Python源码示例：torch.nn.modules.module._addindent()
示例1
def summary(model, show_weights=True, show_parameters=True):
"""
Summarizes torch model by showing trainable parameters and weights.
"""
tmpstr = model.__class__.__name__ + ' (\n'
total_params = 0
for key, module in model._modules.items():
# if it contains layers let call it recursively to get params
# and weights
if type(module) in [
th.nn.modules.container.Container,
th.nn.modules.container.Sequential
]:
modstr = summary(module)
else:
modstr = module.__repr__()
modstr = _addindent(modstr, 2)
params = sum([np.prod(p.size()) for p in module.parameters()])
weights = tuple([tuple(p.size()) for p in module.parameters()])
total_params += params
tmpstr += ' (' + key + '): ' + modstr
if show_weights:
tmpstr += ', weights={}'.format(weights)
if show_parameters:
tmpstr += ', parameters={}'.format(params)
tmpstr += '\n'
tmpstr = tmpstr + ') Total Parameters={}'.format(total_params)
return tmpstr
示例2
def torch_summarize(model, show_weights=True, show_parameters=True):
"""
Summarizes torch model by showing trainable parameters and weights.
Taken from:
https://stackoverflow.com/questions/42480111/model-summary-in-pytorch#42616812
"""
tmpstr = model.__class__.__name__ + ' (\n'
for key, module in model._modules.items():
# if it contains layers let call it recursively to get params and weights
if type(module) in [
torch.nn.modules.container.Container,
torch.nn.modules.container.Sequential
]:
modstr = torch_summarize(module)
else:
modstr = module.__repr__()
modstr = _addindent(modstr, 2)
params = sum([np.prod(p.size()) for p in module.parameters()])
weights = tuple([tuple(p.size()) for p in module.parameters()])
tmpstr += ' (' + key + '): ' + modstr
if show_weights:
tmpstr += ', weights={}'.format(weights)
if show_parameters:
tmpstr += ', parameters={}'.format(params)
tmpstr += '\n'
tmpstr = tmpstr + ')'
return tmpstr
示例3
def hierarchicalsummary(model):
def repr(model):
# We treat the extra repr like the sub-module, one item per line
extra_lines = []
extra_repr = model.extra_repr()
# empty string will be split into list ['']
if extra_repr:
extra_lines = extra_repr.split('\n')
child_lines = []
total_params = 0
for key, module in model._modules.items():
mod_str, num_params = repr(module)
mod_str = _addindent(mod_str, 2)
child_lines.append('(' + key + '): ' + mod_str)
total_params += num_params
lines = extra_lines + child_lines
for name, p in model._parameters.items():
if p is not None:
total_params += reduce(lambda x, y: x * y, p.shape)
main_str = model._get_name() + '('
if lines:
# simple one-liner info, which most builtin Modules will use
if len(extra_lines) == 1 and not child_lines:
main_str += extra_lines[0]
else:
main_str += '\n ' + '\n '.join(lines) + '\n'
main_str += ')'
main_str += ', {:,} params'.format(total_params)
return main_str, total_params
string, count = repr(model)
print(string)
return count
示例4
def torch_summarize(model, show_weights=True, show_parameters=True):
"""Summarizes torch model by showing trainable parameters and weights."""
tmpstr = model.__class__.__name__ + ' (\n'
parameters = 0
convs = 0
for key, module in model._modules.items():
# if it contains layers let call it recursively to get params and weights
if type(module) in [torch.nn.modules.container.Container, torch.nn.modules.container.Sequential]:
modstr, p, cnvs = torch_summarize(module)
parameters += p
convs += cnvs
else:
modstr = module.__repr__()
convs += len(modstr.split('Conv2d')) - 1
modstr = _addindent(modstr, 2)
# if 'conv' in key:
# convs += 1
params = sum([np.prod(p.size()) for p in module.parameters()])
parameters += params
weights = tuple([tuple(p.size()) for p in module.parameters()])
tmpstr += ' (' + key + '): ' + modstr
if show_weights:
tmpstr += ', weights={}'.format(weights)
if show_parameters:
tmpstr += ', parameters={} / {}'.format(params, parameters)
tmpstr += ', convs={}'.format(convs)
tmpstr += '\n'
tmpstr = tmpstr + ')'
return tmpstr, parameters, convs
示例5
def summary(model, show_weights=True, show_parameters=True):
"""
Summarizes torch model by showing trainable parameters and weights.
"""
tmpstr = model.__class__.__name__ + ' (\n'
total_params = 0
for key, module in model._modules.items():
# if it contains layers let call it recursively to get params
# and weights
if type(module) in [
torch.nn.modules.container.Container,
torch.nn.modules.container.Sequential
]:
modstr = summary(module)
else:
modstr = module.__repr__()
modstr = _addindent(modstr, 2)
params = sum([np.prod(p.size()) for p in module.parameters()])
weights = tuple([tuple(p.size()) for p in module.parameters()])
total_params += params
tmpstr += ' (' + key + '): ' + modstr
if show_weights:
tmpstr += ', weights={}'.format(weights)
if show_parameters:
tmpstr += ', parameters={}'.format(params)
tmpstr += '\n'
tmpstr = tmpstr + ') Total Parameters={}'.format(total_params)
return tmpstr
示例6
def summary(model, show_weights=True, show_parameters=True):
"""
Summarizes torch model by showing trainable parameters and weights.
"""
tmpstr = model.__class__.__name__ + ' (\n'
total_params = 0
for key, module in model._modules.items():
# if it contains layers let call it recursively to get params
# and weights
if type(module) in [
th.nn.modules.container.Container,
th.nn.modules.container.Sequential
]:
modstr = summary(module)
else:
modstr = module.__repr__()
modstr = _addindent(modstr, 2)
params = sum([np.prod(p.size()) for p in module.parameters()])
weights = tuple([tuple(p.size()) for p in module.parameters()])
total_params += params
tmpstr += ' (' + key + '): ' + modstr
if show_weights:
tmpstr += ', weights={}'.format(weights)
if show_parameters:
tmpstr += ', parameters={}'.format(params)
tmpstr += '\n'
tmpstr = tmpstr + ') Total Parameters={}'.format(total_params)
return tmpstr
示例7
def summary(model, show_weights=True, show_parameters=True):
"""
Summarizes torch model by showing trainable parameters and weights.
"""
tmpstr = model.__class__.__name__ + ' (\n'
total_params = 0
for key, module in model._modules.items():
# if it contains layers let call it recursively to get params
# and weights
if type(module) in [
torch.nn.modules.container.Container,
torch.nn.modules.container.Sequential
]:
modstr = summary(module)
else:
modstr = module.__repr__()
modstr = _addindent(modstr, 2)
params = sum([np.prod(p.size()) for p in module.parameters()])
weights = tuple([tuple(p.size()) for p in module.parameters()])
total_params += params
tmpstr += ' (' + key + '): ' + modstr
if show_weights:
tmpstr += ', weights={}'.format(weights)
if show_parameters:
tmpstr += ', parameters={}'.format(params)
tmpstr += '\n'
tmpstr = tmpstr + ') Total Parameters={}'.format(total_params)
return tmpstr
示例8
def torch_summarize(model, show_weights = True, show_parameters = True, show_trainable = True):
"""Summarizes torch model by showing trainable parameters and weights."""
tmpstr = model.__class__.__name__ + " (\n"
for key, module in model._modules.items():
# If it contains layers let call it recursively to get params and weights
if type(module) in [
torch.nn.modules.container.Container,
torch.nn.modules.container.Sequential
]:
modstr = torch_summarize(module)
else:
modstr = module.__repr__()
# ====================== Extra stuff (for displaying nn.Parameter) ======================
lst_params = []
for name, p in module.named_parameters():
if(type(p) == torch.nn.parameter.Parameter and "weight" not in name and "bias" not in name):
lst_params.append(" ({}): Parameter{}".format( name, tuple(p.size()) ))
if(lst_params):
modstr = modstr[:-1]
modstr += "\n".join(lst_params)
modstr += "\n)"
# ====================== Extra stuff (for displaying nn.Parameter) ======================
modstr = _addindent(modstr, 2)
weights = tuple([tuple(p.size()) for p in module.parameters()])
params = sum([np.prod(p.size()) for p in module.parameters()])
total_params = sum([torch.LongTensor(list(p.size())).prod() for p in module.parameters()])
trainable_params = sum([torch.LongTensor(list(p.size())).prod() for p in module.parameters() if p.requires_grad])
tmpstr += " (" + key + "): " + modstr
if show_weights:
tmpstr += ", weights = {}".format(weights)
if show_parameters:
tmpstr += ", parameters = {:,}".format(params)
if show_trainable and total_params != 0 and total_params == trainable_params:
tmpstr += " (Trainable)"
tmpstr += "\n"
tmpstr = tmpstr + ")"
return tmpstr
# Generates a summary of the given model
示例9
def summary(model, file=sys.stdout):
def repr(model):
# We treat the extra repr like the sub-module, one item per line
extra_lines = []
extra_repr = model.extra_repr()
# empty string will be split into list ['']
if extra_repr:
extra_lines = extra_repr.split('\n')
child_lines = []
total_params = 0
for key, module in model._modules.items():
mod_str, num_params = repr(module)
mod_str = _addindent(mod_str, 2)
child_lines.append('(' + key + '): ' + mod_str)
total_params += num_params
lines = extra_lines + child_lines
for name, p in model._parameters.items():
if hasattr(p, 'shape'):
total_params += reduce(lambda x, y: x * y, p.shape)
main_str = model._get_name() + '('
if lines:
# simple one-liner info, which most builtin Modules will use
if len(extra_lines) == 1 and not child_lines:
main_str += extra_lines[0]
else:
main_str += '\n ' + '\n '.join(lines) + '\n'
main_str += ')'
if file is sys.stdout:
main_str += ', \033[92m{:,}\033[0m params'.format(total_params)
else:
main_str += ', {:,} params'.format(total_params)
return main_str, total_params
string, count = repr(model)
if file is not None:
if isinstance(file, str):
file = open(file, 'w')
print(string, file=file)
file.flush()
return count
