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806 lines
32 KiB
Python
806 lines
32 KiB
Python
# coding = utf-8
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# Copyright 2025 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 deim model."""
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import inspect
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import math
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import tempfile
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import unittest
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from parameterized import parameterized
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from transformers import (
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DeimConfig,
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HGNetV2Config,
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is_torch_available,
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is_vision_available,
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)
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from transformers.testing_utils import require_torch, require_torch_gpu, require_vision, slow, torch_device
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from transformers.utils import cached_property
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if is_torch_available():
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import torch
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from transformers import DeimForObjectDetection, DeimModel
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if is_vision_available():
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from PIL import Image
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from transformers import RTDetrImageProcessor
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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CHECKPOINT = "ustc-community/dfine_s_coco"
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class DeimModelTester:
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def __init__(
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self,
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parent,
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batch_size=3,
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is_training=True,
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use_labels=True,
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n_targets=3,
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num_labels=10,
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initializer_range=0.02,
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layer_norm_eps=1e-5,
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batch_norm_eps=1e-5,
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# backbone
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backbone_config=None,
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# encoder HybridEncoder
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encoder_hidden_dim=32,
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encoder_in_channels=[128, 256, 512],
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feat_strides=[8, 16, 32],
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encoder_layers=1,
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encoder_ffn_dim=64,
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encoder_attention_heads=2,
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dropout=0.0,
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activation_dropout=0.0,
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encode_proj_layers=[2],
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positional_encoding_temperature=10000,
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encoder_activation_function="gelu",
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activation_function="silu",
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eval_size=None,
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normalize_before=False,
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# decoder DeimTransformer
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d_model=32,
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num_queries=30,
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decoder_in_channels=[32, 32, 32],
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decoder_ffn_dim=64,
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num_feature_levels=3,
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decoder_n_points=[3, 6, 3],
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decoder_n_levels=3,
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decoder_layers=2,
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decoder_attention_heads=2,
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decoder_activation_function="relu",
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attention_dropout=0.0,
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num_denoising=0,
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label_noise_ratio=0.5,
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box_noise_scale=1.0,
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learn_initial_query=False,
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anchor_image_size=None,
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image_size=64,
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disable_custom_kernels=True,
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with_box_refine=True,
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decoder_offset_scale=0.5,
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eval_idx=-1,
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layer_scale=1,
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reg_max=32,
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reg_scale=4.0,
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depth_mult=0.34,
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hidden_expansion=0.5,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.num_channels = 3
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self.is_training = is_training
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self.use_labels = use_labels
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self.n_targets = n_targets
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self.num_labels = num_labels
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.batch_norm_eps = batch_norm_eps
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self.backbone_config = backbone_config
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self.encoder_hidden_dim = encoder_hidden_dim
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self.encoder_in_channels = encoder_in_channels
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self.feat_strides = feat_strides
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self.encoder_layers = encoder_layers
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self.encoder_ffn_dim = encoder_ffn_dim
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self.encoder_attention_heads = encoder_attention_heads
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self.dropout = dropout
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self.activation_dropout = activation_dropout
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self.encode_proj_layers = encode_proj_layers
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self.positional_encoding_temperature = positional_encoding_temperature
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self.encoder_activation_function = encoder_activation_function
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self.activation_function = activation_function
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self.eval_size = eval_size
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self.normalize_before = normalize_before
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self.d_model = d_model
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self.num_queries = num_queries
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self.decoder_in_channels = decoder_in_channels
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self.decoder_ffn_dim = decoder_ffn_dim
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self.num_feature_levels = num_feature_levels
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self.decoder_n_points = decoder_n_points
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self.decoder_n_levels = decoder_n_levels
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self.decoder_layers = decoder_layers
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self.decoder_attention_heads = decoder_attention_heads
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self.decoder_activation_function = decoder_activation_function
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self.attention_dropout = attention_dropout
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self.decoder_offset_scale = decoder_offset_scale
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self.eval_idx = eval_idx
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self.layer_scale = layer_scale
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self.reg_max = reg_max
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self.reg_scale = reg_scale
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self.depth_mult = depth_mult
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self.num_denoising = num_denoising
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self.label_noise_ratio = label_noise_ratio
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self.box_noise_scale = box_noise_scale
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self.learn_initial_query = learn_initial_query
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self.anchor_image_size = anchor_image_size
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self.image_size = image_size
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self.disable_custom_kernels = disable_custom_kernels
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self.with_box_refine = with_box_refine
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self.hidden_expansion = hidden_expansion
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self.encoder_seq_length = math.ceil(self.image_size / 32) * math.ceil(self.image_size / 32)
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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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pixel_mask = torch.ones([self.batch_size, self.image_size, self.image_size], device=torch_device)
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labels = None
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if self.use_labels:
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# labels is a list of Dict (each Dict being the labels for a given example in the batch)
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labels = []
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for i in range(self.batch_size):
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target = {}
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target["class_labels"] = torch.randint(
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high=self.num_labels, size=(self.n_targets,), device=torch_device
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)
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target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device)
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labels.append(target)
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config = self.get_config()
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config.num_labels = self.num_labels
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return config, pixel_values, pixel_mask, labels
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def get_config(self):
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hidden_sizes = [64, 128, 256, 512]
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backbone_config = HGNetV2Config(
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stage_in_channels=[16, 64, 128, 256],
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stage_mid_channels=[16, 32, 64, 128],
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stage_out_channels=[64, 128, 256, 512],
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stage_num_blocks=[1, 1, 2, 1],
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stage_downsample=[False, True, True, True],
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stage_light_block=[False, False, True, True],
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stage_kernel_size=[3, 3, 5, 5],
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stage_numb_of_layers=[3, 3, 3, 3],
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embeddings_size=10,
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hidden_sizes=hidden_sizes,
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depths=[1, 1, 2, 1],
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out_features=["stage2", "stage3", "stage4"],
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out_indices=[2, 3, 4],
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stem_channels=[3, 16, 16],
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use_lab=True,
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)
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return DeimConfig.from_backbone_configs(
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backbone_config=backbone_config,
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encoder_hidden_dim=self.encoder_hidden_dim,
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encoder_in_channels=self.encoder_in_channels,
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feat_strides=self.feat_strides,
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encoder_layers=self.encoder_layers,
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encoder_ffn_dim=self.encoder_ffn_dim,
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encoder_attention_heads=self.encoder_attention_heads,
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dropout=self.dropout,
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activation_dropout=self.activation_dropout,
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encode_proj_layers=self.encode_proj_layers,
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positional_encoding_temperature=self.positional_encoding_temperature,
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encoder_activation_function=self.encoder_activation_function,
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activation_function=self.activation_function,
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eval_size=self.eval_size,
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normalize_before=self.normalize_before,
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d_model=self.d_model,
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num_queries=self.num_queries,
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decoder_in_channels=self.decoder_in_channels,
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decoder_ffn_dim=self.decoder_ffn_dim,
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num_feature_levels=self.num_feature_levels,
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decoder_n_points=self.decoder_n_points,
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decoder_n_levels=self.decoder_n_levels,
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decoder_layers=self.decoder_layers,
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decoder_attention_heads=self.decoder_attention_heads,
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decoder_activation_function=self.decoder_activation_function,
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decoder_offset_scale=self.decoder_offset_scale,
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eval_idx=self.eval_idx,
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layer_scale=self.layer_scale,
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reg_max=self.reg_max,
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reg_scale=self.reg_scale,
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depth_mult=self.depth_mult,
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attention_dropout=self.attention_dropout,
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num_denoising=self.num_denoising,
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label_noise_ratio=self.label_noise_ratio,
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box_noise_scale=self.box_noise_scale,
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learn_initial_query=self.learn_initial_query,
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anchor_image_size=self.anchor_image_size,
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image_size=self.image_size,
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disable_custom_kernels=self.disable_custom_kernels,
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with_box_refine=self.with_box_refine,
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)
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def prepare_config_and_inputs_for_common(self):
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config, pixel_values, pixel_mask, labels = self.prepare_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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def create_and_check_deim_model(self, config, pixel_values, pixel_mask, labels):
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model = DeimModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
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result = model(pixel_values)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.d_model))
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def create_and_check_deim_object_detection_head_model(self, config, pixel_values, pixel_mask, labels):
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model = DeimForObjectDetection(config=config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
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result = model(pixel_values)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
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self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
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result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels)
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self.parent.assertEqual(result.loss.shape, ())
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
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self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
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@require_torch
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class DeimModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (DeimModel, DeimForObjectDetection) if is_torch_available() else ()
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is_encoder_decoder = True
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test_torchscript = False
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test_pruning = False
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test_head_masking = False
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test_missing_keys = False
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test_torch_exportable = True
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# special case for head models
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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if return_labels:
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if model_class.__name__ == "DeimForObjectDetection":
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labels = []
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for i in range(self.model_tester.batch_size):
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target = {}
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target["class_labels"] = torch.ones(
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size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
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)
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target["boxes"] = torch.ones(
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self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
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)
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labels.append(target)
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inputs_dict["labels"] = labels
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return inputs_dict
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def setUp(self):
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self.model_tester = DeimModelTester(self)
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self.config_tester = ConfigTester(
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self,
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config_class=DeimConfig,
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has_text_modality=False,
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common_properties=["hidden_size", "num_attention_heads"],
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)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_deim_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_deim_model(*config_and_inputs)
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def test_deim_object_detection_head_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_deim_object_detection_head_model(*config_and_inputs)
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@unittest.skip(reason="Deim doesn't work well with `nn.DataParallel")
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def test_multi_gpu_data_parallel_forward(self):
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pass
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@unittest.skip(reason="Deim does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="Deim does not use test_inputs_embeds_matches_input_ids")
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def test_inputs_embeds_matches_input_ids(self):
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pass
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@unittest.skip(reason="Deim does not support input and output embeddings")
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def test_model_get_set_embeddings(self):
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pass
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@unittest.skip(reason="Deim does not support input and output embeddings")
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def test_model_common_attributes(self):
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pass
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@unittest.skip(reason="Deim does not use token embeddings")
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def test_resize_tokens_embeddings(self):
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pass
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@unittest.skip(reason="Not relevant for the model")
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def test_can_init_all_missing_weights(self):
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pass
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@unittest.skip(reason="Feed forward chunking is not implemented")
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def test_feed_forward_chunking(self):
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pass
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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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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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attentions = outputs.encoder_attentions
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self.assertEqual(len(attentions), self.model_tester.encoder_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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attentions = outputs.encoder_attentions
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self.assertEqual(len(attentions), self.model_tester.encoder_layers)
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[
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self.model_tester.encoder_attention_heads,
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self.model_tester.encoder_seq_length,
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self.model_tester.encoder_seq_length,
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],
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)
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out_len = len(outputs)
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correct_outlen = 15
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# loss is at first position
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if "labels" in inputs_dict:
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correct_outlen += 1 # loss is added to beginning
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# Object Detection model returns pred_logits and pred_boxes
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if model_class.__name__ == "DeimForObjectDetection":
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correct_outlen += 2
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self.assertEqual(out_len, correct_outlen)
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# decoder attentions
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decoder_attentions = outputs.decoder_attentions
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self.assertIsInstance(decoder_attentions, (list, tuple))
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self.assertEqual(len(decoder_attentions), self.model_tester.decoder_layers)
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self.assertListEqual(
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list(decoder_attentions[0].shape[-3:]),
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[
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self.model_tester.decoder_attention_heads,
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self.model_tester.num_queries,
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self.model_tester.num_queries,
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],
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)
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# cross attentions
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cross_attentions = outputs.cross_attentions
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self.assertIsInstance(cross_attentions, (list, tuple))
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self.assertEqual(len(cross_attentions), self.model_tester.decoder_layers)
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self.assertListEqual(
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list(cross_attentions[0].shape[-3:]),
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[
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self.model_tester.num_queries,
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self.model_tester.decoder_attention_heads,
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self.model_tester.decoder_n_levels * self.model_tester.decoder_n_points
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if isinstance(self.model_tester.decoder_n_points, int)
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else sum(self.model_tester.decoder_n_points),
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],
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)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = 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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if hasattr(self.model_tester, "num_hidden_states_types"):
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added_hidden_states = self.model_tester.num_hidden_states_types
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else:
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# Deim should maintin encoder_hidden_states output
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added_hidden_states = 2
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self.assertEqual(out_len + added_hidden_states, len(outputs))
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self_attentions = outputs.encoder_attentions
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self.assertEqual(len(self_attentions), self.model_tester.encoder_layers)
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self.assertListEqual(
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list(self_attentions[0].shape[-3:]),
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[
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self.model_tester.encoder_attention_heads,
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self.model_tester.encoder_seq_length,
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self.model_tester.encoder_seq_length,
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],
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)
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def test_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, model_class):
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
|
|
|
|
expected_num_layers = getattr(
|
|
self.model_tester, "expected_num_hidden_layers", len(self.model_tester.encoder_in_channels) - 1
|
|
)
|
|
self.assertEqual(len(hidden_states), expected_num_layers)
|
|
|
|
self.assertListEqual(
|
|
list(hidden_states[1].shape[-2:]),
|
|
[
|
|
self.model_tester.image_size // self.model_tester.feat_strides[-1],
|
|
self.model_tester.image_size // self.model_tester.feat_strides[-1],
|
|
],
|
|
)
|
|
|
|
if config.is_encoder_decoder:
|
|
hidden_states = outputs.decoder_hidden_states
|
|
|
|
expected_num_layers = getattr(
|
|
self.model_tester, "expected_num_hidden_layers", self.model_tester.decoder_layers + 1
|
|
)
|
|
|
|
self.assertIsInstance(hidden_states, (list, tuple))
|
|
self.assertEqual(len(hidden_states), expected_num_layers)
|
|
|
|
self.assertListEqual(
|
|
list(hidden_states[0].shape[-2:]),
|
|
[self.model_tester.num_queries, self.model_tester.d_model],
|
|
)
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
inputs_dict["output_hidden_states"] = True
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
# check that output_hidden_states also work using config
|
|
del inputs_dict["output_hidden_states"]
|
|
config.output_hidden_states = True
|
|
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.output_hidden_states = True
|
|
config.output_attentions = True
|
|
|
|
model_class = self.all_model_classes[0]
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
|
|
inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
|
|
outputs = model(**inputs)
|
|
|
|
# we take the first output since last_hidden_state is the first item
|
|
output = outputs[0]
|
|
|
|
encoder_hidden_states = outputs.encoder_hidden_states[0]
|
|
encoder_attentions = outputs.encoder_attentions[0]
|
|
encoder_hidden_states.retain_grad()
|
|
encoder_attentions.retain_grad()
|
|
|
|
decoder_attentions = outputs.decoder_attentions[0]
|
|
decoder_attentions.retain_grad()
|
|
|
|
cross_attentions = outputs.cross_attentions[0]
|
|
cross_attentions.retain_grad()
|
|
|
|
output.flatten()[0].backward(retain_graph=True)
|
|
|
|
self.assertIsNotNone(encoder_hidden_states.grad)
|
|
self.assertIsNotNone(encoder_attentions.grad)
|
|
self.assertIsNotNone(decoder_attentions.grad)
|
|
self.assertIsNotNone(cross_attentions.grad)
|
|
|
|
def test_forward_signature(self):
|
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
signature = inspect.signature(model.forward)
|
|
arg_names = [*signature.parameters.keys()]
|
|
expected_arg_names = ["pixel_values"]
|
|
self.assertListEqual(arg_names[:1], expected_arg_names)
|
|
|
|
def test_different_timm_backbone(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
# let's pick a random timm backbone
|
|
config.encoder_in_channels = [24, 40, 432]
|
|
config.backbone = "tf_mobilenetv3_small_075"
|
|
config.backbone_config = None
|
|
config.use_timm_backbone = True
|
|
config.backbone_kwargs = {"out_indices": [2, 3, 4]}
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
if model_class.__name__ == "DeimForObjectDetection":
|
|
expected_shape = (
|
|
self.model_tester.batch_size,
|
|
self.model_tester.num_queries,
|
|
self.model_tester.num_labels,
|
|
)
|
|
self.assertEqual(outputs.logits.shape, expected_shape)
|
|
# Confirm out_indices was propogated to backbone
|
|
self.assertEqual(len(model.model.backbone.intermediate_channel_sizes), 3)
|
|
else:
|
|
# Confirm out_indices was propogated to backbone
|
|
self.assertEqual(len(model.backbone.intermediate_channel_sizes), 3)
|
|
|
|
self.assertTrue(outputs)
|
|
|
|
def test_hf_backbone(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
# Load a pretrained HF checkpoint as backbone
|
|
config.backbone = "microsoft/resnet-18"
|
|
config.backbone_config = None
|
|
config.use_timm_backbone = False
|
|
config.use_pretrained_backbone = True
|
|
config.backbone_kwargs = {"out_indices": [2, 3, 4]}
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
if model_class.__name__ == "DeimForObjectDetection":
|
|
expected_shape = (
|
|
self.model_tester.batch_size,
|
|
self.model_tester.num_queries,
|
|
self.model_tester.num_labels,
|
|
)
|
|
self.assertEqual(outputs.logits.shape, expected_shape)
|
|
# Confirm out_indices was propogated to backbone
|
|
self.assertEqual(len(model.model.backbone.intermediate_channel_sizes), 3)
|
|
else:
|
|
# Confirm out_indices was propogated to backbone
|
|
self.assertEqual(len(model.backbone.intermediate_channel_sizes), 3)
|
|
|
|
self.assertTrue(outputs)
|
|
|
|
def test_initialization(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
configs_no_init = _config_zero_init(config)
|
|
configs_no_init.initializer_bias_prior_prob = 0.2
|
|
bias_value = -1.3863 # log_e ((1 - 0.2) / 0.2)
|
|
|
|
failed_cases = []
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
# Skip the check for the backbone
|
|
for name, module in model.named_modules():
|
|
if module.__class__.__name__ == "DeimConvEncoder":
|
|
backbone_params = [f"{name}.{key}" for key in module.state_dict().keys()]
|
|
break
|
|
|
|
for name, param in model.named_parameters():
|
|
if param.requires_grad:
|
|
if ("class_embed" in name and "bias" in name) or "enc_score_head.bias" in name:
|
|
bias_tensor = torch.full_like(param.data, bias_value)
|
|
try:
|
|
torch.testing.assert_close(param.data, bias_tensor, atol=1e-4, rtol=1e-4)
|
|
except AssertionError:
|
|
failed_cases.append(
|
|
f"Parameter {name} of model {model_class} seems not properly initialized. "
|
|
f"Biases should be initialized to {bias_value}, got {param.data}"
|
|
)
|
|
elif (
|
|
"level_embed" in name
|
|
or "sampling_offsets.bias" in name
|
|
or "value_proj" in name
|
|
or "output_proj" in name
|
|
or "reference_points" in name
|
|
or "enc_score_head.weight" in name
|
|
or ("class_embed" in name and "weight" in name)
|
|
or name in backbone_params
|
|
):
|
|
continue
|
|
else:
|
|
mean = param.data.mean()
|
|
round_mean = (mean * 1e9).round() / 1e9
|
|
round_mean = round_mean.item()
|
|
if round_mean not in [0.0, 1.0]:
|
|
failed_cases.append(
|
|
f"Parameter {name} of model {model_class} seems not properly initialized. "
|
|
f"Mean is {round_mean}, but should be in [0, 1]"
|
|
)
|
|
|
|
message = "\n" + "\n".join(failed_cases)
|
|
self.assertTrue(not failed_cases, message)
|
|
|
|
@parameterized.expand(["float32", "float16", "bfloat16"])
|
|
@require_torch_gpu
|
|
@slow
|
|
def test_inference_with_different_dtypes(self, torch_dtype_str):
|
|
torch_dtype = {
|
|
"float32": torch.float32,
|
|
"float16": torch.float16,
|
|
"bfloat16": torch.bfloat16,
|
|
}[torch_dtype_str]
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
model.to(torch_device).to(torch_dtype)
|
|
model.eval()
|
|
for key, tensor in inputs_dict.items():
|
|
if tensor.dtype == torch.float32:
|
|
inputs_dict[key] = tensor.to(torch_dtype)
|
|
with torch.no_grad():
|
|
_ = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
@parameterized.expand(["float32", "float16", "bfloat16"])
|
|
@require_torch_gpu
|
|
@slow
|
|
def test_inference_equivalence_for_static_and_dynamic_anchors(self, torch_dtype_str):
|
|
torch_dtype = {
|
|
"float32": torch.float32,
|
|
"float16": torch.float16,
|
|
"bfloat16": torch.bfloat16,
|
|
}[torch_dtype_str]
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
h, w = inputs_dict["pixel_values"].shape[-2:]
|
|
|
|
# convert inputs to the desired dtype
|
|
for key, tensor in inputs_dict.items():
|
|
if tensor.dtype == torch.float32:
|
|
inputs_dict[key] = tensor.to(torch_dtype)
|
|
|
|
for model_class in self.all_model_classes:
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
model_class(config).save_pretrained(tmpdirname)
|
|
model_static = model_class.from_pretrained(
|
|
tmpdirname, anchor_image_size=[h, w], device_map=torch_device, torch_dtype=torch_dtype
|
|
).eval()
|
|
model_dynamic = model_class.from_pretrained(
|
|
tmpdirname, anchor_image_size=None, device_map=torch_device, torch_dtype=torch_dtype
|
|
).eval()
|
|
|
|
self.assertIsNotNone(model_static.config.anchor_image_size)
|
|
self.assertIsNone(model_dynamic.config.anchor_image_size)
|
|
|
|
with torch.no_grad():
|
|
outputs_static = model_static(**self._prepare_for_class(inputs_dict, model_class))
|
|
outputs_dynamic = model_dynamic(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
torch.testing.assert_close(
|
|
outputs_static.last_hidden_state, outputs_dynamic.last_hidden_state, rtol=1e-4, atol=1e-4
|
|
)
|
|
|
|
|
|
TOLERANCE = 1e-4
|
|
|
|
|
|
# We will verify our results on an image of cute cats
|
|
def prepare_img():
|
|
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
|
return image
|
|
|
|
|
|
@require_torch
|
|
@require_vision
|
|
class DeimModelIntegrationTest(unittest.TestCase):
|
|
@cached_property
|
|
def default_image_processor(self):
|
|
return RTDetrImageProcessor.from_pretrained(CHECKPOINT) if is_vision_available() else None
|
|
|
|
def test_inference_object_detection_head(self):
|
|
model = DeimForObjectDetection.from_pretrained(CHECKPOINT).to(torch_device)
|
|
|
|
image_processor = self.default_image_processor
|
|
image = prepare_img()
|
|
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
expected_shape_logits = torch.Size((1, 300, model.config.num_labels))
|
|
self.assertEqual(outputs.logits.shape, expected_shape_logits)
|
|
|
|
expected_logits = torch.tensor(
|
|
[
|
|
[-3.8097816, -4.7724586, -5.994499],
|
|
[-5.2974715, -9.499067, -6.1653666],
|
|
[-5.3502765, -3.9530406, -6.3630295],
|
|
]
|
|
).to(torch_device)
|
|
expected_boxes = torch.tensor(
|
|
[
|
|
[0.7677696, 0.41479152, 0.46441072],
|
|
[0.16912134, 0.19869131, 0.2123824],
|
|
[0.2581653, 0.54818195, 0.47512347],
|
|
]
|
|
).to(torch_device)
|
|
|
|
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, atol=1e-4, rtol=1e-4)
|
|
|
|
expected_shape_boxes = torch.Size((1, 300, 4))
|
|
self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
|
|
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4, rtol=1e-4)
|
|
|
|
# verify postprocessing
|
|
results = image_processor.post_process_object_detection(
|
|
outputs, threshold=0.0, target_sizes=[image.size[::-1]]
|
|
)[0]
|
|
expected_scores = torch.tensor([0.9642, 0.9542, 0.9536, 0.8548], device=torch_device)
|
|
expected_labels = [15, 65, 15, 57]
|
|
expected_slice_boxes = torch.tensor(
|
|
[
|
|
[1.3186283e01, 5.4130211e01, 3.1726535e02, 4.7212445e02],
|
|
[4.0275269e01, 7.2975174e01, 1.7620003e02, 1.1776848e02],
|
|
[3.4276117e02, 2.3427944e01, 6.3998401e02, 3.7477191e02],
|
|
[5.8418274e-01, 1.1794567e00, 6.3933154e02, 4.7485995e02],
|
|
],
|
|
device=torch_device,
|
|
)
|
|
torch.testing.assert_close(results["scores"][:4], expected_scores, atol=1e-3, rtol=1e-4)
|
|
self.assertSequenceEqual(results["labels"][:4].tolist(), expected_labels)
|
|
torch.testing.assert_close(results["boxes"][:4], expected_slice_boxes[:4], atol=1e-3, rtol=1e-4)
|