359 lines
14 KiB
Python
359 lines
14 KiB
Python
# 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 unittest
|
|
|
|
from transformers import DPTConfig
|
|
from transformers.file_utils import is_torch_available, is_vision_available
|
|
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 DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
|
|
from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES
|
|
|
|
|
|
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
|
|
|
|
|
|
@require_torch
|
|
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,
|
|
"image-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()
|
|
|
|
@unittest.skip(reason="DPT does not use inputs_embeds")
|
|
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_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.__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()
|
|
|
|
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.__name__ in MODEL_MAPPING_NAMES.values() 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()
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant_false(self):
|
|
pass
|
|
|
|
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",
|
|
)
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "Intel/dpt-large"
|
|
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
|
|
|
|
|
|
@require_torch
|
|
@require_vision
|
|
@slow
|
|
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)
|