mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 21:30:07 +06:00
26a06814a1
* Fix SAM tests and use smaller checkpoints * Override test_model_from_pretrained to use sam-vit-base as well * make fixup
670 lines
25 KiB
Python
670 lines
25 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Testing suite for the TensorFlow SAM model. """
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import inspect
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import unittest
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import numpy as np
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import requests
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from transformers import SamConfig, SamMaskDecoderConfig, SamPromptEncoderConfig, SamVisionConfig
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from transformers.testing_utils import require_tf, slow
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from transformers.utils import is_tf_available, is_vision_available
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_tf_available():
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import tensorflow as tf
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from transformers import SamProcessor, TFSamModel
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if is_vision_available():
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from PIL import Image
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class TFSamPromptEncoderTester:
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def __init__(
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self,
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hidden_size=32,
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input_image_size=24,
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patch_size=2,
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mask_input_channels=4,
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num_point_embeddings=4,
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hidden_act="gelu",
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):
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self.hidden_size = hidden_size
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self.input_image_size = input_image_size
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self.patch_size = patch_size
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self.mask_input_channels = mask_input_channels
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self.num_point_embeddings = num_point_embeddings
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self.hidden_act = hidden_act
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def get_config(self):
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return SamPromptEncoderConfig(
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image_size=self.input_image_size,
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patch_size=self.patch_size,
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mask_input_channels=self.mask_input_channels,
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hidden_size=self.hidden_size,
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num_point_embeddings=self.num_point_embeddings,
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hidden_act=self.hidden_act,
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)
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def prepare_config_and_inputs(self):
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dummy_points = floats_tensor([self.batch_size, 3, 2])
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config = self.get_config()
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return config, dummy_points
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class TFSamMaskDecoderTester:
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def __init__(
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self,
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hidden_size=32,
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hidden_act="relu",
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mlp_dim=64,
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num_hidden_layers=2,
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num_attention_heads=4,
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attention_downsample_rate=2,
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num_multimask_outputs=3,
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iou_head_depth=3,
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iou_head_hidden_dim=32,
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layer_norm_eps=1e-6,
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):
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self.hidden_size = hidden_size
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self.hidden_act = hidden_act
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self.mlp_dim = mlp_dim
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.attention_downsample_rate = attention_downsample_rate
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self.num_multimask_outputs = num_multimask_outputs
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self.iou_head_depth = iou_head_depth
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self.iou_head_hidden_dim = iou_head_hidden_dim
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self.layer_norm_eps = layer_norm_eps
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def get_config(self):
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return SamMaskDecoderConfig(
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hidden_size=self.hidden_size,
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hidden_act=self.hidden_act,
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mlp_dim=self.mlp_dim,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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attention_downsample_rate=self.attention_downsample_rate,
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num_multimask_outputs=self.num_multimask_outputs,
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iou_head_depth=self.iou_head_depth,
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iou_head_hidden_dim=self.iou_head_hidden_dim,
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layer_norm_eps=self.layer_norm_eps,
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)
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def prepare_config_and_inputs(self):
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config = self.get_config()
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dummy_inputs = {
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"image_embedding": floats_tensor([self.batch_size, self.hidden_size]),
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}
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return config, dummy_inputs
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class TFSamModelTester:
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def __init__(
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self,
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parent,
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hidden_size=36,
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intermediate_size=72,
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projection_dim=62,
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output_channels=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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num_channels=3,
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image_size=24,
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patch_size=2,
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hidden_act="gelu",
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layer_norm_eps=1e-06,
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dropout=0.0,
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attention_dropout=0.0,
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initializer_range=0.02,
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initializer_factor=1.0,
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qkv_bias=True,
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mlp_ratio=4.0,
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use_abs_pos=True,
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use_rel_pos=True,
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rel_pos_zero_init=False,
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window_size=14,
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global_attn_indexes=[2, 5, 8, 11],
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num_pos_feats=16,
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mlp_dim=None,
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batch_size=2,
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):
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self.parent = parent
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self.image_size = image_size
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self.patch_size = patch_size
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self.output_channels = output_channels
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self.num_channels = num_channels
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self.hidden_size = hidden_size
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self.projection_dim = projection_dim
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.initializer_range = initializer_range
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self.initializer_factor = initializer_factor
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self.hidden_act = hidden_act
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self.layer_norm_eps = layer_norm_eps
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self.qkv_bias = qkv_bias
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self.mlp_ratio = mlp_ratio
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self.use_abs_pos = use_abs_pos
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self.use_rel_pos = use_rel_pos
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self.rel_pos_zero_init = rel_pos_zero_init
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self.window_size = window_size
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self.global_attn_indexes = global_attn_indexes
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self.num_pos_feats = num_pos_feats
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self.mlp_dim = mlp_dim
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self.batch_size = batch_size
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# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
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num_patches = (image_size // patch_size) ** 2
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self.seq_length = num_patches + 1
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self.prompt_encoder_tester = TFSamPromptEncoderTester()
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self.mask_decoder_tester = TFSamMaskDecoderTester()
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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config = self.get_config()
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return config, pixel_values
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def get_config(self):
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vision_config = SamVisionConfig(
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image_size=self.image_size,
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patch_size=self.patch_size,
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num_channels=self.num_channels,
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hidden_size=self.hidden_size,
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projection_dim=self.projection_dim,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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dropout=self.dropout,
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attention_dropout=self.attention_dropout,
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initializer_range=self.initializer_range,
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initializer_factor=self.initializer_factor,
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output_channels=self.output_channels,
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qkv_bias=self.qkv_bias,
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mlp_ratio=self.mlp_ratio,
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use_abs_pos=self.use_abs_pos,
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use_rel_pos=self.use_rel_pos,
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rel_pos_zero_init=self.rel_pos_zero_init,
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window_size=self.window_size,
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global_attn_indexes=self.global_attn_indexes,
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num_pos_feats=self.num_pos_feats,
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mlp_dim=self.mlp_dim,
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)
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prompt_encoder_config = self.prompt_encoder_tester.get_config()
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mask_decoder_config = self.mask_decoder_tester.get_config()
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return SamConfig(
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vision_config=vision_config,
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prompt_encoder_config=prompt_encoder_config,
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mask_decoder_config=mask_decoder_config,
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)
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def create_and_check_model(self, config, pixel_values):
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model = TFSamModel(config=config)
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result = model(pixel_values)
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self.parent.assertEqual(result.iou_scores.shape, (self.batch_size, 1, 3))
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self.parent.assertEqual(result.pred_masks.shape[:3], (self.batch_size, 1, 3))
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def create_and_check_get_image_features(self, config, pixel_values):
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model = TFSamModel(config=config)
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result = model.get_image_embeddings(pixel_values)
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self.parent.assertEqual(result[0].shape, (self.output_channels, 12, 12))
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def create_and_check_get_image_hidden_states(self, config, pixel_values):
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model = TFSamModel(config=config)
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result = model.vision_encoder(
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pixel_values,
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output_hidden_states=True,
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return_dict=True,
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)
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# after computing the convolutional features
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expected_hidden_states_shape = (self.batch_size, 12, 12, 36)
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self.parent.assertEqual(len(result[1]), self.num_hidden_layers + 1)
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self.parent.assertEqual(result[1][0].shape, expected_hidden_states_shape)
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result = model.vision_encoder(
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pixel_values,
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output_hidden_states=True,
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return_dict=False,
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)
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# after computing the convolutional features
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expected_hidden_states_shape = (self.batch_size, 12, 12, 36)
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self.parent.assertEqual(len(result[1]), self.num_hidden_layers + 1)
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self.parent.assertEqual(result[1][0].shape, expected_hidden_states_shape)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, pixel_values = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values}
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return config, inputs_dict
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@require_tf
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class TFSamModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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"""
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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,
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attention_mask and seq_length.
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"""
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all_model_classes = (TFSamModel,) if is_tf_available() else ()
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pipeline_model_mapping = (
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{"feature-extraction": TFSamModel, "mask-generation": TFSamModel} if is_tf_available() else {}
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)
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test_pruning = False
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test_resize_embeddings = False
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test_head_masking = False
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test_onnx = False
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# TODO: Fix me @Arthur: `run_batch_test` in `tests/test_pipeline_mixin.py` not working
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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return True
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def setUp(self):
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self.model_tester = TFSamModelTester(self)
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self.vision_config_tester = ConfigTester(self, config_class=SamVisionConfig, has_text_modality=False)
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self.prompt_encoder_config_tester = ConfigTester(
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self,
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config_class=SamPromptEncoderConfig,
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has_text_modality=False,
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num_attention_heads=12,
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num_hidden_layers=2,
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)
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self.mask_decoder_config_tester = ConfigTester(
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self, config_class=SamMaskDecoderConfig, has_text_modality=False
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)
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def test_config(self):
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self.vision_config_tester.run_common_tests()
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self.prompt_encoder_config_tester.run_common_tests()
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self.mask_decoder_config_tester.run_common_tests()
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@unittest.skip(reason="SAM's vision encoder does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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def test_model_common_attributes(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer))
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x = model.get_output_embeddings()
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self.assertTrue(x is None or isinstance(x, tf.keras.layers.Dense))
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.call)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["pixel_values"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_get_image_features(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_get_image_features(*config_and_inputs)
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def test_image_hidden_states(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_get_image_hidden_states(*config_and_inputs)
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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expected_vision_attention_shape = (
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self.model_tester.batch_size * self.model_tester.num_attention_heads,
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196,
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196,
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)
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expected_mask_decoder_attention_shape = (self.model_tester.batch_size, 1, 144, 32)
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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config.return_dict = True
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model = model_class(config)
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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vision_attentions = outputs.vision_attentions
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self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers)
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mask_decoder_attentions = outputs.mask_decoder_attentions
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self.assertEqual(len(mask_decoder_attentions), self.model_tester.mask_decoder_tester.num_hidden_layers)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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vision_attentions = outputs.vision_attentions
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self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers)
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mask_decoder_attentions = outputs.mask_decoder_attentions
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self.assertEqual(len(mask_decoder_attentions), self.model_tester.mask_decoder_tester.num_hidden_layers)
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self.assertListEqual(
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list(vision_attentions[0].shape[-4:]),
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list(expected_vision_attention_shape),
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)
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self.assertListEqual(
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list(mask_decoder_attentions[0].shape[-4:]),
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list(expected_mask_decoder_attention_shape),
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)
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@unittest.skip(reason="Hidden_states is tested in create_and_check_model tests")
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def test_hidden_states_output(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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model = TFSamModel.from_pretrained("facebook/sam-vit-base") # sam-vit-huge blows out our memory
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self.assertIsNotNone(model)
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def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=5e-4, name="outputs", attributes=None):
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super().check_pt_tf_outputs(
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tf_outputs=tf_outputs,
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pt_outputs=pt_outputs,
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model_class=model_class,
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tol=tol,
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name=name,
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attributes=attributes,
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)
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def prepare_image():
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img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
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raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
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return raw_image
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def prepare_dog_img():
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img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dog-sam.png"
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raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
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return raw_image
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@slow
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class SamModelIntegrationTest(unittest.TestCase):
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def test_inference_mask_generation_no_point(self):
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model = TFSamModel.from_pretrained("facebook/sam-vit-base")
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processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
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raw_image = prepare_image()
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inputs = processor(images=raw_image, return_tensors="tf")
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outputs = model(**inputs)
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scores = tf.squeeze(outputs.iou_scores)
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masks = outputs.pred_masks[0, 0, 0, 0, :3]
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self.assertTrue(np.allclose(scores[-1].numpy(), np.array(0.4515), atol=2e-4))
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self.assertTrue(np.allclose(masks.numpy(), np.array([-4.1807, -3.4949, -3.4483]), atol=1e-2))
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def test_inference_mask_generation_one_point_one_bb(self):
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model = TFSamModel.from_pretrained("facebook/sam-vit-base")
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processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
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raw_image = prepare_image()
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input_boxes = [[[650, 900, 1000, 1250]]]
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input_points = [[[820, 1080]]]
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inputs = processor(images=raw_image, input_boxes=input_boxes, input_points=input_points, return_tensors="tf")
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outputs = model(**inputs)
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scores = tf.squeeze(outputs.iou_scores)
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masks = outputs.pred_masks[0, 0, 0, 0, :3]
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self.assertTrue(np.allclose(scores[-1], np.array(0.9566), atol=2e-4))
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self.assertTrue(np.allclose(masks.numpy(), np.array([-12.7657, -12.3683, -12.5985]), atol=2e-2))
|
|
|
|
def test_inference_mask_generation_batched_points_batched_images(self):
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|
model = TFSamModel.from_pretrained("facebook/sam-vit-base")
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|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
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="tf")
|
|
|
|
outputs = model(**inputs)
|
|
scores = tf.squeeze(outputs.iou_scores)
|
|
masks = outputs.pred_masks[0, 0, 0, 0, :3]
|
|
|
|
EXPECTED_SCORES = np.array(
|
|
[
|
|
[
|
|
[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 = np.array([-2.8552, -2.7990, -2.9612])
|
|
self.assertTrue(np.allclose(scores.numpy(), EXPECTED_SCORES, atol=1e-3))
|
|
self.assertTrue(np.allclose(masks.numpy(), EXPECTED_MASKS, atol=3e-2))
|
|
|
|
def test_inference_mask_generation_one_point_one_bb_zero(self):
|
|
model = TFSamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
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="tf",
|
|
)
|
|
|
|
outputs = model(**inputs)
|
|
scores = tf.squeeze(outputs.iou_scores)
|
|
self.assertTrue(np.allclose(scores[-1].numpy(), np.array(0.7894), atol=1e-4))
|
|
|
|
def test_inference_mask_generation_one_point(self):
|
|
model = TFSamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
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="tf")
|
|
|
|
outputs = model(**inputs)
|
|
scores = tf.squeeze(outputs.iou_scores)
|
|
|
|
self.assertTrue(np.allclose(scores[-1], np.array(0.9675), atol=1e-4))
|
|
|
|
# With no label
|
|
input_points = [[[400, 650]]]
|
|
|
|
inputs = processor(images=raw_image, input_points=input_points, return_tensors="tf")
|
|
|
|
outputs = model(**inputs)
|
|
scores = tf.squeeze(outputs.iou_scores)
|
|
|
|
self.assertTrue(np.allclose(scores[-1].numpy(), np.array(0.9675), atol=1e-4))
|
|
|
|
def test_inference_mask_generation_two_points(self):
|
|
model = TFSamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
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="tf")
|
|
|
|
outputs = model(**inputs)
|
|
scores = tf.squeeze(outputs.iou_scores)
|
|
|
|
self.assertTrue(np.allclose(scores[-1].numpy(), np.array(0.9762), atol=1e-4))
|
|
|
|
# no labels
|
|
inputs = processor(images=raw_image, input_points=input_points, return_tensors="tf")
|
|
|
|
outputs = model(**inputs)
|
|
scores = tf.squeeze(outputs.iou_scores)
|
|
|
|
self.assertTrue(np.allclose(scores[-1].numpy(), np.array(0.9762), atol=1e-4))
|
|
|
|
def test_inference_mask_generation_two_points_batched(self):
|
|
model = TFSamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
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="tf"
|
|
)
|
|
|
|
outputs = model(**inputs)
|
|
scores = tf.squeeze(outputs.iou_scores)
|
|
|
|
self.assertTrue(np.allclose(scores[0][-1].numpy(), np.array(0.9762), atol=1e-4))
|
|
self.assertTrue(np.allclose(scores[1][-1], np.array(0.9637), atol=1e-4))
|
|
|
|
def test_inference_mask_generation_one_box(self):
|
|
model = TFSamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
raw_image = prepare_image()
|
|
|
|
input_boxes = [[[75, 275, 1725, 850]]]
|
|
|
|
inputs = processor(images=raw_image, input_boxes=input_boxes, return_tensors="tf")
|
|
|
|
outputs = model(**inputs)
|
|
scores = tf.squeeze(outputs.iou_scores)
|
|
|
|
self.assertTrue(np.allclose(scores[-1].numpy(), np.array(0.7937), atol=1e-4))
|
|
|
|
def test_inference_mask_generation_batched_image_one_point(self):
|
|
model = TFSamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
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="tf")
|
|
|
|
outputs = model(**inputs)
|
|
scores_batched = tf.squeeze(outputs.iou_scores)
|
|
|
|
input_points = [[[220, 470]]]
|
|
|
|
inputs = processor(images=raw_dog_image, input_points=input_points, return_tensors="tf")
|
|
|
|
outputs = model(**inputs)
|
|
scores_single = tf.squeeze(outputs.iou_scores)
|
|
self.assertTrue(np.allclose(scores_batched[1, :].numpy(), scores_single.numpy(), atol=1e-4))
|
|
|
|
def test_inference_mask_generation_two_points_point_batch(self):
|
|
model = TFSamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
raw_image = prepare_image()
|
|
|
|
# fmt: off
|
|
input_points = tf.convert_to_tensor([[[400, 650]], [[220, 470]]])
|
|
# fmt: on
|
|
|
|
input_points = tf.expand_dims(input_points, 0)
|
|
|
|
inputs = processor(raw_image, input_points=input_points, return_tensors="tf")
|
|
|
|
outputs = model(**inputs)
|
|
|
|
iou_scores = outputs.iou_scores
|
|
self.assertTrue(iou_scores.shape == (1, 2, 3))
|
|
self.assertTrue(
|
|
np.allclose(
|
|
iou_scores.numpy(),
|
|
np.array([[[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 = TFSamModel.from_pretrained("facebook/sam-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
|
|
|
|
raw_image = prepare_image()
|
|
|
|
# fmt: off
|
|
input_boxes = tf.convert_to_tensor([[[620, 900, 1000, 1255]], [[75, 275, 1725, 850]], [[75, 275, 1725, 850]]])
|
|
EXPECTED_IOU = np.array([[[0.9773, 0.9881, 0.9522],
|
|
[0.5996, 0.7661, 0.7937],
|
|
[0.5996, 0.7661, 0.7937]]])
|
|
# fmt: on
|
|
input_boxes = tf.expand_dims(input_boxes, 0)
|
|
|
|
inputs = processor(raw_image, input_boxes=input_boxes, return_tensors="tf")
|
|
|
|
outputs = model(**inputs)
|
|
|
|
iou_scores = outputs.iou_scores
|
|
self.assertTrue(iou_scores.shape == (1, 3, 3))
|
|
self.assertTrue(np.allclose(iou_scores.numpy(), EXPECTED_IOU, atol=1e-4, rtol=1e-4))
|