Python miscc.config.cfg.DATA_DIR Examples
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Example #1
Source File: main.py From semantic-object-accuracy-for-generative-text-to-image-synthesis with MIT License | 6 votes |
def get_dataset_indices(split="train", num_max_objects=10): if cfg.TRAIN.OPTIMIZE_DATA_LOADING: label_path = os.path.join(os.path.join(cfg.DATA_DIR, split), 'labels_large.pickle') with open(label_path, "rb") as f: labels = pickle.load(f, encoding='latin1') labels = np.array(labels) dataset_indices = [] for _i in range(num_max_objects+1): dataset_indices.append([]) for index, label in enumerate(labels): for idx, l in enumerate(label): if l == -1: dataset_indices[idx].append(index) break else: dataset_indices[-1].append(index) return dataset_indices
Example #2
Source File: trainer.py From Recipe2ImageGAN with MIT License | 6 votes |
def save_model(netG, avg_param_G, netsD, epoch, model_dir): last_run_dir = cfg.DATA_DIR + '/' + cfg.LAST_RUN_DIR + '/Model' load_params(netG, avg_param_G) torch.save( netG.state_dict(), '%s/netG_%d.pth' % (model_dir, epoch)) torch.save( netG.state_dict(), '%s/netG.pth' % (last_run_dir)) with open(last_run_dir + '/count.txt', 'w') as f: f.write(str(epoch)) for i in range(len(netsD)): netD = netsD[i] torch.save( netD.state_dict(), '%s/netD%d.pth' % (model_dir, i)) torch.save( netD.state_dict(), '%s/netD%d.pth' % (last_run_dir, i)) print('Save G/Ds models.')
Example #3
Source File: eval_trainer.py From Recipe2ImageGAN with MIT License | 6 votes |
def save_model(netG, avg_param_G, netsD, epoch, model_dir): last_run_dir = cfg.DATA_DIR + '/' + cfg.LAST_RUN_DIR + '/Model' load_params(netG, avg_param_G) torch.save( netG.state_dict(), '%s/netG_%d.pth' % (model_dir, epoch)) torch.save( netG.state_dict(), '%s/netG.pth' % (last_run_dir)) with open(last_run_dir + '/count.txt', 'w') as f: f.write(str(epoch)) for i in range(len(netsD)): netD = netsD[i] torch.save( netD.state_dict(), '%s/netD%d.pth' % (model_dir, i)) torch.save( netD.state_dict(), '%s/netD%d.pth' % (last_run_dir, i)) print('Save G/Ds models.')
Example #4
Source File: main.py From DM-GAN with MIT License | 4 votes |
def gen_example(wordtoix, algo): '''generate images from example sentences''' from nltk.tokenize import RegexpTokenizer filepath = '%s/example_filenames.txt' % (cfg.DATA_DIR) data_dic = {} with open(filepath, "r") as f: filenames = f.read().decode('utf8').split('\n') for name in filenames: if len(name) == 0: continue filepath = '%s/%s.txt' % (cfg.DATA_DIR, name) with open(filepath, "r") as f: print('Load from:', name) sentences = f.read().decode('utf8').split('\n') # a list of indices for a sentence captions = [] cap_lens = [] for sent in sentences: if len(sent) == 0: continue sent = sent.replace("\ufffd\ufffd", " ") tokenizer = RegexpTokenizer(r'\w+') tokens = tokenizer.tokenize(sent.lower()) if len(tokens) == 0: print('sent', sent) continue rev = [] for t in tokens: t = t.encode('ascii', 'ignore').decode('ascii') if len(t) > 0 and t in wordtoix: rev.append(wordtoix[t]) captions.append(rev) cap_lens.append(len(rev)) max_len = np.max(cap_lens) sorted_indices = np.argsort(cap_lens)[::-1] cap_lens = np.asarray(cap_lens) cap_lens = cap_lens[sorted_indices] cap_array = np.zeros((len(captions), max_len), dtype='int64') for i in range(len(captions)): idx = sorted_indices[i] cap = captions[idx] c_len = len(cap) cap_array[i, :c_len] = cap key = name[(name.rfind('/') + 1):] data_dic[key] = [cap_array, cap_lens, sorted_indices] algo.gen_example(data_dic)
Example #5
Source File: main.py From multiple-objects-gan with MIT License | 4 votes |
def gen_example(wordtoix, algo): '''generate images from example sentences''' from nltk.tokenize import RegexpTokenizer filepath = '%s/example_filenames.txt' % (cfg.DATA_DIR) data_dic = {} with open(filepath, "r") as f: filenames = f.read().decode('utf8').split('\n') for name in filenames: if len(name) == 0: continue filepath = '%s/%s.txt' % (cfg.DATA_DIR, name) with open(filepath, "r") as f: print('Load from:', name) sentences = f.read().decode('utf8').split('\n') # a list of indices for a sentence captions = [] cap_lens = [] for sent in sentences: if len(sent) == 0: continue sent = sent.replace("\ufffd\ufffd", " ") tokenizer = RegexpTokenizer(r'\w+') tokens = tokenizer.tokenize(sent.lower()) if len(tokens) == 0: print('sent', sent) continue rev = [] for t in tokens: t = t.encode('ascii', 'ignore').decode('ascii') if len(t) > 0 and t in wordtoix: rev.append(wordtoix[t]) captions.append(rev) cap_lens.append(len(rev)) max_len = np.max(cap_lens) sorted_indices = np.argsort(cap_lens)[::-1] cap_lens = np.asarray(cap_lens) cap_lens = cap_lens[sorted_indices] cap_array = np.zeros((len(captions), max_len), dtype='int64') for i in range(len(captions)): idx = sorted_indices[i] cap = captions[idx] c_len = len(cap) cap_array[i, :c_len] = cap key = name[(name.rfind('/') + 1):] data_dic[key] = [cap_array, cap_lens, sorted_indices] algo.gen_example(data_dic)
Example #6
Source File: main.py From attn-gan with MIT License | 4 votes |
def gen_example(wordtoix, algo): '''generate images from example sentences''' from nltk.tokenize import RegexpTokenizer filepath = '%s/example_filenames.txt' % (cfg.DATA_DIR) data_dic = {} with open(filepath, "r") as f: filenames = f.read().decode('utf8').split('\n') for name in filenames: if len(name) == 0: continue filepath = '%s/%s.txt' % (cfg.DATA_DIR, name) with open(filepath, "r") as f: print('Load from:', name) sentences = f.read().decode('utf8').split('\n') # a list of indices for a sentence captions = [] cap_lens = [] for sent in sentences: if len(sent) == 0: continue sent = sent.replace("\ufffd\ufffd", " ") tokenizer = RegexpTokenizer(r'\w+') tokens = tokenizer.tokenize(sent.lower()) if len(tokens) == 0: print('sent', sent) continue rev = [] for t in tokens: t = t.encode('ascii', 'ignore').decode('ascii') if len(t) > 0 and t in wordtoix: rev.append(wordtoix[t]) captions.append(rev) cap_lens.append(len(rev)) max_len = np.max(cap_lens) sorted_indices = np.argsort(cap_lens)[::-1] cap_lens = np.asarray(cap_lens) cap_lens = cap_lens[sorted_indices] cap_array = np.zeros((len(captions), max_len), dtype='int64') for i in range(len(captions)): idx = sorted_indices[i] cap = captions[idx] c_len = len(cap) cap_array[i, :c_len] = cap key = name[(name.rfind('/') + 1):] data_dic[key] = [cap_array, cap_lens, sorted_indices] algo.gen_example(data_dic)
Example #7
Source File: trainer.py From Recipe2ImageGAN with MIT License | 4 votes |
def load_network(gpus): netG = G_NET() netG.apply(weights_init) netG = torch.nn.DataParallel(netG, device_ids=gpus) print(netG) netsD = [] if cfg.TREE.BRANCH_NUM > 0: netsD.append(D_NET64()) if cfg.TREE.BRANCH_NUM > 1: netsD.append(D_NET128()) if cfg.TREE.BRANCH_NUM > 2: netsD.append(D_NET256()) if cfg.TREE.BRANCH_NUM > 3: netsD.append(D_NET512()) if cfg.TREE.BRANCH_NUM > 4: netsD.append(D_NET1024()) # TODO: if cfg.TREE.BRANCH_NUM > 5: for i in range(len(netsD)): netsD[i].apply(weights_init) netsD[i] = torch.nn.DataParallel(netsD[i], device_ids=gpus) # print(netsD[i]) print('# of netsD', len(netsD)) count = 0 if cfg.TRAIN.NET_G != '': state_dict = torch.load(cfg.TRAIN.NET_G) netG.load_state_dict(state_dict) print('Load ', cfg.TRAIN.NET_G) try: istart = cfg.TRAIN.NET_G.rfind('_') + 1 iend = cfg.TRAIN.NET_G.rfind('.') count = cfg.TRAIN.NET_G[istart:iend] count = int(count) except: last_run_dir = cfg.DATA_DIR + '/' + cfg.LAST_RUN_DIR + '/Model' with open(last_run_dir + '/count.txt', 'r') as f: count = int(f.read()) count = int(count) + 1 if cfg.TRAIN.NET_D != '': for i in range(len(netsD)): print('Load %s_%d.pth' % (cfg.TRAIN.NET_D, i)) state_dict = torch.load('%s%d.pth' % (cfg.TRAIN.NET_D, i)) netsD[i].load_state_dict(state_dict) inception_model = INCEPTION_V3() if cfg.CUDA: netG.cuda() for i in range(len(netsD)): netsD[i].cuda() inception_model = inception_model.cuda() inception_model.eval() return netG, netsD, len(netsD), inception_model, count
Example #8
Source File: trainer.py From Recipe2ImageGAN with MIT License | 4 votes |
def save_img_results(imgs_tcpu, fake_imgs, num_imgs, count, image_dir, summary_writer, rec_ids , im_ids): num = cfg.TRAIN.VIS_COUNT last_run_dir = cfg.DATA_DIR + '/' + cfg.LAST_RUN_DIR + '/Image/' # The range of real_img (i.e., self.imgs_tcpu[i][0:num]) # is changed to [0, 1] by function vutils.save_image real_img = imgs_tcpu[-1][0:num] vutils.save_image( real_img, '%s/count_%09d_real_samples.png' % (image_dir, count), normalize=True) vutils.save_image( real_img, last_run_dir + 'real_samples.png', normalize=True) # write images and recipe IDs to filenames rec_ids = [t.tostring().decode('UTF-8') for t in rec_ids.numpy()] im_ids = [t.tostring().decode('UTF-8') for t in im_ids.numpy()] with open('%s/count_%09d_real_samples_IDs.txt' % (image_dir, count),"w") as f: for rec_id, im_id in zip(rec_ids, im_ids): f.write("rec_id=%s, img_id=%s\n" % (rec_id,im_id)) with open(last_run_dir + 'real_samples_IDs.txt',"w") as f: for rec_id, im_id in zip(rec_ids, im_ids): f.write("rec_id=%s, img_id=%s\n" % (rec_id,im_id)) real_img_set = vutils.make_grid(real_img).numpy() real_img_set = np.transpose(real_img_set, (1, 2, 0)) real_img_set = real_img_set * 255 real_img_set = real_img_set.astype(np.uint8) sup_real_img = summary.image('real_img', real_img_set) summary_writer.add_summary(sup_real_img, count) for i in range(num_imgs): fake_img = fake_imgs[i][0:num] # The range of fake_img.data (i.e., self.fake_imgs[i][0:num]) # is still [-1. 1]... vutils.save_image( fake_img.data, '%s/count_%09d_fake_samples%d.png' % (image_dir, count, i), normalize=True) vutils.save_image( fake_img.data, last_run_dir + 'fake_samples%d.png' % (i), normalize=True) fake_img_set = vutils.make_grid(fake_img.data).cpu().numpy() fake_img_set = np.transpose(fake_img_set, (1, 2, 0)) fake_img_set = (fake_img_set + 1) * 255 / 2 fake_img_set = fake_img_set.astype(np.uint8) sup_fake_img = summary.image('fake_img%d' % i, fake_img_set) summary_writer.add_summary(sup_fake_img, count) summary_writer.flush() # ################## For uncondional tasks ######################### #
Example #9
Source File: eval_trainer.py From Recipe2ImageGAN with MIT License | 4 votes |
def load_network(gpus): netG = G_NET() netG.apply(weights_init) netG = torch.nn.DataParallel(netG, device_ids=gpus) print(netG) netsD = [] if cfg.TREE.BRANCH_NUM > 0: netsD.append(D_NET64()) if cfg.TREE.BRANCH_NUM > 1: netsD.append(D_NET128()) if cfg.TREE.BRANCH_NUM > 2: netsD.append(D_NET256()) if cfg.TREE.BRANCH_NUM > 3: netsD.append(D_NET512()) if cfg.TREE.BRANCH_NUM > 4: netsD.append(D_NET1024()) # TODO: if cfg.TREE.BRANCH_NUM > 5: for i in range(len(netsD)): netsD[i].apply(weights_init) netsD[i] = torch.nn.DataParallel(netsD[i], device_ids=gpus) # print(netsD[i]) print('# of netsD', len(netsD)) count = 0 if cfg.TRAIN.NET_G != '': state_dict = torch.load(cfg.TRAIN.NET_G) netG.load_state_dict(state_dict) print('Load ', cfg.TRAIN.NET_G) try: istart = cfg.TRAIN.NET_G.rfind('_') + 1 iend = cfg.TRAIN.NET_G.rfind('.') count = cfg.TRAIN.NET_G[istart:iend] count = int(count) except: last_run_dir = cfg.DATA_DIR + '/' + cfg.LAST_RUN_DIR + '/Model' with open(last_run_dir + '/count.txt', 'r') as f: count = int(f.read()) count = int(count) + 1 if cfg.TRAIN.NET_D != '': for i in range(len(netsD)): print('Load %s_%d.pth' % (cfg.TRAIN.NET_D, i)) state_dict = torch.load('%s%d.pth' % (cfg.TRAIN.NET_D, i)) netsD[i].load_state_dict(state_dict) inception_model = INCEPTION_V3() if cfg.CUDA: netG.cuda() for i in range(len(netsD)): netsD[i].cuda() inception_model = inception_model.cuda() inception_model.eval() return netG, netsD, len(netsD), inception_model, count
Example #10
Source File: main.py From AttnGAN with MIT License | 4 votes |
def gen_example(wordtoix, algo): '''generate images from example sentences''' from nltk.tokenize import RegexpTokenizer filepath = '%s/example_filenames.txt' % (cfg.DATA_DIR) data_dic = {} with open(filepath, "r") as f: filenames = f.read().decode('utf8').split('\n') for name in filenames: if len(name) == 0: continue filepath = '%s/%s.txt' % (cfg.DATA_DIR, name) with open(filepath, "r") as f: print('Load from:', name) sentences = f.read().decode('utf8').split('\n') # a list of indices for a sentence captions = [] cap_lens = [] for sent in sentences: if len(sent) == 0: continue sent = sent.replace("\ufffd\ufffd", " ") tokenizer = RegexpTokenizer(r'\w+') tokens = tokenizer.tokenize(sent.lower()) if len(tokens) == 0: print('sent', sent) continue rev = [] for t in tokens: t = t.encode('ascii', 'ignore').decode('ascii') if len(t) > 0 and t in wordtoix: rev.append(wordtoix[t]) captions.append(rev) cap_lens.append(len(rev)) max_len = np.max(cap_lens) sorted_indices = np.argsort(cap_lens)[::-1] cap_lens = np.asarray(cap_lens) cap_lens = cap_lens[sorted_indices] cap_array = np.zeros((len(captions), max_len), dtype='int64') for i in range(len(captions)): idx = sorted_indices[i] cap = captions[idx] c_len = len(cap) cap_array[i, :c_len] = cap key = name[(name.rfind('/') + 1):] data_dic[key] = [cap_array, cap_lens, sorted_indices] algo.gen_example(data_dic)