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sys.path.append(".")
import pytest
from PIL import Image
import requests
import torch
import time
from src.models.sca import ScaConfig, ScaModel, ScaProcessor
from typing import Sequence
import numpy as np
from torch.nn.utils.rnn import pad_sequence
import torch
import transformers
cache_dir = ".model.cache"
device = "cuda" if torch.cuda.is_available() else "cpu"
sam_model_name = "facebook/sam-vit-base"
text_model_name = "gpt2"
additional_num_hidden_layers = 2
@pytest.fixture
def model():
model = ScaModel.from_sam_text_pretrained(
sam_model_name, text_model_name, additional_num_hidden_layers, cache_dir=cache_dir
).to(device)
return model
@pytest.fixture
def processor():
processor = ScaProcessor.from_sam_text_pretrained(sam_model_name, text_model_name, cache_dir=cache_dir)
return processor
@pytest.fixture
def sam_model():
model = transformers.AutoModel.from_pretrained(sam_model_name, cache_dir=cache_dir).to(device)
return model
@pytest.mark.parametrize("batch_size", [1, 3])
@pytest.mark.parametrize("num_masks", [4, 7])
def test_modeling(batch_size, num_masks, model, processor):
img_url = "https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/truck.jpg"
raw_image = [Image.open(requests.get(img_url, stream=True).raw).convert("RGB")]
input_points = [[[[500, 375]]]] # 2D location of a window in the image
raw_text = [["This is a test sentence."]]
raw_image = raw_image * batch_size
input_points = np.array(input_points)
raw_text = np.array(raw_text, dtype=object)
input_points = input_points.repeat(batch_size, axis=0).repeat(num_masks, axis=1).tolist()
raw_text = raw_text.repeat(batch_size, axis=0).repeat(num_masks, axis=1).reshape(-1).tolist()
inputs = processor(raw_image, input_points=input_points, return_tensors="pt")
# prepare tokenizer
tokenizer = processor.tokenizer
raw_text_inputs = tokenizer(raw_text)
eos_token_id = tokenizer.eos_token_id
if eos_token_id is None:
raise ValueError("tokenizer does not have an eos token id")
pad_token_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else eos_token_id
label_pad_token_id = -100
# get tokenized inputs
tokenized_inputs = tokenizer(raw_text)
raw_input_ids = tokenized_inputs["input_ids"]
raw_attention_mask = tokenized_inputs["attention_mask"]
# add eos token
for i in range(len(raw_input_ids)):
raw_input_ids[i] += [eos_token_id]
raw_attention_mask[i] += [1]
# trim to max length
max_length = tokenizer.model_max_length
for i in range(len(raw_input_ids)):
raw_input_ids[i] = raw_input_ids[i][:max_length]
raw_attention_mask[i] = raw_attention_mask[i][:max_length]
# right pad and get batch of data
input_ids = pad_sequence([torch.tensor(x) for x in raw_input_ids], batch_first=True, padding_value=pad_token_id)
attention_mask = pad_sequence([torch.tensor(x) for x in raw_attention_mask], batch_first=True, padding_value=0)
# get label and left pad the label by 1, to avoid insert BOS token
labels = pad_sequence([torch.tensor(x) for x in raw_input_ids], batch_first=True, padding_value=label_pad_token_id)
labels = torch.nn.functional.pad(labels, (1, 0), value=label_pad_token_id)
# get text inputs
text_inputs = {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels}
for k in text_inputs:
text_inputs[k] = text_inputs[k].view(batch_size, num_masks, -1)
for k, v in inputs.items():
if isinstance(v, torch.Tensor):
print(k, v.shape)
elif isinstance(v, Sequence):
print(k, "sequence of ", type(v[0]), len(v))
else:
print(k, type(v), len(v))
for k, v in text_inputs.items():
if isinstance(v, torch.Tensor):
print(k, v.shape)
elif isinstance(v, Sequence):
print(k, "sequence of ", type(v[0]), len(v))
else:
print(k, type(v), len(v))
for k, v in inputs.items():
if isinstance(v, torch.Tensor):
inputs[k] = v.to(device)
for k, v in text_inputs.items():
if isinstance(v, torch.Tensor):
text_inputs[k] = v.to(device)
# test training
model.train()
outputs = model(**inputs, **text_inputs)
for k, v in outputs.items():
if isinstance(v, torch.Tensor):
print(k, v.shape)
sequence_texts = tokenizer.batch_decode(outputs["logits"].argmax(dim=-1))
sequence_texts = sequence_texts[:1]
print(sequence_texts)
# test inference
model.eval()
with torch.no_grad():
outputs = model.generate(**inputs, **text_inputs)
for k, v in outputs.items():
if isinstance(v, torch.Tensor):
print(k, v.shape)
outputs["sequences"] = outputs["sequences"].view(-1, outputs["sequences"].shape[-1])
sequence_texts = tokenizer.batch_decode(outputs["sequences"])
sequence_texts = sequence_texts[:1]
print(sequence_texts)
# batch_size, num_masks, num_output_heads, 1, hidden_size -> 1, 1, hidden_size
inputs_embeds = outputs["projected_query_logits"][0, 0, 0:1]
inputs_ids = torch.tensor([[tokenizer.eos_token_id]]).to(device)
attention_masks = torch.tensor([[1]]).to(device)
language_model = transformers.AutoModelForCausalLM.from_pretrained(
text_model_name, config=model.config.text_config, cache_dir=cache_dir
).to(device)
language_model.eval()
with torch.no_grad():
original_output = language_model.generate(inputs_embeds=inputs_embeds, attention_mask=attention_masks)
sca_text_output = model.language_model.generate(inputs_embeds=inputs_embeds, attention_mask=attention_masks)
assert torch.allclose(original_output, sca_text_output)
language_model = transformers.AutoModelForCausalLM.from_pretrained(text_model_name, cache_dir=cache_dir).to(device)
language_model.eval()
with torch.no_grad():
original_output = language_model.generate(inputs_embeds=inputs_embeds, attention_mask=attention_masks)
assert torch.allclose(original_output, sca_text_output)
validate_texts = tokenizer.batch_decode(sca_text_output)
validate_texts = validate_texts[:1]
print(validate_texts)
assert validate_texts[0] == sequence_texts[0]
@pytest.mark.parametrize("batch_size", [1, 3])
@pytest.mark.parametrize("num_masks", [4, 7])
def test_modeling_with_sam(batch_size, num_masks, model, sam_model, processor):
img_url = "https://raw.githubusercontent.com/facebookresearch/segment-anything/main/notebooks/images/truck.jpg"
raw_image = [Image.open(requests.get(img_url, stream=True).raw).convert("RGB")]
input_points = [[[[500, 375]]]] # 2D location of a window in the image
raw_text = [["This is a test sentence."]]
raw_image = raw_image * batch_size
input_points = np.array(input_points)
raw_text = np.array(raw_text, dtype=object)
input_points = input_points.repeat(batch_size, axis=0).repeat(num_masks, axis=1).tolist()
raw_text = raw_text.repeat(batch_size, axis=0).repeat(num_masks, axis=1).reshape(-1).tolist()
inputs = processor(raw_image, input_points=input_points, return_tensors="pt")
for k, v in inputs.items():
if isinstance(v, torch.Tensor):
inputs[k] = v.to(device)
sam_model.train()
model.train()
sam_output = sam_model(**inputs)
sca_output = model(**inputs)
sam_output_from_sca = sca_output.segmentation_outputs
for k in sam_output:
if isinstance(sam_output[k], torch.Tensor):
assert torch.allclose(sam_output[k], sam_output_from_sca[k])
sam_model.eval()
model.eval()
with torch.no_grad():
sam_output = sam_model(**inputs)
sca_output = model.generate(**inputs)
sam_output_from_sca = sca_output.segmentation_outputs
for k in sam_output:
if isinstance(sam_output[k], torch.Tensor):
assert torch.allclose(sam_output[k], sam_output_from_sca[k])
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