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import os
import warnings
import cv2
import numpy as np
import torch
from einops import rearrange
from PIL import Image
from custom_controlnet_aux.util import resize_image_with_pad,common_input_validate, custom_hf_download, UNIMATCH_MODEL_NAME
from .utils.flow_viz import save_vis_flow_tofile, flow_to_image
from .unimatch.unimatch import UniMatch
import torch.nn.functional as F
from argparse import Namespace
def inference_flow(model,
image1, #np array of HWC
image2,
padding_factor=8,
inference_size=None,
attn_type='swin',
attn_splits_list=None,
corr_radius_list=None,
prop_radius_list=None,
num_reg_refine=1,
pred_bidir_flow=False,
pred_bwd_flow=False,
fwd_bwd_consistency_check=False,
device="cpu",
**kwargs
):
fixed_inference_size = inference_size
transpose_img = False
image1 = torch.from_numpy(image1).permute(2, 0, 1).float().unsqueeze(0).to(device)
image2 = torch.from_numpy(image2).permute(2, 0, 1).float().unsqueeze(0).to(device)
# the model is trained with size: width > height
if image1.size(-2) > image1.size(-1):
image1 = torch.transpose(image1, -2, -1)
image2 = torch.transpose(image2, -2, -1)
transpose_img = True
nearest_size = [int(np.ceil(image1.size(-2) / padding_factor)) * padding_factor,
int(np.ceil(image1.size(-1) / padding_factor)) * padding_factor]
# resize to nearest size or specified size
inference_size = nearest_size if fixed_inference_size is None else fixed_inference_size
assert isinstance(inference_size, list) or isinstance(inference_size, tuple)
ori_size = image1.shape[-2:]
# resize before inference
if inference_size[0] != ori_size[0] or inference_size[1] != ori_size[1]:
image1 = F.interpolate(image1, size=inference_size, mode='bilinear',
align_corners=True)
image2 = F.interpolate(image2, size=inference_size, mode='bilinear',
align_corners=True)
if pred_bwd_flow:
image1, image2 = image2, image1
results_dict = model(image1, image2,
attn_type=attn_type,
attn_splits_list=attn_splits_list,
corr_radius_list=corr_radius_list,
prop_radius_list=prop_radius_list,
num_reg_refine=num_reg_refine,
task='flow',
pred_bidir_flow=pred_bidir_flow,
)
flow_pr = results_dict['flow_preds'][-1] # [B, 2, H, W]
# resize back
if inference_size[0] != ori_size[0] or inference_size[1] != ori_size[1]:
flow_pr = F.interpolate(flow_pr, size=ori_size, mode='bilinear',
align_corners=True)
flow_pr[:, 0] = flow_pr[:, 0] * ori_size[-1] / inference_size[-1]
flow_pr[:, 1] = flow_pr[:, 1] * ori_size[-2] / inference_size[-2]
if transpose_img:
flow_pr = torch.transpose(flow_pr, -2, -1)
flow = flow_pr[0].permute(1, 2, 0).cpu().numpy() # [H, W, 2]
vis_image = flow_to_image(flow)
# also predict backward flow
if pred_bidir_flow:
assert flow_pr.size(0) == 2 # [2, H, W, 2]
flow_bwd = flow_pr[1].permute(1, 2, 0).cpu().numpy() # [H, W, 2]
vis_image = flow_to_image(flow_bwd)
flow = flow_bwd
return flow, vis_image
MODEL_CONFIGS = {
"gmflow-scale1": Namespace(
num_scales=1,
upsample_factor=8,
attn_type="swin",
feature_channels=128,
num_head=1,
ffn_dim_expansion=4,
num_transformer_layers=6,
attn_splits_list=[2],
corr_radius_list=[-1],
prop_radius_list=[-1],
reg_refine=False,
num_reg_refine=1
),
"gmflow-scale2": Namespace(
num_scales=2,
upsample_factor=4,
padding_factor=32,
attn_type="swin",
feature_channels=128,
num_head=1,
ffn_dim_expansion=4,
num_transformer_layers=6,
attn_splits_list=[2, 8],
corr_radius_list=[-1, 4],
prop_radius_list=[-1, 1],
reg_refine=False,
num_reg_refine=1
),
"gmflow-scale2-regrefine6": Namespace(
num_scales=2,
upsample_factor=4,
padding_factor=32,
attn_type="swin",
feature_channels=128,
num_head=1,
ffn_dim_expansion=4,
num_transformer_layers=6,
attn_splits_list=[2, 8],
corr_radius_list=[-1, 4],
prop_radius_list=[-1, 1],
reg_refine=True,
num_reg_refine=6
)
}
class UnimatchDetector:
def __init__(self, unimatch, config_args):
self.unimatch = unimatch
self.config_args = config_args
self.device = "cpu"
@classmethod
def from_pretrained(cls, pretrained_model_or_path=UNIMATCH_MODEL_NAME, filename="gmflow-scale2-regrefine6-mixdata.pth"):
model_path = custom_hf_download(pretrained_model_or_path, filename)
config_args = None
for key in list(MODEL_CONFIGS.keys())[::-1]:
if key in filename:
config_args = MODEL_CONFIGS[key]
break
assert config_args, f"Couldn't find hardcoded Unimatch config for {filename}"
model = UniMatch(feature_channels=config_args.feature_channels,
num_scales=config_args.num_scales,
upsample_factor=config_args.upsample_factor,
num_head=config_args.num_head,
ffn_dim_expansion=config_args.ffn_dim_expansion,
num_transformer_layers=config_args.num_transformer_layers,
reg_refine=config_args.reg_refine,
task='flow')
sd = torch.load(model_path, map_location="cpu")
model.load_state_dict(sd['model'])
return cls(model, config_args)
def to(self, device):
self.unimatch.to(device)
self.device = device
return self
def __call__(self, image1, image2, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", pred_bwd_flow=False, pred_bidir_flow=False, **kwargs):
assert image1.shape == image2.shape, f"[Unimatch] image1 and image2 must have the same size, got {image1.shape} and {image2.shape}"
image1, output_type = common_input_validate(image1, output_type, **kwargs)
#image1, remove_pad = resize_image_with_pad(image1, detect_resolution, upscale_method)
image2, output_type = common_input_validate(image2, output_type, **kwargs)
#image2, remove_pad = resize_image_with_pad(image2, detect_resolution, upscale_method)
with torch.no_grad():
flow, vis_image = inference_flow(self.unimatch, image1, image2, device=self.device, pred_bwd_flow=pred_bwd_flow, pred_bidir_flow=pred_bidir_flow, **vars(self.config_args))
if output_type == "pil":
vis_image = Image.fromarray(vis_image)
return flow, vis_image
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