pytorch和tensorflow计算Flops和params的详细过程

    net = model()  # 定义好的网络模型
    total = sum([param.nelement() for param in net.parameters()])
    print("Number of parameter: %.2fM" % total)

pip install thop # 安装thop库

import torch
from thop import profile
net = model()  # 定义好的网络模型
img1 = torch.randn(1, 3, 512, 512)
img2 = torch.randn(1, 3, 512, 512)
img3 = torch.randn(1, 3, 512, 512)
Macs, params = profile(net, (img1,img2,img3))
print('flops: ', 2*macs, 'params: ', params)

def get_flops_params():
    sess = tf.compat.v1.Session()
    graph = sess.graph
    flops = tf.compat.v1.profiler.profile(graph, options=tf.compat.v1.profiler.ProfileOptionBuilder.float_operation())
    params = tf.compat.v1.profiler.profile(graph,
                                           options=tf.compat.v1.profiler.ProfileOptionBuilder.trainable_variables_parameter())
    print('FLOPs: {};    Trainable params: {}'.fORMat(flops.total_float_ops, params.total_parameters))
def count2():
    print(np.sum([np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()]))
def get_nb_params_shape(shape):
    '''
    Computes the total number of params for a given shap.
    Works for any number of shapes etc [D,F] or [W,H,C] computes D*F and W*H*C.
    '''
    nb_params = 1
    for dim in shape:
        nb_params = nb_params * int(dim)
    return nb_params
def count3():
    tot_nb_params = 0
    for trainable_variable in tf.trainable_variables():
        shape = trainable_variable.get_shape()  # e.g [D,F] or [W,H,C]
        current_nb_params = get_nb_params_shape(shape)
        tot_nb_params = tot_nb_params + current_nb_params
    print(tot_nb_params)
import tensorflow.compat.v1 as tf
tf.compat.v1.disable_eager_execution()
from model import Model
import keras.backend as K
def get_flops(model):
    run_meta = tf.RunMetadata()
    opts = tf.profiler.ProfileOptionBuilder.float_operation()
    # We use the Keras session graph in the call to the profiler.
    flops = tf.profiler.profile(graph=K.get_session().graph,
                                run_meta=run_meta, cmd='op', options=opts)
    return flops.total_float_ops  # Prints the "flops" of the model.
# .... Define your model here ....
M = Model(BATCH_SIZE=1, INPUT_H=268, INPUT_W=360, is_training=False)
print(get_flops(M))