def test_save_load_pretrained_default(self):
tokenizer_slow = self.get_tokenizer()
tokenizer_fast = self.get_rust_tokenizer()
feature_extractor = self.get_feature_extractor()
processor_slow = CLIPProcessor(tokenizer=tokenizer_slow,
feature_extractor=feature_extractor)
processor_slow.save_pretrained(self.tmpdirname)
processor_slow = CLIPProcessor.from_pretrained(self.tmpdirname,
use_fast=False)
processor_fast = CLIPProcessor(tokenizer=tokenizer_fast,
feature_extractor=feature_extractor)
processor_fast.save_pretrained(self.tmpdirname)
processor_fast = CLIPProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor_slow.tokenizer.get_vocab(),
tokenizer_slow.get_vocab())
self.assertEqual(processor_fast.tokenizer.get_vocab(),
tokenizer_fast.get_vocab())
self.assertEqual(tokenizer_slow.get_vocab(),
tokenizer_fast.get_vocab())
self.assertIsInstance(processor_slow.tokenizer, CLIPTokenizer)
self.assertIsInstance(processor_fast.tokenizer, CLIPTokenizerFast)
self.assertEqual(processor_slow.feature_extractor.to_json_string(),
feature_extractor.to_json_string())
self.assertEqual(processor_fast.feature_extractor.to_json_string(),
feature_extractor.to_json_string())
self.assertIsInstance(processor_slow.feature_extractor,
CLIPFeatureExtractor)
self.assertIsInstance(processor_fast.feature_extractor,
CLIPFeatureExtractor)
def test_inference(self):
model_name = "openai/clip-vit-base-patch32"
model = TFCLIPModel.from_pretrained(model_name)
processor = CLIPProcessor.from_pretrained(model_name)
image = prepare_img()
inputs = processor(text=["a photo of a cat", "a photo of a dog"],
images=image,
padding=True,
return_tensors="tf")
outputs = model(**inputs, training=False)
# verify the logits
self.assertEqual(
outputs.logits_per_image.shape,
tf.TensorShape(
(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
)
self.assertEqual(
outputs.logits_per_text.shape,
tf.TensorShape(
(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
)
expected_logits = tf.constant([[24.5701, 19.3049]])
tf.debugging.assert_near(outputs.logits_per_image,
expected_logits,
atol=1e-3)
def test_inference(self):
model_name = "nvidia/groupvit-gcc-yfcc"
model = GroupViTModel.from_pretrained(model_name)
processor = CLIPProcessor.from_pretrained(model_name)
image = prepare_img()
inputs = processor(text=["a photo of a cat", "a photo of a dog"],
images=image,
padding=True,
return_tensors="pt")
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
self.assertEqual(
outputs.logits_per_image.shape,
torch.Size(
(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
)
self.assertEqual(
outputs.logits_per_text.shape,
torch.Size(
(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
)
expected_logits = torch.tensor([[13.3523, 6.3629]])
self.assertTrue(
torch.allclose(outputs.logits_per_image,
expected_logits,
atol=1e-3))
def test_inference(self):
model_name = "openai/clip-vit-base-patch32"
model = CLIPModel.from_pretrained(model_name).to(torch_device)
processor = CLIPProcessor.from_pretrained(model_name)
image = prepare_img()
inputs = processor(text=["a photo of a cat", "a photo of a dog"],
images=image,
padding=True,
return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
self.assertEqual(
outputs.logits_per_image.shape,
torch.Size(
(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
)
self.assertEqual(
outputs.logits_per_text.shape,
torch.Size(
(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
)
expected_logits = torch.tensor([[24.5701, 19.3049]],
device=torch_device)
self.assertTrue(
torch.allclose(outputs.logits_per_image,
expected_logits,
atol=1e-3))
def test_save_load_pretrained_additional_features(self):
processor = CLIPProcessor(
tokenizer=self.get_tokenizer(),
feature_extractor=self.get_feature_extractor())
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)",
eos_token="(EOS)")
feature_extractor_add_kwargs = self.get_feature_extractor(
do_normalize=False, padding_value=1.0)
processor = CLIPProcessor.from_pretrained(self.tmpdirname,
bos_token="(BOS)",
eos_token="(EOS)",
do_normalize=False,
padding_value=1.0)
self.assertEqual(processor.tokenizer.get_vocab(),
tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, CLIPTokenizerFast)
self.assertEqual(processor.feature_extractor.to_json_string(),
feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor,
CLIPFeatureExtractor)
def test_save_load_pretrained_default(self):
tokenizer = self.get_tokenizer()
feature_extractor = self.get_feature_extractor()
processor = CLIPProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
processor.save_pretrained(self.tmpdirname)
processor = CLIPProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertIsInstance(processor.tokenizer, CLIPTokenizer)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, CLIPFeatureExtractor)
def convert_groupvit_checkpoint(checkpoint_path,
pytorch_dump_folder_path,
model_name="groupvit-gcc-yfcc",
push_to_hub=False):
"""
Copy/paste/tweak model's weights to the Transformers design.
"""
config = GroupViTConfig()
model = GroupViTModel(config).eval()
state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
new_state_dict = convert_state_dict(state_dict, config)
missing_keys, unexpected_keys = model.load_state_dict(new_state_dict,
strict=False)
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"
]) or (len(unexpected_keys) == 0)
# verify result
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
image = prepare_img()
inputs = processor(text=["a photo of a cat", "a photo of a dog"],
images=image,
padding=True,
return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
if model_name == "groupvit-gcc-yfcc":
expected_logits = torch.tensor([[13.3523, 6.3629]])
elif model_name == "groupvit-gcc-redcaps":
expected_logits = torch.tensor([[16.1873, 8.6230]])
else:
raise ValueError(f"Model name {model_name} not supported.")
assert torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3)
processor.save_pretrained(pytorch_dump_folder_path)
model.save_pretrained(pytorch_dump_folder_path)
print("Successfully saved processor and model to",
pytorch_dump_folder_path)
if push_to_hub:
print("Pushing to the hub...")
processor.push_to_hub(model_name, organization="nielsr")
model.push_to_hub(model_name, organization="nielsr")
import random
import matplotlib.pyplot as plt
# IMPORT MODELS
maskrcnn_model = torchvision.models.detection.maskrcnn_resnet50_fpn(
pretrained=True)
maskrcnn_model.eval()
roi_extractor = AgnosticRoIExtractor(maskrcnn_model)
roi_extractor.eval()
clip_model = CLIPModel.from_pretrained(
"/home/gridsan/groups/fastai/omoll/seesaw_root2/models/clip-vit-base-patch32/"
)
clip_processor = CLIPProcessor.from_pretrained(
"/home/gridsan/groups/fastai/omoll/seesaw_root2/models/clip-vit-base-patch32/"
)
def run_clip_proposal(image, boxes, padding):
images, new_boxes = image_clipper(image, boxes, padding)
inputs = clip_processor.feature_extractor(images=images,
return_tensors="pt")
vision_outputs = clip_model.vision_model(**inputs)
image_embeds = vision_outputs[1]
image_embeds = clip_model.visual_projection(image_embeds)
image_embeds = image_embeds / image_embeds.norm(dim=-1, keepdim=True)
return image_embeds
def image_clipper(image, boxes, padding):
from sentence_transformers import SentenceTransformer, util, models
from PIL import ImageFile, Image
import numpy as np
import requests
###########
image = Image.open('two_dogs_in_snow.jpg')
from transformers import CLIPProcessor, CLIPModel
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
inputs = processor(texts=["a cat", "a dog"], images=[image], return_tensors="pt", padding=True)
output = model(**inputs)
#vision_outputs = model.vision_model(pixel_values=inputs['pixel_values'])
#image_embeds = model.visual_projection(vision_outputs[1])
#print(image_embeds.shape)
#exit()
#Load CLIP model
clip = models.CLIPModel()
def preprocess_roi_dataset(
sds: SeesawDatasetManager,
output_path,
clip_model_path=None,
cpu=False,
image_limiter=None,
box_limiter=100,
padding=5,
):
if (not cpu) and torch.cuda.is_available():
device = torch.device("cuda")
print("USING GPU")
else:
device = torch.device("cpu")
print("Using CPU")
dataset = sds.get_pytorch_dataset()
output_path = resolve_path(output_path)
assert not os.path.exists(output_path), "output path already exists"
clip = False
if (clip_model_path):
clip = True
clip_model_path = resolve_path(clip_model_path)
assert os.path.exists(clip_model_path), "clip model path doesn't exist"
dirname = os.path.basename(output_path)
dirpath = os.path.dirname(output_path)
output_path = f"{dirpath}/.tmp.{dirname}"
final_output_path = f"{dirpath}/{dirname}"
os.makedirs(dirpath, exist_ok=True)
if os.path.exists(output_path): # remove old tmpfile
shutil.rmtree(output_path)
os.makedirs(output_path)
'''
vector_path = f"{output_path}/vectors"
os.makedirs(vector_path)
model_link = f"{output_path}/model"
os.symlink(model_path, model_link)
dataset_link = f"{output_path}/dataset"
os.symlink(sds.dataset_root, dataset_link)
real_prefix = f"{os.path.realpath(sds.image_root)}/"
read_paths = ((real_prefix + sds.paths)).tolist()
read_paths = [os.path.normpath(p) for p in read_paths]
meta_dict = dict(zip(read_paths, zip(sds.paths, np.arange(len(sds.paths)))))
'''
maskrcnn_model = torchvision.models.detection.maskrcnn_resnet50_fpn(
pretrained=True).to(device)
maskrcnn_model.eval()
roi_extractor = AgnosticRoIExtractor(maskrcnn_model).to(device)
roi_extractor.eval()
roi_extractor.model.rpn.min_size = 10
roi_extractor.model.rpn.nms_thresh = 0
clip_model = CLIPModel.from_pretrained(clip_model_path).to(device)
clip_processor = CLIPProcessor.from_pretrained(clip_model_path)
print("Models defined")
ims = []
paths = []
with torch.no_grad():
for i in range(len(dataset)):
if i % 2000 == 0:
if i != 0:
ans = list(zip(paths, output))
df = to_dataframe(ans)
df['dbidx'] = dbidx
if clip:
df['clip_feature'] = clip_features
#clip_array = run_clip_on_proposal()
#df.assign(clip_feature_vector=TensorArray(clip_array))
df.to_parquet(output_path + "/" + str(i) + ".parquet")
clip_features = []
output = []
paths = []
dbidx = []
data = dataset[i]
ims.append(data['image'])
paths.append(data['file_path'])
images = torchvision.transforms.ToTensor()(
data['image']).unsqueeze(0).to(device)
print("starting roi")
a = roi_extractor(images)[0]
if a['scores'].shape[0] > box_limiter:
a['boxes'] = torch.split(a['boxes'], box_limiter)[0]
a['scores'] = torch.split(a['scores'], box_limiter)[0]
a['features'] = torch.split(a['features'].detach(),
box_limiter)[0]
dbidx.extend([i] * len(a['scores']))
if clip:
clip_array = run_clip_proposal(data['image'], a['boxes'],
padding, clip_model,
clip_processor, device)
a['clip_feature_vector'] = clip_array
clip_features += clip_array.tolist()
output.append(a)
print(i)
ans = list(zip(paths, output))
df = to_dataframe(ans)
df['dbidx'] = dbidx
if clip:
df['clip_feature'] = clip_features
#clip_array = run_clip_on_proposal()
#df.assign(clip_feature_vector=TensorArray(clip_array))
df.to_parquet(output_path + "/final.parquet")
os.rename(output_path, final_output_path)
reporter = CLIReporter(
parameter_columns=[],
metric_columns=[
metric,
"training_iteration",
],
max_report_frequency=60,
)
base_model = CLIPModel.from_pretrained(
f"/home/gridsan/omoll/xmodexp/notebooks/models/clip-vit-base-patch32")
init_config = base_model.config
init_state_dict = base_model.state_dict()
processor = CLIPProcessor.from_pretrained(
f"/home/gridsan/omoll/xmodexp/notebooks/models/clip-vit-base-patch32")
trainable = make_trainable(
num_epochs=args.max_epochs,
gpus_per_trial=gpus_per_trial,
dataset=bird_dataset,
init_config=init_config,
init_state_dict=init_state_dict,
processor=processor,
)
analysis = tune.run(
trainable,
resources_per_trial={
"cpu": cpus_per_trial,
"gpu": gpus_per_trial