[TorchHub] restructured hubconf and updated SSD and Tacotron2/WaveGlow entrypoints

PyTorch/Detection/SSD/src/__init__.py

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+from .entrypoints import nvidia_ssd, nvidia_ssd_processing_utils

PyTorch/Detection/SSD/src/entrypoints.py

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+import os
+import torch
+import sys
+import urllib.request
+
+# from https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/inference.py
+def checkpoint_from_distributed(state_dict):
+    """
+    Checks whether checkpoint was generated by DistributedDataParallel. DDP
+    wraps model in additional "module.", it needs to be unwrapped for single
+    GPU inference.
+    :param state_dict: model's state dict
+    """
+    ret = False
+    for key, _ in state_dict.items():
+        if key.find('module.') != -1:
+            ret = True
+            break
+    return ret
+
+
+# from https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/inference.py
+def unwrap_distributed(state_dict):
+    """
+    Unwraps model from DistributedDataParallel.
+    DDP wraps model in additional "module.", it needs to be removed for single
+    GPU inference.
+    :param state_dict: model's state dict
+    """
+    new_state_dict = {}
+    for key, value in state_dict.items():
+        new_key = key.replace('module.1.', '')
+        new_key = new_key.replace('module.', '')
+        new_state_dict[new_key] = value
+    return new_state_dict
+
+
+def _download_checkpoint(checkpoint, force_reload):
+    model_dir = os.path.join(torch.hub._get_torch_home(), 'checkpoints')
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    ckpt_file = os.path.join(model_dir, os.path.basename(checkpoint))
+    if not os.path.exists(ckpt_file) or force_reload:
+        sys.stderr.write('Downloading checkpoint from {}\n'.format(checkpoint))
+        urllib.request.urlretrieve(checkpoint, ckpt_file)
+    return ckpt_file
+
+def nvidia_ssd_processing_utils():
+    import numpy as np
+    import skimage
+    from skimage import io, transform
+
+    from .utils import dboxes300_coco, Encoder
+
+    class Processing:
+        @staticmethod
+        def load_image(image_path):
+            """Code from Loading_Pretrained_Models.ipynb - a Caffe2 tutorial"""
+            img = skimage.img_as_float(io.imread(image_path))
+            if len(img.shape) == 2:
+                img = np.array([img, img, img]).swapaxes(0, 2)
+            return img
+
+        @staticmethod
+        def rescale(img, input_height, input_width):
+            """Code from Loading_Pretrained_Models.ipynb - a Caffe2 tutorial"""
+            aspect = img.shape[1] / float(img.shape[0])
+            if (aspect > 1):
+                # landscape orientation - wide image
+                res = int(aspect * input_height)
+                imgScaled = transform.resize(img, (input_width, res))
+            if (aspect < 1):
+                # portrait orientation - tall image
+                res = int(input_width / aspect)
+                imgScaled = transform.resize(img, (res, input_height))
+            if (aspect == 1):
+                imgScaled = transform.resize(img, (input_width, input_height))
+            return imgScaled
+
+        @staticmethod
+        def crop_center(img, cropx, cropy):
+            """Code from Loading_Pretrained_Models.ipynb - a Caffe2 tutorial"""
+            y, x, c = img.shape
+            startx = x // 2 - (cropx // 2)
+            starty = y // 2 - (cropy // 2)
+            return img[starty:starty + cropy, startx:startx + cropx]
+
+        @staticmethod
+        def normalize(img, mean=128, std=128):
+            img = (img * 256 - mean) / std
+            return img
+
+        @staticmethod
+        def prepare_tensor(inputs, fp16=False):
+            NHWC = np.array(inputs)
+            NCHW = np.swapaxes(np.swapaxes(NHWC, 1, 3), 2, 3)
+            tensor = torch.from_numpy(NCHW)
+            tensor = tensor.contiguous()
+            tensor = tensor.cuda()
+            tensor = tensor.float()
+            if fp16:
+                tensor = tensor.half()
+            return tensor
+
+        @staticmethod
+        def prepare_input(img_uri):
+            img = Processing.load_image(img_uri)
+            img = Processing.rescale(img, 300, 300)
+            img = Processing.crop_center(img, 300, 300)
+            img = Processing.normalize(img)
+            return img
+
+        @staticmethod
+        def decode_results(predictions):
+            dboxes = dboxes300_coco()
+            encoder = Encoder(dboxes)
+            ploc, plabel = [val.float() for val in predictions]
+            results = encoder.decode_batch(ploc, plabel, criteria=0.5, max_output=20)
+            return [[pred.detach().cpu().numpy() for pred in detections] for detections in results]
+
+        @staticmethod
+        def pick_best(detections, threshold=0.3):
+            bboxes, classes, confidences = detections
+            best = np.argwhere(confidences > threshold)[:, 0]
+            return [pred[best] for pred in detections]
+
+        @staticmethod
+        def get_coco_object_dictionary():
+            import os
+            file_with_coco_names = "category_names.txt"
+
+            if not os.path.exists(file_with_coco_names):
+                print("Downloading COCO annotations.")
+                import urllib
+                import zipfile
+                import json
+                import shutil
+                urllib.request.urlretrieve("http://images.cocodataset.org/annotations/annotations_trainval2017.zip", "cocoanno.zip")
+                with zipfile.ZipFile("cocoanno.zip", "r") as f:
+                    f.extractall()
+                print("Downloading finished.")
+                with open("annotations/instances_val2017.json", 'r') as COCO:
+                    js = json.loads(COCO.read())
+                class_names = [category['name'] for category in js['categories']]
+                open("category_names.txt", 'w').writelines([c+"\n" for c in class_names])
+                os.remove("cocoanno.zip")
+                shutil.rmtree("annotations")
+            else:
+                class_names = open("category_names.txt").readlines()
+                class_names = [c.strip() for c in class_names]
+            return class_names
+
+    return Processing()
+
+
+def nvidia_ssd(pretrained=True, **kwargs):
+    """Constructs an SSD300 model.
+    For detailed information on model input and output, training recipies, inference and performance
+    visit: github.com/NVIDIA/DeepLearningExamples and/or ngc.nvidia.com
+    Args:
+        pretrained (bool, True): If True, returns a model pretrained on COCO dataset.
+        model_math (str, 'fp32'): returns a model in given precision ('fp32' or 'fp16')
+    """
+
+    from . import model as ssd
+
+    fp16 = "model_math" in kwargs and kwargs["model_math"] == "fp16"
+    force_reload = "force_reload" in kwargs and kwargs["force_reload"]
+
+    m = ssd.SSD300()
+    if fp16:
+        m = m.half()
+
+        def batchnorm_to_float(module):
+            """Converts batch norm to FP32"""
+            if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
+                module.float()
+            for child in module.children():
+                batchnorm_to_float(child)
+            return module
+
+        m = batchnorm_to_float(m)
+        
+    if pretrained:
+        checkpoint = 'https://api.ngc.nvidia.com/v2/models/nvidia/ssd_pyt_ckpt_amp/versions/20.06.0/files/nvidia_ssdpyt_amp_200703.pt'
+        ckpt_file = _download_checkpoint(checkpoint, force_reload)
+        ckpt = torch.load(ckpt_file)
+        ckpt = ckpt['model']
+        if checkpoint_from_distributed(ckpt):
+            ckpt = unwrap_distributed(ckpt)
+        m.load_state_dict(ckpt)
+    return m

PyTorch/SpeechSynthesis/Tacotron2/tacotron2/__init__.py

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+from .entrypoints import nvidia_tacotron2, nvidia_tts_utils

PyTorch/SpeechSynthesis/Tacotron2/tacotron2/entrypoints.py

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+import urllib.request
+import torch
+import os
+import sys
+
+#from https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/inference.py
+def checkpoint_from_distributed(state_dict):
+    """
+    Checks whether checkpoint was generated by DistributedDataParallel. DDP
+    wraps model in additional "module.", it needs to be unwrapped for single
+    GPU inference.
+    :param state_dict: model's state dict
+    """
+    ret = False
+    for key, _ in state_dict.items():
+        if key.find('module.') != -1:
+            ret = True
+            break
+    return ret
+
+
+# from https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/inference.py
+def unwrap_distributed(state_dict):
+    """
+    Unwraps model from DistributedDataParallel.
+    DDP wraps model in additional "module.", it needs to be removed for single
+    GPU inference.
+    :param state_dict: model's state dict
+    """
+    new_state_dict = {}
+    for key, value in state_dict.items():
+        new_key = key.replace('module.1.', '')
+        new_key = new_key.replace('module.', '')
+        new_state_dict[new_key] = value
+    return new_state_dict
+
+def _download_checkpoint(checkpoint, force_reload):
+    model_dir = os.path.join(torch.hub._get_torch_home(), 'checkpoints')
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    ckpt_file = os.path.join(model_dir, os.path.basename(checkpoint))
+    if not os.path.exists(ckpt_file) or force_reload:
+        sys.stderr.write('Downloading checkpoint from {}\n'.format(checkpoint))
+        urllib.request.urlretrieve(checkpoint, ckpt_file)
+    return ckpt_file
+
+def nvidia_tacotron2(pretrained=True, **kwargs):
+    """Constructs a Tacotron 2 model (nn.module with additional infer(input) method).
+    For detailed information on model input and output, training recipies, inference and performance
+    visit: github.com/NVIDIA/DeepLearningExamples and/or ngc.nvidia.com
+    Args (type[, default value]):
+        pretrained (bool, True): If True, returns a model pretrained on LJ Speech dataset.
+        model_math (str, 'fp32'): returns a model in given precision ('fp32' or 'fp16')
+        n_symbols (int, 148): Number of symbols used in a sequence passed to the prenet, see
+                              https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/text/symbols.py
+        p_attention_dropout (float, 0.1): dropout probability on attention LSTM (1st LSTM layer in decoder)
+        p_decoder_dropout (float, 0.1): dropout probability on decoder LSTM (2nd LSTM layer in decoder)
+        max_decoder_steps (int, 1000): maximum number of generated mel spectrograms during inference
+    """
+
+    from tacotron2 import model as tacotron2
+
+    fp16 = "model_math" in kwargs and kwargs["model_math"] == "fp16"
+    force_reload = "force_reload" in kwargs and kwargs["force_reload"]
+
+    if pretrained:
+        if fp16:
+            checkpoint = 'https://api.ngc.nvidia.com/v2/models/nvidia/tacotron2_pyt_ckpt_amp/versions/19.09.0/files/nvidia_tacotron2pyt_fp16_20190427'
+        else:
+            checkpoint = 'https://api.ngc.nvidia.com/v2/models/nvidia/tacotron2_pyt_ckpt_fp32/versions/19.09.0/files/nvidia_tacotron2pyt_fp32_20190427'
+        ckpt_file = _download_checkpoint(checkpoint, force_reload)
+        ckpt = torch.load(ckpt_file)
+        state_dict = ckpt['state_dict']
+        if checkpoint_from_distributed(state_dict):
+            state_dict = unwrap_distributed(state_dict)
+        config = ckpt['config']
+    else:
+        config = {'mask_padding': False, 'n_mel_channels': 80, 'n_symbols': 148,
+                  'symbols_embedding_dim': 512, 'encoder_kernel_size': 5,
+                  'encoder_n_convolutions': 3, 'encoder_embedding_dim': 512,
+                  'attention_rnn_dim': 1024, 'attention_dim': 128,
+                  'attention_location_n_filters': 32,
+                  'attention_location_kernel_size': 31, 'n_frames_per_step': 1,
+                  'decoder_rnn_dim': 1024, 'prenet_dim': 256,
+                  'max_decoder_steps': 1000, 'gate_threshold': 0.5,
+                  'p_attention_dropout': 0.1, 'p_decoder_dropout': 0.1,
+                  'postnet_embedding_dim': 512, 'postnet_kernel_size': 5,
+                  'postnet_n_convolutions': 5, 'decoder_no_early_stopping': False}
+        for k,v in kwargs.items():
+            if k in config.keys():
+                config[k] = v
+
+    m = tacotron2.Tacotron2(**config)
+
+    if pretrained:
+        m.load_state_dict(state_dict)
+
+    return m
+
+def nvidia_tts_utils():
+    
+    class Processing:
+        
+        from tacotron2.text import text_to_sequence
+        
+        @staticmethod
+        def pad_sequences(batch):
+            # Right zero-pad all one-hot text sequences to max input length
+            input_lengths, ids_sorted_decreasing = torch.sort(
+                torch.LongTensor([len(x) for x in batch]),
+                dim=0, descending=True)
+            max_input_len = input_lengths[0]
+
+            text_padded = torch.LongTensor(len(batch), max_input_len)
+            text_padded.zero_()
+            for i in range(len(ids_sorted_decreasing)):
+                text = batch[ids_sorted_decreasing[i]]
+                text_padded[i, :text.size(0)] = text
+
+            return text_padded, input_lengths
+        
+        @staticmethod
+        def prepare_input_sequence(texts, cpu_run=False):
+
+            d = []
+            for i,text in enumerate(texts):
+                d.append(torch.IntTensor(
+                    Processing.text_to_sequence(text, ['english_cleaners'])[:]))
+
+            text_padded, input_lengths = Processing.pad_sequences(d)
+            if not cpu_run:
+                text_padded = text_padded.cuda().long()
+                input_lengths = input_lengths.cuda().long()
+            else:
+                text_padded = text_padded.long()
+                input_lengths = input_lengths.long()
+
+            return text_padded, input_lengths
+    
+    return Processing()

PyTorch/SpeechSynthesis/Tacotron2/waveglow/__init__.py

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+from .entrypoints import nvidia_waveglow

PyTorch/SpeechSynthesis/Tacotron2/waveglow/entrypoints.py

@@ -0,0 +1,90 @@
+import urllib.request
+import torch
+import os
+import sys
+
+
+# from https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/inference.py
+def checkpoint_from_distributed(state_dict):
+    """
+    Checks whether checkpoint was generated by DistributedDataParallel. DDP
+    wraps model in additional "module.", it needs to be unwrapped for single
+    GPU inference.
+    :param state_dict: model's state dict
+    """
+    ret = False
+    for key, _ in state_dict.items():
+        if key.find('module.') != -1:
+            ret = True
+            break
+    return ret
+
+
+# from https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/inference.py
+def unwrap_distributed(state_dict):
+    """
+    Unwraps model from DistributedDataParallel.
+    DDP wraps model in additional "module.", it needs to be removed for single
+    GPU inference.
+    :param state_dict: model's state dict
+    """
+    new_state_dict = {}
+    for key, value in state_dict.items():
+        new_key = key.replace('module.1.', '')
+        new_key = new_key.replace('module.', '')
+        new_state_dict[new_key] = value
+    return new_state_dict
+
+
+def _download_checkpoint(checkpoint, force_reload):
+    model_dir = os.path.join(torch.hub._get_torch_home(), 'checkpoints')
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    ckpt_file = os.path.join(model_dir, os.path.basename(checkpoint))
+    if not os.path.exists(ckpt_file) or force_reload:
+        sys.stderr.write('Downloading checkpoint from {}\n'.format(checkpoint))
+        urllib.request.urlretrieve(checkpoint, ckpt_file)
+    return ckpt_file
+
+def nvidia_waveglow(pretrained=True, **kwargs):
+    """Constructs a WaveGlow model (nn.module with additional infer(input) method).
+    For detailed information on model input and output, training recipies, inference and performance
+    visit: github.com/NVIDIA/DeepLearningExamples and/or ngc.nvidia.com
+    Args:
+        pretrained (bool): If True, returns a model pretrained on LJ Speech dataset.
+        model_math (str, 'fp32'): returns a model in given precision ('fp32' or 'fp16')
+    """
+
+    from waveglow import model as waveglow
+
+    fp16 = "model_math" in kwargs and kwargs["model_math"] == "fp16"
+    force_reload = "force_reload" in kwargs and kwargs["force_reload"]
+
+    if pretrained:
+        if fp16:
+            checkpoint = 'https://api.ngc.nvidia.com/v2/models/nvidia/waveglow_ckpt_amp/versions/19.09.0/files/nvidia_waveglowpyt_fp16_20190427'
+        else:
+            checkpoint = 'https://api.ngc.nvidia.com/v2/models/nvidia/waveglow_ckpt_fp32/versions/19.09.0/files/nvidia_waveglowpyt_fp32_20190427'
+        ckpt_file = _download_checkpoint(checkpoint, force_reload)
+        ckpt = torch.load(ckpt_file)
+        state_dict = ckpt['state_dict']
+        if checkpoint_from_distributed(state_dict):
+            state_dict = unwrap_distributed(state_dict)
+        config = ckpt['config']
+    else:
+        config = {'n_mel_channels': 80, 'n_flows': 12, 'n_group': 8,
+                  'n_early_every': 4, 'n_early_size': 2,
+                  'WN_config': {'n_layers': 8, 'kernel_size': 3,
+                                'n_channels': 512}}
+        for k,v in kwargs.items():
+            if k in config.keys():
+                config[k] = v
+            elif k in config['WN_config'].keys():
+                config['WN_config'][k] = v
+
+    m = waveglow.WaveGlow(**config)
+
+    if pretrained:
+        m.load_state_dict(state_dict)
+
+    return m

hubconf.py

@@ -1,35 +1,10 @@
-def relocated():
-    raise ValueError(
-        "NVIDIA entrypoints moved to branch torchhub \n"
-        "Use torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', ...) to access the models"
-    )
+import os
+import sys
 
+from PyTorch.Detection.SSD.src import nvidia_ssd, nvidia_ssd_processing_utils
+sys.path.append(os.path.join(sys.path[0], 'PyTorch/Detection/SSD'))
 
-def nvidia_ncf(**kwargs):
-    """Entrypoints moved to branch torchhub
-    """
-    relocated()
-
-
-def nvidia_tacotron2(**kwargs):
-    """Entrypoints moved to branch torchhub
-    """
-    relocated()
-
-
-def nvidia_waveglow(**kwargs):
-    """Entrypoints moved to branch torchhub
-    """
-    relocated()
-
-
-def nvidia_ssd_processing_utils():
-    """Entrypoints moved to branch torchhub
-    """
-    relocated()
-
-
-def nvidia_ssd(**kwargs):
-    """Entrypoints moved to branch torchhub
-    """
-    relocated()
+from PyTorch.SpeechSynthesis.Tacotron2.tacotron2 import nvidia_tacotron2
+from PyTorch.SpeechSynthesis.Tacotron2.tacotron2 import nvidia_tts_utils
+from PyTorch.SpeechSynthesis.Tacotron2.waveglow import nvidia_waveglow
+sys.path.append(os.path.join(sys.path[0], 'PyTorch/SpeechSynthesis/Tacotron2'))