update for batches

.gitignore

@@ -0,0 +1 @@
+env/

handler.py

@@ -10,10 +10,632 @@ import torch
 from torch import nn
 
 
-import subprocess
-result = subprocess.run(["pip", "install", "git+https://github.com/sberbank-ai/Real-ESRGAN.git"], check=True)
-print(f"git+https://github.com/sberbank-ai/Real-ESRGAN.git = {result}")
-from RealESRGAN import RealESRGAN
+# import subprocess
+# result = subprocess.run(["pip", "install", "git+https://github.com/sberbank-ai/Real-ESRGAN.git"], check=True)
+# print(f"git+https://github.com/sberbank-ai/Real-ESRGAN.git = {result}")
+# from RealESRGAN import RealESRGAN
+
+# no need to install, just take in all of the necessary files from the notebook
+import math
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+from torch.nn import init as init
+from torch.nn.modules.batchnorm import _BatchNorm
+
+@torch.no_grad()
+def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
+    """Initialize network weights.
+
+    Args:
+        module_list (list[nn.Module] | nn.Module): Modules to be initialized.
+        scale (float): Scale initialized weights, especially for residual
+            blocks. Default: 1.
+        bias_fill (float): The value to fill bias. Default: 0
+        kwargs (dict): Other arguments for initialization function.
+    """
+    if not isinstance(module_list, list):
+        module_list = [module_list]
+    for module in module_list:
+        for m in module.modules():
+            if isinstance(m, nn.Conv2d):
+                init.kaiming_normal_(m.weight, **kwargs)
+                m.weight.data *= scale
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+            elif isinstance(m, nn.Linear):
+                init.kaiming_normal_(m.weight, **kwargs)
+                m.weight.data *= scale
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+            elif isinstance(m, _BatchNorm):
+                init.constant_(m.weight, 1)
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+
+
+def make_layer(basic_block, num_basic_block, **kwarg):
+    """Make layers by stacking the same blocks.
+
+    Args:
+        basic_block (nn.module): nn.module class for basic block.
+        num_basic_block (int): number of blocks.
+
+    Returns:
+        nn.Sequential: Stacked blocks in nn.Sequential.
+    """
+    layers = []
+    for _ in range(num_basic_block):
+        layers.append(basic_block(**kwarg))
+    return nn.Sequential(*layers)
+
+
+class ResidualBlockNoBN(nn.Module):
+    """Residual block without BN.
+
+    It has a style of:
+        ---Conv-ReLU-Conv-+-
+         |________________|
+
+    Args:
+        num_feat (int): Channel number of intermediate features.
+            Default: 64.
+        res_scale (float): Residual scale. Default: 1.
+        pytorch_init (bool): If set to True, use pytorch default init,
+            otherwise, use default_init_weights. Default: False.
+    """
+
+    def __init__(self, num_feat=64, res_scale=1, pytorch_init=False):
+        super(ResidualBlockNoBN, self).__init__()
+        self.res_scale = res_scale
+        self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
+        self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
+        self.relu = nn.ReLU(inplace=True)
+
+        if not pytorch_init:
+            default_init_weights([self.conv1, self.conv2], 0.1)
+
+    def forward(self, x):
+        identity = x
+        out = self.conv2(self.relu(self.conv1(x)))
+        return identity + out * self.res_scale
+
+
+class Upsample(nn.Sequential):
+    """Upsample module.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+    """
+
+    def __init__(self, scale, num_feat):
+        m = []
+        if (scale & (scale - 1)) == 0:  # scale = 2^n
+            for _ in range(int(math.log(scale, 2))):
+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+                m.append(nn.PixelShuffle(2))
+        elif scale == 3:
+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+            m.append(nn.PixelShuffle(3))
+        else:
+            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+        super(Upsample, self).__init__(*m)
+
+
+def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True):
+    """Warp an image or feature map with optical flow.
+
+    Args:
+        x (Tensor): Tensor with size (n, c, h, w).
+        flow (Tensor): Tensor with size (n, h, w, 2), normal value.
+        interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'.
+        padding_mode (str): 'zeros' or 'border' or 'reflection'.
+            Default: 'zeros'.
+        align_corners (bool): Before pytorch 1.3, the default value is
+            align_corners=True. After pytorch 1.3, the default value is
+            align_corners=False. Here, we use the True as default.
+
+    Returns:
+        Tensor: Warped image or feature map.
+    """
+    assert x.size()[-2:] == flow.size()[1:3]
+    _, _, h, w = x.size()
+    # create mesh grid
+    grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x))
+    grid = torch.stack((grid_x, grid_y), 2).float()  # W(x), H(y), 2
+    grid.requires_grad = False
+
+    vgrid = grid + flow
+    # scale grid to [-1,1]
+    vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0
+    vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0
+    vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3)
+    output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners)
+
+    # TODO, what if align_corners=False
+    return output
+
+
+def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False):
+    """Resize a flow according to ratio or shape.
+
+    Args:
+        flow (Tensor): Precomputed flow. shape [N, 2, H, W].
+        size_type (str): 'ratio' or 'shape'.
+        sizes (list[int | float]): the ratio for resizing or the final output
+            shape.
+            1) The order of ratio should be [ratio_h, ratio_w]. For
+            downsampling, the ratio should be smaller than 1.0 (i.e., ratio
+            < 1.0). For upsampling, the ratio should be larger than 1.0 (i.e.,
+            ratio > 1.0).
+            2) The order of output_size should be [out_h, out_w].
+        interp_mode (str): The mode of interpolation for resizing.
+            Default: 'bilinear'.
+        align_corners (bool): Whether align corners. Default: False.
+
+    Returns:
+        Tensor: Resized flow.
+    """
+    _, _, flow_h, flow_w = flow.size()
+    if size_type == 'ratio':
+        output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1])
+    elif size_type == 'shape':
+        output_h, output_w = sizes[0], sizes[1]
+    else:
+        raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.')
+
+    input_flow = flow.clone()
+    ratio_h = output_h / flow_h
+    ratio_w = output_w / flow_w
+    input_flow[:, 0, :, :] *= ratio_w
+    input_flow[:, 1, :, :] *= ratio_h
+    resized_flow = F.interpolate(
+        input=input_flow, size=(output_h, output_w), mode=interp_mode, align_corners=align_corners)
+    return resized_flow
+
+
+# TODO: may write a cpp file
+def pixel_unshuffle(x, scale):
+    """ Pixel unshuffle.
+
+    Args:
+        x (Tensor): Input feature with shape (b, c, hh, hw).
+        scale (int): Downsample ratio.
+
+    Returns:
+        Tensor: the pixel unshuffled feature.
+    """
+    print('PIXEL UNSHUFFLE X SIZE', x.size())
+    output = []
+    # new batch size for it here
+    b, c, hh, hw = x.size()
+
+    # okay ugh, what is this all doing ...
+    # i mean you could concat each of those in a llok
+    out_channel = c * (scale**2)
+    assert hh % scale == 0 and hw % scale == 0
+    h = hh // scale
+    w = hw // scale
+    x_view = x.view(b, c, h, scale, w, scale)
+    x_view = x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
+    
+    # output = torch.stack(output)
+    print('output shape', x_view.shape)
+    # 1/0
+    return x_view
+
+
+import os
+import torch
+from torch.nn import functional as F
+from PIL import Image
+import numpy as np
+from huggingface_hub import hf_hub_url, cached_download
+
+
+HF_MODELS = {
+    2: dict(
+        repo_id='sberbank-ai/Real-ESRGAN',
+        filename='RealESRGAN_x2.pth',
+    ),
+    4: dict(
+        repo_id='sberbank-ai/Real-ESRGAN',
+        filename='RealESRGAN_x4.pth',
+    ),
+    8: dict(
+        repo_id='sberbank-ai/Real-ESRGAN',
+        filename='RealESRGAN_x8.pth',
+    ),
+}
+
+
+class RealESRGAN:
+    def __init__(self, device, scale=4):
+        self.device = device
+        self.scale = scale
+        self.model = RRDBNet(
+            num_in_ch=3, num_out_ch=3, num_feat=64,
+            num_block=23, num_grow_ch=32, scale=scale
+        )
+
+    def load_weights(self, model_path, download=True):
+        if not os.path.exists(model_path) and download:
+            assert self.scale in [2,4,8], 'You can download models only with scales: 2, 4, 8'
+            config = HF_MODELS[self.scale]
+            cache_dir = os.path.dirname(model_path)
+            local_filename = os.path.basename(model_path)
+            config_file_url = hf_hub_url(repo_id=config['repo_id'], filename=config['filename'])
+            cached_download(config_file_url, cache_dir=cache_dir, force_filename=local_filename)
+            print('Weights downloaded to:', os.path.join(cache_dir, local_filename))
+
+        loadnet = torch.load(model_path)
+        if 'params' in loadnet:
+            self.model.load_state_dict(loadnet['params'], strict=True)
+        elif 'params_ema' in loadnet:
+            self.model.load_state_dict(loadnet['params_ema'], strict=True)
+        else:
+            self.model.load_state_dict(loadnet, strict=True)
+        self.model.eval()
+        self.model.to(self.device)
+
+    @torch.cuda.amp.autocast()
+    def predict(self, numpy_images, batch_size=4, patches_size=192,
+                padding=24, pad_size=15):
+        import time
+        start = time.time()
+        # okay i think that's good with variability for now ... 
+        # ***IMPORTANT VARIABLE***
+        batch_size = len(numpy_images) * 4
+        scale = self.scale
+        device = self.device
+
+        list_of_inputs = []
+        for lr_image in numpy_images:
+            lr_image = np.array(lr_image)
+            lr_image = pad_reflect(lr_image, pad_size)
+
+            patches, p_shape = split_image_into_overlapping_patches(
+                lr_image, patch_size=patches_size, padding_size=padding
+            )
+
+            print('patches.shape', patches.shape)
+            print('p_shape', p_shape)
+
+            img = torch.FloatTensor(patches/255).permute((0,3,1,2)).to(device).detach()
+            list_of_inputs.append(img)
+
+
+        input_batch = torch.concat(list_of_inputs)
+
+        print('input_batch.shape', input_batch.shape)
+
+
+        with torch.no_grad():
+            # res = self.model(input_batch[0:batch_size])
+
+            # okay what does the input size really need to be?
+
+            print('input_batch.shape', input_batch.shape)
+            print('input_batch[0:batch_size].shape', input_batch[0:batch_size].shape)
+            # 1/0
+            res = self.model(input_batch[0:batch_size])
+
+            print('res.shape 1', res.shape)
+            print('batch_size', batch_size)
+            # 1/0
+            for i in range(batch_size, img.shape[0], batch_size):
+                print('i is', i)
+                res = torch.cat((res, self.model(img[i:i+batch_size])), 0)
+                print('res.shape 2', res.shape)
+
+        print('res.shape 3', res.shape)
+
+        # 1/0
+
+        sr_image = res.permute((0,2,3,1)).clamp_(0, 1).cpu()
+        np_sr_image_batch = sr_image.numpy()
+
+        print('np_sr_image_batch.shape', np_sr_image_batch.shape)
+        print('np_sr_image_batch[0].shape', np_sr_image_batch[0].shape)
+        # 1/0
+
+        padded_size_scaled = tuple(np.multiply(p_shape[0:2], scale)) + (3,)
+
+        output_images = []
+        for i in range(0,batch_size,4):
+            # get first time from original input image size
+            scaled_image_shape = tuple(np.multiply(lr_image.shape[0:2], scale)) + (3,)
+            print('scaled_image_shape', scaled_image_shape)
+            print('padded_size_scaled', padded_size_scaled)
+            print("padding * scale", padding * scale)
+            np_sr_image = stich_together(
+                np_sr_image_batch[i:i+4], padded_image_shape=padded_size_scaled,
+                target_shape=scaled_image_shape, padding_size=padding * scale
+            )
+            sr_img = (np_sr_image*255).astype(np.uint8)
+            print('sr_img.shape', sr_img.shape)
+            sr_img = unpad_image(sr_img, pad_size*scale)
+            sr_img = Image.fromarray(sr_img)
+            output_images.append(sr_img)
+
+        print('len of output_images', len(output_images))
+
+        # for debugging
+        # for idx, image in enumerate(output_images):
+        #     image.save(f'output_image_{idx}.png')
+
+
+        print("EVERYTHING TOOK", time.time() - start)
+
+        return output_images
+
+
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+
+class ResidualDenseBlock(nn.Module):
+    """Residual Dense Block.
+
+    Used in RRDB block in ESRGAN.
+
+    Args:
+        num_feat (int): Channel number of intermediate features.
+        num_grow_ch (int): Channels for each growth.
+    """
+
+    def __init__(self, num_feat=64, num_grow_ch=32):
+        super(ResidualDenseBlock, self).__init__()
+        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
+        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+        # initialization
+        default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
+
+    def forward(self, x):
+        x1 = self.lrelu(self.conv1(x))
+        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
+        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
+        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+        # Emperically, we use 0.2 to scale the residual for better performance
+        return x5 * 0.2 + x
+
+
+class RRDB(nn.Module):
+    """Residual in Residual Dense Block.
+
+    Used in RRDB-Net in ESRGAN.
+
+    Args:
+        num_feat (int): Channel number of intermediate features.
+        num_grow_ch (int): Channels for each growth.
+    """
+
+    def __init__(self, num_feat, num_grow_ch=32):
+        super(RRDB, self).__init__()
+        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
+        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
+        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
+
+    def forward(self, x):
+        # this part happens 23 times per pass 
+        out = self.rdb1(x)
+        out = self.rdb2(out)
+        out = self.rdb3(out)
+        # Emperically, we use 0.2 to scale the residual for better performance
+        return out * 0.2 + x
+
+
+class RRDBNet(nn.Module):
+    """Networks consisting of Residual in Residual Dense Block, which is used
+    in ESRGAN.
+
+    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
+
+    We extend ESRGAN for scale x2 and scale x1.
+    Note: This is one option for scale 1, scale 2 in RRDBNet.
+    We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size
+    and enlarge the channel size before feeding inputs into the main ESRGAN architecture.
+
+    Args:
+        num_in_ch (int): Channel number of inputs.
+        num_out_ch (int): Channel number of outputs.
+        num_feat (int): Channel number of intermediate features.
+            Default: 64
+        num_block (int): Block number in the trunk network. Defaults: 23
+        num_grow_ch (int): Channels for each growth. Default: 32.
+    """
+
+    def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32):
+        super(RRDBNet, self).__init__()
+
+        self.scale = scale
+        if scale == 2:
+            num_in_ch = num_in_ch * 4
+        elif scale == 1:
+            num_in_ch = num_in_ch * 16
+
+        print('num_in_ch', num_in_ch)
+
+        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
+        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        # upsample
+        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        if scale == 8:
+            self.conv_up3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+        print('IN FORWARD, X.shape is', x.shape)
+        if self.scale == 2:
+            feat = pixel_unshuffle(x, scale=2)
+        elif self.scale == 1:
+            feat = pixel_unshuffle(x, scale=4)
+        else:
+            feat = x
+        print('feat shape', feat.shape)
+        # breaks here ...
+        feat = self.conv_first(feat)
+        body_feat = self.conv_body(self.body(feat))
+        feat = feat + body_feat
+        # upsample
+        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
+        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
+        if self.scale == 8:
+            feat = self.lrelu(self.conv_up3(F.interpolate(feat, scale_factor=2, mode='nearest')))
+        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
+        return out
+
+import numpy as np
+import torch
+from PIL import Image
+import os
+import io
+
+def pad_reflect(image, pad_size):
+    imsize = image.shape
+    height, width = imsize[:2]
+    print('imsize', imsize)
+    new_img = np.zeros([height+pad_size*2, width+pad_size*2, imsize[2]]).astype(np.uint8)
+    new_img[pad_size:-pad_size, pad_size:-pad_size, :] = image
+    print('new_img.shape 1', new_img.shape)
+
+    new_img[0:pad_size, pad_size:-pad_size, :] = np.flip(image[0:pad_size, :, :], axis=0) #top
+    new_img[-pad_size:, pad_size:-pad_size, :] = np.flip(image[-pad_size:, :, :], axis=0) #bottom
+    new_img[:, 0:pad_size, :] = np.flip(new_img[:, pad_size:pad_size*2, :], axis=1) #left
+    new_img[:, -pad_size:, :] = np.flip(new_img[:, -pad_size*2:-pad_size, :], axis=1) #right
+    print('new_img.shape 2', new_img.shape)
+
+    return new_img
+
+def unpad_image(image, pad_size):
+    return image[pad_size:-pad_size, pad_size:-pad_size, :]
+
+
+def process_array(image_array, expand=True):
+    """ Process a 3-dimensional array into a scaled, 4 dimensional batch of size 1. """
+
+    image_batch = image_array / 255.0
+    if expand:
+        image_batch = np.expand_dims(image_batch, axis=0)
+    return image_batch
+
+
+def process_output(output_tensor):
+    """ Transforms the 4-dimensional output tensor into a suitable image format. """
+
+    sr_img = output_tensor.clip(0, 1) * 255
+    sr_img = np.uint8(sr_img)
+    return sr_img
+
+
+def pad_patch(image_patch, padding_size, channel_last=True):
+    """ Pads image_patch with with padding_size edge values. """
+
+    if channel_last:
+        return np.pad(
+            image_patch,
+            ((padding_size, padding_size), (padding_size, padding_size), (0, 0)),
+            'edge',
+        )
+    else:
+        return np.pad(
+            image_patch,
+            ((0, 0), (padding_size, padding_size), (padding_size, padding_size)),
+            'edge',
+        )
+
+
+def unpad_patches(image_patches, padding_size):
+    return image_patches[:, padding_size:-padding_size, padding_size:-padding_size, :]
+
+
+def split_image_into_overlapping_patches(image_array, patch_size, padding_size=2):
+    """ Splits the image into partially overlapping patches.
+    The patches overlap by padding_size pixels.
+    Pads the image twice:
+        - first to have a size multiple of the patch size,
+        - then to have equal padding at the borders.
+    Args:
+        image_array: numpy array of the input image.
+        patch_size: size of the patches from the original image (without padding).
+        padding_size: size of the overlapping area.
+    """
+
+    xmax, ymax, _ = image_array.shape
+    x_remainder = xmax % patch_size
+    y_remainder = ymax % patch_size
+
+    # modulo here is to avoid extending of patch_size instead of 0
+    x_extend = (patch_size - x_remainder) % patch_size
+    y_extend = (patch_size - y_remainder) % patch_size
+
+    # make sure the image is divisible into regular patches
+    extended_image = np.pad(image_array, ((0, x_extend), (0, y_extend), (0, 0)), 'edge')
+
+    # add padding around the image to simplify computations
+    padded_image = pad_patch(extended_image, padding_size, channel_last=True)
+
+    xmax, ymax, _ = padded_image.shape
+    patches = []
+
+    x_lefts = range(padding_size, xmax - padding_size, patch_size)
+    y_tops = range(padding_size, ymax - padding_size, patch_size)
+
+    for x in x_lefts:
+        for y in y_tops:
+            x_left = x - padding_size
+            y_top = y - padding_size
+            x_right = x + patch_size + padding_size
+            y_bottom = y + patch_size + padding_size
+            patch = padded_image[x_left:x_right, y_top:y_bottom, :]
+            patches.append(patch)
+
+    return np.array(patches), padded_image.shape
+
+
+def stich_together(patches, padded_image_shape, target_shape, padding_size=4):
+    """ Reconstruct the image from overlapping patches.
+    After scaling, shapes and padding should be scaled too.
+    Args:
+        patches: patches obtained with split_image_into_overlapping_patches
+        padded_image_shape: shape of the padded image contructed in split_image_into_overlapping_patches
+        target_shape: shape of the final image
+        padding_size: size of the overlapping area.
+    """
+
+    xmax, ymax, _ = padded_image_shape
+    patches = unpad_patches(patches, padding_size)
+    patch_size = patches.shape[1]
+    n_patches_per_row = ymax // patch_size
+
+    complete_image = np.zeros((xmax, ymax, 3))
+
+    row = -1
+    col = 0
+    for i in range(len(patches)):
+        if i % n_patches_per_row == 0:
+            row += 1
+            col = 0
+        complete_image[
+        row * patch_size: (row + 1) * patch_size, col * patch_size: (col + 1) * patch_size,:
+        ] = patches[i]
+        col += 1
+    return complete_image[0: target_shape[0], 0: target_shape[1], :]
+
 
 
 class EndpointHandler():
@@ -32,18 +654,42 @@ class EndpointHandler():
             A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
         """
         inputs = data.pop("inputs", data)
+        if isinstance(inputs['image'], list) and len(inputs['image']) > 1:
+            input_images = []
+            for base64_string in inputs['image']:
+                image = Image.open(BytesIO(base64.b64decode(base64_string)))
+                input_images.append(image)
+
+            for i in range(len(input_images)):
+                input_images[i] = input_images[i].resize((194, 250))
+
+            numpy_images = [np.array(img) for img in input_images]
+            output_images = self.model.predict(numpy_images)
+
+            base64_strings = []
+            for output_image in output_images:
+                buffered = BytesIO()
+                output_image = output_image.convert('RGB')
+                output_image.save(buffered, format="png")
+                img_str = base64.b64encode(buffered.getvalue())
+                base64_strings.append(img_str)
+
+            return base64_strings
+        
+        else:
+            inputs = data.pop("inputs", data)
 
-        # decode base64 image to PIL
-        image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
+            # decode base64 image to PIL
+            image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
 
-        # forward pass
-        output_image = self.model.predict(image)
+            # forward pass
+            output_image = self.model.predict(image)
 
-        # base64 encode output
-        buffered = BytesIO()
-        output_image = output_image.convert('RGB')
-        output_image.save(buffered, format="png")
-        img_str = base64.b64encode(buffered.getvalue())
+            # base64 encode output
+            buffered = BytesIO()
+            output_image = output_image.convert('RGB')
+            output_image.save(buffered, format="png")
+            img_str = base64.b64encode(buffered.getvalue())
 
-        # postprocess the prediction
-        return {"image": img_str.decode()}
+            # postprocess the prediction
+            return {"image": img_str.decode()}

local_test.py

@@ -0,0 +1,61 @@
+from handler import EndpointHandler
+import json
+
+# init handler
+my_handler = EndpointHandler(path=".")
+
+import base64
+
+# prepare sample payload
+# non_holiday_payload = {"inputs": "I am quite excited how this will turn out", "date": "2022-08-08"}
+# holiday_payload = {"inputs": "Today is a though day", "date": "2022-07-04"}
+
+# with open('sample_input.json', 'r') as file:
+#     data = json.load(file)
+import io
+from PIL import Image
+import requests
+response = requests.get('https://mystore-12345-product-images.s3-us-east-2.amazonaws.com/0c817b58-2774-4f02-95b8-3ae379aa2e98.jpeg')
+image = Image.open(io.BytesIO(response.content)).convert('RGB')
+
+response2 = requests.get('https://mystore-12345-product-images.s3-us-east-2.amazonaws.com/3b9c698b-b7ae-4c5d-a978-2179ccc08d12.jpeg')
+image2 = Image.open(io.BytesIO(response2.content)).convert('RGB')
+
+response3 = requests.get('https://mystore-12345-product-images.s3-us-east-2.amazonaws.com/72801dfa-5d6a-442e-91cd-80bdb394a323.jpeg')
+image3 = Image.open(io.BytesIO(response3.content)).convert('RGB')
+
+pil_images = [image.copy() for i in range(10)]
+pil_images[1] = image2.copy()
+pil_images[2] = image3.copy()
+
+base64_strings = []
+for output_image in pil_images:
+    buffered = io.BytesIO()
+    output_image = output_image.convert('RGB')
+    output_image.save(buffered, format="png")
+    img_str = base64.b64encode(buffered.getvalue())
+    base64_strings.append(img_str)
+
+inputs = {
+    'image': base64_strings
+}
+
+
+# test the handler
+import time 
+start =  time.time()
+prediction=my_handler(inputs)
+# holiday_payload=my_handler(holiday_payload)
+print("inference time itself is", time.time() - start)
+
+# show results
+# print("prediction", prediction)
+
+print("type of prediction", type(prediction))
+
+data_json = prediction
+
+
+print("type of prediction", data_json.keys())
+
+img_str = data_json['image']