813 lines
32 KiB
Python
813 lines
32 KiB
Python
# coding=utf-8
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# Copyright 2022 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 CLIPSeg model."""
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import inspect
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import os
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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 requests
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import transformers
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from transformers import CLIPSegConfig, CLIPSegProcessor, CLIPSegTextConfig, CLIPSegVisionConfig
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from transformers.testing_utils import (
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is_flax_available,
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is_pt_flax_cross_test,
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require_torch,
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require_vision,
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slow,
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torch_device,
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)
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from transformers.utils import is_torch_available, is_vision_available
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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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_config_zero_init,
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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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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 CLIPSegForImageSegmentation, CLIPSegModel, CLIPSegTextModel, CLIPSegVisionModel
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from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES
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if is_vision_available():
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from PIL import Image
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if is_flax_available():
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import jax.numpy as jnp
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from transformers.modeling_flax_pytorch_utils import (
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convert_pytorch_state_dict_to_flax,
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load_flax_weights_in_pytorch_model,
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)
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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=2,
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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.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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# 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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@require_torch
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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,) 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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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.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_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(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant_false(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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model_name = "CIDAS/clipseg-rd64-refined"
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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=2,
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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.to(torch_device)
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model.eval()
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with torch.no_grad():
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result = model(input_ids, attention_mask=input_mask)
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result = model(input_ids)
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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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@require_torch
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class CLIPSegTextModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (CLIPSegTextModel,) if is_torch_available() else ()
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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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model_split_percents = [0.5, 0.8, 0.9]
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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(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant_false(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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model_name = "CIDAS/clipseg-rd64-refined"
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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__(
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self,
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parent,
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text_kwargs=None,
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vision_kwargs=None,
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is_training=True,
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# This should respect the `num_hidden_layers` in `CLIPSegVisionModelTester`
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extract_layers=(1,),
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):
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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.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test
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self.is_training = is_training
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self.extract_layers = extract_layers
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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=self.extract_layers,
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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).to(torch_device).eval()
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with torch.no_grad():
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result = model(input_ids, pixel_values, attention_mask)
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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).to(torch_device).eval()
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with torch.no_grad():
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result = model(input_ids, pixel_values)
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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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@require_torch
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class CLIPSegModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (CLIPSegModel, CLIPSegForImageSegmentation) if is_torch_available() else ()
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pipeline_model_mapping = {"feature-extraction": CLIPSegModel} if is_torch_available() else {}
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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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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"] = torch.zeros(
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[batch_size, height, width], device=torch_device, dtype=torch.float
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)
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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)
|
|
|
|
@unittest.skip(reason="Hidden_states is tested in individual model tests")
|
|
def test_hidden_states_output(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Retain_grad is tested in individual model tests")
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="CLIPSegModel does not have input/output embeddings")
|
|
def test_model_common_attributes(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(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
|
|
|
|
# override as the some parameters require custom initialization
|
|
def test_initialization(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
configs_no_init = _config_zero_init(config)
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
for name, param in model.named_parameters():
|
|
if param.requires_grad:
|
|
# check if `logit_scale` is initilized as per the original implementation
|
|
if "logit_scale" in name:
|
|
self.assertAlmostEqual(
|
|
param.data.item(),
|
|
np.log(1 / 0.07),
|
|
delta=1e-3,
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
elif "film" in name or "transposed_conv" in name or "reduce" in name:
|
|
# those parameters use PyTorch' default nn.Linear initialization scheme
|
|
pass
|
|
else:
|
|
self.assertIn(
|
|
((param.data.mean() * 1e9).round() / 1e9).item(),
|
|
[0.0, 1.0],
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
|
|
def _create_and_check_torchscript(self, config, inputs_dict):
|
|
if not self.test_torchscript:
|
|
return
|
|
|
|
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
|
|
configs_no_init.torchscript = True
|
|
configs_no_init.return_dict = False
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
try:
|
|
input_ids = inputs_dict["input_ids"]
|
|
pixel_values = inputs_dict["pixel_values"] # CLIPSeg needs pixel_values
|
|
traced_model = torch.jit.trace(model, (input_ids, pixel_values))
|
|
except RuntimeError:
|
|
self.fail("Couldn't trace module.")
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
|
|
|
|
try:
|
|
torch.jit.save(traced_model, pt_file_name)
|
|
except Exception:
|
|
self.fail("Couldn't save module.")
|
|
|
|
try:
|
|
loaded_model = torch.jit.load(pt_file_name)
|
|
except Exception:
|
|
self.fail("Couldn't load module.")
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
loaded_model.to(torch_device)
|
|
loaded_model.eval()
|
|
|
|
model_state_dict = model.state_dict()
|
|
loaded_model_state_dict = loaded_model.state_dict()
|
|
|
|
non_persistent_buffers = {}
|
|
for key in loaded_model_state_dict.keys():
|
|
if key not in model_state_dict.keys():
|
|
non_persistent_buffers[key] = loaded_model_state_dict[key]
|
|
|
|
loaded_model_state_dict = {
|
|
key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
|
|
}
|
|
|
|
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
|
|
|
|
model_buffers = list(model.buffers())
|
|
for non_persistent_buffer in non_persistent_buffers.values():
|
|
found_buffer = False
|
|
for i, model_buffer in enumerate(model_buffers):
|
|
if torch.equal(non_persistent_buffer, model_buffer):
|
|
found_buffer = True
|
|
break
|
|
|
|
self.assertTrue(found_buffer)
|
|
model_buffers.pop(i)
|
|
|
|
models_equal = True
|
|
for layer_name, p1 in model_state_dict.items():
|
|
p2 = loaded_model_state_dict[layer_name]
|
|
if p1.data.ne(p2.data).sum() > 0:
|
|
models_equal = False
|
|
|
|
self.assertTrue(models_equal)
|
|
|
|
def test_load_vision_text_config(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
# Save CLIPSegConfig and check if we can load CLIPSegVisionConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
vision_config = CLIPSegVisionConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
|
|
|
|
# Save CLIPSegConfig and check if we can load CLIPSegTextConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
text_config = CLIPSegTextConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
|
|
|
|
# overwrite from common since FlaxCLIPSegModel returns nested output
|
|
# which is not supported in the common test
|
|
@is_pt_flax_cross_test
|
|
def test_equivalence_pt_to_flax(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
with self.subTest(model_class.__name__):
|
|
# load PyTorch class
|
|
pt_model = model_class(config).eval()
|
|
# Flax models don't use the `use_cache` option and cache is not returned as a default.
|
|
# So we disable `use_cache` here for PyTorch model.
|
|
pt_model.config.use_cache = False
|
|
|
|
fx_model_class_name = "Flax" + model_class.__name__
|
|
|
|
if not hasattr(transformers, fx_model_class_name):
|
|
return
|
|
|
|
fx_model_class = getattr(transformers, fx_model_class_name)
|
|
|
|
# load Flax class
|
|
fx_model = fx_model_class(config, dtype=jnp.float32)
|
|
# make sure only flax inputs are forward that actually exist in function args
|
|
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
|
|
|
|
# prepare inputs
|
|
pt_inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
|
|
# remove function args that don't exist in Flax
|
|
pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}
|
|
|
|
fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
|
|
fx_model.params = fx_state
|
|
|
|
with torch.no_grad():
|
|
pt_outputs = pt_model(**pt_inputs).to_tuple()
|
|
|
|
# convert inputs to Flax
|
|
fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)}
|
|
fx_outputs = fx_model(**fx_inputs).to_tuple()
|
|
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
|
|
for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]):
|
|
self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
pt_model.save_pretrained(tmpdirname)
|
|
fx_model_loaded = fx_model_class.from_pretrained(tmpdirname, from_pt=True)
|
|
|
|
fx_outputs_loaded = fx_model_loaded(**fx_inputs).to_tuple()
|
|
self.assertEqual(
|
|
len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch"
|
|
)
|
|
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4], pt_outputs[:4]):
|
|
self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 4e-2)
|
|
|
|
# overwrite from common since FlaxCLIPSegModel returns nested output
|
|
# which is not supported in the common test
|
|
@is_pt_flax_cross_test
|
|
def test_equivalence_flax_to_pt(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
with self.subTest(model_class.__name__):
|
|
# load corresponding PyTorch class
|
|
pt_model = model_class(config).eval()
|
|
|
|
# So we disable `use_cache` here for PyTorch model.
|
|
pt_model.config.use_cache = False
|
|
|
|
fx_model_class_name = "Flax" + model_class.__name__
|
|
|
|
if not hasattr(transformers, fx_model_class_name):
|
|
# no flax model exists for this class
|
|
return
|
|
|
|
fx_model_class = getattr(transformers, fx_model_class_name)
|
|
|
|
# load Flax class
|
|
fx_model = fx_model_class(config, dtype=jnp.float32)
|
|
# make sure only flax inputs are forward that actually exist in function args
|
|
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
|
|
|
|
pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
|
|
|
|
# make sure weights are tied in PyTorch
|
|
pt_model.tie_weights()
|
|
|
|
# prepare inputs
|
|
pt_inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
|
|
# remove function args that don't exist in Flax
|
|
pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}
|
|
|
|
with torch.no_grad():
|
|
pt_outputs = pt_model(**pt_inputs).to_tuple()
|
|
|
|
fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)}
|
|
|
|
fx_outputs = fx_model(**fx_inputs).to_tuple()
|
|
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
|
|
|
|
for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]):
|
|
self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
fx_model.save_pretrained(tmpdirname)
|
|
pt_model_loaded = model_class.from_pretrained(tmpdirname, from_flax=True)
|
|
|
|
with torch.no_grad():
|
|
pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple()
|
|
|
|
self.assertEqual(
|
|
len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch"
|
|
)
|
|
for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs_loaded[:4]):
|
|
self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
|
|
|
|
def test_training(self):
|
|
if not self.model_tester.is_training:
|
|
return
|
|
|
|
for model_class in self.all_model_classes:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.return_dict = True
|
|
|
|
if model_class.__name__ in MODEL_MAPPING_NAMES.values():
|
|
continue
|
|
|
|
print("Model class:", model_class)
|
|
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.train()
|
|
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
for k, v in inputs.items():
|
|
print(k, v.shape)
|
|
loss = model(**inputs).loss
|
|
loss.backward()
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "CIDAS/clipseg-rd64-refined"
|
|
model = CLIPSegModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
# We will verify our results on an image of cute cats
|
|
def prepare_img():
|
|
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
image = Image.open(requests.get(url, stream=True).raw)
|
|
return image
|
|
|
|
|
|
@require_vision
|
|
@require_torch
|
|
class CLIPSegModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference_image_segmentation(self):
|
|
model_name = "CIDAS/clipseg-rd64-refined"
|
|
processor = CLIPSegProcessor.from_pretrained(model_name)
|
|
model = CLIPSegForImageSegmentation.from_pretrained(model_name).to(torch_device)
|
|
|
|
image = prepare_img()
|
|
texts = ["a cat", "a remote", "a blanket"]
|
|
inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# verify the predicted masks
|
|
self.assertEqual(
|
|
outputs.logits.shape,
|
|
torch.Size((3, 352, 352)),
|
|
)
|
|
expected_masks_slice = torch.tensor(
|
|
[[-7.4613, -7.4785, -7.3628], [-7.3268, -7.0899, -7.1333], [-6.9838, -6.7900, -6.8913]]
|
|
).to(torch_device)
|
|
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_masks_slice, atol=1e-3))
|
|
|
|
# verify conditional and pooled output
|
|
expected_conditional = torch.tensor([0.5601, -0.0314, 0.1980]).to(torch_device)
|
|
expected_pooled_output = torch.tensor([0.5036, -0.2681, -0.2644]).to(torch_device)
|
|
self.assertTrue(torch.allclose(outputs.conditional_embeddings[0, :3], expected_conditional, atol=1e-3))
|
|
self.assertTrue(torch.allclose(outputs.pooled_output[0, :3], expected_pooled_output, atol=1e-3))
|