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Fixed issue where evaluation would error when large image was loaded (#194)

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This commit is contained in:
Aidan Dempster
2022-07-08 20:11:34 -04:00
committed by GitHub
parent f28dc6dc01
commit 870aeeca62
1 changed files with 4 additions and 9 deletions
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13
train_decoder.py
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@@ -132,7 +132,7 @@ def get_example_data(dataloader, device, n=5):
break break
return list(zip(images[:n], img_embeddings[:n], text_embeddings[:n], captions[:n])) return list(zip(images[:n], img_embeddings[:n], text_embeddings[:n], captions[:n]))
def generate_samples(trainer, example_data, condition_on_text_encodings=False, text_prepend=""): def generate_samples(trainer, example_data, condition_on_text_encodings=False, text_prepend="", match_image_size=True):
""" """
Takes example data and generates images from the embeddings Takes example data and generates images from the embeddings
Returns three lists: real images, generated images, and captions Returns three lists: real images, generated images, and captions
@@ -160,6 +160,9 @@ def generate_samples(trainer, example_data, condition_on_text_encodings=False, t
samples = trainer.sample(**sample_params) samples = trainer.sample(**sample_params)
generated_images = list(samples) generated_images = list(samples)
captions = [text_prepend + txt for txt in txts] captions = [text_prepend + txt for txt in txts]
if match_image_size:
generated_image_size = generated_images[0].shape[-1]
real_images = [resize_image_to(image, generated_image_size, clamp_range=(0, 1)) for image in real_images]
return real_images, generated_images, captions return real_images, generated_images, captions
def generate_grid_samples(trainer, examples, condition_on_text_encodings=False, text_prepend=""): def generate_grid_samples(trainer, examples, condition_on_text_encodings=False, text_prepend=""):
@@ -167,14 +170,6 @@ def generate_grid_samples(trainer, examples, condition_on_text_encodings=False,
Generates samples and uses torchvision to put them in a side by side grid for easy viewing Generates samples and uses torchvision to put them in a side by side grid for easy viewing
""" """
real_images, generated_images, captions = generate_samples(trainer, examples, condition_on_text_encodings, text_prepend) real_images, generated_images, captions = generate_samples(trainer, examples, condition_on_text_encodings, text_prepend)
real_image_size = real_images[0].shape[-1]
generated_image_size = generated_images[0].shape[-1]
# training images may be larger than the generated one
if real_image_size > generated_image_size:
real_images = [resize_image_to(image, generated_image_size) for image in real_images]
grid_images = [torchvision.utils.make_grid([original_image, generated_image]) for original_image, generated_image in zip(real_images, generated_images)] grid_images = [torchvision.utils.make_grid([original_image, generated_image]) for original_image, generated_image in zip(real_images, generated_images)]
return grid_images, captions return grid_images, captions