demonstration program for the project (32*32)

implementation/demo32.py

@@ -0,0 +1,136 @@
+import torch as th
+import numpy as np
+import data_processing.DataLoader as dl
+import yaml
+
+
+current_depth = 4
+
+from networks.TextEncoder import Encoder
+from networks.ConditionAugmentation import ConditionAugmentor
+from networks.C_PRO_GAN import ProGAN
+
+# define the device for the training script
+device = th.device("cuda" if th.cuda.is_available() else "cpu")
+
+############################################################################
+#load my generator.
+
+
+def get_config(conf_file):
+    """
+    parse and load the provided configuration
+    :param conf_file: configuration file
+    :return: conf => parsed configuration
+    """
+    from easydict import EasyDict as edict
+
+    with open(conf_file, "r") as file_descriptor:
+        data = yaml.load(file_descriptor)
+
+    # convert the data into an easyDictionary
+    return edict(data)
+
+config = get_config("configs\\11.conf")
+
+c_pro_gan = ProGAN(
+    embedding_size=config.hidden_size,
+    depth=config.depth,
+    latent_size=config.latent_size,
+    learning_rate=config.learning_rate,
+    beta_1=config.beta_1,
+    beta_2=config.beta_2,
+    eps=config.eps,
+    drift=config.drift,
+    n_critic=config.n_critic,
+    device=device
+)
+
+c_pro_gan.gen.load_state_dict(th.load("training_runs\\11\\saved_models\\GAN_GEN_3_20.pth"))
+
+###################################################################################
+#load my embedding and conditional augmentor
+
+dataset = dl.Face2TextDataset(
+        pro_pick_file=config.processed_text_file,
+        img_dir=config.images_dir,
+        img_transform=dl.get_transform(config.img_dims),
+        captions_len=config.captions_length
+    )
+
+
+text_encoder = Encoder(
+        embedding_size=config.embedding_size,
+        vocab_size=dataset.vocab_size,
+        hidden_size=config.hidden_size,
+        num_layers=config.num_layers,
+        device=device
+    )
+text_encoder.load_state_dict(th.load("training_runs\\11\\saved_models\\Encoder_3_20.pth"))
+
+condition_augmenter = ConditionAugmentor(
+        input_size=config.hidden_size,
+        latent_size=config.ca_out_size,
+        device=device
+    )
+condition_augmenter.load_state_dict(th.load("training_runs\\11\\saved_models\\Condition_Augmentor_3_20.pth"))
+
+
+
+###################################################################################
+# #ask for text description/caption
+
+#caption to text encoding
+caption = input('Enter your desired description : ')
+seq = []
+for word in caption.split():
+    seq.append(dataset.rev_vocab[word])
+for i in range(len(seq), 100):
+    seq.append(0)
+
+seq = th.LongTensor(seq)
+seq = seq.cuda()
+print(type(seq))
+print('\nInput : ', caption)
+
+list_seq = [seq for i in range(16)]
+print(len(list_seq))
+list_seq = th.stack(list_seq)
+list_seq = list_seq.cuda()
+
+
+
+embeddings = text_encoder(list_seq)
+
+
+c_not_hats, mus, sigmas = condition_augmenter(embeddings)
+
+
+z = th.randn(list_seq.shape[0],
+             c_pro_gan.latent_size - c_not_hats.shape[-1]
+            ).to(device)
+
+gan_input = th.cat((c_not_hats, z), dim=-1)
+
+alpha = 0.007352941176470588
+
+samples=c_pro_gan.gen(gan_input,
+                      current_depth,
+                      alpha)
+
+from torchvision.utils import save_image
+from torch.nn.functional import upsample
+#from train_network import create_grid
+
+img_file = caption + '.png'
+samples = (samples / 2) + 0.5
+if int(np.power(2, c_pro_gan.depth - current_depth - 1)) > 1:
+    samples = upsample(samples, scale_factor=current_depth)
+
+
+#save image to the disk, the resulting image is <caption>.png
+save_image(samples, img_file, nrow=int(np.sqrt(20)))
+
+
+###################################################################################
+# #output the image.