def __getitem__(self, index):
metainfo = GView(self.get_metainfo(index))
feed_dict = GView()
# scene annotations
if self.incl_scene:
feed_dict.scene = metainfo.scene
feed_dict.update(gdef.annotate_objects(metainfo.scene))
feed_dict.objects_raw = feed_dict.objects.copy()
feed_dict.update(gdef.annotate_scene(metainfo.scene))
# image
feed_dict.image_index = metainfo.image_index
feed_dict.image_filename = metainfo.image_filename
# video_folder
feed_dict.video_folder = metainfo.video_folder
feed_dict.video = []
if self.image_root is not None:
feed_dict.image = Image.open(
osp.join(self.image_root,
feed_dict.image_filename)).convert("RGB")
feed_dict.image, feed_dict.objects = self.image_transform(
feed_dict.image, feed_dict.objects)
if self.image_root is not None:
import glob
print("Got video 3")
for name in glob.glob(
osp.join(self.image_root, feed_dict.video_folder) +
"/*.png"):
feed_dict.video += [Image.open(name).convert("RGB")]
return feed_dict.raw()
def __getitem__(self, index):
metainfo = GView(self.get_metainfo(index))
feed_dict = GView()
# scene annotations
if self.incl_scene:
feed_dict.scene = metainfo.scene
feed_dict.update(gdef.annotate_objects(metainfo.scene))
feed_dict.objects_raw = feed_dict.objects.copy()
feed_dict.update(gdef.annotate_scene(metainfo.scene))
# image
feed_dict.image_index = metainfo.image_index
feed_dict.image_filename = metainfo.image_filename
if self.image_root is not None:
feed_dict.image = Image.open(osp.join(self.image_root, feed_dict.image_filename)).convert('RGB')
feed_dict.image, feed_dict.objects = self.image_transform(feed_dict.image, feed_dict.objects)
# program
feed_dict.program_raw = metainfo.program_raw
feed_dict.program_seq = metainfo.program_seq
feed_dict.program_tree = metainfo.program_tree
feed_dict.program_qsseq = metainfo.program_qsseq
feed_dict.program_qstree = metainfo.program_qstree
feed_dict.question_type = metainfo.question_type
# question
feed_dict.answer = True
return feed_dict.raw()
def __getitem__(self, index):
metainfo = GView(self.get_metainfo(index))
feed_dict = GView()
# scene annotations
if self.incl_scene:
feed_dict.scene = metainfo.scene
feed_dict.update(gdef.annotate_objects(metainfo.scene))
feed_dict.objects_raw = feed_dict.objects.copy()
feed_dict.update(gdef.annotate_scene(metainfo.scene))
# image
feed_dict.image_index = metainfo.image_index
feed_dict.image_filename = metainfo.image_filename
# video
feed_dict.video_folder = metainfo.video_folder
video = []
original_objects = feed_dict.objects
if self.image_root is not None:
feed_dict.image = Image.open(
osp.join(self.image_root,
feed_dict.image_filename)).convert("RGB")
feed_dict.image, feed_dict.objects = self.image_transform(
feed_dict.image, feed_dict.objects)
if self.image_root is not None and feed_dict.video_folder is not None:
import glob
for name in glob.glob(
osp.join(self.image_root, feed_dict.video_folder) +
"/*.png"):
image = Image.open(name).convert("RGB")
image, _ = self.image_transform(image, original_objects)
video += [image]
feed_dict.video = torch.cat(video)
# program
feed_dict.program_raw = metainfo.program_raw
feed_dict.program_seq = metainfo.program_seq
feed_dict.program_tree = metainfo.program_tree
feed_dict.program_qsseq = metainfo.program_qsseq
feed_dict.program_qstree = metainfo.program_qstree
feed_dict.question_type = metainfo.question_type
# question
feed_dict.answer = True
return feed_dict.raw()
def __getitem__(self, index):
metainfo = GView(self.get_metainfo(index))
feed_dict = GView()
# scene annotations
if self.incl_scene:
feed_dict.scene = metainfo.scene
feed_dict.update(gdef.annotate_objects(metainfo.scene))
feed_dict.objects_raw = feed_dict.objects.copy()
feed_dict.update(gdef.annotate_scene(metainfo.scene))
# image
feed_dict.image_index = metainfo.image_index
feed_dict.image_filename = metainfo.image_filename
if self.image_root is not None:
feed_dict.image = Image.open(osp.join(self.image_root, feed_dict.image_filename)).convert('RGB')
feed_dict.image, feed_dict.objects = self.image_transform(feed_dict.image, feed_dict.objects)
return feed_dict.raw()
def __getitem__(self, index):
# index = index % 200
metainfo = GView(self.get_metainfo(index))
metainfo.view_id = 1
feed_dict = GView()
feed_dict.scene = metainfo.scene
feed_dict.attribute_name = "shape"
feed_dict.concept_name = metainfo.scene["objects"][0][feed_dict.attribute_name]
if self.incl_scene:
feed_dict.scene = metainfo.scene
feed_dict.update(gdef.annotate_objects(metainfo.scene))
if "objects" in feed_dict:
# NB(Jiayuan Mao): in some datasets, object information might be completely unavailable.
feed_dict.objects_raw = feed_dict.objects.copy()
feed_dict.update(gdef.annotate_scene(metainfo.scene))
# image
feed_dict.image_index = metainfo.image_index
feed_dict.image_filename = metainfo.image_filename
if self.image_root is not None and feed_dict.image_filename is not None:
feed_dict.image = Image.open(
osp.join(self.image_root, feed_dict.image_filename)
).convert("RGB")
feed_dict.image, feed_dict.objects = self.image_transform(
feed_dict.image, feed_dict.objects
)
if self.depth_root is not None and feed_dict.image_filename is not None:
depth_filename = feed_dict.image_filename.split(".")[0] + ".exr"
feed_dict.depth = torch.tensor(
load_depth(osp.join(self.depth_root, depth_filename))
)
# program
# Scene
# feed_dict.bboxes = torch.tensor(feed_dict.scene["obj_bboxes"][0]).reshape(-1, 9)
# # feed_dict.bboxes_len = torch.tensor(feed_dict.bboxes.size(0))
# feed_dict.pix_T_cam = torch.tensor(metainfo.scene["pix_T_cams"]).float()
# feed_dict.origin_T_cam = torch.tensor(
# metainfo.scene["origin_T_cams"][metainfo.view_id]
# ).float()
return feed_dict.raw()
def __getitem__(self, index):
metainfo = GView(self.get_metainfo(index))
feed_dict = GView()
# metainfo annotations
if self.incl_scene:
feed_dict.scene = metainfo.scene
feed_dict.update(gdef.annotate_objects(metainfo.scene))
if "objects" in feed_dict:
# NB(Jiayuan Mao): in some datasets, object information might be completely unavailable.
feed_dict.objects_raw = feed_dict.objects.copy()
feed_dict.update(gdef.annotate_scene(metainfo.scene))
# image
feed_dict.image_index = metainfo.image_index
feed_dict.image_filename = metainfo.image_filename
if self.image_root is not None and feed_dict.image_filename is not None:
feed_dict.image = Image.open(
osp.join(self.image_root, feed_dict.image_filename)
).convert("RGB")
feed_dict.image, feed_dict.objects = self.image_transform(
feed_dict.image, feed_dict.objects
)
if self.depth_root is not None and feed_dict.image_filename is not None:
depth_filename = feed_dict.image_filename.split(".")[0] + ".exr"
feed_dict.depth = torch.tensor(
load_depth(osp.join(self.depth_root, depth_filename))
)
# program
if "program_raw" in metainfo:
feed_dict.program_raw = metainfo.program_raw
feed_dict.program_seq = metainfo.program_seq
feed_dict.program_tree = metainfo.program_tree
feed_dict.program_qsseq = metainfo.program_qsseq
feed_dict.program_qstree = metainfo.program_qstree
feed_dict.question_type = metainfo.question_type
# question
feed_dict.question_index = metainfo.question_index
feed_dict.question_raw = metainfo.question
feed_dict.question_raw_tokenized = metainfo.question_tokenized
feed_dict.question_metainfo = gdef.annotate_question_metainfo(metainfo)
feed_dict.question = metainfo.question_tokenized
feed_dict.answer = gdef.canonize_answer(metainfo.answer, metainfo.question_type)
feed_dict.update(gdef.annotate_question(metainfo))
if self.question_transform is not None:
self.question_transform(feed_dict)
feed_dict.question = np.array(
self.vocab.map_sequence(feed_dict.question), dtype="int64"
)
return feed_dict.raw()
def __getitem__(self, index):
metainfo = GView(self.get_metainfo(index))
feed_dict = GView()
# metainfo annotations
if self.incl_scene:
feed_dict.scene = metainfo.scene
feed_dict.update(gdef.annotate_objects(metainfo.scene))
if "objects" in feed_dict:
# NB(Jiayuan Mao): in some datasets, object information might be completely unavailable.
feed_dict.objects_raw = feed_dict.objects.copy()
feed_dict.update(gdef.annotate_scene(metainfo.scene))
# image
feed_dict.image_index = metainfo.image_index
feed_dict.image_filename = metainfo.image_filename
# video
feed_dict.video_folder = metainfo.video_folder
video = []
original_objects = feed_dict.objects
if self.image_root is not None and feed_dict.image_filename is not None:
feed_dict.image = Image.open(
osp.join(self.image_root,
feed_dict.image_filename)).convert("RGB")
feed_dict.image, feed_dict.objects = self.image_transform(
feed_dict.image, feed_dict.objects)
# print("Image:", feed_dict.image.shape)
# print(feed_dict.objects)
if self.image_root is not None and feed_dict.video_folder is not None:
import glob
for name in glob.glob(
osp.join(self.image_root, feed_dict.video_folder) +
"/*.png"):
image = Image.open(name).convert("RGB")
image, _ = self.image_transform(image, original_objects)
video += [image]
feed_dict.video = torch.stack(video)
# Tensor
# print("Video:", feed_dict.video.shape)
# program
if "program_raw" in metainfo:
feed_dict.program_raw = metainfo.program_raw
feed_dict.program_seq = metainfo.program_seq
feed_dict.program_tree = metainfo.program_tree
feed_dict.program_qsseq = metainfo.program_qsseq
feed_dict.program_qstree = metainfo.program_qstree
feed_dict.question_type = metainfo.question_type
# question
feed_dict.question_index = metainfo.question_index
feed_dict.question_raw = metainfo.question
feed_dict.question_raw_tokenized = metainfo.question_tokenized
feed_dict.question_metainfo = gdef.annotate_question_metainfo(metainfo)
feed_dict.question = metainfo.question_tokenized
feed_dict.answer = gdef.canonize_answer(metainfo.answer,
metainfo.question_type)
feed_dict.update(gdef.annotate_question(metainfo))
if self.question_transform is not None:
self.question_transform(feed_dict)
feed_dict.question = np.array(self.vocab.map_sequence(
feed_dict.question),
dtype="int64")
return feed_dict.raw()
class ForwardContext(object):
def __init__(self, training, *, loss=0, monitors=None, output_dict=None):
self.training = training
self.loss = loss
self.monitors = GView(monitors)
self.output_dict = GView(output_dict)
self.hyperparameters = dict()
def set_hyperparameter(self, key, value):
self.hyperparameters[key] = value
def get_hyperparameter(self, key, default=None):
return self.hyperparameters.get(key, default=default)
def add_loss(self, loss, key=None, accumulate=True):
if float(accumulate) > 0:
self.loss = self.loss + loss * float(accumulate)
if key is not None:
if f'loss/{key}' in self.monitors:
self.monitors[f'loss/{key}'] += float(loss)
else:
self.monitors[f'loss/{key}'] = float(loss)
return self
def add_accuracy(self, accuracy, key):
self.monitors[f'accuracy/{key}'] = float(accuracy)
return self
def add_output(self, output, key):
self.output_dict[key] = output
return self
def update_monitors(self, monitors):
self.monitors.update(monitors)
return self
def update_mo(self, monitors, output_dict):
self.monitors.update(monitors)
self.output_dict.update(output_dict)
return self
binary_classification_accuracy = _wrap_monitor_function(
monitor.binary_classification_accuracy)
classification_accuracy = _wrap_monitor_function(
monitor.classification_accuracy)
regression_accuracy = _wrap_monitor_function(monitor.regression_accuracy)
monitor_rms = _wrap_monitor_function(monitor.monitor_rms)
monitor_param_saturation = _wrap_monitor_function(
monitor.monitor_param_saturation)
monitor_param_rms = _wrap_monitor_function(monitor.monitor_param_rms)
monitor_param_gradrms = _wrap_monitor_function(
monitor.monitor_param_gradrms)
monitor_param_gradrms_ratio = _wrap_monitor_function(
monitor.monitor_param_gradrms_ratio)
@wrap_custom_as_default(is_local=True)
def as_default(self) -> 'ForwardContext':
yield self
def finalize(self):
if self.training:
return self.loss, self.monitors, self.output_dict
else:
self.output_dict.monitors = self.monitors
return self.output_dict
