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import cv2
import torch
import warnings
import numpy as np
from datetime import datetime
import cv2
import warnings
import time
from videollava.mm_utils import tokenizer_image_token, KeywordsStoppingCriteria
from videollava.conversation import conv_templates, SeparatorStyle
from videollava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_VIDEO_TOKEN
def replace_video_token(prompt, image_paths, prompt_strategy):
if prompt_strategy is None:
vid_replace_token = DEFAULT_IMAGE_TOKEN * len(image_paths)
elif prompt_strategy == 'interleave':
vid_replace_token = ''.join(f"Image {i+1}: {DEFAULT_IMAGE_TOKEN}" for i in range(len(image_paths)))
else:
raise ValueError(f"Unknown prompt strategy: {prompt_strategy}")
return prompt.replace(DEFAULT_VIDEO_TOKEN, vid_replace_token)
def run_inference_single(
model,
processor,
tokenizer,
conv_mode,
inp,
image_paths,
metadata=None,
use_video_data=False,
repeat_frames=None,
prompt_strategy=None,
chronological_prefix=True,
delete_system_prompt=False,
print_prompt=False,
return_prompt=False,
last_image=False,
prompt=None
):
conv = conv_templates[conv_mode].copy()
if prompt is None:
conv.append_message(conv.roles[0], inp)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
if chronological_prefix:
prompt = prompt.replace("times:", "times in chronological order:")
if metadata is not None:
# Sort by time
image_paths, metadata = zip(*sorted(
zip(image_paths, metadata),
key=lambda t: datetime.strptime(t[1]["timestamp"], "%Y-%m-%d")
))
if delete_system_prompt:
if "This is" in prompt:
start_index = prompt.find("This is")
elif "These are" in prompt:
start_index = prompt.find("These are")
end_index = prompt.find(":", start_index)
if start_index != -1 and end_index != -1:
prompt = prompt[:start_index] + prompt[end_index+1:]
else:
warnings.warn("Impossible to remove the system message from the prompt.")
if use_video_data:
image_paths = list(image_paths)
if repeat_frames == "uniform":
# Repeat up to 8 for now
num_frames = 8
if len(image_paths) < num_frames:
num_repeats = num_frames // len(image_paths)
index = len(image_paths) - num_frames % len(image_paths)
image_paths = list(np.repeat(image_paths[:index], num_repeats)) + list(np.repeat(image_paths[index:], num_repeats+1))
elif repeat_frames == "first":
# Repeat the first frame
num_frames = 8
if len(image_paths) < num_frames:
repeat_frames = [image_paths[0]] * (num_frames - len(image_paths)) + image_paths
elif repeat_frames == "last":
# Repeat the last frame
num_frames = 8
if len(image_paths) < num_frames:
repeat_frames = image_paths + [image_paths[-1]] * (num_frames - len(image_paths))
video_tensor = processor.preprocess(image_paths, return_tensors='pt')['pixel_values']
tensor = [video_tensor.to(model.device, dtype=torch.float16)]
else:
image_tensors = [processor.preprocess(i, return_tensors='pt')['pixel_values'][0] for i in image_paths]
tensor = [image_tensor.to(model.device, dtype=torch.float16) for image_tensor in image_tensors]
if last_image:
tensor = [tensor[-1]]
image_paths = [image_paths[-1]]
if metadata is not None:
metadata = [metadata[-1]]
prompt = replace_video_token(prompt, image_paths, prompt_strategy)
if print_prompt:
print(prompt)
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(model.device)
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
with torch.inference_mode():
output_ids = model.generate(
input_ids=input_ids,
images=tensor,
do_sample=True,
temperature=0.2,
max_new_tokens=256,
use_cache=True,
stopping_criteria=[stopping_criteria],
)
# .replace removes the end sentence token "</s>" from the output
outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).replace('</s>', '').strip()
if return_prompt:
return prompt, outputs
else:
return outputs
def create_mask(poly, im_size):
"""
Create mask of given height and width where entries
inside polygon are 1.
params:
- poly (shapely polygon object): polygon to create mask for
- im_size (tuple): size of image (height, width)
returns:
- img_mask (np.array): mask of polygon"""
img_mask = np.zeros(im_size, np.uint8)
def int_coords(x): return np.array(x).round().astype(np.int32)
try:
exteriors = [int_coords(pol.exterior.coords) for pol in poly]
except:
exteriors = [int_coords(poly.exterior.coords)]
cv2.fillPoly(img_mask, exteriors, 1)
try:
interiors = [int_coords(pol.interior.coords) for pol in poly]
cv2.fillPoly(img_mask, interiors, 0)
except:
pass
try:
interiors = [int_coords(poly.interior.coords)]
cv2.fillPoly(img_mask, interiors, 0)
except:
pass
return img_mask
def create_mask_s2looking(img_id, split=None, question=None):
if split == None:
raise ValueError("split must be provided for S2Looking evaluation")
if question == None:
raise ValueError("question must be provided for S2Looking evaluation")
im1_path = f'/scr/geovlm/S2Looking/{split}/label1' # built
img2_path = f'/scr/geovlm/S2Looking/{split}/label2' # destroyed
id, chunk = img_id.split('_')
# Load image as numpy array
im1 = cv2.imread(f'{im1_path}/{id}.png', cv2.IMREAD_GRAYSCALE)
im2 = cv2.imread(f'{img2_path}/{id}.png', cv2.IMREAD_GRAYSCALE)
# replace any value different from 0 with 1
im1[im1 != 0] = 1
im2[im2 != 0] = 1
# get the corresponding of the 16 chunks
# 1 is upper left, 16 is lower right
if chunk == '1':
mask1 = im1[:256, :256]
mask2 = im2[:256, :256]
elif chunk == '2':
mask1 = im1[:256, 256:2*256]
mask2 = im2[:256, 256:2*256]
elif chunk == '3':
mask1 = im1[:256, 2*256:3*256]
mask2 = im2[:256, 2*256:3*256]
elif chunk == '4':
mask1 = im1[:256, 3*256:]
mask2 = im2[:256, 3*256:]
elif chunk == '5':
mask1 = im1[256:2*256, :256]
mask2 = im2[256:2*256, :256]
elif chunk == '6':
mask1 = im1[256:2*256, 256:2*256]
mask2 = im2[256:2*256, 256:2*256]
elif chunk == '7':
mask1 = im1[256:2*256, 2*256:3*256]
mask2 = im2[256:2*256, 2*256:3*256]
elif chunk == '8':
mask1 = im1[256:2*256, 3*256:]
mask2 = im2[256:2*256, 3*256:]
elif chunk == '9':
mask1 = im1[2*256:3*256, :256]
mask2 = im2[2*256:3*256, :256]
elif chunk == '10':
mask1 = im1[2*256:3*256, 256:2*256]
mask2 = im2[2*256:3*256, 256:2*256]
elif chunk == '11':
mask1 = im1[2*256:3*256, 2*256:3*256]
mask2 = im2[2*256:3*256, 2*256:3*256]
elif chunk == '12':
mask1 = im1[2*256:3*256, 3*256:]
mask2 = im2[2*256:3*256, 3*256:]
elif chunk == '13':
mask1 = im1[3*256:, :256]
mask2 = im2[3*256:, :256]
elif chunk == '14':
mask1 = im1[3*256:, 256:2*256]
mask2 = im2[3*256:, 256:2*256]
elif chunk == '15':
mask1 = im1[3*256:, 2*256:3*256]
mask2 = im2[3*256:, 2*256:3*256]
elif chunk == '16':
mask1 = im1[3*256:, 3*256:]
mask2 = im2[3*256:, 3*256:]
task = None
if 'built' in question or 'constructed' in question:
task = 'constructing'
if 'destroyed' in question or 'torn down' in question or 'demolished' in question:
task = 'destroying'
if 'changed' in question:
task = 'changing'
if (('built' in question) or ('constructed' in question)) and (('destroyed' in question) or ('torn down' in question) or ('demolished' in question)):
print(question)
raise ValueError("Question cannot contain both 'built' and 'destroyed'")
if task is None:
print(question)
raise ValueError("Question must contain either 'built', 'destroyed', or 'changed'")
if task == 'constructing':
mask = mask1
elif task == 'destroying':
mask = mask2
elif task == 'changing':
mask = np.logical_or(mask1, mask2)
return mask
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