CocoCaptions in PyTorch (3)

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*Memos:

• My post explains CocoCaptions() using train2014 with captions_train2014.json, instances_train2014.json and person_keypoints_train2014.json, val2014 with captions_val2014.json, instances_val2014.json and person_keypoints_val2014.json and test2017 with image_info_test2014.json, image_info_test2015.json and image_info_test-dev2015.json.
• My post explains CocoCaptions() using train2017 with captions_train2017.json, instances_train2017.json and person_keypoints_train2017.json, val2017 with captions_val2017.json, instances_val2017.json and person_keypoints_val2017.json and test2017 with image_info_test2017.json and image_info_test-dev2017.json.
• My post explains CocoDetection() using train2014 with captions_train2014.json, instances_train2014.json and person_keypoints_train2014.json, val2014 with captions_val2014.json, instances_val2014.json and person_keypoints_val2014.json and test2017 with image_info_test2014.json, image_info_test2015.json and image_info_test-dev2015.json.
• My post explains CocoDetection() using train2017 with captions_train2017.json, instances_train2017.json and person_keypoints_train2017.json, val2017 with captions_val2017.json, instances_val2017.json and person_keypoints_val2017.json and test2017 with image_info_test2017.json and image_info_test-dev2017.json.
• My post explains CocoDetection() using train2017 with stuff_train2017.json, val2017 with stuff_val2017.json, stuff_train2017_pixelmaps with stuff_train2017.json, stuff_val2017_pixelmaps with stuff_val2017.json, panoptic_train2017 with panoptic_train2017.json, panoptic_val2017 with panoptic_val2017.json and unlabeled2017 with image_info_unlabeled2017.json.
• My post explains MS COCO.

CocoCaptions() can use MS COCO dataset as shown below. *This is for train2017 with stuff_train2017.json, val2017 with stuff_val2017.json, stuff_train2017_pixelmaps with stuff_train2017.json, stuff_val2017_pixelmaps with stuff_val2017.json, panoptic_train2017 with panoptic_train2017.json, panoptic_val2017 with panoptic_val2017.json and unlabeled2017 with image_info_unlabeled2017.json:

from torchvision.datasets import CocoCaptions

stf_train2017_data = CocoCaptions(
    root="data/coco/imgs/train2017",
    annFile="data/coco/anns/stuff_trainval2017/stuff_train2017.json"
)

stf_val2017_data = CocoCaptions(
    root="data/coco/imgs/val2017",
    annFile="data/coco/anns/stuff_trainval2017/stuff_val2017.json"
)

len(stf_train2017_data), len(stf_val2017_data)
# (118287, 5000)

pms_stf_train2017_data = CocoCaptions(
    root="data/coco/anns/stuff_trainval2017/stuff_train2017_pixelmaps",
    annFile="data/coco/anns/stuff_trainval2017/stuff_train2017.json"
)

pms_stf_val2017_data = CocoCaptions(
    root="data/coco/anns/stuff_trainval2017/stuff_val2017_pixelmaps",
    annFile="data/coco/anns/stuff_trainval2017/stuff_val2017.json"
)

len(pms_stf_train2017_data), len(pms_stf_val2017_data)
# (118287, 5000)

# pan_train2017_data = CocoCaptions(
#     root="data/coco/anns/panoptic_trainval2017/panoptic_train2017",
#     annFile="data/coco/anns/panoptic_trainval2017/panoptic_train2017.json"
# ) # Error

# pan_val2017_data = CocoCaptions(
#     root="data/coco/anns/panoptic_trainval2017/panoptic_val2017",
#     annFile="data/coco/anns/panoptic_trainval2017/panoptic_val2017.json"
# ) # Error

unlabeled2017_data = CocoCaptions(
    root="data/coco/imgs/unlabeled2017",
    annFile="data/coco/anns/unlabeled2017/image_info_unlabeled2017.json"
)

len(unlabeled2017_data)
# 123403

stf_train2017_data[2] # Error

stf_train2017_data[47] # Error

stf_train2017_data[64] # Error

stf_val2017_data[2] # Error

stf_val2017_data[47] # Error

stf_val2017_data[64] # Error

pms_stf_train2017_data[2] # Error

pms_stf_train2017_data[47] # Error

pms_stf_train2017_data[64] # Error

pms_stf_val2017_data[2] # Error

pms_stf_val2017_data[47] # Error

pms_stf_val2017_data[64] # Error

unlabeled2017_data[2]
# (<PIL.Image.Image image mode=RGB size=640x427>, [])

unlabeled2017_data[47]
# (<PIL.Image.Image image mode=RGB size=428x640>, [])

unlabeled2017_data[64]
# (<PIL.Image.Image image mode=RGB size=640x480>, [])

import matplotlib.pyplot as plt

def show_images(data, ims, main_title=None):
    file = data.root.split('/')[-1]
    fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(14, 8))
    fig.suptitle(t=main_title, y=0.9, fontsize=14)
    for i, axis in zip(ims, axes.ravel()):
        if not data[i][1]:
            im, _ = data[i]
            axis.imshow(X=im)
    fig.tight_layout()
    plt.show()

ims = (2, 47, 64)

show_images(data=unlabeled2017_data, ims=ims,
            main_title="unlabeled2017_data")