RegNet (#16188)

* base model done

* make style

* done

* added files

* Apply suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Trigger doc build

* resolved conversations

* resolved conversations

* seer models

* minor changes

* minor changes

* make fixup

* glob variables

* minor changes

* fix copies

* config when possibile

* resolved conflicts

* resolved conflicts

* resolved conflicts

* CI

* conversion script for 10b param

* fixed for 10b model

* minor updates in the doc + make style

* removed unused code

* Apply suggestions from code review
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* removed unused code

* removed unused code

* updated modeling_utils from main
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>

tests/regnet/test_modeling_regnet.py

+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Testing suite for the PyTorch RegNet model. """
+
+
+import inspect
+import unittest
+
+from transformers import RegNetConfig
+from transformers.file_utils import cached_property, is_torch_available, is_vision_available
+from transformers.testing_utils import require_torch, require_vision, slow, torch_device
+
+from ..test_configuration_common import ConfigTester
+from ..test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+
+
+if is_torch_available():
+    import torch
+    from torch import nn
+
+    from transformers import RegNetForImageClassification, RegNetModel
+    from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import AutoFeatureExtractor
+
+
+class RegNetModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=3,
+        image_size=32,
+        num_channels=3,
+        embeddings_size=10,
+        hidden_sizes=[10, 20, 30, 40],
+        depths=[1, 1, 2, 1],
+        is_training=True,
+        use_labels=True,
+        hidden_act="relu",
+        num_labels=3,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.num_channels = num_channels
+        self.embeddings_size = embeddings_size
+        self.hidden_sizes = hidden_sizes
+        self.depths = depths
+        self.is_training = is_training
+        self.use_labels = use_labels
+        self.hidden_act = hidden_act
+        self.num_labels = num_labels
+        self.scope = scope
+        self.num_stages = len(hidden_sizes)
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+        labels = None
+        if self.use_labels:
+            labels = ids_tensor([self.batch_size], self.num_labels)
+
+        config = self.get_config()
+
+        return config, pixel_values, labels
+
+    def get_config(self):
+        return RegNetConfig(
+            num_channels=self.num_channels,
+            embeddings_size=self.embeddings_size,
+            hidden_sizes=self.hidden_sizes,
+            depths=self.depths,
+            hidden_act=self.hidden_act,
+            num_labels=self.num_labels,
+        )
+
+    def create_and_check_model(self, config, pixel_values, labels):
+        model = RegNetModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+        # expected last hidden states: B, C, H // 32, W // 32
+        self.parent.assertEqual(
+            result.last_hidden_state.shape,
+            (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),
+        )
+
+    def create_and_check_for_image_classification(self, config, pixel_values, labels):
+        config.num_labels = self.num_labels
+        model = RegNetForImageClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values, labels=labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, labels = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class RegNetModelTest(ModelTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as RegNet does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
+
+    test_pruning = False
+    test_torchscript = False
+    test_resize_embeddings = False
+    test_head_masking = False
+    has_attentions = False
+
+    def setUp(self):
+        self.model_tester = RegNetModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=RegNetConfig, has_text_modality=False)
+
+    def test_config(self):
+        self.create_and_test_config_common_properties()
+        self.config_tester.create_and_test_config_to_json_string()
+        self.config_tester.create_and_test_config_to_json_file()
+        self.config_tester.create_and_test_config_from_and_save_pretrained()
+        self.config_tester.create_and_test_config_with_num_labels()
+        self.config_tester.check_config_can_be_init_without_params()
+        self.config_tester.check_config_arguments_init()
+
+    def create_and_test_config_common_properties(self):
+        return
+
+    @unittest.skip(reason="RegNet does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="RegNet does not support input and output embeddings")
+    def test_model_common_attributes(self):
+        pass
+
+    def test_forward_signature(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            signature = inspect.signature(model.forward)
+            # signature.parameters is an OrderedDict => so arg_names order is deterministic
+            arg_names = [*signature.parameters.keys()]
+
+            expected_arg_names = ["pixel_values"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_initialization(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config=config)
+            for name, module in model.named_modules():
+                if isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)):
+                    self.assertTrue(
+                        torch.all(module.weight == 1),
+                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
+                    )
+                    self.assertTrue(
+                        torch.all(module.bias == 0),
+                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
+                    )
+
+    def test_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
+
+            expected_num_stages = self.model_tester.num_stages
+            self.assertEqual(len(hidden_states), expected_num_stages + 1)
+
+            # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
+            self.assertListEqual(
+                list(hidden_states[0].shape[-2:]),
+                [self.model_tester.image_size // 2, self.model_tester.image_size // 2],
+            )
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        layers_type = ["basic", "bottleneck"]
+        for model_class in self.all_model_classes:
+            for layer_type in layers_type:
+                config.layer_type = layer_type
+                inputs_dict["output_hidden_states"] = True
+                check_hidden_states_output(inputs_dict, config, model_class)
+
+                # check that output_hidden_states also work using config
+                del inputs_dict["output_hidden_states"]
+                config.output_hidden_states = True
+
+                check_hidden_states_output(inputs_dict, config, model_class)
+
+    def test_for_image_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = RegNetModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+    return image
+
+
+@require_torch
+@require_vision
+class RegNetModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_feature_extractor(self):
+        return (
+            AutoFeatureExtractor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
+            if is_vision_available()
+            else None
+        )
+
+    @slow
+    def test_inference_image_classification_head(self):
+        model = RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(torch_device)
+
+        feature_extractor = self.default_feature_extractor
+        image = prepare_img()
+        inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        # verify the logits
+        expected_shape = torch.Size((1, 1000))
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        expected_slice = torch.tensor([-0.4180, -1.5051, -3.4836]).to(torch_device)
+
+        self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))