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demo.py
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demo.py
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#!/usr/bin/env python
# coding: utf-8
# ## Inverse Cooking: Recipe Generation from Food Images
import torch
import os
from src.args import get_parser
import pickle
from src.model import get_model
from torchvision import transforms
from src.utils.output_utils import prepare_output
from PIL import Image
import time
import sys
import requests
from io import BytesIO
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
urls = []
ids = []
ids.append("hello")
urls.append(sys.argv[1])
# print(urls)
# sys.stdout.flush()
# print(ids)
# print(sys.argv[1])
# -----------------------------------------
data_dir = './data/'
# code will run in gpu if available and if the flag is set to True, else it will run on cpu
use_gpu = False
device = torch.device('cuda' if torch.cuda.is_available() and use_gpu else 'cpu')
map_loc = None if torch.cuda.is_available() and use_gpu else 'cpu'
# print(device)
# print(map_loc)
# code below was used to save vocab files so that they can be loaded without Vocabulary class
# ingrs_vocab = pickle.load(open(os.path.join(data_dir, 'final_recipe1m_vocab_ingrs.pkl'), 'rb'))
# ingrs_vocab = [min(w, key=len) if not isinstance(w, str) else w for w in ingrs_vocab.idx2word.values()]
# vocab = pickle.load(open(os.path.join(data_dir, 'final_recipe1m_vocab_toks.pkl'), 'rb')).idx2word
# pickle.dump(ingrs_vocab, open('../demo/ingr_vocab.pkl', 'wb'))
# pickle.dump(vocab, open('../demo/instr_vocab.pkl', 'wb'))
ingrs_vocab = pickle.load(open(os.path.join(data_dir, 'ingr_vocab.pkl'), 'rb'))
vocab = pickle.load(open(os.path.join(data_dir, 'instr_vocab.pkl'), 'rb'))
ingr_vocab_size = len(ingrs_vocab)
instrs_vocab_size = len(vocab)
output_dim = instrs_vocab_size
t = time.time()
sys.argv = ['']
# del sys
args = get_parser()
args.maxseqlen = 15
args.ingrs_only = False
model = get_model(args, ingr_vocab_size, instrs_vocab_size)
# Load the trained model parameters
model_path = os.path.join(data_dir, 'modelbest.ckpt')
#print(os.path.exists('./data/' + 'modelbest.ckpt'))
#sys.stdout.flush()
# model_path = os.path.join("https://drive.google.com/open?id=1dpSIUZFtl03dvH1midHn67EZKyDzEAvy", "")
#dicty = torch.load(model_path, map_location=torch.device('cpu'))
#import torchvision.models as models
#resnet50 = models.resnet50(pretrained=True)
#print(resnet50)
#print(os.listdir("./.torch/models"))
#os.remove('./.torch/models/resnet50-19c8e357.pth')
#print(os.listdir("./.torch/models"))
dicty = torch.load("file.pt")
#print(type(dicty))
#print(dicty['state_dict'])
# torch.save(dicty, 'file.pt')
#try:
#dicty = torch.load("resnet50-19c8e357.pth")
#print(dicty)
#except:
#os.remove('./.torch/models/resnet50-19c8e357.pth')
model.load_state_dict(dicty) # map_location=map_loc
#print(model.load_state_dict(dicty))
model.to(device)
model.eval()
model.ingrs_only = False
model.recipe_only = False
# print('loaded model')
# print("Elapsed time:", time.time() - t)
transf_list_batch = [transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))]
to_input_transf = transforms.Compose(transf_list_batch)
greedy = [True, False, False, False]
beam = [-1, -1, -1, -1]
temperature = 1.0
numgens = 1
# Set ```use_urls = True``` to get recipes for images in ```demo_urls```.
#
# You can also set ```use_urls = False``` and get recipes for images in the path in ```data_dir/test_imgs```.
use_urls = True # set to true to load images from demo_urls instead of those in test_imgs folder
show_anyways = False # if True, it will show the recipe even if it's not valid
final_output = {}
response = requests.get(urls[0])
image = Image.open(BytesIO(response.content))
transf_list = [transforms.Resize(256), transforms.CenterCrop(224)]
transform = transforms.Compose(transf_list)
image_transf = transform(image)
image_tensor = to_input_transf(image_transf).unsqueeze(0).to(device)
# plt.imshow(image_transf)
# plt.axis('off')
# plt.show()
# plt.close()
num_valid = 1
for i in range(numgens):
with torch.no_grad():
outputs = model.sample(image_tensor, greedy=greedy[i],
temperature=temperature, beam=beam[i], true_ingrs=None)
ingr_ids = outputs['ingr_ids'].cpu().numpy()
recipe_ids = outputs['recipe_ids'].cpu().numpy()
outs, valid = prepare_output(recipe_ids[0], ingr_ids[0], ingrs_vocab, vocab)
print(outs['ingrs'])
# print(final_output)
final_output[ids[0]] = outs['ingrs']
# ---------------------------- Retrieving Cuisines ----------------------------
from gensim.models import Word2Vec
import numpy
model = Word2Vec.load("word2vec.model")
clf = pickle.load(open('./trained_models/svc_model_cuisine_100.sav', 'rb'))
# ---------------------------- Calculating total weight for each recipe
recipe_weight = len(outs['ingrs'])
# ---------------------------- Weighting Contribution each Ingredient
print(recipe_weight)
recipe_vector = []
for ingredient in outs['ingrs']:
recipe_vector.append(numpy.array([model[lemmatizer.lemmatize(ingredient.replace("_", " ").lower())]]) * 1)
recipe_vector = numpy.sum(numpy.array(recipe_vector), 0) / recipe_weight
print(clf.predict(recipe_vector))
sys.stdout.flush()