calling kubectl delete hello-world-wascc-assemblyscript hangs

It looks as though the waSCC provider does not acknowledge the DELETE request, as there are no logs to indicate that the actor was stopped.
kubectl logs:
><> k delete po hello-world-wascc-assemblyscript -v 10
I0402 15:48:29.630949   26668 loader.go:375] Config loaded from file:  /Users/bacongobbler/.kube/config
I0402 15:48:29.631919   26668 cert_rotation.go:137] Starting client certificate rotation controller
I0402 15:48:29.632904   26668 cached_discovery.go:114] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/servergroups.json
I0402 15:48:29.633639   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/discovery.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.633686   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/apiregistration.k8s.io/v1/serverresources.json
I0402 15:48:29.633708   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/apps/v1/serverresources.json
I0402 15:48:29.633728   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/authorization.k8s.io/v1/serverresources.json
I0402 15:48:29.633752   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/policy/v1beta1/serverresources.json
I0402 15:48:29.633787   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/autoscaling/v2beta1/serverresources.json
I0402 15:48:29.633798   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/extensions/v1beta1/serverresources.json
I0402 15:48:29.633806   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/authentication.k8s.io/v1/serverresources.json
I0402 15:48:29.633844   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/authentication.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.633849   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/authorization.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.633932   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/events.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.633937   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/autoscaling/v1/serverresources.json
I0402 15:48:29.633948   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/rbac.authorization.k8s.io/v1/serverresources.json
I0402 15:48:29.633944   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/apiregistration.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.633984   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/apiextensions.k8s.io/v1/serverresources.json
I0402 15:48:29.633989   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/storage.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.634054   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/batch/v1beta1/serverresources.json
I0402 15:48:29.634055   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/autoscaling/v2beta2/serverresources.json
I0402 15:48:29.634067   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/apiextensions.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.634086   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/rbac.authorization.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.634107   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/admissionregistration.k8s.io/v1/serverresources.json
I0402 15:48:29.634124   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/storage.k8s.io/v1/serverresources.json
I0402 15:48:29.634184   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/scheduling.k8s.io/v1/serverresources.json
I0402 15:48:29.634181   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/networking.k8s.io/v1/serverresources.json
I0402 15:48:29.634240   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/admissionregistration.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.634242   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/certificates.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.634263   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/networking.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.634261   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/batch/v1/serverresources.json
I0402 15:48:29.634288   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/scheduling.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.634297   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/coordination.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.634353   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/node.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.634360   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/coordination.k8s.io/v1/serverresources.json
I0402 15:48:29.634455   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/v1/serverresources.json
I0402 15:48:29.643271   26668 cached_discovery.go:114] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/servergroups.json
I0402 15:48:29.643518   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/discovery.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.643524   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/authentication.k8s.io/v1/serverresources.json
I0402 15:48:29.643578   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/rbac.authorization.k8s.io/v1/serverresources.json
I0402 15:48:29.643608   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/apps/v1/serverresources.json
I0402 15:48:29.643649   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/events.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.643680   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/apiregistration.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.643695   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/rbac.authorization.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.643710   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/authentication.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.643713   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/extensions/v1beta1/serverresources.json
I0402 15:48:29.643805   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/v1/serverresources.json
I0402 15:48:29.643827   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/apiregistration.k8s.io/v1/serverresources.json
I0402 15:48:29.643838   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/authorization.k8s.io/v1/serverresources.json
I0402 15:48:29.643860   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/storage.k8s.io/v1/serverresources.json
I0402 15:48:29.643864   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/batch/v1/serverresources.json
I0402 15:48:29.643939   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/autoscaling/v1/serverresources.json
I0402 15:48:29.643950   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/networking.k8s.io/v1/serverresources.json
I0402 15:48:29.643975   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/autoscaling/v2beta1/serverresources.json
I0402 15:48:29.643993   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/networking.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.643998   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/storage.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.644100   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/batch/v1beta1/serverresources.json
I0402 15:48:29.644073   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/authorization.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.644148   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/autoscaling/v2beta2/serverresources.json
I0402 15:48:29.644148   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/policy/v1beta1/serverresources.json
I0402 15:48:29.644245   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/admissionregistration.k8s.io/v1/serverresources.json
I0402 15:48:29.644281   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/coordination.k8s.io/v1/serverresources.json
I0402 15:48:29.644305   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/apiextensions.k8s.io/v1/serverresources.json
I0402 15:48:29.644318   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/certificates.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.644334   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/admissionregistration.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.644358   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/coordination.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.644380   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/scheduling.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.644414   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/apiextensions.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.644428   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/node.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.644455   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/scheduling.k8s.io/v1/serverresources.json
I0402 15:48:29.645532   26668 cached_discovery.go:114] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/servergroups.json
I0402 15:48:29.645993   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/discovery.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.645993   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/extensions/v1beta1/serverresources.json
I0402 15:48:29.645993   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/events.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.646408   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/apiextensions.k8s.io/v1/serverresources.json
I0402 15:48:29.646771   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/authentication.k8s.io/v1/serverresources.json
I0402 15:48:29.646797   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/rbac.authorization.k8s.io/v1/serverresources.json
I0402 15:48:29.646820   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/authorization.k8s.io/v1/serverresources.json
I0402 15:48:29.646856   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/apiextensions.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.646927   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/rbac.authorization.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.646938   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/apps/v1/serverresources.json
I0402 15:48:29.647288   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/authorization.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.647323   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/apiregistration.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.647459   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/autoscaling/v1/serverresources.json
I0402 15:48:29.647508   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/admissionregistration.k8s.io/v1/serverresources.json
I0402 15:48:29.647569   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/v1/serverresources.json
I0402 15:48:29.647594   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/autoscaling/v2beta1/serverresources.json
I0402 15:48:29.647694   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/storage.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.647725   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/admissionregistration.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.647752   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/autoscaling/v2beta2/serverresources.json
I0402 15:48:29.647752   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/storage.k8s.io/v1/serverresources.json
I0402 15:48:29.647957   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/certificates.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.647973   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/networking.k8s.io/v1/serverresources.json
I0402 15:48:29.648013   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/scheduling.k8s.io/v1/serverresources.json
I0402 15:48:29.648071   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/coordination.k8s.io/v1/serverresources.json
I0402 15:48:29.648101   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/batch/v1/serverresources.json
I0402 15:48:29.648144   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/networking.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.648214   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/policy/v1beta1/serverresources.json
I0402 15:48:29.645993   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/authentication.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.648263   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/scheduling.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.648341   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/node.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.648364   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/batch/v1beta1/serverresources.json
I0402 15:48:29.648395   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/coordination.k8s.io/v1beta1/serverresources.json
I0402 15:48:29.648646   26668 cached_discovery.go:71] returning cached discovery info from /Users/bacongobbler/.kube/cache/discovery/<IP_ADDRESS>_8443/apiregistration.k8s.io/v1/serverresources.json
I0402 15:48:29.652193   26668 request.go:1068] Request Body: {"propagationPolicy":"Background"}
I0402 15:48:29.652300   26668 round_trippers.go:423] curl -k -v -XDELETE  -H "Accept: application/json" -H "Content-Type: application/json" -H "User-Agent: kubectl/v1.18.0 (darwin/amd64) kubernetes/9e99141" 'https://<IP_ADDRESS>:8443/api/v1/namespaces/default/pods/hello-world-wascc-assemblyscript'
I0402 15:48:29.669796   26668 round_trippers.go:443] DELETE https://<IP_ADDRESS>:8443/api/v1/namespaces/default/pods/hello-world-wascc-assemblyscript 200 OK in 17 milliseconds
I0402 15:48:29.669818   26668 round_trippers.go:449] Response Headers:
I0402 15:48:29.669825   26668 round_trippers.go:452]     Content-Type: application/json
I0402 15:48:29.669831   26668 round_trippers.go:452]     Content-Length: 2736
I0402 15:48:29.669837   26668 round_trippers.go:452]     Date: Thu, 02 Apr 2020 22:48:29 GMT
I0402 15:48:29.669884   26668 request.go:1068] Response Body: {"kind":"Pod","apiVersion":"v1","metadata":{"name":"hello-world-wascc-assemblyscript","namespace":"default","selfLink":"/api/v1/namespaces/default/pods/hello-world-wascc-assemblyscript","uid":"b7123e72-0285-4f48-b69e-9b6d15827f02","resourceVersion":"1396","creationTimestamp":"2020-04-02T22:43:50Z","deletionTimestamp":"2020-04-02T22:48:59Z","deletionGracePeriodSeconds":30,"annotations":{"deislabs.io/wascc-action-key":"MA6DECXXYRIRPWYJAIKQT7ZBPUCJ7HRBLK3ZSDTGE7WWIJ6VU52PZOCI"},"managedFields":[{"manager":"kubectl","operation":"Update","apiVersion":"v1","time":"2020-04-02T22:43:50Z","fieldsType":"FieldsV1","fieldsV1":{"f:metadata":{"f:annotations":{".":{},"f:deislabs.io/wascc-action-key":{}}},"f:spec":{"f:containers":{"k:{\"name\":\"hello-world-wascc-assemblyscript\"}":{".":{},"f:image":{},"f:imagePullPolicy":{},"f:name":{},"f:resources":{},"f:terminationMessagePath":{},"f:terminationMessagePolicy":{}}},"f:dnsPolicy":{},"f:enableServiceLinks":{},"f:nodeSelector":{".":{},"f:beta.kubernetes.io/arch":{}},"f:restartPolicy":{},"f:schedulerName":{},"f:securityContext":{},"f:terminationGracePeriodSeconds":{}}}},{"manager":"unknown","operation":"Update","apiVersion":"v1","time":"2020-04-02T22:43:52Z","fieldsType":"FieldsV1","fieldsV1":{"f:status":{"f:containerStatuses":{},"f:phase":{}}}}]},"spec":{"volumes":[{"name":"default-token-f9d8r","secret":{"secretName":"default-token-f9d8r","defaultMode":420}}],"containers":[{"name":"hello-world-wascc-assemblyscript","image":"webassembly.azurecr.io/hello-world-wascc-assemblyscript:v0.1.0","resources":{},"volumeMounts":[{"name":"default-token-f9d8r","readOnly":true,"mountPath":"/var/run/secrets/kubernetes.io/serviceaccount"}],"terminationMessagePath":"/dev/termination-log","terminationMessagePolicy":"File","imagePullPolicy":"IfNotPresent"}],"restartPolicy":"Always","terminationGracePeriodSeconds":30,"dnsPolicy":"ClusterFirst","nodeSelector":{"beta.kubernetes.io/arch":"wasm32-wascc"},"serviceAccountName":"default","serviceAccount":"default","nodeName":"krustlet-wascc","securityContext":{},"schedulerName":"default-scheduler","tolerations":[{"key":"node.kubernetes.io/not-ready","operator":"Exists","effect":"NoExecute","tolerationSeconds":300},{"key":"node.kubernetes.io/unreachable","operator":"Exists","effect":"NoExecute","tolerationSeconds":300}],"priority":0,"enableServiceLinks":true},"status":{"phase":"Running","conditions":[{"type":"PodScheduled","status":"True","lastProbeTime":null,"lastTransitionTime":"2020-04-02T22:43:50Z"}],"containerStatuses":[{"name":"hello-world-wascc-assemblyscript","state":{"running":{"startedAt":"2020-04-02T22:43:52Z"}},"lastState":{},"ready":true,"restartCount":0,"image":"","imageID":"","started":true}],"qosClass":"BestEffort"}}
pod "hello-world-wascc-assemblyscript" deleted
I0402 15:48:29.681503   26668 round_trippers.go:423] curl -k -v -XGET  -H "Accept: application/json" -H "User-Agent: kubectl/v1.18.0 (darwin/amd64) kubernetes/9e99141" 'https://<IP_ADDRESS>:8443/api/v1/namespaces/default/pods?fieldSelector=metadata.name%3Dhello-world-wascc-assemblyscript'
I0402 15:48:29.686827   26668 round_trippers.go:443] GET https://<IP_ADDRESS>:8443/api/v1/namespaces/default/pods?fieldSelector=metadata.name%3Dhello-world-wascc-assemblyscript 200 OK in 5 milliseconds
I0402 15:48:29.686868   26668 round_trippers.go:449] Response Headers:
I0402 15:48:29.686883   26668 round_trippers.go:452]     Content-Type: application/json
I0402 15:48:29.686890   26668 round_trippers.go:452]     Content-Length: 2835
I0402 15:48:29.686898   26668 round_trippers.go:452]     Date: Thu, 02 Apr 2020 22:48:29 GMT
I0402 15:48:29.686969   26668 request.go:1068] Response Body: {"kind":"PodList","apiVersion":"v1","metadata":{"selfLink":"/api/v1/namespaces/default/pods","resourceVersion":"1396"},"items":[{"metadata":{"name":"hello-world-wascc-assemblyscript","namespace":"default","selfLink":"/api/v1/namespaces/default/pods/hello-world-wascc-assemblyscript","uid":"b7123e72-0285-4f48-b69e-9b6d15827f02","resourceVersion":"1396","creationTimestamp":"2020-04-02T22:43:50Z","deletionTimestamp":"2020-04-02T22:48:59Z","deletionGracePeriodSeconds":30,"annotations":{"deislabs.io/wascc-action-key":"MA6DECXXYRIRPWYJAIKQT7ZBPUCJ7HRBLK3ZSDTGE7WWIJ6VU52PZOCI"},"managedFields":[{"manager":"kubectl","operation":"Update","apiVersion":"v1","time":"2020-04-02T22:43:50Z","fieldsType":"FieldsV1","fieldsV1":{"f:metadata":{"f:annotations":{".":{},"f:deislabs.io/wascc-action-key":{}}},"f:spec":{"f:containers":{"k:{\"name\":\"hello-world-wascc-assemblyscript\"}":{".":{},"f:image":{},"f:imagePullPolicy":{},"f:name":{},"f:resources":{},"f:terminationMessagePath":{},"f:terminationMessagePolicy":{}}},"f:dnsPolicy":{},"f:enableServiceLinks":{},"f:nodeSelector":{".":{},"f:beta.kubernetes.io/arch":{}},"f:restartPolicy":{},"f:schedulerName":{},"f:securityContext":{},"f:terminationGracePeriodSeconds":{}}}},{"manager":"unknown","operation":"Update","apiVersion":"v1","time":"2020-04-02T22:43:52Z","fieldsType":"FieldsV1","fieldsV1":{"f:status":{"f:containerStatuses":{},"f:phase":{}}}}]},"spec":{"volumes":[{"name":"default-token-f9d8r","secret":{"secretName":"default-token-f9d8r","defaultMode":420}}],"containers":[{"name":"hello-world-wascc-assemblyscript","image":"webassembly.azurecr.io/hello-world-wascc-assemblyscript:v0.1.0","resources":{},"volumeMounts":[{"name":"default-token-f9d8r","readOnly":true,"mountPath":"/var/run/secrets/kubernetes.io/serviceaccount"}],"terminationMessagePath":"/dev/termination-log","terminationMessagePolicy":"File","imagePullPolicy":"IfNotPresent"}],"restartPolicy":"Always","terminationGracePeriodSeconds":30,"dnsPolicy":"ClusterFirst","nodeSelector":{"beta.kubernetes.io/arch":"wasm32-wascc"},"serviceAccountName":"default","serviceAccount":"default","nodeName":"krustlet-wascc","securityContext":{},"schedulerName":"default-scheduler","tolerations":[{"key":"node.kubernetes.io/not-ready","operator":"Exists","effect":"NoExecute","tolerationSeconds":300},{"key":"node.kubernetes.io/unreachable","operator":"Exists","effect":"NoExecute","tolerationSeconds":300}],"priority":0,"enableServiceLinks":true},"status":{"phase":"Running","conditions":[{"type":"PodScheduled","status":"True","lastProbeTime":null,"lastTransitionTime":"2020-04-02T22:43:50Z"}],"containerStatuses":[{"name":"hello-world-wascc-assemblyscript","state":{"running":{"startedAt":"2020-04-02T22:43:52Z"}},"lastState":{},"ready":true,"restartCount":0,"image":"","imageID":"","started":true}],"qosClass":"BestEffort"}}]}
I0402 15:48:29.702001   26668 round_trippers.go:423] curl -k -v -XGET  -H "Accept: application/json" -H "User-Agent: kubectl/v1.18.0 (darwin/amd64) kubernetes/9e99141" 'https://<IP_ADDRESS>:8443/api/v1/namespaces/default/pods?fieldSelector=metadata.name%3Dhello-world-wascc-assemblyscript&resourceVersion=1396&watch=true'
I0402 15:48:29.703864   26668 round_trippers.go:443] GET https://<IP_ADDRESS>:8443/api/v1/namespaces/default/pods?fieldSelector=metadata.name%3Dhello-world-wascc-assemblyscript&resourceVersion=1396&watch=true 200 OK in 1 milliseconds
I0402 15:48:29.703892   26668 round_trippers.go:449] Response Headers:
I0402 15:48:29.703900   26668 round_trippers.go:452]     Content-Type: application/json
I0402 15:48:29.703906   26668 round_trippers.go:452]     Date: Thu, 02 Apr 2020 22:48:29 GMT
^C

And here's Krustlet's logs:
[2020-04-02T22:43:29Z INFO  wascc_host::capability] Loaded capability: wascc:http_server, native provider: waSCC Default HTTP Server (Actix Web)
[2020-04-02T22:43:29Z INFO  wascc_host::host] Native capability provider 'wascc:http_server' ready
[2020-04-02T22:43:50Z DEBUG wascc_provider] Pod added "hello-world-wascc-assemblyscript"
[2020-04-02T22:43:50Z DEBUG wascc_provider] Using public key: "MA6DECXXYRIRPWYJAIKQT7ZBPUCJ7HRBLK3ZSDTGE7WWIJ6VU52PZOCI"
[2020-04-02T22:43:52Z INFO  wascc_provider] Starting containers for pod "hello-world-wascc-assemblyscript"
[2020-04-02T22:43:52Z DEBUG wascc_provider] Starting container hello-world-wascc-assemblyscript on thread
[2020-04-02T22:43:52Z INFO  wascc_provider] wascc run
[2020-04-02T22:43:52Z INFO  wascc_host::authz] Discovered capability attestations: wascc:http_server
[2020-04-02T22:43:52Z INFO  wascc_host::host] Native capability provider 'wascc:extras' ready
[2020-04-02T22:43:52Z INFO  wascc_host::host] Adding actor MA6DECXXYRIRPWYJAIKQT7ZBPUCJ7HRBLK3ZSDTGE7WWIJ6VU52PZOCI to host
[2020-04-02T22:43:52Z INFO  wascc_host::host] Loading actor module...
[2020-04-02T22:43:52Z INFO  wascc_host::host] Actor MA6DECXXYRIRPWYJAIKQT7ZBPUCJ7HRBLK3ZSDTGE7WWIJ6VU52PZOCI ready for communications
[2020-04-02T22:43:52Z INFO  wascc_provider] configuring capability wascc:http_server
[2020-04-02T22:43:52Z INFO  wascc_host::host] Attempting to configure actor MA6DECXXYRIRPWYJAIKQT7ZBPUCJ7HRBLK3ZSDTGE7WWIJ6VU52PZOCI for capability wascc:http_server
[2020-04-02T22:43:52Z INFO  wascc_host::host] Capability wascc:http_server received invocation for target wascc:http_server
[2020-04-02T22:43:52Z INFO  wascc_provider] Instance executing
[2020-04-02T22:43:52Z INFO  wascc_provider] All containers started for pod "hello-world-wascc-assemblyscript". Updating status
[2020-04-02T22:43:52Z INFO  wascc_provider] Pod modified
[2020-04-02T22:43:52Z INFO  wascc_provider] Modified pod spec: PodStatus {
        conditions: Some(
            [
                PodCondition {
                    last_probe_time: None,
                    last_transition_time: Some(
                        Time(
                            2020-04-02T22:43:50Z,
                        ),
                    ),
                    message: None,
                    reason: None,
                    status: "True",
                    type_: "PodScheduled",
                },
            ],
        ),
        container_statuses: None,
        ephemeral_container_statuses: None,
        host_ip: None,
        init_container_statuses: None,
        message: None,
        nominated_node_name: None,
        phase: Some(
            "Pending",
        ),
        pod_ip: None,
        pod_ips: None,
        qos_class: Some(
            "BestEffort",
        ),
        reason: None,
        start_time: None,
    }
[2020-04-02T22:43:52Z INFO  wascc_provider] Pod modified
[2020-04-02T22:43:52Z INFO  wascc_provider] Modified pod spec: PodStatus {
        conditions: Some(
            [
                PodCondition {
                    last_probe_time: None,
                    last_transition_time: Some(
                        Time(
                            2020-04-02T22:43:50Z,
                        ),
                    ),
                    message: None,
                    reason: None,
                    status: "True",
                    type_: "PodScheduled",
                },
            ],
        ),
        container_statuses: Some(
            [
                ContainerStatus {
                    container_id: None,
                    image: "",
                    image_id: "",
                    last_state: Some(
                        ContainerState {
                            running: None,
                            terminated: None,
                            waiting: None,
                        },
                    ),
                    name: "hello-world-wascc-assemblyscript",
                    ready: true,
                    restart_count: 0,
                    started: Some(
                        true,
                    ),
                    state: Some(
                        ContainerState {
                            running: Some(
                                ContainerStateRunning {
                                    started_at: Some(
                                        Time(
                                            2020-04-02T22:43:52Z,
                                        ),
                                    ),
                                },
                            ),
                            terminated: None,
                            waiting: None,
                        },
                    ),
                },
            ],
        ),
        ephemeral_container_statuses: None,
        host_ip: None,
        init_container_statuses: None,
        message: None,
        nominated_node_name: None,
        phase: Some(
            "Running",
        ),
        pod_ip: None,
        pod_ips: None,
        qos_class: Some(
            "BestEffort",
        ),
        reason: None,
        start_time: None,
    }
[2020-04-02T22:48:29Z INFO  wascc_provider] Pod modified
[2020-04-02T22:48:29Z INFO  wascc_provider] Modified pod spec: PodStatus {
        conditions: Some(
            [
                PodCondition {
                    last_probe_time: None,
                    last_transition_time: Some(
                        Time(
                            2020-04-02T22:43:50Z,
                        ),
                    ),
                    message: None,
                    reason: None,
                    status: "True",
                    type_: "PodScheduled",
                },
            ],
        ),
        container_statuses: Some(
            [
                ContainerStatus {
                    container_id: None,
                    image: "",
                    image_id: "",
                    last_state: Some(
                        ContainerState {
                            running: None,
                            terminated: None,
                            waiting: None,
                        },
                    ),
                    name: "hello-world-wascc-assemblyscript",
                    ready: true,
                    restart_count: 0,
                    started: Some(
                        true,
                    ),
                    state: Some(
                        ContainerState {
                            running: Some(
                                ContainerStateRunning {
                                    started_at: Some(
                                        Time(
                                            2020-04-02T22:43:52Z,
                                        ),
                                    ),
                                },
                            ),
                            terminated: None,
                            waiting: None,
                        },
                    ),
                },
            ],
        ),
        ephemeral_container_statuses: None,
        host_ip: None,
        init_container_statuses: None,
        message: None,
        nominated_node_name: None,
        phase: Some(
            "Running",
        ),
        pod_ip: None,
        pod_ips: None,
        qos_class: Some(
            "BestEffort",
        ),
        reason: None,
        start_time: None,
    }

I couldn't reproduce this locally, and the pod seems to be deleted.
I agree that we might want to log some more here, but it does seem to work for me.

I think what happens is when the wascc provider gets a pod it can’t handle, it freezes at some point after it pulls down the image (trying to figure out where). Because the kubelet has a mutex lock on it, the delete request (which uses the same lock), ends up waiting for a request

Looks like I was wrong. I filtered out those pods and it was still hanging on delete. Not sure if this is a wascc issue or something else we may have missed

Ok, I am 99% sure I figured out what was causing this. It is because all kubernetes does is set things to terminating (by setting the metadata.deletionTimestamp to a specific time). If the pod is already terminated, the API deletes it and we get the event. Otherwise, the event shows up as a “Modify” and we don’t handle those yet. So this means we'll need to handle modify events (or at least this specific kind of event) to delete things properly

Updated the title of the issue to provide more clarity


File download feature

https://github.com/mattmezza/vue-beautiful-chat/issues/173

Here we can see the preview of the document now.

We use different types of files we add the preview of each one:

word file

pdf file

excel file

power point file

zip file

jpg/jpeg file

png file

-svg file

csv file

and if we have an other type file we will have this icon

Hi @mattmezza,
can you process at the code review please ?
If missing something don't hesitate.
Thanks.

Hi @arabakevin , @a-kriya ,
i've try to resolve each point.
Can you verify if all it's ok for you ? And if you need something more don't hesitate.
Thanks.

Hi,
Do you need something or more details on README or  demo/src/App.vue ?
Thanks


Add basic conversion between ttir and linalg

Goal: The end-to-end goal is to integrate a path to compile and execute specific ops or sets of ops on the CPU.
Context:
The entire task will be split into (tentatively) 7 PRs, as follows:

Hoist specific ops into isolated funcs in a separate module
Convert TTIR ops to linalg ops within the module of hoisted funcs
Build a pipeline to lower linalg to llvm from existing conversion passes
Translate LLVM Dialect into a dynamic library for packing into flatbuffer
Generate helper functions so that we can call all of our hoisted funcs with a common signature
Insert TTNN instructions to move operands to host before executing hoisted func, then back to device afterwards
Update ttir-to-ttnn and ttnn-to-flatbuffer pipelines to use new passes, generate dylibs, and embed them into output flatbuffers, and update update runtime to consume dylibs from flatbuffers

This PR represents the 2nd subtask above--it converts TTIR ops in the "cpu" module into their linalg equivalents.  In this case, we only enable a few basic operations for now, in an attempt to get basic end-to-end conversions working before fleshing out full conversion.  Definitely open to adding some other specific ops to conversion before merging, not sure which ops would make most sense.
Example
Input:
module  {
  func.func @add(%arg0: tensor<32x32xf32>, %arg1: tensor<32x32xf32>, %arg2: tensor<32x32xf32>) -> tensor<32x32xf32> {
    %1 = "ttir.add"(%arg0, %arg1, %arg2) <{operandSegmentSizes = array<i32: 2, 1>}> : (tensor<32x32xf32>, tensor<32x32xf32>, tensor<32x32xf32>) -> tensor<32x32xf32>
    return %1 : tensor<32x32xf32>
  }
}

Output:
module {
  func.func @add(%arg0: tensor<32x32xf32>, %arg1: tensor<32x32xf32>, %arg2: tensor<32x32xf32>) -> tensor<32x32xf32> {
    %0 = linalg.add ins(%arg0, %arg1 : tensor<32x32xf32>, tensor<32x32xf32>) outs(%arg2 : tensor<32x32xf32>) -> tensor<32x32xf32>
    return %0 : tensor<32x32xf32>
  }
}

Could be wrong, but I don't see any CMakeLists.txt  changed here, do these new Conversion/ files get compiled a different way?

Could be wrong, but I don't see any CMakeLists.txt changed here, do these new Conversion/ files get compiled a different way?

Let me double-check, there were quite a few interleaved commits I tried to manually split out, so wouldn't surprise me if I missed one.  Thanks!

Hey Vincent, awesome change, I'm really excited to see the CPU path working e2e!
Just a heads up, we've recently started paying more attention towards making our code as readable as possible. This guide should be useful: https://docs.tenstorrent.com/tt-mlir/coding-guidelines.html
Also, if you have any suggestions on how we can make it even better, they would be most welcome!
I see @mtopalovicTT has covered most of the functional stuff, so I'll defer the approval to him.

Thanks!  One question about style guide: Milan mentioned on another PR we should prefer llvm:: types vs std:: types where possible--should this be added to the style guide somewhere?


Mask looks very wrong

I trained > 100k, the loss looks fine, the bounding box looks too much, but the mask looks very wrong. Is it the visualization bug or we need more training?

Masks have to be in the form of 0s and 1s, something is problematic with the mask code. I also obtained very similar results.

Can you share the evaluation code? how to visualize the masks and bounding boxes.

It seem like pred_mask has some random values.

Can someone @zhuwenzhen @souryuu @realwecan manage to correct above predicted mask? Thanks

I also see this. Looking at the code, I see:
From https://github.com/CharlesShang/FastMaskRCNN/blob/a70dcdbb16b75f145cba4a5eda92400935ab863b/libs/nets/pyramid_network.py#L344
        ## mask head
        m = cropped_rois
        for _ in range(4):
            m = slim.conv2d(m, 256, [3, 3], stride=1, padding='SAME', activation_fn=tf.nn.relu)
        # to 28 x 28
        m = slim.conv2d_transpose(m, 256, 2, stride=2, padding='VALID', activation_fn=tf.nn.relu)
        tf.add_to_collection('__TRANSPOSED__', m)
        m = slim.conv2d(m, num_classes, [1, 1], stride=1, padding='VALID', activation_fn=None)

        # add a mask, given the predicted boxes and classes
        outputs['mask'] = {'mask':m, 'cls': classes, 'score': scores}

From https://github.com/CharlesShang/FastMaskRCNN/blob/a70dcdbb16b75f145cba4a5eda92400935ab863b/libs/nets/pyramid_network.py#L606
    # final network visualization
    first_mask = outputs['mask']['mask'][:1]
    first_mask = tf.transpose(first_mask, [3, 1, 2, 0])

    visualize_final_predictions(outputs['final_boxes']["box"], end_points["input"], first_mask)

From https://github.com/CharlesShang/FastMaskRCNN/blob/a70dcdbb16b75f145cba4a5eda92400935ab863b/libs/visualization/summary_utils.py#L37
def visualize_final_predictions(boxes, image, masks):
    visualize_masks(masks, "pred_mask")
    visualize_bb(image, boxes, "final_bb_pred")

Seems like there isn't a sigmoid. I'm not exactly sure how to add it though.

Did anyone found a solution to fix the above on mask accuracy?
thanks
Tets

I thought tf.sigmoid(outputs['mask']['mask']) should do a fine work. However, it did not work when I tried to visualize masks on tensorboard. So I wrote another visualization function using PIL to create .png. It works fine for drawing predicted masks on images during training. If you are interested, you can look at my visualization fork. However, I changed a lot of things in that fork and the code is totally mess.

Thanks @souryuu , I try your code and it works fine.
BTW, you really change a lot  :-)

@lengly Just wondering if you were able to reproduce any experimental evaluation results from the paper (or obtaining a close performance)? Thanks!

@realwecan You can try souryuu's code, it'll save some images which make sense. I only try 60k iter. So the result isn't as good as the paper.

@souryuu Your fork is truly beneficial thank you very much for your efforts. However I didn't understand why test.py and train.py differs that much? Images created by test.py predicts only human labels even they are not humans sometimes. On the contrary, images created by train.py involve various types of labels with intensive number of bounding box compared to test.py.

@Cangrc Hi, does your test.py work without any modification? I run train.py for 50k iter. and when I run test.py, there is no mask or bbox.

Yes it works without any modification. I think I am somewhere near 200k iteration.

If you use the code in my fork without any modification, your batch normalization should not be properly trained up until now because update_bn in config_v1.py was set to False. Change that to True and train the network again. You should see significant improvement.

@souryuu Thanks heaps! Just wanted to clarify here, in your latest code you have added the IS_TRAINING flag. I can see that when IS_TRAINING==True, update_bn will be set to True (and False otherwise). Am I correct to assume that I can just use this version of code as is, as long as I set IS_TRAINING=True when training, and IS_TRAINING=False during test?

@realwecan Yes it should be something like that. However, in the latest version on "fix_testing" branch, all "is_training" in train.py and test.py are already set to True and False respectively. BN should work fine without any setting.

@souryuu Thanks for your prompt reply! Just wondering if you have ever encountered a problem where you see the regular_loss constantly rising. Could this caused by improper settings of the learning rate and the loss weights? I have been experiencing this in a few versions of the code.

@realwecan Yes I encountered that in the early stage of training. Afterward, it gradually reduced.
Setting learning rate and loss weight too high should temporary increase the regular loss. However, I am not sure if it matters From what I understand,. It is just a loss from regularization term that helps you not overfitting to the training data.

@souryuu When testing with the latest code in the fix_testing branch I met the following error:
Traceback (most recent call last):
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/script_ops.py", line 82, in call
ret = func(*args)
File "/home/twang/work/segmentation/baselines/FastMaskRCNN/train/../libs/layers/sample.py", line 127, in sample_rpn_outputs_wrt_gt_boxes
gt_argmax_overlaps = overlaps.argmax(axis=0) # G
ValueError: attempt to get argmax of an empty sequence
This error does not seem to exist in the code in your master branch. Do you have any idea what this might be coming from? Thanks!

@souryuu I try your latest code in master branch. I set is_training=True and train it for 60k iterations. When I set is_training=False, and run test.py, there're still no bbox & mask. Why? Is it because my iteration is not enough?

@realwecan in pyramid_networks line 272, set only_positive flag in sample_rpn_outputs to False should solve your issue. It is the flag to filter rois with RPN confidence value < 0.5 for speed up the training. Since at the beginning of training RPN can not provide reliable confidence value, this should be turned off.
@lengy Can you see mask and bounding box from images created during training ?

@souryuu Yes, when training, it works fine. the box & mask in training image are just like Cangrc's result above.

@lengly Check if update_bn in config_v1.py is set to True during training. If not, set it to True and train the network again.

@souryuu Yeah, update_bn is true when training. I use your latest code in master branch, and the only change is per_process_gpu_memory_fraction=0.95, but I think it should not affect result.  Did the latest master branch test.py work fine in your side?

It works fine for me. During training, did "target" and "predicted" match ?
If no, I think your RCNN did not learn well. you can keep RPN and begin training RCNN and mask network by commenting out line 76 and uncomment line 80-125 in train.py.
If yes, I recommend you to use the code in fix_testing branch instead. However, you need to retrain the network again since the anchors in both branches do not match.

@souryuu Yeah they're nearly match.  I'll try fix_testing branch. I really appreciate for your help!

@souryuu fix_testing branch is being trained 3 or 4 times slower than the previous ones what might be the reason for that? Thank you very much for allocating time

@Cangrc @realwecan @lengly It would be greatly appreciated if some of you could maybe share the trained weights. It would take a long time for my computer to reach 600k iterations. Thanks!

Can you share the trained weights?Thanks very much!! @souryuu @gengyixuan @lengly @realwecan

@souryuu  When I run train.py for 300k iter, the result looks bad, target and predict do not match. What should I do? commenting out line 76 and uncomment line 80-125 in train.py? And then training the code again?

@souryuu  I set is_training=True and train it for 300k iterations. When I set is_training=False, and run test.py, the result looks bad, target and predict do not match. What should I do? commenting out line 76 and uncomment line 80-125 in train.py? And then training the code again?

I trained until 400K but the prediction looks very bad. Anyone succeed?
Btw, t=it seems we have a new extended version of Mask RCNN on Caffe AffordanceNet. The result looks interesting to me.

@souryuu Thanks for your code! I tried your code following the 'README.md' but error happened.
It said:"IOError: [Errno 2] No such file or directory: './output/mask_rcnn/est_imgs/train_est_0.jpg'
"
From train.py I could not see any code about how to generate file 'est_imgs'. So can you tell me if i missed some steps about it?
I will appreciate it.


Support external log rotation tools via SIGUSR2

This is to support third party log rotation mechanisms. It is the responsibility of the external log rotation tool to move the current log out of the way and issue a SIGUSR2 to traffic_manager. With the change contained herein, traffic_manager will then create and begin logging to the new files it manages (manager.out, traffic.out). It then passes down the signal to traffic_server which does the same with its logs (squid.log, diags.log, etc.).

Going to move this out to 9.1.x.

[approve ci docs]

[approve ci autest]

[approve ci autest]

[approve ci autest]

[approve ci autest]


Font changes if editor contains a special unicode code point

VSCode Version: 1.10.2
OS Version: Ubuntu 16.04

Steps to Reproduce:

Type an arbitrary text containing the unicode symbol '(̈́'. Python claims the encoding to be '\xcd\x84', but I cannot find this code point in unicode.

Using this code point results in the following layout for the text:
print d.getVar(̈́'VERSION',True)
The fixed spaced font changes into a proportional font for that line

I could also reproduce on Windows when using the Hack font, but not when using Consolas.

FYI, the symbol is Combining Greek Dialytika Tonos (U+0344) combined with a standard left peren.

This is an upstream issue (possibly within Chromium), and there are no known workarounds that we could implement on our side. This means there is no possible code change for us to do in VS Code sources to improve things.
To keep the number of issues in our inbox at a manageable level, we’re closing issues that have been on the backlog for a long time but haven’t gained traction or that are not actionable. Thank you for your time and understanding.


[T4A3][T13-B2] Zhang Zongyi

Added parent class contact

@zongyizzy : Some comments added. Please close the PR after reading comments


fixes #845 so we can use an annotation to filter out openshift only or kubernetes only resources for different platforms

Agreed; though I'm on the fence of the idea of removing it; as I can imagine we may need it there again when we start combining resources together again into packages. Though maybe we just have to be a bit smarter in how we compose packages better


Cannot init any type of Executor

Hello.
I want to try this library in our environment. I have successfully installed it. It almost works, but I can't pass the compiling while I init a Executor:
auto exec = gko::CudaExecutor::create(0, gko::OmpExecutor::create(), true);

I use command as follows to compile it:
/usr/bin/g++ -lginkgo -lginkgo_omp -lginkgo_cuda -lginkgo_reference -lginkgo_device -lginkgo_dpcpp -lginkgo_hip main.cc

And error like this appears:
/usr/bin/ld: /tmp/ccCIpD0K.o: in function `main':
sell.cc:(.text+0x1a8): undefined reference to `gko::CudaExecutor::create(int, std::shared_ptr<gko::Executor>, bool, gko::allocation_mode)'
/usr/bin/ld: /tmp/ccCIpD0K.o: in function `gko::MachineTopology::get_instance()':
sell.cc:(.text._ZN3gko15MachineTopology12get_instanceEv[_ZN3gko15MachineTopology12get_instanceEv]+0x3e): undefined reference to `gko::MachineTopology::MachineTopology()'
/usr/bin/ld: /tmp/ccCIpD0K.o: in function `gko::OmpExecutor::OmpExecutor()':
sell.cc:(.text._ZN3gko11OmpExecutorC2Ev[_ZN3gko11OmpExecutorC5Ev]+0x32): undefined reference to `vtable for gko::OmpExecutor'
/usr/bin/ld: sell.cc:(.text._ZN3gko11OmpExecutorC2Ev[_ZN3gko11OmpExecutorC5Ev]+0x50): undefined reference to `gko::OmpExecutor::populate_exec_info(gko::MachineTopology const*)'
collect2: error: ld returned 1 exit status

I try code like this, and it can pass the compiles, so I guest this error only appears in executor init.
using mtx = gko::matrix::Dense<double>;

I use like command to config ginkgo while installing:
cmake  -G "Unix Makefiles" -H. -DGINKGO_DEVEL_TOOLS=OFF  -DGINKGO_BUILD_TESTS=ON -DGINKGO_BUILD_REFERENCE=ON -DGINKGO_BUILD_OMP=ON -DGINKGO_BUILD_CUDA=ON -DGINKGO_BUILD_HWLOC=ON ..

-- Found OpenMP_C: -fopenmp (Required is at least version "3.0")
-- Found OpenMP_CXX: -fopenmp (Required is at least version "3.0")
-- GINKGO_BUILD_TESTS is ON, enabling GINKGO_BUILD_REFERENCE
-- Could NOT find PAPI (missing: PAPI_LIBRARY PAPI_INCLUDE_DIR)
-- Could NOT find GTest (missing: GTEST_LIBRARY GTEST_INCLUDE_DIR GTEST_MAIN_LIBRARY) (Required is at least version "1.10.0")
-- Looking for HWLOC - found version 2.1.0
-- Looking for hwloc_topology_init
-- Looking for hwloc_topology_init - found
-- Fetching external GTest
fatal: not a git repository (or any of the parent directories): .git
-- Ginkgo is not in a git repository, so no git hook was installed
-- Fetching external GFlags
-- Fetching external RapidJSON
-- Found OpenMP_C: -fopenmp (Required is at least version "3.0")
-- Found OpenMP_CXX: -fopenmp (Required is at least version "3.0")
-- Found OpenMP_C: -fopenmp (Required is at least version "3.0")
-- Found OpenMP_CXX: -fopenmp (Required is at least version "3.0")
-- No OpenCV found, disabling heat-equation example

---------------------------------------------------------------------------------------------------------
--
--    Summary of Configuration for Ginkgo (version 1.4.0 with tag master)
--
--    Ginkgo configuration:
--        CMAKE_BUILD_TYPE:                           Release
--        BUILD_SHARED_LIBS:                          ON
--        CMAKE_INSTALL_PREFIX:                       /usr/local
--        PROJECT_SOURCE_DIR:                         /home/duzhen/ginkgo-1.4.0
--        PROJECT_BINARY_DIR:                         /home/duzhen/ginkgo-1.4.0/build
--        CMAKE_CXX_COMPILER:                         GNU 9.3.0 on platform Linux x86_64
--                                                    /usr/bin/c++
--    User configuration:
--      Enabled modules:
--        GINKGO_BUILD_OMP:                           ON
--        GINKGO_BUILD_REFERENCE:                     ON
--        GINKGO_BUILD_CUDA:                          ON
--        GINKGO_BUILD_HIP:                           OFF
--        GINKGO_BUILD_DPCPP:                         OFF
--      Enabled features:
--        GINKGO_MIXED_PRECISION:                     OFF
--      Tests, benchmarks and examples:
--        GINKGO_BUILD_TESTS:                         ON
--        GINKGO_FAST_TESTS:                          OFF
--        GINKGO_BUILD_EXAMPLES:                      ON
--        GINKGO_EXTLIB_EXAMPLE:
--        GINKGO_BUILD_BENCHMARKS:                    ON
--        GINKGO_BENCHMARK_ENABLE_TUNING:             OFF
--      Documentation:
--        GINKGO_BUILD_DOC:                           OFF
--        GINKGO_VERBOSE_LEVEL:                       1
--
---------------------------------------------------------------------------------------------------------
--
--      Developer Tools:
--        GINKGO_DEVEL_TOOLS:                         OFF
--        GINKGO_WITH_CLANG_TIDY:                     OFF
--        GINKGO_WITH_IWYU:                           OFF
--        GINKGO_CHECK_CIRCULAR_DEPS:                 OFF
--        GINKGO_WITH_CCACHE:                         ON
---------------------------------------------------------------------------------------------------------
--
--      Components:
--        GINKGO_BUILD_HWLOC:                         ON
--
--  Detailed information (More compiler flags, module configuration) can be found in detailed.log
--
--
--  Now, run  cmake --build .  to compile Ginkgo!
--
---------------------------------------------------------------------------------------------------------

-- Configuring done
-- Generating done
-- Build files have been written to: /home/duzhen/ginkgo-1.4.0/build

My g++ version is 9.3.0. And CUDA version is 11.

Could you try
/usr/bin/g++ main.cc -lginkgo -lginkgo_omp -lginkgo_cuda -lginkgo_reference -lginkgo_device -lginkgo_dpcpp -lginkgo_hip
and
/usr/bin/g++ main.cc -lginkgo -lginkgo_omp -lginkgo_cuda -lginkgo_reference -lginkgo_hip -lginkgo_dpcpp -lginkgo_device?
both might also need -lhwloc

The "canonical" way to link against Ginkgo in a Makefile environment is using the output of pkg-config --libs ginkgo, since that models the link dependencies correctly, in your case you are missing -lginkgo_device, in total:
-lginkgo -lginkgo_device -lginkgo_omp -lginkgo_cuda -lginkgo_reference -lginkgo_hip -lginkgo_dpcpp
HWLOC might be missing in the output, but that is being fixed in https://github.com/ginkgo-project/ginkgo/pull/923
I would heavily recommend using CMake though, since it gives the most smooth integration.

@upsj The OP was already using -lginkgo_device. From Mike's reply, the main difference on my setup was that the main.cc needs to come before all the libraries when building and linking in one step. Maybe this is a noob thing but I was not expecting that to matter.

@duzhen1996 were you able to make any progress? Please let us know if we can assist any further, otherwise we will close this issue soon.

@upsj Thanks for your attention! You can close this issue now.


Support for Web Share Target

I cherry picked psmakov's commit (223a17c) and adapted the code.
Everything works ok on most devices, but there are still problems where things go wrong. Perhaps someone with more knowledge about Android, androidx or android-browser-helper could help me out here.
Problem 1: On my Pixel 4XL w/ Android 10 (QQ2A.200305.003) and Google Chrome (80.0.3987.162) and my Nexus 10 tablet w/ Android 8 (8.1.0 Pixel C Build OPM8.190605.005) and the same Google Chrome version sharing with our TWA sometimes works for images (only) and only if our TWA is already running. (On a Pixel 3 XL w/ Android 10 (QQ2A.200305.002) and the same Google Chrome version everything works as expected.)
What happens is that I get a blank splash-screen. When I attach the Google Chrome  device inspector on it, I see that there is only an empty tab open on the device and no service-worker. So clearly the tab has not been started correctly.
In the logs of the phone (via logcat in Android Studio) I just see:
2020-04-05 22:04:22.877 1345-1953/? I/ActivityTaskManager: START u0 {act=android.intent.action.SEND typ=image/jpeg flg=0x10000001 cmp=com.clingmobileapp/.ClingApplicationLauncherShare clip={image/jpeg U:content://com.google.android.apps.photos.contentprovider/0/1/content%3A%2F%2Fmedia%2Fexternal%2Fimages%2Fmedia%2F3537/REQUIRE_ORIGINAL/NONE/image%2Fjpeg/190640420} (has extras)} from uid 10173

Everything is working fine if I disable Asset Link checking as described here. I even tried to disable the splash screen, but still it does not work on my phone. It works with a Google Pixel 4 / Android 10 emulator btw.
Problem 2: This is more of a UX problem. If the installed Google Chrome version does not support Share API v2 w/ TWA I would like to disable the share-intent or at least be able to react to it and show an info message to the user that an update of Google Chrome is necessary to use sharing. Is there any way to know, which Chrome version is available?

@googlebot I signed it!

I had a skim over the code, nothing stands out to me, but I'll need to have a more in depth look through it later - did you end up changing a lot or is it mostly unchanged from the commit you cherry picked?
I don't have any ideas off the top of my head for the first issue - I'll have to look into it a bit further (time permitting).
For your second question, yeah, you can figure out the version of Chrome that is available (look at ChromeLegacyUtils.java). However, I'd prefer we do things a slightly different way.
When Chrome added support for this, we added the CATEGORY_WEB_SHARE_TARGET_V2 to its AndroidManifest. We can then check whether this category is present (we do something similar here). That way we can handle other browsers supporting this.
Once we find out whether the browser supports it, it should be easy to show a Toast to the users. We could even disable the intent filter for next time (eg).

@PEConn thanks for your reply.
I did change very little. I mainly fixed a bug in SharingUtils, where we tried to read intent.getParcelableArrayListExtra(Intent.EXTRA_STREAM), which throws an error if there is no streaming data.
I structured the commits in a way that should make it clear, what was changed and what came straight from the commit I cherry picked.

I just rebased this PR on top of the upstream master. The problems (App won't start sometimes when used as a share target) persist unfortunately. It would be great if someone could step in.

Might this be the problem?
I had to adapt the way the PWA is launched, otherwise sharing would not work at all. I got this piece of code from the original commit by pshmakov.

@flunderpero I'm not sure this is the problem. TrustedWebActivityIntent.launchTrustedWebActivity does have have additional behaviour that seems to handle permission for the Share Target URIs.
Do you have a test app you've been using? I'd like to give it a try myself.

Since the problem only occurs if I enable Digital Asset Linking, I cannot provide a simple demo-app. (To be precise: If I disable the verification in Chrome (--disable-digital-asset-link-verification-for-url=https://cling.com/c) everything works fine so far.)
You can join our internal test channel and try the app for yourself. You'll need to sign-up with our service (cling.com), which is a free platform focused on content creation, curation and collaboration.
https://play.google.com/apps/internaltest/4698063214496940258
Then try to share an image, a link or text with the app. It might work if the app is open in the background, but it never works if the app is not running AFAICS. I am currently using a Pixel 4 XL with Android 10 (see above).
You can then compare this with the PWA, which works smoothly all the time.

Sounds like the testing is invite-only: Your account hasn't yet been invited to participate in this app's internal testing program.

Sorry, I didn't check that. Now you should be able to download the test version.

Hi Peter, I'm looking into this issue. Does it still reproduce? Can I test it with your app? Thanks!

Hi @mvano, yes it still reproduces (unfortunately). And also yes, you can use our app: https://play.google.com/apps/internaltest/4698063214496940258

Thanks @flunderpero! Could you put me on the allowlist for the test app? I signed up with timmytester1337 at gmail.com.

@mvano I just added you. Please let me know if I can help out in any way.

Thanks @flunderpero though I still don't seem to have access. In the meantime, I've landed #160 which has two test sites, one for text and one for photos. Perhaps we can continue on that more accessible basis, if the bug still happens there.

@mvano That's great news. I updated our app with the lastest main build of android-browser-helper and the problems persist unfortunately. I granted access to timmytester...@ in the console (sorry, I made a mistake with that before). Perhaps you can give it a try and see something more.
I will take a deeper look into the demos in order to find differences in the usage of the library.

I adapted my manifest to match the manifest in the demo (I had an activity-alias for example), but the problems are still there.
OTOH, I cannot share an image with the demo-app at all. I see the launcher or a white page and nothing ever happens.


First release of budget app

What does this PR do?
This PR is raised to implement the following features: 👍 :
Description of the task to be completed ↓↓

[x]  Working on the Mobile version
[x] View the List of categories
[x] Add category
[x] List of Spends
[x] Add spend based on category
[x] Authentication with device
[x] Testing
[x] Project Presentation
[x] Project Deploying

Account to test the application

email

<EMAIL_ADDRESS>

password

testing

How to run
Clone the repository
git clone https://github.com/SamTush/budget.git

How to run the tests
    rspec

Thanks for reviewing my PR 😊

Thank you @Lucas-Masaba for reviewing my PR👍🏽 cheer and happy coding 😎

Your welcome @SamTush . Happy coding to you too :surfer:

Thank you @Felix45 for reviewing my PR👍🏽 cheer and happy coding


Agent not starting on Mac Intel

Two users who previously couldn't load the app on Mac Intel are now unable to start their agents.
PealLogs.zip
Severity: very high. Totally blocks the happy path – assume for all Intel users.

user 2 pearl_logs_2024-08-19T08_31_08.540Z-0.1.0-rc86.zip


Don't cast NaN to integer

Do not try to convert NaN to integer type, as the operation is undefined
and results in random values.  This fixes all testsuite failures.

Hi @andreas-schwab, and welcome to xarray!
Can you tell what specific failures this change fixed for you? If there was something failing we want to capture it in our test suite, but I am not sure what failure you are referring to.

It's already perfectly covered by the testsuite.
=========================== short test summary info ============================
FAILED xarray/tests/test_backends.py::TestScipyInMemoryData::test_roundtrip_numpy_datetime_data
FAILED xarray/tests/test_backends.py::TestScipyFileObject::test_roundtrip_numpy_datetime_data
FAILED xarray/tests/test_backends.py::TestGenericNetCDFData::test_roundtrip_numpy_datetime_data
FAILED xarray/tests/test_backends.py::TestScipyFilePath::test_roundtrip_numpy_datetime_data
= 4 failed, 4636 passed, 5632 skipped, 19 xfailed, 22 xpassed, 38 warnings in 266.18s (0:04:26) =

It's already perfectly covered by the testsuite.

Okay great, but then why are these tests failing for you (locally?) and not in our CI runs? (Our main branch just passed all automated tests just now.) Do you have a different version of some package that we aren't testing against?
I'm asking because if our current CI test runs don't reproduce this error, then we (a) have no way to check that your change fixed the error and (b) will not know if some regression causes this error to resurface in future. Standard practice would be to raise an issue that demonstrates the problem, then link the fix PR to that issue.

That's the beauty of undefined behaviour.  It comes in many flavors, and
you never know which one you get.  Which also means that it is
impossible to reliably test for it.

@TomNicholas Something different will need to happen with that cast eventually. See #6191 for something that is failing on some systems that users have but is currently unable to be captured in the tests. Numpy has already added runtime warnings about doing this, and is "thinking about" making nan to int casts raise https://github.com/numpy/numpy/issues/14412. Xarray's own @shoyer has hit issues like this before as well https://github.com/numpy/numpy/issues/6109.

Thank you very much for the context @DocOtak !

I think the real solution here is to explicitly handle NaNs during the decoding step. We do want these to be NaT in the output.
cc @spencerclark

Hi, is there any reason that keeps this pr from being merged?  This PR is a fix for some failing tests on riscv64.

@andreas-schwab Thanks again for this PR. It turned out it was a bit more involved to make this work and we hope #7827 solves the underlying issue.


Missing Headers Problem

Hello,
While working on a development related to Spring Cloud Stream, I noticed a situation that is also relevant to Spring Cloud Function.
To explain the issue clearly, we will first examine a method from Spring Cloud Function. The following method exhibits different behaviors. In some cases, it generates the output message using the input header, while in other cases, it does not consider the input header at all. This results in some variations in the code.
In other words, the code below can sometimes directly set the headers provided in the input to the output header. Sometimes, it creates the output headers without using the input headers.

Now, let's examine the method affected by the above code. I have written some tests, and based on the results, the target-protocol header is sometimes set and sometimes not. Therefore, I have made the relevant development.
However, I still have some questions that I do not fully understand. I would greatly appreciate your help. Is the target-protocol header a value that should be set for every message? If so, there are many places that we need to correct. Could you please provide me with information on this matter?
I am a bit confused, so I wanted to ask you before continuing with the development. Thank you.

@olegz, hello. Thank you very much for your interest. However, there are other situations where these headers are not set. If this target_protocol needs to be set in all cases, I will need to make further developments accordingly. Could you inform me about this?

Please do


Update python script shebang to explicitly reference used python vers…

…ion 3
Signed-off-by: Gary Brown<EMAIL_ADDRESS>

As found by Pavol: https://stackoverflow.com/questions/6908143/should-i-put-shebang-in-python-scripts-and-what-form-should-it-take/19305076#19305076

Does the docker build for python images work? I don't think it's executed on PR build (only on merge to master)

@pavolloffay Yes, I ran make docker-images-elastic and docker run ... help on the es-index-cleaner image and it worked fine.


Enable cpu usage

These changes fix some issues I had when torch is used without cuda (cpu only).

I implemented #561 as an alternative approach. I like your approach of defaulting to CPU mode if CUDA isn't available, but I think it would be desirable to allow the user to explicitly choose CPU even if CUDA is available (as my PR does). Feel free to borrow pieces of my PR; seems desirable to combine the respective strengths of both PR's.

I implemented #561 as an alternative approach (wasn't intentionally duplicating your effort; I wrote my PR last week before yours was submitted). I like your approach of defaulting to CPU mode if CUDA isn't available, but I think it would be desirable to allow the user to explicitly choose CPU even if CUDA is available (as my PR does). Feel free to borrow pieces of my PR; seems desirable to combine the respective strengths of both PR's.

@xinntao any thoughts on this?


Export-RubrikDatabase

I am unable to perform a simple export with the command. Export-RubrikDatabase
Even after giving correct time, (i am getting  Timestamp<PHONE_NUMBER>000 is not recoverable) while i have verified the backup took place and the time conversion is correct per error.. )
please assist..

@rafeim Can you share your script via gist or email me at michaelfalrubrikcom? I'd like to see how you're calling it so we can further assist.

`
Connect-Rubrik  “IPAddess”
$db = "dbatools"
[DateTime]$DateTime = "2017-10-03 23:00:00.000"
$DBid= (Get-RubrikDatabase -Name "aspnetdb" -Instance "MSSQLSERVER").id
##Retrieving the instance Id
$targetInstanceId = (Get-RubrikDatabase -Name "databaseNameFromInstance" -Instance "MSSQLSERVER" -Hostname "HOSTNAME").instanceid
Export-RubrikDatabase -Id $DBid -Server "Rubrik-IP" -TargetInstanceId $targetInstanceId -TargetDatabaseName "TargetDBNAme" -MaxDataStreams 4 -RecoveryDateTime $DateTime
`
Below is also an example from function Export-RubrikDatabase,  but the latestrecoveryPoint attribute doesn’t exist.. I attempted that to no avail.
.EXAMPLE       Export-RubrikDatabase -id $db.id -recoveryDateTime (Get-Date (Get-RubrikDatabase $db).latestRecoveryPoint) -targetInstanceId $db2.instanceId -targetDatabaseName 'BAR_EXP' -targetFilePaths $targetfiles -maxDataStreams 1
Please assist with how I can retrieve the latest recoverable time from a database backup within rubrik using the Module..

Sorry for the late response on this. I see what the issue is. This is a gotcha for the summary data vs detailed data. latestRecoveryPoint only exists in the detail of the call, which is only returned by Get-RubrikDatabase when you call it for an explicit Rubrik database id.
Try this:
$db = Get-RubrikDatabase -Name aspnetdb -Instance 'MSSQLSERVER' | Get-RubrikDatabase Export-RubrikDatabase -ID $db.id -Server 'Rubrik-IP' -TargetInstanceID $TargetInstanceId -TargetDatabaseName 'aspnetdb-export' -MaxDataStreams 4 -RecoveryDateTime $db.latestRecoveryPoint


Identity is not the same thing as equality in Python

Use ==/!= to compare str, bytes, and int literals
flake8 testing of https://github.com/leaderj1001/Attention-Augmented-Conv2d on Python 3.7.1
$ flake8 . --count --select=E9,F63,F72,F82 --show-source --statistics
./attention_augmented_conv.py:106:12: F632 use ==/!= to compare str, bytes, and int literals
        if case is "w":
           ^
./attention_augmented_conv.py:108:14: F632 use ==/!= to compare str, bytes, and int literals
        elif case is "h":
             ^
./AA-Wide-ResNet/attention_augmented_conv.py:107:12: F632 use ==/!= to compare str, bytes, and int literals
        if case is "w":
           ^
./AA-Wide-ResNet/attention_augmented_conv.py:109:14: F632 use ==/!= to compare str, bytes, and int literals
        elif case is "h":
             ^
./AA-Wide-ResNet/preprocess.py:13:8: F632 use ==/!= to compare str, bytes, and int literals
    if args.dataset_mode is "CIFAR100":
       ^
./AA-Wide-ResNet/preprocess.py:38:10: F632 use ==/!= to compare str, bytes, and int literals
    elif args.dataset_mode is "CIFAR10":
         ^
./AA-Wide-ResNet/preprocess.py:63:10: F632 use ==/!= to compare str, bytes, and int literals
    elif args.dataset_mode is "MNIST":
         ^
./AA-Wide-ResNet/main.py:86:8: F632 use ==/!= to compare str, bytes, and int literals
    if args.dataset_mode is "CIFAR10":
       ^
./AA-Wide-ResNet/main.py:88:10: F632 use ==/!= to compare str, bytes, and int literals
    elif args.dataset_mode is "CIFAR100":
         ^
./AA-Wide-ResNet/main.py:90:10: F632 use ==/!= to compare str, bytes, and int literals
    elif args.dataset_mode is "MNIST":
         ^
10    F632 use ==/!= to compare str, bytes, and int literals
10

Oh, Thank you for your advise. I fixed that ! Thank you :)


Add Sling (https://slingdata.io/)

Hello! Just wanted to suggest adding Sling (https://slingdata.io/).

Hi @vordimous, please review when you have a moment. Let me know if any questions!


apk execution error

OS Platform and Distribution
android
Compiler version
colab
Programming Language and version
python
Installed using virtualenv? pip? Conda?(if python)
No response
MediaPipe version
No response
Bazel version
No response
XCode and Tulsi versions (if iOS)
No response
Android SDK and NDK versions (if android)
No response
Android AAR (if android)
None
OpenCV version (if running on desktop)
No response
Describe the problem
I made an apk file in colab just fine.
With Python + Camera + OpenCV + Colab
The apk file worked fine on the phone.

However, I did use Mediapipe for analysis.
Python+Camera+Opencv+Mediapipe+Colab
The APK file was created well in Colab, but
Doesn't run on phone.

I would like some help on this matter.

Error when applying 'import mediapipe as mp'

Complete Logs
It runs on the phone, so it's unconfirmed

Hello @zeroche72

Would you please let us know if you are using Kivy to build android apps with Python?
Also, whcih MediaPipe solution are you building?

Hello @zeroche72

Would you please let us know if you are using Kivy to build android apps with Python?
Also, whcih MediaPipe solution are you building?

Thanks for the answer.
I compiled it from the colab site.
'https://colab.research.google.com/'
The environment is like this:
Python 3.8.10
mediapipe <IP_ADDRESS>

@zeroche72 Please use AAR solutions for working on Android. Python APIs cannot be used in Android. Also, you will need to first setup the system with Android Studio, SDK, and NDK. Please check the guide here.
Google Colab cannot be used for creating Android apps.

@zeroche72 Please use AAR solutions for working on Android. Python APIs cannot be used in Android. Also, you will need to first setup the system with Android Studio, SDK, and NDK. Please check the guide here. Google Colab cannot be used for creating Android apps.
Also, if you decide to explore Hands, Face Detection or Face Mesh, we have Maven packages prebuilt. So you can follow this guide to build solutions in easier way.

I thank you for providing a lot of information from you.
My purpose is to create android app in python language.
First, I created a simple Android app using Google Colab in Python language.
An app that controls the camera has also been successful.
I'd go a step further and try using a media pipe.
Other modules can be used by importing them.
The current mission is to find information about why the MediaPipes module is failing.

@zeroche72 Android apps cannot be created in Python natively. You will need to use Kivy for that. And frameworks like Kivy are not supported officially by MediaPipe.
I would reiterate the ask - Please share a colab notebook with ANY minimal android app that you are creating with Python.


CPU BiasOp only supports NHWC.

Any solution on the error below?
E tensorflow/core/common_runtime/executor.cc:594] Executor failed to create kernel. Invalid argument: CPU BiasOp only supports NHWC.

Can be fixed by changing below from NCHW to the CPU compatible NHCW
./tflib/ops/batchnorm.py:            return tf.nn.fused_batch_norm(inputs, scale, offset, epsilon=1e-5, data_format='NCHW')
./tflib/ops/batchnorm.py:            #     data_format='NCHW'
Binary file ./tflib/ops/batchnorm.pyc matches
./tflib/ops/conv1d.py:            data_format='NCHW'
./tflib/ops/conv1d.py:            result = tf.nn.bias_add(result, _biases, data_format='NCHW')
./tflib/ops/conv2d.py:            data_format='NCHW'
./tflib/ops/conv2d.py:            result = tf.nn.bias_add(result, _biases, data_format='NCHW')
Binary file ./tflib/ops/conv2d.pyc matches
./tflib/ops/deconv2d.py:        inputs = tf.transpose(inputs, [0,2,3,1], name='NCHW_to_NHWC')
./tflib/ops/deconv2d.py:        result = tf.transpose(result, [0,3,1,2], name='NHWC_to_NCHW')
Binary file ./tflib/ops/deconv2d.pyc matches

It seems like you're trying to run the model without a GPU. Is there any reason you want to do this? I think it would be too slow to be practical on a CPU.

I need to run the MNIST experiments on this repo and can't do it on my GPU because it's busy running some other experiments.
It might also be that people without GPUs want to run the toy example or MNIST...

Thanks for creating this repo. I agree with rafaelvalle - it seems unreasonable that the code should break entirely if not using GPU...

Totally agree. This is extremely helpful for me who can't afford a GPU and want to run this code as a toy example...

However, in order to make this code run through in CPU mode, I have to make extra changes apart from the changes mentioned by rafaelvalle.
For example, in conv1d.py, I have to  change result = tf.expand_dims(result, 3) to result = tf.expand_dims(result, **1**) since the data format is  changed from 'NCHW' to 'NHWC'.
As  I am pretty new to DL and TF, I am struggling to understand the data structure, and the changes I made to this code run through are pretty ugly. Anyway, I am asking my boss to buy a GPU so that I can get rid of these headaches...

In order to run MNIST experiments on CPU in wgan-gp mode, beside performing the changes suggested by @rafaelvalle , it is necessary to also perform the following changes:

conv2d.py from strides = [1, 1, stride, stride] to strides = [1, stride, stride, 1]
gan_mnist.py the output tensor in Discriminator should be changed from output = tf.reshape(inputs, [-1, 1, 28, 28]) to output = tf.reshape(inputs, [-1, 28, 28, 1])

Hi, I'm running gan_laguage.py, after changing all the NCHW to the CPU compatible NHWC, i still get problem as follows:
ValueError: Dimensions must be equal, but are 32 and 512 for 'Generator.1.1/conv1d/Conv2D' (op: 'Conv2D') with input shapes: [64,1,512,32], [1,5,512,512].
Can anyone tell me how to fix the problem? Thank you very much!

@georgiazhang I am also facing a similar problem.
@igul222 , how can we mitigate this while running on CPU?

@rafaelvalle , how long does it take for each iteration in case of MNIST training on CPU? For me it is taking about 12s. Is it too high for one mini-batch?

@georgiazhang I am facing the same problem. Do you have fixed this issue?

@kaiyu-tang
@MLEnthusiast
@georgiazhang
I am facing the same problem. Do you have fixed this issue?

@kaiyu-tang
@MLEnthusiast
@georgiazhang
＠Samt7
I am facing the same problem. Do you have fixed this issue?

@kaiyu-tang
@MLEnthusiast
@georgiazhang
＠Samt7
@nickyoungforu
I am facing the same problem. Do you have fixed this issue?

@1213999170
i modified tensors shape after changed data_format=NHWC:
./tflib/ops/conv1d.py: 104 add result = tf.transpose(result, [0, 1, 3, 2])
./gan_language.py: 71 # output = tf.transpose(output, [0, 2, 1])
./gan_language.py: 76 # output = tf.transpose(inputs, [0,2,1])

I want to know how to solve this problems ,can you tell me ?
I am facing the same problem. Do you have fixed this issue?


QR code Image

The Qr code url or temp doesn't clear after it has been displayed, all text keep showing in the QRcode.

yes the image does not fade, it is just to illustrate the information contained in the QR code I think I can add as a feature


Masthead:  Persistent search text in Masthead searchbar after search is executed functional specifications

User Story

As a [user role below]:
DDS developer

I need to:
have the Masthead search functional specs

so that I can:
so that I can properly code the Masthead search component that meets the Design functions and intent

Additional information

Overview and scope of the engineering work should be included in the linked epic (#TBD )
No visual design changes are needed
React Development issue (#5203 )
Web Components dev issue (#5232 )
Visual Designs & Functional Design Specs Box folder link(TBD)

Acceptance criteria

[ ] Documented Masthead search functional specifications
[ ] Sent and/or presented the specs to Design and Engineering for their review and comments
[ ] Additional stories are created, if needed, for any work to be done in future sprints

Must have gotten distracted, sorry about that!
https://ibm.box.com/s/89qwjyjy6kwbi69qnz4nzatwfv3mh2kc


Delete Items

Swipe left or right to delete items in the List Page

Hi
can you  assign this issue to me?


Listen Connection Events from WaveWebSocketClient properly

In WaveWebSocketClient, some GWT events are trigger for network changes.
Handle them in a proper way to be consumed in the SwellRT API
Consider make particular copy of WaveWebSocketClient for SwellRT

If the connectios gets a error in Atmosphere layer, WaveWebSocketClient doesn't handle it properly and client app is not informed.


ci(workflow): run script in headless mode on VMs

Run script in headless mode on all supported distributions, on Digital Ocean VMs, with GitHub Actions

Overview from DO:


Pessimistic operator doesn't handle beta versions properly

[10] pry(main)> v = Semantic::Version.new('2.0.0-beta')
=> #<Semantic::Version:0x000055d8286340b0 @build=nil, @major=2, @minor=0, @patch=0, @pre="beta", @version="2.0.0-beta">
[11] pry(main)> v.satisfies?('~> 1.0')
=> true
It should return false for it, since it's no longer version 1

Also the same happens with
[29] pry(main)> v.satisfies?('<2')
=> true
I think <2 should behave as MAJOR < 2, not version < 2.0.0

So I ran into the same issue and did some digging. The responsible code is here: version.rb#L35.
After reading through the Semantic Versioning Spec it actually specifies this behavior in point #9, so the library is working as intended.
As to dealing with this behavior, I use the earliest possible pre-release version as my comparisons instead. For example, first the old comparison, then then the way I propose:
irb(main):021:0> Semantic::Version.new("9.2.0-b22") >= Semantic::Version.new("9.2.0")
=> false
irb(main):022:0> Semantic::Version.new("9.2.0-b22") >= Semantic::Version.new("9.2.0-b1")
=> true

@jlindsey Looks like this can be closed. Might be worth adding a line or two to the README


wheels for python 3.10 pretty please?

When i try to pip install pycolmap with python 3.10 i get "No matching distribution found for pycolmap"
Looking at the files on pypi, it looks like there's just no wheel yet for 3.10:
https://pypi.org/project/pycolmap/#files
But it seems wheel building for 3.10 was added here: https://github.com/colmap/pycolmap/pull/109
Maybe the wheels are pushed manually to pypi?  I think it's all write-once so you have to be careful with releases...
@johnnynunez since you authored the 3.10 update, do you happen to have permission to publish wheels to pypi?

For this we'll need to push a new release. In the meantime, are you able to download them from the CI ?

thanks @Skydes for the quick response! yeah i hear you about needing to just wait for next release.
ohh interesting, i see so CI preserves all wheels for every PR it seems, e.g. for the latest run (at time of writing)

https://github.com/colmap/pycolmap/actions/runs/4161103130

thank you! yes this is a good stopgap.  hope lazyweb finds this solution as well.

I’m trying to do for windows CI with similar strategies but it’s more complicated.

ah ok thanks @johnnynunez ! i m on linux / docker FWIW. i got local compile to work so im certainly unblocked for now, but would love to hear if / when wheels are released for python 3.10.  Cool to know the CI keeps the wheels up for public use.

ah ok thanks @johnnynunez ! i m on linux / docker FWIW. i got local compile to work so im certainly unblocked for now, but would love to hear if / when wheels are released for python 3.10. Cool to know the CI keeps the wheels up for public use.

when they publish a release, it's upload automatically on pypi

@Skydes Thank you so much!!  So this does help, although as I went further down the rabbit hole, it turns out I needed Windows wheels, and that appears to require COLMAP for Windows ... https://github.com/colmap/pycolmap/pull/114
Is it a longshot to think perhaps the COLMAP folks might just build / include pycolmap in their own release system?

cc https://github.com/nerfstudio-project/nerfstudio/pull/2113  Nerfstudio has moved to pycolmap 0.4.0 at least for development, but windows install is still very manual ( https://github.com/colmap/pycolmap/issues/34#issuecomment-1562449270 )

Closing this since:

wheels for linux are now available
@Skydes says ( https://github.com/nerfstudio-project/nerfstudio/pull/2199 )  pycolmap is trying to track the dev branch of COLMAP, so perhaps a synchronized release of COLMAP + pycolmap for both linux and windows is forthcoming

We now have instructions to build PyCOLMAP on Windows. We also have pre-built wheels that are available here and can be installed with pip. I don't have a Windows machine at hand so it'd be great if someone could confirm that they work. We'll then release them on PyPI.


use: Support a plugin version field.

I thought I'd implement this for consistency with Unexpected, even though we don't really need it at present.
@sunesimonsen Feel free to leave this open until we actually need it.

Thanks.


Mention amd64 build requirement in Custom Image Registry docs

Is your feature request related to a problem? Please describe.
Custom image registry build fails on arm64. This is not warned in the docs.
https://docs.pixielabs.ai/reference/admin/deploy-options/#custom-image-registry
Describe the solution you'd like
Docs which state that the build must be performed on amd64, also mentioning Apple M1 chips in that context.

Pixie know supports arm64 and amd64, and the images we publish are multiarch. So amd64 should no longer be a requirement.

This issue was relating to the build process for custom image hosting. Part of the OLM build failed on arm64. I will retest when I can and create a new issue with details if needed.

Sounds good. We recently updated our OLM images to support arm64. There's still one known issue with kubernetes.io/arch taints affecting scheduling of some of the OLM jobs, but other than that they should work, so if you run into issues I'd be happy to help.


Module build issues with 4.0.0

Anyone else seeing this issue in Angular 15 when they upgrade to 4.0.0? earlier version 3.3.0 does not cause this, currently am forced to use 3.3.0
./src/styles.scss - Error: Module build failed (from ./node_modules/@angular-devkit/build-angular/node_modules/postcss-loader/dist/cjs.js):
TypeError: withAnimations is not a function

./src/styles.scss?ngGlobalStyle - Error: Module build failed (from ./node_modules/mini-css-extract-plugin/dist/loader.js):
HookWebpackError: Module build failed (from ./node_modules/@angular-devkit/build-angular/node_modules/postcss-loader/dist/cjs.js):
TypeError: withAnimations is not a function

 "dependencies": {
    "@algolia/autocomplete-js": "^1.7.4",
    "@angular/animations": "^15.1.4",
    "@angular/cdk": "^15.1.4",
    "@angular/common": "^15.1.4",
    "@angular/compiler": "^15.1.4",
    "@angular/core": "^15.1.4",
    "@angular/forms": "^15.1.4",
    "@angular/platform-browser": "^15.1.4",
    "@angular/platform-browser-dynamic": "^15.1.4",
    "@angular/router": "^15.1.4",
    "@arcgis/core": "^4.25.5",
    "@faker-js/faker": "^7.6.0",
    "@floating-ui/dom": "^1.2.0",
    "@ng-web-apis/common": "^2.1.0",
    "@ngneat/overview": "^4.0.1",
    "@ngneat/until-destroy": "^9.2.3",
    "@ngx-loading-bar/core": "^6.0.2",
    "@ngx-loading-bar/router": "^6.0.2",
    "@supabase/supabase-js": "^2.7.1",
    "@twilio/conversations": "^2.2.1",
    "algoliasearch": "^4.14.3",
    "hammerjs": "^2.0.8",
    "lodash-es": "^4.17.21",
    "ngx-device-detector": "^5.0.1",
    "ngx-pipes": "^3.2.0",
    "phone": "^3.1.33",
    "popmotion": "^11.0.5",
    "rxjs": "^7.8.0",
    "tslib": "^2.5.0",
    "uuid": "^9.0.0",
    "zone.js": "^0.12.0"
  },
  "devDependencies": {
    "@angular-devkit/build-angular": "^15.1.5",
    "@angular/cli": "^15.1.5",
    "@angular/compiler-cli": "^15.1.4",
    "@tailwindcss/container-queries": "^0.1.0",
    "@tailwindcss/forms": "^0.5.3",
    "@types/jasmine": "^4.3.1",
    "@types/lodash-es": "^4.17.6",
    "@types/node": "^18.13.0",
    "@types/twilio": "^3.19.3",
    "animated-tailwindcss": "3.3.0",
    "copyfiles": "^2.4.1",
    "jasmine-core": "^4.5.0",
    "karma": "^6.4.1",
    "karma-chrome-launcher": "^3.1.1",
    "karma-coverage": "~2.2.0",
    "karma-jasmine": "^5.1.0",
    "karma-jasmine-html-reporter": "^2.0.0",
    "resize-observer-polyfill": "^1.5.1",
    "tailwind-scrollbar": "^2.1.0",
    "tailwindcss": "^3.2.6",
    "typescript": "^4.8.4"
  }

You probably haven't updated the import. Now it's a named export instead of default one. https://brc-dd.github.io/animated-tailwindcss/advanced#from-v3-to-v4

Why would this be in Miscellaneous? Needs to be on readme of the github imo


Improve json identification with FSA

This mainly resolves #363
I only added a small test to pinpoint the problem, I'll work on putting more tests in there in this PR

Very cool! Thanks for adding more tests, the automata is quite involved :) Can we also have an example of "wrong json followed by valid json" to check that it recovers? (also json anywhere in a line since you also fixed that!)

Thanks!

@ElectreAAS I tried testing this with the local-test-repo/Makefile and it seems to error out.  Could you take a look?
You could trigger a build using this Makefile by running make local-make-bench, for instance.

@punchagan I don't want to be that person, but... it works on my machine :laughing:
if I cd local-test-repo; make bench as advised by @art-w, it seems to work...? It prints a lot of things, but looking at the Makefile it doesn't do anything, just calls to echo. What errors do you have?
If at the root I make local-make-bench it will try to git commit stuff, but will stop since there are no changes... Very weird makefiles

By the way the only thing make local-make-bench does is this: cd local-test-repo/; git add .; git commit --amend -m "New commit: $$(date)"; cd .., which is... not very useful?

Thanks for adding more context to my comment, @art-w !


Align reference frame sign issue

Align geometry has a sign issue for 2D cone beam and aligns in the wrong direction on the Y axis
https://github.com/TomographicImaging/CIL/blob/0a434c8b8086d2b759b4f3d66f7cc42e8099dfe9/Wrappers/Python/cil/framework/framework.py#L883-L887
Potentially Parallel3D has the same issue.

Also, I find this on the centre slice of the Walnut dataset:

data = TXRMDataReader(file_name=filename).read()
data = TransmissionAbsorptionConverter()(data)
data.reorder('tigre')
data2d = data.get_slice(vertical='centre')
print(data2d.geometry)

prints
2D Cone-beam tomography
System configuration:
	Source position: [   0.     , -105.05081]
	Rotation axis position: [0., 0.]
	Detector position: [ 0.      , 45.087574]
	Detector direction x: [1., 0.]
Panel configuration:
	Number of pixels: [1024    1]
	Pixel size: [0.0658543 0.0658543]
	Pixel origin: bottom-left
Channel configuration:
	Number of channels: 1
Acquisition description:
	Number of positions: 1601
	Angles 0-20 in degrees:
[-179.9985 , -179.77707, -179.55138, -179.32538, -179.0972 , -178.87242,
 -178.65372, -178.42287, -178.20068, -177.97687, -177.74826, -177.52678,
 -177.30148, -177.0751 , -176.84659, -176.62518, -176.40147, -176.17467,
 -175.94827, -175.72462]

Changing the centre of rotation
data2d.geometry.config.system.rotation_axis.position = (.2,0)
data2d.geometry.config.system.align_reference_frame()
print(data2d.geometry)

prints
2D Cone-beam tomography
System configuration:
	Source position: [2.2858695e-09, 1.0505100e+02]
	Rotation axis position: [0., 0.]
	Detector position: [  0.28583905, -45.087112  ]
	Detector direction x: [-0.9999982 , -0.00190384]
Panel configuration:
	Number of pixels: [1024    1]
	Pixel size: [0.0658543 0.0658543]
	Pixel origin: bottom-left
Channel configuration:
	Number of channels: 1
Acquisition description:
	Number of positions: 1601
	Angles 0-20 in degrees:
[-179.9985 , -179.77707, -179.55138, -179.32538, -179.0972 , -178.87242,
 -178.65372, -178.42287, -178.20068, -177.97687, -177.74826, -177.52678,
 -177.30148, -177.0751 , -176.84659, -176.62518, -176.40147, -176.17467,
 -175.94827, -175.72462]