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# Copyright 2021 The HuggingFace 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.
import unittest
import datasets
from huggingface_hub import DepthEstimationOutput
from huggingface_hub.utils import insecure_hashlib
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
compare_pipeline_output_to_hub_spec,
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class Image:
@staticmethod
def open(*args, **kwargs):
pass
def hashimage(image: Image) -> str:
m = insecure_hashlib.md5(image.tobytes())
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class DepthEstimationPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
_dataset = None
@classmethod
def _load_dataset(cls):
# Lazy loading of the dataset. Because it is a class method, it will only be loaded once per pytest process.
if cls._dataset is None:
# we use revision="refs/pr/1" until the PR is merged
# https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1
cls._dataset = datasets.load_dataset(
"hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1"
)
def get_test_pipeline(
self,
model,
tokenizer=None,
image_processor=None,
feature_extractor=None,
processor=None,
torch_dtype="float32",
):
depth_estimator = DepthEstimationPipeline(
model=model,
tokenizer=tokenizer,
feature_extractor=feature_extractor,
image_processor=image_processor,
processor=processor,
torch_dtype=torch_dtype,
)
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def run_pipeline_test(self, depth_estimator, examples):
self._load_dataset()
outputs = depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png")
self.assertEqual({"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)}, outputs)
outputs = depth_estimator(
[
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
"http://images.cocodataset.org/val2017/000000039769.jpg",
# RGBA
self._dataset[0]["image"],
# LA
self._dataset[1]["image"],
# L
self._dataset[2]["image"],
]
)
self.assertEqual(
[
{"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
{"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
{"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
{"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
{"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
],
outputs,
)
for single_output in outputs:
compare_pipeline_output_to_hub_spec(single_output, DepthEstimationOutput)
@require_tf
@unittest.skip(reason="Depth estimation is not implemented in TF")
def test_small_model_tf(self):
pass
@slow
@require_torch
def test_large_model_pt(self):
model_id = "Intel/dpt-large"
depth_estimator = pipeline("depth-estimation", model=model_id)
outputs = depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg")
outputs["depth"] = hashimage(outputs["depth"])
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()), 29.306)
self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()), 2.662)
@require_torch
def test_small_model_pt(self):
# This is highly irregular to have no small tests.
self.skipTest(reason="There is not hf-internal-testing tiny model for either GLPN nor DPT")
@require_torch
def test_multiprocess(self):
depth_estimator = pipeline(
model="hf-internal-testing/tiny-random-DepthAnythingForDepthEstimation",
num_workers=2,
)
outputs = depth_estimator(
[
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
)
self.assertEqual(
[
{"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
{"predicted_depth": ANY(torch.Tensor), "depth": ANY(Image.Image)},
],
outputs,
)
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