mirror of
https://github.com/huggingface/transformers.git
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d3eacbb829
* Squash all commits of modeling_detr_v7 branch into one * Improve docs * Fix tests * Style * Improve docs some more and fix most tests * Fix slow tests of ViT, DeiT and DETR * Improve replacement of batch norm * Restructure timm backbone forward * Make DetrForSegmentation support any timm backbone * Fix name of output * Address most comments by @LysandreJik * Give better names for variables * Conditional imports + timm in setup.py * Address additional comments by @sgugger * Make style, add require_timm and require_vision to testsé * Remove train_backbone attribute of DetrConfig, add methods to freeze/unfreeze backbone * Add png files to fixtures * Fix type hint * Add timm to workflows * Add `BatchNorm2d` to the weight initialization * Fix retain_grad test * Replace model checkpoints by Facebook namespace * Fix name of checkpoint in test * Add user-friendly message when scipy is not available * Address most comments by @patrickvonplaten * Remove return_intermediate_layers attribute of DetrConfig and simplify Joiner * Better initialization * Scipy is necessary to get sklearn metrics * Rename TimmBackbone to DetrTimmConvEncoder and rename DetrJoiner to DetrConvModel * Make style * Improve docs and add 2 community notebooks Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
102 lines
3.8 KiB
Python
102 lines
3.8 KiB
Python
# coding=utf-8
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# Copyright 2021 HuggingFace Inc.
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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 json
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import os
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import tempfile
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from transformers.file_utils import is_torch_available, is_vision_available
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if is_torch_available():
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import numpy as np
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import torch
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if is_vision_available():
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from PIL import Image
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def prepare_image_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False):
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"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
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or a list of PyTorch tensors if one specifies torchify=True.
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"""
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assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
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if equal_resolution:
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image_inputs = []
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for i in range(feature_extract_tester.batch_size):
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image_inputs.append(
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np.random.randint(
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255,
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size=(
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feature_extract_tester.num_channels,
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feature_extract_tester.max_resolution,
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feature_extract_tester.max_resolution,
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),
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dtype=np.uint8,
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)
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)
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else:
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image_inputs = []
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for i in range(feature_extract_tester.batch_size):
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width, height = np.random.choice(
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np.arange(feature_extract_tester.min_resolution, feature_extract_tester.max_resolution), 2
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)
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image_inputs.append(
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np.random.randint(255, size=(feature_extract_tester.num_channels, width, height), dtype=np.uint8)
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)
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if not numpify and not torchify:
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# PIL expects the channel dimension as last dimension
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image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
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if torchify:
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image_inputs = [torch.from_numpy(x) for x in image_inputs]
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return image_inputs
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class FeatureExtractionSavingTestMixin:
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def test_feat_extract_to_json_string(self):
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feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
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obj = json.loads(feat_extract.to_json_string())
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for key, value in self.feat_extract_dict.items():
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self.assertEqual(obj[key], value)
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def test_feat_extract_to_json_file(self):
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feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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json_file_path = os.path.join(tmpdirname, "feat_extract.json")
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feat_extract_first.to_json_file(json_file_path)
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feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
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self.assertEqual(feat_extract_second.to_dict(), feat_extract_first.to_dict())
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def test_feat_extract_from_and_save_pretrained(self):
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feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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feat_extract_first.save_pretrained(tmpdirname)
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feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
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self.assertEqual(feat_extract_second.to_dict(), feat_extract_first.to_dict())
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def test_init_without_params(self):
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feat_extract = self.feature_extraction_class()
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self.assertIsNotNone(feat_extract)
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