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trajGRU.py
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trajGRU.py
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import torch
from torch import nn
from nowcasting.config import cfg
from nowcasting.models.model import activation
import torch.nn.functional as F
# input: B, C, H, W
# flow: [B, 2, H, W]
def wrap(input, flow):
B, C, H, W = input.size()
# mesh grid
xx = torch.arange(0, W).view(1, -1).repeat(H, 1).to(cfg.GLOBAL.DEVICE)
yy = torch.arange(0, H).view(-1, 1).repeat(1, W).to(cfg.GLOBAL.DEVICE)
xx = xx.view(1, 1, H, W).repeat(B, 1, 1, 1)
yy = yy.view(1, 1, H, W).repeat(B, 1, 1, 1)
grid = torch.cat((xx, yy), 1).float()
vgrid = grid + flow
# scale grid to [-1,1]
vgrid[:, 0, :, :] = 2.0 * vgrid[:, 0, :, :].clone() / max(W - 1, 1) - 1.0
vgrid[:, 1, :, :] = 2.0 * vgrid[:, 1, :, :].clone() / max(H - 1, 1) - 1.0
vgrid = vgrid.permute(0, 2, 3, 1)
output = torch.nn.functional.grid_sample(input, vgrid)
return output
class BaseConvRNN(nn.Module):
def __init__(self, num_filter, b_h_w,
h2h_kernel=(3, 3), h2h_dilate=(1, 1),
i2h_kernel=(3, 3), i2h_stride=(1, 1),
i2h_pad=(1, 1), i2h_dilate=(1, 1),
act_type=torch.tanh,
prefix='BaseConvRNN'):
super(BaseConvRNN, self).__init__()
self._prefix = prefix
self._num_filter = num_filter
self._h2h_kernel = h2h_kernel
assert (self._h2h_kernel[0] % 2 == 1) and (self._h2h_kernel[1] % 2 == 1), \
"Only support odd number, get h2h_kernel= %s" % str(h2h_kernel)
self._h2h_pad = (h2h_dilate[0] * (h2h_kernel[0] - 1) // 2,
h2h_dilate[1] * (h2h_kernel[1] - 1) // 2)
self._h2h_dilate = h2h_dilate
self._i2h_kernel = i2h_kernel
self._i2h_stride = i2h_stride
self._i2h_pad = i2h_pad
self._i2h_dilate = i2h_dilate
self._act_type = act_type
assert len(b_h_w) == 3
i2h_dilate_ksize_h = 1 + (self._i2h_kernel[0] - 1) * self._i2h_dilate[0]
i2h_dilate_ksize_w = 1 + (self._i2h_kernel[1] - 1) * self._i2h_dilate[1]
self._batch_size, self._height, self._width = b_h_w
self._state_height = (self._height + 2 * self._i2h_pad[0] - i2h_dilate_ksize_h)\
// self._i2h_stride[0] + 1
self._state_width = (self._width + 2 * self._i2h_pad[1] - i2h_dilate_ksize_w) \
// self._i2h_stride[1] + 1
self._curr_states = None
self._counter = 0
class TrajGRU(BaseConvRNN):
# b_h_w: input feature map size
def __init__(self, input_channel, num_filter, b_h_w, zoneout=0.0, L=5,
i2h_kernel=(3, 3), i2h_stride=(1, 1), i2h_pad=(1, 1),
h2h_kernel=(5, 5), h2h_dilate=(1, 1),
act_type=cfg.MODEL.RNN_ACT_TYPE):
super(TrajGRU, self).__init__(num_filter=num_filter,
b_h_w=b_h_w,
h2h_kernel=h2h_kernel,
h2h_dilate=h2h_dilate,
i2h_kernel=i2h_kernel,
i2h_pad=i2h_pad,
i2h_stride=i2h_stride,
act_type=act_type,
prefix='TrajGRU')
self._L = L
self._zoneout = zoneout
# 对应 wxz, wxr, wxh
# reset_gate, update_gate, new_mem
self.i2h = nn.Conv2d(in_channels=input_channel,
out_channels=self._num_filter*3,
kernel_size=self._i2h_kernel,
stride=self._i2h_stride,
padding=self._i2h_pad,
dilation=self._i2h_dilate)
# inputs to flow
self.i2f_conv1 = nn.Conv2d(in_channels=input_channel,
out_channels=32,
kernel_size=(5, 5),
stride=1,
padding=(2, 2),
dilation=(1, 1))
# hidden to flow
self.h2f_conv1 = nn.Conv2d(in_channels=self._num_filter,
out_channels=32,
kernel_size=(5, 5),
stride=1,
padding=(2, 2),
dilation=(1, 1))
# generate flow
self.flows_conv = nn.Conv2d(in_channels=32,
out_channels=self._L * 2,
kernel_size=(5, 5),
stride=1,
padding=(2, 2))
# 对应 hh, hz, hr，为 1 * 1 的卷积核
self.ret = nn.Conv2d(in_channels=self._num_filter*self._L,
out_channels=self._num_filter*3,
kernel_size=(1, 1),
stride=1)
# inputs: B*C*H*W
def _flow_generator(self, inputs, states):
if inputs is not None:
i2f_conv1 = self.i2f_conv1(inputs)
else:
i2f_conv1 = None
h2f_conv1 = self.h2f_conv1(states)
f_conv1 = i2f_conv1 + h2f_conv1 if i2f_conv1 is not None else h2f_conv1
f_conv1 = self._act_type(f_conv1)
flows = self.flows_conv(f_conv1)
flows = torch.split(flows, 2, dim=1)
return flows
# inputs 和 states 不同时为空
# inputs: S*B*C*H*W
def forward(self, inputs=None, states=None, seq_len=cfg.HKO.BENCHMARK.IN_LEN):
if states is None:
states = torch.zeros((inputs.size(1), self._num_filter, self._state_height,
self._state_width), dtype=torch.float).to(cfg.GLOBAL.DEVICE)
if inputs is not None:
S, B, C, H, W = inputs.size()
i2h = self.i2h(torch.reshape(inputs, (-1, C, H, W)))
i2h = torch.reshape(i2h, (S, B, i2h.size(1), i2h.size(2), i2h.size(3)))
i2h_slice = torch.split(i2h, self._num_filter, dim=2)
else:
i2h_slice = None
prev_h = states
outputs = []
for i in range(seq_len):
if inputs is not None:
flows = self._flow_generator(inputs[i, ...], prev_h)
else:
flows = self._flow_generator(None, prev_h)
wrapped_data = []
for j in range(len(flows)):
flow = flows[j]
wrapped_data.append(wrap(prev_h, -flow))
wrapped_data = torch.cat(wrapped_data, dim=1)
h2h = self.ret(wrapped_data)
h2h_slice = torch.split(h2h, self._num_filter, dim=1)
if i2h_slice is not None:
reset_gate = torch.sigmoid(i2h_slice[0][i, ...] + h2h_slice[0])
update_gate = torch.sigmoid(i2h_slice[1][i, ...] + h2h_slice[1])
new_mem = self._act_type(i2h_slice[2][i, ...] + reset_gate * h2h_slice[2])
else:
reset_gate = torch.sigmoid(h2h_slice[0])
update_gate = torch.sigmoid(h2h_slice[1])
new_mem = self._act_type(reset_gate * h2h_slice[2])
next_h = update_gate * prev_h + (1 - update_gate) * new_mem
if self._zoneout > 0.0:
mask = F.dropout2d(torch.zeros_like(prev_h), p=self._zoneout)
next_h = torch.where(mask, next_h, prev_h)
outputs.append(next_h)
prev_h = next_h
# return torch.cat(outputs), next_h
return torch.stack(outputs), next_h