wehub-resource-sync
535 lines
20 KiB
Python
535 lines
20 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2022 The OpenAI Team Authors and The HuggingFace 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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import copy
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import inspect
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import tempfile
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import unittest
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import numpy as np
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import paddle
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import requests
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from paddle import nn
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from PIL import Image
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from paddlenlp.transformers import (
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CLIPSegConfig,
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CLIPSegForImageSegmentation,
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CLIPSegModel,
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CLIPSegProcessor,
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CLIPSegTextConfig,
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CLIPSegTextModel,
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CLIPSegVisionConfig,
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CLIPSegVisionModel,
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)
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from paddlenlp.transformers.clipseg.modeling import (
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CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
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)
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from ...testing_utils import slow
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from ..test_configuration_common import ConfigTester
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from ..test_modeling_common import (
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ModelTesterMixin,
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floats_tensor,
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ids_tensor,
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random_attention_mask,
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)
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def _config_zero_init(config):
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configs_no_init = copy.deepcopy(config)
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for key in configs_no_init.__dict__.keys():
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if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
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setattr(configs_no_init, key, 1e-10)
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return configs_no_init
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class CLIPSegVisionModelTester:
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def __init__(
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self,
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parent,
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batch_size=12,
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image_size=30,
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patch_size=2,
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num_channels=3,
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is_training=True,
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hidden_size=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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dropout=0.1,
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attention_dropout=0.1,
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initializer_range=0.02,
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scope=None,
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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.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.is_training = is_training
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self.hidden_size = hidden_size
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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.scope = scope
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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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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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return CLIPSegVisionConfig(
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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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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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)
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def create_and_check_model(self, config, pixel_values):
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model = CLIPSegVisionModel(config=config)
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model.eval()
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with paddle.no_grad():
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result = model(pixel_values, return_dict=True)
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# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
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image_size = (self.image_size, self.image_size)
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patch_size = (self.patch_size, self.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.parent.assertEqual(result.last_hidden_state.shape, [self.batch_size, num_patches + 1, self.hidden_size])
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self.parent.assertEqual(result.pooler_output.shape, [self.batch_size, self.hidden_size])
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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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class CLIPSegVisionModelTest(ModelTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as CLIPSeg 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 = (CLIPSegVisionModel,)
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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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use_test_model_name_list = False
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def setUp(self):
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self.model_tester = CLIPSegVisionModelTester(self)
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self.config_tester = ConfigTester(
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self, config_class=CLIPSegVisionConfig, has_text_modality=False, hidden_size=37
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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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@unittest.skip(reason="CLIPSeg does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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def test_model_common_attributes(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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self.assertIsInstance(model.get_input_embeddings(), (nn.Layer))
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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_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["pixel_values"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_training(self):
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pass
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(reason="CLIPSegVisionModel has no base class and is not available in MODEL_MAPPING")
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def test_save_load_fast_init_from_base(self):
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pass
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@unittest.skip(reason="CLIPSegVisionModel has no base class and is not available in MODEL_MAPPING")
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def test_save_load_fast_init_to_base(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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for model_name in CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = CLIPSegVisionModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class CLIPSegTextModelTester:
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def __init__(
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self,
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parent,
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batch_size=12,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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dropout=0.1,
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attention_dropout=0.1,
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max_position_embeddings=512,
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initializer_range=0.02,
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scope=None,
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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.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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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.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.scope = scope
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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if input_mask is not None:
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batch_size, seq_length = input_mask.shape
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rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
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for batch_idx, start_index in enumerate(rnd_start_indices):
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input_mask[batch_idx, :start_index] = 1
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input_mask[batch_idx, start_index:] = 0
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config = self.get_config()
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return config, input_ids, input_mask
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def get_config(self):
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return CLIPSegTextConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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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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max_position_embeddings=self.max_position_embeddings,
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initializer_range=self.initializer_range,
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)
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def create_and_check_model(self, config, input_ids, input_mask):
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model = CLIPSegTextModel(config=config)
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model.eval()
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with paddle.no_grad():
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result = model(input_ids, attention_mask=input_mask, return_dict=True)
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result = model(input_ids, return_dict=True)
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self.parent.assertEqual(result.last_hidden_state.shape, [self.batch_size, self.seq_length, self.hidden_size])
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self.parent.assertEqual(result.pooler_output.shape, [self.batch_size, self.hidden_size])
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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, input_ids, input_mask = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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class CLIPSegTextModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (CLIPSegTextModel,)
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fx_compatible = False
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test_pruning = False
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test_head_masking = False
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use_test_model_name_list = False
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def setUp(self):
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self.model_tester = CLIPSegTextModelTester(self)
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self.config_tester = ConfigTester(self, config_class=CLIPSegTextConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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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_training(self):
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pass
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(reason="CLIPSeg 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="CLIPSegTextModel has no base class and is not available in MODEL_MAPPING")
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def test_save_load_fast_init_from_base(self):
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pass
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@unittest.skip(reason="CLIPSegTextModel has no base class and is not available in MODEL_MAPPING")
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def test_save_load_fast_init_to_base(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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for model_name in CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = CLIPSegTextModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class CLIPSegModelTester:
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def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
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if text_kwargs is None:
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text_kwargs = {}
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if vision_kwargs is None:
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vision_kwargs = {}
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self.parent = parent
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self.text_model_tester = CLIPSegTextModelTester(parent, **text_kwargs)
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self.vision_model_tester = CLIPSegVisionModelTester(parent, **vision_kwargs)
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self.is_training = is_training
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def prepare_config_and_inputs(self):
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text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
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vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
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config = self.get_config()
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return config, input_ids, attention_mask, pixel_values
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def get_config(self):
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return CLIPSegConfig.from_text_vision_configs(
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self.text_model_tester.get_config(),
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self.vision_model_tester.get_config(),
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projection_dim=64,
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reduce_dim=32,
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extract_layers=[1, 2, 3],
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)
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def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
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model = CLIPSegModel(config)
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model.eval()
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with paddle.no_grad():
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result = model(input_ids, pixel_values, attention_mask, return_dict=True)
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self.parent.assertEqual(
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result.logits_per_image.shape, [self.vision_model_tester.batch_size, self.text_model_tester.batch_size]
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)
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self.parent.assertEqual(
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result.logits_per_text.shape, [self.text_model_tester.batch_size, self.vision_model_tester.batch_size]
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)
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def create_and_check_model_for_image_segmentation(self, config, input_ids, attention_maks, pixel_values):
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model = CLIPSegForImageSegmentation(config)
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model.eval()
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with paddle.no_grad():
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result = model(input_ids, pixel_values, return_dict=True)
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self.parent.assertEqual(
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result.logits.shape,
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[
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self.vision_model_tester.batch_size,
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self.vision_model_tester.image_size,
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self.vision_model_tester.image_size,
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],
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)
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self.parent.assertEqual(
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result.conditional_embeddings.shape, [self.text_model_tester.batch_size, config.projection_dim]
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)
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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, input_ids, attention_mask, pixel_values = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"pixel_values": pixel_values,
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}
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return config, inputs_dict
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class CLIPSegModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (CLIPSegModel, CLIPSegForImageSegmentation)
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pipeline_model_mapping = {"feature-extraction": CLIPSegModel}
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fx_compatible = False
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test_head_masking = False
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test_pruning = False
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test_resize_embeddings = False
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test_attention_outputs = False
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use_test_model_name_list = False
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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# CLIPSegForImageSegmentation requires special treatment
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if return_labels:
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if model_class.__name__ == "CLIPSegForImageSegmentation":
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batch_size, _, height, width = inputs_dict["pixel_values"].shape
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inputs_dict["labels"] = paddle.zeros([batch_size, height, width], dtype="float32")
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return inputs_dict
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def setUp(self):
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self.model_tester = CLIPSegModelTester(self)
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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_model_for_image_segmentation(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_for_image_segmentation(*config_and_inputs)
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@unittest.skip(reason="Hidden_states is tested in individual model tests")
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def test_hidden_states_output(self):
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pass
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@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="Retain_grad is tested in individual model tests")
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def test_retain_grad_hidden_states_attentions(self):
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pass
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@unittest.skip(reason="CLIPSegModel does not have input/output embeddings")
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def test_model_common_attributes(self):
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pass
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# override as the some parameters require custom initialization
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def test_initialization(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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if param.stop_gradient is False:
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# check if `logit_scale` is initialized as per the original implementation]
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if "logit_scale" in name:
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self.assertAlmostEqual(
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param.item(),
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np.log(1 / 0.07),
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delta=1e-3,
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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elif "film" in name or "transposed_conv" in name or "reduce" in name:
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# those parameters use PyTorch' default nn.Linear initialization scheme
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pass
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else:
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self.assertIn(
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((param.mean() * 1e9).round() / 1e9).item(),
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[0.0, 1.0],
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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def test_load_vision_text_config(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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# Save CLIPSegConfig and check if we can load CLIPSegVisionConfig from it
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with tempfile.TemporaryDirectory() as tmp_dir_name:
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config.save_pretrained(tmp_dir_name)
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vision_config = CLIPSegVisionConfig.from_pretrained(tmp_dir_name)
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self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
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# Save CLIPSegConfig and check if we can load CLIPSegTextConfig from it
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with tempfile.TemporaryDirectory() as tmp_dir_name:
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config.save_pretrained(tmp_dir_name)
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text_config = CLIPSegTextConfig.from_pretrained(tmp_dir_name)
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self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
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@slow
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def test_model_from_pretrained(self):
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for model_name in CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = CLIPSegModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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|
|
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# We will verify our results on an image of cute cats
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def prepare_img():
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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return image
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|
|
|
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class CLIPSegModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_image_segmentation(self):
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model_name = "CIDAS/clipseg-rd64-refined"
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processor = CLIPSegProcessor.from_pretrained(model_name)
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model = CLIPSegForImageSegmentation.from_pretrained(model_name)
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|
|
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image = prepare_img()
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texts = ["a cat", "a remote", "a blanket"]
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inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pd")
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|
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# forward pass
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with paddle.no_grad():
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outputs = model(**inputs, return_dict=True)
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|
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# verify the predicted masks
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|
self.assertEqual(
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outputs.logits.shape,
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[3, 352, 352],
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|
)
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expected_masks_slice = paddle.to_tensor(
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[[-7.4613, -7.4785, -7.3628], [-7.3268, -7.0899, -7.1333], [-6.9838, -6.7900, -6.8913]]
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)
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# Modified atol for tf32
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self.assertTrue(paddle.allclose(outputs.logits[0, :3, :3], expected_masks_slice, atol=1e-2))
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|
# verify conditional and pooled output
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|
expected_conditional = paddle.to_tensor([0.5601, -0.0314, 0.1980])
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expected_pooled_output = paddle.to_tensor([0.5036, -0.2681, -0.2644])
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|
self.assertTrue(paddle.allclose(outputs.conditional_embeddings[0, :3], expected_conditional, atol=1e-2))
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|
self.assertTrue(paddle.allclose(outputs.pooled_output[0, :3], expected_pooled_output, atol=1e-2))
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