inference.py

import numpy as np
import cv2
import torch
import torch.nn as nn
from segment_anything import SamPredictor, sam_model_registry, SamAutomaticMaskGenerator
from segment_anything_kd.modeling.image_encoder import add_decomposed_rel_pos
import matplotlib.pyplot as plt
import torch_pruning as tp

def calculate_iou(mask1, mask2):
    """
    Calculate Intersection over Union (IoU) for two binary masks.

    Parameters:
        mask1 (numpy.ndarray): The first binary mask.
        mask2 (numpy.ndarray): The second binary mask.

    Returns:
        float: The IoU score.
    """
    # Make sure the input masks have the same shape
    assert mask1.shape == mask2.shape, "Both masks must have the same shape."

    # Calculate the intersection and union of the masks
    intersection = np.logical_and(mask1, mask2)
    union = np.logical_or(mask1, mask2)

    # Compute the IoU score
    iou_score = np.sum(intersection) / np.sum(union)

    return iou_score

def show_mask(mask, ax, random_color=False):
    if random_color:
        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
    else:
        color = np.array([30/255, 144/255, 255/255, 0.6])
    h, w = mask.shape[-2:]
    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
    ax.imshow(mask_image)
    
def show_points(coords, labels, ax, marker_size=375):
    pos_points = coords[labels==1]
    neg_points = coords[labels==0]
    ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
    ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)   
    
def show_box(box, ax):
    x0, y0 = box[0], box[1]
    w, h = box[2] - box[0], box[3] - box[1]
    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))  
            
def show_anns(anns):
    if len(anns) == 0:
        return
    sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
    ax = plt.gca()
    ax.set_autoscale_on(False)

    img = np.ones((sorted_anns[0]['segmentation'].shape[0], sorted_anns[0]['segmentation'].shape[1], 4))
    img[:,:,3] = 0
    for ann in sorted_anns:
        m = ann['segmentation']
        color_mask = np.concatenate([np.random.random(3), [0.35]])
        img[m] = color_mask
    ax.imshow(img)

def test_model():

    device = torch.device("cuda")
    print("CUDA visible devices: " + str(torch.cuda.device_count()))
    print("CUDA Device Name: " + str(torch.cuda.get_device_name(device)))

    # teacher_model_type = 'vit_b'
    # checkpoint = 'checkpoints/sam_vit_b_qkv.pth'
    # teacher_model = sam_model_registry[teacher_model_type](checkpoint=checkpoint)
    # teacher_model.to(device)
    # teacher_model.eval()

    # teacher_model_type = 'vit_p77'
    # checkpoint = 'checkpoints/SlimSAM-77-uniform.pth'
    # SlimSAM_model = sam_model_registry[teacher_model_type](checkpoint=checkpoint)
    # SlimSAM_model.to(device)
    # SlimSAM_model.eval()

    model_path = "checkpoints/SlimSAM-77.pth"
    SlimSAM_model = torch.load(model_path)
    SlimSAM_model.image_encoder = SlimSAM_model.image_encoder.module
    SlimSAM_model.to(device)
    SlimSAM_model.eval()
    print("model_path:",model_path)

    def forward(self, x):

        x = self.patch_embed(x)
        if self.pos_embed is not None:
            x = x + self.pos_embed

        for blk in self.blocks:
            x,qkv_emb,mid_emb,x_emb = blk(x)

        x = self.neck(x.permute(0, 3, 1, 2))
        
        return x

    import types
    funcType = types.MethodType
    SlimSAM_model.image_encoder.forward = funcType(forward, SlimSAM_model.image_encoder)

    example_inputs = torch.randn(1, 3, 1024, 1024).to(device)
    ori_macs, ori_size = tp.utils.count_ops_and_params(SlimSAM_model.image_encoder, example_inputs)
    print("MACs(G):",ori_macs/1e9,"Params(M):",ori_size/1e6)

    #mask_generator = SamAutomaticMaskGenerator(teacher_model)
    mask_generator = SamAutomaticMaskGenerator(
    model=SlimSAM_model,
    points_per_side=32,
    pred_iou_thresh=0.88,
    stability_score_thresh=0.90,
    crop_n_layers=1,
    crop_n_points_downscale_factor=2,
    min_mask_region_area=100,  # Requires open-cv to run post-processing
)

    predictor = SamPredictor(SlimSAM_model)

    with torch.no_grad():
 
        path = 'images/truck.jpg'
        print(path)
        image = cv2.imread(path)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        
        predictor.set_image(image)

        
################ Point Prompt ################
        input_point = np.array([[750, 370]])
        input_label = np.array([1])
        masks, scores, logits = predictor.predict(
        point_coords=input_point,
        point_labels=input_label,
        box = None,
        multimask_output=False,
    )   
        plt.figure(figsize=(15,10))
        plt.imshow(image)
        show_mask(masks, plt.gca())
        show_points(input_point, input_label, plt.gca())
        plt.axis('off')
        plt.tight_layout()
        plt.savefig("images/"+'demo_point' + ".png")


################ Box Prompt ################
        input_box = np.array([75, 275, 1725, 850])
        masks, scores, logits = predictor.predict(
        point_coords=None,
        point_labels=None,
        box = input_box,
        multimask_output=False,
    )   
        plt.figure(figsize=(15,10))
        plt.imshow(image)
        show_mask(masks, plt.gca())
        show_box(input_box, plt.gca())
        plt.axis('off')
        plt.tight_layout()
        plt.savefig("images/"+'demo_box' + ".png")


################ Segment everything prompt ################
        masks = mask_generator.generate(image)
        plt.figure(figsize=(15,10))
        plt.imshow(image)
        show_anns(masks)
        plt.axis('off')
        plt.show()
        plt.tight_layout()
        plt.savefig("images/"+"demo_everything" + ".png")




            











if __name__ == '__main__':
    test_model()