14319828903

Committed 07 Apr 2025 09:27PM UTC coverage: 97.259% (+0.2%) from 97.035%

Build # 14319828903

Build Type

Pull #86

github

Committed by

web-flow

Commit Message

Merge 44b7c25ee into e9c323c4f

Pull Request Pull Request #86: Smallclasses

Run Details

2275 of 2409 new or added lines in 54 files covered. (94.44%)

1 existing line in 1 file now uncovered.

11637 of 11965 relevant lines covered (97.26%)

0.97 hits per line

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78.38

/nnodely/operators/memory.py

import  torch

from nnodely.support.utils import  TORCH_DTYPE, check, enforce_types

class Memory:
    def __init__(self):
        check(type(self) is not Memory, TypeError, "Loader class cannot be instantiated directly")

        # Model definition
        self._states = {}
        self._input_n_samples = {}
        self._input_ns_backward = {}
        self._input_ns_forward = {}
        self._max_samples_backward = None
        self._max_samples_forward = None
        self._max_n_samples = 0

    def _removeVirtualStates(self, connect, closed_loop):
        for key in (connect.keys() | closed_loop.keys()):
            if key in self._states.keys():
                del self._states[key]

    def _updateState(self, X, out_closed_loop, out_connect):
        ## Update
        for key, val in out_closed_loop.items():
            shift = val.shape[1]  ## take the output time dimension
            X[key] = torch.roll(X[key], shifts=-1, dims=1)  ## Roll the time window
            X[key][:, -shift:, :] = val  ## substitute with the predicted value
            self._states[key] = X[key].clone().detach()
        for key, value in out_connect.items():
            X[key] = value
            self._states[key] = X[key].clone().detach()

    @enforce_types
    def resetStates(self, states:set={}, batch:int=1) -> None:
        if states: ## reset only specific states
            for key in states:
                window_size = self._input_n_samples[key]
                dim = self._model_def['States'][key]['dim']
                self._states[key] = torch.zeros(size=(batch, window_size, dim), dtype=TORCH_DTYPE, requires_grad=False)
        else: ## reset all states
            self._states = {}
            for key, state in self._model_def['States'].items():
                window_size = self._input_n_samples[key]
                dim = state['dim']
                self._states[key] = torch.zeros(size=(batch, window_size, dim), dtype=TORCH_DTYPE, requires_grad=False)


1	import torch	1✔
2
3	from nnodely.support.utils import TORCH_DTYPE, check, enforce_types	1✔
4
5	class Memory:	1✔
6	def __init__(self):	1✔
NEW 7	check(type(self) is not Memory, TypeError, "Loader class cannot be instantiated directly")	×
8
9	# Model definition
NEW 10	self._states = {}	×
NEW 11	self._input_n_samples = {}	×
NEW 12	self._input_ns_backward = {}	×
NEW 13	self._input_ns_forward = {}	×
NEW 14	self._max_samples_backward = None	×
NEW 15	self._max_samples_forward = None	×
NEW 16	self._max_n_samples = 0	×
17
18	def _removeVirtualStates(self, connect, closed_loop):	1✔
19	for key in (connect.keys() \| closed_loop.keys()):	1✔
20	if key in self._states.keys():	1✔
21	del self._states[key]	1✔
22
23	def _updateState(self, X, out_closed_loop, out_connect):	1✔
24	## Update
25	for key, val in out_closed_loop.items():	1✔
26	shift = val.shape[1] ## take the output time dimension	1✔
27	X[key] = torch.roll(X[key], shifts=-1, dims=1) ## Roll the time window	1✔
28	X[key][:, -shift:, :] = val ## substitute with the predicted value	1✔
29	self._states[key] = X[key].clone().detach()	1✔
30	for key, value in out_connect.items():	1✔
31	X[key] = value	1✔
32	self._states[key] = X[key].clone().detach()	1✔
33
34	@enforce_types	1✔
35	def resetStates(self, states:set={}, batch:int=1) -> None:	1✔
36	if states: ## reset only specific states	1✔
37	for key in states:	1✔
38	window_size = self._input_n_samples[key]	1✔
39	dim = self._model_def['States'][key]['dim']	1✔
40	self._states[key] = torch.zeros(size=(batch, window_size, dim), dtype=TORCH_DTYPE, requires_grad=False)	1✔
41	else: ## reset all states
42	self._states = {}	1✔
43	for key, state in self._model_def['States'].items():	1✔
44	window_size = self._input_n_samples[key]	1✔
45	dim = state['dim']	1✔
46	self._states[key] = torch.zeros(size=(batch, window_size, dim), dtype=TORCH_DTYPE, requires_grad=False)	1✔
47

tonegas / nnodely / 14319828903

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