COSMO-demo / app.py
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import os
import time
import argparse
import numpy as np
import random
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import sys
import json
from collections import defaultdict
import math
from model import DistMult
from tqdm import tqdm
from utils import collate_list, detach_and_clone, move_to
from PIL import Image
from torchvision import transforms
_DEFAULT_IMAGE_TENSOR_NORMALIZATION_MEAN = [0.485, 0.456, 0.406]
_DEFAULT_IMAGE_TENSOR_NORMALIZATION_STD = [0.229, 0.224, 0.225]
def evaluate(model, id2entity, target_list, args):
model.eval()
torch.set_grad_enabled(False)
overall_id_to_name = json.load(open('data/iwildcam_v2.0/overall_id_to_name.json'))
img = Image.open(args.img_path).convert('RGB')
transform_steps = transforms.Compose([transforms.Resize((448, 448)), transforms.ToTensor(), transforms.Normalize(_DEFAULT_IMAGE_TENSOR_NORMALIZATION_MEAN, _DEFAULT_IMAGE_TENSOR_NORMALIZATION_STD)])
h = transform_steps(img)
r = torch.tensor([3])
h = move_to(h, args.device).unsqueeze(0)
r = move_to(r, args.device).unsqueeze(0)
outputs = model.forward_ce(h, r, triple_type=('image', 'id'))
y_pred = detach_and_clone(outputs.cpu())
y_pred = y_pred.argmax(-1)
pred_label = target_list[y_pred].item()
species_label = overall_id_to_name[str(id2entity[pred_label])]
print('species label = {}'.format(species_label))
return
def _get_id(dict, key):
id = dict.get(key, None)
if id is None:
id = len(dict)
dict[key] = id
return id
def generate_target_list(data, entity2id):
sub = data.loc[(data["datatype_h"] == "image") & (data["datatype_t"] == "id"), ['t']]
sub = list(sub['t'])
categories = []
for item in tqdm(sub):
if entity2id[str(int(float(item)))] not in categories:
categories.append(entity2id[str(int(float(item)))])
# print('categories = {}'.format(categories))
# print("No. of target categories = {}".format(len(categories)))
return torch.tensor(categories, dtype=torch.long).unsqueeze(-1)
if __name__=='__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data-dir', type=str, default='data/iwildcam_v2.0/')
parser.add_argument('--img-path', type=str, required=True, help='path to species image to be classified')
parser.add_argument('--seed', type=int, default=813765)
parser.add_argument('--ckpt-path', type=str, default=None, help='path to ckpt for restarting expt')
parser.add_argument('--debug', action='store_true')
parser.add_argument('--no-cuda', action='store_true')
parser.add_argument('--batch_size', type=int, default=16)
parser.add_argument('--embedding-dim', type=int, default=512)
parser.add_argument('--location_input_dim', type=int, default=2)
parser.add_argument('--time_input_dim', type=int, default=1)
parser.add_argument('--mlp_location_numlayer', type=int, default=3)
parser.add_argument('--mlp_time_numlayer', type=int, default=3)
parser.add_argument('--img-embed-model', choices=['resnet18', 'resnet50'], default='resnet50')
parser.add_argument('--use-data-subset', action='store_true')
parser.add_argument('--subset-size', type=int, default=10)
args = parser.parse_args()
print('args = {}'.format(args))
args.device = torch.device('cuda') if not args.no_cuda and torch.cuda.is_available() else torch.device('cpu')
# Set random seed
torch.manual_seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
datacsv = pd.read_csv(os.path.join(args.data_dir, 'dataset_subtree.csv'), low_memory=False)
entity_id_file = os.path.join(args.data_dir, 'entity2id_subtree.json')
if not os.path.exists(entity_id_file):
entity2id = {} # each of triple types have their own entity2id
for i in tqdm(range(datacsv.shape[0])):
if datacsv.iloc[i,1] == "id":
_get_id(entity2id, str(int(float(datacsv.iloc[i,0]))))
if datacsv.iloc[i,-2] == "id":
_get_id(entity2id, str(int(float(datacsv.iloc[i,-3]))))
json.dump(entity2id, open(entity_id_file, 'w'))
else:
entity2id = json.load(open(entity_id_file, 'r'))
id2entity = {v:k for k,v in entity2id.items()}
num_ent_id = len(entity2id)
# print('len(entity2id) = {}'.format(len(entity2id)))
target_list = generate_target_list(datacsv, entity2id)
model = DistMult(args, num_ent_id, target_list, args.device)
model.to(args.device)
# restore from ckpt
if args.ckpt_path:
ckpt = torch.load(args.ckpt_path, map_location=args.device)
model.load_state_dict(ckpt['model'], strict=False)
print('ckpt loaded...')
evaluate(model, id2entity, target_list, args)