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Merge pull request #1 from aria1th/patch-11

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fix dropouts for future hypernetworks
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guaneec 2022-10-27 14:14:03 +08:00 committed by GitHub
commit 80844ac861
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2 changed files with 20 additions and 11 deletions
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27
modules/hypernetworks/hypernetwork.py
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@ -34,7 +34,8 @@ class HypernetworkModule(torch.nn.Module):
} }
activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'}) activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False, activate_output=False): def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
add_layer_norm=False, use_dropout=False, activate_output=False, last_layer_dropout=True):
super().__init__() super().__init__()
assert layer_structure is not None, "layer_structure must not be None" assert layer_structure is not None, "layer_structure must not be None"
@ -60,7 +61,7 @@ class HypernetworkModule(torch.nn.Module):
linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
# Add dropout except last layer # Add dropout except last layer
if use_dropout and i < len(layer_structure) - 3: if use_dropout and (i < len(layer_structure) - 3 or last_layer_dropout and i < len(layer_structure) - 2):
linears.append(torch.nn.Dropout(p=0.3)) linears.append(torch.nn.Dropout(p=0.3))
self.linear = torch.nn.Sequential(*linears) self.linear = torch.nn.Sequential(*linears)
@ -74,7 +75,7 @@ class HypernetworkModule(torch.nn.Module):
w, b = layer.weight.data, layer.bias.data w, b = layer.weight.data, layer.bias.data
if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm: if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm:
normal_(w, mean=0.0, std=0.01) normal_(w, mean=0.0, std=0.01)
normal_(b, mean=0.0, std=0.005) normal_(b, mean=0.0, std=0)
elif weight_init == 'XavierUniform': elif weight_init == 'XavierUniform':
xavier_uniform_(w) xavier_uniform_(w)
zeros_(b) zeros_(b)
@ -126,7 +127,7 @@ class Hypernetwork:
filename = None filename = None
name = None name = None
def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False): def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs):
self.filename = None self.filename = None
self.name = name self.name = name
self.layers = {} self.layers = {}
@ -139,11 +140,14 @@ class Hypernetwork:
self.add_layer_norm = add_layer_norm self.add_layer_norm = add_layer_norm
self.use_dropout = use_dropout self.use_dropout = use_dropout
self.activate_output = activate_output self.activate_output = activate_output
self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True
for size in enable_sizes or []: for size in enable_sizes or []:
self.layers[size] = ( self.layers[size] = (
HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output), HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output), self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
) )
def weights(self): def weights(self):
@ -172,7 +176,8 @@ class Hypernetwork:
state_dict['sd_checkpoint'] = self.sd_checkpoint state_dict['sd_checkpoint'] = self.sd_checkpoint
state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
state_dict['activate_output'] = self.activate_output state_dict['activate_output'] = self.activate_output
state_dict['last_layer_dropout'] = self.last_layer_dropout
torch.save(state_dict, filename) torch.save(state_dict, filename)
def load(self, filename): def load(self, filename):
@ -193,12 +198,16 @@ class Hypernetwork:
self.use_dropout = state_dict.get('use_dropout', False) self.use_dropout = state_dict.get('use_dropout', False)
print(f"Dropout usage is set to {self.use_dropout}" ) print(f"Dropout usage is set to {self.use_dropout}" )
self.activate_output = state_dict.get('activate_output', True) self.activate_output = state_dict.get('activate_output', True)
print(f"Activate last layer is set to {self.activate_output}")
self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
for size, sd in state_dict.items(): for size, sd in state_dict.items():
if type(size) == int: if type(size) == int:
self.layers[size] = ( self.layers[size] = (
HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output), HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init,
HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output), self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init,
self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
) )
self.name = state_dict.get('name', self.name) self.name = state_dict.get('name', self.name)

4
modules/ui.py
View File

@ -1238,8 +1238,8 @@ def create_ui(wrap_gradio_gpu_call):
new_hypernetwork_name = gr.Textbox(label="Name") new_hypernetwork_name = gr.Textbox(label="Name")
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'") new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys) new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"]) new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Normal is default, for experiments, relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout") new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork") overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")