325 lines
12 KiB
Python
325 lines
12 KiB
Python
# coding=utf-8
|
|
# Copyright 2023 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 Dinov2Config, 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 transformers import DPTForDepthEstimation
|
|
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,
|
|
num_channels=3,
|
|
image_size=32,
|
|
patch_size=16,
|
|
use_labels=True,
|
|
num_labels=3,
|
|
is_training=True,
|
|
hidden_size=4,
|
|
num_hidden_layers=2,
|
|
num_attention_heads=2,
|
|
intermediate_size=8,
|
|
out_features=["stage1", "stage2"],
|
|
apply_layernorm=False,
|
|
reshape_hidden_states=False,
|
|
neck_hidden_sizes=[2, 2],
|
|
fusion_hidden_size=6,
|
|
):
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.num_channels = num_channels
|
|
self.image_size = image_size
|
|
self.patch_size = patch_size
|
|
self.hidden_size = hidden_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
self.intermediate_size = intermediate_size
|
|
self.out_features = out_features
|
|
self.apply_layernorm = apply_layernorm
|
|
self.reshape_hidden_states = reshape_hidden_states
|
|
self.use_labels = use_labels
|
|
self.num_labels = num_labels
|
|
self.is_training = is_training
|
|
self.neck_hidden_sizes = neck_hidden_sizes
|
|
self.fusion_hidden_size = fusion_hidden_size
|
|
# DPT's sequence length
|
|
self.seq_length = (self.image_size // self.patch_size) ** 2 + 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(
|
|
backbone_config=self.get_backbone_config(),
|
|
backbone=None,
|
|
neck_hidden_sizes=self.neck_hidden_sizes,
|
|
fusion_hidden_size=self.fusion_hidden_size,
|
|
)
|
|
|
|
def get_backbone_config(self):
|
|
return Dinov2Config(
|
|
image_size=self.image_size,
|
|
patch_size=self.patch_size,
|
|
num_channels=self.num_channels,
|
|
hidden_size=self.hidden_size,
|
|
num_hidden_layers=self.num_hidden_layers,
|
|
num_attention_heads=self.num_attention_heads,
|
|
intermediate_size=self.intermediate_size,
|
|
is_training=self.is_training,
|
|
out_features=self.out_features,
|
|
reshape_hidden_states=self.reshape_hidden_states,
|
|
)
|
|
|
|
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 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 = (DPTForDepthEstimation,) if is_torch_available() else ()
|
|
pipeline_model_mapping = {"depth-estimation": DPTForDepthEstimation} 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 with AutoBackbone does not have a base model and hence no input_embeddings")
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
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_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()
|
|
|
|
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",
|
|
)
|
|
|
|
@unittest.skip(reason="DPT with AutoBackbone does not have a base model and hence no input_embeddings")
|
|
def test_model_common_attributes(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="DPT with AutoBackbone does not have a base model")
|
|
def test_save_load_fast_init_from_base(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="DPT with AutoBackbone does not have a base model")
|
|
def test_save_load_fast_init_to_base(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(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
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "Intel/dpt-large"
|
|
model = DPTForDepthEstimation.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_dinov2(self):
|
|
image_processor = DPTImageProcessor.from_pretrained("facebook/dpt-dinov2-small-kitti")
|
|
model = DPTForDepthEstimation.from_pretrained("facebook/dpt-dinov2-small-kitti").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, 576, 736))
|
|
self.assertEqual(predicted_depth.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor(
|
|
[[6.0433, 7.1636, 7.4268], [6.9047, 7.2471, 7.2355], [7.9261, 8.0631, 8.0244]]
|
|
).to(torch_device)
|
|
|
|
self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4))
|
|
|
|
def test_inference_depth_estimation_beit(self):
|
|
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-beit-base-384")
|
|
model = DPTForDepthEstimation.from_pretrained("Intel/dpt-beit-base-384").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(
|
|
[[2669.7061, 2663.7144, 2674.9399], [2633.9326, 2650.9092, 2665.4270], [2621.8271, 2632.0129, 2637.2290]]
|
|
).to(torch_device)
|
|
|
|
self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4))
|
|
|
|
def test_inference_depth_estimation_swinv2(self):
|
|
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-swinv2-tiny-256")
|
|
model = DPTForDepthEstimation.from_pretrained("Intel/dpt-swinv2-tiny-256").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, 256, 256))
|
|
self.assertEqual(predicted_depth.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor(
|
|
[[1032.7719, 1025.1886, 1030.2661], [1023.7619, 1021.0075, 1024.9121], [1022.5667, 1018.8522, 1021.4145]]
|
|
).to(torch_device)
|
|
|
|
self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4))
|