester.image_size // 32) ** 2
            expected_reduced_seq_len = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
            self.assertListEqual(
                list(attentions[-1].shape[-3:]),
                [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len],
            )
            out_len = len(outputs)

            # Check attention is always last and order is fine
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            self.assertEqual(out_len + 1, len(outputs))

            self_attentions = outputs.attentions

            self.assertEqual(len(self_attentions), expected_num_attentions)
            # verify the first attentions (first block, first layer)
            expected_seq_len = (self.model_tester.image_size // 4) ** 2
            expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
            self.assertListEqual(
                list(self_attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],
            )

    def test_hidden_states_output(self):
        def check_hidden_states_output(inputs_dict, config, model_class):
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            hidden_states = outputs.hidden_states

            expected_num_layers = self.model_tester.num_encoder_blocks
            self.assertEqual(len(hidden_states), expected_num_layers)

            # verify the first hidden states (first block)
            self.assertListEqual(
                list(hidden_states[0].shape[-3:]),
                [
                    self.model_tester.hidden_sizes[0],
                    self.model_tester.image_size // 4,
                    self.model_tester.image_size // 4,
                ],
            )

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            inputs_dict["output_hidden_states"] = True
            check_hidden_states_output(inputs_dict, config, model_class)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            config.output_hidden_states = True

            check_hidden_states_output(inputs_dict, config, model_class)

    def test_training(self):
        if not self.model_tester.is_training:
            return

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        for model_class in self.all_model_classes:
            if model_class.__name__ in MODEL_MAPPING_NAMES.values():
                continue

            model = model_class(config)
            model.to(torch_device)
            model.train()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            loss = model(**inputs).loss
            loss.backward()

    @slow
    def test_model_from_pretrained(self):
        model_name = "nvidia/segformer-b0-finetuned-ade-512-512"
        model = SegformerModel.from_pretrained(model_name)
        self.assertIsNotNone(model)


# We will verify our results on an image of cute cats
def prepare_img():
    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
    return image


@require_torch
class SegformerModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference_image_segmentation_ade(self):
        # only resize + normalize
        image_processor = SegformerImageProcessor(
            image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False
        )
        model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to(
            torch_device
        )

        image = prepare_img()
        encoded_inputs = image_processor(images=image, return_tensors="pt")
        pixel_values = encoded_inputs.pixel_values.to(torch_device)

        with torch.no_grad():
            outputs = model(pixel_values)

        expected_shape = torch.Size((1, model.config.num_labels, 128, 128))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = torch.tensor(
            [
                [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
            ]
        ).to(torch_device)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3], expected_slice, atol=1e-4))

    @slow
    def test_inference_image_segmentation_city(self):
        # only resize + normalize
        image_processor = SegformerImageProcessor(
            image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False
        )
        model = SegformerForSemanticSegmentation.from_pretrained(
            "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"
        ).to(torch_device)

        image = prepare_img()
        encoded_inputs = image_processor(images=image, return_tensors="pt")
        pixel_values = encoded_inputs.pixel_values.to(torch_device)

        with torch.no_grad():
            outputs = model(pixel_values)

        expected_shape = torch.Size((1, model.config.num_labels, 128, 128))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = torch.tensor(
            [
                [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
            ]
        ).to(torch_device)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3], expected_slice, atol=1e-1))

    @slow
    def test_post_processing_semantic_segmentation(self):
        # only resize + normalize
        image_processor = SegformerImageProcessor(
            image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False
        )
        model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to(
            torch_device
        )

        image = prepare_img()
        encoded_inputs = image_processor(images=image, return_tensors="pt")
        pixel_values = encoded_inputs.pixel_values.to(torch_device)

        with torch.no_grad():
            outputs = model(pixel_values)

        outputs.logits = outputs.logits.detach().cpu()

        segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(500, 300)])
        expected_shape = torch.Size((500, 300))
        self.assertEqual(segmentation[0].shape, expected_shape)

        segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs)
        expected_shape = torch.Size((128, 128))
        self.assertEqual(segmentation[0].shape, expected_shape)