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# coding=utf-8 | |
# Copyright 2022 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 DPT model. """ | |
import inspect | |
import unittest | |
from transformers import DPTConfig | |
from transformers.file_utils import is_torch_available, is_vision_available | |
from transformers.models.auto import get_values | |
from transformers.testing_utils import require_torch, require_vision, slow, torch_device | |
from ...test_configuration_common import ConfigTester | |
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor | |
from ...test_pipeline_mixin import PipelineTesterMixin | |
if is_torch_available(): | |
import torch | |
from torch import nn | |
from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel | |
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST | |
if is_vision_available(): | |
from PIL import Image | |
from transformers import DPTImageProcessor | |
class DPTModelTester: | |
def __init__( | |
self, | |
parent, | |
batch_size=2, | |
image_size=32, | |
patch_size=16, | |
num_channels=3, | |
is_training=True, | |
use_labels=True, | |
hidden_size=32, | |
num_hidden_layers=2, | |
backbone_out_indices=[0, 1, 2, 3], | |
num_attention_heads=4, | |
intermediate_size=37, | |
hidden_act="gelu", | |
hidden_dropout_prob=0.1, | |
attention_probs_dropout_prob=0.1, | |
initializer_range=0.02, | |
num_labels=3, | |
neck_hidden_sizes=[16, 32], | |
is_hybrid=False, | |
scope=None, | |
): | |
self.parent = parent | |
self.batch_size = batch_size | |
self.image_size = image_size | |
self.patch_size = patch_size | |
self.num_channels = num_channels | |
self.is_training = is_training | |
self.use_labels = use_labels | |
self.hidden_size = hidden_size | |
self.num_hidden_layers = num_hidden_layers | |
self.backbone_out_indices = backbone_out_indices | |
self.num_attention_heads = num_attention_heads | |
self.intermediate_size = intermediate_size | |
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 | |
self.is_hybrid = is_hybrid | |
self.neck_hidden_sizes = neck_hidden_sizes | |
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token) | |
num_patches = (image_size // patch_size) ** 2 | |
self.seq_length = num_patches + 1 | |
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 DPTConfig( | |
image_size=self.image_size, | |
patch_size=self.patch_size, | |
num_channels=self.num_channels, | |
hidden_size=self.hidden_size, | |
fusion_hidden_size=self.hidden_size, | |
num_hidden_layers=self.num_hidden_layers, | |
backbone_out_indices=self.backbone_out_indices, | |
num_attention_heads=self.num_attention_heads, | |
intermediate_size=self.intermediate_size, | |
hidden_act=self.hidden_act, | |
hidden_dropout_prob=self.hidden_dropout_prob, | |
attention_probs_dropout_prob=self.attention_probs_dropout_prob, | |
is_decoder=False, | |
initializer_range=self.initializer_range, | |
is_hybrid=self.is_hybrid, | |
neck_hidden_sizes=self.neck_hidden_sizes, | |
) | |
def create_and_check_model(self, config, pixel_values, labels): | |
model = DPTModel(config=config) | |
model.to(torch_device) | |
model.eval() | |
result = model(pixel_values) | |
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) | |
def create_and_check_for_depth_estimation(self, config, pixel_values, labels): | |
config.num_labels = self.num_labels | |
model = DPTForDepthEstimation(config) | |
model.to(torch_device) | |
model.eval() | |
result = model(pixel_values) | |
self.parent.assertEqual(result.predicted_depth.shape, (self.batch_size, self.image_size, self.image_size)) | |
def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels): | |
config.num_labels = self.num_labels | |
model = DPTForSemanticSegmentation(config) | |
model.to(torch_device) | |
model.eval() | |
result = model(pixel_values, labels=labels) | |
self.parent.assertEqual( | |
result.logits.shape, (self.batch_size, self.num_labels, self.image_size, self.image_size) | |
) | |
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 | |
class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): | |
""" | |
Here we also overwrite some of the tests of test_modeling_common.py, as DPT does not use input_ids, inputs_embeds, | |
attention_mask and seq_length. | |
""" | |
all_model_classes = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else () | |
pipeline_model_mapping = ( | |
{ | |
"depth-estimation": DPTForDepthEstimation, | |
"feature-extraction": DPTModel, | |
"image-segmentation": DPTForSemanticSegmentation, | |
} | |
if is_torch_available() | |
else {} | |
) | |
test_pruning = False | |
test_resize_embeddings = False | |
test_head_masking = False | |
def setUp(self): | |
self.model_tester = DPTModelTester(self) | |
self.config_tester = ConfigTester(self, config_class=DPTConfig, has_text_modality=False, hidden_size=37) | |
def test_config(self): | |
self.config_tester.run_common_tests() | |
def test_inputs_embeds(self): | |
pass | |
def test_model_common_attributes(self): | |
config, _ = self.model_tester.prepare_config_and_inputs_for_common() | |
for model_class in self.all_model_classes: | |
model = model_class(config) | |
self.assertIsInstance(model.get_input_embeddings(), (nn.Module)) | |
x = model.get_output_embeddings() | |
self.assertTrue(x is None or isinstance(x, nn.Linear)) | |
def test_forward_signature(self): | |
config, _ = self.model_tester.prepare_config_and_inputs_for_common() | |
for model_class in self.all_model_classes: | |
model = model_class(config) | |
signature = inspect.signature(model.forward) | |
# signature.parameters is an OrderedDict => so arg_names order is deterministic | |
arg_names = [*signature.parameters.keys()] | |
expected_arg_names = ["pixel_values"] | |
self.assertListEqual(arg_names[:1], expected_arg_names) | |
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_depth_estimation(self): | |
config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
self.model_tester.create_and_check_for_depth_estimation(*config_and_inputs) | |
def test_for_semantic_segmentation(self): | |
config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
self.model_tester.create_and_check_for_semantic_segmentation(*config_and_inputs) | |
def test_training(self): | |
for model_class in self.all_model_classes: | |
if model_class.__name__ == "DPTForDepthEstimation": | |
continue | |
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
config.return_dict = True | |
if model_class in get_values(MODEL_MAPPING): | |
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() | |
def test_training_gradient_checkpointing(self): | |
for model_class in self.all_model_classes: | |
if model_class.__name__ == "DPTForDepthEstimation": | |
continue | |
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
config.use_cache = False | |
config.return_dict = True | |
if model_class in get_values(MODEL_MAPPING) or not model_class.supports_gradient_checkpointing: | |
continue | |
model = model_class(config) | |
model.to(torch_device) | |
model.gradient_checkpointing_enable() | |
model.train() | |
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) | |
loss = model(**inputs).loss | |
loss.backward() | |
def test_initialization(self): | |
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() | |
configs_no_init = _config_zero_init(config) | |
for model_class in self.all_model_classes: | |
model = model_class(config=configs_no_init) | |
# Skip the check for the backbone | |
backbone_params = [] | |
for name, module in model.named_modules(): | |
if module.__class__.__name__ == "DPTViTHybridEmbeddings": | |
backbone_params = [f"{name}.{key}" for key in module.state_dict().keys()] | |
break | |
for name, param in model.named_parameters(): | |
if param.requires_grad: | |
if name in backbone_params: | |
continue | |
self.assertIn( | |
((param.data.mean() * 1e9).round() / 1e9).item(), | |
[0.0, 1.0], | |
msg=f"Parameter {name} of model {model_class} seems not properly initialized", | |
) | |
def test_model_from_pretrained(self): | |
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: | |
model = DPTModel.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 | |
class DPTModelIntegrationTest(unittest.TestCase): | |
def test_inference_depth_estimation(self): | |
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large") | |
model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large").to(torch_device) | |
image = prepare_img() | |
inputs = image_processor(images=image, return_tensors="pt").to(torch_device) | |
# forward pass | |
with torch.no_grad(): | |
outputs = model(**inputs) | |
predicted_depth = outputs.predicted_depth | |
# verify the predicted depth | |
expected_shape = torch.Size((1, 384, 384)) | |
self.assertEqual(predicted_depth.shape, expected_shape) | |
expected_slice = torch.tensor( | |
[[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]] | |
).to(torch_device) | |
self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4)) | |
def test_inference_semantic_segmentation(self): | |
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large-ade") | |
model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade").to(torch_device) | |
image = prepare_img() | |
inputs = image_processor(images=image, return_tensors="pt").to(torch_device) | |
# forward pass | |
with torch.no_grad(): | |
outputs = model(**inputs) | |
# verify the logits | |
expected_shape = torch.Size((1, 150, 480, 480)) | |
self.assertEqual(outputs.logits.shape, expected_shape) | |
expected_slice = torch.tensor( | |
[[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]] | |
).to(torch_device) | |
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3], expected_slice, atol=1e-4)) | |
def test_post_processing_semantic_segmentation(self): | |
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large-ade") | |
model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade").to(torch_device) | |
image = prepare_img() | |
inputs = image_processor(images=image, return_tensors="pt").to(torch_device) | |
# forward pass | |
with torch.no_grad(): | |
outputs = model(**inputs) | |
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((480, 480)) | |
self.assertEqual(segmentation[0].shape, expected_shape) | |