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model_1440_721_cn.py
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import torch
from torch import nn
import torch.optim as optim
import torch.nn.functional as F
from timm.models.layers import DropPath, trunc_normal_
import math
import numpy as np
from model2023.modules.vit import VisionTransformer
class Model_x(nn.Module):
def __init__(self, in_shape, out_ch, hid_S=32, hid_T=256, N_S=4, N_T=4, model_type='gSTA',
mlp_ratio=8., drop=0.1, drop_path=0.0, spatio_kernel_enc=3,
spatio_kernel_dec=3, act_inplace=True, args=None, **kwargs):
super(Model_x, self).__init__()
B, T, C, H, W = in_shape # T is input_time_length
self.args = args
self.hid_S = hid_S
self.out_ch = out_ch
self.H_d, self.W_d = args.H_d, args.W_d #
# Encoder, Decoder
act_inplace = False
self.enc = Encoder(C, hid_S, N_S, spatio_kernel_enc, H, W, self.H_d, self.W_d, act_inplace=act_inplace) # C_in, C_hid, N_S, spatio_kernel, act_inplace=True
self.dec = Decoder(hid_S, out_ch, N_S, spatio_kernel_dec, H_tar=args.tar_size, W_tar=args.tar_size, H_d=self.H_d, W_d=self.W_d,
act_inplace=act_inplace) # 1 means the total_precipitation_6hr var
self.time_embedding = nn.Sequential(
nn.Linear(args.input_time_length*args.time_emb_num, 128),
nn.LeakyReLU(),
nn.Linear(128, 256), # hid_S ***
nn.LeakyReLU(),
nn.Linear(256, int(hid_S*0.5))
)
# prediction model
scale_fac = args.input_time_length
num_block = 12
N_S_pred = 4
norm_band_num = 4 # in self attention
patch_size = 4 # in self attention
step = 2
self.hid = [nn.Sequential(
Encoder(int(hid_S * (T+0.5)), scale_fac * hid_S, N_S_pred, spatio_kernel_enc, H=self.H_d, W=self.W_d,
H_d=self.H_d, W_d=self.W_d, act_inplace=act_inplace),
VisionTransformer([self.H_d, self.W_d], patch_size=[4,4], inp_chans=scale_fac*hid_S, out_chans=scale_fac*hid_S, # from makani
embed_dim=768, depth=8, num_heads=12, mlp_ratio=4., qkv_bias=True, mlp_drop_rate=0.0,
attn_drop_rate=0.0, path_drop_rate=0.0, norm_layer="layer_norm", comm_inp_name="fin",
comm_hidden_name="fout"),
Decoder(scale_fac * hid_S, hid_S, N_S_pred, spatio_kernel_dec, H_tar=self.H_d, W_tar=self.W_d, H_d=self.H_d,
W_d=self.W_d, act_inplace=act_inplace))
for i in range(len(self.args.time_inte))] # args.drop
self.hid = nn.ModuleList(self.hid)
def forward(self, x_raw, const_data, time_data, labels, diff_ori=None, aft_seq_length=1, hid_i=0, shrink=1, mode='train', device=None, **kwargs):
# print(x_raw.shape)
B, T, C, H, W = x_raw.shape
x = x_raw.flatten(0,1)
embed = self.enc(x)
# print(embed.shape)
_, C_, H_, W_ = embed.shape
if mode == 'train':
embed_label = self.enc(labels.detach().flatten(0,1)).unflatten(0, [B, aft_seq_length])
label_pred = self.dec(embed).reshape((B, T, -1, H, W))
embed_diff = None
embed_label_dec = self.dec(embed_label.view(-1, self.hid_S, self.H_d, self.W_d)).view(B, aft_seq_length, -1, H, W)
# embed_label, label_pred, embed_diff, embed_tmp, embed_label_dec = None, None, None, None, None
embed_tmp = embed.view(B, self.args.input_time_length, self.hid_S, self.H_d, self.W_d)
else:
embed_label, label_pred, embed_diff, embed_tmp, embed_label_dec = None, None, None, None, None
z = embed.view(B, -1, H_, W_)#.gather()
if mode == 'train':
hid = self._predict(z, time_data, aft_seq_length, hid_i=hid_i, shrink=shrink, mode=mode,
device=device)
else:
hid = self._predict_pangu(z, time_data, aft_seq_length, shrink=shrink, mode=mode,
device=device)
# print(hid.shape)
hid = hid.unflatten(1, [aft_seq_length, self.hid_S]).flatten(0,1)
Y = self.dec(hid) # , self.res_conv(skip).reshape(B*self.args.aft_seq_length, -1, H_, W_)
Y = Y.unflatten(0, [B, aft_seq_length])
return Y, hid.unflatten(0, [B, aft_seq_length]), embed_label, label_pred, \
embed_tmp, embed_diff, embed_label_dec
def _predict_pangu(self, cur_seq, cur_time_data, aft_seq_length, shrink=False, mode='val', device=None, **kwargs):
# pred_len means the var length that the model would predict
# fast version
pred_y = []
re_time_inte = self.args.time_inte[::-1]
tmp_arr = np.arange(self.args.time_inte[-1])
tmp_list = list(tmp_arr+1)
tmp_list.insert(0, self.args.time_inte[-1])
map_dict_tmp = dict(zip(tmp_arr, np.array(tmp_list[:-1]))) # *************
tmp_last_lat_fea = None
for pred_i in range(1, aft_seq_length+1):
if pred_i <=4 :
iter_idx_num = 0
pred_i_tmp = pred_i
cur_seq_tmp = cur_seq.clone()
else:
iter_idx_num = (pred_i // self.args.time_inte[-1]) * self.args.time_inte[-1] if \
pred_i % self.args.time_inte[-1] != 0 else ((pred_i-1) // self.args.time_inte[-1]) * self.args.time_inte[-1]
pred_i_tmp = map_dict_tmp[pred_i % self.args.time_inte[-1]]
cur_seq_tmp = tmp_last_lat_fea
iter_num = []
for i in reversed(self.args.time_inte):
iter_num.append(pred_i_tmp // i)
pred_i_tmp = pred_i_tmp % i
# print(iter_num)
for j in range(len(iter_num)):
if iter_num[j] != 0 and j == 0:
# print(f'predict model {len(iter_num)-j-1} iterate {iter_num[j]}')
for k in range(iter_num[j]):
tmp_last_lat_fea = self.forward_recur(cur_seq_tmp,
cur_time_data[:, :, iter_idx_num:iter_idx_num+self.args.input_time_length]
, hid_i=len(iter_num)-j-1)
cur_seq_tmp = tmp_last_lat_fea.clone()
iter_idx_num += self.args.input_time_length * re_time_inte[j]
elif iter_num[j] != 0 and j != 0:
for k in range(iter_num[j]):
cur_seq_tmp = self.forward_recur(cur_seq_tmp,
cur_time_data[:, :, iter_idx_num:iter_idx_num + self.args.input_time_length]
, hid_i=len(iter_num) - j - 1)
iter_idx_num += self.args.input_time_length * re_time_inte[j]
pred_y.append(cur_seq_tmp) # **********************************
pred_y = torch.cat(pred_y, dim=1)
return pred_y
def _predict(self, cur_seq, cur_time_data, aft_seq_length, hid_i=0, batch_y=None, shrink=0, mode='val', device=None, **kwargs):
"""Forward the model"""
if aft_seq_length == self.args.pre_seq_length:
pred_y = self.forward_recur(cur_seq,
cur_time_data[:, :, 0*self.args.pre_seq_length:self.args.input_time_length+0*self.args.pre_seq_length], hid_i=hid_i)
elif aft_seq_length < self.args.pre_seq_length:
pred_y = self.forward_recur(cur_seq, cur_time_data)
pred_y = pred_y[:, :aft_seq_length]
elif aft_seq_length > self.args.pre_seq_length:
pred_y = []
d = aft_seq_length // self.args.pre_seq_length
m = aft_seq_length % self.args.pre_seq_length
for i in range(d):
# print(i)
if shrink: # means the output length is shorter than the input
if mode == 'train':
cur_seq = cur_seq + (torch.randn(size=cur_seq.size(), dtype=torch.float32)/100.0).type(torch.float32).to(device) # ablation
temp_out = self.forward_recur(cur_seq,
cur_time_data[:, :, i*self.args.pre_seq_length:i*self.args.pre_seq_length+self.args.input_time_length], hid_i=hid_i)
cur_seq = torch.cat([cur_seq[:, -(cur_seq.shape[1]-temp_out.shape[1]):, ...], temp_out], 1)
pred_y.append(temp_out)
else:
if mode == 'train':
cur_seq = cur_seq + (torch.randn(size=cur_seq.size(), dtype=torch.float32)/100.0).type(torch.float32).to(device) # ablation
cur_seq = self.forward_recur(cur_seq,
cur_time_data[:, :, i*self.args.input_time_length:(i+1)*self.args.input_time_length], hid_i=hid_i) # assume that the length of input and output of the model is same
pred_y.append(cur_seq)
if m != 0:
cur_seq = self.forward_recur(cur_seq, cur_time_data[:, :, -(m+self.args.input_time_length+self.args.pre_seq_length):-m])
pred_y.append(cur_seq[:, :m])
pred_y = torch.cat(pred_y, dim=1)
return pred_y
def forward_recur(self, x, time_data, hid_i=0, **kwargs): # const_emb coube be a const, but the time is changed
B, C, H, W = x.shape
# print(x.shape)
x_res = x[:, -self.hid_S:, ...]
time_emb = self.time_embedding(time_data.reshape(B, -1))[..., None, None].repeat(1, 1, H, W)#reshape(B, -1, H_, W_)#.contigous().view(B, C_, H_, W_)
Y = self.hid[hid_i](torch.cat([x, time_emb], 1))
# print(Y.shape)
return Y + x_res
class BasicConv2d(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=0,
dilation=1,
upsampling=False,
act_norm=False,
act_inplace=True):
super(BasicConv2d, self).__init__()
self.act_norm = act_norm
self.in_channels = in_channels
if upsampling is True:
self.conv = nn.Sequential(*[
nn.Conv2d(in_channels, out_channels*4, kernel_size=kernel_size,
stride=1, padding=padding, dilation=dilation, padding_mode='circular'),
nn.PixelShuffle(2)
])
else:
self.conv = nn.Conv2d(
in_channels, out_channels, kernel_size=kernel_size,
stride=stride, padding=padding, dilation=dilation, padding_mode='circular')
self.norm = nn.GroupNorm(2, out_channels) # group number: 2
# self.norm = nn.BatchNorm2d(out_channels)
self.act = nn.SiLU(inplace=act_inplace)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, (nn.Conv2d)):
trunc_normal_(m.weight, std=.02) # math.sqrt(2.0 / self.in_channels)
nn.init.constant_(m.bias, 0)
def forward(self, x):
y = self.conv(x)
if self.act_norm:
y = self.act(self.norm(y))
return y # try more conv and resnet
class ConvSC(nn.Module):
def __init__(self,
C_in,
C_out,
kernel_size=3,
downsampling=False,
upsampling=False,
act_norm=True,
act_inplace=True):
super(ConvSC, self).__init__()
stride = 2 if downsampling is True else 1
padding = (kernel_size - stride + 1) // 2
self.conv = BasicConv2d(C_in, C_out, kernel_size=kernel_size, stride=stride,
upsampling=upsampling, padding=padding,
act_norm=act_norm, act_inplace=act_inplace)
self.conv2 = BasicConv2d(C_in, C_out, kernel_size=5, stride=stride,
upsampling=upsampling, padding=2,
act_norm=act_norm, act_inplace=act_inplace)
self.cat_conv = nn.Conv2d(C_out*2, C_out, 1)
self.res_conv = nn.Conv2d(C_in, C_out, kernel_size=3, stride=1, padding=1,
padding_mode='circular') if C_in != C_out else nn.Identity()
def forward(self, x):
# x0 = x.clone()
y = self.conv(x)
y2 = self.conv2(x)
return self.cat_conv(torch.cat([y, y2], 1)) + self.res_conv(x)
def sampling_generator(N, reverse=False):
samplings = [False, False] * (N // 2)
if reverse: return list(reversed(samplings[:N]))
else: return samplings[:N]
class Encoder(nn.Module):
def __init__(self, C_in, C_hid, N_S, spatio_kernel, H=64, W=32, H_d=120, W_d=60, act_inplace=True, tar_dim=None):
samplings = sampling_generator(N_S)
# print(samplings)
super(Encoder, self).__init__()
self.tar_dim = tar_dim
mid_ch = (C_hid + C_in) // 2 if ((C_hid + C_in) // 2) % 2 == 0 else (C_hid + C_in) // 2 + 1
if tar_dim is not None:
self.corr_linear = nn.Sequential(
nn.Linear(C_in, C_in//2),
nn.SiLU(),
nn.Linear(C_in//2, C_in),
nn.Sigmoid())
self.enc = nn.Sequential(
ConvSC(C_in, mid_ch, spatio_kernel, downsampling=samplings[0],
act_inplace=act_inplace),
ConvSC(mid_ch, C_hid, spatio_kernel, downsampling=samplings[1],
act_inplace=act_inplace),
*[ConvSC(C_hid, C_hid, spatio_kernel, downsampling=s,
act_inplace=act_inplace) for s in samplings[2:]]
)
self.H, self.W, self.H_d, self.W_d = H, W, H_d, W_d
print('self.H, self.W, self.H_d, self.W_d', self.H, self.W, self.H_d, self.W_d)
if H != H_d and W != W_d:
self.pre_process0 = nn.Conv2d(C_hid, C_hid, kernel_size=3, padding=1, padding_mode='circular', groups=C_hid,
stride=int(H/H_d), dilation=int((H/H_d)/2))
self.pre_process1 = nn.Conv2d(C_hid, C_hid, kernel_size=3, padding=1, padding_mode='circular', groups=C_hid)
else:
self.pre_process0 = nn.Conv2d(C_hid, C_hid, kernel_size=3, padding=1, padding_mode='circular', groups=C_hid,
stride=1, dilation=int((H/H_d)/2))
self.pre_process1 = nn.Conv2d(C_hid, C_hid, kernel_size=3, padding=1, padding_mode='circular', groups=C_hid)
def forward(self, x): # B*4, 3, 128, 128
# print('x.shape', x.shape)
B, C, H, W = x.shape
if self.tar_dim is not None:
corr_mat = cross_att_matrix(x.view(B, C, -1), tar_dim=self.tar_dim)
# print(corr_mat.shape)
x = x * self.corr_linear(corr_mat)[..., None, None]
latent = self.enc[0](x)
for i in range(1, len(self.enc)):
latent = self.enc[i](latent)
# if self.H != self.H_d and self.W != self.W_d:
# print(latent.shape)
latent = self.pre_process0(latent)
# print(latent.shape)
latent = F.interpolate(latent, size=[self.H_d, self.W_d], mode='bilinear')
# print(latent.shape)
latent = self.pre_process1(latent)
# print(latent.shape)
return latent #, enc1
class Decoder(nn.Module):
def __init__(self, C_hid, C_out, N_S, spatio_kernel, H_tar=145, W_tar=145, H_d=64, W_d=64, act_inplace=True):
samplings = sampling_generator(N_S, reverse=True)
super(Decoder, self).__init__()
mid_ch = (C_hid+C_out)//2 if ((C_hid+C_out)//2) % 2 ==0 else (C_hid+C_out)//2 + 1
self.H, self.W = H_tar, W_tar
self.H_d, self.W_d = H_d, W_d
if self.H != self.H_d or self.W != self.W_d:
self.pre_process0 = nn.Conv2d(C_hid, C_hid, kernel_size=3, padding=1, padding_mode='circular', groups=C_hid)
self.dec = nn.Sequential(
*[ConvSC(C_hid, C_hid, spatio_kernel, upsampling=s,
act_inplace=act_inplace) for s in samplings[:-1]],
ConvSC(C_hid, mid_ch, spatio_kernel, upsampling=samplings[-1],
act_inplace=act_inplace)
)
self.readout = nn.Conv2d(mid_ch, C_out, 3, padding=1, padding_mode='circular')
def forward(self, hid, enc1=None):
if self.H != self.H_d or self.W != self.W_d:
hid = F.interpolate(hid, size=[self.H, self.W], mode='bilinear')
hid = self.pre_process0(hid)
for i in range(0, len(self.dec)-1):
hid = self.dec[i](hid)
# print(hid.shape)
# print(enc1.shape)
Y = self.dec[-1](hid) # + enc1
Y = self.readout(Y)
return Y
class Decoder_recon(nn.Module):
"""Decoder"""
def __init__(self, C_hid, out_era5_ch, N_S, spatio_kernel, H=64, W=64, out_era5_size=145, act_inplace=True, min_rain=0.1, device=None):
samplings = sampling_generator(N_S, reverse=True)
super(Decoder_recon, self).__init__()
self.min_rain = min_rain
self.era5_size = out_era5_size
self.dec = nn.Sequential(
*[ConvSC(C_hid, C_hid, spatio_kernel, upsampling=s,
act_inplace=act_inplace) for s in samplings[:-1]],
ConvSC(C_hid, C_hid, spatio_kernel, upsampling=samplings[-1],
act_inplace=act_inplace)
)
self.readout_era5 = nn.Sequential(
nn.Conv2d(C_hid, (out_era5_ch+C_hid)//2, 3, padding=1, padding_mode='circular'),
nn.LeakyReLU(),
nn.Conv2d((out_era5_ch+C_hid)//2, out_era5_ch, 3, padding=1, padding_mode='circular'))
def forward(self, hid, enc1=None):
# print(pred_mean.size())
# print(hid.size())
for i in range(0, len(self.dec)-1):
hid = self.dec[i](hid)
Y = self.dec[-1](hid) # + enc1
recon_era5 = F.interpolate(Y, size=[self.era5_size, self.era5_size], mode='bilinear')
recon_era5 = self.readout_era5(recon_era5) # pred_class的为降雨发生与否的概率大小值
# print('self.scale', self.scale)
# print(f'Decoder', torch.max(-self.scale*Y), torch.min(-self.scale*Y))
# mask = (1 - (Y >= self.exp_b).type(torch.int)) # no rain=0
# print(self.exp_b)
return recon_era5#, recon_fy4b # -np.log(self.min_rain) - Y - exp_b 考虑到 exp_b的空间变化