My network is a 1d CNN, I want to compute the number of FLOPs and params. I used public method 'flops_counter', but I am not sure the size of the input. When I run it with size(128,1,50), I get error 'Expected 3-dimensional input for 3-dimensional weight [128, 1, 50], but got 4-dimensional input of size [1, 128, 1, 50] instead'. When I run it with size(128,50), I get error 'RuntimeError: Given groups=1, weight of size [128, 1, 50], expected input[1, 128, 50] to have 1 channels, but got 128 channels instead'.
import torch
from models.cnn import net
from flops_counter import get_model_complexity_info
model = net()
# Flops¶ms
flops, params = get_model_complexity_info(model, (128,1,50), as_strings=True, print_per_layer_stat=True)
print('Flops: ' + flops)
print('Params: ' + params)
Here is my 1d CNN.
from __future__ import print_function
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
# create cnn
class net(nn.Module):
def __init__(self):
super(net, self).__init__()
self.conv1 = nn.Conv1d(1, 128, 50, stride=3)
self.conv2 = nn.Conv1d(128, 32, 7, stride=1)
self.conv3 = nn.Conv1d(32, 32, 9, stride=1)
self.fc1 = nn.Linear(32, 128)
self.fc2 = nn.Linear(128, 5)
self.bn1 = nn.BatchNorm1d(128)
self.bn2 = nn.BatchNorm1d(32)
self.dropout = nn.Dropout2d(0.5)
self.faltten = nn.Flatten()
# forward propagation
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.bn1(x)
x = F.max_pool1d(x, 2, stride=3)
x = self.dropout(F.relu(self.conv2(x)))
# x = F.relu(self.conv2(x))
x = self.bn2(x)
x = F.max_pool1d(x, 2, stride=2)
x = self.dropout(F.relu(self.conv3(x)))
x = self.faltten(x)
x = self.dropout(self.fc1(x))
output = self.fc2(x)
return output
Here is the code of flops_counter:
'''
Copyright (C) 2019 Sovrasov V. - All Rights Reserved
* You may use, distribute and modify this code under the
* terms of the MIT license.
* You should have received a copy of the MIT license with
* this file. If not visit https://opensource.org/licenses/MIT
'''
import sys
from functools import partial
import torch
import torch.nn as nn
import numpy as np
def get_model_complexity_info(model, input_res,
print_per_layer_stat=True,
as_strings=True,
input_constructor=None, ost=sys.stdout,
verbose=False, ignore_modules=[],
custom_modules_hooks={}):
assert type(input_res) is tuple
assert len(input_res) >= 1
assert isinstance(model, nn.Module)
global CUSTOM_MODULES_MAPPING
CUSTOM_MODULES_MAPPING = custom_modules_hooks
flops_model = add_flops_counting_methods(model)
flops_model.eval()
flops_model.start_flops_count(ost=ost, verbose=verbose, ignore_list=ignore_modules)
if input_constructor:
input = input_constructor(input_res)
_ = flops_model(**input)
else:
try:
batch = torch.ones(()).new_empty((1, *input_res),
dtype=next(flops_model.parameters()).dtype,
device=next(flops_model.parameters()).device)
except StopIteration:
batch = torch.ones(()).new_empty((1, *input_res))
_ = flops_model(batch)
flops_count, params_count = flops_model.compute_average_flops_cost()
if print_per_layer_stat:
print_model_with_flops(flops_model, flops_count, params_count, ost=ost)
flops_model.stop_flops_count()
CUSTOM_MODULES_MAPPING = {}
if as_strings:
return flops_to_string(flops_count), params_to_string(params_count)
return flops_count, params_count
def flops_to_string(flops, units='GMac', precision=2):
if units is None:
if flops // 10**9 > 0:
return str(round(flops / 10.**9, precision)) + ' GMac'
elif flops // 10**6 > 0:
return str(round(flops / 10.**6, precision)) + ' MMac'
elif flops // 10**3 > 0:
return str(round(flops / 10.**3, precision)) + ' KMac'
else:
return str(flops) + ' Mac'
else:
if units == 'GMac':
return str(round(flops / 10.**9, precision)) + ' ' + units
elif units == 'MMac':
return str(round(flops / 10.**6, precision)) + ' ' + units
elif units == 'KMac':
return str(round(flops / 10.**3, precision)) + ' ' + units
else:
return str(flops) + ' Mac'
def params_to_string(params_num, units=None, precision=2):
if units is None:
if params_num // 10 ** 6 > 0:
return str(round(params_num / 10 ** 6, 2)) + ' M'
elif params_num // 10 ** 3:
return str(round(params_num / 10 ** 3, 2)) + ' k'
else:
return str(params_num)
else:
if units == 'M':
return str(round(params_num / 10.**6, precision)) + ' ' + units
elif units == 'K':
return str(round(params_num / 10.**3, precision)) + ' ' + units
else:
return str(params_num)
def print_model_with_flops(model, total_flops, total_params, units='GMac',
precision=3, ost=sys.stdout):
def accumulate_params(self):
if is_supported_instance(self):
return self.__params__
else:
sum = 0
for m in self.children():
sum += m.accumulate_params()
return sum
def accumulate_flops(self):
if is_supported_instance(self):
return self.__flops__ / model.__batch_counter__
else:
sum = 0
for m in self.children():
sum += m.accumulate_flops()
return sum
def flops_repr(self):
accumulated_params_num = self.accumulate_params()
accumulated_flops_cost = self.accumulate_flops()
return ', '.join([params_to_string(accumulated_params_num, units='M', precision=precision),
'{:.3%} Params'.format(accumulated_params_num / total_params),
flops_to_string(accumulated_flops_cost, units=units, precision=precision),
'{:.3%} MACs'.format(accumulated_flops_cost / total_flops),
self.original_extra_repr()])
def add_extra_repr(m):
m.accumulate_flops = accumulate_flops.__get__(m)
m.accumulate_params = accumulate_params.__get__(m)
flops_extra_repr = flops_repr.__get__(m)
if m.extra_repr != flops_extra_repr:
m.original_extra_repr = m.extra_repr
m.extra_repr = flops_extra_repr
assert m.extra_repr != m.original_extra_repr
def del_extra_repr(m):
if hasattr(m, 'original_extra_repr'):
m.extra_repr = m.original_extra_repr
del m.original_extra_repr
if hasattr(m, 'accumulate_flops'):
del m.accumulate_flops
model.apply(add_extra_repr)
print(model, file=ost)
model.apply(del_extra_repr)
def get_model_parameters_number(model):
params_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
return params_num
def add_flops_counting_methods(net_main_module):
# adding additional methods to the existing module object,
# this is done this way so that each function has access to self object
net_main_module.start_flops_count = start_flops_count.__get__(net_main_module)
net_main_module.stop_flops_count = stop_flops_count.__get__(net_main_module)
net_main_module.reset_flops_count = reset_flops_count.__get__(net_main_module)
net_main_module.compute_average_flops_cost = compute_average_flops_cost.__get__(net_main_module)
net_main_module.reset_flops_count()
return net_main_module
def compute_average_flops_cost(self):
"""
A method that will be available after add_flops_counting_methods() is called
on a desired net object.
Returns current mean flops consumption per image.
"""
batches_count = self.__batch_counter__
flops_sum = 0
params_sum = 0
for module in self.modules():
if is_supported_instance(module):
flops_sum += module.__flops__
params_sum = get_model_parameters_number(self)
return flops_sum / batches_count, params_sum
def start_flops_count(self, **kwargs):
"""
A method that will be available after add_flops_counting_methods() is called
on a desired net object.
Activates the computation of mean flops consumption per image.
Call it before you run the network.
"""
add_batch_counter_hook_function(self)
seen_types = set()
def add_flops_counter_hook_function(module, ost, verbose, ignore_list):
if type(module) in ignore_list:
seen_types.add(type(module))
if is_supported_instance(module):
module.__params__ = 0
elif is_supported_instance(module):
if hasattr(module, '__flops_handle__'):
return
if type(module) in CUSTOM_MODULES_MAPPING:
handle = module.register_forward_hook(CUSTOM_MODULES_MAPPING[type(module)])
else:
handle = module.register_forward_hook(MODULES_MAPPING[type(module)])
module.__flops_handle__ = handle
seen_types.add(type(module))
else:
if verbose and not type(module) in (nn.Sequential, nn.ModuleList) and not type(module) in seen_types:
print('Warning: module ' + type(module).__name__ + ' is treated as a zero-op.', file=ost)
seen_types.add(type(module))
self.apply(partial(add_flops_counter_hook_function, **kwargs))
def stop_flops_count(self):
"""
A method that will be available after add_flops_counting_methods() is called
on a desired net object.
Stops computing the mean flops consumption per image.
Call whenever you want to pause the computation.
"""
remove_batch_counter_hook_function(self)
self.apply(remove_flops_counter_hook_function)
def reset_flops_count(self):
"""
A method that will be available after add_flops_counting_methods() is called
on a desired net object.
Resets statistics computed so far.
"""
add_batch_counter_variables_or_reset(self)
self.apply(add_flops_counter_variable_or_reset)
# ---- Internal functions
def empty_flops_counter_hook(module, input, output):
module.__flops__ += 0
def upsample_flops_counter_hook(module, input, output):
output_size = output[0]
batch_size = output_size.shape[0]
output_elements_count = batch_size
for val in output_size.shape[1:]:
output_elements_count *= val
module.__flops__ += int(output_elements_count)
def relu_flops_counter_hook(module, input, output):
active_elements_count = output.numel()
module.__flops__ += int(active_elements_count)
def linear_flops_counter_hook(module, input, output):
input = input[0]
output_last_dim = output.shape[-1] # pytorch checks dimensions, so here we don't care much
module.__flops__ += int(np.prod(input.shape) * output_last_dim)
def pool_flops_counter_hook(module, input, output):
input = input[0]
module.__flops__ += int(np.prod(input.shape))
def bn_flops_counter_hook(module, input, output):
module.affine
input = input[0]
batch_flops = np.prod(input.shape)
if module.affine:
batch_flops *= 2
module.__flops__ += int(batch_flops)
def deconv_flops_counter_hook(conv_module, input, output):
# Can have multiple inputs, getting the first one
input = input[0]
batch_size = input.shape[0]
input_height, input_width = input.shape[2:]
kernel_height, kernel_width = conv_module.kernel_size
in_channels = conv_module.in_channels
out_channels = conv_module.out_channels
groups = conv_module.groups
filters_per_channel = out_channels // groups
conv_per_position_flops = kernel_height * kernel_width * in_channels * filters_per_channel
active_elements_count = batch_size * input_height * input_width
overall_conv_flops = conv_per_position_flops * active_elements_count
bias_flops = 0
if conv_module.bias is not None:
output_height, output_width = output.shape[2:]
bias_flops = out_channels * batch_size * output_height * output_height
overall_flops = overall_conv_flops + bias_flops
conv_module.__flops__ += int(overall_flops)
def conv_flops_counter_hook(conv_module, input, output):
# Can have multiple inputs, getting the first one
input = input[0]
batch_size = input.shape[0]
output_dims = list(output.shape[2:])
kernel_dims = list(conv_module.kernel_size)
in_channels = conv_module.in_channels
out_channels = conv_module.out_channels
groups = conv_module.groups
filters_per_channel = out_channels // groups
conv_per_position_flops = int(np.prod(kernel_dims)) * in_channels * filters_per_channel
active_elements_count = batch_size * int(np.prod(output_dims))
overall_conv_flops = conv_per_position_flops * active_elements_count
bias_flops = 0
if conv_module.bias is not None:
bias_flops = out_channels * active_elements_count
overall_flops = overall_conv_flops + bias_flops
conv_module.__flops__ += int(overall_flops)
def batch_counter_hook(module, input, output):
batch_size = 1
if len(input) > 0:
# Can have multiple inputs, getting the first one
input = input[0]
batch_size = len(input)
else:
pass
print('Warning! No positional inputs found for a module, assuming batch size is 1.')
module.__batch_counter__ += batch_size
def rnn_flops(flops, rnn_module, w_ih, w_hh, input_size):
# matrix matrix mult ih state and internal state
flops += w_ih.shape[0]*w_ih.shape[1]
# matrix matrix mult hh state and internal state
flops += w_hh.shape[0]*w_hh.shape[1]
if isinstance(rnn_module, (nn.RNN, nn.RNNCell)):
# add both operations
flops += rnn_module.hidden_size
elif isinstance(rnn_module, (nn.GRU, nn.GRUCell)):
# hadamard of r
flops += rnn_module.hidden_size
# adding operations from both states
flops += rnn_module.hidden_size*3
# last two hadamard product and add
flops += rnn_module.hidden_size*3
elif isinstance(rnn_module, (nn.LSTM, nn.LSTMCell)):
# adding operations from both states
flops += rnn_module.hidden_size*4
# two hadamard product and add for C state
flops += rnn_module.hidden_size + rnn_module.hidden_size + rnn_module.hidden_size
# final hadamard
flops += rnn_module.hidden_size + rnn_module.hidden_size + rnn_module.hidden_size
return flops
def rnn_flops_counter_hook(rnn_module, input, output):
"""
Takes into account batch goes at first position, contrary
to pytorch common rule (but actually it doesn't matter).
IF sigmoid and tanh are made hard, only a comparison FLOPS should be accurate
"""
flops = 0
inp = input[0] # input is a tuble containing a sequence to process and (optionally) hidden state
batch_size = inp.shape[0]
seq_length = inp.shape[1]
num_layers = rnn_module.num_layers
for i in range(num_layers):
w_ih = rnn_module.__getattr__('weight_ih_l' + str(i))
w_hh = rnn_module.__getattr__('weight_hh_l' + str(i))
if i == 0:
input_size = rnn_module.input_size
else:
input_size = rnn_module.hidden_size
flops = rnn_flops(flops, rnn_module, w_ih, w_hh, input_size)
if rnn_module.bias:
b_ih = rnn_module.__getattr__('bias_ih_l' + str(i))
b_hh = rnn_module.__getattr__('bias_hh_l' + str(i))
flops += b_ih.shape[0] + b_hh.shape[0]
flops *= batch_size
flops *= seq_length
if rnn_module.bidirectional:
flops *= 2
rnn_module.__flops__ += int(flops)
def rnn_cell_flops_counter_hook(rnn_cell_module, input, output):
flops = 0
inp = input[0]
batch_size = inp.shape[0]
w_ih = rnn_cell_module.__getattr__('weight_ih')
w_hh = rnn_cell_module.__getattr__('weight_hh')
input_size = inp.shape[1]
flops = rnn_flops(flops, rnn_cell_module, w_ih, w_hh, input_size)
if rnn_cell_module.bias:
b_ih = rnn_cell_module.__getattr__('bias_ih')
b_hh = rnn_cell_module.__getattr__('bias_hh')
flops += b_ih.shape[0] + b_hh.shape[0]
flops *= batch_size
rnn_cell_module.__flops__ += int(flops)
def add_batch_counter_variables_or_reset(module):
module.__batch_counter__ = 0
def add_batch_counter_hook_function(module):
if hasattr(module, '__batch_counter_handle__'):
return
handle = module.register_forward_hook(batch_counter_hook)
module.__batch_counter_handle__ = handle
def remove_batch_counter_hook_function(module):
if hasattr(module, '__batch_counter_handle__'):
module.__batch_counter_handle__.remove()
del module.__batch_counter_handle__
def add_flops_counter_variable_or_reset(module):
if is_supported_instance(module):
if hasattr(module, '__flops__') or hasattr(module, '__params__'):
print('Warning: variables __flops__ or __params__ are already '
'defined for the module' + type(module).__name__ +
' ptflops can affect your code!')
module.__flops__ = 0
module.__params__ = get_model_parameters_number(module)
CUSTOM_MODULES_MAPPING = {}
MODULES_MAPPING = {
# convolutions
nn.Conv1d: conv_flops_counter_hook,
nn.Conv2d: conv_flops_counter_hook,
nn.Conv3d: conv_flops_counter_hook,
# activations
nn.ReLU: relu_flops_counter_hook,
nn.PReLU: relu_flops_counter_hook,
nn.ELU: relu_flops_counter_hook,
nn.LeakyReLU: relu_flops_counter_hook,
nn.ReLU6: relu_flops_counter_hook,
# poolings
nn.MaxPool1d: pool_flops_counter_hook,
nn.AvgPool1d: pool_flops_counter_hook,
nn.AvgPool2d: pool_flops_counter_hook,
nn.MaxPool2d: pool_flops_counter_hook,
nn.MaxPool3d: pool_flops_counter_hook,
nn.AvgPool3d: pool_flops_counter_hook,
nn.AdaptiveMaxPool1d: pool_flops_counter_hook,
nn.AdaptiveAvgPool1d: pool_flops_counter_hook,
nn.AdaptiveMaxPool2d: pool_flops_counter_hook,
nn.AdaptiveAvgPool2d: pool_flops_counter_hook,
nn.AdaptiveMaxPool3d: pool_flops_counter_hook,
nn.AdaptiveAvgPool3d: pool_flops_counter_hook,
# BNs
nn.BatchNorm1d: bn_flops_counter_hook,
nn.BatchNorm2d: bn_flops_counter_hook,
nn.BatchNorm3d: bn_flops_counter_hook,
# FC
nn.Linear: linear_flops_counter_hook,
# Upscale
nn.Upsample: upsample_flops_counter_hook,
# Deconvolution
nn.ConvTranspose2d: deconv_flops_counter_hook,
# RNN
nn.RNN: rnn_flops_counter_hook,
nn.GRU: rnn_flops_counter_hook,
nn.LSTM: rnn_flops_counter_hook,
nn.RNNCell: rnn_cell_flops_counter_hook,
nn.LSTMCell: rnn_cell_flops_counter_hook,
nn.GRUCell: rnn_cell_flops_counter_hook
}
def is_supported_instance(module):
if type(module) in MODULES_MAPPING or type(module) in CUSTOM_MODULES_MAPPING:
return True
return False
def remove_flops_counter_hook_function(module):
if is_supported_instance(module):
if hasattr(module, '__flops_handle__'):
module.__flops_handle__.remove()
del module.__flops_handle__
Here is working code using the
,
ptflopspackage. You need to take care of the length of your input sequence. The pytorch doc for Conv1d reads:which lets you backtrace the input size you need from the first fully connected layer (see my comments in the model definition).
output: