I'm trying to make this version work on my CPU (Linux):
https://github.com/bes-dev/stable_diffusion.openvino
And it works fine without any initial image. But when I try to pass an initial image, I get this error:
Traceback (most recent call last):
File "/home/ideruga/workspace/stable_diffusion.openvino/demo.py", line 79, in <module>
main(args)
File "/home/ideruga/workspace/stable_diffusion.openvino/demo.py", line 39, in main
image = engine(
File "/home/ideruga/workspace/stable_diffusion.openvino/stable_diffusion_engine.py", line 188, in __call__
noise_pred = result(self.unet.infer_new_request({
File "/home/ideruga/anaconda3/lib/python3.9/site-packages/openvino/runtime/ie_api.py", line 266, in infer_new_request
return self.create_infer_request().infer(inputs)
......
File "/home/ideruga/anaconda3/lib/python3.9/site-packages/openvino/runtime/ie_api.py", line 31, in set_scalar_tensor
request.set_tensor(key, tensor)
RuntimeError: [ PARAMETER_MISMATCH ] Failed to set input blob with precision: I64, if CNNNetwork input blob precision is: FP64
It's bizarre, because I am not messing with any parameters. It's as if model that it downloads is not compatible with parsed input image.
I've actually found a bug in the linked repository, I'll submit a fix later today. The used model expects f64 but is fed with i64 value. I'll post a comment with the PR when it's submitted.
Related
I am trying to implement Hierarchical time series forecasting on azureautoml pipelines.
I followed this notebook for implementation
https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb
While I ran training pipeline on compute instance it worked, but when I am running the same on compute cluster it breaks at hts-proportion-calculation part.
This is the error I am getting,
system error:
Encountered an internal AutoML error. Error Message/Code: ClientException. Additional Info: ClientException:
Message: No objects to concatenate
InnerException: None
ErrorResponse
{
"error": {
"message": "No objects to concatenate"
}
}
logs :
Loading arguments for scenario proportions-calculation
adding argument --input-medatadata
adding argument --hts-graph
adding argument --enable-event-logger
Input arguments dict is {'--input-medatadata': '/mnt/azureml/cr/j/85509be625484b6caa3c1d97b7ab2e33/cap/data-capability/wd/INPUT_automl_training_workspaceblobstore/azureml/17ca5ae7-7269-4246-888f-e781071e3f5c/automl_training', '--hts-graph': '/mnt/azureml/cr/j/85509be625484b6caa3c1d97b7ab2e33/cap/data-capability/wd/INPUT_hts_graph_workspaceblobstore/azureml/a2c1b15a-c895-41e8-b6a6-1ca37ebe9e77/hts_graph', '--enable-event-logger': None}
Unknown file to proceed outputs.txt
processing: outputs.txt with type None.
Cleaning up all outstanding Run operations, waiting 300.0 seconds
3 items cleaning up...
Cleanup took 0.001676321029663086 seconds
Traceback (most recent call last):
File "proportions_calculation_wrapper.py", line 47, in <module>
runtime_wrapper.run()
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/azureml/train/automl/runtime/_many_models/automl_pipeline_step_wrapper.py", line 63, in run
self._run()
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/azureml/train/automl/runtime/_hts/proportions_calculation.py", line 44, in _run
proportions_calculation(self.arguments_dict, self.event_logger, script_run=self.step_run)
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/azureml/train/automl/runtime/_hts/proportions_calculation.py", line 173, in proportions_calculation
proportion_files_list, forecasting_parameters.time_column_name, graph.label_column_name
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/azureml/train/automl/runtime/_hts/proportions_calculation.py", line 92, in calculate_time_agg_sum_for_all_files
df = pd.concat(pool.map(concat_func, files_batches), ignore_index=True)
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/pandas/util/_decorators.py", line 311, in wrapper
return func(*args, **kwargs)
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/pandas/core/reshape/concat.py", line 304, in concat
sort=sort,
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/pandas/core/reshape/concat.py", line 351, in __init__
raise ValueError("No objects to concatenate")
ValueError: No objects to concatenate
Please let me know how can I resolve this issue ?
This error was incurred as Iteration timeout was not less than experiment timeout , but the system error & logs are a kind of misleading.
df = pd.concat(pool.map(concat_func, files_batches), ignore_index=True)
logs was pointing to pandas "No objects to concatenate"
This error can be overcome by setting iterationtimeout value less than experimenttime out value.
I had set iteration_timeout_minutes=60 which caused the error.
automl_settings = AutoMLConfig(
task="forecasting",
primary_metric="normalized_root_mean_squared_error",
experiment_timeout_hours=1,
label_column_name=label_column_name,
track_child_runs=False,
forecasting_parameters=forecasting_parameters,
pipeline_fetch_max_batch_size=15,
model_explainability=model_explainability,
n_cross_validations="auto", # Feel free to set to a small integer (>=2) if runtime is an issue.
cv_step_size="auto",
# The following settings are specific to this sample and should be adjusted according to your own needs.
iteration_timeout_minutes=10,
iterations=15,
)
We are able to run the sample successfully using the compute cluster as given below.
from azureml.core.compute import ComputeTarget, AmlCompute
# Name your cluster
compute_name = "hts-compute"
if compute_name in ws.compute_targets:
compute_target = ws.compute_targets[compute_name]
if compute_target and type(compute_target) is AmlCompute:
print("Found compute target: " + compute_name)
else:
print("Creating a new compute target...")
provisioning_config = AmlCompute.provisioning_configuration(
vm_size="STANDARD_D16S_V3", max_nodes=20
)
# Create the compute target
compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)
# Can poll for a minimum number of nodes and for a specific timeout.
# If no min node count is provided it will use the scale settings for the cluster
compute_target.wait_for_completion(
show_output=True, min_node_count=None, timeout_in_minutes=20
)
# For a more detailed view of current cluster status, use the 'status' property
print(compute_target.status.serialize())
I see that several Python-package related files have gibberish at their end.
Due to this, I am unable to do several pip operations (even basic ones like "pip list").
(Usually, I use conda by the way)
For example. When I pressed pip list. I get the following error.
ERROR: Exception:
Traceback (most recent call last):
File "C:\Users\shan_jaffry\Miniconda3\envs\SQL_version\lib\site-packages\pip\_internal\cli\base_command.py", line 173, in _main
status = self.run(options, args)
File "C:\Users\shan_jaffry\Miniconda3\envs\SQL_version\lib\site-packages\pip\_internal\commands\list.py", line 179, in run
self.output_package_listing(packages, options)
File "C:\Users\shan_jaffry\Miniconda3\envs\SQL_version\lib\site-packages\pip\_internal\commands\list.py", line 255, in output_package_listing
data, header = format_for_columns(packages, options)
File "C:\Users\shan_jaffry\Miniconda3\envs\SQL_version\lib\site-packages\pip\_internal\commands\list.py", line 307, in format_for_columns
row = [proj.raw_name, str(proj.version)]
File "C:\Users\shan_jaffry\Miniconda3\envs\SQL_version\lib\site-packages\pip\_internal\metadata\base.py", line 163, in raw_name
return self.metadata.get("Name", self.canonical_name)
File "C:\Users\shan_jaffry\Miniconda3\envs\SQL_version\lib\site-packages\pip\_internal\metadata\pkg_resources.py", line 96, in metadata
return get_metadata(self._dist)
File "C:\Users\shan_jaffry\Miniconda3\envs\SQL_version\lib\site-packages\pip\_internal\utils\packaging.py", line 48, in get_metadata
metadata = dist.get_metadata(metadata_name)
File "C:\Users\shan_jaffry\Miniconda3\envs\SQL_version\lib\site-packages\pip\_vendor\pkg_resources\__init__.py", line 1424, in get_metadata
return value.decode('utf-8')
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xfd in position 14097: invalid start byte in METADATA file at path: c:\users\shan_jaffry\miniconda3\envs\sql_version\lib\site-packages\hupper-1.10.2.dist-info\METADATA
I went into the file META and found the following gibberish at the end. This (I found) has been done in several other files i.e. end of files are appended with gibberish and the actual thin is removed. Any help?
> 0.1 (2016-10-21)
> ================
> -
> - Initial rele9ýl·øA
I found that the by manually going to the site-packages folder, and removing the two folders, :: hupper and hupper-1.10.2.dist-info and then installing hupper again using "pip install hupper", problem was solved.
The issue was that the hupper package (and hupper-1.10.2.dist-info) were corrupted. Hence uninstall and re-install helped.
I'm running a Dataflow job which I think should work, and is failing after 1.5 hrs with what looks like network errors. It works fine when run against a subset of the data.
The first trouble sign is a whole string of warnings like this:
Refusing to split <dataflow_worker.shuffle.GroupedShuffleRangeTracker object at 0x7f2bcb629950> at b'\xa4r\xa6\x85\x00\x01': proposed split position is out of range [b'\xa4^E\xd2\x00\x01', b'\xa4r\xa6\x85\x00\x01'). Position of last group processed was b'\xa4r\xa6\x84\x00\x01'.
Then there are four errors which seem to be about writing CSV files to GCS:
Error in _start_upload while inserting file gs://(redacted).csv: Traceback (most recent call last): File "/usr/local/lib/python3.7/site-packages/apache_beam/io/gcp/gcsio.py", line 565, in _start_upload self._client.objects.Insert(self._insert_request, upload=self._upload) File "/usr/local/lib/python3.7/site-packages/apache_beam/io/gcp/internal/clients/storage/storage_v1_client.py", line 1156, in Insert upload=upload, upload_config=upload_config) File "/usr/local/lib/python3.7/site-packages/apitools/base/py/base_api.py", line 731, in _RunMethod return self.ProcessHttpResponse(method_config, http_response, request) File "/usr/local/lib/python3.7/site-packages/apitools/base/py/base_api.py", line 737, in ProcessHttpResponse self.__ProcessHttpResponse(method_config, http_response, request)) File "/usr/local/lib/python3.7/site-packages/apitools/base/py/base_api.py", line 604, in __ProcessHttpResponse http_response, method_config=method_config, request=request) apitools.base.py.exceptions.HttpError: HttpError accessing <https://www.googleapis.com/resumable/upload/storage/v1/b/(redacted).csv&uploadType=resumable&upload_id=(redacted)>: response: <{'content-type': 'text/plain; charset=utf-8', 'x-guploader-uploadid': '(redacted)', 'content-length': '0', 'date': 'Wed, 08 Jul 2020 22:17:28 GMT', 'server': 'UploadServer', 'status': '503'}>, content <>
Error in _start_upload while inserting file gs://(redacted).csv: Traceback (most recent call last): File "/usr/local/lib/python3.7/site-packages/apache_beam/io/gcp/gcsio.py", line 565, in _start_upload self._client.objects.Insert(self._insert_request, upload=self._upload) File "/usr/local/lib/python3.7/site-packages/apache_beam/io/gcp/internal/clients/storage/storage_v1_client.py", line 1156, in Insert upload=upload, upload_config=upload_config) File "/usr/local/lib/python3.7/site-packages/apitools/base/py/base_api.py", line 715, in _RunMethod http_request, client=self.client) File "/usr/local/lib/python3.7/site-packages/apitools/base/py/transfer.py", line 908, in InitializeUpload return self.StreamInChunks() File "/usr/local/lib/python3.7/site-packages/apitools/base/py/transfer.py", line 1020, in StreamInChunks additional_headers=additional_headers) File "/usr/local/lib/python3.7/site-packages/apitools/base/py/transfer.py", line 971, in __StreamMedia self.RefreshResumableUploadState() File "/usr/local/lib/python3.7/site-packages/apitools/base/py/transfer.py", line 873, in RefreshResumableUploadState self.stream.seek(self.progress) File "/usr/local/lib/python3.7/site-packages/apache_beam/io/filesystemio.py", line 301, in seek offset, whence, self.position, self.last_block_position)) NotImplementedError: offset: 0, whence: 0, position: 411, last: 411
The Dataflow job ID is 2020-07-07_13_08_31-7649894576933400587 -- if anyone from Google Cloud Support is able to look at this I'd be very grateful. Thanks very much.
P.S I asked a similar question last year (Dataflow job fails at BigQuery write with backend errors), the resolution was to use --experiments=use_beam_bq_sink -- I am already doing this.
You can safely ignore "Refusing to split" errors. This just means that the split position Dataflow service provided probably was received by the worker after that position was already read by the worker. Hence the worker has to ignore the split request.
Error "Error in _start_upload while inserting" seems more problematic and seems to be similar to https://issues.apache.org/jira/browse/BEAM-7014. I suspect this to be a rare flake though so I'm not sure if this was the reason for your job failure (the job only fails of the same workitem failed four times).
Can you contact Google Cloud support so that they can look into your job ?
I will mention this in the JIRA.
I can get SIFT keypoints and descriptors from two, seperate, large images (~2GB) when I run sift.detectAndCompute from the command line. I run it on one image, wait a very long time, but eventually get the keypoints and descriptors. Then I repeat for the second image, and again it takes a long time, but I DO eventually get my keypoints and descriptors. Here are the two lines I run from the IPython console in Spyder, which I am running on my machine with 32 GB of RAM. (MAX_MATCHES = 50000 in the code below):
sift = cv2.xfeatures2d.SIFT_create(MAX_MATCHES)
keypoints, descriptors = sift.detectAndCompute(imgGray, None)
This takes 10 minutes to finish, but it does finish. Next, I run this:
keypoints2, descriptors2 = sift.detectAndCompute(refimgGray, None)
When done, keypoints and keypoints2 DO contain 50000 keypoint objects.
However, if I run my script, which calls a function that uses sift.detectAndCompute and returns keypoints and descriptors, the process takes a long time, uses 100% of my memory and ~95% of my disk BW and then fails with this traceback:
runfile('C:/AV GIS/python scripts/img_align_w_geo_w_mask_refactor_ret_1.py', wdir='C:/AV GIS/python scripts')
Reading reference image : C:\Users\kellett\Downloads\3074_transparent_mosaic_group1.tif
xfrm for image = (584505.1165100001, 0.027370000000000002, 0.0, 4559649.608440001, 0.0, -0.027370000000000002)
Reading image to align : C:\Users\kellett\Downloads\3071_transparent_mosaic_group1.tif
xfrm for image = (584499.92168, 0.02791, 0.0, 4559648.80372, 0.0, -0.02791)
Traceback (most recent call last):
File "<ipython-input-75-571660ddab7f>", line 1, in <module>
runfile('C:/AV GIS/python scripts/img_align_w_geo_w_mask_refactor_ret_1.py', wdir='C:/AV GIS/python scripts')
File "C:\Users\kellett\AppData\Local\Continuum\anaconda3\envs\testgdal\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 668, in runfile
execfile(filename, namespace)
File "C:\Users\kellett\AppData\Local\Continuum\anaconda3\envs\testgdal\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 108, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/AV GIS/python scripts/img_align_w_geo_w_mask_refactor_ret_1.py", line 445, in <module>
matches = find_matches(refKP, refDesc, imgKP, imgDesc)
File "C:/AV GIS/python scripts/img_align_w_geo_w_mask_refactor_ret_1.py", line 301, in find_matches
matches = matcher.match(dsc1, dsc2)
error: C:\ci\opencv_1512688052760\work\modules\core\src\stat.cpp:4024: error: (-215) (type == 0 && dtype == 4) || dtype == 5 in function cv::batchDistance
The function is simply called once for each image thusly:
print("Reading image to align : ", imFilename);
img, imgGray, imgEdgmask, imgXfrm, imgGeoInfo = read_ortho4align(imFilename)
refKP, refDesc = extractKeypoints(refimgGray, refEdgmask)
imgKP, imgDesc = extractKeypoints(imgGray, imgEdgmask)
HERE IS MY QUESTION (sorry for shouting): Do you think Python tries to run the two lines above concurrently in some way? If so, how can I force it to run serially? If not, do you have any idea why the two keypoint detections would work individually, but not when they come one after another in a script?
One more clue - I put in a statement to see if the script proceeds to the second detectAndCompute statement before it fails, and it does. (I just put a print statement in between the two.)
My error was coming later in my script where I was finding matches.
I have no reason to believe the two SIFT keypoint finding processes are occurring at the same time.
I downsampled the images I was searching for SIFT keypoints and was able to iterate my troubleshooting more quickly and found my error.
I will look at my error more closely next time before asking a question.
I was looking for pinterest API endpoints...
I've founded some URL..
https://api.pinterest.com/v3/domains/<domains>/search/pins/?query=<query>&access_token=<access_token>
I was able to generate the access_token..but every time I've tried a POST on that URL it gave me:
{
"status":"failure",
"code":12,
"host":"ngapi2-b2fc674c",
"generated_at":"Mon, 09 Feb 2015 17:45:29 +0000",
"message":"Something went wrong on our end. Sorry about that.",
"data":"path: /v3/domains/www.vtracker.com.br/search/pins/\nparams:
[('access_token', [u'blablahblahblah']), ('query',
[u'como'])]\nTraceback (most recent call last):\n File
\"/mnt/virtualenv/local/lib/python2.7/site-packages/flask/app.py\",
line 1504, in wsgi_app\n response =
self.full_dispatch_request()\n File
\"/mnt/virtualenv/local/lib/python2.7/site-packages/flask/app.py\",
line 1264, in full_dispatch_request\n rv =
self.handle_user_exception(e)\n File
\"/mnt/virtualenv/local/lib/python2.7/site-packages/flask/app.py\",
line 1262, in full_dispatch_request\n rv =
self.dispatch_request()\n File
\"/mnt/virtualenv/local/lib/python2.7/site-packages/flask/app.py\",
line 1248, in dispatch_request\n return
self.view_functions[rule.endpoint](**req.view_args)\n File
\"../api/pin_api.py\", line 715, in __call__\n
self._perform_auth()\n File \"../api/pin_api.py\", line 848, in
_perform_auth\n authorization.perform(dictified_values,
request.cookies, request.headers)\n File \"../api/pin_api.py\",
line 271, in perform\n params, cookies, headers)\n File
\"../api/pin_api.py\", line 121, in perform\n headers=headers)\n
File \"../api/decorators.py\", line 212, in
verify_user_authorization\n
core.Consumer.manager.get_scope_as_int(required_scope)):\n File
\"../core/managers/consumer_manager.py\", line 479, in
check_scope\n scope = migrate_legacy_scope(scope)\n File
\"../core/managers/consumer_manager.py\", line 475, in
migrate_legacy_scope\n if ~scope & old == 0:\nTypeError: bad
operand type for unary ~: 'NoneType'\n"
}
Is Pinterest API v3 closed or some other problem is going on?
Thks
You must first ensure that your app has been approved by Pinterest. You may need to reapply for approval (I had to reapply for my app). Once you are approved, you will see a link on your app page called "Visit API docs". This will link to the V3 documentation (https://developers.pinterest.com/docs/redoc/pinner_app). At least, this is the documentation I have been given access to. If you have a different type of app, maybe you will have access to other documentation.
After your app has been approved, the section of the documentation you will be interested in is "Search user pins" (https://developers.pinterest.com/docs/redoc/pinner_app/#tag/search).
The endpoint is: https://api.pinterest.com/v3/search/user_pins/{user}/
The documentation provides details about the query parameters that are allowed and the response data.