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pck_tss.py
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pck_tss.py
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import os
import sys
import torch
import os
import numpy as np
from PIL import Image
from tqdm import tqdm
import torch.nn.functional as F
from utils.utils_correspondence import co_pca, resize, find_nearest_patchs, find_nearest_patchs_replace
import matplotlib.pyplot as plt
import time
from utils.logger import get_logger
from loguru import logger
import argparse
from extractor_sd import load_model, process_features_and_mask, get_mask
from extractor_dino import ViTExtractor
from utils.utils_tss import TSSDataset
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
import imageio
from imageio import imwrite
from utils.utils_flow import remap_using_flow_fields, flow_to_image, convert_flow_to_mapping, overlay_semantic_mask
import torch.nn.functional as F
def get_smooth(img, mask=None):
if mask is not None:
img_smooth=img.clone().permute(0, 2, 3, 1)
img_smooth[~mask] = 0
img=img_smooth.permute(0, 3, 1, 2)
def _gradient_x(img,mask): #tobe implemented
img = F.pad(img, (0, 1, 0, 0), mode="replicate")
gx = img[:, :, :, :-1] - img[:, :, :, 1:] # NCHW
return gx
def _gradient_y(img,mask):
img = F.pad(img, (0, 0, 0, 1), mode="replicate")
gy = img[:, :, :-1, :] - img[:, :, 1:, :] # NCHW
return gy
img_grad_x = _gradient_x(img, mask)
img_grad_y = _gradient_y(img, mask)
if mask is not None:
smooth = (torch.abs(img_grad_x).sum() + torch.abs(img_grad_y).sum())/torch.sum(mask)
else:
smooth = torch.mean(torch.abs(img_grad_x)) + torch.mean(torch.abs(img_grad_y))
return smooth
def plot_individual_images(save_path, name_image, source_image, target_image, flow_est, flow_gt,
mask_used=None, color=[255, 102, 51]):
if not isinstance(source_image, np.ndarray):
source_image = source_image.squeeze().permute(1, 2, 0).cpu().numpy().astype(np.uint8)
target_image = target_image.squeeze().permute(1, 2, 0).cpu().numpy().astype(np.uint8)
else:
# numpy array
if not source_image.shape[2] == 3:
source_image = source_image.transpose(1, 2, 0)
target_image = target_image.transpose(1, 2, 0)
flow_target = flow_est.detach().permute(0, 2, 3, 1)[0].cpu().numpy()
flow_gt = flow_gt.detach().permute(0, 2, 3, 1)[0].cpu().numpy()
remapped_est = remap_using_flow_fields(source_image, flow_target[:, :, 0], flow_target[:, :, 1])
max_mapping = 520
max_flow = 400
rgb_flow = flow_to_image(flow_target, max_flow)
rgb_flow_gt = flow_to_image(flow_gt, max_flow)
rgb_mapping = flow_to_image(convert_flow_to_mapping(flow_target, False), max_mapping)
if not os.path.isdir(os.path.join(save_path, 'individual_images')):
os.makedirs(os.path.join(save_path, 'individual_images'))
# save the rgb flow
imageio.imwrite(os.path.join(save_path, 'individual_images', "{}_rgb_flow.png".format(name_image)), rgb_flow)
imageio.imwrite(os.path.join(save_path, 'individual_images', "{}_rgb_flow_gt.png".format(name_image)), rgb_flow_gt)
imageio.imwrite(os.path.join(save_path, 'individual_images', "{}_rgb_mapping.png".format(name_image)),rgb_mapping)
imageio.imwrite(os.path.join(save_path, 'individual_images', "{}_image_s.png".format(name_image)), source_image)
imageio.imwrite(os.path.join(save_path, 'individual_images', "{}_image_t.png".format(name_image)), target_image)
imageio.imwrite(os.path.join(save_path, 'individual_images', "{}_warped_s.png".format(name_image)),
remapped_est)
if mask_used is not None:
mask_used = mask_used.squeeze().cpu().numpy()
imageio.imwrite(os.path.join(save_path, 'individual_images', "{}_mask.png".format(name_image)),
mask_used.astype(np.uint8) * 255)
imageio.imwrite(
os.path.join(save_path, 'individual_images', "{}_image_s_warped_and_mask.png".format(name_image)),
remapped_est * np.tile(np.expand_dims(mask_used.astype(np.uint8), axis=2), (1, 1, 3)))
# overlay mask on warped image
img_mask_overlay_color = overlay_semantic_mask(remapped_est.astype(np.uint8),
255 - mask_used.astype(np.uint8) * 255, color=color)
imwrite(os.path.join(save_path, 'individual_images',
'{}_warped_overlay_mask_color.png'.format(name_image)), img_mask_overlay_color)
flow_mask_overlay_color = overlay_semantic_mask(rgb_flow, 255 - mask_used.astype(np.uint8) * 255, color=color)
imwrite(os.path.join(save_path, 'individual_images',
'{}_flow_overlay_mask_color.png'.format(name_image)), flow_mask_overlay_color)
flow_gt_mask_overlay_color = overlay_semantic_mask(rgb_flow_gt, 255 - mask_used.astype(np.uint8) * 255, color=color)
imwrite(os.path.join(save_path, 'individual_images',
'{}_flow_gt_overlay_mask_color.png'.format(name_image)), flow_gt_mask_overlay_color)
def nearest_neighbor_flow(src_descriptor, trg_descriptor, ori_shape, mask1=None, mask2=None):
B, C, H, W = src_descriptor.shape
if mask1 is not None and mask2 is not None:
resized_mask1 = F.interpolate(mask1.cuda().unsqueeze(0).unsqueeze(0).float(), size=src_descriptor.shape[2:], mode='nearest')
resized_mask2 = F.interpolate(mask2.cuda().unsqueeze(0).unsqueeze(0).float(), size=trg_descriptor.shape[2:], mode='nearest')
src_descriptor = src_descriptor * resized_mask1.repeat(1, src_descriptor.shape[1], 1, 1)
trg_descriptor = trg_descriptor * resized_mask2.repeat(1, trg_descriptor.shape[1], 1, 1)
# set where mask==0 a very large number
src_descriptor[(src_descriptor.sum(1)==0).repeat(1, src_descriptor.shape[1], 1, 1)] = 100000
trg_descriptor[(trg_descriptor.sum(1)==0).repeat(1, trg_descriptor.shape[1], 1, 1)] = 100000
real_H, real_W = ori_shape
long_edge = max(real_H, real_W)
src_descriptor = src_descriptor.view(B, C, -1).permute(0, 2, 1).squeeze()
trg_descriptor = trg_descriptor.view(B, C, -1).permute(0, 2, 1).squeeze()
# Compute distance matrix using broadcasting and torch.cdist
distances = torch.cdist(trg_descriptor, src_descriptor)
# Find the indices of the minimum distances
indices = torch.argmin(distances, dim=1).reshape(B, H, W)
# Convert indices to coordinates
trg_y = torch.div(indices, W).to(torch.float32)
trg_x = torch.fmod(indices, W).to(torch.float32)
# Create coordinate grid
grid_y, grid_x = torch.meshgrid(torch.arange(H, dtype=torch.float32, device=src_descriptor.device), torch.arange(W, dtype=torch.float32, device=src_descriptor.device))
# Compare target coordinates with source coordinate grid
flow_x = trg_x - grid_x
flow_y = trg_y - grid_y
# Stack the flow fields together to form the final optical flow
flow = torch.stack([flow_x, flow_y], dim=1)
# Perform bilinear interpolation to adjust the optical flow from (60, 60) to (real_H, real_W)
flow = F.interpolate(flow, size=(long_edge, long_edge), mode='bilinear', align_corners=False)
flow *= torch.tensor([long_edge / 60.0, long_edge / 60.0], dtype=torch.float32, device=src_descriptor.device).view(1, 2, 1, 1)
# Crop the flow field to the original image size
if long_edge == real_H:
flow = flow[:, :, :, (long_edge - real_W) // 2:(long_edge - real_W) // 2 + real_W]
else:
flow = flow[:, :, (long_edge - real_H) // 2:(long_edge - real_H) // 2 + real_H, :]
return flow
def compute_flow(model, aug, source_img, target_img, save_path, batch_num=0, category=['car'], mask=False, dist='cos', real_size=960):
if type(category) == str:
category = [category]
img_size = 840 if DINOV2 else 480
model_dict={'small':'dinov2_vits14',
'base':'dinov2_vitb14',
'large':'dinov2_vitl14',
'giant':'dinov2_vitg14'}
model_type = model_dict[MODEL_SIZE] if DINOV2 else 'dino_vits8'
layer = 11 if DINOV2 else 9
if 'l' in model_type:
layer = 23
elif 'g' in model_type:
layer = 39
facet = 'token' if DINOV2 else 'key'
stride = 14 if DINOV2 else 8
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# indiactor = 'v2' if DINOV2 else 'v1'
# model_size = model_type.split('vit')[-1]
extractor = ViTExtractor(model_type, stride, device=device)
patch_size = extractor.model.patch_embed.patch_size[0] if DINOV2 else extractor.model.patch_embed.patch_size
num_patches = int(patch_size / stride * (img_size // patch_size - 1) + 1)
input_text = "a photo of "+category[-1][0] if TEXT_INPUT else None
current_save_results = 0
N = 1
result = []
for pair_idx in range(N):
shape = source_img.shape[2:]
# Load image 1
img1=Image.fromarray(source_img.squeeze().numpy().transpose(1,2,0).astype(np.uint8))
img1_input = resize(img1, real_size, resize=True, to_pil=True, edge=EDGE_PAD)
img1 = resize(img1, img_size, resize=True, to_pil=True, edge=EDGE_PAD)
# Load image 2
img2=Image.fromarray(target_img.squeeze().numpy().transpose(1,2,0).astype(np.uint8))
img2_input = resize(img2, real_size, resize=True, to_pil=True, edge=EDGE_PAD)
img2 = resize(img2, img_size, resize=True, to_pil=True, edge=EDGE_PAD)
with torch.no_grad():
if not CO_PCA:
if not ONLY_DINO:
img1_desc = process_features_and_mask(model, aug, img1_input, input_text=input_text, mask=False).reshape(1,1,-1, num_patches**2).permute(0,1,3,2)
img2_desc = process_features_and_mask(model, aug, img2_input, category[-1], input_text=input_text, mask=mask).reshape(1,1,-1, num_patches**2).permute(0,1,3,2)
if FUSE_DINO:
img1_batch = extractor.preprocess_pil(img1)
img1_desc_dino = extractor.extract_descriptors(img1_batch.to(device), layer, facet)
img2_batch = extractor.preprocess_pil(img2)
img2_desc_dino = extractor.extract_descriptors(img2_batch.to(device), layer, facet)
else:
if not ONLY_DINO:
features1 = process_features_and_mask(model, aug, img1_input, input_text=input_text, mask=False, raw=True)
features2 = process_features_and_mask(model, aug, img2_input, category[-1], input_text=input_text, mask=mask, raw=True)
processed_features1, processed_features2 = co_pca(features1, features2, PCA_DIMS)
img1_desc = processed_features1.reshape(1, 1, -1, num_patches**2).permute(0,1,3,2)
img2_desc = processed_features2.reshape(1, 1, -1, num_patches**2).permute(0,1,3,2)
if FUSE_DINO:
img1_batch = extractor.preprocess_pil(img1)
img1_desc_dino = extractor.extract_descriptors(img1_batch.to(device), layer, facet)
img2_batch = extractor.preprocess_pil(img2)
img2_desc_dino = extractor.extract_descriptors(img2_batch.to(device), layer, facet) # (1,1,3600,768)
if dist == 'l1' or dist == 'l2':
# normalize the features
img1_desc = img1_desc / img1_desc.norm(dim=-1, keepdim=True)
img2_desc = img2_desc / img2_desc.norm(dim=-1, keepdim=True)
if FUSE_DINO:
img1_desc_dino = img1_desc_dino / img1_desc_dino.norm(dim=-1, keepdim=True)
img2_desc_dino = img2_desc_dino / img2_desc_dino.norm(dim=-1, keepdim=True)
if FUSE_DINO and not ONLY_DINO:
# cat two features together
img1_desc = torch.cat((img1_desc, img1_desc_dino), dim=-1)
img2_desc = torch.cat((img2_desc, img2_desc_dino), dim=-1)
img1_desc[...,:PCA_DIMS[0]]*=WEIGHT[0]
img1_desc[...,PCA_DIMS[0]:PCA_DIMS[1]+PCA_DIMS[0]]*=WEIGHT[1]
img1_desc[...,PCA_DIMS[1]+PCA_DIMS[0]:PCA_DIMS[2]+PCA_DIMS[1]+PCA_DIMS[0]]*=WEIGHT[2]
img2_desc[...,:PCA_DIMS[0]]*=WEIGHT[0]
img2_desc[...,PCA_DIMS[0]:PCA_DIMS[1]+PCA_DIMS[0]]*=WEIGHT[1]
img2_desc[...,PCA_DIMS[1]+PCA_DIMS[0]:PCA_DIMS[2]+PCA_DIMS[1]+PCA_DIMS[0]]*=WEIGHT[2]
if ONLY_DINO:
img1_desc = img1_desc_dino
img2_desc = img2_desc_dino
# logger.info(img1_desc.shape, img2_desc.shape)
if DRAW_DENSE:
mask1 = get_mask(model, aug, img1, category[0])
mask2 = get_mask(model, aug, img2, category[-1])
if ONLY_DINO or not FUSE_DINO:
img1_desc = img1_desc / img1_desc.norm(dim=-1, keepdim=True)
img2_desc = img2_desc / img2_desc.norm(dim=-1, keepdim=True)
img1_desc_reshaped = img1_desc.permute(0,1,3,2).reshape(-1, img1_desc.shape[-1], num_patches, num_patches)
img2_desc_reshaped = img2_desc.permute(0,1,3,2).reshape(-1, img2_desc.shape[-1], num_patches, num_patches)
trg_dense_output, src_color_map = find_nearest_patchs(mask2, mask1, img2, img1, img2_desc_reshaped, img1_desc_reshaped, mask=mask)
if current_save_results!=TOTAL_SAVE_RESULT:
if not os.path.exists(f'{save_path}/{category[0]}'):
os.makedirs(f'{save_path}/{category[0]}')
fig_colormap, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))
ax1.axis('off')
ax2.axis('off')
ax1.imshow(src_color_map)
ax2.imshow(trg_dense_output)
fig_colormap.savefig(f'{save_path}/{category[0]}/{batch_num}_colormap.png')
plt.close(fig_colormap)
if DRAW_SWAP:
if not DRAW_DENSE:
mask1 = get_mask(model, aug, img1, category[0])
mask2 = get_mask(model, aug, img2, category[-1])
if (ONLY_DINO or not FUSE_DINO) and not DRAW_DENSE:
img1_desc = img1_desc / img1_desc.norm(dim=-1, keepdim=True)
img2_desc = img2_desc / img2_desc.norm(dim=-1, keepdim=True)
img1_desc_reshaped = img1_desc.permute(0,1,3,2).reshape(-1, img1_desc.shape[-1], num_patches, num_patches)
img2_desc_reshaped = img2_desc.permute(0,1,3,2).reshape(-1, img2_desc.shape[-1], num_patches, num_patches)
trg_dense_output, src_color_map = find_nearest_patchs_replace(mask2, mask1, img2, img1, img2_desc_reshaped, img1_desc_reshaped, mask=mask, resolution=156)
if current_save_results!=TOTAL_SAVE_RESULT:
if not os.path.exists(f'{save_path}/{category[0]}'):
os.makedirs(f'{save_path}/{category[0]}')
fig_colormap, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))
ax1.axis('off')
ax2.axis('off')
ax1.imshow(src_color_map)
ax2.imshow(trg_dense_output)
fig_colormap.savefig(f'{save_path}/{category[0]}/{batch_num}_swap.png')
plt.close(fig_colormap)
# compute the flow map based on the nearest neighbor
# reshape the descriptors (1,dim,80,60)
img1_desc_reshaped = img1_desc.permute(0,1,3,2).reshape(-1, img1_desc.shape[-1], num_patches, num_patches)
img2_desc_reshaped = img2_desc.permute(0,1,3,2).reshape(-1, img2_desc.shape[-1], num_patches, num_patches)
# compute the flow map based on the nearest neighbor
if MASK:
mask1 = get_mask(model, aug, img1, category[0])
mask2 = get_mask(model, aug, img2, category[-1])
result = nearest_neighbor_flow(img1_desc_reshaped, img2_desc_reshaped, shape, mask1, mask2)
else:
result = nearest_neighbor_flow(img1_desc_reshaped, img2_desc_reshaped, shape)
return result
def run_evaluation_semantic(model, aug, test_dataloader, device,
path_to_save=None, plot=False, plot_100=False, plot_ind_images=False):
current_save_results = 0
pbar = tqdm(enumerate(test_dataloader), total=len(test_dataloader))
mean_epe_list, epe_all_list, pck_0_05_list, pck_0_01_list, pck_0_1_list, pck_0_15_list = [], [], [], [], [], []
smooth_est_list, smooth_gt_list = [], []
eval_buf = {'cls_pck': dict(), 'vpvar': dict(), 'scvar': dict(), 'trncn': dict(), 'occln': dict()}
# pck curve per image
pck_thresholds = [0.01]
pck_thresholds.extend(np.arange(0.05, 0.4, 0.05).tolist())
pck_per_image_curve = np.zeros((len(pck_thresholds), len(test_dataloader)), np.float32)
for i_batch, mini_batch in pbar:
source_img = mini_batch['source_image']
target_img = mini_batch['target_image']
flow_gt = mini_batch['flow_map'].to(device)
mask_valid = mini_batch['correspondence_mask'].to(device)
category = mini_batch['category']
if 'pckthres' in list(mini_batch.keys()):
L_pck = mini_batch['pckthres'][0].float().item()
else:
raise ValueError('No pck threshold in mini_batch')
flow_est = compute_flow(model, aug, source_img, target_img, batch_num=i_batch, save_path=path_to_save, category=category)
if plot_ind_images or current_save_results < TOTAL_SAVE_RESULT:
plot_individual_images(path_to_save, 'image_{}'.format(i_batch), source_img, target_img, flow_est,flow_gt , mask_used=mask_valid)
current_save_results += 1
smooth_est_list.append(get_smooth(flow_est,mask_valid).cpu().numpy())
smooth_gt_list.append(get_smooth(flow_gt,mask_valid).cpu().numpy())
flow_est = flow_est.permute(0, 2, 3, 1)[mask_valid]
flow_gt = flow_gt.permute(0, 2, 3, 1)[mask_valid]
epe = torch.sum((flow_est - flow_gt) ** 2, dim=1).sqrt()
epe_all_list.append(epe.view(-1).cpu().numpy())
mean_epe_list.append(epe.mean().item())
pck_0_05_list.append(epe.le(0.05*L_pck).float().mean().item())
pck_0_01_list.append(epe.le(0.01*L_pck).float().mean().item())
pck_0_1_list.append(epe.le(0.1*L_pck).float().mean().item())
pck_0_15_list.append(epe.le(0.15*L_pck).float().mean().item())
for t in range(len(pck_thresholds)):
pck_per_image_curve[t, i_batch] = epe.le(pck_thresholds[t]*L_pck).float().mean().item()
epe_all = np.concatenate(epe_all_list)
pck_0_05_dataset = np.mean(epe_all <= 0.05 * L_pck)
pck_0_01_dataset = np.mean(epe_all <= 0.01 * L_pck)
pck_0_1_dataset = np.mean(epe_all <= 0.1 * L_pck)
pck_0_15_dataset = np.mean(epe_all <= 0.15 * L_pck)
smooth_est_dataset = np.mean(smooth_est_list)
smooth_gt_dataset = np.mean(smooth_gt_list)
output = {'AEPE': np.mean(mean_epe_list), 'PCK_0_05_per_image': np.mean(pck_0_05_list),
'PCK_0_01_per_image': np.mean(pck_0_01_list), 'PCK_0_1_per_image': np.mean(pck_0_1_list),
'PCK_0_15_per_image': np.mean(pck_0_15_list),
'PCK_0_01_per_dataset': pck_0_01_dataset, 'PCK_0_05_per_dataset': pck_0_05_dataset,
'PCK_0_1_per_dataset': pck_0_1_dataset, 'PCK_0_15_per_dataset': pck_0_15_dataset,
'pck_threshold_alpha': pck_thresholds, 'pck_curve_per_image': np.mean(pck_per_image_curve, axis=1).tolist()
}
logger.info("Validation EPE: %f, alpha=0_01: %f, alpha=0.05: %f" % (output['AEPE'], output['PCK_0_01_per_image'],
output['PCK_0_05_per_image']))
logger.info("smooth_est: %f, smooth_gt: %f" % (smooth_est_dataset, smooth_gt_dataset))
for name in eval_buf.keys():
output[name] = {}
for cls in eval_buf[name]:
if eval_buf[name] is not None:
cls_avg = sum(eval_buf[name][cls]) / len(eval_buf[name][cls])
output[name][cls] = cls_avg
return output
def main(args):
global MASK, SAMPLE, DIST, TOTAL_SAVE_RESULT, VER, CO_PCA, PCA_DIMS, SIZE, FUSE_DINO, DINOV2, MODEL_SIZE, DRAW_DENSE, TEXT_INPUT, DRAW_SWAP, ONLY_DINO, SEED, EDGE_PAD, WEIGHT
MASK = args.MASK
SAMPLE = args.SAMPLE
DIST = args.DIST
TOTAL_SAVE_RESULT = args.TOTAL_SAVE_RESULT
VER = args.VER
CO_PCA = args.CO_PCA
PCA_DIMS = args.PCA_DIMS
SIZE = args.SIZE
INDICES = args.INDICES
EDGE_PAD = args.EDGE_PAD
FUSE_DINO = False if args.NOT_FUSE else True
ONLY_DINO = args.ONLY_DINO
DINOV2 = False if args.DINOV1 else True
MODEL_SIZE = args.MODEL_SIZE
DRAW_DENSE = args.DRAW_DENSE
DRAW_SWAP = args.DRAW_SWAP
TEXT_INPUT = args.TEXT_INPUT
SEED = args.SEED
WEIGHT = args.WEIGHT # corresponde to three groups for the sd features, and one group for the dino features
if SAMPLE == 0:
SAMPLE = None
if DRAW_DENSE or DRAW_SWAP:
TOTAL_SAVE_RESULT = SAMPLE
if ONLY_DINO:
FUSE_DINO = True
if FUSE_DINO and not ONLY_DINO:
DIST = "l2"
else:
DIST = "cos"
np.random.seed(args.SEED)
torch.manual_seed(args.SEED)
torch.cuda.manual_seed(args.SEED)
torch.backends.cudnn.benchmark = True
model, aug = load_model(diffusion_ver=VER, image_size=SIZE, num_timesteps=args.TIMESTEP, block_indices=tuple(INDICES))
save_path=f'./results_tss/pck_tss_mask_{MASK}_dist_{DIST}_{args.TIMESTEP}{VER}_{MODEL_SIZE}_{SIZE}_copca_{CO_PCA}_{INDICES[0]}_{PCA_DIMS[0]}_{INDICES[1]}_{PCA_DIMS[1]}_{INDICES[2]}_{PCA_DIMS[2]}_text_{TEXT_INPUT}_sd_{not ONLY_DINO}_dino_{FUSE_DINO}'
if EDGE_PAD:
save_path += '_edge_pad'
if not os.path.exists(save_path):
os.makedirs(save_path)
logger = get_logger(save_path+'/result.log')
logger.info(args)
data_dir = "data/TSS_CVPR2016"
start_time=time.time()
class ArrayToTensor(object):
"""Converts a numpy.ndarray (H x W x C) to a torch.FloatTensor of shape (C x H x W)."""
def __init__(self, get_float=True):
self.get_float = get_float
def __call__(self, array):
if not isinstance(array, np.ndarray):
array = np.array(array)
array = np.transpose(array, (2, 0, 1))
# handle numpy array
tensor = torch.from_numpy(array)
# put it from HWC to CHW format
if self.get_float:
# carefull, this is not normalized to [0, 1]
return tensor.float()
else:
return tensor
co_transform = None
target_transform = transforms.Compose([ArrayToTensor()]) # only put channel first
input_transform = transforms.Compose([ArrayToTensor(get_float=False)]) # only put channel first
output = {}
for sub_data in ['FG3DCar', 'JODS', 'PASCAL']:
test_set = TSSDataset(os.path.join(data_dir, sub_data),
source_image_transform=input_transform,
target_image_transform=input_transform, flow_transform=target_transform,
co_transform=co_transform,
num_samples=SAMPLE)
test_dataloader = DataLoader(test_set, batch_size=1, num_workers=8)
results = run_evaluation_semantic(model,aug, test_dataloader, device='cuda', path_to_save=save_path+'/'+sub_data, plot_ind_images=DRAW_SWAP)
output[sub_data] = results
end_time=time.time()
minutes, seconds = divmod(end_time-start_time, 60)
logger.info(f"Time: {minutes:.0f}m {seconds:.0f}s")
torch.save(output, save_path+'/result.pth')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--SEED', type=int, default=42)
parser.add_argument('--MASK', action='store_true', default=False)
parser.add_argument('--SAMPLE', type=int, default=0)
parser.add_argument('--DIST', type=str, default='l2')
parser.add_argument('--TOTAL_SAVE_RESULT', type=int, default=5)
parser.add_argument('--VER', type=str, default="v1-5")
parser.add_argument('--CO_PCA', type=bool, default=True)
parser.add_argument('--PCA_DIMS', nargs=3, type=int, default=[256, 256, 256])
parser.add_argument('--TIMESTEP', type=int, default=100)
parser.add_argument('--SIZE', type=int, default=960)
parser.add_argument('--INDICES', nargs=4, type=int, default=[2,5,8,11])
parser.add_argument('--WEIGHT', nargs=4, type=float, default=[1,1,1,1])
parser.add_argument('--EDGE_PAD', action='store_true', default=False)
parser.add_argument('--NOT_FUSE', action='store_true', default=False)
parser.add_argument('--ONLY_DINO', action='store_true', default=False)
parser.add_argument('--DINOV1', action='store_true', default=False)
parser.add_argument('--MODEL_SIZE', type=str, default='base')
parser.add_argument('--DRAW_DENSE', action='store_true', default=False)
parser.add_argument('--DRAW_SWAP', action='store_true', default=False)
parser.add_argument('--TEXT_INPUT', action='store_true', default=False)
args = parser.parse_args()
main(args)