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program_declaration.py
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import torch
from domiknows.sensor.pytorch.sensors import ReaderSensor, ConcatSensor, FunctionalSensor, JointSensor
from domiknows.sensor.pytorch.learners import ModuleLearner, LSTMLearner
from models import *
from utils import *
from domiknows.sensor.pytorch.relation_sensors import CompositionCandidateSensor
from sklearn import preprocessing
from transformers import RobertaModel
import numpy as np
def program_declaration(cur_device, *, pmd=False, beta=0.5, sampling=False, sampleSize=1, dropout=False,
constraints=False, model='bert'):
from graph import graph, story, question, answer_class, story_contain, \
symmetric, s_quest1, s_quest2, reverse, r_quest1, r_quest2, \
transitive, t_quest1, t_quest2, t_quest3, transitive_topo, tt_quest1, tt_quest2, tt_quest3, tt_quest4
story["questions"] = ReaderSensor(keyword="questions")
story["stories"] = ReaderSensor(keyword="stories")
story["relations"] = ReaderSensor(keyword="relation")
story["question_ids"] = ReaderSensor(keyword="question_ids")
story["labels"] = ReaderSensor(keyword="labels")
def str_to_int_list(x):
return torch.LongTensor([int(i) for i in x])
def make_labels(label_list):
labels = label_list.split("@@")
label_nums = [0 if label == "Yes" else 1 if label == "No" else 2 for label in labels]
return str_to_int_list(label_nums)
def make_question(questions, stories, relations, q_ids, labels):
num_labels = make_labels(labels)
ids = str_to_int_list(q_ids.split("@@"))
return torch.ones(len(questions.split("@@")), 1), questions.split("@@"), stories.split("@@"), \
relations.split("@@"), ids, num_labels
question[story_contain, "question", "story", "relation", "id", "label"] = \
JointSensor(story["questions"], story["stories"], story["relations"],
story["question_ids"], story["labels"], forward=make_question, device=cur_device)
def read_label(_, label):
return label
question[answer_class] = FunctionalSensor(story_contain, "label", forward=read_label, label=True, device=cur_device)
print("Using the {:} as the baseline model".format(model))
if model == "roberta":
question["input_ids"] = JointSensor(story_contain, 'question', "story",
forward=RoBERTaTokenizer(), device=cur_device)
clf = MultipleClassYNRoberta.from_pretrained('roberta-base', device=cur_device, drp=dropout)
elif model == "t5-adapter":
question["input_ids"] = JointSensor(story_contain, 'question', "story",
forward=T5Tokenizer('google/flan-t5-base'), device=cur_device)
clf = MultipleClassYNT5('google/flan-t5-base', device=cur_device, adapter=True)
elif model == "llama3":
model_ids = "meta-llama/Meta-Llama-3-8B"
tokenizer = Llama3Tokenizer(model_ids)
question["input_ids"] = JointSensor(story_contain, 'question', "story",
forward=tokenizer, device=cur_device)
clf = MultipleClassYNLlama3(model_ids, tokenizer, device=cur_device, adapter=True)
else:
question["input_ids"] = JointSensor(story_contain, 'question', "story",
forward=BERTTokenizer(), device=cur_device)
clf = MultipleClassYN.from_pretrained('bert-base-uncased', device=cur_device, drp=dropout)
question[answer_class] = ModuleLearner("input_ids", module=clf, device=cur_device)
poi_list = [question, answer_class]
# Including the constraints relation check
if constraints:
print("Include logical constraints")
symmetric[s_quest1.reversed, s_quest2.reversed] = \
CompositionCandidateSensor(
relations=(s_quest1.reversed, s_quest2.reversed),
forward=check_symmetric, device=cur_device)
reverse[r_quest1.reversed, r_quest2.reversed] = \
CompositionCandidateSensor(
relations=(r_quest1.reversed, r_quest2.reversed),
forward=check_reverse, device=cur_device)
transitive[t_quest1.reversed, t_quest2.reversed, t_quest3.reversed] = \
CompositionCandidateSensor(
relations=(t_quest1.reversed, t_quest2.reversed, t_quest3.reversed),
forward=check_transitive, device=cur_device)
transitive_topo[tt_quest1.reversed, tt_quest2.reversed, tt_quest3.reversed, tt_quest4.reversed] = \
CompositionCandidateSensor(
relations=(tt_quest1.reversed, tt_quest2.reversed, tt_quest3.reversed, tt_quest4.reversed),
forward=check_transitive_topo, device=cur_device)
poi_list.extend([symmetric, reverse, transitive, transitive_topo])
from domiknows.program.metric import PRF1Tracker, PRF1Tracker, DatanodeCMMetric, MacroAverageTracker, ValueTracker
from domiknows.program.loss import NBCrossEntropyLoss, BCEWithLogitsIMLoss, BCEFocalLoss
from domiknows.program import LearningBasedProgram, SolverPOIProgram
from domiknows.program.lossprogram import SampleLossProgram, PrimalDualProgram
from domiknows.program.model.pytorch import model_helper, PoiModel, SolverModel
infer_list = ['local/argmax'] # ['ILP', 'local/argmax']
if pmd:
print("Using Primal Dual Program")
program = PrimalDualProgram(graph, SolverModel, poi=poi_list,
inferTypes=infer_list,
loss=MacroAverageTracker(NBCrossEntropyLoss()),
beta=beta,
metric={'ILP': PRF1Tracker(DatanodeCMMetric()),
'argmax': PRF1Tracker(DatanodeCMMetric('local/argmax'))},
device=cur_device)
elif sampling:
program = SampleLossProgram(graph, SolverModel, poi=poi_list,
inferTypes=infer_list,
loss=MacroAverageTracker(NBCrossEntropyLoss()),
metric={'ILP': PRF1Tracker(DatanodeCMMetric()),
'argmax': PRF1Tracker(DatanodeCMMetric('local/argmax'))},
sample=True,
sampleSize=sampleSize,
sampleGlobalLoss=False,
beta=1,
device=cur_device)
else:
print("Using Base Program")
program = SolverPOIProgram(graph,
poi=poi_list,
inferTypes=infer_list,
loss=MacroAverageTracker(NBCrossEntropyLoss()),
metric={'ILP': PRF1Tracker(DatanodeCMMetric()),
'argmax': PRF1Tracker(DatanodeCMMetric('local/argmax'))},
device=cur_device)
return program
def program_declaration_spartun_fr(device, *, pmd=False, beta=0.5, sampling=False, sampleSize=1, dropout=False,
constraints=False, spartun=True, model="bert"):
program = None
from graph_spartun_rel import graph, story, story_contain, question, \
left, right, above, below, behind, front, near, far, disconnected, touch, \
overlap, coveredby, inside, cover, contain, inverse, inv_question1, inv_question2, \
transitive, tran_quest1, tran_quest2, tran_quest3, tran_topo, tran_topo_quest1, \
tran_topo_quest2, tran_topo_quest3, tran_topo_quest4
story["questions"] = ReaderSensor(keyword="questions")
story["stories"] = ReaderSensor(keyword="stories")
story["relations"] = ReaderSensor(keyword="relation")
story["question_ids"] = ReaderSensor(keyword="question_ids")
story["labels"] = ReaderSensor(keyword="labels")
all_labels = ["left", "right", "above", "below", "behind", "front", "near", "far", "dc", "ec", "po", "tpp", "ntpp",
"tppi", "ntppi"]
def to_int_list(x):
return torch.LongTensor([int(i) for i in x])
def make_labels(label_list):
labels = label_list.split("@@")
all_labels_list = [[] for _ in range(15)]
for bits_label in labels:
bits_label = int(bits_label)
cur_label = 1
for ind, label in enumerate(all_labels):
all_labels_list[ind].append(1 if bits_label & cur_label else 0)
cur_label *= 2
# label_nums = [0 if label == "Yes" else 1 if label == "No" else 2 for label in labels]
return [to_int_list(labels_list) for labels_list in all_labels_list]
def make_question(questions, stories, relations, q_ids, labels):
all_labels = make_labels(labels)
ids = to_int_list(q_ids.split("@@"))
left_list, right_list, above_list, below_list, behind_list, \
front_list, near_list, far_list, dc_list, ec_list, po_list, \
tpp_list, ntpp_list, tppi_list, ntppi_list = all_labels
return torch.ones(len(questions.split("@@")), 1), questions.split("@@"), stories.split("@@"), \
relations.split("@@"), ids, left_list, right_list, above_list, below_list, behind_list, \
front_list, near_list, far_list, dc_list, ec_list, po_list, \
tpp_list, ntpp_list, tppi_list, ntppi_list
question[story_contain, "question", "story", "relation", "id", "left_label", "right_label",
"above_label", "below_label", "behind_label", "front_label", "near_label", "far_label", "dc_label", "ec_label", "po_label",
"tpp_label", "ntpp_label", "tppi_label", "ntppi_label"] = \
JointSensor(story["questions"], story["stories"], story["relations"],
story["question_ids"], story["labels"], forward=make_question, device=device)
def read_label(_, label):
return label
# Model
if model == "t5-adapter":
t5_model_id = "google/flan-t5-base"
print("Using", t5_model_id)
question["input_ids"] = JointSensor(story_contain, 'question', "story",
forward=T5Tokenizer(t5_model_id), device=device)
all_answers = [left, right, above, below, behind, front,
near, far, disconnected, touch, overlap, coveredby,
inside, cover, contain]
expected_label = ["left", "right", "above", "below", "behind", "front",
"near", "far", "disconnected", "touch", "overlap", "covered by",
"inside", "cover", "contain"]
clf1 = MultipleClassFRT5(t5_model_id, expected_label, device=device, adapter=True)
question["hidden_layer"] = ModuleLearner("input_ids", module=clf1, device=device)
question[left] = ModuleLearner("hidden_layer",
module=ClassifyLabelT5(expected_label[0], map_index=clf1.map_label,
device=device),
device=device)
question[right] = ModuleLearner("hidden_layer",
module=ClassifyLabelT5(expected_label[1], map_index=clf1.map_label,
device=device),
device=device)
question[above] = ModuleLearner("hidden_layer",
module=ClassifyLabelT5(expected_label[2], map_index=clf1.map_label,
device=device),
device=device)
question[below] = ModuleLearner("hidden_layer",
module=ClassifyLabelT5(expected_label[3], map_index=clf1.map_label,
device=device),
device=device)
question[behind] = ModuleLearner("hidden_layer",
module=ClassifyLabelT5(expected_label[4], map_index=clf1.map_label,
device=device),
device=device)
question[front] = ModuleLearner("hidden_layer",
module=ClassifyLabelT5(expected_label[5], map_index=clf1.map_label,
device=device),
device=device)
question[near] = ModuleLearner("hidden_layer",
module=ClassifyLabelT5(expected_label[6], map_index=clf1.map_label,
device=device),
device=device)
question[far] = ModuleLearner("hidden_layer",
module=ClassifyLabelT5(expected_label[7], map_index=clf1.map_label,
device=device),
device=device)
question[disconnected] = ModuleLearner("hidden_layer",
module=ClassifyLabelT5(expected_label[8], map_index=clf1.map_label,
device=device),
device=device)
question[touch] = ModuleLearner("hidden_layer",
module=ClassifyLabelT5(expected_label[9], map_index=clf1.map_label,
device=device),
device=device)
question[overlap] = ModuleLearner("hidden_layer",
module=ClassifyLabelT5(expected_label[10], map_index=clf1.map_label,
device=device),
device=device)
question[coveredby] = ModuleLearner("hidden_layer",
module=ClassifyLabelT5(expected_label[11], map_index=clf1.map_label,
device=device),
device=device)
question[inside] = ModuleLearner("hidden_layer",
module=ClassifyLabelT5(expected_label[12], map_index=clf1.map_label,
device=device),
device=device)
question[cover] = ModuleLearner("hidden_layer",
module=ClassifyLabelT5(expected_label[13], map_index=clf1.map_label,
device=device),
device=device)
question[contain] = ModuleLearner("hidden_layer",
module=ClassifyLabelT5(expected_label[14], map_index=clf1.map_label,
device=device),
device=device)
else:
print("Using BERT")
question["input_ids"] = JointSensor(story_contain, 'question', "story",
forward=BERTTokenizer(), device=device)
clf1 = MultipleClassYN_Hidden.from_pretrained('bert-base-uncased', device=device, drp=dropout)
question["hidden_layer"] = ModuleLearner("input_ids", module=clf1, device=device)
all_answers = [left, right, above, below, behind, front,
near, far, disconnected, touch, overlap, coveredby,
inside, cover, contain]
question[left] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[right] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[above] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[below] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[behind] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[front] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[near] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[far] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[disconnected] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[touch] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[overlap] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[coveredby] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[inside] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[cover] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[contain] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
# Reading label
question[left] = FunctionalSensor(story_contain, "left_label", forward=read_label, label=True, device=device)
question[right] = FunctionalSensor(story_contain, "right_label", forward=read_label, label=True, device=device)
question[above] = FunctionalSensor(story_contain, "above_label", forward=read_label, label=True, device=device)
question[below] = FunctionalSensor(story_contain, "below_label", forward=read_label, label=True, device=device)
question[behind] = FunctionalSensor(story_contain, "behind_label", forward=read_label, label=True, device=device)
question[front] = FunctionalSensor(story_contain, "front_label", forward=read_label, label=True, device=device)
question[near] = FunctionalSensor(story_contain, "near_label", forward=read_label, label=True, device=device)
question[far] = FunctionalSensor(story_contain, "far_label", forward=read_label, label=True, device=device)
question[disconnected] = FunctionalSensor(story_contain, "dc_label", forward=read_label, label=True, device=device)
question[touch] = FunctionalSensor(story_contain, "ec_label", forward=read_label, label=True, device=device)
question[overlap] = FunctionalSensor(story_contain, "po_label", forward=read_label, label=True, device=device)
question[coveredby] = FunctionalSensor(story_contain, "tpp_label", forward=read_label, label=True, device=device)
question[inside] = FunctionalSensor(story_contain, "ntpp_label", forward=read_label, label=True, device=device)
question[cover] = FunctionalSensor(story_contain, "tppi_label", forward=read_label, label=True, device=device)
question[contain] = FunctionalSensor(story_contain, "ntppi_label", forward=read_label, label=True, device=device)
# question[left] = ModuleLearner("hidden_layer",
# module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
# device=device)
# question[right] = ModuleLearner("hidden_layer",
# module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
# device=device)
# question[above] = ModuleLearner("hidden_layer",
# module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
# device=device)
# question[below] = ModuleLearner("hidden_layer",
# module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
# device=device)
# question[behind] = ModuleLearner("hidden_layer",
# module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
# device=device)
# question[front] = ModuleLearner("hidden_layer",
# module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
# device=device)
# question[near] = ModuleLearner("hidden_layer",
# module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
# device=device)
# question[far] = ModuleLearner("hidden_layer",
# module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
# device=device)
# question[disconnected] = ModuleLearner("hidden_layer",
# module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
# device=device)
# question[touch] = ModuleLearner("hidden_layer",
# module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout), device=device)
# question[overlap] = ModuleLearner("hidden_layer",
# module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout), device=device)
# question[coveredby] = ModuleLearner("hidden_layer",
# module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
# device=device)
# question[inside] = ModuleLearner("hidden_layer",
# module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
# device=device)
#
# question[cover] = ModuleLearner("hidden_layer",
# module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
# device=device)
#
# question[contain] = ModuleLearner("hidden_layer",
# module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
# device=device)
poi_list = [question, left, right, above, below, behind, front, near, far,
disconnected, touch, overlap, coveredby, inside, cover, contain]
if constraints:
print("Included constraints")
inverse[inv_question1.reversed, inv_question2.reversed] = \
CompositionCandidateSensor(
relations=(inv_question1.reversed, inv_question2.reversed),
forward=check_symmetric, device=device)
transitive[tran_quest1.reversed, tran_quest2.reversed, tran_quest3.reversed] = \
CompositionCandidateSensor(
relations=(tran_quest1.reversed, tran_quest2.reversed, tran_quest3.reversed),
forward=check_transitive, device=device)
tran_topo[tran_topo_quest1.reversed, tran_topo_quest2.reversed,
tran_topo_quest3.reversed, tran_topo_quest4.reversed] = \
CompositionCandidateSensor(
relations=(tran_topo_quest1.reversed, tran_topo_quest2.reversed
, tran_topo_quest3.reversed, tran_topo_quest4.reversed),
forward=check_transitive_topo, device=device)
poi_list.extend([inverse, transitive, tran_topo])
from domiknows.program.metric import PRF1Tracker, PRF1Tracker, DatanodeCMMetric, MacroAverageTracker, ValueTracker
from domiknows.program.loss import NBCrossEntropyLoss, BCEWithLogitsIMLoss, BCEFocalLoss
from domiknows.program import LearningBasedProgram, SolverPOIProgram
from domiknows.program.lossprogram import SampleLossProgram, PrimalDualProgram
from domiknows.program.model.pytorch import model_helper, PoiModel, SolverModel
infer_list = ['ILP', 'local/argmax'] # ['ILP', 'local/argmax']
if pmd:
print("Using PMD program")
program = PrimalDualProgram(graph, SolverModel, poi=poi_list,
inferTypes=infer_list,
loss=MacroAverageTracker(NBCrossEntropyLoss()),
beta=beta,
metric={
'ILP': PRF1Tracker(DatanodeCMMetric()),
'argmax': PRF1Tracker(DatanodeCMMetric('local/argmax'))},
device=device)
elif sampling:
program = SampleLossProgram(graph, SolverModel, poi=poi_list,
inferTypes=infer_list,
loss=MacroAverageTracker(NBCrossEntropyLoss()),
metric={
'ILP': PRF1Tracker(DatanodeCMMetric()),
'argmax': PRF1Tracker(DatanodeCMMetric('local/argmax'))},
sample=True,
sampleSize=sampleSize,
sampleGlobalLoss=False,
beta=1,
device=device)
else:
print("Using Base program")
program = SolverPOIProgram(graph,
poi=poi_list,
inferTypes=infer_list,
loss=MacroAverageTracker(NBCrossEntropyLoss()),
metric={
'ILP': PRF1Tracker(DatanodeCMMetric()),
'argmax': PRF1Tracker(DatanodeCMMetric('local/argmax'))},
device=device)
return program
def program_declaration_StepGame(device, *, pmd=False, beta=0.5, sampling=False, sampleSize=1, dropout=False,
constraints=False, spartun=True):
program = None
from graph_stepgame import graph, story, story_contain, question, \
left, right, above, below, lower_left, lower_right, upper_left, upper_right, overlap
story["questions"] = ReaderSensor(keyword="questions")
story["stories"] = ReaderSensor(keyword="stories")
story["relations"] = ReaderSensor(keyword="relation")
story["question_ids"] = ReaderSensor(keyword="question_ids")
story["labels"] = ReaderSensor(keyword="labels")
all_labels = ["left", "right", "above", "below", "lower-left",
"lower-right", "upper-left", "upper-right", "overlap"]
def to_int_list(x):
return torch.LongTensor([int(i) for i in x])
def make_labels(label_list):
labels = label_list.split("@@")
all_labels_list = [[] for _ in range(9)]
for cur_label in labels:
for ind, label in enumerate(all_labels):
all_labels_list[ind].append(1 if label == cur_label else 0)
# label_nums = [0 if label == "Yes" else 1 if label == "No" else 2 for label in labels]
return [to_int_list(labels_list) for labels_list in all_labels_list]
def make_question(questions, stories, relations, q_ids, labels):
all_labels = make_labels(labels)
ids = to_int_list(q_ids.split("@@"))
left_list, right_list, above_list, below_list, lower_left_list, \
lower_right_list, upper_left_list, upper_right_list, over_lap_list = all_labels
return torch.ones(len(questions.split("@@")), 1), questions.split("@@"), stories.split("@@"), \
relations.split("@@"), ids, left_list, right_list, above_list, below_list, lower_left_list, \
lower_right_list, upper_left_list, upper_right_list, over_lap_list
question[story_contain, "question", "story", "relation", "id", "left_label", "right_label",
"above_label", "below_label", "lower_left_label", "lower_right_label", "upper_left_label", "upper_right_label", "overlap_label"] = \
JointSensor(story["questions"], story["stories"], story["relations"],
story["question_ids"], story["labels"], forward=make_question, device=device)
def read_label(_, label):
return label
# question[answer_class] =
# FunctionalSensor(story_contain, "label", forward=read_label, label=True, device=cur_device)
# Replace with all classes
question["input_ids"] = JointSensor(story_contain, 'question', "story",
forward=BERTTokenizer(), device=device)
clf1 = MultipleClassYN_Hidden.from_pretrained('bert-base-uncased', device=device, drp=dropout)
question["hidden_layer"] = ModuleLearner("input_ids", module=clf1, device=device)
question[left] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[left] = FunctionalSensor(story_contain, "left_label", forward=read_label, label=True, device=device)
question[right] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[right] = FunctionalSensor(story_contain, "right_label", forward=read_label, label=True, device=device)
question[above] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[above] = FunctionalSensor(story_contain, "above_label", forward=read_label, label=True, device=device)
question[below] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[below] = FunctionalSensor(story_contain, "below_label", forward=read_label, label=True, device=device)
question[lower_left] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[lower_left] = FunctionalSensor(story_contain, "lower_left_label", forward=read_label, label=True,
device=device)
question[lower_right] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[lower_right] = FunctionalSensor(story_contain, "lower_right_label", forward=read_label, label=True,
device=device)
question[upper_left] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[upper_left] = FunctionalSensor(story_contain, "upper_left_label", forward=read_label, label=True,
device=device)
question[upper_right] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[upper_right] = FunctionalSensor(story_contain, "upper_right_label", forward=read_label, label=True,
device=device)
question[overlap] = ModuleLearner("hidden_layer",
module=ClassifyLayer(clf1.hidden_size, device=device, drp=dropout),
device=device)
question[overlap] = FunctionalSensor(story_contain, "overlap_label", forward=read_label, label=True, device=device)
poi_list = [question, left, right, above, below, lower_left, lower_right, upper_left, upper_right,
overlap]
from domiknows.program.metric import PRF1Tracker, PRF1Tracker, DatanodeCMMetric, MacroAverageTracker, ValueTracker
from domiknows.program.loss import NBCrossEntropyLoss, BCEWithLogitsIMLoss, BCEFocalLoss
from domiknows.program import LearningBasedProgram, SolverPOIProgram
from domiknows.program.lossprogram import SampleLossProgram, PrimalDualProgram
from domiknows.program.model.pytorch import model_helper, PoiModel, SolverModel
infer_list = ['local/argmax'] # ['ILP', 'local/argmax']
if pmd:
program = PrimalDualProgram(graph, SolverModel, poi=poi_list,
inferTypes=infer_list,
loss=MacroAverageTracker(NBCrossEntropyLoss()),
beta=beta,
metric={'argmax': PRF1Tracker(DatanodeCMMetric('local/argmax'))},
device=device)
elif sampling:
program = SampleLossProgram(graph, SolverModel, poi=poi_list,
inferTypes=infer_list,
loss=MacroAverageTracker(NBCrossEntropyLoss()),
metric={'argmax': PRF1Tracker(DatanodeCMMetric('local/argmax'))},
sample=True,
sampleSize=sampleSize,
sampleGlobalLoss=False,
beta=1,
device=device)
else:
program = SolverPOIProgram(graph,
poi=poi_list,
inferTypes=infer_list,
loss=MacroAverageTracker(NBCrossEntropyLoss()),
metric={'argmax': PRF1Tracker(DatanodeCMMetric('local/argmax'))},
device=device)
return program
def program_declaration_spartun_fr_T5(device, *, pmd=False, beta=0.5, sampling=False, sampleSize=1, dropout=False,
constraints=False, spartun=True):
from graph_spartun_rel import graph, story, story_contain, question, \
left, right, above, below, behind, front, near, far, disconnected, touch, \
overlap, coveredby, inside, cover, contain, inverse, inv_question1, inv_question2, \
transitive, tran_quest1, tran_quest2, tran_quest3, tran_topo, tran_topo_quest1, \
tran_topo_quest2, tran_topo_quest3, tran_topo_quest4, output_for_loss
story["questions"] = ReaderSensor(keyword="questions")
story["stories"] = ReaderSensor(keyword="stories")
story["relations"] = ReaderSensor(keyword="relation")
story["question_ids"] = ReaderSensor(keyword="question_ids")
story["labels"] = ReaderSensor(keyword="labels")
all_labels = ["left", "right", "above", "below", "behind", "front",
"near", "far", "disconnected", "touch", "overlap", "covered by",
"inside", "cover", "contain"]
map_label_index = {text: i for i, text in enumerate(all_labels)}
def to_int_list(x):
return torch.LongTensor([int(i) for i in x])
def to_float_list(x):
return torch.Tensor([float(i) for i in x])
def make_labels(label_list):
labels = label_list.split("@@")
text_label = ["" for _ in range(len(labels))]
for ind, bits_label in enumerate(labels):
bits_label = int(bits_label)
cur_bit = 1
for label in all_labels:
if bits_label & cur_bit:
text_label[ind] += label if text_label[ind] == "" else (", " + label)
cur_bit *= 2
# label_nums = [0 if label == "Yes" else 1 if label == "No" else 2 for label in labels]
return text_label
def make_question(questions, stories, relations, q_ids, labels):
text_label = make_labels(labels)
ids = to_int_list(q_ids.split("@@"))
return torch.ones(len(questions.split("@@")), 1), questions.split("@@"), stories.split("@@"), relations.split(
"@@"), ids, text_label
question[story_contain, "question", "story", "relation", "id", "text_labels"] = \
JointSensor(story["questions"], story["stories"], story["relations"],
story["question_ids"], story["labels"], forward=make_question, device=device)
T5_model = T5WithLora("google/flan-t5-base", device=device, adapter=True)
# defined loss based on the model
LossT5 = T5LossFunction(T5_model=T5_model)
t5_outTokenizer = T5TokenizerOutput('google/flan-t5-base')
t5_inTokenizer = T5TokenizerInput('google/flan-t5-base')
question[output_for_loss] = JointSensor(story_contain, 'question', "story",
forward=t5_inTokenizer, device=device)
question["input_ids"] = JointSensor(story_contain, 'question', "story", True,
forward=t5_inTokenizer, device=device)
question[output_for_loss] = FunctionalSensor(story_contain,
'text_labels',
forward=t5_outTokenizer,
label=True,
device=device)
all_answers = [left, right, above, below, behind, front,
near, far, disconnected, touch, overlap, coveredby,
inside, cover, contain]
question["output_encoder"] = ModuleLearner(story_contain, "input_ids", module=T5_model, device=device)
question["output_decoder"] = FunctionalSensor(story_contain, "output_encoder",
forward=T5TokenizerDecoder('google/flan-t5-base'), device=device)
def read_decoder(_, decoder_list):
text_label = [[0] * 15 for _ in range(len(decoder_list))]
for ind, text_decode in enumerate(decoder_list):
text_decode = text_decode.replace("and", "")
all_relations = text_decode.strip().split(", ")
for relation in all_relations:
relation = relation.strip()
if relation not in map_label_index:
continue
text_label[ind][map_label_index[relation]] = 1
list_tensor = [to_float_list(labels_list) for labels_list in text_label]
return torch.stack(list_tensor)
def read_label(_, relation_list, index):
label = relation_list[:, index].reshape((-1, 1))
label = torch.concat((torch.ones_like(label) - label, label), dim=-1)
return label
question["output_relations"] = FunctionalSensor(story_contain, "output_decoder", forward=read_decoder,
device=device)
question[left] = FunctionalSensor(story_contain, "output_relations", 0, forward=read_label, device=device)
question[right] = FunctionalSensor(story_contain, "output_relations", 1, forward=read_label, device=device)
question[above] = FunctionalSensor(story_contain, "output_relations", 2, forward=read_label, device=device)
question[below] = FunctionalSensor(story_contain, "output_relations", 3, forward=read_label, device=device)
question[behind] = FunctionalSensor(story_contain, "output_relations", 4, forward=read_label, device=device)
question[front] = FunctionalSensor(story_contain, "output_relations", 5, forward=read_label, device=device)
question[near] = FunctionalSensor(story_contain, "output_relations", 6, forward=read_label, device=device)
question[far] = FunctionalSensor(story_contain, "output_relations", 7, forward=read_label, device=device)
question[disconnected] = FunctionalSensor(story_contain, "output_relations", 8, forward=read_label, device=device)
question[touch] = FunctionalSensor(story_contain, "output_relations", 9, forward=read_label, device=device)
question[overlap] = FunctionalSensor(story_contain, "output_relations", 10, forward=read_label, device=device)
question[coveredby] = FunctionalSensor(story_contain, "output_relations", 11, forward=read_label, device=device)
question[inside] = FunctionalSensor(story_contain, "output_relations", 12, forward=read_label, device=device)
question[cover] = FunctionalSensor(story_contain, "output_relations", 13, forward=read_label, device=device)
question[contain] = FunctionalSensor(story_contain, "output_relations", 14, forward=read_label, device=device)
poi_list = [question, left, right, above, below, behind, front, near, far,
disconnected, touch, overlap, coveredby, inside, cover, contain, output_for_loss]
if constraints:
inverse[inv_question1.reversed, inv_question2.reversed] = \
CompositionCandidateSensor(
relations=(inv_question1.reversed, inv_question2.reversed),
forward=check_symmetric, device=device)
transitive[tran_quest1.reversed, tran_quest2.reversed, tran_quest3.reversed] = \
CompositionCandidateSensor(
relations=(tran_quest1.reversed, tran_quest2.reversed, tran_quest3.reversed),
forward=check_transitive, device=device)
tran_topo[tran_topo_quest1.reversed, tran_topo_quest2.reversed,
tran_topo_quest3.reversed, tran_topo_quest4.reversed] = \
CompositionCandidateSensor(
relations=(tran_topo_quest1.reversed, tran_topo_quest2.reversed
, tran_topo_quest3.reversed, tran_topo_quest4.reversed),
forward=check_transitive_topo, device=device)
poi_list.extend([inverse, transitive, tran_topo])
from domiknows.program.metric import PRF1Tracker, PRF1Tracker, DatanodeCMMetric, MacroAverageTracker, ValueTracker
from domiknows.program.loss import NBCrossEntropyLoss, BCEWithLogitsIMLoss, BCEFocalLoss
from domiknows.program import LearningBasedProgram, SolverPOIProgram
from domiknows.program.lossprogram import SampleLossProgram, PrimalDualProgram
from domiknows.program.model.pytorch import model_helper, PoiModel, SolverModel
infer_list = ['local/argmax'] # ['ILP', 'local/argmax']
if pmd:
print("Using PMD program")
program = PrimalDualProgram(graph, SolverModel, poi=poi_list,
inferTypes=infer_list,
loss=ValueTracker(LossT5),
beta=beta,
device=device)
elif sampling:
program = SampleLossProgram(graph, SolverModel, poi=poi_list,
inferTypes=infer_list,
loss=ValueTracker(LossT5),
sample=True,
sampleSize=sampleSize,
sampleGlobalLoss=False,
beta=1,
device=device)
else:
print("Using Base program")
program = SolverPOIProgram(graph,
poi=poi_list,
inferTypes=infer_list,
loss=ValueTracker(LossT5),
device=device)
return program
def program_declaration_spartun_fr_T5_v2(device, *, pmd=False, beta=0.5, sampling=False, sampleSize=1, dropout=False,
constraints=False, spartun=True):
from graph_spartun_rel import graph, story, story_contain, question, \
left, right, above, below, behind, front, near, far, disconnected, touch, \
overlap, coveredby, inside, cover, contain, inverse, inv_question1, inv_question2, \
transitive, tran_quest1, tran_quest2, tran_quest3, tran_topo, tran_topo_quest1, \
tran_topo_quest2, tran_topo_quest3, tran_topo_quest4, output_for_loss
story["questions"] = ReaderSensor(keyword="questions")
story["stories"] = ReaderSensor(keyword="stories")
story["relations"] = ReaderSensor(keyword="relation")
story["question_ids"] = ReaderSensor(keyword="question_ids")
story["labels"] = ReaderSensor(keyword="labels")
all_labels = ["left", "right", "above", "below", "behind", "front",
"near", "far", "disconnected", "touch", "overlap", "covered by",
"inside", "cover", "contain"]
map_label_index = {text: i for i, text in enumerate(all_labels)}
def to_int_list(x):
return torch.LongTensor([int(i) for i in x])
def to_float_list(x):
return torch.Tensor([float(i) for i in x])
def make_labels(label_list):
labels = label_list.split("@@")
text_label = ["" for _ in range(len(labels))]
for ind, bits_label in enumerate(labels):
bits_label = int(bits_label)
cur_bit = 1
for label in all_labels:
text_label[ind] += label + ":" + ("yes" if bits_label & cur_bit else "no") + " "
cur_bit *= 2
# label_nums = [0 if label == "Yes" else 1 if label == "No" else 2 for label in labels]
# print(text_label)
return text_label
def make_question(questions, stories, relations, q_ids, labels):
text_label = make_labels(labels)
ids = to_int_list(q_ids.split("@@"))
return torch.ones(len(questions.split("@@")), 1), questions.split("@@"), stories.split("@@"), relations.split(
"@@"), ids, text_label
question[story_contain, "question", "story", "relation", "id", "text_labels"] = \
JointSensor(story["questions"], story["stories"], story["relations"],
story["question_ids"], story["labels"], forward=make_question, device=device)
T5_model = T5WithLora("google/flan-t5-base", device=device, adapter=True)
# defined loss based on the model
LossT5 = T5LossFunction(T5_model=T5_model)
t5_outTokenizer = T5TokenizerOutput('google/flan-t5-base')
t5_inTokenizer = T5TokenizerInput('google/flan-t5-base')
question[output_for_loss] = JointSensor(story_contain, 'question', "story",
forward=t5_inTokenizer, device=device)
question["input_ids"] = JointSensor(story_contain, 'question', "story", True,
forward=t5_inTokenizer, device=device)
question[output_for_loss] = FunctionalSensor(story_contain,
'text_labels',
forward=t5_outTokenizer,
label=True,
device=device)
all_answers = [left, right, above, below, behind, front,
near, far, disconnected, touch, overlap, coveredby,
inside, cover, contain]
question["output_encoder"] = ModuleLearner(story_contain, "input_ids", module=T5_model, device=device)
question["output_decoder"] = FunctionalSensor(story_contain, "output_encoder",
forward=T5TokenizerDecoder('google/flan-t5-base'), device=device)
def read_decoder(_, decoder_list):
text_label = [[0] * 15 for _ in range(len(decoder_list))]
for ind, text_decode in enumerate(decoder_list):
all_relations = text_decode.strip()
for label in all_labels:
if all_relations.find(label + ":" + "yes"): # This is may be wrong
text_label[ind][map_label_index[label]] = 1
list_tensor = [to_float_list(labels_list) for labels_list in text_label]
return torch.stack(list_tensor)
def read_label(_, relation_list, index):
label = relation_list[:, index].reshape((-1, 1))
label = torch.concat((torch.ones_like(label) - label, label), dim=-1)
return label
question["output_relations"] = FunctionalSensor(story_contain, "output_decoder", forward=read_decoder,
device=device)
question[left] = FunctionalSensor(story_contain, "output_relations", 0, forward=read_label, device=device)
question[right] = FunctionalSensor(story_contain, "output_relations", 1, forward=read_label, device=device)
question[above] = FunctionalSensor(story_contain, "output_relations", 2, forward=read_label, device=device)
question[below] = FunctionalSensor(story_contain, "output_relations", 3, forward=read_label, device=device)
question[behind] = FunctionalSensor(story_contain, "output_relations", 4, forward=read_label, device=device)
question[front] = FunctionalSensor(story_contain, "output_relations", 5, forward=read_label, device=device)
question[near] = FunctionalSensor(story_contain, "output_relations", 6, forward=read_label, device=device)
question[far] = FunctionalSensor(story_contain, "output_relations", 7, forward=read_label, device=device)
question[disconnected] = FunctionalSensor(story_contain, "output_relations", 8, forward=read_label, device=device)
question[touch] = FunctionalSensor(story_contain, "output_relations", 9, forward=read_label, device=device)
question[overlap] = FunctionalSensor(story_contain, "output_relations", 10, forward=read_label, device=device)
question[coveredby] = FunctionalSensor(story_contain, "output_relations", 11, forward=read_label, device=device)
question[inside] = FunctionalSensor(story_contain, "output_relations", 12, forward=read_label, device=device)
question[cover] = FunctionalSensor(story_contain, "output_relations", 13, forward=read_label, device=device)
question[contain] = FunctionalSensor(story_contain, "output_relations", 14, forward=read_label, device=device)
poi_list = [question, left, right, above, below, behind, front, near, far,
disconnected, touch, overlap, coveredby, inside, cover, contain, output_for_loss]
if constraints:
inverse[inv_question1.reversed, inv_question2.reversed] = \
CompositionCandidateSensor(
relations=(inv_question1.reversed, inv_question2.reversed),
forward=check_symmetric, device=device)
transitive[tran_quest1.reversed, tran_quest2.reversed, tran_quest3.reversed] = \
CompositionCandidateSensor(
relations=(tran_quest1.reversed, tran_quest2.reversed, tran_quest3.reversed),
forward=check_transitive, device=device)
tran_topo[tran_topo_quest1.reversed, tran_topo_quest2.reversed,
tran_topo_quest3.reversed, tran_topo_quest4.reversed] = \
CompositionCandidateSensor(
relations=(tran_topo_quest1.reversed, tran_topo_quest2.reversed
, tran_topo_quest3.reversed, tran_topo_quest4.reversed),
forward=check_transitive_topo, device=device)
poi_list.extend([inverse, transitive, tran_topo])
from domiknows.program.metric import PRF1Tracker, PRF1Tracker, DatanodeCMMetric, MacroAverageTracker, ValueTracker
from domiknows.program.loss import NBCrossEntropyLoss, BCEWithLogitsIMLoss, BCEFocalLoss
from domiknows.program import LearningBasedProgram, SolverPOIProgram
from domiknows.program.lossprogram import SampleLossProgram, PrimalDualProgram
from domiknows.program.model.pytorch import model_helper, PoiModel, SolverModel
infer_list = ['local/argmax'] # ['ILP', 'local/argmax']
if pmd:
print("Using PMD program")
program = PrimalDualProgram(graph, SolverModel, poi=poi_list,
inferTypes=infer_list,
loss=ValueTracker(LossT5),
beta=beta,
device=device)
elif sampling:
program = SampleLossProgram(graph, SolverModel, poi=poi_list,
inferTypes=infer_list,
loss=ValueTracker(LossT5),
sample=True,
sampleSize=sampleSize,
sampleGlobalLoss=False,
beta=1,
device=device)
else:
print("Using Base program")
program = SolverPOIProgram(graph,
poi=poi_list,
inferTypes=infer_list,
loss=ValueTracker(LossT5),
device=device)
return program
def program_declaration_spartun_fr_T5_v3(device, *, pmd=False, beta=0.5, sampling=False, sampleSize=1, dropout=False,
constraints=False, spartun=True):
program = None
from graph_spartun_rel import graph, story, story_contain, question, \
left, right, above, below, behind, front, near, far, disconnected, touch, \
overlap, coveredby, inside, cover, contain, inverse, inv_question1, inv_question2, \
transitive, tran_quest1, tran_quest2, tran_quest3, tran_topo, tran_topo_quest1, \
tran_topo_quest2, tran_topo_quest3, tran_topo_quest4
story["questions"] = ReaderSensor(keyword="questions")
story["stories"] = ReaderSensor(keyword="stories")
story["relations"] = ReaderSensor(keyword="relation")
story["question_ids"] = ReaderSensor(keyword="question_ids")
story["labels"] = ReaderSensor(keyword="labels")
all_labels = ["left", "right", "above", "below", "behind", "front", "near", "far", "dc", "ec", "po", "tpp", "ntpp",
"tppi", "ntppi"]
def to_int_list(x):
return torch.LongTensor([int(i) for i in x])
def make_labels(label_list):
labels = label_list.split("@@")
all_labels_list = [[] for _ in range(15)]
for bits_label in labels:
bits_label = int(bits_label)
cur_label = 1
for ind, label in enumerate(all_labels):
all_labels_list[ind].append(1 if bits_label & cur_label else 0)
cur_label *= 2
# label_nums = [0 if label == "Yes" else 1 if label == "No" else 2 for label in labels]
return [to_int_list(labels_list) for labels_list in all_labels_list]
def make_text_labels(label_list):
labels = label_list.split("@@")
text_label = ["" for _ in range(len(labels))]
for ind, bits_label in enumerate(labels):
bits_label = int(bits_label)
cur_bit = 1
for label in all_labels:
if bits_label & cur_bit:
text_label[ind] += label if text_label[ind] == "" else (", " + label)
cur_bit *= 2
# label_nums = [0 if label == "Yes" else 1 if label == "No" else 2 for label in labels]
return text_label
def make_question(questions, stories, relations, q_ids, labels):
all_labels = make_labels(labels)
text_labels = make_text_labels(labels)
ids = to_int_list(q_ids.split("@@"))
left_list, right_list, above_list, below_list, behind_list, \
front_list, near_list, far_list, dc_list, ec_list, po_list, \
tpp_list, ntpp_list, tppi_list, ntppi_list = all_labels
return torch.ones(len(questions.split("@@")), 1), questions.split("@@"), stories.split("@@"), \
relations.split("@@"), ids, left_list, right_list, above_list, below_list, behind_list, \
front_list, near_list, far_list, dc_list, ec_list, po_list, \
tpp_list, ntpp_list, tppi_list, ntppi_list, text_labels
question[story_contain, "question", "story", "relation", "id", "left_label", "right_label",
"above_label", "below_label", "behind_label", "front_label", "near_label", "far_label", "dc_label", "ec_label", "po_label",
"tpp_label", "ntpp_label", "tppi_label", "ntppi_label", "text_label"] = \
JointSensor(story["questions"], story["stories"], story["relations"],
story["question_ids"], story["labels"], forward=make_question, device=device)
def read_label(_, label):
return label
print("Using T5")
t5_outtokenizer = T5TokenizerOutput('google/flan-t5-base')
t5_inTokenizer = T5TokenizerInput('google/flan-t5-base')
t5_normal_tokenizer = AutoTokenizer.from_pretrained('google/flan-t5-base')
label_text = ["left", "right", "above", "below", "behind", "front",
"near", "far", "disconnected", "touch", "overlap", "covered by",
"inside", "cover", "contain"]
t5_model = T5WithLoraGenerativeCLF("google/flan-t5-base",
label=label_text,