52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
# script to test data loader by itself
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# run from training root, edit the data_root manually
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# python ldm/data/test_dl.py
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import ed_validate
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import time
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s = time.perf_counter()
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data_root = "r:/everydream-trainer/training_samples/multiaspect4"
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batch_size = 1
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repeats = 1
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ed_val_batch = ed_validate.EDValidateBatch(data_root=data_root, flip_p=0.0, debug_level=0, batch_size=batch_size, repeats=repeats)
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print(f"batch type: {type(ed_val_batch)}")
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i = 0
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is_next = True
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curr_batch = []
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while is_next and i < 84:
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try:
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example = ed_val_batch[i]
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if example is not None:
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#print(f"example type: {type(example)}") # dict
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#print(f"example keys: {example.keys()}") # dict_keys(['image', 'caption'])
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#print(f"example image type: {type(example['image'])}") # numpy.ndarray
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if i%batch_size == 0:
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curr_batch = example['image'].shape
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img_in_right_batch = curr_batch == example['image'].shape
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print(f"example image shape: {example['image'].shape} {i%batch_size} {img_in_right_batch}") # (256, 256, 3)
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if not img_in_right_batch:
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raise Exception("Current image in wrong batch")
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#print(f"example caption: {example['caption']}") # str
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else:
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is_next = False
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i += 1
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except IndexError:
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is_next = False
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print(f"IndexError: {i}")
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pass
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# for idx, batches in every_dream_batch:
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# print(f"inner example type: {type(batches)}")
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# print(type(batches))
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# print(type(batches[0]))
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# print(dir(batches))
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#h, w = batches.image.size
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#print(f"{idx:05d}-{idx%6:02d}EveryDreamBatch image caption pair: w:{w} h:{h} {batches.caption[1]}")
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print(f" *TEST* test cycles: {i}")
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print(f" *TEST* EDValidateBatch epoch image length: {len(every_dream_batch)}")
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elapsed = time.perf_counter() - s
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print(f"{__file__} executed in {elapsed:5.2f} seconds.") |