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(5) fastai application

2022-07-31 00:22:00 【_helen_520】

目前：fastai lesson8~lesson11parts have been refactored

mnist数据集比较简单、28×28的像素,都是一样的.The background is also relatively clean,It is also a classification task,It can be handled with a simple network.

1. 使用线性模型

# 在这里对mnistThe dataset is classified,实现acc的提升
from exp.nb_09c import *
""" 0.数据准备
        I didn't write it myselfDataBunch,ItemList等接口.ImageList的get是要去open的
    mnist走的还是pytorch的Dataloader的接口
"""

x_train,y_train,x_valid,y_valid = get_data() # 这个函数在nb_02.py中定义
x_train,x_valid = normalize_to(x_train,x_valid)  # nb_05.py中
n,m = x_train.shape
c = y_train.max().item() + 1
bs = 512

# 使用Dataset来管理batch数据： nb_03.py
train_ds,valid_ds = Dataset(x_train, y_train),Dataset(x_valid, y_valid)
# nb_08.py  get_dls在nb_03.py,使用的是Dataloader
data = DataBunch(*get_dls(train_ds, valid_ds, bs), c)

loss_func = F.cross_entropy

""" 1. 线性模型（50,10）,使用pytorch的nn.Module基类,not refactored
"""
nh = 50

def init_linear_(m, f):
    if isinstance(m, nn.Linear):
        f(m.weight, a=0.1)
        if getattr(m, 'bias', None) is not None: m.bias.data.zero_()
    for l in m.children(): init_linear_(l, f)

def init_linear(m, uniform=False):
    f = init.kaiming_uniform_ if uniform else init.kaiming_normal_
    init_linear_(m, f)

# ① model,Because it is a custom linear model,没有初始化
model = nn.Sequential(nn.Linear(m, nh), nn.ReLU(), nn.Linear(nh, c))

lr = 0.5
# get_runner nb_06.py 由于不是CNN网络,所以不是get_cnn_runner
# 使用get_runner而不是get_learner
# device = torch.device('cuda', 0)
# torch.cuda.set_device(device)
cbfs = [partial(AvgStatsCallback, accuracy), CudaCallback, Recorder, ProgressCallback]

phases = combine_scheds([0.3, 0.7], cos_1cycle_anneal(0.2, 0.6, 0.2))
sched = ParamScheduler('lr', phases)

# Learner在nb_09b.py   线性模型、交叉熵loss、lr、cbfs、opt  在Learner.fit中有opt的初始化函数的.
# ② 优化器 nb_09b.py 简单的sgd梯度下降,weight_decay是l2正则化
learn = Learner(model=model, data=data, loss_func=loss_func, lr=lr, cb_funcs=cbfs)

# 可以在fit的时候添加一个cbs

# sgd: p = p - lr*p.grad 
# weight_decay: p = p * ( 1 - lr*wd)
def append_stats(hook, mod, inp, outp):
    if not hasattr(hook,'stats'): hook.stats = ([],[],[])
    means,stds,hists = hook.stats
    means.append(outp.data.mean().cpu()) # The value of the activation element
    stds .append(outp.data.std().cpu())
    hists.append(outp.data.cpu().histc(40,0,10)) #histc isn't implemented on the GPU

def get_hist(h): 
    return torch.stack(h.stats[2]).t().float().log1p()  # h.stats[2]为直方图

with Hooks(model, append_stats) as hooks:
    learn.fit(1)    # pytorch_init + sgd
    fig, [ax0, ax1] = plt.subplots(1,2, figsize=(10,4))
    for h in hooks:
        ms, ss, hi = h.stats
        ax0.plot(ms), ax0.set_title("act_means", loc='center'), ax0.set_xlabel('batches')
        ax0.legend(range(3))
        ax1.plot(ss), ax1.set_title("act_stds", loc='center'), ax1.set_xlabel('batches')
        ax1.legend(range(3))

fig,axes = plt.subplots(2,2, figsize=(15,6))
for ax,h in zip(axes.flatten(), hooks[:3]):
    ax.imshow(get_hist(h), origin='lower'), ax.set_title("acts_hist", loc='center'), ax.set_xlabel('activiations')
    ax.axis('off')
plt.tight_layout()

def get_min(h): # Add up the first two numbers of the histogram
    h1 = torch.stack(h.stats[2]).t().float()
    return h1[:2].sum(0)/h1.sum(0)

fig,axes = plt.subplots(2,2, figsize=(15,6))
for ax,h in zip(axes.flatten(), hooks[:3]):
    ax.plot(get_min(h)), ax.set_title("hist[:2] zero ratio", loc='center'), plt.xlabel('batches')
    ax.set_ylim(0,1)
plt.tight_layout()