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[to #51336898] fix minor problems in deploying, convert img output to cv2

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Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/14009870
This commit is contained in:
lingcai.wl
2023-09-20 15:10:41 +08:00
parent 6b09cb3d7a
commit 9c4cdb15d0
1 changed files with 28 additions and 25 deletions
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53
modelscope/utils/input_output.py
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@@ -7,6 +7,7 @@ from io import BytesIO
from typing import Any
from urllib.parse import urlparse
import cv2
import numpy as np
from modelscope.hub.api import HubApi
@@ -437,7 +438,17 @@ class PipelineInfomation():
def _analyze(self):
input, parameters = get_pipeline_input_parameters(
self._source_path, self._class_name)
if input is not None: # custom pipeline __call__ asr_inferrnce_pipeline
# use base pipeline __call__ if inputs and outputs are defined in modelscope lib
if self._task_name in TASK_INPUTS and self._task_name in TASK_OUTPUTS:
# delete the first default input which is defined by task.
if parameters is None:
self._parameters_schema = {}
else:
self._parameters_schema = generate_pipeline_parameters_schema(
parameters)
self._input_schema = get_input_schema(self._task_name, None)
self._output_schema = get_output_schema(self._task_name)
elif input is not None: # custom pipeline implemented it's own __call__ method
self._is_custom_call_method = True
self._input_schema = generate_pipeline_parameters_schema(input)
self._input_schema[
@@ -449,27 +460,18 @@ class PipelineInfomation():
if self._task_name in TASK_OUTPUTS:
self._output_schema = get_output_schema(self._task_name)
else:
# use base pipeline __call__
if self._task_name in TASK_INPUTS and self._task_name in TASK_OUTPUTS:
# delete the first default input which is defined by task.
self._parameters_schema = generate_pipeline_parameters_schema(
parameters)
logger.warning(
'Task: %s input is defined: %s, output is defined: %s which is not completed'
% (self._task_name, self._task_name
in TASK_INPUTS, self._task_name in TASK_OUTPUTS))
self._input_schema = None
self._output_schema = None
if self._task_name in TASK_INPUTS:
self._input_schema = get_input_schema(self._task_name, None)
if self._task_name in TASK_OUTPUTS:
self._output_schema = get_output_schema(self._task_name)
else:
logger.warning(
'Task: %s input is defined: %s, output is defined: %s which is not completed'
% (self._task_name, self._task_name
in TASK_INPUTS, self._task_name in TASK_OUTPUTS))
self._input_schema = None
self._output_schema = None
if self._task_name in TASK_INPUTS:
self._input_schema = get_input_schema(
self._task_name, None)
if self._task_name in TASK_OUTPUTS:
self._output_schema = get_output_schema(self._task_name)
self._parameters_schema = generate_pipeline_parameters_schema(
parameters)
self._parameters_schema = generate_pipeline_parameters_schema(
parameters)
@property
def task_name(self):
@@ -663,11 +665,8 @@ def service_base64_input_to_pipeline_input(task_name, body):
def encode_numpy_image_to_base64(image):
from PIL import Image
with BytesIO() as output_bytes:
pil_image = Image.fromarray(image.astype(np.uint8))
pil_image.save(output_bytes, 'PNG')
bytes_data = output_bytes.getvalue()
_, img_encode = cv2.imencode('.png', image)
bytes_data = img_encode.tobytes()
base64_str = str(base64.b64encode(bytes_data), 'utf-8')
return base64_str
@@ -718,6 +717,10 @@ def _convert_to_python_type(inputs):
return res
elif isinstance(inputs, np.ndarray):
return inputs.tolist()
elif isinstance(inputs, np.floating):
return float(inputs)
elif isinstance(inputs, np.integer):
return int(inputs)
else:
return inputs