demo_inference.py

image_path = './demo/demo.jpg'
sentence = 'the most handsome guy'
weights = './checkpoints/refcoco.pth'
device = 'cuda:0'

# pre-process the input image
from PIL import Image
import torchvision.transforms as T
import numpy as np
img = Image.open(image_path).convert("RGB")
img_ndarray = np.array(img)  # (orig_h, orig_w, 3); for visualization
original_w, original_h = img.size  # PIL .size returns width first and height second

image_transforms = T.Compose(
    [
     T.Resize(480),
     T.ToTensor(),
     T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ]
)

img = image_transforms(img).unsqueeze(0)  # (1, 3, 480, 480)
img = img.to(device)  # for inference (input)

# pre-process the raw sentence
from bert.tokenization_bert import BertTokenizer
import torch
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
sentence_tokenized = tokenizer.encode(text=sentence, add_special_tokens=True)
sentence_tokenized = sentence_tokenized[:20]  # if the sentence is longer than 20, then this truncates it to 20 words
# pad the tokenized sentence
padded_sent_toks = [0] * 20
padded_sent_toks[:len(sentence_tokenized)] = sentence_tokenized
# create a sentence token mask: 1 for real words; 0 for padded tokens
attention_mask = [0] * 20
attention_mask[:len(sentence_tokenized)] = [1]*len(sentence_tokenized)
# convert lists to tensors
padded_sent_toks = torch.tensor(padded_sent_toks).unsqueeze(0)  # (1, 20)
attention_mask = torch.tensor(attention_mask).unsqueeze(0)  # (1, 20)
padded_sent_toks = padded_sent_toks.to(device)  # for inference (input)
attention_mask = attention_mask.to(device)  # for inference (input)

# initialize model and load weights
from bert.modeling_bert import BertModel
from lib import segmentation

# construct a mini args class; like from a config file


class args:
    swin_type = 'base'
    window12 = True
    mha = ''
    fusion_drop = 0.0


single_model = segmentation.__dict__['lavt'](pretrained='', args=args)
single_model.to(device)
model_class = BertModel
single_bert_model = model_class.from_pretrained('bert-base-uncased')
single_bert_model.pooler = None

checkpoint = torch.load(weights, map_location='cpu')
single_bert_model.load_state_dict(checkpoint['bert_model'])
single_model.load_state_dict(checkpoint['model'])
model = single_model.to(device)
bert_model = single_bert_model.to(device)


# inference
import torch.nn.functional as F
last_hidden_states = bert_model(padded_sent_toks, attention_mask=attention_mask)[0]
embedding = last_hidden_states.permute(0, 2, 1)
output = model(img, embedding, l_mask=attention_mask.unsqueeze(-1))
output = output.argmax(1, keepdim=True)  # (1, 1, 480, 480)
output = F.interpolate(output.float(), (original_h, original_w))  # 'nearest'; resize to the original image size
output = output.squeeze()  # (orig_h, orig_w)
output = output.cpu().data.numpy()  # (orig_h, orig_w)


# show/save results
def overlay_davis(image, mask, colors=[[0, 0, 0], [255, 0, 0]], cscale=1, alpha=0.4):
    from scipy.ndimage.morphology import binary_dilation

    colors = np.reshape(colors, (-1, 3))
    colors = np.atleast_2d(colors) * cscale

    im_overlay = image.copy()
    object_ids = np.unique(mask)

    for object_id in object_ids[1:]:
        # Overlay color on  binary mask
        foreground = image*alpha + np.ones(image.shape)*(1-alpha) * np.array(colors[object_id])
        binary_mask = mask == object_id

        # Compose image
        im_overlay[binary_mask] = foreground[binary_mask]

        # countours = skimage.morphology.binary.binary_dilation(binary_mask) - binary_mask
        countours = binary_dilation(binary_mask) ^ binary_mask
        # countours = cv2.dilate(binary_mask, cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3))) - binary_mask
        im_overlay[countours, :] = 0

    return im_overlay.astype(image.dtype)


output = output.astype(np.uint8)  # (orig_h, orig_w), np.uint8
# Overlay the mask on the image
visualization = overlay_davis(img_ndarray, output)  # red
visualization = Image.fromarray(visualization)
# show the visualization
#visualization.show()
# Save the visualization
visualization.save('./demo/demo_result.jpg')