Add missing LHQ README samples

README.md

 # Unconditional Diffusion model
  
-This repo presents our paper implementation of the unconditional diffusion model ,with its popular optimizations, from *Denoising Diffusion Probabilistic Models* by Ho et al and *Improved Denoising Diffusion Probabilistic Models* by Nichol and Dhariwal. The pipeline contains training, sampling and evaluation functions meant to be run on the HPC.
+This repo presents our paper implementation of the unconditional diffusion model, with its popular optimizations, from [*Denoising Diffusion Probabilistic Models* ](https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html) by Ho et al and [*Improved Denoising Diffusion Probabilistic Models* ](https://proceedings.mlr.press/v139/nichol21a.html) by Nichol and Dhariwal. The pipeline contains training, sampling, and evaluation functions meant to be run on the HPC.
  
-We show correctness of our pipeline by training unconditional diffusion models on the landscapes (LHQ) and celebrity A (CelebAHQ) datasets, generating realistic images with a resolution of 128x128px.
+We show the correctness of our pipeline by training unconditional diffusion models on the landscapes (LHQ) and celebrity A (CelebAHQ) datasets, generating realistic images with a resolution of 128x128px.
 
 Diffusion models are a class of generative models that offer a unique approach to modeling complex data distributions by simulating a stochastic process, known as a diffusion process, that gradually transforms data from a simple initial distribution into a complex data distribution. More specifically, the simple distribution is given by Gaussian Noise which is iteratively denoised into coherent images through modeling the entire data distribution present in the training set.
 
 ## Sample Examples
 ### Unconditional Landscape Generation: 
+<table>
+  <tr>
+    <td><img src="imgs/landscapes/sample_0_0.png" alt="land"></td>
+    <td><img src="imgs/landscapes/sample_0_26.png" alt="land"></td>
+    <td><img src="imgs/landscapes/sample_1_13.png" alt="land"></td>
+    <td><img src="imgs/landscapes/sample_0_5.png" alt="land"></td>
+    <td><img src="imgs/landscapes/sample_1_2.png" alt="land"></td>
+  </tr>
+  <tr>
+    <td><img src="imgs/landscapes/sample_1_4.png" alt="land"></td>
+    <td><img src="imgs/landscapes/sample_0_13.png" alt="land"></td>
+    <td><img src="imgs/landscapes/sample_0_6.png" alt="land"></td>
+    <td><img src="imgs/landscapes/sample_0_35.png" alt="land"></td>
+    <td><img src="imgs/landscapes/sample_0_36.png" alt="land"></td>
+  </tr>
+  <tr>
+    <td><img src="imgs/landscapes/sample_0_9.png" alt="land"></td>
+    <td><img src="imgs/landscapes/sample_0_11.png" alt="land"></td>
+    <td><img src="imgs/landscapes/sample_0_1.png" alt="land"></td>
+    <td><img src="imgs/landscapes/sample_0_15.png" alt="land"></td>
+    <td><img src="imgs/landscapes/sample_0_18.png" alt="land"></td>
+  </tr>
+</table>
 
 ### Unconditional Celebrity Face Generation:
 <table>

evaluation/evaluate.py

@@ -10,6 +10,105 @@ from torchvision.models import resnet50
 from evaluation.helpers.kNN import *
 from evaluation.helpers.metrics import *
 
+def simple_evaluator(model, 
+                     device,
+                     dataloader,
+                     checkpoint,
+                     experiment_path,
+                     sample_idx=0,
+                     **args,
+                     ):
+  '''
+  Takes a trained diffusion model from 'checkpoint' and evaluates its performance on the test 
+  dataset 'dataloader' w.r.t. the three most important perfromance metrics; FID, IS, KID. We continue
+  the progress of our evaluation function for the LDM upscalaer and may update this function accordingly.
+    
+  checkpoint:      Name of the saved pth. file containing the trained weights and biases  
+  experiment_path: Path to the experiment folder where the evaluation results will be stored  
+  dataloader:      Loads the test dataset for evaluation
+  sample_idx:      Integer that denotes which sample directory sample_{sample_idx} from the checkpoint model shall be used for evaluation
+  '''
+
+  checkpoint_path = f'{experiment_path}trained_ddpm/{checkpoint}'
+  # create evaluation directory for the complete experiment (if first time sampling images)
+  output_dir = f'{experiment_path}evaluations/'
+  os.makedirs(output_dir, exist_ok=True)
+
+  # create evaluation directory for the current version of the trained model
+  model_name = os.path.basename(checkpoint_path)
+  epoch = re.findall(r'\d+', model_name)
+  if epoch:
+      e = int(epoch[0])
+  else:
+      raise ValueError(f"No digit found in the filename: {filename}")
+  model_dir = os.path.join(output_dir,f'epoch_{e}')
+  os.makedirs(model_dir, exist_ok=True)
+
+  # create the evaluation directory for this evaluation run for the current version of the model 
+  eval_dir_list = [d for d in os.listdir(model_dir) if os.path.isdir(os.path.join(model_dir, d))]
+  indx_list = [int(d.split('_')[1]) for d in eval_dir_list if d.startswith('evaluation_')]
+  j = max(indx_list, default=-1) + 1
+  eval_dir = os.path.join(model_dir, f'evaluation_{j}')
+  os.makedirs(eval_dir, exist_ok=True)
+  
+  # Compute Metrics   
+  eval_path = os.path.join(eval_dir, 'eval.txt')
+ 
+  # get sampled images
+  transform = transforms.Compose([transforms.ToTensor(), transforms.Lambda(lambda x: (x * 255).type(torch.uint8))])
+  sample_path =  os.path.join(f'{experiment_path}samples/',f'epoch_{e}',f'sample_{sample_idx}')
+  ignore_tensor = f'image_tensor{j}'
+  images = []
+  for samplename in os.listdir(sample_path):
+        if samplename == ignore_tensor:
+            continue
+        img = Image.open(os.path.join(sample_path, samplename))
+        img = transform(img)
+        images.append(img)
+  # split them into batches for GPU memory
+  generated = torch.stack(images).to(device)
+  generated_batches = torch.split(generated, dataloader.batch_size)
+  nr_generated_batches = len(generated_batches)
+  nr_real_batches = len(dataloader)
+
+  # Init FID, IS and KID scores
+  fid = FrechetInceptionDistance(normalize = False).to(device)
+  iscore = InceptionScore(normalize=False).to(device)
+  kid = KernelInceptionDistance(normalize=False, subset_size=32).to(device)
+
+  # Update scores for the full testing dataset w.r.t. the sampled batches
+  for idx,(data, _) in enumerate(dataloader):
+    data = data.to(device)
+    fid.update(data, real=True)
+    kid.update(data, real=True)
+    if idx < nr_generated_batches:
+        gen = generated_batches[idx].to(device)
+        fid.update(gen, real=False)
+        kid.update(gen, real=False)
+        iscore.update(gen)
+
+  # If there are sampled images left, add them too
+  for idx in range(nr_real_batches, nr_generated_batches):
+    gen = generated_batches[idx].to(device)
+    fid.update(gen, real=False)
+    kid.update(gen, real=False)
+    iscore.update(gen)
+ 
+  # compute total FID, IS and KID 
+  fid_score = fid.compute()
+  i_score = iscore.compute()
+  kid_score = kid.compute()
+
+   # store results in txt file
+  with open(str(eval_path), 'a') as txt:
+    result = f'FID_epoch_{e}_sample_{sample_idx}:'
+    txt.write(result + str(fid_score.item()) + '\n')
+    result = f'KID_epoch_{e}_sample_{sample_idx}:'
+    txt.write(result + str(kid_score) + '\n')
+    result =  f'IS_epoch_{e}_sample_{sample_idx}:'
+    txt.write(result + str(i_score) + '\n')
+
+
 
 def ddpm_evaluator(experiment_path, realpath, genpath, data='lhq', size=128, arch='clip', mode='kNN', k=3, sample=10, name_appendix='', fid='no'):