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import torch | |
import torch.nn as nn | |
import torch.optim as optim | |
import numpy as np | |
import torch.nn.functional as F | |
class MLP(nn.Module): | |
def __init__(self, input_size, hidden_size, num_classes, dropout_prob=0.1): | |
super(MLP, self).__init__() | |
self.fc1 = nn.Linear(input_size, hidden_size) | |
self.relu = nn.ReLU() | |
self.dropout = nn.Dropout(dropout_prob) | |
self.fc2 = nn.Linear(hidden_size, num_classes) | |
def forward(self, x): | |
out = self.fc1(x) | |
out = self.relu(out) | |
out = self.dropout(out) | |
out = self.fc2(out) | |
return out | |
def show_anns(anns, color_code='auto'): | |
if len(anns) == 0: | |
return | |
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True) | |
ax = plt.gca() | |
ax.set_autoscale_on(False) | |
polygons = [] | |
color = [] | |
for ann in sorted_anns: | |
m = ann['segmentation'] | |
img = np.ones((m.shape[0], m.shape[1], 3)) | |
color_mask = np.random.random((1, 3)).tolist()[0] | |
if color_code == 'auto': | |
for i in range(3): | |
img[:,:,i] = color_mask[i] | |
elif color_code == 'red': | |
for i in range(3): | |
img[:,:,0] = 1 | |
img[:,:,1] = 0 | |
img[:,:,2] = 0 | |
else: | |
for i in range(3): | |
img[:,:,0] = 0 | |
img[:,:,1] = 0 | |
img[:,:,2] = 1 | |
return np.dstack((img, m*0.35)) | |
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 ram_show_mask(m): | |
img = np.ones((m.shape[0], m.shape[1], 3)) | |
color_mask = np.random.random((1, 3)).tolist()[0] | |
for i in range(3): | |
img[:,:,0] = 1 | |
img[:,:,1] = 0 | |
img[:,:,2] = 0 | |
return np.dstack((img, m*0.35)) | |
def iou(mask1, mask2): | |
intersection = np.logical_and(mask1, mask2) | |
union = np.logical_or(mask1, mask2) | |
iou_score = np.sum(intersection) / np.sum(union) | |
return iou_score | |
def sort_and_deduplicate(sam_masks, iou_threshold=0.8): | |
# Sort the sam_masks list based on the area value | |
sorted_masks = sorted(sam_masks, key=lambda x: x['area'], reverse=True) | |
# Deduplicate masks based on the given iou_threshold | |
filtered_masks = [] | |
for mask in sorted_masks: | |
duplicate = False | |
for filtered_mask in filtered_masks: | |
if iou(mask['segmentation'], filtered_mask['segmentation']) > iou_threshold: | |
duplicate = True | |
break | |
if not duplicate: | |
filtered_masks.append(mask) | |
return filtered_masks | |
relation_classes = ['over', | |
'in front of', | |
'beside', | |
'on', | |
'in', | |
'attached to', | |
'hanging from', | |
'on back of', | |
'falling off', | |
'going down', | |
'painted on', | |
'walking on', | |
'running on', | |
'crossing', | |
'standing on', | |
'lying on', | |
'sitting on', | |
'flying over', | |
'jumping over', | |
'jumping from', | |
'wearing', | |
'holding', | |
'carrying', | |
'looking at', | |
'guiding', | |
'kissing', | |
'eating', | |
'drinking', | |
'feeding', | |
'biting', | |
'catching', | |
'picking', | |
'playing with', | |
'chasing', | |
'climbing', | |
'cleaning', | |
'playing', | |
'touching', | |
'pushing', | |
'pulling', | |
'opening', | |
'cooking', | |
'talking to', | |
'throwing', | |
'slicing', | |
'driving', | |
'riding', | |
'parked on', | |
'driving on', | |
'about to hit', | |
'kicking', | |
'swinging', | |
'entering', | |
'exiting', | |
'enclosing', | |
'leaning on',] | |