{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Description\nThis kernel performs inference for [Grapheme fast.ai starter](https://www.kaggle.com/iafoss/grapheme-fast-ai-starter) kernel. Check it for more training details. The image preprocessing pipline is provided [here](https://www.kaggle.com/iafoss/image-preprocessing-128x128)."},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"%reload_ext autoreload\n%autoreload 2\n%matplotlib inline\n\nimport cv2\nfrom tqdm import tqdm_notebook as tqdm\nimport fastai\nfrom fastai.vision import *\nimport os\nfrom mish_activation import *\nimport warnings\nwarnings.filterwarnings(\"ignore\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"os.listdir(\"../input/efficientnet-mixup-256\")","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"HEIGHT = 137\nWIDTH = 236\nSIZE = 256\nbs = 128\nstats = (0.0692, 0.2051)\narch = models.densenet201\nMODEL = '../input/efficientnet-mixup-256/efficientnet_with_mixup_256.pth'\nnworkers = 2\n\nTEST = ['/kaggle/input/bengaliai-cv19/test_image_data_0.parquet',\n        '/kaggle/input/bengaliai-cv19/test_image_data_1.parquet',\n        '/kaggle/input/bengaliai-cv19/test_image_data_2.parquet',\n        '/kaggle/input/bengaliai-cv19/test_image_data_3.parquet']\n\nLABELS = '../input/bengaliai-cv19/train.csv'\n\ndf = pd.read_csv(LABELS)\nnunique = list(df.nunique())[1:-1]","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model"},{"metadata":{"trusted":true},"cell_type":"code","source":"class Mish(nn.Module):\n    def __init__(self):\n        super().__init__()\n\n    def forward(self, x):\n        #inlining this saves 1 second per epoch (V100 GPU) vs having a temp x and then returning x(!)\n        return x *( torch.tanh(F.softplus(x)))\n    \ndef to_Mish(layers):\n    for i in range(len(layers)):\n        layer = layers[i]\n        if isinstance(layer, nn.ReLU):\n            layers[i] = Mish()","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-output":false,"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"class Head(nn.Module):\n    def __init__(self, nc, n, ps=0.5):\n        super().__init__()\n        layers = [AdaptiveConcatPool2d(), Mish(), Flatten()] + \\\n            bn_drop_lin(nc*2, 512, True, ps, nn.ReLU(inplace=True)) + \\\n            bn_drop_lin(512, n, True, ps)\n        self.fc = nn.Sequential(*layers)\n        to_Mish(self.fc)\n        self._init_weight()\n        \n    def _init_weight(self):\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                torch.nn.init.kaiming_normal_(m.weight)\n            elif isinstance(m, nn.BatchNorm2d):\n                m.weight.data.fill_(1.0)\n                m.bias.data.zero_()\n        \n    def forward(self, x):\n        return self.fc(x)\n\n#change the first conv to accept 1 chanel input\nclass Dnet_1ch(nn.Module):\n    def __init__(self, arch=arch, n=nunique, pre=True, ps=0.5):\n        super().__init__()\n        m = arch(True) if pre else arch()\n        \n        conv = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n        w = (m.features.conv0.weight.sum(1)).unsqueeze(1)\n        conv.weight = nn.Parameter(w)\n        \n        self.layer0 = nn.Sequential(conv, m.features.norm0, nn.ReLU(inplace=True))\n        self.layer1 = nn.Sequential(\n            nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False),\n            m.features.denseblock1)\n        self.layer2 = nn.Sequential(m.features.transition1,m.features.denseblock2)\n        self.layer3 = nn.Sequential(m.features.transition2,m.features.denseblock3)\n        self.layer4 = nn.Sequential(m.features.transition3,m.features.denseblock4,\n                                    m.features.norm5)\n        \n        nc = self.layer4[-1].weight.shape[0]\n        self.head1 = Head(nc,n[0])\n        self.head2 = Head(nc,n[1])\n        self.head3 = Head(nc,n[2])\n        to_Mish(self.layer0), to_Mish(self.layer1), to_Mish(self.layer2)\n        to_Mish(self.layer3), to_Mish(self.layer4)\n        \n    def forward(self, x):    \n        #x =  F.interpolate(x, scale_factor=2, mode='bilinear')\n        x = self.layer0(x)\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n        \n        x1 = self.head1(x)\n        x2 = self.head2(x)\n        x3 = self.head3(x)\n        \n        return x1,x2,x3","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import re\nimport math\nimport collections\nfrom functools import partial\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch.utils import model_zoo\n\n########################################################################\n############### HELPERS FUNCTIONS FOR MODEL ARCHITECTURE ###############\n########################################################################\n\n\n# Parameters for the entire model (stem, all blocks, and head)\nGlobalParams = collections.namedtuple('GlobalParams', [\n    'batch_norm_momentum', 'batch_norm_epsilon', 'dropout_rate',\n    'num_classes', 'width_coefficient', 'depth_coefficient',\n    'depth_divisor', 'min_depth', 'drop_connect_rate', 'image_size'])\n\n# Parameters for an individual model block\nBlockArgs = collections.namedtuple('BlockArgs', [\n    'kernel_size', 'num_repeat', 'input_filters', 'output_filters',\n    'expand_ratio', 'id_skip', 'stride', 'se_ratio'])\n\n# Change namedtuple defaults\nGlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields)\nBlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields)\n\n\nclass SwishImplementation(torch.autograd.Function):\n    @staticmethod\n    def forward(ctx, i):\n        result = i * torch.sigmoid(i)\n        ctx.save_for_backward(i)\n        return result\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        i = ctx.saved_variables[0]\n        sigmoid_i = torch.sigmoid(i)\n        return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))\n\n\nclass MemoryEfficientSwish(nn.Module):\n    def forward(self, x):\n        return SwishImplementation.apply(x)\n\nclass Swish(nn.Module):\n    def forward(self, x):\n        return x * torch.sigmoid(x)\n\n\ndef round_filters(filters, global_params):\n    \"\"\" Calculate and round number of filters based on depth multiplier. \"\"\"\n    multiplier = global_params.width_coefficient\n    if not multiplier:\n        return filters\n    divisor = global_params.depth_divisor\n    min_depth = global_params.min_depth\n    filters *= multiplier\n    min_depth = min_depth or divisor\n    new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)\n    if new_filters < 0.9 * filters:  # prevent rounding by more than 10%\n        new_filters += divisor\n    return int(new_filters)\n\n\ndef round_repeats(repeats, global_params):\n    \"\"\" Round number of filters based on depth multiplier. \"\"\"\n    multiplier = global_params.depth_coefficient\n    if not multiplier:\n        return repeats\n    return int(math.ceil(multiplier * repeats))\n\n\ndef drop_connect(inputs, p, training):\n    \"\"\" Drop connect. \"\"\"\n    if not training: return inputs\n    batch_size = inputs.shape[0]\n    keep_prob = 1 - p\n    random_tensor = keep_prob\n    random_tensor += torch.rand([batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device)\n    binary_tensor = torch.floor(random_tensor)\n    output = inputs / keep_prob * binary_tensor\n    return output\n\n\ndef get_same_padding_conv2d(image_size=None):\n    \"\"\" Chooses static padding if you have specified an image size, and dynamic padding otherwise.\n        Static padding is necessary for ONNX exporting of models. \"\"\"\n    if image_size is None:\n        return Conv2dDynamicSamePadding\n    else:\n        return partial(Conv2dStaticSamePadding, image_size=image_size)\n\n\nclass Conv2dDynamicSamePadding(nn.Conv2d):\n    \"\"\" 2D Convolutions like TensorFlow, for a dynamic image size \"\"\"\n\n    def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True):\n        super().__init__(in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)\n        self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2\n\n    def forward(self, x):\n        ih, iw = x.size()[-2:]\n        kh, kw = self.weight.size()[-2:]\n        sh, sw = self.stride\n        oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)\n        pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)\n        pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)\n        if pad_h > 0 or pad_w > 0:\n            x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])\n        return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)\n\n\nclass Conv2dStaticSamePadding(nn.Conv2d):\n    \"\"\" 2D Convolutions like TensorFlow, for a fixed image size\"\"\"\n\n    def __init__(self, in_channels, out_channels, kernel_size, image_size=None, **kwargs):\n        super().__init__(in_channels, out_channels, kernel_size, **kwargs)\n        self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2\n\n        # Calculate padding based on image size and save it\n        assert image_size is not None\n        ih, iw = image_size if type(image_size) == list else [image_size, image_size]\n        kh, kw = self.weight.size()[-2:]\n        sh, sw = self.stride\n        oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)\n        pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)\n        pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)\n        if pad_h > 0 or pad_w > 0:\n            self.static_padding = nn.ZeroPad2d((pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2))\n        else:\n            self.static_padding = Identity()\n\n    def forward(self, x):\n        x = self.static_padding(x)\n        x = F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)\n        return x\n\n\nclass Identity(nn.Module):\n    def __init__(self, ):\n        super(Identity, self).__init__()\n\n    def forward(self, input):\n        return input\n\n\n########################################################################\n############## HELPERS FUNCTIONS FOR LOADING MODEL PARAMS ##############\n########################################################################\n\n\ndef efficientnet_params(model_name):\n    \"\"\" Map EfficientNet model name to parameter coefficients. \"\"\"\n    params_dict = {\n        # Coefficients:   width,depth,res,dropout\n        'efficientnet-b0': (1.0, 1.0, 224, 0.2),\n        'efficientnet-b1': (1.0, 1.1, 240, 0.2),\n        'efficientnet-b2': (1.1, 1.2, 260, 0.3),\n        'efficientnet-b3': (1.2, 1.4, 300, 0.3),\n        'efficientnet-b4': (1.4, 1.8, 380, 0.4),\n        'efficientnet-b5': (1.6, 2.2, 456, 0.4),\n        'efficientnet-b6': (1.8, 2.6, 528, 0.5),\n        'efficientnet-b7': (2.0, 3.1, 600, 0.5),\n    }\n    return params_dict[model_name]\n\n\nclass BlockDecoder(object):\n    \"\"\" Block Decoder for readability, straight from the official TensorFlow repository \"\"\"\n\n    @staticmethod\n    def _decode_block_string(block_string):\n        \"\"\" Gets a block through a string notation of arguments. \"\"\"\n        assert isinstance(block_string, str)\n\n        ops = block_string.split('_')\n        options = {}\n        for op in ops:\n            splits = re.split(r'(\\d.*)', op)\n            if len(splits) >= 2:\n                key, value = splits[:2]\n                options[key] = value\n\n        # Check stride\n        assert (('s' in options and len(options['s']) == 1) or\n                (len(options['s']) == 2 and options['s'][0] == options['s'][1]))\n\n        return BlockArgs(\n            kernel_size=int(options['k']),\n            num_repeat=int(options['r']),\n            input_filters=int(options['i']),\n            output_filters=int(options['o']),\n            expand_ratio=int(options['e']),\n            id_skip=('noskip' not in block_string),\n            se_ratio=float(options['se']) if 'se' in options else None,\n            stride=[int(options['s'][0])])\n\n    @staticmethod\n    def _encode_block_string(block):\n        \"\"\"Encodes a block to a string.\"\"\"\n        args = [\n            'r%d' % block.num_repeat,\n            'k%d' % block.kernel_size,\n            's%d%d' % (block.strides[0], block.strides[1]),\n            'e%s' % block.expand_ratio,\n            'i%d' % block.input_filters,\n            'o%d' % block.output_filters\n        ]\n        if 0 < block.se_ratio <= 1:\n            args.append('se%s' % block.se_ratio)\n        if block.id_skip is False:\n            args.append('noskip')\n        return '_'.join(args)\n\n    @staticmethod\n    def decode(string_list):\n        \"\"\"\n        Decodes a list of string notations to specify blocks inside the network.\n        :param string_list: a list of strings, each string is a notation of block\n        :return: a list of BlockArgs namedtuples of block args\n        \"\"\"\n        assert isinstance(string_list, list)\n        blocks_args = []\n        for block_string in string_list:\n            blocks_args.append(BlockDecoder._decode_block_string(block_string))\n        return blocks_args\n\n    @staticmethod\n    def encode(blocks_args):\n        \"\"\"\n        Encodes a list of BlockArgs to a list of strings.\n        :param blocks_args: a list of BlockArgs namedtuples of block args\n        :return: a list of strings, each string is a notation of block\n        \"\"\"\n        block_strings = []\n        for block in blocks_args:\n            block_strings.append(BlockDecoder._encode_block_string(block))\n        return block_strings\n\n\ndef efficientnet(width_coefficient=None, depth_coefficient=None, dropout_rate=0.2,\n                 drop_connect_rate=0.2, image_size=None, num_classes=1000):\n    \"\"\" Creates a efficientnet model. \"\"\"\n\n    blocks_args = [\n        'r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25',\n        'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25',\n        'r3_k5_s11_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25',\n        'r1_k3_s11_e6_i192_o320_se0.25',\n    ]\n    blocks_args = BlockDecoder.decode(blocks_args)\n\n    global_params = GlobalParams(\n        batch_norm_momentum=0.99,\n        batch_norm_epsilon=1e-3,\n        dropout_rate=dropout_rate,\n        drop_connect_rate=drop_connect_rate,\n        # data_format='channels_last',  # removed, this is always true in PyTorch\n        num_classes=num_classes,\n        width_coefficient=width_coefficient,\n        depth_coefficient=depth_coefficient,\n        depth_divisor=8,\n        min_depth=None,\n        image_size=image_size,\n    )\n\n    return blocks_args, global_params\n\n\ndef get_model_params(model_name, override_params):\n    \"\"\" Get the block args and global params for a given model \"\"\"\n    if model_name.startswith('efficientnet'):\n        w, d, s, p = efficientnet_params(model_name)\n        # note: all models have drop connect rate = 0.2\n        blocks_args, global_params = efficientnet(\n            width_coefficient=w, depth_coefficient=d, dropout_rate=p, image_size=s)\n    else:\n        raise NotImplementedError('model name is not pre-defined: %s' % model_name)\n    if override_params:\n        # ValueError will be raised here if override_params has fields not included in global_params.\n        global_params = global_params._replace(**override_params)\n    return blocks_args, global_params\n\n\nurl_map = {\n    'efficientnet-b0': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b0-355c32eb.pth',\n    'efficientnet-b1': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b1-f1951068.pth',\n    'efficientnet-b2': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b2-8bb594d6.pth',\n    'efficientnet-b3': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b3-5fb5a3c3.pth',\n    'efficientnet-b4': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b4-6ed6700e.pth',\n    'efficientnet-b5': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b5-b6417697.pth',\n    'efficientnet-b6': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b6-c76e70fd.pth',\n    'efficientnet-b7': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b7-dcc49843.pth',\n}\n\n\ndef load_pretrained_weights(model, model_name, load_fc=True):\n    \"\"\" Loads pretrained weights, and downloads if loading for the first time. \"\"\"\n    state_dict = model_zoo.load_url(url_map[model_name])\n    if load_fc:\n        model.load_state_dict(state_dict)\n    else:\n        state_dict.pop('_fc.weight')\n        state_dict.pop('_fc.bias')\n        res = model.load_state_dict(state_dict, strict=False)\n        assert set(res.missing_keys) == set(['_fc.weight', '_fc.bias']), 'issue loading pretrained weights'\n    print('Loaded pretrained weights for {}'.format(model_name))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class MBConvBlock(nn.Module):\n    \"\"\"\n    Mobile Inverted Residual Bottleneck Block\n    Args:\n        block_args (namedtuple): BlockArgs, see above\n        global_params (namedtuple): GlobalParam, see above\n    Attributes:\n        has_se (bool): Whether the block contains a Squeeze and Excitation layer.\n    \"\"\"\n\n    def __init__(self, block_args, global_params):\n        super().__init__()\n        self._block_args = block_args\n        self._bn_mom = 1 - global_params.batch_norm_momentum\n        self._bn_eps = global_params.batch_norm_epsilon\n        self.has_se = (self._block_args.se_ratio is not None) and (0 < self._block_args.se_ratio <= 1)\n        self.id_skip = block_args.id_skip  # skip connection and drop connect\n\n        # Get static or dynamic convolution depending on image size\n        Conv2d = get_same_padding_conv2d(image_size=global_params.image_size)\n\n        # Expansion phase\n        inp = self._block_args.input_filters  # number of input channels\n        oup = self._block_args.input_filters * self._block_args.expand_ratio  # number of output channels\n        if self._block_args.expand_ratio != 1:\n            self._expand_conv = Conv2d(in_channels=inp, out_channels=oup, kernel_size=1, bias=False)\n            self._bn0 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps)\n\n        # Depthwise convolution phase\n        k = self._block_args.kernel_size\n        s = self._block_args.stride\n        self._depthwise_conv = Conv2d(\n            in_channels=oup, out_channels=oup, groups=oup,  # groups makes it depthwise\n            kernel_size=k, stride=s, bias=False)\n        self._bn1 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps)\n\n        # Squeeze and Excitation layer, if desired\n        if self.has_se:\n            num_squeezed_channels = max(1, int(self._block_args.input_filters * self._block_args.se_ratio))\n            self._se_reduce = Conv2d(in_channels=oup, out_channels=num_squeezed_channels, kernel_size=1)\n            self._se_expand = Conv2d(in_channels=num_squeezed_channels, out_channels=oup, kernel_size=1)\n\n        # Output phase\n        final_oup = self._block_args.output_filters\n        self._project_conv = Conv2d(in_channels=oup, out_channels=final_oup, kernel_size=1, bias=False)\n        self._bn2 = nn.BatchNorm2d(num_features=final_oup, momentum=self._bn_mom, eps=self._bn_eps)\n        self._swish = MemoryEfficientSwish()\n\n    def forward(self, inputs, drop_connect_rate=None):\n        \"\"\"\n        :param inputs: input tensor\n        :param drop_connect_rate: drop connect rate (float, between 0 and 1)\n        :return: output of block\n        \"\"\"\n\n        # Expansion and Depthwise Convolution\n        x = inputs\n        if self._block_args.expand_ratio != 1:\n            x = self._swish(self._bn0(self._expand_conv(inputs)))\n        x = self._swish(self._bn1(self._depthwise_conv(x)))\n\n        # Squeeze and Excitation\n        if self.has_se:\n            x_squeezed = F.adaptive_avg_pool2d(x, 1)\n            x_squeezed = self._se_expand(self._swish(self._se_reduce(x_squeezed)))\n            x = torch.sigmoid(x_squeezed) * x\n\n        x = self._bn2(self._project_conv(x))\n\n        # Skip connection and drop connect\n        input_filters, output_filters = self._block_args.input_filters, self._block_args.output_filters\n        if self.id_skip and self._block_args.stride == 1 and input_filters == output_filters:\n            if drop_connect_rate:\n                x = drop_connect(x, p=drop_connect_rate, training=self.training)\n            x = x + inputs  # skip connection\n        return x\n\n    def set_swish(self, memory_efficient=True):\n        \"\"\"Sets swish function as memory efficient (for training) or standard (for export)\"\"\"\n        self._swish = MemoryEfficientSwish() if memory_efficient else Swish()\n\n\nclass EfficientNet(nn.Module):\n    \"\"\"\n    An EfficientNet model. Most easily loaded with the .from_name or .from_pretrained methods\n    Args:\n        blocks_args (list): A list of BlockArgs to construct blocks\n        global_params (namedtuple): A set of GlobalParams shared between blocks\n    Example:\n        model = EfficientNet.from_pretrained('efficientnet-b0')\n    \"\"\"\n\n    def __init__(self, blocks_args=None, global_params=None):\n        super().__init__()\n        assert isinstance(blocks_args, list), 'blocks_args should be a list'\n        assert len(blocks_args) > 0, 'block args must be greater than 0'\n        self._global_params = global_params\n        self._blocks_args = blocks_args\n\n        # Get static or dynamic convolution depending on image size\n        Conv2d = get_same_padding_conv2d(image_size=global_params.image_size)\n\n        # Batch norm parameters\n        bn_mom = 1 - self._global_params.batch_norm_momentum\n        bn_eps = self._global_params.batch_norm_epsilon\n\n        # Stem\n        in_channels = 3  # rgb\n        out_channels = round_filters(32, self._global_params)  # number of output channels\n        self._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False)\n        self._bn0 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps)\n\n        # Build blocks\n        self._blocks = nn.ModuleList([])\n        for block_args in self._blocks_args:\n\n            # Update block input and output filters based on depth multiplier.\n            block_args = block_args._replace(\n                input_filters=round_filters(block_args.input_filters, self._global_params),\n                output_filters=round_filters(block_args.output_filters, self._global_params),\n                num_repeat=round_repeats(block_args.num_repeat, self._global_params)\n            )\n\n            # The first block needs to take care of stride and filter size increase.\n            self._blocks.append(MBConvBlock(block_args, self._global_params))\n            if block_args.num_repeat > 1:\n                block_args = block_args._replace(input_filters=block_args.output_filters, stride=1)\n            for _ in range(block_args.num_repeat - 1):\n                self._blocks.append(MBConvBlock(block_args, self._global_params))\n\n        # Head\n        in_channels = block_args.output_filters  # output of final block\n        out_channels = round_filters(1280, self._global_params)\n        self._conv_head = Conv2d(in_channels, out_channels, kernel_size=1, bias=False)\n        self._bn1 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps)\n\n        # Final linear layer\n        self._avg_pooling = nn.AdaptiveAvgPool2d(1)\n        self._dropout = nn.Dropout(self._global_params.dropout_rate)\n        self._fc = nn.Linear(out_channels, self._global_params.num_classes)\n        self._swish = MemoryEfficientSwish()\n\n    def set_swish(self, memory_efficient=True):\n        \"\"\"Sets swish function as memory efficient (for training) or standard (for export)\"\"\"\n        self._swish = MemoryEfficientSwish() if memory_efficient else Swish()\n        for block in self._blocks:\n            block.set_swish(memory_efficient)\n\n\n    def extract_features(self, inputs):\n        \"\"\" Returns output of the final convolution layer \"\"\"\n\n        # Stem\n        x = self._swish(self._bn0(self._conv_stem(inputs)))\n\n        # Blocks\n        for idx, block in enumerate(self._blocks):\n            drop_connect_rate = self._global_params.drop_connect_rate\n            if drop_connect_rate:\n                drop_connect_rate *= float(idx) / len(self._blocks)\n            x = block(x, drop_connect_rate=drop_connect_rate)\n\n        # Head\n        x = self._swish(self._bn1(self._conv_head(x)))\n\n        return x\n\n    def forward(self, inputs):\n        \"\"\" Calls extract_features to extract features, applies final linear layer, and returns logits. \"\"\"\n        bs = inputs.size(0)\n        # Convolution layers\n        x = self.extract_features(inputs)\n\n        # Pooling and final linear layer\n        x = self._avg_pooling(x)\n        x = x.view(bs, -1)\n        x = self._dropout(x)\n        x = self._fc(x)\n        return x\n\n    @classmethod\n    def from_name(cls, model_name, override_params=None):\n        cls._check_model_name_is_valid(model_name)\n        blocks_args, global_params = get_model_params(model_name, override_params)\n        return cls(blocks_args, global_params)\n\n    @classmethod\n    def from_pretrained(cls, model_name, num_classes=1000, in_channels = 3):\n        model = cls.from_name(model_name, override_params={'num_classes': num_classes})\n        load_pretrained_weights(model, model_name, load_fc=(num_classes == 1000))\n        if in_channels != 3:\n            Conv2d = get_same_padding_conv2d(image_size = model._global_params.image_size)\n            out_channels = round_filters(32, model._global_params)\n            model._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False)\n        return model\n    \n    @classmethod\n    def from_pretrained(cls, model_name, num_classes=1000):\n        model = cls.from_name(model_name, override_params={'num_classes': num_classes})\n        load_pretrained_weights(model, model_name, load_fc=(num_classes == 1000))\n\n        return model\n\n    @classmethod\n    def get_image_size(cls, model_name):\n        cls._check_model_name_is_valid(model_name)\n        _, _, res, _ = efficientnet_params(model_name)\n        return res\n\n    @classmethod\n    def _check_model_name_is_valid(cls, model_name, also_need_pretrained_weights=False):\n        \"\"\" Validates model name. None that pretrained weights are only available for\n        the first four models (efficientnet-b{i} for i in 0,1,2,3) at the moment. \"\"\"\n        num_models = 4 if also_need_pretrained_weights else 8\n        valid_models = ['efficientnet-b'+str(i) for i in range(num_models)]\n        if model_name not in valid_models:\n            raise ValueError('model_name should be one of: ' + ', '.join(valid_models))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# !pip install efficientnet_pytorch\n\n# from efficientnet_pytorch import EfficientNet\n# from efficientnet_pytorch.utils import *","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#change the first conv to accept 1 chanel input\nclass EfficientNet_1Channel(nn.Module):\n    def __init__(self, arch=arch, n=nunique, pre=True, ps=0.5):\n        super().__init__()\n        \n        model = EfficientNet.from_name('efficientnet-b7')\n\n\n        _conv_stem = Conv2dStaticSamePadding(1, 48, kernel_size=(3, 3), stride=(2, 2), bias=False, image_size=[600,600])\n        w = (model._conv_stem.weight.sum(1)).unsqueeze(1)\n        _conv_stem.weight = nn.Parameter(w)\n\n        self.layer0 = nn.Sequential(_conv_stem, model._bn0)\n        self.layer1 = nn.Sequential(*model._blocks[0:])\n        self.layer2 = nn.Sequential(model._conv_head)\n        self.layer3 = nn.Sequential(nn.BatchNorm2d(2560, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True),\n                                    nn.AdaptiveAvgPool2d(output_size=1),\n                                    nn.Dropout(p=0.5, inplace=False))\n\n        # nc = self.layer3[-1].weight.shape[0]\n        self.head1 = Head(2560,n[0])\n        self.head2 = Head(2560,n[1])\n        self.head3 = Head(2560,n[2])\n        to_Mish(self.layer0), to_Mish(self.layer1), to_Mish(self.layer2)\n        to_Mish(self.layer3)\n        \n    def forward(self, x):    \n        #x =  F.interpolate(x, scale_factor=2, mode='bilinear')\n        x = self.layer0(x)\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        \n        x1 = self.head1(x)\n        x2 = self.head2(x)\n        x3 = self.head3(x)\n        \n        return x1,x2,x3","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = EfficientNet_1Channel().cuda()\nmodel.load_state_dict(torch.load(MODEL, map_location=torch.device('cpu')));\nmodel.eval();","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Data"},{"metadata":{"trusted":true},"cell_type":"code","source":"#check https://www.kaggle.com/iafoss/image-preprocessing-128x128\n\ndef bbox(img):\n    rows = np.any(img, axis=1)\n    cols = np.any(img, axis=0)\n    rmin, rmax = np.where(rows)[0][[0, -1]]\n    cmin, cmax = np.where(cols)[0][[0, -1]]\n    return rmin, rmax, cmin, cmax\n\ndef crop_resize(img0, size=SIZE, pad=16):\n    #crop a box around pixels large than the threshold \n    #some images contain line at the sides\n    ymin,ymax,xmin,xmax = bbox(img0[5:-5,5:-5] > 80)\n    #cropping may cut too much, so we need to add it back\n    xmin = xmin - 13 if (xmin > 13) else 0\n    ymin = ymin - 10 if (ymin > 10) else 0\n    xmax = xmax + 13 if (xmax < WIDTH - 13) else WIDTH\n    ymax = ymax + 10 if (ymax < HEIGHT - 10) else HEIGHT\n    img = img0[ymin:ymax,xmin:xmax]\n    #remove lo intensity pixels as noise\n    img[img < 28] = 0\n    lx, ly = xmax-xmin,ymax-ymin\n    l = max(lx,ly) + pad\n    #make sure that the aspect ratio is kept in rescaling\n    img = np.pad(img, [((l-ly)//2,), ((l-lx)//2,)], mode='constant')\n    return cv2.resize(img,(size,size))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class GraphemeDataset(Dataset):\n    def __init__(self, fname):\n        self.df = pd.read_parquet(fname)\n        self.data = 255 - self.df.iloc[:, 1:].values.reshape(-1, HEIGHT, WIDTH).astype(np.uint8)\n\n    def __len__(self):\n        return len(self.data)\n\n    def __getitem__(self, idx):\n        name = self.df.iloc[idx,0]\n        #normalize each image by its max val\n        img = (self.data[idx]*(255.0/self.data[idx].max())).astype(np.uint8)\n        img = crop_resize(img)\n        img = (img.astype(np.float32)/255.0 - stats[0])/stats[1]\n        return img, name","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Prediction"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"row_id,target = [],[]\nfor fname in TEST:\n    ds = GraphemeDataset(fname)\n    dl = DataLoader(ds, batch_size=bs, num_workers=nworkers, shuffle=False)\n    with torch.no_grad():\n        for x,y in tqdm(dl):\n            x = x.unsqueeze(1).cuda()\n            p1,p2,p3 = model(x)\n            p1 = p1.argmax(-1).view(-1).cpu()\n            p2 = p2.argmax(-1).view(-1).cpu()\n            p3 = p3.argmax(-1).view(-1).cpu()\n            for idx,name in enumerate(y):\n                row_id += [f'{name}_grapheme_root',f'{name}_vowel_diacritic',\n                           f'{name}_consonant_diacritic']\n                target += [p1[idx].item(),p2[idx].item(),p3[idx].item()]\n                \nsub_df = pd.DataFrame({'row_id': row_id, 'target': target})\nsub_df.to_csv('submission.csv', index=False)\nsub_df.head()","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}