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Adjust Roboflow models to primarily use base64 payloads #798

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Adjust Roboflow models to primarily use base64 payloads #798

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2 changes: 1 addition & 1 deletion inference/core/workflows/core_steps/models/foundation/clip_comparison/v2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -207,7 +207,7 @@ def run_remotely(
tasks = [
partial(
client.clip_compare,
subject=single_image.numpy_image,
subject=single_image.base64_image,
prompt=classes,
clip_version=version,
)
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2 changes: 1 addition & 1 deletion inference/core/workflows/core_steps/models/foundation/ocr/v1.py
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Expand Up @@ -169,7 +169,7 @@ def run_remotely(
max_concurrent_requests=WORKFLOWS_REMOTE_EXECUTION_MAX_STEP_CONCURRENT_REQUESTS,
)
client.configure(configuration)
non_empty_inference_images = [i.numpy_image for i in images]
non_empty_inference_images = [i.base64_image for i in images]
predictions = client.ocr_image(
inference_input=non_empty_inference_images,
)
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2 changes: 1 addition & 1 deletion inference/core/workflows/core_steps/models/foundation/yolo_world/v1.py
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Expand Up @@ -217,7 +217,7 @@ def run_remotely(
client.configure(inference_configuration=configuration)
if WORKFLOWS_REMOTE_API_TARGET == "hosted":
client.select_api_v0()
inference_images = [i.to_inference_format(numpy_preferred=True) for i in images]
inference_images = [i.to_inference_format() for i in images]
image_sub_batches = list(
make_batches(
iterable=inference_images,
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2 changes: 1 addition & 1 deletion inference/core/workflows/core_steps/models/roboflow/instance_segmentation/v1.py
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Expand Up @@ -339,7 +339,7 @@ def run_remotely(
source="workflow-execution",
)
client.configure(inference_configuration=client_config)
inference_images = [i.numpy_image for i in images]
inference_images = [i.base64_image for i in images]
predictions = client.infer(
inference_input=inference_images,
model_id=model_id,
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2 changes: 1 addition & 1 deletion inference/core/workflows/core_steps/models/roboflow/keypoint_detection/v1.py
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Expand Up @@ -324,7 +324,7 @@ def run_remotely(
source="workflow-execution",
)
client.configure(inference_configuration=client_config)
inference_images = [i.numpy_image for i in images]
inference_images = [i.base64_image for i in images]
predictions = client.infer(
inference_input=inference_images,
model_id=model_id,
Expand Down
2 changes: 1 addition & 1 deletion inference/core/workflows/core_steps/models/roboflow/multi_class_classification/v1.py
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Expand Up @@ -230,7 +230,7 @@ def run_remotely(
source="workflow-execution",
)
client.configure(inference_configuration=client_config)
non_empty_inference_images = [i.numpy_image for i in images]
non_empty_inference_images = [i.base64_image for i in images]
predictions = client.infer(
inference_input=non_empty_inference_images,
model_id=model_id,
Expand Down
2 changes: 1 addition & 1 deletion inference/core/workflows/core_steps/models/roboflow/multi_label_classification/v1.py
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Expand Up @@ -230,7 +230,7 @@ def run_remotely(
source="workflow-execution",
)
client.configure(inference_configuration=client_config)
non_empty_inference_images = [i.numpy_image for i in images]
non_empty_inference_images = [i.base64_image for i in images]
predictions = client.infer(
inference_input=non_empty_inference_images,
model_id=model_id,
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2 changes: 1 addition & 1 deletion inference/core/workflows/core_steps/models/roboflow/object_detection/v1.py
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Expand Up @@ -306,7 +306,7 @@ def run_remotely(
source="workflow-execution",
)
client.configure(inference_configuration=client_config)
non_empty_inference_images = [i.numpy_image for i in images]
non_empty_inference_images = [i.base64_image for i in images]
predictions = client.infer(
inference_input=non_empty_inference_images,
model_id=model_id,
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2 changes: 1 addition & 1 deletion inference/core/workflows/execution_engine/entities/base.py
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Expand Up @@ -375,7 +375,7 @@ def base64_image(self) -> str:
return self._base64_image
numpy_image = self.numpy_image
self._base64_image = base64.b64encode(
encode_image_to_jpeg_bytes(numpy_image)
encode_image_to_jpeg_bytes(numpy_image, jpeg_quality=95)
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Probably want to do whatever we do during the export process for training. I think it’s quality 70

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I'd vote to keep default compression used by opencv, if we introduce higher compression we will have users complaining that inference works differently when they run it locally vs when they make request to our platform..

).decode("ascii")
return self._base64_image

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6 changes: 5 additions & 1 deletion inference_cli/lib/cloud_adapter.py
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Expand Up @@ -83,14 +83,18 @@
""",
}


def check_sky_installed():
try:
global sky
import sky
except ImportError as e:
print("Please install cloud deploy dependencies with 'pip install inference[cloud-deploy]'")
print(
"Please install cloud deploy dependencies with 'pip install inference[cloud-deploy]'"
)
raise e


def _random_char(y):
return "".join(random.choice(string.ascii_lowercase) for x in range(y))

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16 changes: 9 additions & 7 deletions tests/inference/unit_tests/core/cache/test_serializers.py
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@@ -1,26 +1,28 @@
import os
from unittest.mock import MagicMock

import pytest

from inference.core.cache.serializers import (
to_cachable_inference_item,
build_condensed_response,
to_cachable_inference_item,
)
from inference.core.entities.requests.inference import (
ClassificationInferenceRequest,
ObjectDetectionInferenceRequest,
)
from inference.core.entities.responses.inference import (
ClassificationInferenceResponse,
MultiLabelClassificationInferenceResponse,
ClassificationPrediction,
InstanceSegmentationInferenceResponse,
InstanceSegmentationPrediction,
Keypoint,
KeypointsDetectionInferenceResponse,
KeypointsPrediction,
MultiLabelClassificationInferenceResponse,
MultiLabelClassificationPrediction,
ObjectDetectionInferenceResponse,
ObjectDetectionPrediction,
ClassificationPrediction,
MultiLabelClassificationPrediction,
InstanceSegmentationPrediction,
KeypointsPrediction,
Keypoint,
Point,
)

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12 changes: 9 additions & 3 deletions tests/inference/unit_tests/usage_tracking/test_collector.py
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Expand Up @@ -764,7 +764,7 @@ def test_zip_usage_payloads_with_different_exec_session_ids():
"fps": 10,
"exec_session_id": "session_2",
},
}
},
},
{
"fake_api1_hash": {
Expand Down Expand Up @@ -831,7 +831,11 @@ def test_zip_usage_payloads_with_different_exec_session_ids():

def test_system_info_with_dedicated_deployment_id():
# given
system_info = UsageCollector.system_info(ip_address="w.x.y.z", hostname="hostname01", dedicated_deployment_id="deployment01")
system_info = UsageCollector.system_info(
ip_address="w.x.y.z",
hostname="hostname01",
dedicated_deployment_id="deployment01",
)

# then
expected_system_info = {
Expand All @@ -845,7 +849,9 @@ def test_system_info_with_dedicated_deployment_id():

def test_system_info_with_no_dedicated_deployment_id():
# given
system_info = UsageCollector.system_info(ip_address="w.x.y.z", hostname="hostname01")
system_info = UsageCollector.system_info(
ip_address="w.x.y.z", hostname="hostname01"
)

# then
expected_system_info = {
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14 changes: 12 additions & 2 deletions tests/workflows/unit_tests/core_steps/analytics/test_line_counter_v2.py
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Expand Up @@ -63,8 +63,18 @@ def test_line_counter() -> None:
)

# then
assert frame1_result == {"count_in": 0, "count_out": 0, "detections_in": frame1_detections[[False, False, False, False]], "detections_out": frame1_detections[[False, False, False, False]]}
assert frame2_result == {"count_in": 1, "count_out": 1, "detections_in": frame2_detections[[True, False, False, False]], "detections_out": frame2_detections[[False, True, False, False]]}
assert frame1_result == {
"count_in": 0,
"count_out": 0,
"detections_in": frame1_detections[[False, False, False, False]],
"detections_out": frame1_detections[[False, False, False, False]],
}
assert frame2_result == {
"count_in": 1,
"count_out": 1,
"detections_in": frame2_detections[[True, False, False, False]],
"detections_out": frame2_detections[[False, True, False, False]],
}


def test_line_counter_no_trackers() -> None:
Expand Down