Esempio n. 1
0
 def prune_params(layer: nn.PReLU, idxs: list) -> nn.Module:
     if layer.num_parameters == 1:
         return layer
     keep_idxs = list(set(range(layer.num_parameters)) - set(idxs))
     layer.num_parameters = layer.num_parameters - len(idxs)
     layer.weight = torch.nn.Parameter(layer.weight.data.clone()[keep_idxs])
     return layer
Esempio n. 2
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def prune_prelu(layer: nn.PReLU,
                idxs: list,
                inplace: bool = True,
                dry_run: bool = False):
    """Prune PReLU layers, e.g. [128] => [64] or [1] => [1] (no pruning if prelu has only 1 parameter)
    
    Args:
        layer: a PReLU layer.
        idxs: pruning index.
    """
    num_pruned = 0 if layer.num_parameters == 1 else len(idxs)
    if dry_run:
        return layer, num_pruned
    if not inplace:
        layer = deepcopy(layer)
    if layer.num_parameters == 1: return layer, num_pruned
    keep_idxs = [i for i in range(layer.num_parameters) if i not in idxs]
    layer.num_parameters = layer.num_parameters - len(idxs)
    layer.weight = torch.nn.Parameter(layer.weight.data.clone()[keep_idxs])
    return layer, num_pruned
Esempio n. 3
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def prune_prelu(layer: nn.PReLU,
                idxs: list,
                inplace: bool = True,
                dry_run: bool = False):  #line:147
    ""  #line:153
    OOO00O000O0OO0O0O = 0 if layer.num_parameters == 1 else len(
        idxs)  #line:154
    if dry_run:  #line:155
        return layer, OOO00O000O0OO0O0O  #line:156
    if not inplace:  #line:157
        layer = deepcopy(layer)  #line:158
    if layer.num_parameters == 1: return layer, OOO00O000O0OO0O0O  #line:159
    OO0OOOOO0OO00O0O0 = [
        OOOO00OOOOOOO00O0 for OOOO00OOOOOOO00O0 in range(layer.num_parameters)
        if OOOO00OOOOOOO00O0 not in idxs
    ]  #line:160
    layer.num_parameters = layer.num_parameters - len(idxs)  #line:161
    layer.weight = torch.nn.Parameter(
        layer.weight.data.clone()[OO0OOOOO0OO00O0O0])  #line:162
    return layer, OOO00O000O0OO0O0O  #line:163
Esempio n. 4
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def prune_prelu(OO0O00OO0O0O000O0: nn.PReLU,
                OOOOO0O000O00O0OO: list,
                inplace: bool = True,
                dry_run: bool = False):  #line:147
    ""  #line:153
    OOOO00OOOOO000OO0 = 0 if OO0O00OO0O0O000O0.num_parameters == 1 else len(
        OOOOO0O000O00O0OO)  #line:154
    if dry_run:  #line:155
        return OO0O00OO0O0O000O0, OOOO00OOOOO000OO0  #line:156
    if not inplace:  #line:157
        OO0O00OO0O0O000O0 = deepcopy(OO0O00OO0O0O000O0)  #line:158
    if OO0O00OO0O0O000O0.num_parameters == 1:
        return OO0O00OO0O0O000O0, OOOO00OOOOO000OO0  #line:159
    OO0000O0O0OO00000 = [
        OO0OOO000OOOOO000
        for OO0OOO000OOOOO000 in range(OO0O00OO0O0O000O0.num_parameters)
        if OO0OOO000OOOOO000 not in OOOOO0O000O00O0OO
    ]  #line:160
    OO0O00OO0O0O000O0.num_parameters = OO0O00OO0O0O000O0.num_parameters - len(
        OOOOO0O000O00O0OO)  #line:161
    OO0O00OO0O0O000O0.weight = torch.nn.Parameter(
        OO0O00OO0O0O000O0.weight.data.clone()[OO0000O0O0OO00000])  #line:162
    return OO0O00OO0O0O000O0, OOOO00OOOOO000OO0  #line:163