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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .update import BasicUpdateBlock, SmallUpdateBlock
from .extractor import BasicEncoder, SmallEncoder
from .corr import CorrBlock, AlternateCorrBlock
from .utils.utils import bilinear_sampler, coords_grid, upflow8
import argparse
from pathlib import Path
try:
autocast = torch.cuda.amp.autocast
except:
# dummy autocast for PyTorch < 1.6
class autocast:
def __init__(self, enabled):
pass
def __enter__(self):
pass
def __exit__(self, *args):
pass
class Dummy:
def __init__(self, enabled):
pass
def __enter__(self):
pass
def __exit__(self, *args):
pass
def get_args(cmd=None):
parser = argparse.ArgumentParser()
parser.add_argument('--corr_levels', type=int, default=4)
parser.add_argument('--corr_radius', type=int, default=4)
parser.add_argument('--dropout', type=float, default=0.0)
parser.add_argument('--mixed_precision', action='store_true')
parser.add_argument('--small', action='store_true')
parser.add_argument('--gpus', type=int, nargs='+', default=[0])
if cmd is None:
args = parser.parse_args()
else:
args = parser.parse_args(cmd)
return args
def load_raft_model(load_path,
ignore_prefix=None,
multiframe=False,
scale_inputs=False,
**kwargs):
path = Path(load_path) if load_path else None
args = get_args("")
for k,v in kwargs.items():
args.__setattr__(k,v)
args.multiframe = multiframe
args.scale_inputs = scale_inputs
model = RAFT(args)
if load_path is not None:
weight_dict = torch.load(load_path, map_location=torch.device("cpu"))
new_dict = dict()
for k in weight_dict.keys():
if 'module' in k:
new_dict[k.replace('module.', '')] = weight_dict[k]
else:
new_dict[k] = weight_dict[k]
if ignore_prefix is not None:
new_dict_1 = dict()
for k, v in new_dict.items():
new_dict_1[k.replace(ignore_prefix, '')] = v
new_dict = new_dict_1
did_load = model.load_state_dict(new_dict, strict=False)
print(did_load, type(model).__name__, load_path)
else:
print("created a new %s with %d parameters" % (
type(model).__name__,
sum([v.numel() for v in model.parameters()])))
return model
def get_raft_flow(x, raft_model, iters=24, backward=False, t_dim=1):
assert len(x.shape) == 5, x.shape
assert x.shape[t_dim] >= 2, x.shape
x = x * 255.0
inds = torch.tensor([0,1]).to(x.device)
x1, x2 = torch.index_select(x, t_dim, inds).unbind(t_dim)
if backward:
flow = raft_model(x2, x1, test_mode=True, iters=iters)[-1]
else:
flow = raft_model(x1, x2, test_mode=True, iters=iters)[-1]
return flow
class RAFT(nn.Module):
def __init__(self, args):
super(RAFT, self).__init__()
self.args = args
self.multiframe = self.args.multiframe
self.scale_inputs = self.args.scale_inputs
if args.small:
self.hidden_dim = hdim = 96
self.context_dim = cdim = 64
args.corr_levels = 4
args.corr_radius = 3
else:
self.hidden_dim = hdim = 128
self.context_dim = cdim = 128
args.corr_levels = 4
args.corr_radius = 4
if 'dropout' not in self.args:
self.args.dropout = 0
if 'alternate_corr' not in self.args:
self.args.alternate_corr = False
# feature network, context network, and update block
if args.small:
self.fnet = SmallEncoder(output_dim=128, norm_fn='instance', dropout=args.dropout)
self.cnet = SmallEncoder(output_dim=hdim+cdim, norm_fn='none', dropout=args.dropout)
self.update_block = SmallUpdateBlock(self.args, hidden_dim=hdim)
else:
self.fnet = BasicEncoder(output_dim=256, norm_fn='instance', dropout=args.dropout)
self.cnet = BasicEncoder(output_dim=hdim+cdim, norm_fn='batch', dropout=args.dropout)
self.update_block = BasicUpdateBlock(self.args, hidden_dim=hdim)
def freeze_bn(self):
for m in self.modules():
if isinstance(m, nn.BatchNorm2d):
m.eval()
def initialize_flow(self, img):
""" Flow is represented as difference between two coordinate grids flow = coords1 - coords0"""
N, C, H, W = img.shape
coords0 = coords_grid(N, H//8, W//8, device=img.device, dtype=img.dtype)
coords1 = coords_grid(N, H//8, W//8, device=img.device, dtype=img.dtype)
# optical flow computed as difference: flow = coords1 - coords0
return coords0, coords1
def upsample_flow(self, flow, mask):
""" Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination """
N, _, H, W = flow.shape
mask = mask.view(N, 1, 9, 8, 8, H, W)
mask = torch.softmax(mask, dim=2)
up_flow = F.unfold(8 * flow, [3,3], padding=1)
up_flow = up_flow.view(N, 2, 9, 1, 1, H, W)
up_flow = torch.sum(mask * up_flow, dim=2)
up_flow = up_flow.permute(0, 1, 4, 2, 5, 3)
return up_flow.reshape(N, 2, 8*H, 8*W)
@property
def iters(self):
if getattr(self, '_iters', None) is None:
return None
return self._iters
@iters.setter
def iters(self, value=None):
self._iters = value
def _forward_two_images(
self,
image1, image2,
iters=24, flow_init=None,
upsample=True, test_mode=True, **kwargs):
""" Estimate optical flow between pair of frames """
if self.iters is not None:
iters = self.iters
image1 = 2 * (image1 / 255.0) - 1.0
image2 = 2 * (image2 / 255.0) - 1.0
image1 = image1.contiguous()
image2 = image2.contiguous()
hdim = self.hidden_dim
cdim = self.context_dim
# run the feature network
decorator = autocast(enabled=True) if \
(self.args.mixed_precision or (image1.dtype in [torch.float16, torch.bfloat16])) \
else Dummy(enabled=False)
with decorator:
fmap1, fmap2 = self.fnet([image1, image2])
fmap1 = fmap1.float()
fmap2 = fmap2.float()
if self.args.alternate_corr:
corr_fn = AlternateCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
else:
corr_fn = CorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
# run the context network
# with autocast(enabled=self.args.mixed_precision):
with decorator:
cnet = self.cnet(image1)
net, inp = torch.split(cnet, [hdim, cdim], dim=1)
net = torch.tanh(net)
inp = torch.relu(inp)
coords0, coords1 = self.initialize_flow(image1)
if flow_init is not None:
coords1 = coords1 + flow_init
flow_predictions = []
for itr in range(iters):
coords1 = coords1.detach()
corr = corr_fn(coords1) # index correlation volume
flow = coords1 - coords0
# with autocast(enabled=self.args.mixed_precision):
with decorator:
net, up_mask, delta_flow, motion_features = self.update_block(net, inp, corr, flow)
# F(t+1) = F(t) + \Delta(t)
coords1 = coords1 + delta_flow
# upsample predictions
if up_mask is None:
flow_up = upflow8(coords1 - coords0)
else:
flow_up = self.upsample_flow(coords1 - coords0, up_mask)
flow_predictions.append(flow_up)
if test_mode:
return coords1 - coords0, flow_up, motion_features
return flow_predictions, motion_features
def forward(self, *args, **kwargs):
if not self.multiframe:
return self._forward_two_images(*args, **kwargs)
x = (args[0] * 255.0) if self.scale_inputs else args[0]
assert len(x.shape) == 5, x.shape
assert x.shape[1] >= 2, x.shape
num_frames = x.size(1)
flows = []
motion_features = []
backward = kwargs.get('backward', False)
for t in range(num_frames-1):
x1, x2 = torch.index_select(
x, 1, torch.tensor([t,t+1]).to(x.device)).unbind(1)
_args = (x2, x1) if backward else (x1, x2)
_, flow, features = self._forward_two_images(*_args, *args[1:], **kwargs)
flows.insert(0, flow) if backward else flows.append(flow)
motion_features.append(features)
return torch.stack(flows, 1), torch.stack(motion_features, 1)
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