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874 lines
32 KiB
Python
874 lines
32 KiB
Python
# coding=utf-8
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# Copyright 2024 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 PyTorch SAM2 model."""
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import gc
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import unittest
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import requests
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from transformers import (
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Sam2Config,
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Sam2MaskDecoderConfig,
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Sam2MemoryEncoderConfig,
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Sam2Processor,
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Sam2PromptEncoderConfig,
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Sam2VisionConfig,
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pipeline,
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)
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from transformers.testing_utils import backend_empty_cache, require_torch, slow, torch_device
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from transformers.utils import is_torch_available, is_vision_available
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from transformers.video_utils import load_video
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from ...test_modeling_common import ModelTesterMixin, floats_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from torch import nn
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from transformers import Sam2Model, SamProcessor
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if is_vision_available():
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from PIL import Image
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class Sam2PromptEncoderTester:
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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 Sam2PromptEncoderConfig(
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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 Sam2MaskDecoderTester:
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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_downsam2ple_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_downsam2ple_rate = attention_downsam2ple_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 Sam2MaskDecoderConfig(
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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_downsam2ple_rate=self.attention_downsam2ple_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 Sam2MemoryEncoderTester:
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def __init__(
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self,
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hidden_size=32,
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num_heads=1,
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num_channels=3,
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image_size=24,
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patch_kernel_size=2,
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patch_stride=2,
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patch_padding=1,
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drop_path_rate=0.0,
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q_pool=3,
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q_stride=(2, 2),
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stages=(1, 2, 7, 2),
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dim_mul=2.0,
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head_mul=2.0,
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window_positional_embedding_background_size=(7, 7),
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window_spec=(8, 4, 14, 7),
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global_attention_blocks=(5, 7, 9),
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backbone_channel_list=[768, 384, 192, 96],
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backbone_feature_sizes=[[256, 256], [128, 128], [64, 64]],
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fpn_hidden_size=256,
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fpn_kernel_size=1,
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fpn_stride=1,
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fpn_padding=0,
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fpn_top_down_levels=[2, 3],
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fpn_interpolation_mode="nearest",
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num_feature_levels=3,
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fuse_type="sum",
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hidden_act="gelu",
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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.num_heads = num_heads
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self.num_channels = num_channels
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self.image_size = image_size
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self.patch_kernel_size = patch_kernel_size
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self.patch_stride = patch_stride
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self.patch_padding = patch_padding
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self.drop_path_rate = drop_path_rate
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self.q_pool = q_pool
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self.q_stride = q_stride
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self.stages = stages
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self.dim_mul = dim_mul
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self.head_mul = head_mul
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self.window_positional_embedding_background_size = window_positional_embedding_background_size
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self.window_spec = window_spec
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self.global_attention_blocks = global_attention_blocks
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self.backbone_channel_list = backbone_channel_list
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self.backbone_feature_sizes = backbone_feature_sizes
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self.fpn_hidden_size = fpn_hidden_size
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self.fpn_kernel_size = fpn_kernel_size
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self.fpn_stride = fpn_stride
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self.fpn_padding = fpn_padding
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self.fpn_top_down_levels = fpn_top_down_levels
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self.fpn_interpolation_mode = fpn_interpolation_mode
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self.num_feature_levels = num_feature_levels
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self.fuse_type = fuse_type
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self.hidden_act = hidden_act
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self.layer_norm_eps = layer_norm_eps
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def get_config(self):
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return Sam2MemoryEncoderConfig(
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hidden_size=self.hidden_size,
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num_heads=self.num_heads,
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num_channels=self.num_channels,
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image_size=self.image_size,
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patch_kernel_size=self.patch_kernel_size,
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patch_stride=self.patch_stride,
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patch_padding=self.patch_padding,
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drop_path_rate=self.drop_path_rate,
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q_pool=self.q_pool,
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q_stride=self.q_stride,
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stages=self.stages,
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dim_mul=self.dim_mul,
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head_mul=self.head_mul,
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window_positional_embedding_background_size=self.window_positional_embedding_background_size,
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window_spec=self.window_spec,
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global_attention_blocks=self.global_attention_blocks,
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backbone_channel_list=self.backbone_channel_list,
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backbone_feature_sizes=self.backbone_feature_sizes,
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fpn_hidden_size=self.fpn_hidden_size,
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fpn_kernel_size=self.fpn_kernel_size,
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fpn_stride=self.fpn_stride,
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fpn_padding=self.fpn_padding,
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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 Sam2ModelTester:
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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 = Sam2PromptEncoderTester()
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self.mask_decoder_tester = Sam2MaskDecoderTester()
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self.memory_encoder_tester = Sam2MemoryEncoderTester()
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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 = Sam2VisionConfig(
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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 Sam2Config(
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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 = Sam2Model(config=config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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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 = Sam2Model(config=config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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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 = Sam2Model(config=config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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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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with torch.no_grad():
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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_torch
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class Sam2ModelTest(ModelTesterMixin, 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 SAM2'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 = (Sam2Model,) if is_torch_available() else ()
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fx_compatible = False
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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_torchscript = False
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@unittest.skip(reason="SAM2'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_get_set_embeddings(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(), (nn.Module))
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x = model.get_output_embeddings()
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self.assertTrue(x is None or isinstance(x, nn.Linear))
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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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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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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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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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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(
|
|
list(mask_decoder_attentions[0].shape[-4:]),
|
|
list(expected_mask_decoder_attention_shape),
|
|
)
|
|
|
|
@unittest.skip(reason="Sam2Model does not support training")
|
|
def test_training(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Sam2Model 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="Sam2Model has no base class and is not available in MODEL_MAPPING")
|
|
def test_save_load_fast_init_from_base(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Sam2Model has no base class and is not available in MODEL_MAPPING")
|
|
def test_save_load_fast_init_to_base(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Sam2Model 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):
|
|
model_name = "facebook/sam2-hiera-large"
|
|
model = Sam2Model.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
def prepare_image():
|
|
img_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg"
|
|
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
|
|
|
|
|
|
def prepare_video():
|
|
video_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/bedroom.mp4"
|
|
raw_video, _ = load_video(video_url)
|
|
return raw_video
|
|
|
|
|
|
@slow
|
|
class Sam2ModelIntegrationTest(unittest.TestCase):
|
|
def setUp(self):
|
|
super().setUp()
|
|
self.model = Sam2Model.from_pretrained("../sam2_hf_implem/sam2_tiny_hf")
|
|
self.processor = Sam2Processor.from_pretrained("../sam2_hf_implem/sam2_tiny_hf")
|
|
self.model.to(torch_device)
|
|
self.model.eval()
|
|
|
|
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):
|
|
pass
|
|
|
|
# model = Sam2Model.from_pretrained("facebook/sam2-vit-base")
|
|
|
|
# processor = SamProcessor.from_pretrained("facebook/sam2-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_multimask(self):
|
|
raw_image = prepare_image()
|
|
input_points = [[[[500, 375]]]]
|
|
input_labels = [[[1]]]
|
|
|
|
inputs = self.processor(
|
|
images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt"
|
|
).to(torch_device)
|
|
# to_tensor = ToTensor()
|
|
# transforms = torch.jit.script(
|
|
# nn.Sequential(
|
|
# Resize((1024, 1024)),
|
|
# Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
|
|
# )
|
|
# )
|
|
# inputs["pixel_values"] = transforms(to_tensor(raw_image)).unsqueeze(0).to("cuda")
|
|
|
|
with torch.no_grad():
|
|
outputs = self.model(**inputs)
|
|
self.assertEqual(outputs.iou_scores.shape, (1, 1, 3))
|
|
self.assertEqual(outputs.low_res_masks.shape, (1, 1, 3, 256, 256))
|
|
sorted_indices = torch.argsort(outputs.iou_scores.squeeze(), descending=True)
|
|
scores = outputs.iou_scores.squeeze()[sorted_indices]
|
|
masks_logits = outputs.low_res_masks.squeeze()[sorted_indices][0, :3, :3]
|
|
|
|
torch.testing.assert_close(
|
|
scores, torch.tensor([0.9546, 0.4937, 0.0428]).to(torch_device), atol=1e-4, rtol=1e-4
|
|
)
|
|
torch.testing.assert_close(
|
|
masks_logits,
|
|
torch.tensor(
|
|
[[-25.0963, -41.5728, -30.8723], [-34.7112, -30.7988, -36.4013], [-25.3061, -37.4575, -33.1899]]
|
|
).to(torch_device),
|
|
atol=1e-4,
|
|
rtol=1e-4,
|
|
)
|
|
|
|
def test_inference_mask_generation_one_point_no_multimask(self):
|
|
raw_image = prepare_image()
|
|
input_points = [[[[500, 375]]]]
|
|
input_labels = [[[1]]]
|
|
|
|
inputs = self.processor(
|
|
images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt"
|
|
).to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = self.model(**inputs, multimask_output=False)
|
|
self.assertEqual(outputs.iou_scores.shape, (1, 1, 1))
|
|
self.assertEqual(outputs.low_res_masks.shape, (1, 1, 1, 256, 256))
|
|
scores = outputs.iou_scores.squeeze((0, 1))
|
|
masks_logits = outputs.low_res_masks.squeeze((0, 1))[0, :3, :3]
|
|
|
|
torch.testing.assert_close(scores, torch.tensor([0.9366]).to(torch_device), atol=1e-4, rtol=1e-4)
|
|
torch.testing.assert_close(
|
|
masks_logits,
|
|
torch.tensor(
|
|
[[-7.1674, -13.4459, -9.6908], [-10.6038, -9.7242, -12.4059], [-7.4478, -12.4997, -10.5906]]
|
|
).to(torch_device),
|
|
atol=1e-4,
|
|
rtol=1e-4,
|
|
)
|
|
|
|
def test_inference_mask_generation_video_one_point(self):
|
|
pass
|
|
# raw_video = prepare_video()
|
|
# self.processor.init_state(video_path="./videos/bedroom_light")
|
|
|
|
# inputs = processor.add_new_points_or_box(
|
|
# frame_idx=0,
|
|
# obj_id=1,
|
|
# points=[[[[210, 350]]]],
|
|
# labels=[[[1]]],
|
|
# )
|
|
|
|
# def test_inference_mask_generation_one_point_one_bb(self):
|
|
# model = Sam2Model.from_pretrained("../sam2_hf_implem/sam2_tiny_hf")
|
|
# processor = SamProcessor.from_pretrained("../sam2_hf_implem/sam2_tiny_hf")
|
|
|
|
# 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):
|
|
raw_image1 = prepare_image()
|
|
raw_image2 = prepare_dog_img()
|
|
input_points = [[[[500, 375], [10, 10]]], [[[770, 200], [730, 120]]]]
|
|
input_labels = [[[1, -10]], [[1, 0]]]
|
|
|
|
inputs = self.processor(
|
|
images=[raw_image1, raw_image2], input_points=input_points, input_labels=input_labels, return_tensors="pt"
|
|
).to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = self.model(**inputs)
|
|
self.assertEqual(outputs.iou_scores.shape, (2, 1, 3))
|
|
self.assertEqual(outputs.low_res_masks.shape, (2, 1, 3, 256, 256))
|
|
|
|
sorted_indices = torch.argsort(outputs.iou_scores[0].squeeze(), descending=True)
|
|
scores1 = outputs.iou_scores[0].squeeze()[sorted_indices]
|
|
masks_logits1 = outputs.low_res_masks[0].squeeze()[sorted_indices][0, :3, :3]
|
|
sorted_indices = torch.argsort(outputs.iou_scores[1].squeeze(), descending=True)
|
|
scores2 = outputs.iou_scores[1].squeeze()[sorted_indices]
|
|
masks_logits2 = outputs.low_res_masks[1].squeeze()[sorted_indices][0, :3, :3]
|
|
|
|
torch.testing.assert_close(
|
|
scores1, torch.tensor([0.9584, 0.4898, 0.0445]).to(torch_device), atol=1e-4, rtol=1e-4
|
|
)
|
|
torch.testing.assert_close(
|
|
masks_logits1,
|
|
torch.tensor(
|
|
[[-22.4127, -37.7623, -27.7642], [-31.0563, -27.6730, -32.6308], [-22.4559, -33.8773, -29.5238]]
|
|
).to(torch_device),
|
|
atol=1e-4,
|
|
rtol=1e-4,
|
|
)
|
|
|
|
torch.testing.assert_close(
|
|
scores2, torch.tensor([0.9504, 0.8117, 0.7426]).to(torch_device), atol=1e-4, rtol=1e-4
|
|
)
|
|
torch.testing.assert_close(
|
|
masks_logits2,
|
|
torch.tensor(
|
|
[[-13.1202, -17.3222, -14.9687], [-16.2375, -12.7737, -17.6353], [-13.5025, -17.1528, -15.6627]]
|
|
).to(torch_device),
|
|
atol=1e-4,
|
|
rtol=1e-4,
|
|
)
|
|
|
|
def test_inference_mask_generation_one_point_one_bb_zero(self):
|
|
model = Sam2Model.from_pretrained("facebook/sam2-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam2-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_two_points_batched(self):
|
|
model = Sam2Model.from_pretrained("facebook/sam2-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam2-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 = Sam2Model.from_pretrained("facebook/sam2-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam2-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 = Sam2Model.from_pretrained("facebook/sam2-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam2-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 = Sam2Model.from_pretrained("facebook/sam2-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam2-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_close(
|
|
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 = Sam2Model.from_pretrained("facebook/sam2-vit-base")
|
|
processor = SamProcessor.from_pretrained("facebook/sam2-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_close(iou_scores, EXPECTED_IOU, atol=1e-4, rtol=1e-4)
|
|
|
|
def test_dummy_pipeline_generation(self):
|
|
generator = pipeline("mask-generation", model="facebook/sam2-vit-base", device=torch_device)
|
|
raw_image = prepare_image()
|
|
|
|
_ = generator(raw_image, points_per_batch=64)
|