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import numpy as np
from physion_evaluator.feature_extract_interface import PhysionFeatureExtractor
from physion_evaluator.utils import DataAugmentationForVideoMAE
from torch.functional import F
from cwm.eval.Flow.flow_utils import get_occ_masks
from cwm.model.model_factory import model_factory
import torch
def load_predictor(
model_func_,
load_path_,
**kwargs):
predictor = model_func_(**kwargs).eval().requires_grad_(False)
did_load = predictor.load_state_dict(
torch.load(load_path_, map_location=torch.device("cpu"))['model'])
predictor._predictor_load_path = load_path_
print(did_load, load_path_)
return predictor
class CWM(PhysionFeatureExtractor):
def __init__(self, model_name, aggregate_embeddings=False):
super().__init__()
self.model = model_factory.load_model(model_name).cuda().half()
self.num_frames = self.model.num_frames
self.timestamps = np.arange(self.num_frames)
ps = (224 // self.model.patch_size[1]) ** 2
self.bool_masked_pos = np.zeros([ps * self.num_frames])
self.bool_masked_pos[ps * (self.num_frames - 1):] = 1
self.ps = ps
self.aggregate_embeddings = aggregate_embeddings
def transform(self):
return DataAugmentationForVideoMAE(
imagenet_normalize=True,
rescale_size=224,
), 150, 4
def fwd(self, videos):
bool_masked_pos = torch.tensor(self.bool_masked_pos).to(videos.device).unsqueeze(0).bool()
bool_masked_pos = torch.cat([bool_masked_pos] * videos.shape[0])
x_encoded = self.model(videos.half(), bool_masked_pos, forward_full=True,
return_features=True)
return x_encoded
def extract_features(self, videos, for_flow=False):
'''
videos: [B, T, C, H, W], T is usually 4 and videos are normalized with imagenet norm
returns: [B, T, D] extracted features
'''
videos = videos.transpose(1, 2)
all_features = []
# repeat the last frame of the video
videos = torch.cat([videos, videos[:, :, -1:]], dim=2)
for x in range(0, 4, self.num_frames - 1):
vid = videos[:, :, x:x + self.num_frames, :, :]
all_features.append(self.fwd(vid))
if self.aggregate_embeddings:
feats = all_features[-1].mean(dim=1, keepdim=True)
all_features[-1] = feats
# feats = feats.view(feats.shape[0], -1, self.model.num_patches_per_frame, feats.shape[-1])
# feats = feats.mean(dim=2)
# all_features[-1] = feats
x_encoded = torch.cat(all_features, dim=1)
return x_encoded
class CWM_Keypoints(PhysionFeatureExtractor):
def __init__(self, model_name):
super().__init__()
self.model = model_factory.load_model(model_name).cuda().half()
self.frames = [[0, 1, 2], [1, 2, 3]]
self.num_frames = self.model.num_frames
self.ps = (224 // self.model.patch_size[1]) ** 2
self.bool_masked_pos = np.zeros([self.ps * self.num_frames])
self.bool_masked_pos[self.ps * (self.num_frames - 1):] = 1
self.frame_gap = 150
self.num_frames_dataset = 4
self.res = 224
def transform(self):
return DataAugmentationForVideoMAE(
imagenet_normalize=True,
rescale_size=self.res,
), self.frame_gap, self.num_frames_dataset
def fwd(self, videos):
bool_masked_pos = torch.tensor(self.bool_masked_pos).to(videos.device).unsqueeze(0).bool()
bool_masked_pos = torch.cat([bool_masked_pos] * videos.shape[0])
_, x_encoded = self.model(videos.half(), bool_masked_pos, forward_full=True,
return_features=True)
return x_encoded
def extract_features(self, videos, segments=None):
'''
videos: [B, T, C, H, W], T is usually 4 and videos are normalized with imagenet norm
returns: [B, T, D] extracted features
'''
videos = videos.transpose(1, 2)
all_features = []
for x, arr in enumerate(self.frames):
#use the downsampled videos for keypoints
vid = videos[:, :, arr, :, :].half()
frame0 = vid[:, :, 0]
frame1 = vid[:, :, 1]
frame2 = vid[:, :, 2]
#extract features from the video frames frame0 and frame1 and include features at keypoint regions of frame2
mask, choices, err_array, k_feat, keypoint_recon = self.model.get_keypoints(frame0, frame1, frame2, 10, 1)
#reshape the features to [batch size, num_features]
k_feat = k_feat.view(k_feat.shape[0], -1)
all_features.append(k_feat)
x_encoded = torch.cat(all_features, dim=1)
return x_encoded
class CWM_KeypointsFlow(PhysionFeatureExtractor):
def __init__(self, model_name):
super().__init__()
self.model = model_factory.load_model(model_name).cuda().half()
self.frames = [[0, 3, 6], [3, 6, 9], [6, 9, 9]]
self.num_frames = self.model.num_frames
self.timestamps = np.arange(self.num_frames)
self.ps = (224 // self.model.patch_size[1]) ** 2
self.bool_masked_pos = np.zeros([self.ps * self.num_frames])
self.bool_masked_pos[self.ps * (self.num_frames - 1):] = 1
self.frame_gap = 50
self.num_frames_dataset = 9
self.res = 512
def transform(self):
return DataAugmentationForVideoMAE(
imagenet_normalize=True,
rescale_size=self.res,
), self.frame_gap, self.num_frames_dataset
def fwd(self, videos):
bool_masked_pos = torch.tensor(self.bool_masked_pos).to(videos.device).unsqueeze(0).bool()
bool_masked_pos = torch.cat([bool_masked_pos] * videos.shape[0])
_, x_encoded = self.model(videos.half(), bool_masked_pos, forward_full=True,
return_features=True)
return x_encoded
def get_forward_flow(self, videos):
fid = 6
forward_flow = self.model.get_flow(videos[:, :, fid], videos[:, :, fid + 1], conditioning_img=videos[:, :, fid + 2], mode='cosine')
backward_flow = self.model.get_flow(videos[:, :, fid + 1], videos[:, :, fid], conditioning_img=videos[:, :, fid - 1], mode='cosine')
occlusion_mask = get_occ_masks(forward_flow, backward_flow)[0]
forward_flow = forward_flow * occlusion_mask
forward_flow = torch.stack([forward_flow, forward_flow, forward_flow], dim=1)
forward_flow = forward_flow.to(videos.device)
forward_flow = F.interpolate(forward_flow, size=(2, 224, 224), mode='nearest')
return forward_flow
def extract_features(self, videos, segments=None):
'''
videos: [B, T, C, H, W], T is usually 4 and videos are normalized with imagenet norm
returns: [B, T, D] extracted features
Note:
For efficiency, the optical flow is computed and added for a single frame (300ms) as we found this to be sufficient
for capturing temporal dynamics in our experiments. This approach can be extended to multiple frames if needed,
depending on the complexity of the task.
'''
#resize to 224 to get keypoints and features
videos_downsampled = F.interpolate(videos.flatten(0, 1), size=(224, 224), mode='bilinear', align_corners=False)
videos_downsampled = videos_downsampled.view(videos.shape[0], videos.shape[1], videos.shape[2], 224, 224)
#for computing flow at higher resolution
videos_ = F.interpolate(videos.flatten(0, 1), size=(1024, 1024), mode='bilinear', align_corners=False)
videos = videos_.view(videos.shape[0], videos.shape[1], videos.shape[2], 1024, 1024)
videos = videos.transpose(1, 2).half()
videos_downsampled = videos_downsampled.transpose(1, 2).half()
# Get the forward flow for the frame at 300ms
forward_flow = self.get_forward_flow(videos)
# Verify that there are no nans forward flow
assert not torch.isnan(forward_flow).any(), "Forward flow is nan"
all_features = []
for x, arr in enumerate(self.frames):
#use the downsampled videos for keypoints
vid = videos_downsampled[:, :, arr, :, :]
frame0 = vid[:, :, 0]
frame1 = vid[:, :, 1]
frame2 = vid[:, :, 2]
#extract features from the video frames frame0 and frame1 and include features at keypoint regions of frame2
mask, choices, err_array, k_feat, keypoint_recon = self.model.get_keypoints(frame0, frame1, frame2, 10, 1)
#for the last set of frames only use features at keypoint regions of frame2
if (x == 2):
k_feat = k_feat[:, -10:, :]
#reshape the features to [batch size, num_features]
k_feat = k_feat.view(k_feat.shape[0], -1)
choices_image_resolution = choices * self.model.patch_size[1]
# At 300ms, add optical flow patches at the detected keypoint locations
# For the first frame (x == 0)
if x == 0:
# Extract the optical flow information from the forward flow matrix for the second channel (index 2)
flow_keyp = forward_flow[:, 2]
# Initialize a result tensor to store the flow patches
# Tensor shape: [batch_size, 8x8 patch (flattened to 64) * 2 channels, 10 keypoints]
flow = torch.zeros(vid.shape[0], 8 * 8 * 2, 10).to(videos.device)
# Patch size shift (since 8x8 patches are being extracted)
shift = 8
# Loop over each element in the batch to process individual video frames
for b in range(flow_keyp.size(0)):
# Extract the x and y coordinates of the keypoint locations for this batch element
x_indices = choices_image_resolution[b, :, 0]
y_indices = choices_image_resolution[b, :, 1]
# For each keypoint (10 total keypoints in this case)
for ind in range(10):
# Extract the 8x8 patch of optical flow at each keypoint's (x, y) location
# Flatten the patch and assign it to the corresponding slice in the result tensor
flow[b, :, ind] = flow_keyp[b, :, y_indices[ind]:y_indices[ind] + shift,
x_indices[ind]:x_indices[ind] + shift].flatten()
# Reshape the flow tensor for easier concatenation (flatten across all patches)
flow = flow.view(flow.shape[0], -1)
# Concatenate the extracted optical flow features with the existing feature tensor (k_feat)
k_feat = torch.cat([k_feat, flow], dim=1)
all_features.append(k_feat)
x_encoded = torch.cat(all_features, dim=1)
return x_encoded
class CWM_base_8x8_3frame(CWM):
def __init__(self,):
super().__init__('vitb_8x8patch_3frames')
class CWM_base_8x8_3frame_mean_embed(CWM):
def __init__(self,):
super().__init__('vitb_8x8patch_3frames', aggregate_embeddings=True)
# CWM* (keypoints only) 74.7
class CWM_base_8x8_3frame_keypoints(CWM_Keypoints):
def __init__(self,):
super().__init__('vitb_8x8patch_3frames')
# CWM* (keypoints + Flow) 75.4
class CWM_base_8x8_3frame_keypoints_flow(CWM_KeypointsFlow):
def __init__(self,):
super().__init__('vitb_8x8patch_3frames')
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