Added Text2Vid in Azure

TrainedModels/marinabaysands/2024_04_12_20_49_12_generation_train_depth_3_lr_scale_0.5_act_lrelu_0.05/main.py

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+import datetime
+import dateutil.tz
+import os
+import os.path as osp
+from shutil import copyfile, copytree
+import glob
+import time
+import random
+import torch
+import math
+import argparse
+from SoloSynthGAN import functions
+
+
+def get_arguments():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--not_cuda', action='store_true', help='disables cuda', default=0)
+    parser.add_argument('--manualSeed', type=int, help='manual seed')
+
+
+    parser.add_argument('--nfc', type=int,help='number of filters per conv layer', default=64)
+    parser.add_argument('--ker_size',type=int,help='kernel size',default=3)
+    parser.add_argument('--num_layer',type=int,help='number of layers per stage',default=3)
+    parser.add_argument('--padd_size',type=int,help='net pad size',default=0)
+
+
+    parser.add_argument('--nc_im',type=int,help='image # channels',default=3)
+    parser.add_argument('--noise_amp',type=float,help='additive noise cont weight',default=0.1)
+    parser.add_argument('--min_size',type=int,help='image minimal size at the coarser scale',default=25)
+    parser.add_argument('--max_size', type=int,help='image minimal size at the coarser scale', default=250)
+    parser.add_argument('--train_depth', type=int, help='how many layers are trained if growing', default=3)
+    parser.add_argument('--start_scale', type=int, help='at which stage to start training', default=0)
+
+
+    parser.add_argument('--niter', type=int, default=1500, help='number of epochs to train per scale')
+    parser.add_argument('--gamma',type=float,help='scheduler gamma',default=0.1)
+    parser.add_argument('--lr_g', type=float, default=0.0005, help='learning rate, default=0.0005')
+    parser.add_argument('--lr_d', type=float, default=0.0005, help='learning rate, default=0.0005')
+    parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')
+    parser.add_argument('--Gsteps',type=int, help='Generator inner steps',default=3)
+    parser.add_argument('--Dsteps',type=int, help='Discriminator inner steps',default=3)
+    parser.add_argument('--lambda_grad',type=float, help='gradient penalty weight',default=0.1)
+    parser.add_argument('--alpha',type=float, help='reconstruction loss weight',default=10)
+    parser.add_argument('--activation', default='lrelu', help="activation function {lrelu, prelu, elu, selu}")
+    parser.add_argument('--lrelu_alpha', type=float, help='alpha for leaky relu', default=0.05)
+    parser.add_argument('--batch_norm', action='store_true', help='use batch norm in generator', default=0)
+
+    return parser
+
+
+
+
+if __name__ == '__main__':
+    parser = get_arguments()
+    parser.add_argument('--input_name', help='input image name for training', required=True)
+    parser.add_argument('--naive_img', help='naive input image  (harmonization or editing)', default="")
+    parser.add_argument('--gpu', type=int, help='which GPU to use', default=0)
+    parser.add_argument('--train_mode', default='generation',
+                        choices=['generation', 'retarget', 'harmonization', 'editing', 'animation'],
+                        help="generation, retarget, harmonization, editing, animation")
+    parser.add_argument('--lr_scale', type=float, help='scaling of learning rate for lower stages', default=0.5)
+    parser.add_argument('--train_stages', type=int, help='how many stages to use for training', default=6)
+
+    parser.add_argument('--fine_tune', action='store_true', help='whether to fine tune on a given image', default=0)
+    parser.add_argument('--model_dir', help='model to be used for fine tuning (harmonization or editing)', default="")
+
+    opt = parser.parse_args()
+    print(opt)
+    opt = functions.post_config(opt)
+
+
+    if opt.fine_tune:
+        _gpu = opt.gpu
+        _model_dir = opt.model_dir
+        _timestamp = opt.timestamp
+        _naive_img = opt.naive_img
+        _niter = opt.niter
+
+        opt = functions.load_config(opt)
+
+        opt.gpu = _gpu
+        opt.model_dir = _model_dir
+        opt.start_scale = opt.train_stages - 1
+        opt.timestamp = _timestamp
+        opt.fine_tune = True
+        opt.naive_img = _naive_img
+        opt.niter = _niter
+
+    if not os.path.exists(opt.input_name):
+        print("Image does not exist: {}".format(opt.input_name))
+        print("Please specify a valid image.")
+        exit()
+
+    if torch.cuda.is_available():
+        torch.cuda.set_device(opt.gpu)
+
+
+    if opt.train_mode == "generation" or opt.train_mode == "retarget" or opt.train_mode == "animation":
+        if opt.train_mode == "animation":
+            opt.min_size = 20
+        from SoloSynthGAN.training_generation import *
+
+    dir2save = functions.generate_dir2save(opt)
+
+    if osp.exists(dir2save):
+        print('Trained model already exist: {}'.format(dir2save))
+        exit()
+    try:
+        os.makedirs(dir2save)
+    except OSError:
+        pass
+
+    with open(osp.join(dir2save, 'parameters.txt'), 'w') as f:
+        for o in opt.__dict__:
+            f.write("{}\t-\t{}\n".format(o, opt.__dict__[o]))
+    current_path = os.path.dirname(os.path.abspath(__file__))
+    for py_file in glob.glob(osp.join(current_path, "*.py")):
+        copyfile(py_file, osp.join(dir2save, py_file.split("/")[-1]))
+    copytree(osp.join(current_path, "SoloSynthGAN"), osp.join(dir2save, "SoloSynthGAN"))
+
+
+
+    print("Training model ({})".format(dir2save))
+    start = time.time()
+    train(opt)
+    end = time.time()
+    elapsed_time = end - start
+    print("Time for training: {} seconds".format(elapsed_time))

TrainedModels/marinabaysands/2024_04_12_20_49_12_generation_train_depth_3_lr_scale_0.5_act_lrelu_0.05/parameters.txt

@@ -0,0 +1,35 @@
+not_cuda	-	0
+manualSeed	-	5060
+nfc	-	64
+ker_size	-	3
+num_layer	-	3
+padd_size	-	0
+nc_im	-	3
+noise_amp	-	0.1
+min_size	-	25
+max_size	-	250
+train_depth	-	3
+start_scale	-	0
+niter	-	1500
+gamma	-	0.1
+lr_g	-	0.0005
+lr_d	-	0.0005
+beta1	-	0.5
+Gsteps	-	3
+Dsteps	-	3
+lambda_grad	-	0.1
+alpha	-	10
+activation	-	lrelu
+lrelu_alpha	-	0.05
+batch_norm	-	0
+input_name	-	Images/marinabaysands.jpg
+naive_img	-	
+gpu	-	0
+train_mode	-	generation
+lr_scale	-	0.5
+train_stages	-	6
+fine_tune	-	0
+model_dir	-	
+device	-	cuda:0
+noise_amp_init	-	0.1
+timestamp	-	2024_04_12_20_49_12

TrainedModels/stonehenge/2024_02_09_16_42_52_generation_train_depth_3_lr_scale_0.12_act_lrelu_0.05/.amlignore

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+## This file was auto generated by the Azure Machine Learning Studio. Please do not remove. 
+## Read more about the .amlignore file here: https://docs.microsoft.com/azure/machine-learning/how-to-save-write-experiment-files#storage-limits-of-experiment-snapshots
+
+.ipynb_aml_checkpoints/ 
+*.amltmp 
+*.amltemp

TrainedModels/stonehenge/2024_02_09_16_42_52_generation_train_depth_3_lr_scale_0.12_act_lrelu_0.05/.amlignore.amltmp

@@ -0,0 +1,6 @@
+## This file was auto generated by the Azure Machine Learning Studio. Please do not remove. 
+## Read more about the .amlignore file here: https://docs.microsoft.com/azure/machine-learning/how-to-save-write-experiment-files#storage-limits-of-experiment-snapshots
+
+.ipynb_aml_checkpoints/ 
+*.amltmp 
+*.amltemp