764 lines
28 KiB
Python
764 lines
28 KiB
Python
# coding=utf-8
|
|
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
""" Testing suite for the PyTorch SAM model. """
|
|
|
|
|
|
import gc
|
|
import unittest
|
|
|
|
import requests
|
|
|
|
from transformers import SamConfig, SamMaskDecoderConfig, SamPromptEncoderConfig, SamVisionConfig, pipeline
|
|
from transformers.testing_utils import backend_empty_cache, require_torch, slow, torch_device
|
|
from transformers.utils import is_torch_available, is_vision_available
|
|
|
|
from ...test_configuration_common import ConfigTester
|
|
from ...test_modeling_common import ModelTesterMixin, floats_tensor
|
|
from ...test_pipeline_mixin import PipelineTesterMixin
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
from torch import nn
|
|
|
|
from transformers import SamModel, SamProcessor
|
|
from transformers.models.sam.modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST
|
|
|
|
|
|
if is_vision_available():
|
|
from PIL import Image
|
|
|
|
|
|
class SamPromptEncoderTester:
|
|
def __init__(
|
|
self,
|
|
hidden_size=32,
|
|
input_image_size=24,
|
|
patch_size=2,
|
|
mask_input_channels=4,
|
|
num_point_embeddings=4,
|
|
hidden_act="gelu",
|
|
):
|
|
self.hidden_size = hidden_size
|
|
self.input_image_size = input_image_size
|
|
self.patch_size = patch_size
|
|
self.mask_input_channels = mask_input_channels
|
|
self.num_point_embeddings = num_point_embeddings
|
|
self.hidden_act = hidden_act
|
|
|
|
def get_config(self):
|
|
return SamPromptEncoderConfig(
|
|
image_size=self.input_image_size,
|
|
patch_size=self.patch_size,
|
|
mask_input_channels=self.mask_input_channels,
|
|
hidden_size=self.hidden_size,
|
|
num_point_embeddings=self.num_point_embeddings,
|
|
hidden_act=self.hidden_act,
|
|
)
|
|
|
|
def prepare_config_and_inputs(self):
|
|
dummy_points = floats_tensor([self.batch_size, 3, 2])
|
|
config = self.get_config()
|
|
|
|
return config, dummy_points
|
|
|
|
|
|
class SamMaskDecoderTester:
|
|
def __init__(
|
|
self,
|
|
hidden_size=32,
|
|
hidden_act="relu",
|
|
mlp_dim=64,
|
|
num_hidden_layers=2,
|
|
num_attention_heads=4,
|
|
attention_downsample_rate=2,
|
|
num_multimask_outputs=3,
|
|
iou_head_depth=3,
|
|
iou_head_hidden_dim=32,
|
|
layer_norm_eps=1e-6,
|
|
):
|
|
self.hidden_size = hidden_size
|
|
self.hidden_act = hidden_act
|
|
self.mlp_dim = mlp_dim
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
self.attention_downsample_rate = attention_downsample_rate
|
|
self.num_multimask_outputs = num_multimask_outputs
|
|
self.iou_head_depth = iou_head_depth
|
|
self.iou_head_hidden_dim = iou_head_hidden_dim
|
|
self.layer_norm_eps = layer_norm_eps
|
|
|
|
def get_config(self):
|
|
return SamMaskDecoderConfig(
|
|
hidden_size=self.hidden_size,
|
|
hidden_act=self.hidden_act,
|
|
mlp_dim=self.mlp_dim,
|
|
num_hidden_layers=self.num_hidden_layers,
|
|
num_attention_heads=self.num_attention_heads,
|
|
attention_downsample_rate=self.attention_downsample_rate,
|
|
num_multimask_outputs=self.num_multimask_outputs,
|
|
iou_head_depth=self.iou_head_depth,
|
|
iou_head_hidden_dim=self.iou_head_hidden_dim,
|
|
layer_norm_eps=self.layer_norm_eps,
|
|
)
|
|
|
|
def prepare_config_and_inputs(self):
|
|
config = self.get_config()
|
|
|
|
dummy_inputs = {
|
|
"image_embedding": floats_tensor([self.batch_size, self.hidden_size]),
|
|
}
|
|
|
|
return config, dummy_inputs
|
|
|
|
|
|
class SamModelTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
hidden_size=36,
|
|
intermediate_size=72,
|
|
projection_dim=62,
|
|
output_channels=32,
|
|
num_hidden_layers=2,
|
|
num_attention_heads=4,
|
|
num_channels=3,
|
|
image_size=24,
|
|
patch_size=2,
|
|
hidden_act="gelu",
|
|
layer_norm_eps=1e-06,
|
|
dropout=0.0,
|
|
attention_dropout=0.0,
|
|
initializer_range=0.02,
|
|
initializer_factor=1.0,
|
|
qkv_bias=True,
|
|
mlp_ratio=4.0,
|
|
use_abs_pos=True,
|
|
use_rel_pos=True,
|
|
rel_pos_zero_init=False,
|
|
window_size=14,
|
|
global_attn_indexes=[2, 5, 8, 11],
|
|
num_pos_feats=16,
|
|
mlp_dim=None,
|
|
batch_size=2,
|
|
):
|
|
self.parent = parent
|
|
self.image_size = image_size
|
|
self.patch_size = patch_size
|
|
self.output_channels = output_channels
|
|
self.num_channels = num_channels
|
|
self.hidden_size = hidden_size
|
|
self.projection_dim = projection_dim
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
self.intermediate_size = intermediate_size
|
|
self.dropout = dropout
|
|
self.attention_dropout = attention_dropout
|
|
self.initializer_range = initializer_range
|
|
self.initializer_factor = initializer_factor
|
|
self.hidden_act = hidden_act
|
|
self.layer_norm_eps = layer_norm_eps
|
|
self.qkv_bias = qkv_bias
|
|
self.mlp_ratio = mlp_ratio
|
|
self.use_abs_pos = use_abs_pos
|
|
self.use_rel_pos = use_rel_pos
|
|
self.rel_pos_zero_init = rel_pos_zero_init
|
|
self.window_size = window_size
|
|
self.global_attn_indexes = global_attn_indexes
|
|
self.num_pos_feats = num_pos_feats
|
|
self.mlp_dim = mlp_dim
|
|
self.batch_size = batch_size
|
|
|
|
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
|
|
num_patches = (image_size // patch_size) ** 2
|
|
self.seq_length = num_patches + 1
|
|
|
|
self.prompt_encoder_tester = SamPromptEncoderTester()
|
|
self.mask_decoder_tester = SamMaskDecoderTester()
|
|
|
|
def prepare_config_and_inputs(self):
|
|
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
|
|
config = self.get_config()
|
|
|
|
return config, pixel_values
|
|
|
|
def get_config(self):
|
|
vision_config = SamVisionConfig(
|
|
image_size=self.image_size,
|
|
patch_size=self.patch_size,
|
|
num_channels=self.num_channels,
|
|
hidden_size=self.hidden_size,
|
|
projection_dim=self.projection_dim,
|
|
num_hidden_layers=self.num_hidden_layers,
|
|
num_attention_heads=self.num_attention_heads,
|
|
intermediate_size=self.intermediate_size,
|
|
dropout=self.dropout,
|
|
attention_dropout=self.attention_dropout,
|
|
initializer_range=self.initializer_range,
|
|
initializer_factor=self.initializer_factor,
|
|
output_channels=self.output_channels,
|
|
qkv_bias=self.qkv_bias,
|
|
mlp_ratio=self.mlp_ratio,
|
|
use_abs_pos=self.use_abs_pos,
|
|
use_rel_pos=self.use_rel_pos,
|
|
rel_pos_zero_init=self.rel_pos_zero_init,
|
|
window_size=self.window_size,
|
|
global_attn_indexes=self.global_attn_indexes,
|
|
num_pos_feats=self.num_pos_feats,
|
|
mlp_dim=self.mlp_dim,
|
|
)
|
|
|
|
prompt_encoder_config = self.prompt_encoder_tester.get_config()
|
|
|
|
mask_decoder_config = self.mask_decoder_tester.get_config()
|
|
|
|
return SamConfig(
|
|
vision_config=vision_config,
|
|
prompt_encoder_config=prompt_encoder_config,
|
|
mask_decoder_config=mask_decoder_config,
|
|
)
|
|
|
|
def create_and_check_model(self, config, pixel_values):
|
|
model = SamModel(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
result = model(pixel_values)
|
|
self.parent.assertEqual(result.iou_scores.shape, (self.batch_size, 1, 3))
|
|
self.parent.assertEqual(result.pred_masks.shape[:3], (self.batch_size, 1, 3))
|
|
|
|
def create_and_check_get_image_features(self, config, pixel_values):
|
|
model = SamModel(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
result = model.get_image_embeddings(pixel_values)
|
|
self.parent.assertEqual(result[0].shape, (self.output_channels, 12, 12))
|
|
|
|
def create_and_check_get_image_hidden_states(self, config, pixel_values):
|
|
model = SamModel(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
result = model.vision_encoder(
|
|
pixel_values,
|
|
output_hidden_states=True,
|
|
return_dict=True,
|
|
)
|
|
|
|
# after computing the convolutional features
|
|
expected_hidden_states_shape = (self.batch_size, 12, 12, 36)
|
|
self.parent.assertEqual(len(result[1]), self.num_hidden_layers + 1)
|
|
self.parent.assertEqual(result[1][0].shape, expected_hidden_states_shape)
|
|
|
|
with torch.no_grad():
|
|
result = model.vision_encoder(
|
|
pixel_values,
|
|
output_hidden_states=True,
|
|
return_dict=False,
|
|
)
|
|
|
|
# after computing the convolutional features
|
|
expected_hidden_states_shape = (self.batch_size, 12, 12, 36)
|
|
self.parent.assertEqual(len(result[1]), self.num_hidden_layers + 1)
|
|
self.parent.assertEqual(result[1][0].shape, expected_hidden_states_shape)
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
config_and_inputs = self.prepare_config_and_inputs()
|
|
config, pixel_values = config_and_inputs
|
|
inputs_dict = {"pixel_values": pixel_values}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class SamModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
"""
|
|
Here we also overwrite some of the tests of test_modeling_common.py, as SAM's vision encoder does not use input_ids, inputs_embeds,
|
|
attention_mask and seq_length.
|
|
"""
|
|
|
|
all_model_classes = (SamModel,) if is_torch_available() else ()
|
|
pipeline_model_mapping = (
|
|
{"feature-extraction": SamModel, "mask-generation": SamModel} if is_torch_available() else {}
|
|
)
|
|
fx_compatible = False
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_head_masking = False
|
|
test_torchscript = False
|
|
|
|
# TODO: Fix me @Arthur: `run_batch_test` in `tests/test_pipeline_mixin.py` not working
|
|
def is_pipeline_test_to_skip(
|
|
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
|
|
):
|
|
return True
|
|
|
|
def setUp(self):
|
|
self.model_tester = SamModelTester(self)
|
|
self.vision_config_tester = ConfigTester(self, config_class=SamVisionConfig, has_text_modality=False)
|
|
self.prompt_encoder_config_tester = ConfigTester(
|
|
self,
|
|
config_class=SamPromptEncoderConfig,
|
|
has_text_modality=False,
|
|
num_attention_heads=12,
|
|
num_hidden_layers=2,
|
|
)
|
|
self.mask_decoder_config_tester = ConfigTester(
|
|
self, config_class=SamMaskDecoderConfig, has_text_modality=False
|
|
)
|
|
|
|
def test_config(self):
|
|
self.vision_config_tester.run_common_tests()
|
|
self.prompt_encoder_config_tester.run_common_tests()
|
|
self.mask_decoder_config_tester.run_common_tests()
|
|
|
|
@unittest.skip(reason="SAM's vision encoder does not use inputs_embeds")
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
def test_model_common_attributes(self):
|
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
|
|
x = model.get_output_embeddings()
|
|
self.assertTrue(x is None or isinstance(x, nn.Linear))
|
|
|
|
def test_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_model(*config_and_inputs)
|
|
|
|
def test_get_image_features(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_get_image_features(*config_and_inputs)
|
|
|
|
def test_image_hidden_states(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_get_image_hidden_states(*config_and_inputs)
|
|
|
|
def test_attention_outputs(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.return_dict = True
|
|
|
|
expected_vision_attention_shape = (
|
|
self.model_tester.batch_size * self.model_tester.num_attention_heads,
|
|
196,
|
|
196,
|
|
)
|
|
expected_mask_decoder_attention_shape = (self.model_tester.batch_size, 1, 144, 32)
|
|
|
|
for model_class in self.all_model_classes:
|
|
inputs_dict["output_attentions"] = True
|
|
inputs_dict["output_hidden_states"] = False
|
|
config.return_dict = True
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
vision_attentions = outputs.vision_attentions
|
|
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers)
|
|
|
|
mask_decoder_attentions = outputs.mask_decoder_attentions
|
|
self.assertEqual(len(mask_decoder_attentions), self.model_tester.mask_decoder_tester.num_hidden_layers)
|
|
|
|
# check that output_attentions also work using config
|
|
del inputs_dict["output_attentions"]
|
|
config.output_attentions = True
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
vision_attentions = outputs.vision_attentions
|
|
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers)
|
|
|
|
mask_decoder_attentions = outputs.mask_decoder_attentions
|
|
self.assertEqual(len(mask_decoder_attentions), self.model_tester.mask_decoder_tester.num_hidden_layers)
|
|
|
|
self.assertListEqual(
|
|
list(vision_attentions[0].shape[-4:]),
|
|
list(expected_vision_attention_shape),
|
|
)
|
|
|
|
self.assertListEqual(
|
|
list(mask_decoder_attentions[0].shape[-4:]),
|
|
list(expected_mask_decoder_attention_shape),
|
|
)
|
|
|
|
@unittest.skip(reason="SamModel does not support training")
|
|
def test_training(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="SamModel does not support training")
|
|
def test_training_gradient_checkpointing(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant_false(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="SamModel has no base class and is not available in MODEL_MAPPING")
|
|
def test_save_load_fast_init_from_base(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="SamModel has no base class and is not available in MODEL_MAPPING")
|
|
def test_save_load_fast_init_to_base(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="SamModel does not support training")
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Hidden_states is tested in create_and_check_model tests")
|
|
def test_hidden_states_output(self):
|
|
pass
|
|
|
|
def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=5e-5, name="outputs", attributes=None):
|
|
# Use a slightly higher default tol to make the tests non-flaky
|
|
super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol=tol, name=name, attributes=attributes)
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
for model_name in SAM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
|
model = SamModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
def prepare_image():
|
|
img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
|
|
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
|
|
return raw_image
|
|
|
|
|
|
def prepare_dog_img():
|
|
img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dog-sam.png"
|
|
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
|
|
return raw_image
|
|
|
|
|
|
@slow
|
|
class SamModelIntegrationTest(unittest.TestCase):
|
|
def tearDown(self):
|
|
super().tearDown()
|
|
# clean-up as much as possible GPU memory occupied by PyTorch
|
|
gc.collect()
|
|
backend_empty_cache(torch_device)
|
|
|
|
def test_inference_mask_generation_no_point(self):
|
|
model = SamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
raw_image = prepare_image()
|
|
inputs = processor(images=raw_image, return_tensors="pt").to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
scores = outputs.iou_scores.squeeze()
|
|
masks = outputs.pred_masks[0, 0, 0, 0, :3]
|
|
self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.4515), atol=2e-4))
|
|
self.assertTrue(torch.allclose(masks, torch.tensor([-4.1800, -3.4948, -3.4481]).to(torch_device), atol=2e-4))
|
|
|
|
def test_inference_mask_generation_one_point_one_bb(self):
|
|
model = SamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
raw_image = prepare_image()
|
|
input_boxes = [[[650, 900, 1000, 1250]]]
|
|
input_points = [[[820, 1080]]]
|
|
|
|
inputs = processor(
|
|
images=raw_image, input_boxes=input_boxes, input_points=input_points, return_tensors="pt"
|
|
).to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
scores = outputs.iou_scores.squeeze()
|
|
masks = outputs.pred_masks[0, 0, 0, 0, :3]
|
|
self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.9566), atol=2e-4))
|
|
self.assertTrue(
|
|
torch.allclose(masks, torch.tensor([-12.7729, -12.3665, -12.6061]).to(torch_device), atol=2e-4)
|
|
)
|
|
|
|
def test_inference_mask_generation_batched_points_batched_images(self):
|
|
model = SamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
raw_image = prepare_image()
|
|
input_points = [
|
|
[[[820, 1080]], [[820, 1080]], [[820, 1080]], [[820, 1080]]],
|
|
[[[510, 1080]], [[820, 1080]], [[820, 1080]], [[820, 1080]]],
|
|
]
|
|
|
|
inputs = processor(images=[raw_image, raw_image], input_points=input_points, return_tensors="pt").to(
|
|
torch_device
|
|
)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
scores = outputs.iou_scores.squeeze().cpu()
|
|
masks = outputs.pred_masks[0, 0, 0, 0, :3].cpu()
|
|
|
|
EXPECTED_SCORES = torch.tensor(
|
|
[
|
|
[
|
|
[0.6765, 0.9379, 0.8803],
|
|
[0.6765, 0.9379, 0.8803],
|
|
[0.6765, 0.9379, 0.8803],
|
|
[0.6765, 0.9379, 0.8803],
|
|
],
|
|
[
|
|
[0.3317, 0.7264, 0.7646],
|
|
[0.6765, 0.9379, 0.8803],
|
|
[0.6765, 0.9379, 0.8803],
|
|
[0.6765, 0.9379, 0.8803],
|
|
],
|
|
]
|
|
)
|
|
EXPECTED_MASKS = torch.tensor([-2.8550, -2.7988, -2.9625])
|
|
self.assertTrue(torch.allclose(scores, EXPECTED_SCORES, atol=1e-3))
|
|
self.assertTrue(torch.allclose(masks, EXPECTED_MASKS, atol=1e-3))
|
|
|
|
def test_inference_mask_generation_one_point_one_bb_zero(self):
|
|
model = SamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
raw_image = prepare_image()
|
|
input_boxes = [[[620, 900, 1000, 1255]]]
|
|
input_points = [[[820, 1080]]]
|
|
labels = [[0]]
|
|
|
|
inputs = processor(
|
|
images=raw_image,
|
|
input_boxes=input_boxes,
|
|
input_points=input_points,
|
|
input_labels=labels,
|
|
return_tensors="pt",
|
|
).to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
scores = outputs.iou_scores.squeeze()
|
|
|
|
self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.7894), atol=1e-4))
|
|
|
|
def test_inference_mask_generation_one_point(self):
|
|
model = SamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
raw_image = prepare_image()
|
|
|
|
input_points = [[[400, 650]]]
|
|
input_labels = [[1]]
|
|
|
|
inputs = processor(
|
|
images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt"
|
|
).to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
scores = outputs.iou_scores.squeeze()
|
|
self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.9675), atol=1e-4))
|
|
|
|
# With no label
|
|
input_points = [[[400, 650]]]
|
|
|
|
inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt").to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
scores = outputs.iou_scores.squeeze()
|
|
self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.9675), atol=1e-4))
|
|
|
|
def test_inference_mask_generation_two_points(self):
|
|
model = SamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
raw_image = prepare_image()
|
|
|
|
input_points = [[[400, 650], [800, 650]]]
|
|
input_labels = [[1, 1]]
|
|
|
|
inputs = processor(
|
|
images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt"
|
|
).to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
scores = outputs.iou_scores.squeeze()
|
|
self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.9762), atol=1e-4))
|
|
|
|
# no labels
|
|
inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt").to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
scores = outputs.iou_scores.squeeze()
|
|
|
|
self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.9762), atol=1e-4))
|
|
|
|
def test_inference_mask_generation_two_points_batched(self):
|
|
model = SamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
raw_image = prepare_image()
|
|
|
|
input_points = [[[400, 650], [800, 650]], [[400, 650]]]
|
|
input_labels = [[1, 1], [1]]
|
|
|
|
inputs = processor(
|
|
images=[raw_image, raw_image], input_points=input_points, input_labels=input_labels, return_tensors="pt"
|
|
).to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
scores = outputs.iou_scores.squeeze()
|
|
self.assertTrue(torch.allclose(scores[0][-1], torch.tensor(0.9762), atol=1e-4))
|
|
self.assertTrue(torch.allclose(scores[1][-1], torch.tensor(0.9637), atol=1e-4))
|
|
|
|
def test_inference_mask_generation_one_box(self):
|
|
model = SamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
raw_image = prepare_image()
|
|
|
|
input_boxes = [[[75, 275, 1725, 850]]]
|
|
|
|
inputs = processor(images=raw_image, input_boxes=input_boxes, return_tensors="pt").to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
scores = outputs.iou_scores.squeeze()
|
|
self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.7937), atol=1e-4))
|
|
|
|
def test_inference_mask_generation_batched_image_one_point(self):
|
|
model = SamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
raw_image = prepare_image()
|
|
raw_dog_image = prepare_dog_img()
|
|
|
|
input_points = [[[820, 1080]], [[220, 470]]]
|
|
|
|
inputs = processor(images=[raw_image, raw_dog_image], input_points=input_points, return_tensors="pt").to(
|
|
torch_device
|
|
)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
scores_batched = outputs.iou_scores.squeeze()
|
|
|
|
input_points = [[[220, 470]]]
|
|
|
|
inputs = processor(images=raw_dog_image, input_points=input_points, return_tensors="pt").to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
scores_single = outputs.iou_scores.squeeze()
|
|
self.assertTrue(torch.allclose(scores_batched[1, :], scores_single, atol=1e-4))
|
|
|
|
def test_inference_mask_generation_two_points_point_batch(self):
|
|
model = SamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
raw_image = prepare_image()
|
|
|
|
input_points = torch.Tensor([[[400, 650]], [[220, 470]]]).cpu() # fmt: skip
|
|
|
|
input_points = input_points.unsqueeze(0)
|
|
|
|
inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
iou_scores = outputs.iou_scores.cpu()
|
|
self.assertTrue(iou_scores.shape == (1, 2, 3))
|
|
torch.testing.assert_allclose(
|
|
iou_scores, torch.tensor([[[0.9105, 0.9825, 0.9675], [0.7646, 0.7943, 0.7774]]]), atol=1e-4, rtol=1e-4
|
|
)
|
|
|
|
def test_inference_mask_generation_three_boxes_point_batch(self):
|
|
model = SamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
raw_image = prepare_image()
|
|
|
|
# fmt: off
|
|
input_boxes = torch.Tensor([[[620, 900, 1000, 1255]], [[75, 275, 1725, 850]], [[75, 275, 1725, 850]]]).cpu()
|
|
EXPECTED_IOU = torch.tensor([[[0.9773, 0.9881, 0.9522],
|
|
[0.5996, 0.7661, 0.7937],
|
|
[0.5996, 0.7661, 0.7937]]])
|
|
# fmt: on
|
|
input_boxes = input_boxes.unsqueeze(0)
|
|
|
|
inputs = processor(raw_image, input_boxes=input_boxes, return_tensors="pt").to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
iou_scores = outputs.iou_scores.cpu()
|
|
self.assertTrue(iou_scores.shape == (1, 3, 3))
|
|
torch.testing.assert_allclose(iou_scores, EXPECTED_IOU, atol=1e-4, rtol=1e-4)
|
|
|
|
def test_dummy_pipeline_generation(self):
|
|
generator = pipeline("mask-generation", model="facebook/sam-vit-base", device=torch_device)
|
|
raw_image = prepare_image()
|
|
|
|
_ = generator(raw_image, points_per_batch=64)
|